The proportion mediated is given by the ratio of NIE to TE.


The average proportion mediated and corresponding P value were reported for each biomarker.

Models were fitted overall and within age subgroups (<65 or ≥65 years) based on data from adult patients (≥18 years of age) with at least one recorded biomarker measure prior to ventilation.

A complete case analysis was performed assuming data were missing completely at random.

Summary level data on characteristics of patients with and without missing data are provided in Supplement Table S3 (24).


Primary analysis was based on data derived from the MGH co- hort.

Replication analysis was based on the CUIMC/NYP cohort.

Additional analyses using the MGH cohort were performed to check the consistency of our conclusions when adjusting for additional potential confounders (type 2 diabetes mellitus [T2DM], hypertension, dyslipidemia, and pulmonary disease) in the multivariable models or including CRP measurements after mechanical ventilation in the calculation of peak CRP.

All analyses were completed using R version 3.5.0.

Mediation analysis was performed using the R package "mediation."


The Partners HealthCare Institutional Review Board (IRB) (#2020P000829) approved collection of curated data based on comprehensive manual chart reviews and data extrac- tions from the EHRs on patients who receive care through the Mass General Brigham (formerly Partners) system.

The CUIMC IRB approved this study (#AAAS9835) and waived the requirement for obtaining informed consent.


Table 2 provides results of the causal mediation analysis of peak CRP overall and stratified by age (<65 or ≥65 years) in the MGH cohort.

In the adjusted Total Effect Model (Supplement Table S2) (24), obesity was a significant pre- dictor of ventilation or death (OR = 1.73; 95% CI = [1.25, 2.41]) and in the adjusted Mediator Model obesity of was significant predictor of peak CRP (estimate = 0.261, P < 0.001).

After additional adjustment for peak CRP in the adjusted Outcome Model, the effect of obesity on ven- tilation or death was partially attenuated (OR = 1.49; 95% CI = [1.03, 2.16]).

A statistically significant positive inter- action between peak CRP and age on severe outcome was also observed, suggesting a greater effect of peak CRP on COVID-19 outcomes with increasing age.

There was no observed interaction between peak CRP and obesity on se- vere outcome.

Summary data on patients with and without complete data are in Supplement Table S3 (24).

The average proportion of the effect of obesity on ventilation/death that was mediated by peak CRP is 0.49 (P < 0.001).

In stratified analysis, the proportion mediated was 0.44 (P = 0.180) in patients ≥65 years of age and 0.52 (P < 0.001) in pa- tients < 65 years of age.


The model results for 5 additional inflammatory bio- markers are provided in Table 2 and Supplement Figure S1 (24).

In all cases, a statistically significant positive asso- ciation was observed between peak biomarker value and the probability of ventilation or death (Outcome Model).

Strikingly, with the exception of ESR, there was no detect- able association between obesity and the peak values for Downloaded from https://academic.oup.com/jcem/article/107/2/e698/6362597 by Robert Crown Law Library user on 07 October 2025 e702 The Journal of Clinical Endocrinology & Metabolism, 2022, Vol. 107, No. 2 any of these additional biomarkers, overall nor within age strata (Mediator Model).

This result suggests that peak D-dimer, ferritin, WBC, and IL-6 are unlikely to be in the causal pathway between obesity and severe outcomes in this population.

Similar to CRP although to a lesser extent, peak levels of ESR also mediated the effect of obesity on ventilation/death and, like CRP, this was more pronounced in the younger age group (<65 years old) though this was not statistically significant.


Replication analysis was performed using the CUIMC/ NYP COVID-19 cohort (N = 2626).

Supplement Table S4 (24) provides characteristics of this cohort overall and by obesity.

Similar to MGH, in the CUIMC/NYP co- hort, obese patients were more likely to be female (51% vs 37%, P < 0.001) and were younger (60 vs 69 median age, P < 0.001) compared with nonobese patients.

Obese patients were also more likely to be Black (14% vs 11%, P = 0.027).

Despite younger age, a history of hyperten- sion and T2DM were numerically greater in obese patients compared with nonobese patients, although not statistic- ally significantly different in unadjusted analysis.


Table 3 presents the results of causal mediation ana- lysis of CRP in the CUIMC/NYP cohort.

The overall esti- mated average proportion mediated was 0.67 (P = 0.002).

In stratified analysis, the proportion mediated was 0.63 (P = 0.230) in patients ≥65 years of age and 0.68 (P < 0.001) in patients <65 years of age.

