Bayes factors provide a continuous measure of evidence for one hypothesis (e.g., the null, H0) relative to another (e.g., the alternative, H1) [1].

Warmelink et al. ( 2019) reported Bayes factors alongside p-values to draw inferences about whether the order of expected versus unexpected questions influenced the amount of details interviewees provided during an interview [2].

Mac Giolla & Ly (2019) provided several recommendations to improve the reporting of Bayesian analyses and used Warmelink et al. ( 2019) as a concrete example [3].

These included (I) not to over-rely on cut-offs when interpreting Bayes factors; (II) to rely less on Bayes factors, and switch to ‘nominal support’; and (III) to report the posterior distribution.

This paper elaborates on their recommendations and provides two further suggestions for improvement.

First, we recommend deception researchers report Robustness Regions to demonstrate the sensitivity of their conclusions to the model of H1 used.

Second, we demonstrate a method that deception researchers can use to estimate, a priori, the sample size likely to be required to provide conclusive evidence.

Mac Giolla and Ly’s (2019) commentary on Warmelink, Subramanian, Tkacheva, and McLatchie (2019) discussed how deception researchers could improve their use and reporting of Bayes factors, which quantify relative evidence for competing hypotheses [3].

Mac Giolla and Ly’s advice included: 1.

Not to over-rely on cut-offs when interpreting Bayes factors.

To report alternatives to Bayes factors, including ‘nominal support’.

3. To report the posterior distribution.

We broadly agree, but in the first part of our reply we add a number of clarifications that we believe deception researchers should consider before following these suggestions.

We also add two further suggestions: 1. Report Robustness Regions to demonstrate the sensitivity of conclusions to the different models of H1 that could be used to calculate Bayes factors.

2. Design informative studies by estimating the sample size required to obtain a minimum level of evidence.

In this section, we comment on the three suggestions made by Mac Giolla and Ly (2019) on the reporting of Bayesian analyses.


Do not over-rely on cut-offs Mac Giolla and Ly begin by ‘warning against an over-reliance on cut-offs’ (p. 2) [3].

A cut-off is the level of evidence that a researcher considers sufficient to conclude that their data support either hypothesis, or that the data are inconclusive.

Although Mac Giolla and Ly suggest that cut-offs ‘encourage categorical rather than continuous thinking’ (p. 2), we believe it is possible to interpret Bayes factors as a continuous measure while simultaneously acknowledging whether the Bayes factor meets a pre-specified threshold of evidence to enable a categorical interpretation; for example, to state whether an intervention was successful or not, or whether the evidence was inconclusive.

This judgement will influence subsequent behaviours, such as whether the researchers encourage or discourage uptake of the intervention when disseminating the results.

Thus, although the use of cut-offs should not prevent the interpretation of the Bayes factor as a continuous measure, cut-offs can nonetheless guide the researcher’s decision-making process.

Our purpose is not to discuss where the minimum cut-off should be set, which is a matter of ongoing debate in psychology (e.g., Aczel et al., 2018; Benjamin et al., 2018; for an argument against cut-offs, see Morey, 2015) [4][5][6].

Instead, we urge researchers to clearly specify when they are using cut-offs, and what those cut-offs are.

We believe that Warmelink et al. ( 2019) provide a reasonable and not over-reliant model of how cut-offs can be used: (I) introduce the Bayes factor as a continuous measure, (II) specify the interpretation of the Bayes factor cut-offs, (III) use labels that indicate the increasing strength of evidence, and (IV) always report the exact Bayes factor [2].

Violating these guidelines might suggest an over-reliance on cut-offs.


Researchers should report ‘nominal support’ (posterior odds) The term ‘nominal support’ in Mac Giolla and Ly’s commentary is not part of the standard Bayesian vernacular (see Box 1 for a short glossary), but appears to be equivalent to interpreting posterior odds [3].

We would like to suggest three things that deception researchers should consider before reporting posterior odds.

1. If posterior odds are reported, they should be reported alongside Bayes factors.

Van Ravenzwaaij and Wagenmakers (2019) recently argued that, as the posterior odds are the product of the Bayes factor and the prior odds, both should be reported and justified alongside posterior odds [7].

2. Mac Giolla and Ly suggest that the use of cut-offs is problematic for Bayes factors, and they then offer posterior odds as an alternative [3].

Therefore, readers might assume that posterior odds avoid the use of cut-offs in a manner that Bayes factors do not.

However, both posterior odds and Bayes factors can be interpreted using cut-offs by researchers who either want to or are required to categorically interpret their results.

Again, the use of such cut-offs to interpret either Bayes factors or posterior odds does not diminish their value as continuous measures.

Researchers can correctly interpret a Bayes factor or posterior odds as a continuous measure and acknowledge whether it met a pre-specified minimum threshold of evidence in order to draw certain categorical conclusions.

The use of such thresholds can help with identifying the required sample size and limit researcher flexibility when interpreting results.

Further guidance is required on how deception researchers can specify scientifically informed prior odds.

Mac Giolla and Ly specify 8% ‘for the sake of argument’, but not all experimental hypotheses are equally (im)plausible, and the degree of scepticism will differ between researchers [3].

Meanwhile, reporting the Bayes factor allows readers to calculate their own posterior odds based on their own prior odds.

Report the posterior distribution Mac Giolla & Ly encourage deception researchers to make their studies more informative by reporting the posterior distribution [3].

A distinction exists in statistical inference between estimation (e.g., using posterior distributions) and hypothesis testing (e.g., using Bayes factors) (see Dienes, 2020) [8].

To calculate posterior distributions, researchers use vague ‘default’ prior models, which allow the posterior distribution to be primarily determined by the data.

However, vague prior models are not very useful for testing theories, as the vague prior model might not represent the scientific theory of interest.

In contrast, Warmelink et al. ( 2019) used prior models that represented their scientific theory (i.e., the prior model is a ‘model of H1’, see Dienes, 2019) to calculate informed Bayes factors [2][9].

Researchers should exercise caution before automatically calculating posterior distributions using the model of H1 used to calculate informed Bayes factors.

Consider the Bayes factor reported on p. 7 of Warmelink et al. ( 2019): BH(0, 7.05) = 3.64 [2].

The model of H1 was specified using a half-normal distribution (indicated by the subscript H in the notation and depicted by the solid black line in Figure 1A,B).

The half-normal distribution represents several aspects of the theory: (I) only effects in one direction are considered plausible, and these are represented as positive in the analysis; (II) smaller effect sizes are considered more plausible than larger effect sizes; and (III) effect sizes up to twice the approximate scale-of-effect are considered plausible (the approximate scale-of-effect was 7.05, based on the results of Warmelink, 2012) [10].

To see why caution should be exercised before calculating a posterior distribution, consider Figure 1.

The red dotted line in Panel A shows the actual posterior distribution for the data in this example.

There is nothing particularly disconcerting about Panel A – the most plausible effect sizes according to the posterior distribution lie midway between the most plausible effect sizes according to the model of H1 (i.e., smaller effect sizes closer to zero), and the most plausible effect sizes according to the data (i.e., those close to the observed mean difference of 9.41).

Now consider Panel B. The red dotted line in Panel B shows the posterior distribution assuming that Warmelink et al. ( 2019) had observed the same magnitude of effect, but in the opposite direction predicted by theory [2].

The posterior distribution is clearly not an accurate estimation of effect sizes in light of the data.

The model of H1 assigned zero plausibility to results in the opposite direction to the prediction and consequently so does the posterior distribution.

In sum, posterior distributions can be informative for estimation purposes when a vague prior model has been used, but researchers should exercise caution before automatically calculating posterior distributions when reporting informed Bayes factors.


Additional suggestions In this section, we provide two further suggestions that we believe will improve the reporting of Bayes factors.

Report Robustness Regions to demonstrate the robustness of conclusions to models of H1 In line with Jeffrey’s platitude that ‘the answer should depend on the question’, the result of the Bayes factor depends on the specified models of H1.

APA guidelines state that one should therefore report how sensitive the inference is to the specification of the model of H1 whenever reporting Bayes factors (Appelbaum et al., 2020, p. 20) [4].

This can be done by reporting robustness regions alongside Bayes factors (e.g., Lin, McLatchie & Linkenauger (2020) and Palfi, Parris, McLatchie, Kekecs & Dienes (In Press)) [11][12].

Robustness regions are reported as RR[LL, UL] where LL corresponds to the smallest scale-of-effect and UL corresponds to the largest scale-of-effect that lead to the same conclusion as the model of H1 used to calculate the Bayes factor, assuming the minimum threshold of evidence is constant.

Consider again the Bayes factor discussed above: BH(0, 7.05) = 3.64 [2].

Warmelink et al. ( 2019) concluded that they had obtained moderate evidence for an effect, when specifying their model of H1 with a scale-of-effect of 7.05 and a minimum threshold of B = 3 [2].

To create robustness regions, we calculated a series of Bayes factors, each time manipulating the scale-of-effect to identify the smallest and largest scale-of-effect that still produced B > 3 (see DeBruine & Dienes, 2020, for R code that calculates Robustness Regions non-iteratively) [6].

In this case, the robustness region is RR[4.44, 15.74].

Whether this can be considered robust or not depends on the scientific context.

If effects outside the robustness region are scientifically plausible, the inference is not robust.

If the robustness regions are judged to cover all theoretically plausible effect sizes, the inference is robust.

In this example, we conclude that the result is not robust.

Many deception researchers may argue that an effect smaller than 4.44 details would be too small to be of interest.

However, an effect larger than 15.74 can be considered plausible and interesting (e.g., Vrij, Leal, Jupe, & Harvey, 2018, report a mean difference of 29 details between two interviewing conditions) [13].

Estimate the sample size required to obtain conclusive evidence As Mac Giolla and Ly highlight, 19 of the 22 Bayes factors reported by Warmelink et al. ( 2019) were inconclusive [3][2].

Here, we provide a simple method (with corresponding R script available at https://osf.io/34nb6/, based on original by Palfi & Dienes, 2019) that researchers can adopt to estimate the number of participants required in order to obtain a pre-specified level of evidence (originally outlined by Dienes, 2015) [14][15].

As sample size increases, and the standard error reduces towards zero, Bayes factors are better able to distinguish between evidence for two competing hypotheses.

The process requires one to (1) estimate an approximate scale-of-effect if H1 were true (for a tutorial on how to estimate what your theory predicts, see Dienes, 2019) [9], (2) estimate the standard error for a given sample size (e.g., the standard error from a past study), and (3) specify a minimum level of evidence one wishes to obtain.