Consistent also with the MGH registry data, an increase in peak values was associated with an increased odds of ventilator or death for ESR, WBC, D-dimer, ferritin, and IL-6.

There was no association, however, between obesity and peak values of D-dimer, WBC, and IL-6, while obesity was negatively associated with peak ferritin in patients ≥65 years of age (Supplement Table S5) (24).

Similar to MGH data, there was evidence of modest mediation effect captured by ele- vated ESR that was more pronounced in the younger age strata (<65 years of age).


Analysis based on a more fully adjusted model that in- cluded additional potential confounders type 2 diabetes mellitus, hypertension, dyslipidemia, and pulmonary dis- ease yielded similar results of a proportion mediated equal to 0.57 (P < 0.001) overall, 0.51 (P = 0.220) in patients ≥65 years of age and 0.53 (P = 0.002) in patients < 65 years of age.

The results of using all available CRP measurements before and after ventilation in the calculation of peak CRP are reported in Supplement Table S6 (24).

Here, the esti- mated average proportion mediated was 0.63 (P < 0.001) overall, 0.76 (P = 0.260) in patients ≥65 years of age, and 0.47 (P = 0.004) in patients < 65 years of age.


We find compelling and reproducible evidence for me- diation by CRP and ESR-related inflammation, but not other biomarkers of outcomes, in obesity-associated severe disease in 3828 COVID-19 patients at 2 independent qua- ternary care centers.


Obesity is one of the strongest risk factors for crit- ical illness in COVID-19 (1-4) while dysregulation of in- nate and adaptive immunity is a hallmark of patients with poor COVID-19 outcomes (10-12).

A direct causal role of obesity, especially by immuno-metabolically abnormal adipose tissue, in an exaggerated inflammatory response to SARS-CoV-2 infection could underlie the convergence of obesity, severe systemic inflammation, and poor out- comes.

Experimental mouse models and clinical studies have shown immune cell infiltration in obese adipose (25) and obesity converges with low-grade, systemic inflamma- tion in increased risk for cardiometabolic diseases (26-28).

Previous work has shown that obesity is also associated with inflammatory responses and worse outcomes in sepsis and acute respiratory distress syndrome (29).

Here, we show that obesity-associated systemic inflammation, as re- flected in peak circulating CRP, is indeed a mediator of se- vere outcomes in COVID-19, especially in younger obese patients, with greater mediation in patients <65 relative to >65 years of age.


It is notable that the mediation effect was specific to peak CRP and ESR but not peak levels of other inter- rogated biomarkers.

This is explained in part by the interesting result that peak CRP during hospitalization The outcome model had a significant CRP by age interaction and therefore the main effect of CRP was not reported here.

See Supplement Table S2 for more de- tailed results of this model fit.


The findings herein are consistent with data from clinical trials.

For example, the recent success of trials of systemic corticosteroids in reducing all-cause mortality in patients with severe COVID-19 (30-32) provides clear evidence of large clinical benefits by targeting activation of innate immunity, which is captured by peak CRP levels.

Indeed, there is an established link between corticosteroid benefit and reductions in CRP and ESR levels in non-COVID-19 inflammatory disorders (33).

Whether reductions in CRP and ESR track with therapeutic benefit of corticosteroids in COVID-19 remains to be determined.


In contrast, the apparent failure of early trials targeting IL-6 signaling in COVID-19 (34, 35) is consistent with our finding that IL-6 was not a significant mediator of obesity on severe COVID- 19 outcomes.

Moving forward, elevated CRP levels may be useful for stratifying COVID-19 patients into subgroups in clinical trials of anti-inflammatory strategies as well as to monitor therapeutic efficacy of novel interventions in COVID-19.

Failure to design statistically powered COVID- 19 clinical trials to target or detect effects in such subgroups may contribute to apparent lack of therapeutic efficacy (36) and limit progress due to false negative trial data.


Methodological strengths of our work include the novel application of mediation analysis as well as robust and in- dependent replication.

Our work also has limitations.


First, there are inherent biases in use of observational data to draw causal inference.

The causal mediation framework assumes sequential ignorability (21), which relies on meth- odical consideration of potential confounding variables and assumes no unmeasured confounding.


Second, missing data and measurement error are ubiquitous in observa- tional studies that rely on EHR data and this can result in biased findings (37).

An alternative to our complete case analysis strategy is to multiply impute missing values as- suming missing at random.

Additionally, patients enter the hospital at different stages of disease severity and there- fore the biomarker trajectories are not fully observed.

Moreover, Hispanic and Black individuals have disparity in availability and comorbidity reporting in EHR data and a greater propensity than White individuals to obesity at younger ages (38).


Third, despite robust replication across MGH and CUIMC/NYP, these 2 observational studies have differences in design.

It is critical moving forward to test anti-inflammatory therapies in prospective ran- domized controlled trials that are designed for targeted subgroup analyses, for example, using obesity and CRP- based stratification.


Fourth, given that central obesity measures are stronger predictors than BMI of outcomes in cardiometabolic diseases (39, 40), future studies should de- termine if CRP-related inflammation plays an even more powerful role in central obesity effects on outcomes in COVID-19 (36, 41).

This may be particularly important given the impact of central obesity on respiratory-related COVID-19 outcomes, including impaired mechanics of the respiratory system, the inability to pronate patients, and difficulties with intubation procedures.


Fifth, a latent prediabetic state, for example, glucose intolerance or in- sulin resistance, could correlate with obesity and be the cause of severe outcomes.

Testing this hypothesis is not yet feasible given the available clinical laboratory data and in the setting of acute illness.


Finally, given the complex rela- tionships among obesity and severe COVID-19 outcomes, additional work is required to define optimal clinical trial and therapeutic stratification for high-risk patients with obesity.


In conclusion, our results elucidate the mediating role of systemic inflammation in obesity-associated severe COVID- 19 outcomes.

The highly consistent pattern for all bio- markers and by age strata in both MGH and CUIMC/NYP cohorts supports generalizability of a specific mediating role captured by peak CRP (and to a lesser extent ESR) levels in the pathways between obesity and severe disease.

Stronger evidence of mediation in younger obese patients highlights clinical importance and potential therapeutic op- portunity to target systemic inflammatory pathways and to monitor therapeutic interventions in high-risk, obese sub- groups with elevated CRP levels.


We have read and appreciate the hypothesis and excellent data collected and analyzed to show that inflammation related to obesity is associated with increased severity of COVID-19 illness [1].

The COVID-19 pandemic has illustrated and exacerbated existing inequalities in health and well-being.

At the intersection of many of the pandemic drivers, sex plays an essential and frequently overlooked role.

There are several reasons why the analyses, as presented, warrant an in-depth analysis of sex.

First, the published data indicate that obese patients were more likely to be female (page e701) [1].

Which is sufficient reason, alone, to analyze outcomes separately by sex.

Second, rates of morbidity and mortality from COVID-19, as defined by the need for assisted ventilation, longer hospitalizations, and intensive care, appear to be greater in men than in women [2, 3] despite equivalent incidence of SARS-CoV-2 disease by sex.

Third, the C-reactive protein variable used to identify inflammation is reported to have a different mean population level in men vs women as documented by many years of National Health and Nutrition Examination Survey data [4].


The concept that data from women and men (female and male animals, and now female and male cells) be disaggregated and that outcomes be stratified by sex is now longstanding and supported by multiple scientific institutions [5].

The Endocrine Society, importantly, has specific sex-considerations in its published publication policy (https://academic.oup.com/jcem/pages/author_guidelines#OriginalArticles).

The “Reporting of the Sex of Research Subjects” guidelines specify that “the sex of research subjects must be indicated.

If both males and females were included in the study, the numbers of subjects from each sex should be indicated, and it must be indicated whether sex was considered a factor in the statistical analysis of data.”


These authors did clearly show how many women and men they studied. But, they have not indicated whether sex was considered in the statistical analysis, or even if they adjusted for sex in the analysis.

Furthermore, we note that the current JCEM policy does not meet guidelines that require separate statistical analysis by sex as do the international Sex and Gender Equity in Research (SAGER) guidelines [5, 6].

Science needs to follow scientific principles and guidelines, especially when not following them introduces sex and gender bias.

Women and men differ.

Both sexes deserve accurate representation in research and scientific publications.


We agree fully with Nelson et al [1] that rigor in the analysis of sex is critically important in studies of severe outcomes associated with SARS-CoV-2 infection, and particularly in investigations of obesity, inflammation, and the mechanistic underpinnings of severe disease.

Indeed, in tandem to the article in JCEM [2], members of our team published an article specifically investigating biomarkers of cardiac injury, renal injury, and inflammation as potential mediators of sex-associated mortality among SARS-CoV-2–infected individuals [3].