The R script then simulates the standard error in light of increasing sample size and calculates Bayes factors for each sample size.

It does this assuming the evidence supports H1 and H0 separately.

The script also outputs a graph to show the Bayes factor for H0 and H1 at each tested sample size.

Figure 2 shows the graph created assuming a researcher wanted to replicate the result from Warmelink et al. ( 2019), BH(0, 7.05) = 3.64, with a minimum cut-off of 3 [2].

In this case, a replication would require 130 participants to obtain a Bayes factor of 3 in favour of H1 assuming the study supported H1, or 328 participants to obtain a Bayes factor of 3 in favour of H0 assuming the study supported H0.

It is common that more participants are required to obtain evidence for the point-null hypothesis than for the alternative hypothesis.

In this example, to ensure enough participants are recruited that the study could obtain sufficient evidence for either hypothesis, a replication study should look to recruit at least 328 participants.


Conclusion We whole-heartedly agree with Mac Giolla and Ly when they encourage researchers to design more informative studies, and we are grateful for their commentary that sought to educate deception researchers on matters of reporting and interpreting Bayesian analyses [3].

Here, we have sought to add a number of clarifications and also specified two additional ways that the Bayesian analyses in Warmelink et al. ( 2019) could have been more informative [2].

If researchers adopt these recommendations, we believe they will be better able to assess the evidence provided by their research.


Gastroenteropancreatic neuroendocrine neoplasms (NENs) are rare tumors defined by the expression of specific diagnostic biomarkers [1-3].

Cell differentiation is a major prognostic marker of neuroendocrine neoplasms [4, 5].

Indeed, regardless of the stage or the location of the primary tumor, it has been highlighted [3] that well-differentiated lesions have a better prognosis than poorly differentiated ones [4, 6-8].

In 2010, the World Health Organization (WHO) classification of neuroendocrine neoplasms was reviewed and validated the crucial role of the proliferative rate [9-14].

The WHO Classification 2010 defined three groups of tumors, according to the combination of the morphological characteristics and the mitotic index as well as the Ki-67 index [15]: grades 1 and 2 corresponded to well-differentiated neuroendocrine tumors (NETs), whereas grade 3 corresponded to poorly differentiated lesions called neuroendocrine carcinomas (NECs) (Table 1).


Initially, it was assumed that no NET with a mitotic or a Ki-67 index above 20% could exist.


According to the 2010 WHO classification, gastroenter- opancreatic NENs are classified as NET G-1 or G-2 in up to 84% of cases or NEC in 6%-8% [16, 17].

Mitoses .20 per high- power field (HPF) or a Ki-67 index .20% are identified in grade 3 tumors.

Recently, a proportion of NET presenting a high proliferation, either mitoses or Ki-67 index cutoff above 20% and a well-differentiated morphology, have been identified, thereby calling for a new category, well-differentiated grade 3 NET (NET G-3).

NET G-3 has been estimated between 5.6% and 8% of gastroenteropancreatic NENs [16, 18].

This category appeared as a distinct entity from NECs because of their better overall survival [16, 18].

NET G-3 suffered from a lack of guide- lines or consensus-based recommendations.

We herein stress the histopathological identification, frequency, treatment, and outcome of NET G-3.


The majority of gastroenteropancreatic NENs are classified in the well-differentiated category because they retain the organoid architecture typical of neuroendocrine organs and have a relatively low proliferative rate.

It is stated that all gastroenteropancreatic NETs-with the exception of the pan- creatic microadenomas and minute, gastrin-driven type 1 neuroendocrine tumors in the stomach-are potentially ma- lignant neoplasms.

Different classifications have been used to distinguish pure neuroendocrine tumors from mixed endocrine- exocrine tumors, and to distinguish within pure neuroendocrine tumors different categories according to their behavior (well- differentiated NETs with benign behavior, well-differentiated NETs with uncertain behavior, well-differentiated NETs with malignant behavior, and poorly differentiated endocrine carcinomas with high-grade malignant behavior) [19, 20].

These classifications were based on their site of origin, size, gross and/or microscopic tumor extension, vascular invasion, and/or proliferative activity (Ki-67 index), as well as their syndromatic clinical/functioning features.


The prognostic assessment of gastroenteropancreatic NENs has improved significantly since the introduction of the European Neuroendocrine Tumor Society (ENETS) and the WHO 2010 staging and grading systems [21].

The unifying grading scheme is based solely on the proliferative rate (mitoses and the Ki-67 index), permitting classification of all gastroenteropancreatic NENs as follows: low-grade, or G-1 (mitoses #2/10 HPF and Ki-67 index #2%); intermediate- grade, or G-2 (mitoses 2-20/10 HPF or Ki-67 index 3%-20%); and high-grade, or G-3 (mitoses .20/HPF or Ki-67 index .20%) (Table 1).

The WHO recommends that mitoses and the Ki-67 index should be counted in 50 high-power fields and 500-2,000 cells in the highest labeling regions, respectively.


According to this system, a distinction can be made between well-differentiated gastroenteropancreatic NETs (low-grade G-1 and intermediate-grade G-2), the most frequent form, and poorly differentiated gastroenteropancreatic NECs (high- grade G-3) that include small- and large-cell NECs.

Microscop- ically, well-differentiated NETs are composed of monotonous regular cells presenting round or oval nuclei with "salt and pepper" chromatin and eosinophilic granular cytoplasm.

The tumor nests are arranged in trabecular, insular, or sheet-like patterns (Fig. 1A, 1B).

In some cases, important nuclear pleo- morphisms may be encountered, but are not associated with tumor aggressiveness (Fig. 1B).

Poorly differentiated NECs show highly proliferative atypical cells that are organized in diffuse sheets with little nesting (Fig. 1C).

Apoptotic bodies and necrosis are usually observed, whereas the Ki-67 index is usually .50% (Fig. 1D).


However, it has now become clear that the G-3 group is heterogeneous, comprising, in addition to poorly differenti- ated NECs, tumors that retain the cytoarchitectural features of well-differentiated gastroenteropancreatic NETs, despite a mitoses/Ki-67 count above 20% (Fig. 1E, 1F).

NETs of the G-3 group seemed not to be as aggressive as classic poorly differentiated NECs, but showed a worse outcome than the G-2 group [18, 22].

The prognosis is considered as intermediate between NET G-2 and NEC.Thus, it recently appeared of clinical interest that pathologists distinguished them both from NECs by their well-differentiated pattern and their lower mitosis/ Ki-67 count, which usually ranges from 20% to 50%, but also from G-1/2 NET.

In some cases, the morphological distinction with NECs (i.e., the distinction between well and poorly dif- ferentiated NENs) remained problematic in case of important tumoral heterogeneity, with limited areas showing less dif- ferentiated patterns and/or necrosis.


Recently, it has been shown that sporadic NETs and NECs occurring in the pancreas are genetically different [23].

Whereas inactivating mutations in DAXX and ATRX were exclusively found in NETs, abnormal immunolabeling for p53 and the Rb/p16 pathways was, conversely, detected in virtually all NECs (Table 2).

Some of these molecular alterations may help pathologists to separate morphologically NEC from NET, in addition to morphological cellular characteristics.


An accurate pathological assessment of the proliferation index appears critical to rigorously identify highly proliferative NET G-3.

Determination of the Ki-67 index has become a critical element in NET disease management.

Discussions remain regarding the lack of uniformity and re- producibility between the different methods used, such as "eyeballing," manual counting, or digital image analysis.

De- spite good agreement for tumors showing a very low (,1%) or high (.70%) index, because of the marked interobserver and intraobserver variability, the eyeballing estimation did not appear to be reliable.

This was particularly observed around the cutoffs for grading a tumor as G-2 (2%-5% range) or G-3 (15% to .20%) [24].

Compared with the image analysis Well-Differentiated Grade 3 NET method, eye observation/counting tended to overestimate the Ki-67 index [24-26].

Thus, in practice, the "acceptable gold standard" for determining the Ki-67 index remained the manual counting of a sufficient number of cells (.500), through the microscope or more confidently on a printed version of a captured screenshot as demonstrated by some authors [27].

Besides the problems of quantification, other factors, such as the fixative process, the staining technique, or the antibody used, may potentially affect the reproducibility of Ki-67 values.

Underestimated grading has also been demonstrated when histological analysis is performed by endoscopic ultrasound- guided fine-needle aspiration [28, 29].


Most well-differentiated NETs (G-1 and G-2) are recognizable without difficulty by pathologists.

It is very unusual that an expert pathologist does not confirm an initial diagnosis.

Based on the 2010 WHO classification, morphology has, for the most part, unfortunately been disregarded.

The lackof NET G-3 in the current classification could lead to classifying a tumor as a poorly differentiated G-3, mainly because of a high prolifer- ative index, instead of a well-differentiated G-3 [30].

Over- diagnosis can be observed for tumors with a Ki-67 index .20%, showing a marked nuclear pleomorphism or a crushed artifact, particularly on biopsies.

However, it is clear that in rare cases, a gray zone persists for pathologists to distinguish NECs from highly proliferative NET G-3.

Some authors underline the need to revise criteria concerning G-3 tumors.

Revision should put emphasis on differentiation, modification of the Ki-67 cutoff value (50% instead of 20%), and integration of molecular characteristics that could allow a better distinction between these two types of proliferative tumors.

Mixed tumors and metastasis, from the lung in particular, also need to be dis- tinguished from gastroenteropancreatic NEC.

This remains a problem because these tumors are often diagnosed at a multimetastatic stage, and the diagnosis made sometimes requires the use of a microbiopsy performed under endoscopic ultrasound.

Furthermore, in contrast to NET, TTF1 and CDX2 are not reliable markers to establish the site of origin of a metastatic NEC.

Differential diagnosis with an acinar cell carcinoma is even more problematic and requires the use of a panel of immunohistochemical stains, including both neuro- endocrine and acinar markers (trypsin, chymotrypsin, and Bcl10) [31].


This distinction is particularly difficult for mixed acinar-neuroendocrine carcinomas [32].


The incidence of gastroenteropancreatic NENs is rising, prob- ably because of the increasing quality and quantity of com- puted tomography imaging [1, 33].

Epidemiologic data on NEN G-3 (NEC 1 NET G-3) are scarce.

Some studies have only reported NECs with a small-cell morphology [34, 35], whereas in contrast with lung NECs, large-cell NECs are more common than small-cell NECs in the gastroenteropancreatic area [36, 37].