In other work, we report sex-stratified analyses in the study of signature biomarker states and mortality [4] and characterize sex differences in inflammatory markers among patients hospitalized with SARS-CoV-2 [5].

Appropriate handling of sex as a potential moderator or confounder is of paramount importance and for over a decade, we have included this focus in our clinical and translational research, for instance [6, 7].

We thank Nelson et al for highlighting this need, which we may have underemphasized in [2] as we were undertaking a much more comprehensive sex-specific analysis [3].


Notably, this second article is published within the section on Sex and Gender in Cardiovascular Medicine, focused specifically on examining sex differences in outcomes.

Importantly, we do want to clarify that, in our JCEM article [2], models were adjusted for sex and this fact was indicated throughout (see paragraph 2 of Statistical Methods, legend to Figure 1, footnote b of Table 2, and footnote b of Table 3).

We fully endorse the SAGER guidelines [8] and concur unequivocally with Nelson et al that “Science needs to follow scientific principles and guidelines, especially when not following them introduces sex and gender bias.”


Eighty-eight patients were included, 44 in each group.

Patient characteristics were similar although more patients in the combination group had renal disease.

Mortality was 36.4% and 15.9% in the monotherapy and combination therapy groups, respectively (P = .03).

Secondary outcomes of clinical success, length of stay, and length of ICU stay did not differ between groups.


Our data are supportive of the use of combination therapy with oral vancomycin and IV metro- nidazole in critically ill patients with CDI.

However, prospective, randomized studies are required to define optimal treatment regimens in this limited population of CDI patients.


Keywords.

Clostridium difficile infection; vancomycin; metronidazole; mortality; sepsis.

Clostridium difficile infections (CDI) have generated a significant burden on the healthcare system and are linked to 14 000 deaths per year in the United States [1].

The increasing prevalence of the fluoroquinolone- resistant BI/NAP1/027 epidemic strain of C. difficile, which has been shown to cause more severe disease than historical strains, has contributed to increased morbidity and mortality [2, 3].

The attributable mortal- ity rate of CDI at 30 days and 1 year after diagnosis was estimated at 6.9% and 16.7%, respectively, which was noted during outbreaks with the NAP1 strain [2, 4, 5].

In addition, it has been estimated that management of CDI costs US hospitals $3.2 billion annually [5].


The Society for Healthcare Epidemiology of America (SHEA) and Infectious Diseases Society of America (IDSA) developed recommendations to distinguish se- verity of CDI [6].

Patients presenting with an initial C. difficile episode with a white blood cell (WBC) count of ≥15 000 cells/mL or serum creatinine ≥1.5 times baseline are cat- egorized as having severe disease.

The recommended treatment for severe disease is oral vancomycin (BI recommendation).

Pa- tients with life-threatening megacolon, ileus, hypotension, or shock are categorized as having severe, complicated disease.

The recommended treatment for severe, complicated disease is oral vancomycin plus intravenous (IV) metronidazole (CIII recom- mendation).

This recommendation is based on expert opinion.

There are presently no data to support a preferred regimen for critically ill patients with CDI requiring admission to an intensive care unit (ICU).

The purpose of our study was to evaluate mortality among critically ill patients with CDI who received oral vancomycin (monotherapy) vs oral vancomycin with IV metronidazole (combination therapy).


A single-center, retrospective, observational, comparative study was conducted among critically ill patients with CDI.

This study was approved by the institutional review board of Wake Forest Baptist Medical Center.

Patients with a positive C. difficile polymerase chain reaction or toxin assay who were admitted to an ICU between June 2007 and September 2012 were screened for inclusion.

CDI was not required to be the attributable reason for ICU admission.

However, at least 3 of the following seven cri- teria had to be present within 24 hours of CDI treatment initia- tion: albumin <2.5 g/dL, heart rate >90 bpm, mean arterial pressure (MAP) <60 mmHg, WBC count ≥15 000 cells/mL, age >60 years, serum creatinine ≥1.5 times baseline, or temperature ≥100.4°F (38°C).


All patients received oral vancomycin as treat- ment of CDI.

Patients in the combination therapy group received concurrent IV metronidazole for at least 72 hours started within 48 hours after oral vancomycin.

Patients were excluded if they were <18 years of age or had unrelated gastrointestinal disease.

Pa- tients were matched 1:1 based on Acute Physiology and Chronic Health Evaluation II (APACHE II) scores.


The primary outcome measure was in-hospital mortality.

Secondary outcomes included clinical success at days 6, 10, and 21 (using predefined criteria), hospital length of stay (LOS) after CDI diagnosis, and length of ICU stay after CDI di- agnosis.

Multivariable analysis was performed to determine fac- tors independently associated with survival.


Clinical success was defined as improvement of diarrhea (a de- crease in the number of stools and/or volume), absence of fever, WBC count <15 000 cells/mL, normalization of tachycardia (if present), and a MAP >60 mmHg without the use of vasopres- sors.

Patients who died were counted as clinical failures.

Patients were classified as immunosuppressed if they met any of the following criteria: absolute neutrophil count (ANC) <1000 cells/mm 3 , receiving chronic immunosuppressive thera- py (eg, chemotherapy, monoclonal antibody, corticosteroids, methotrexate, azathioprine), or human immunodeficiency virus-positive with a CD4 count <200 cells/mm 3 .

Antimicrobial risk for CDI was categorized using criteria published elsewhere [7].

A high-risk antimicrobial was defined as 2nd-, 3rd-, 4th- generation cephalosporin, fluoroquinolone, or lincosamide.

A moderate-risk antimicrobial was defined as penicillin, penicillin combination, 1st-generation cephalosporin, macrolide, mono- bactam, or streptogramin.

A low-risk antimicrobial was any other systemic antimicrobial.


Standardized case definitions were used to classify CDI expo- sures [8].

Health-care facility-onset health-care facility associat- ed (HO-HCFA) CDI was defined as CDI symptom onset >48 hours after admission to a HCF.

Community-onset HCFA (CO-HCFA) CDI was defined as CDI symptom onset in the community or ≤48 hours after admission to a HCF, given that symptom onset was <4 weeks from the last discharge from a HCF.

Indeterminate disease was defined as CDI symp- tom onset in the community but discharge from the same or another HCF occurred 4-12 weeks prior to symptom onset.

Pa- tients with symptom onset in the community and without dis- charge from a HCF in the previous 12 weeks were assigned community associated CDI (CA-CDI).


Comparisons between groups were analyzed using chi-square or Fisher's exact test for categorical data.

Mann-Whitney U test and Student t test were used for ordinal and continuous data, depend- ing on distribution of the data.

A 2-sided P value of <.05 was con- sidered statistically significant.

Logistic regression was used to determine factors independently associated with survival.

WBC counts were adjusted using a square root transformation to make the data more normally distributed than the original data.

A full model was then fit with variables with univariate P values <.20.

Ad- ditionally, the effect of MAP was added as a control in the initial model.

A final model was fit by removing nonsignificant effects singularly until only significant variables (P < .05) remained.


Of the 187 patients who were prescribed oral vancomycin and admitted to an ICU, 88 patients failed to meet 3 of 7 criteria for establishing critical illness, leaving 99 patients who met study criteria.


Of these, 88 patients were matched based on APACHE II score, 44 patients in each treatment group.


Patient character- istics of the study population are shown in Table 1.


Patients in the combination therapy group were more likely to have The percentage of patients meeting each of the predefined se- verity criteria are displayed in Figure 1.


Despite matching on APACHE II scores, patients in the combination therapy arm met more severity criteria than patients in the monotherapy arm.


Thirty patients in the monotherapy group and 37 patients in the combination therapy group met at least 4 of 7 severity criteria (68.2% vs 84.1%, P = .08).


Sixteen and 24 patients met at least 5 of 7 severity criteria, respectively (36.4% vs 54.5%, P = .09).


A higher percentage of patients in the combination therapy group had a WBC count ≥15 000 cells/mm 3 (43.2% vs 68.2%, P = .03) and were hypotensive (36.4% vs 56.8%, P = .05) within 24 hours of initiation of study treatment.


When Table 1 continued.

Downloaded from https://academic.oup.com/cid/article/61/6/934/450824 by Institut Armand-Frappier user on 16 May 2024 analyzed as continuous data (Table 1), patients in the combina- tion therapy group had a higher median WBC count (13.8 vs 20.2 cells/mm 3 , P = .004) and a lower MAP (65 vs 59 mmHg, P = .025).


Table 2 displays results of outcome measures.

In-hospital mortal- ity was higher in the monotherapy group compared to the com- bination therapy group; 16 patients died in the monotherapy group, whereas 7 patients died in the combination therapy group (36.4% vs 15.9%, P = .03).

Nine patients (20.5%) in the monotherapy group compared to 6 patients (13.6%) in the com- bination therapy group met criteria for clinical success by day 6 of therapy (P = .57).