Among gastroenteropancreatic NENs, 7%-21% are poorly differentiated (NEC) [1, 17, 35], but no data on G-3 tumors were available in these studies.


Some studies reported a frequency of 9%-16% of gastroenteropancreatic NEN G-3 [17, 38, 39].

Studies, which have reported both characteristics, have al- ways found more NET G-3 than NEC: 9.1% versus 6.7% in the Austrian study [17] and 13.5% versus 9% in the Pronet study [39].

Therefore, we can speculate that the NET G-3 incidence is probably underestimated.

For instance, we can expect some NET G-3 in the NORDIC NEC study [36], especially from the pancreas, because (a) no pathological review was performed in this population of 305 patients selected on Ki-67 .20% to confirm whether all tumors were poorly differenti- ated or not and (b) patients with pancreatic tumors had higher rates of positive somatostatin receptor scintigraphy (SRS) (46%), lower rates of Ki-67 (70% with Ki-67 ,55%), and longer overall survival.

These factors are more frequent in NET G-3 than in NEC.


Very few data comparing NET G-3 and NEC are available.

This was the goal of only four recent studies [16, 18, 37, 39].

Therefore, it is difficult to get an idea of the true distribution between NET G-3 and NEC, but NET G-3 would represent approximately one third of NEN G-3.The only prospective data come from the Pronet study, a 1-year prospective, epidemi- ologic study to assess the characteristics of newly diagnosed NEN, including a review of their diagnostic pathology.

This study reported 778 patients with gastroenteropancreatic NEN, including 104 (13.5%) NEN G-3.

From the 104 NEN G-3, the proportions of NEC, NET G-3, and mixed adenoneuroendocrine carcinoma were 69% (n 5 72), 20% (n 5 21), and 11% (n 5 11), respectively [39].

Another prospective national cohort, which focused on NEC (NET G-3 excluded) with a pathological review by the French national referent pathological network (TENpath), gathered interesting data on characteristics, prognosis, and treatments of NEC without comparisons with NET G-3 [40].


The paucity of available data on presentation of NET G-3 warrants particular caution regarding their distribution (Table 3)


In nonmetastatic tumors, the ENETS and U.S. guidelines recommend a surgical resection for localized NEN, irrespective of tumor grading [42-44].

Based on the treatment paradigm for limited-stage small-cell lung cancer, neoadjuvant chemo- therapy and radiation can be considered in many gastro- enteropancreatic NEC patients, particularly when surgical resection is difficult [43, 44].

No data exist on the efficacy of such an approach in NET G-3 patients.

As for well-differentiated NET G-1/2, surgery is recommended as the first option in nonmetastatic NET G-3 patients [43, 44].


In pancreatic tumors, the standard treatment is surgical resection with a 5% mortality-and a 50% morbidity-rate according to the type of surgery [45, 46].The ENETS guidelines suggest that a "wait-and-see" approach might be preferred for asymptomatic patients in case of G-1, ,2 cm tumors.

Con- sidering the lack of data, a similar approach cannot be rec- ommended in NET G-3.


Ingastrictumors, a cancersurgery iswarranted.

Antrectomy, indicated in selected type 1 gastric NET patients to avoid gastrin stimulation, is not recommended in NET G-3 [47, 48].

In colonic and rectal tumors, NET G-3 should be treated with appropriate cancer surgery combined with lymph node re- section [49].


Surgical management of metastatic NET is controversial.

The overall prognosis of these tumors is mainly based on growth of liver metastasis, cell differentiation, and Ki-67 index.

In NET G-1/2, liver surgery can be a valid option.

In pancreatic NET G-3, a similar overall survival of patients with NET G-2 and G-3 has been recently reported (67.8 and 54.1 months, respectively), significantly higher than in NEC (11 months) [18].

Therefore, from a surgical point of view, metastatic NET G-3 should be considered as NET G-2.


In well-differentiated NET G-1/2, no data favor the adminis- tration of adjuvant chemotherapy.

In NEC, there are no studies examining adjuvant postoperative treatments.

Nevertheless, their aggressive behavior and high rate of recurrence have led experts to consider adjuvant treatment, using platinum-based combinations as in small-cell lung tumors [44].

In NET G-3, no data suggest a benefit of such an adjuvant therapy.

In addition,

Based on experts considerations and from the NORDIC NEC study (Ki-67 with a cutoff for response found at 55%), we proposed a Ki-67 cutoff between 50% and 60% as the minimum level that should be considered satisfactory to propose plati- num based chemotherapy (Fig. 2).

Therefore, it appears reasonable to manage NET G-3 and NET G-1/2 patients similarly, without adjuvant therapy although there is a higher risk of recurrence [37].


In pancreatic well-differentiated NET, chemotherapy is the benchmark if the main treatment goal is the reduction of the tumor mass, particularly if it would allow secondary surgery.

A long time ago, streptozotocin plus doxorubicin was shown to be superior to streptozotocin plus 5-fluorouracil, both in terms of tumor regression rate (69% versus 45%; p 5 .005) and length of time to tumor progression (median: 20 months versus 6.9 months; p 5 .001) [52].

However, in view of renal toxicity of streptozotocin and cardiac toxicity of doxorubicin, different chemotherapy regimens have been investigated and may be recommended as a first-line option.


Altimari et al. identified a 50% efficacy of dacarbazine in patients with nonresectable intra-abdominal NET [53].

In line with this report, Bajetta et al. showed a 30% response rate with a doublet of dacarbazine and 5-fluorouracil (LV5FU2-dacarbazine) in a cohort of 30 patients [54].

Strosberg et al. identified a major response rate (radiologic objective response: 70%; stable disease: 20%) using capecitabine from days 1 to 14 in addition to temozolomide from day 10 to 14 [12].

In the BETTER trial, chemotherapy-naïve pancreatic NET G-1/2 patients were exposed to a bevacizumab-5-fluorouracil-streptozotocin asso- ciation and experienced a significant disease control rate (56% of partial responses and 44% of stabilizations) [55].

Irinotecan in addition to 5-fluorouracil (FOLFIRI) has also been considered as an option in second-line pancreatic NET.

This regimen was associated with an 80% disease control rate but without objective tumor response [56].

In pancreatic NEC, the association of cisplatin to etoposide appeared as the first chemotherapy option [7, 57].

Recently, Nakano et al. presented another platinum-based regimen with irinotecan and cisplatin and experienced a 50% overall response rate [58].

Similarly to pancreatic NET, FOLFIRI regimen has been evaluated as showing 37% overall response rate in second-line [59].

In a In nonpancreatic NET, no randomized study identi- fied a standard of care.


Interferon is mainly proposed in cases of persistent secretory syndrome despite the somato- statin analogs [61, 62].

Interferon did not provide any significant benefit in terms of progression-free survival versus 5-fluorouracil plus streptozotocin (14.1 versus 5.5 months; hazard ratio 5 0.75 [0.41-1.36]) [62].

Interferon-a (3 million IU, three times per week) was used, whereas other forms seem better-tolerated and possibly as effective [63].

Considering the lack of validated chemotherapy, some experts in line with well-differentiated NET recommended dacarbazine [53] with or without 5-fluorouracil (LV5FU2- dacarbazine) [64] or temozolomide [65].

An alternative could be the combination of capecitabine and temozolomide [12].

Cassier et al., using a platinum-based chemotherapy (gemcitabine and oxaliplatin regimen), suggested some efficacy in pretreated non-pancreatic-NET G-1/2 patients with an 84% overall response rate [66].

Nevertheless, physicians should keep in mind that the response rates to alkylant agents are very low in nonpancreatic NET, possi- bly due to a strong expression of O(6)-methylguanine DNA methyltransferase [67, 68].

The best results of a chemo- therapy regimen were obtained in a nonrandomized phase II study of naive patients with nonpancreatic NET [69].

In patients receiving a capecitabine-bevacizumab combina- tion, the response rate was reported as 18%, the overall disease control rate was 88%, and the progression-free survival was approximately 23 months [69].

These results require confirmation.

In nonpancreatic NET G-3, no chemo- therapy regimen should be considered as a standard of first-line care, considering the very small amount of data available.


In nonpancreatic NET G-3, no chemotherapy regimen should be considered as a standard of first-line care, considering the very small amount of data available.


In pancreatic NET G-1/2, two randomized phase III studies demonstrated the efficacy of a targeted therapy, either an mam- malian target of rapamycin (mTOR) inhibitor [9] (everolimus) or a tyrosine kinase inhibitor [14] (sunitinib).

The ENETS guidelines recommend targeted therapies in second-line and their use in first-line when chemotherapy is not appropriate [70].

In pancreatic NEC, there is no validation of a targeted therapy (except bevacizumab; see above).


In nonpancreatic NET G-1/2, an mTOR inhibitor (everolimus) has been evaluated.

In the Radiant 2 study, everolimus plus octreotide, compared with placebo plus octreotide, tends Well-Differentiated Grade 3 NET to demonstrate some efficacy [71].

Everolimus improved progression-free survival in patients with advanced NET associated with the carcinoid syndrome.

The median progression-free survival by central review was 16.4 months in the treated group versus 11.3 months in the placebo group (hazard ratio 0.77, 95% CI 0.59-1.00; one- sided log-rank test p 5 .026).

These data were significantly confirmed by the Radiant 4 trial, which reported that everolimus was associated with a 52% reduction in the estimated risk of progression or death in nonpancreatic NET G-1/2 (hazard ratio [HR] 0.48 [95% CI (0.35-0.67], p , .00001) [72].

Today, there are no data to propose a mTOR inhibitor or a tyrosine kinase inhibitor as first-line treatment in NET G-3.


NETs express a high density of somatostatin (SST) recep- tors involved in the antiproliferative effects of somatostatin analogs.

In the Promid study, long-acting octreotide signifi- cantly lengthened time to tumor progression compared with placebo in patients with metastatic nonpancreatic (midgut) NET [73].

After 6 months of treatment, stable disease was observed in 67% of treated patients versus 37% in nontreated.

Interestingly, the most favorable effect was observed in patients with low hepatic tumor load.


The Clarinet study confirmed the efficacy of somatostatin analog, lanreotide in nonpancreatic NET G-1/2, and identified a benefit in pancreatic NET G-1/2 [74].

Lanreotide was associated with a significantly prolonged progression-free survival among pa- tients with metastatic NET G-1/2 (Ki-67 , 10%).