At day 6, improvement in diarrhea occurred in 47.7% and 52.3% of patients in the monotherapy and combina- tion therapy groups, respectively (P = .67).

Similarly, there were no differences in clinical success rates at days 10 and 21.

Length of stay (median 20.5 vs 18.0 days, P = .99) and length of ICU stay after CDI diagnosis (median 9.0 vs 11.0 days, P = .93) did not differ between the 2 groups.

Four patients (9.1%) in the mono- therapy group compared to 3 patients (6.8%) in the combina- tion therapy group had recurrent CDI (P = 1.0).


Multivariable analysis revealed 2 factors independently associated with sur- vival, receiving concurrent IV metronidazole and albumin (Table 3).

For albumin, survival was associated with increasing albumin values.


Initial oral vancomycin dose did not differ significantly between the 2 groups.

Most patients received oral vancomycin 125 mg every 6 hours, 79.5% and 59.0% in the monotherapy and com- bination therapy groups, respectively.

Other vancomycin doses prescribed were either 250 mg every 6 hours or 500 mg every 6 hours.

Oral vancomycin dose was modified during therapy in 18.2% in the monotherapy group compared to 25% in the com- bination therapy group (P = .44).

All dosing modifications were among the aforementioned doses.

Median duration of oral vancomycin was 15.0 days (range 6-39) in the monotherapy group and 15.5 days (6-59) in the combination therapy group (P = .15).

Of the patients receiving IV metronidazole in combi- nation, 25.0% received 500 mg every 6 hours, 72.7% received 500 mg every 8 hours, and 2.3% received 250 mg every 6


The specific aim of our study was to evaluate the difference in mortality among critically ill patients who received monotherapy or combination therapy.

In the 2010 SHEA and IDSA guideline, the recommendation to add IV metronidazole is based on expert opinion [6].

The European Society of Clinical Microbiology and Infectious Diseases recently published recommendations for management of CDI but did not include specific recommenda- tions on the use of combination therapy [9].

To our knowledge, the present study is the first to evaluate monotherapy vs combi- nation therapy in critically ill patients with CDI.

We found that mortality is higher in patients who receive monotherapy.


IV metronidazole for CDI has not been well studied.

Small trials and anecdotal evidence suggest IV metronidazole alone may have efficacy [10, 11], but this evidence is not sufficient to support routine use of IV metronidazole alone in the treat- ment of CDI.

The advantage of combination therapy observed in our study could be attributable to the dual route of IV and oral administration.

Gut dysmotility in critically ill patients may inhibit delivery of oral vancomycin to the colon, thereby limiting its effectiveness.

Achieving therapeutic metronidazole concentrations at the site of CDI may still occur with IV therapy because it is not dependent on gastrointestinal transit.

For this reason, IV metronidazole should not be substituted with oral metronidazole when used as combination therapy.


Zar and colleagues evaluated clinical cure at day 6 in a study of oral metronidazole compared with oral vancomycin [12].

Among patients with severe disease who received oral vancomy- cin, clinical cure was 97%.

However, of the 31 patients who re- ceived oral vancomycin for severe disease, only 2 patients were hospitalized in the ICU, which limits the applicability of these results to the treatment of critically ill patients.


One factor that has the potential to influence mortality asso- ciated with CDI is immune suppression.

There were more im- munocompromised patients in the monotherapy group (13 vs 10).

The combination therapy group had more patients on chronic immunosuppressive medication whereas the mono- therapy group had more neutropenic patients.

Multivariable analysis did not find an association between types of immune suppression and mortality.

Cancer is another factor to consider, and there were more patients with cancer in the monotherapy group (18 vs 15).

This may have influenced how many patients were housed in an Oncology ICU, which also was a greater number in the monotherapy group.

However, in our hospital, the most critically ill oncology patients are transferred to the Medicine ICU, and there were more of these patients in the combination therapy group.

Similarly, multivariable analysis failed to identify ICU location as a predictor of survival.


Although mortality was lower in the combination therapy group, a difference between treatment groups in the number of patients meeting the definition of clinical success was not ob- served.

Clinical success was similar between the groups at all time points evaluated (6, 10, and 21 days).

In addition, the overall clinical success rates were relatively low; only 20.5% in the mono- therapy group and 13.6% in the combination therapy group achieved clinical success by day 6.