Patients with well-differentiated metastatic NET, Ki-67 proliferation index up to 5%, stable disease before treatment, and low-to-moderate hepatic tumor involvement (,25%) have been associated with tumor control during lanreotide treatment [75].

As more precise understanding of NET cell biology evolves, more accurate identification of individual tumors SST receptor (SSTR) profile will probably facilitate a more precise delinea- tion of SST analog treatments [74-76].

Nevertheless, because of the lack of data in NET G-3, the use of somatostatin analogs might be restricted to low hepatic tumor involve- ment and stable disease.

The use of somatostatin analogs in NET G-3 should be only considered with a close monitoring and may be after confirmation of SSTR expression on SST imaging.


Presence of liver metastases secondary to NET of pancreatic and nonpancreatic origin has been shown to be a major factor in the alteration of both quality of life and prognosis [77].

Hepatic artery embolization or chemoembolization, as well as radioembolization, have been shown to have good clinical, biochemical, and morphological responses when liver burden does not permit ablative therapies [78].

Similar efficacy of hepatic arterial embolization versus chemoembolization in the treatment of liver metastases from well-differentiated non- pancreatic NET in a prospective study has been identified [79].

These data do not favor the hypothesis of an additive efficacy of arterial chemotherapy versus embolization alone.

To date, no randomized study has sought to compare the efficacy of either embolization or chemoembolization in NET G-3.

The choice of management depends on liver burden, metastases pattern, origin of the primary tumor, tumor differentiation, and tumor proliferative activity.

In the presence of extrahe- patic metastases, especially bone, a systemic treatment is preferred.


Peptide receptor radionuclide therapy (PRRT) is an effective form of treatment for patients with metastatic NET.

However, delivering sufficient radiation dose to the tumor to result in a high percentage of long-term tumor remissions remains challenging because of the limits imposed on administered activity levels by radiation damage to normal tissues.

Sabet et al. identified a specific efficacy of peptide receptor radionuclide therapy with 177Lu-octreotate in advanced nonpancreatic NET [80].

PRRT was well tolerated and very effective in well-differentiated midgut NET.

Disease control rate was similar in patients with a ,2% Ki-67 and 3%-20% Ki-67 NET, 97% and 87%, respectively (p 5 .36) [80].

The treatment with 177Lu-DOTA0-Tyr3-Octreotate plus best supportive care (30 mg Octreotide long-acting repeatable [LAR]) was compared with high-dose (60 mg) Octreotide LAR in patients with inoperable, progressive somatostatin receptor-positive, well-differentiated NET G-1/2 of the small bowel and confirmed its efficacy (median overall survival: not reached, 8.4 months; hazard ratio [95% CI]: 0.21 [0.129- 0.338]; p , .0001) [81].

To date, no data exist that would justify PRRT in NET G-3.


The identification of NET G-3 is the cornerstone of an adapted therapy.

This entity does exist and represents up to 6% of NET.

Prognosis and response rate seem to be close to well- differentiated NET G-1/2 with a worse overall survival.

It seems that the cell differentiation should be first taken in consider- ation when discussing the therapeutic options.

To date, cell differentiation, mitoses, and Ki-67 index must be known before treatment choice.

The identification of a biological marker would guide the physicians to the best therapy.

Our recom- mendations for management are based on extrapolations of the literature, which has scant data on this particular subset of patients, warranting future studies further focusing on the clinical differences and molecular underpinnings in these patients.


We read with great interest the excellent article by Coriat et al. titled “Gastroenteropancreatic on Well-Differentiated Grade 3 Neuroendocrine Tumors: Review and Position Statement,” published in The Oncologist [1].

The authors reviewed in detail the epidemiology and made recommendations for the management of this new entity.

They concluded that Gastroenteropancreatic (GEP) Neuroendocrine Tumors (NETs), Grade 3 (NET G-3), exist and differ from Poorly Differentiated (PD) GEP Neuroendocrine Carcinomas (NECs) in terms of prognosis.


Almost simultaneously, we published a multicenter Italian retrospective analysis of a large series of GEP NECs that was not referenced in this article [2].

Our report identified an 18% prevalence of NET G3 (24/136) and confirmed that the category we named “NEC Type A”—defining a well-differentiated (WD) tumor with a Ki-67 index of 21%–55%—had a significantly different better survival than that seen with PD GEP NEC.

Furthermore, our study revealed that not only morphology but also Ki-67 index can affect survival.

In addition, patients with neoplasms falling into the PD GEP NEC category and with a Ki-67 index ≤55% had significantly longer survival than those with Ki-67 index >55%.


On the other hand, Coriat et al. suggested that a pancreatic primary may be more likely classified as a G3 NET, rather than as NEC, although the references for that statement did not provide information on the frequency of NET G3 at different sites.

In our study, we observed that the prevalence of the pancreatic primary site was remarkably represented in 21% (29/136) of the total cohort; of those patients, 34.5% (10/29) showed WD morphology [2].


Regarding therapy, Coriat et al. reported that everolimus and sunitinib were studied in two phase III randomized trials of G1-G2 pancreatic NETs and concluded that “there is no validation of a targeted therapy except bevacizumab.” However, given that NET G3 are well differentiated, as the vast majority of tumor populations included in the two aforementioned trials, everolimus and sunitinib should be considered among the possible therapeutic options.

It cannot be excluded that some NET G3 were included in those studies, provided that Ki-67 was unknown in the main part of the tumors (on this basis the terms “G1” and “G2” should not be used).

Furthermore, data on a limited number of patients with pancreatic NET G3 treated with everolimus were presented at the most recent European Neuroendocrine Tumor Society meeting [3].


Finally, we disagree with Coriat and colleagues’ statement that bevacizumab was validated in pancreatic NECs, based on the current evidence.

We believe that biological therapies, such as molecular targeted agents and somatostatin receptor-targeted treatments, should be prospectively investigated in patients with advanced NET G3 in comparison with platinum-based chemotherapy, which is the current debatable standard treatment.


We thank Fazio and Milione [1] for their comment on our position paper recently published in The Oncologist [2].

As highlighted in our paper, evidence-based medicine in response to chemotherapy is lacking, and the choice of the best therapeutic option remains a challenge.

Fazio and Milione described in their letter that the main primary localization of a well-differentiated grade 3 neuroendocrine tumor (NET G-3) is the pancreas.

They identified in their retrospective cohort that the incidence of pancreatic NET G-3 is 42% of all NET G-3 (10 out of 24).

Similarly, in our position paper, we stressed that the main localizations of NET G-3 are the pancreas, the stomach, and the colon.


In addition, we factually reported that there is no validated phase III study on NET G-3 and there are no prospective data ongoing on the targeted therapy efficacy.

Furthermore, we agree with their comment that in “low” NET G-3, the proven efficacy of targeted therapy should be evaluated and that in some cases such therapy, either everolimus or sunitinib, should be considered.


Fazio and Milione believe that somatostatin receptor-targeted treatments might have a therapeutic efficacy in NET G-3.

In 2016, the NETTER-1 phase III study validated the efficacy of the 177Lu-DOTA0-Tyr3-Octreotate (Lutathera, Advanced Accelerator Applications, Saint-Genis-Pouilly, France) in patients with inoperable, progressive, somatostatin receptor-positive midgut grade 1–2 neuroendocrine tumor.


The NETTER-1 study presented as an inclusion criterion patients with a Ki-67 level below 20.

No data on the “aggressive” grade 2 NET subgroup treated in the NETTER-1 study that helped to propose 177Lu-DOTA0-Tyr3-Octreotate in NET G-3 have been published.


Moreover, it has been reported that patients with pancreatic tumors had higher rates of positive somatostatin receptor scintigraphy (46%), lower rates of Ki-67 (70% with Ki-67 <55%), and longer overall survival.


Such data pave the way for an evaluation in NET G-3 with a Ki-67 below 55% of the efficacy of the 177Lu-DOTA0-Tyr3-Octreotate (Lutathera).

Finally, the 2016 European Neuroendocrine Tumor Society (ENETS) guidelines state that chemotherapy as first-line treatment with a temozolomide plus capecitabine or streptozotocin plus 5-fluorouracil regimen [3] appears as the recommended option in NET G-3.


As stressed by Milione et al. in a recent review, there is a clinicopathologic heterogeneity of NET G-3 that deserves specific clinical trials [4].

Cell differentiation and Ki-67 level appear as the key points in the management of NET G-3 with a Ki-67 cut-off.


Homelessness in the United States is a risk factor for a myriad of poor health outcomes, including infectious dis- eases, chronic diseases, mental health disorders, and sub- stance abuse disorders (1).

However, much is unknown about the full extent of the health risks associated with homeless- ness.

Gaining a better understanding of the epidemiologic context of the experience of homelessness is valuable to public health for 2 reasons: First, epidemiologic data can inform public health practitioners and clinical providers in designing interventions that protect people who are cur- rently experiencing homelessness, and second, these data can provide evidence to support housing and homelessness prevention.


To calculate rates of disease for people experiencing homelessness, data sources that quantify the number of people exposed to homelessness are necessary.

Although several agencies collect data on homelessness in the United States, 2 federal agencies provide yearly, publicly available, local-level data: the US Department of Housing and Urban Development (HUD) and the US Department of Education (ED).

HUD defines any individual (of any age) or family as homeless if they lack a fixed, regular, and adequate nighttime residence.

This includes situations where their primary nighttime residence is a public or private place not meant for human habitation, if they are living in a temporary shelter (including emergency homeless shelters and transitional housing), or if they are exiting an institution where they have resided for 90 days or less and resided in an emergency shelter or place not meant for human habitation immediately before entering that institution (2).

ED defines homelessness differently than HUD, counting school-aged children who lack a fixed, regular, and adequate nighttime residence, inclusive of those who are 1) sharing the housing of other people; 2) living in motels, hotels, trailer parks, or campgrounds; 3) living in emergency or transitional shelters; 3) abandoned in hospitals; or 4) residing in places not designed for or ordinarily used as regular sleeping accommodations for human beings (such as cars, parks, public spaces, abandoned buildings, substandard housing, bus or train stations, or similar settings) (3).

While these federal definitions reflect eligibility for the homeless assistance programs of HUD and ED, the data collection requirements may result in capturing fewer adults and Through consultation with federal agencies, we identified and reviewed 3 publicly available data sources that enu- merate persons experiencing homelessness with the purpose of outlining the challenges and opportunities for use in epidemiologic analyses.