The authors acknowledge that the definition for clinical success used in this study may not be appropriate for evaluating response to CDI treatment in this patient population, and this may account for the discrepancy between clinical success and mortality.

Many critically ill patients who get CDI are not critically ill only because of CDI.

Underlying comorbidities in addition to CDI can contribute to their level of illness.

For instance, it is possible patients could have had another reason besides CDI to exhibit tachycardia (part of clinical success criteria) which may not have resolved with effective CDI treat- ment.

This is supported by the much higher rates of improvement in diarrhea at day 6 compared with the rates of clinical success.


This study was not designed to determine the cause of death or the extent to which CDI contributed to death among patients who died during their hospitalization.

Although the median time to death for each treatment group was similar, the range was rather large for the monotherapy group (5-174 days).

These data are not normally distributed, and 174 days repre- sents an outlier result.

Although death at 174 days is not as like- ly to be directly attributable to CDI, it is difficult to know how much early events in a hospitalization impact the ultimate dis- position of any given patient.


It is noteworthy that more patients in the combination ther- apy group received vancomycin per rectum (8 vs 2).

Even though the multivariable analysis did not identify receipt of per rectum vancomycin as an independent predictor of survival, the use of vancomycin by this route would afford the same pro- posed benefit as IV metronidazole by ensuring delivery of anti- biotic to the site of infection.

Although this study was not designed to assess the impact of per rectum vancomycin, the study's findings suggest possible benefit and support future ef- forts to study this treatment in the critically ill population.


Critically ill patients represent a unique subgroup within the 'severe disease' category of current guidelines.

Within guidelines, categorization as severe CDI can be based on laboratory data alone (eg, WBC count or serum creatinine), but these criteria may not adequately represent the severity of disease present in a critically ill patient.

As such, the terms "severe disease" and "critically ill" as they relate to CDI may not be appropriately in- terchangeable.

Consideration of other factors is important when deciding who should get concurrent IV metronidazole.

Such fac- tors include admission to an ICU, advanced age, temperature There are limited data regarding the preferred dosing regi- men of oral vancomycin for critically ill patients with CDI.


The SHEA/IDSA guideline recommends 125 mg every 6 hours for most patients, but for patients with ileus, toxic megacolon, hypotension, or shock the recommended dose of oral vanco- mycin is 500 mg every 6 hours [6].

Abdominal distention is considered an indication for the 500 mg dose in another set of guidelines [16].

This seems reasonable as abdominal disten- tion is considered a sign of potential ileus.

Even with consistent recommendations to administer the higher dose for patients with severe, complicated CDI, the evidence to support this rec- ommendation is lacking.


In our study, different oral vancomy- cin doses were used in both groups.

The effect of vancomycin dose on clinical outcomes is unknown.

Available literature sug- gests there is no difference in clinical outcomes between low- and high-dose regimens [17-19].

In addition, vancomycin 125 mg every 6 hours achieves fecal concentrations that far exceeds the necessary amount needed to inhibit C. difficile [20, 21].

Gonzales et al noted lower fecal concentrations during the first 24 hours of treatment; however, concentrations were still well above the MIC 90 of C. difficile (1 mg/L), and clinical significance of this finding is unknown [21].

Likewise, there is no evidence to support a higher dose for critically ill patients with CDI, although there is potential for gut dysmotility in this patient population.


Our study is not without other limitations.

The retrospective study design instills reliance on accurate documentation in the medical record and limits interpretation of outcomes that are not completely objective.

Although efforts were made to match the groups, lack of randomization imparts risk that the groups may not be drawn from the same population.

There is particular risk of confounding by indication; assignment according to treat- ment may be inherently biased because a relationship may exist between choice of treatment and certain medical conditions or health outcomes.

The small sample size of this study undermines statistical power and decreases the likelihood of accurate results.

However, this study represents the largest study of critically ill, ICU patients with CDI.

We evaluated management of CDI in a limited subgroup of patients where practice guidelines lack suffi- cient evidence to support treatment recommendations.


Clostridium difficile infections have generated a significant bur- den on the healthcare system contributing to elevated healthcare costs and mortality.

Based on our findings, critically ill patients with CDI who require admission to an ICU may benefit from combination therapy with oral vancomycin and IV metronida- zole.

It is important to emphasize that these results are best ap- plied in the care of the most severely ill patients with CDI.

Prospective, randomized studies to define optimal treatment reg- imens in critically ill patients with CDI are warranted.


Potential conflicts of interest.

All authors: No potential conflicts of interest.


All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.