To provide examples of the impli- cations of the data sources, we describe the use of these data sets in the context of calculating the incidence of tuberculosis and other infectious diseases.


ED publicly releases school-district-level data with an unduplicated number of homeless students enrolled in public schools, regardless of age, at any time point during the school year.

These data are collected in alignment with the education portion of the McKinney-Vento Act to ensure that students who are experiencing homelessness do not face barriers to school enrollment and are provided with academic support.

Data are available from the 2013-2014 to 2017-2018 school years.

These local-level data include type of homelessness (sheltered, unsheltered, doubled up, motel/hotel) and some characteristics of the students, such as the number of homeless students who have disabilities, are English learners, are migrants, or are unaccompanied.

Counts are suppressed if the number of students in a category is less than 2 (9).


These data provide the number of enrolled school-aged children who have been recorded to have been exposed to homelessness (inclusive of those doubled up, staying in hotels/motels, or residing in other temporary situations as described above) over the course of a school year (Table 1).

ED data can serve to define a retrospective exposure group of students within the population.

Using ED data, the definition of homelessness in case data should be "a case in a student enrolled in public school who experienced homelessness (as defined) at some point during the school year." Utilizing these data as a denominator for an incident case of a disease, the state or local case report form would ask about a student's exposure to homelessness over the course of the school year.


ED data are not comparable to HUD data due to differing definitions of homelessness.

ED data also may not be com- parable across years: Prior to the 2016-2017 school year, students awaiting foster-care placement were counted as homeless.

In more recent years, states have begun excluding those students from their counts.

Like the HMIS data, ED data cannot be used to estimate traditional person-time for incidence calculations and cannot be used in combination with a case report form that collects data on immediate homelessness.


The Homeless Management Information System (HMIS) is a set of locally administered information systems used by CoCs to confidentially collect data on individuals and fami- lies who access any homeless services throughout the year, including but not limited to emergency shelters, transitional housing, permanent supportive housing, or rapid rehousing (6).

People who are unsheltered are only included in HMIS data through street outreach programs, if they utilize pro- grams that participate in the HMIS, such as day shelters, or if a local CoC decides to add their own customized fields on unsheltered people to their database.

People entering Vio- lence Against Women Act-funded programs for survivors of domestic violence are not included in the HMIS.

Lastly, Native Americans who may be experiencing homelessness or seeking services on tribal lands are not included.


Like the PIT, the purpose of the HMIS is to allow com- munities to follow performance and allocate resources while providing data to Congress and the public to better inform homeless policy and decision-making (7).

Each CoC sub- contracts with an HMIS software provider of their choosing but must follow HUD data standards.

All CoCs have been


More epidemiologic research is needed to protect the health of people experiencing homelessness.

The data from HUD and ED provide an opportunity to expand current re- search and develop actionable analyses.

However, as de- scribed here, the limitations of the data must be kept in mind.

New data on both sheltered and unsheltered homelessness will also become available on the local level from the 2020 US Census, which can be useful for validation and may provide further geographic granularity (10).

With cautious alignment of definitions and consideration of the limita- tions, data from the 3 sources outlined here can be used as denominator estimates for understanding the epidemiology and risks associated with homelessness.


In the accompanying article, Mosites et al. ( Am J Epidemiol.

2021;190(11):2432–2436) evaluate data sources that enumerate people experiencing homelessness in the United States with respect to their strengths and limitations for conducting epidemiologic research in homeless populations.

We largely agree with their key arguments, yet offer additional points that provide important context about these data for researchers and other stakeholders.

Overall, we believe that it is possible to address many of the noted shortcomings of these data, and once addressed, the data could be more effectively leveraged to improve the health, housing stability, and quality of life of people experiencing homelessness.


Homelessness consistently ranks as a leading problem in the minds of the American public [1].

Yet, the true scope of the problem has historically been a subject of fierce debate among advocates, researchers, policy-makers, and other stakeholders.

According to the most recent estimates from the US Department of Housing and Urban Development (HUD), in 2019 an estimated 568,000 people were experiencing homelessness in the United States, and throughout 2018, more than 1.4 million spent time in a shelter [2], [3].


Estimates of the size of the homeless population have been reported by HUD every year since 2007 in the Annual Homeless Assessment Report (AHAR).

HUD is the only agency that collects data with which to estimate the number of people experiencing homelessness in the United States on a systematic and routine basis.

Additionally, the AHAR creates a descriptive profile of people experiencing homelessness [4].


However, HUD estimates probably undercount the true number of people experiencing homeless in the United States—especially those who are unsheltered, homeless youth, immigrants, and homeless families [5].

Estimate inaccuracies interfere with appropriate resource allocation and interventions.

For example, during the beginning of the coronavirus disease 2019 pandemic, it was unclear what resources were needed to protect people experiencing homelessness in different localities because of inaccurate estimates.

More accurate estimates of the size of the homeless population—especially at the county and city levels, where most programs are implemented—would have facilitated the delivery of resources to homeless people affected by coronavirus disease 2019 [6].


In their article in this issue of the Journal, Mosites et al. [ 7] argue that population-level data for epidemiologic exploration are necessary to understand the full extent of social and health-related risks associated with homelessness, and in turn to inform interventions that will ultimately improve outcomes.

The authors describe the potential epidemiologic applications of 3 main data sources: HUD Point-in-Time (PIT) estimates, which are annual counts of the sheltered and unsheltered homeless population on a single night in January; HUD annual estimates of persons using emergency shelter, derived from local Homeless Management Information System data; and US Department of Education estimates of the number of students experiencing homelessness during a school year [7].

They argue that each has serious shortcomings that limit how and when the data can be used in epidemiologic research and should thus be approached and interpreted with caution.

The authors’ argument also implicitly calls for making improvements to these data sources.


Although we agree with Mosites et al. ’s main argument about the limitations of these data, we believe epidemiologists and other stakeholders interested in understanding and improving the health outcomes of persons experiencing homelessness should keep 3 additional points in mind.

First, while flawed, the current data sources that enumerate the homeless population are vast improvements over what was available until the late 2000s.

Second, there is a wide array of proposals for improving the accuracy of enumeration efforts that could be easily implemented and would strengthen the utility of these data for epidemiologic research and other purposes.

Finally, improvements in the accuracy of these data would have benefits that would extend far beyond their potential use for epidemiologic research and even the original reasons for collecting the data.


Current homeless population-level data collection and estimates are improvements


Enumeration of the homeless population has been a longstanding methodological challenge, as conventional means of counting the general population (e.g., through the Census Bureau’s household-based surveys) are inadequate for people experiencing homelessness.

Before the first AHAR report in 2007, estimates of the homeless population were conducted sporadically and were of questionable accuracy—varying wildly from 200,000 to 3.5 million [8]–[10].

However, since 2007, annual estimates from HUD have been available, and these have been valuable, useful, and easily retrievable from HUD.

Additionally, the community-level data comprising the AHAR have also been available since 2007 and add a crucial resource for tracking trends in the size of the homeless population over time at multiple levels of geography.

The collection of these data has opened avenues for conducting important new research to understand community-level predictors of homelessness and the impact of policy interventions on homelessness [11]–[13].

For example, in 2020, the US Government Accountability Office conducted regression analyses using HUD PIT data and found that rent level is an important driver of homelessness [5].

Likewise, researchers have used PIT data to show that a concerted policy effort to expand the availability of permanent supportive housing for military veterans has been a key factor in declines in veteran homelessness over time [11].


Counts underestimate the homeless population but can be easily improved


Mosites et al. correctly note that there are serious limitations in estimates of people experiencing homelessness [7].

They call particular attention to problems with the undercounting of the unsheltered population, and those concerns are echoed by the Government Accountability Office report, as well as the New York Times [5], [14].

However, they provide little guidance on how to improve these estimates, despite numerous recent and past proposals for improvement [5], [15].


Undercounting of individuals in any given homeless population probably occurs more than expected and more for unsheltered persons than for sheltered individuals, given the wide year-to-year fluctuations in numbers reported for the unsheltered population [5], [15].

The Homeless Management Information System estimates of the sheltered population are thought to be more reliable given fewer year-to-year fluctuations, but they could also be improved [5].


Enumeration of the unsheltered population depends on many factors: weather, the number of enumerators, the use of sampling or a census, a change in methods, the ability to find people, the area of focus (e.g., urban areas vs. large rural areas), and the level of training [5].

Some of these factors introduce bias to PIT counts.

That is, some homeless people have a lower probability of being included in the count than others.

During counts, enumerators sometimes estimate the number of people inside a tent or in an abandoned building without going inside, because of risk or out of respect [5].

In addition, characteristics such as age, sex, and race/ethnicity are assumed if not confirmed through interview [5].


We feel that a few simple strategies could vastly improve the estimates and help address these issues.


First, HUD could provide clearer and more accessible guidance and training on PIT counting methods [5].

This would result in more accurate and more consistent estimates from year to year [5].

In particular, HUD could provide communities with the resources and training necessary to calculate margins of error for their estimates [5].

One way to improve accuracy for the unsheltered population PIT count, through improved estimates of the error, would be to use plant-capture methods (in which people impersonating homeless individuals are “planted” in predetermined locations to help assess what percentage of homeless persons are being missed during the count), which have previously been used in certain communities on an ad hoc basis [15].


Additional options HUD could consider for improving estimates of people experiencing homelessness include conducting the count over several days and nights or over the course of a week or increasing the number of communities that use postcount service-based surveys.

These surveys involve interviewing people receiving services from soup kitchens, drop-in centers, or other places frequented by people experiencing homelessness to assess whether they were homeless on the night of the PIT count.

It may also be worthwhile to expand homeless enumeration efforts to include people who are “doubled up” (e.g., sleeping on a friend’s sofa) and do not meet HUD’s current definition of homelessness and thus are not currently included in HUD’s estimates.

Estimating the size of this population poses additional methodological challenges, but it would be a worthwhile endeavor for understanding how conditions that may be understood as homelessness affect health.


Finally, it need not be solely the responsibility of HUD to routinely collect data on the scope of homelessness in the United States.

Key population-based health surveys, such as the National Health and Nutrition Examination Survey and the National Health Interview Survey, could promote understanding of the risk homelessness poses to health by incorporating measures of current housing status, recent exposure to homelessness, and risk of becoming homeless.


Epidemiologic purposes are just one of many uses for population-level homelessness data


Mosites et al. note that more accurate population-level data would lead to better epidemiologic research focused on homelessness [7].

While that is true, there is already abundant evidence of the negative health impacts of homelessness, and improving counts of people experiencing homelessness solely for the purpose of developing more precise estimates of these negative health impacts is likely to be insufficient for achieving improved health and housing outcomes in this population.

Indeed, there are reasons to improve these counts beyond their ability to produce better research.

Currently, these data inform Congress on the magnitude of the problem and the characteristics of the homeless population, and they directly affect the amount of funding allocated to federal homelessness programs and the ability of programs that serve homeless populations to plan and respond to homelessness [5].

For this reason, it is imperative that these data be accurate.

Improving data collection methods and accuracy is essential for supporting and serving the homeless population and other hard-to-reach groups, such as immigrants.

Beyond assessment of health risks, improved data collection will inform interventions and natural experiments which have the potential to improve health and quality of life.


CONCLUSION


The annual data from HUD are improvements over past ad hoc and sporadic information-gathering, which varied considerably.

Nevertheless, the HUD data, especially the PIT counts, are considered underestimates.

Fortunately, methods for improving the estimates are well known.

Commitment of resources from federal and state entities will be needed, as well as thoughtful use of well-regarded population-based surveys.

There is broad agreement that improved estimates of the homeless population would be desirable and could lead to progress in addressing the health and housing needs of those experiencing homelessness.

Yet, better estimates require both the political will and adequate resources.

Given the marginalized nature of this population and its significant intersection with other historically marginalized racial groups and people with disabilities, we hope that such political will and resources will be forthcoming.


We appreciate the insights provided by Roncarati et al. [ 1] in their commentary on our article [2].

The authors raised important considerations about recent progress related to data collection on homelessness, possible opportunities to improve data collection processes, and nonepidemiologic uses for data on homelessness.

In particular, we appreciate that the authors outlined specific opportunities to improve data collection.


We emphasize that the primary goal of our article was to ensure that epidemiologists and public health practitioners are aware of the available data for use in their analyses.

Despite the limitations and opportunities for improvement, these data sources are available for use now.

Use of population data on homelessness is especially timely and critical during the response to coronavirus disease 2019 in order to understand the burden of disease and estimate resource needs for people experiencing homelessness.


We agree with Roncarati et al. that data collection can be improved [1].

Efforts to improve these data, especially for epidemiologic purposes, should be a collaboration across health and social service sectors.

As the authors note, collecting information on housing status in health surveys is one way to increase understanding of risks of and exposure to homelessness.

However, because concerns about the health of people experiencing homelessness inherently cross sectors, collaboration between public health, health care, social services, and homeless service agencies is necessary to make wider improvements in the representativeness of homelessness data.


Although Roncarati et al. suggest that “there is already abundant evidence of the negative health impacts of homelessness” [1, p. 2439], quantification of health inequities, whether they are socioeconomic, geographic, or racial, remains crucial.

There are large gaps in the peer-reviewed literature related to homelessness, which can lead to misunderstanding of risk and misallocation of public health resources.

Epidemiologic analyses can help to protect the health of people currently experiencing homelessness while ultimately supporting the importance of stable housing and the end of homelessness.

We look forward to working collaboratively across sectors to achieve these goals.


The University of Tokyo Institutional Review Board approved this study.

The board waived the requirement for informed pa- tient consent because of the anonymous nature of the data.


For this study, we used the Japanese diagnosis procedure com- bination (DPC) database, which was described in detail previ- ously [21].

In short, the DPC database includes administrative claims and discharge abstract data for all inpatients discharged from more than 1000 participating hospitals; it covers approx- imately 92% (244/266) of all tertiary-care emergency hospitals in Japan and 90% (90/100) of institutions certified for training burn specialists by the Japanese Society for Burn Injuries [22].

The database includes the following information for each pa- tient: age; sex; primary diagnosis; comorbidities on admission and post-admission complications coded with the International Classification of Diseases, 10th Revision (ICD-10), codes and written in Japanese; medical procedures, including types of sur- gery, coded with original Japanese codes; daily records of drug administration and devices used; length of stay; and discharge status.

The dates of hospital admission, surgery, bedside proce- dures, drugs administered, and hospital discharge are recorded using a uniform data submission format [21-25].


Several lists of scores are also available in the database, in- cluding burn index and Japan coma scale (JCS) scores.

The burn index takes both the surface area and thickness of the burned area into consideration: burn index = full thickness of total burn surface area + 1/2 partial thickness of total burn sur- face area [22, 26].

The JCS correlates well with the Glasgow coma scale; consciousness scored at 100 points on the JCS is equivalent to a score of 6-9 on the Glasgow coma scale.

We cat- egorized the JCS scores into 4 groups: 0, alert; 1-3, delirium; 10-30, somnolence; and 100-300, coma [21-25].

To quantify the extent of comorbidities, the ICD-10 code for each comor- bidity was converted to a score, and the sum was used to calcu- late the Charlson comorbidity index (CCI) as previously described [22, 27, 28].

Briefly, the CCI is a method of predicting mortality by classifying or weighting comorbidities.

It has been widely used by health researchers to measure case mix and the burden of disease [27].

We categorized patients into 1 of 3 groups on the basis of the CCI: low, 0; medium, 1; and high, ≥2 [22].


We categorized the hospital types as academic or nonaca- demic.

We defined teaching hospitals as to whether or not the institution was designated for postgraduate clinical training.

We designated hospital volume as the number of eligible patients treated for the current study and categorized into quartiles (very low, low, high, and very high).

We defined the use of prophylactic antibiotics as treatment with first-generation ceph- alosporin and ampicillin/sulbactam (ie, targeted for burn infec- tion) within 2 days after admission without being used for perioperative management.


We identified all patients with severe burns (burn index ≥10) [22] who were recorded in the database from 1 July 2010 to 31 March 2013.

The exclusion criteria for this study were the following types of patients: out-of-hospital cardiac arrest, dis- charged within 2 days after admission (to avoid immortal time bias), administered nonprophylactic antibiotics within 2 days after admission, and underwent surgery (skin grafting) within 2 days after admission.

We thus compared patients who were administered prophylactic antibiotics within 2 days after admission ( prophylaxis group) and those who were not administered any antibiotics within that time (control group).

Because the DPC is an administrative database with informa- tion input as to when patients are discharged, patient follow- up began on the day of admission and ended on the hospital discharge date (to home, transfer to another hospital, or death).

We could not follow up patients after discharge from the hospital since the information was not available in this dis- charge database.

Although physicians in charge were obliged to record the burn index, we were unable to evaluate patients for whom these data were missing.


The endpoint used in this study was all-cause 28-day in- hospital mortality.

The secondary endpoints were in-hospital mortality and the use of carbapenem, tazobactam/piperacillin, fourth-generation cephalosporins, and drugs for methicillin- resistant Staphylococcus aureus (MRSA; eg, vancomycin, teico- planin, arbekacin sulfate, linezolid daptomycin) started after day 2 or later.


Since inhalation injury that requires mechanical ventilation sig- nificantly affects the outcome for burn patients [22, 26, 29-32], we divided the patients into 2 categories: those who had re- ceived mechanical ventilation within 2 days after admission and those who had not.

We compared categorical variables using the χ 2 or Fisher exact test.

To assess the propensity score, we fitted a logistic regression model for prophylactic an- tibiotic use as a function of patients' demographic and clinical characteristics as well as hospital factors, which included the following: age; sex; burn index; CCI, level of consciousness on admission; hospital type (academic or nonacademic); teaching hospital status and hospital volume; use of catecholamine Prophylactic Antibiotics for Severe Burns • CID 2016:62 (1 January) • 61 (dobutamine, norepinephrine, and/or dopamine); antithrom- bin use; treatment with recombinant human soluble thrombo- modulin; treatment with haptoglobin; and requirement for escharotomy and debridement [1, 21-26, 29-34].

We performed propensity score-adjusted logistic regression analyses to evalu- ate the effect of prophylactic antibiotics (with and without me- chanical ventilation, respectively), fitted with a generalized estimating equation to adjust for institutional clustering [35].


In addition, we also performed a 1-to-1 propensity score- matched analysis (of prophylactic antibiotic and control groups) using nearest-neighbor matching.

A match occurred when a patient in the prophylaxis group had an estimated score within 0.2 standard deviations of a patient in the control group.

We examined the balance in baseline variables using standardized differences, where >10% was regarded as imbalanced [36].

We performed logistic regression analysis fitted with generalized estimating equations to examine the association between pro- phylactic antibiotic use and 28-day in-hospital mortality and accounted for the paired nature of the propensity score- matched patients.

We used Cox regression analysis to assess dif- ferences in in-hospital survival rates between patients with and without prophylactic treatment in the propensity score- matched groups.

We performed all statistical analyses using IBM SPSS, version 22 (IBM Corp., Armonk, New York).


We identified 2893 severe burn patients treated at 583 hospitals during the 33-month study period (Figure 1).

We categorized the patients according to whether they received mechanical ventilation within 2 days after admission (n = 692) or not (n = 2201).

We further divided the patients into those with and without prophylaxis.

From the groups with and without mechanical ventilation, we generated 232 and 526 propensity score-matched pairs, respectively.


Overall, 35% (1013/2893) re- ceived prophylactic antibiotics: 60% (412/692) with mechanical ventilation and 27.3% (601/2201) without mechanical ventila- tion.

The mean doses and durations of prophylactic first- generation cephalosporin or ampicillin/sulbactam were 2.1 g/ day for 6.9 days and 4.6 g/day for 6.7 days, respectively.


Table 1 shows the baseline characteristics of the unmatched and propensity score-matched groups among mechanically ventilated patients.

In the unmatched groups, patients were more likely to have received prophylaxis if they were younger, required more norepinephrine or other drugs, or needed escharotomy and debridement.


Table 2 shows the baseline char- acteristics of the unmatched and propensity score-matched groups without mechanical ventilation.

Among the unmatched groups, patients were more likely to have received prophylaxis if they were treated at teaching hospitals, required higher doses of cat- echolamines or other drugs, or required debridement.


After pro- pensity score matching, the baseline patient characteristics were well balanced between the groups, as evident in Tables 1 and 2.


Overall 28-day in-hospital mortality among all patients was 15.2% (441/2893).

There was a significant 28-day in-hospital mortality difference between patients who were mechanically ventilated and those who were not (42.5%, 294/692 vs 6.7%, 147/2201).

Pro- pensity score- adjusted logistic regression analyses adjusted for institutional clustering revealed a significant association between the use of prophylactic antibiotics and lower 28-day in-hospital mortality in 692 mechanically ventilated patients (odds ratio [OR], 0.71; 95% confidence interval [CI], .52 to .96), but there was no significant relationship among the 2201 patients who were not mechanically ventilated (OR, 0.94; 95% CI, .61 to 1.5).


Among the mechanically ventilated patients, significant dif- ferences in 28-day in-hospital mortality occurred between the 2 62 • CID 2016:62 (1 January) • Tagami et al groups for both the unmatched (control vs prophylaxis, 48.6% vs 38.3%; difference, 10.2%; 95% CI, 2.7 to 17.7) and propensi- ty-matched groups (47.0% vs 36.6%; difference, 10.3%; 95% CI, 1.4 to 19.3; Table 3).

Logistic regression analyses showed a sig- nificant association between the use of prophylactic antibiotics and lower 28-day in-hospital mortality in the propensity- matched groups (OR, 0.65; 95% CI, .45 to .95).

There was a sig- nificant in-hospital mortality difference between the 2 groups for both the unmatched (56.8% vs 48.5%; difference, 8.2%; 95% CI, .6 to 15.8) and propensity-matched groups (55.6% vs 45.6%; difference, 9.9%; 95% CI, .8 to 19.0).

Cox regression anal- ysis showed a significant in-hospital mortality difference between the control and prophylaxis groups in the propensity-matched groups (hazard ratio, 0.70; 95% CI, .54 to .91; Figure 2).

In the propensity-matched groups, after day 2, there were no significant differences in the proportion administered anti-MRSA drugs (43.1% vs. 38.8%; difference, 4.3%; 95% CI, −4.6 to 13.2), carba- penem (34.1% vs. 37.9%; difference, −3.9%; 95% CI, −12.6 to 4.9), tazobactam/piperacillin (31.0% vs. 29.3%; difference, 2.0%; 95% CI, −7.8 to 12.0), or fourth-generation cephalosporins (12.5% vs. 14.2%; difference, −1.7%; 95% CI, −7.9 to 9.6).


Among the patients who did not receive mechanical ventila- tion, there was no significant difference in 28-day in-hospital mortality between the 2 groups for both the unmatched and propensity-matched groups (Table 3).

Logistic regression anal- yses showed that there was no significant association between the use of prophylactic antibiotics and lower 28-day in-hospital mortality in the propensity score-matched groups (OR, 0.81; 95% CI, .45 to 1.4).

There were no significant in-hospital mor- tality differences between the 2 groups for both the unmatched (control vs prophylaxis; 11.3% vs 10.1%; difference, 1.1%; 95% CI, −1.8 to 4.0) and propensity-matched groups (8.6% vs 8.4%; difference, 0.2%; 95% CI, −3.2 to 3.6).

Cox regression analysis did not indicate significant in-hospital mortality differences between the control and prophylaxis groups for the propensi- ty-matched groups (hazard ratio, 0.94; 95% CI, .61 to 1.5).

Although more patients received carbapenem and tazobactam/ piperacillin after day 2 in the prophylaxis group than the control group (10.5% vs 18.1%; difference, −7.6%; 95% CI, −8.4 to −3.4 and 8.7% vs 13.3%; difference, −4.6%; 95% CI, −8.9 to −.8, re- spectively), there was no significant difference between the pro- portion administered anti-MRSA drugs (14.8% vs 18.4%; difference, −3.6%; 95% CI, −8.1 to 1.6) or fourth-generation cephalosporins (4.2% vs 5.3%; difference, −1.1%; 95% CI, -3.7 to 1.4) after day 2 in the prophylaxis and control groups.


The results of this study, which used a nationwide database, suggest that there may be a significant association between the use of prophylactic antibiotics and lower mortality in Several recommendations and guidelines for the initial man- agement of severe burns [17, 18, 20], including Japanese guide- lines (Japanese Society for Burn Injuries) [19], do not address systemic antibiotic prophylaxis or explicitly state that prophy- lactic antibiotics are not recommended.

The use of prophylactic antibiotics may not be safe because they could also promote the emergence of resistant strains of microorganisms, further im- peding the treatment of infections.

However, in terms of actual clinical practice in a general setting (ie, not for perioperative management) across Japan, the present study found that pro- phylactic antibiotics were administered to approximately 35% of all patients with severe burns and to 60% of those receiving mechanical ventilation.


These results are consistent with the findings of the system- atic review study by Avni et al [15].

They analyzed 136 paired cases from 5 studies that did not take the use of mechanical ven- tilation into consideration and determined that systemic antibi- otic prophylaxis significantly reduces all-cause mortality.


The methodological strength of our study lies in the evaluation of a large sample of patients across Japan with severe burns.

We conducted robust adjustment for measured confounders using propensity-matched analyses and focused on patients who did or did not receive prophylaxis within 2 days after admission.


In our study, the prophylactic antibiotics mainly targeted microor- ganisms, which in general heavily colonize the wound surface within the first 48 hours.

In severe burn patients, coagulase-neg- ative staphylococci and methicillin-sensitive S. aureus have been found to be the most prevalent isolates in admission cultures [4].

A gradual decrease in the number of isolates of coagulase- negative staphylococci and a marked increase in the numbers of S. aureus and Pseudomonas aeruginosa have been observed from admission to day 21 [4].

There was subsequently a constant increase in MRSA [4].

Accordingly, as the secondary outcome, we compared the incidence of patients who required anti-MRSA drug administration with the prophylaxis group and controls.

We found that the use of prophylactic antibiotics did not lead to an increase in the proportion of anti-MRSA drug administration.


Several small sample studies [10, 11] and systematic reviews [15, 16] have found reduced incidence of pneumonia in burn patients to be associated with systemic antibiotic regimens.

Several studies have indicated the presence of immune defects in the early phase following thermal injury among patients with severe burns.

The defects include impaired cytotoxic T-lymphocyte response, myeloid maturation arrest causing neutropenia, impaired neutrophil function, and decreased mac- rophage production [6-8] .The results of the current propensity score-matched analysis suggest that mechanically ventilated pa- tients with severe burns who received prophylactic antibiotics were more likely to survive than similar patients who did not receive the antibiotics.

These findings were robust, as demon- strated by the results obtained by the logistic regression and Cox regression analyses.

Although in the present study we were unable to determine the indications for mechanical venti- lation for each patient, mechanically ventilated patients were apparently in a more severe condition (higher burn index and mortality rate) than those who did not undergo the procedure.

Although we cannot infer a robust cause-and-effect relationship from this study, we can speculate that severe burn patients who required mechanical ventilation could have (severe enough to) had immune defects and may have suffered from sepsis (includ- ing pneumonia) in the early phase after burn injury.


This study has some limitations.

First, although it used a na- tionwide database, it was retrospective and observational, with- out randomization.

Even though we adopted propensity score matching to adjust for differences in baseline characteristics and disease severity, there may still have been bias in the form of confounders that were not measured.

However, we were able to identify the major factors that potentially affect mortality in patients with severe burns, such as age, sex, burn index, comorbidities, and use of mechanical ventilation [5, 20, 22, 26, 28, 33], and the baseline characteristics of selected pa- tients with severe burns were well balanced in the propensity score-matched groups.

Although large randomized trials are necessary to confirm these results, it may be not easy to perform such trials in a large number of patients for this life-threatening condition, especially for mechanically ventilated patients with Prophylactic Antibiotics for Severe Burns • CID 2016:62 (1 January) • 65 severe burns (mortality rate of 42.5%).

Thus, the present nation- wide study may provide the best attainable level of evidence on this issue.


Second, the database does not include data on micro- organisms, the actual incidence of infection, or the cause of death, that is, death caused by sepsis due to burn site infection or pneumonia.


Third, we did not consider topical antibiotics when we analyzed the data.

However, recent studies have sug- gested that topical antibiotic prophylaxis confers no beneficial effects on outcome [15, 16].


Finally, we evaluated only severe burn patients in a general setting (ie, not in a perioperative set- ting), as defined in previous studies [15, 16].

Thus, our results cannot be generalized to the use of prophylactic antibiotics for severe burns in perioperative settings.


The current nationwide database study using propensity score analyses found that the use of systemic prophylactic antibiotics in general settings may result in improved 28-day in-hospital mortality in mechanically ventilated patients with severe burns but not in patients who do not receive mechanical ventilation.

Future multination studies are required to validate our results.


All authors assisted with study conception and design and with data acquisition.

T. T., H. M., and H. Y. were responsible for the statistical analyses and the first draft of the manuscript.

All authors amended and commented on the manuscript and approved the final version.


This work was supported by grants for Research on Policy Planning and Evaluation from the Ministry of Health, Labour and Welfare, Japan (grant numbers H27-Policy-Designated-009 to K. F. and H. Y. and H27-Policy-Strategy-011 to H. Y.) and Grants-in-Aid for Scientific Research, Japan (grant number KAKENHI-15H05685 to T. T.).

The funders had no role in the execution of this study or interpretation of the results.


Potential conflicts of interest.

All authors: No potential conflicts of in- terest.

All authors have submitted the ICMJE Form for Disclosure of Poten- tial Conflicts of Interest.

Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.


TO THE EDITOR—We read with great interest the article by Tagami et al [1] suggesting that antibiotic prophylaxis may improve outcome in patients with burn injuries requiring invasive mechanical ventilation.

They reached their conclusion using a propensity score analysis from a Japanese nationwide database.

The authors deserve credit for addressing such an important question, because outcomes in severely ill patients with burn injuries remain poor, with most dying of sepsis.

Although we agree that antibiotic prophylaxis has been such questioned in critically ill patients with burn injuries [2], we also believe that too much reliance on the results reported by Tagami et al [1] may lead to inappropriate conclusions regarding the effectiveness of antibiotic prophylaxis in this specific population.


One major point is the setting in which patients with burn injuries were treated.

Such patients have longer intensive care unit (ICU) stays than most medical and surgical patients and most often receive several courses of antibiotic treatment owing to repeated sepsis.

The need to control antibiotic exposure to limit selection of drug-resistant pathogens therefore seems crucial and must be considered in the benefit-risk balance for antibiotic prophylaxis in these patients.

In the current study [1], most mechanically ventilated patients did receive antibiotic prophylaxis (60%), which seems a very high proportion given current recommendations [3].

However, we do not know the basis on which antibiotic prophylaxis was administrated.


Previous meta-analysis has shown that the risk of death was reduced with selective digestive decontamination in ICU patients and that systemic antimicrobial therapy was essential to this reduced risk [4].

Although short-term emergence of antimicrobial resistance seems limited, the risk of long-term resistance remains largely unexplored.

It is likely that some centers had a protocol of antibiotic prophylaxis for mechanically ventilated patients (ie, not patients with burn injuries specifically).

Centers with such protocols are likely to have implemented healthcare-associated infection prevention bundles, which may have a large impact on prognosis.

This important point is missed by the propensity score adjustment.


In other words, it is unclear whether some mechanically ventilated patients with burn injuries may have benefited from selective digestive decontamination and systemic antibiotic prophylaxis implemented for all mechanically ventilated patients.

This question is reinforced by the fact that most patients with burn injuries seem to have been treated in ICUs that were not dedicated burn units and had a low volume of such patients (>80% of mechanically ventilated patients with burn injuries were admitted to centers with <12 such admissions a year).

A control group to determine the effect of antibiotic prophylaxis in mechanically ventilated patients without burn injuries would have provided important insight in this regard.


Finally, we are concerned by the low doses of antibiotics used (2.1 g/d for first-generation cephalosporin or 4.6 g/d for ampicillin-sulbactam), given the high risk of underdosing in patients with burn injuries.

Underdosing may dramatically decrease efficacy and increase the risk of selecting drug-resistant bacteria.

In sum, we believe that Tagami et al [1] may have missed the specificity of treatment for patients with severe burn injuries in specialized centers.

The question of antibiotic prophylaxis and infection control in such patients remains open.


TO THE EDITOR—We thank Legrand and Lafaurie [1] for their interest and comments on our study [2].

Their major concerns were within-hospital clustering and the doses of antibiotics used in our study [2].

We agree with their view [1] that between-hospital differences in medical care for patients with severe burns, particularly in specialized centers, may influence outcomes.

To reduce these differences, we included several hospital factors (hospital type, teaching hospital status, and hospital volume), as well as patient-level demographic and clinical characteristics, among the dependent variables in the logistic regression model for estimating the propensity score analysis [2].

After the propensity score matching, the baseline patient characteristics related to the outcome of burns [3] and the hospital factors were well balanced between the groups [2, Tables 1 and 2].


Moreover, as a different confirmatory analysis for the propensity score matching, we performed propensity score-adjusted logistic regression analyses to evaluate the effects of prophylactic antibiotics fitted with a generalized estimating equation to adjust for within-hospital clustering [4].

Although neither the propensity score-matching analyses nor the logistic generalized estimating equation regression analyses could completely avoid the effect of within-hospital clustering, the consistent results from these 2 analyses may serve to reflect the robustness of our results.


As mentioned by Legrand and Lafaurie [1], the doses of antibiotics used in our study were lower than those used in Western countries.

However, these are the standard doses used in Japan and were approved by the Japanese Ministry of Health, Labour and Welfare.


Finally, we agree with the final comment by Legrand and Lafaurie [1] that the question of antibiotic prophylaxis and infection control in patients with burns remains open.

Further multinational studies are warranted to clarify the true effectiveness of prophylactic antibiotics in patients with severe burns.

Our study may help ignite this into a more “burning” question and spark the initiation of multinational trials.


Snake venoms are a complex mixture of proteins and polypeptides which cause a broad range of toxic effects when released by the snake into the prey during a bite.

Although snake toxins have proba- bly evolved to assist in prey capture and defense, they inadvertently cause a range of systemic effects when humans are bitten.

Many snake venoms contain procoagulant toxins that activate the clotting pathway, which results in venom induced consumption coagulopa- thy (VICC) in human envenoming, and this is the most common im- portant clinical manifestation of snakebites worldwide.

1,2 Australian elapids contain serine proteases, which closely resemble the mam- malian prothrombinase complex.

3 Exposure to these prothrombin activators leads to widespread activation of the coagulation pathway resulting in consumption of the key clotting factors, including fibrinogen, FV, and FVIII.

2,4 More recently, microangiopathic hemolytic anemia (MAHA), acute kidney injury, and thrombocytopenia have also been recognized to be as- sociated with VICC.

Although this is thought to be a form of throm- botic microangiopathy, the pathophysiology remains unclear. 2-4 Circulating microvesicles (MV) or small cell- derived extracellular vesicles have been implicated in hemostatic disorders-an increase in MV associated with pathological thrombosis, and a decrease contrib- uting to potential bleeding.

5-7 Several cell- derived MV are frequently found in the circulation-these include red cell, platelet, leukocyte, and endothelial cell- derived.

8,9 Flow cytometry is a popular and easily acces- sible technique for measuring MV and determining their cellular origin.

10 The role of circulating MV in snakebite patients is not known and we present the results of a unique investigation of MV in snakebite re- lated VICC and MAHA.


Changes in MV levels after envenoming could indicate direct toxin mediated damage to the endovascular system which has been hitherto difficult to evaluate or measure in a system- atic fashion.

To explore MV in snake envenoming further, we analyzed the levels of circulating platelet, endothelial, and red blood cell- derived MV in patients recruited to the Australian Snakebite Project (ASP) with confirmed envenoming from brown snake, tiger snake, or taipan.


Samples and clinical information were sourced from patients re- cruited to the ASP with elapid envenoming (including brown snake, tiger snake, and taipan envenoming), as previously described.

11 Citrated plasma was collected from each snakebite patient re- cruited to the study.

Peripheral blood was collected in 0.109 mol/L tri- sodium citrate and platelet- free plasma was prepared by double centrifugation of whole blood for 15 minutes at 2500×g. All sam- ples were processed within 2 hours of collection, and aliquots were stored at −80°C for further batched analysis.

Samples were then thawed at 37°C, 15 minutes before testing.

Control samples were obtained from healthy volunteer blood donors after appropriate consent-these were processed and stored in an identical fashion to the patient samples.


Prothrombin time (PT), activated partial thromboplastin time (aPTT), fibrinogen, and D- dimer concentrations were measured on a Behring Coagulation System (BCS; Dade Behring, Marburg, Germany) at a centralized laboratory.

Full blood count was mea- sured as per local routine laboratory protocol and blood films were examined by an independent pathologist for presence of red cell fragmentation for confirmation of MAHA.

VICC was defined as an international normalized ratio (INR) >3 with undetectable fibrino- gen, and MAHA as the occurrence of thrombocytopenia and red cell fragmentation.

11 Flow cytometry was done on a BD FACS Canto flow cytometer (BD Biosciences, San Jose, CA, USA) with combinations of antibodies specific for platelet marker (CD41a- PE; Clone HIP8, BD Biosciences), red cell marker (CD235a- APC; Clone GA- R2, BD Biosciences), en- dothelial cell marker (CD62e- APC; Clone 68- 5H11) and appropriate isotype controls in a final volume of 100 μL of PBS.

Megamix beads (Biocytex, Marseille, France) were used to gate events less than 1 μm in diameter and analysis was undertaken using FACSDiva software.

Control samples were obtained from healthy volunteer blood do- nors from the Australian Red Cross blood service.

They ranged in age from 21 to 60 years and had a mix of both sexes.

These subjects were screened for any significant medical conditions and samples obtained were processed within 4 hours from collection in a citrated tube in a manner similar to the snakebite patients' samples.


Microsoft Excel 2007 and SPSS, (IBM SPSS Statistics for Macintosh, Version 19.0., IBM Corp., Armonk, NY, USA) were used to collate and evaluate the data.

Prism 7.0a (Graphpad Software, La Jolla, CA, USA) was used for graphical representations and non- parametric analysis was undertaken to evaluate differences given the large standard deviations in the MV levels.

The normal co- hort, VICC without MAHA (VICC no MAHA) and VICC with MAHA


• Snake bites are associated with venom induced coagulopathy, microangiopathic hemolytic anaemia (MAHA) and thrombocytopaenia.


• The mechanisms of MAHA and role of MV in its pathophysiology in snake bite are not known.


• This results of this study show that red cell MV are increased in snake bites associated with MAHA whilst endothelial MV are reduced in all snakebites.


• Evaluation of circulating endovascular microvesicles provide an important mechanistic tool and have a potential role as predictive markers for studying the effects of snake venom.


(VICC + MAHA) groups were compared.

One way ANOVA (Kruskal- Wallis) test was used for non- parametric comparisons between the groups and a level of 0.05 was set for statistical significance.

The study was approved by the local ethics committee (Hunter New England ethics committee approval number 06/12/13/5.05) and by all major State and Territory human research ethics committees for the hospitals involved in the ASP.


A cohort of snakebite patient samples who had envenoming and VICC, and had baseline samples collected on admission to hospital, were evaluated.

There were 17 patient- samples with VICC alone (no evidence of MAHA; designated as VICC no MAHA) and 13 patient- samples with VICC with MAHA (designated as VICC + MAHA); and 28 normal healthy controls.

The age range for all snakebite patients was 24- 73 years and there were more males than females (see Table 1).

However, the differences between the age and gender between the groups compared was not statistically significant (Kruskal- Wallis test for age and z- test for gender).

All envenomation was by brown snake bites apart from five patients (two tiger snake and three where the type could not be determined due to insufficient pre- antivenom sam- ples).


results for baseline blood counts and circulating MV levels in the three groups are shown in the table and illustrated in Figure 1.

As expected, snakebite patients with MAHA had lower hemoglobin and platelets compared to controls and those without MAHA (P < 0.001).

Platelet MV were significantly elevated in snakebite patients with- out MAHA (VICC no MAHA) compared to controls (P = 0.009), but there were no differences between the snakebite patients with and without MAHA (P = 0.21).

Interestingly, the endothelial MV were significantly reduced in all snakebite samples compared to the con- trols (P < 0.001).

The levels of red cell MV in snakebite subjects with MAHA (VICC + MAHA group), however, were significantly higher than those without MAHA (P = 0.002) and when compared to con- trols (see Figure 1).


A markedly elevated red cell MV level may be used as a sur- rogate marker for MAHA whereas thrombocytopenia alone may Increased levels of red cell MV in snakebite patient samples with MAHA is consistent with red cell breakdown and hemolysis.

If this is a consistent finding then measuring MV at presentation could be useful as a predictive marker for MAHA in snakebite pa- tients.

This suggests an alternative mechanism for the observed microangiopathy other than endothelial damage alone due to the snake toxin.

The rapid generation of red cell MV may subsequently lead on to a consumptive coagulopathy and microvascular throm- bosis, which is often accompanied by thrombocytopenia that is typical of MAHA.