Conflicts that the editors consider relevant to the con- tent of the manuscript have been disclosed.


TO THE EDITOR—We read with great interest the study by Rokas et al in which they report a mortality benefit from combining intravenous metronidazole with oral vancomycin for the treatment of severe Clostridium difficile infection (CDI) in critically ill patients [1].

Improving the evidence base for CDI treatment in this population is clearly important, and it is difficult to perform randomized, controlled trials.

Use of in-hospital mortality as the primary endpoint and identification of only a relatively small sample size (88) with a low proportion of patients admitted to the intensive care unit because of CDI (30%) are understandable, pragmatic limitations to the study [2].

However, even if we accept these limitations, we have a number of concerns with the statistical analysis and question the accuracy and generalizability of the study findings.

The authors describe the study as “comparative.” Two cohorts were assembled on the basis of exposure to intravenous metronidazole and after matching on the Acute Physiology and Chronic Health Evaluation II score.

Matching in cohort studies, similar to case-control studies, aims to reduce the effects of confounding and increase efficiency [3].

It is an uncommonly used study design, in favor of more familiar methods of adjusting for confounding during the statistical analysis.

Reducing the sample size, as was done in this study, may exclude valuable data, and unfortunately, neither the matching methodology nor the characteristics of excluded patients was described.


Statistical methods to analyze matched cohort studies have received relatively little attention.

Mantel–Haenszel matched-pair analysis or other techniques that account for the matching variable are generally recommended [4].

An unconditional logistic regression, as described in the study, may overestimate the odds ratios, resulting in spurious associations.

Of greater concern, interpretation of the generalizability of the multivariable regression model is impossible due to the failure to include a number of important variables such as age and renal function.

Both are associated with severe CDI and mortality and are important variables to consider [5, 6].


Finally, for the matched cohort study, the choice of Fisher exact test or χ2 test is not reported for individual analysis.

It appears Fisher exact test was used to test for statistically significant differences in all the dichotomous endpoints, except for mortality, where χ2 test was applied.

The authors did not provide reasons for the switch; however, we note that use of Fisher exact test produces a P value of .051.

Which test is “better” is not important here, but failure to clearly describe the statistical methodology raises the concern of a search by the authors for a “significant” P value.

Given these limitations, it is unfortunately not possible to conclude that intravenous metronidazole is independently associated with improved outcomes.

A short course of metronidazole is not likely to cause harm, and further studies are required to determine if there really is evidence of benefit.


TO THE EDITOR—We appreciate the correspondence by Young and coauthors [1] who question certain aspects of our study analysis [2].

Their concerns are addressed here.


Patients in our study were matched on Acute Physiology and Chronic Health Evaluation II (APACHE II) scores; acceptable matches were limited to no more than a 2-point difference.

Of 99 patients who met study criteria, only 11 (11%) patients were not included in the study because of the inability to match within this limit.

Of the 88 patients included, 84% were matched with no difference or a 1-point difference.

Excluding patients because of matching methodology is a limitation of such retrospective studies; however, we were pleased with the number of patients who could be matched and believe the small proportion of patients excluded for this reason minimizes associated risks of generalizability.


Our study did not compare 2 distinct groups as in case-control studies.

Rather, patients were selected from the same single cohort and then comparisons were made with regard to treatment.

Due to the chance that separation by treatment could impose selection bias and as a way to promote equal disease severity, the treatment groups were matched based on the APACHE II score.

It is important to acknowledge this about the study design because of its bearing on analysis.

Unlike matched case-control studies where paired analyses are imperative to prevent bias, paired analyses are an enhancement rather than a necessity for matched cohort studies [3].


Nevertheless, as suggested by Young and colleagues, we performed matched analyses to demonstrate the robustness of our results.

An odds ratio using Mantel–Haenszel matched-pair analysis was generated.

However, because this analysis does not accommodate continuous confounders, a conditional logistic regression analysis was also performed.

As was done in the original analysis, a final model was fit by removing nonsignificant effects singularly until only significant variables (P < .05) remained.

With regard to age and renal function, we agree with Young and colleagues that these are important factors to consider.

Both age and serum creatinine at the time of Clostridium difficile infection (CDI) diagnosis were included in the model selection.

An association between concurrent intravenous metronidazole and improved survival was upheld in both the Mantel–Haenszel analysis and conditional logistic regression.

These 2 analyses resulted in the same odds ratio (5.50) and 95% confidence interval (1.22, 24.8; P = .027), owing to the fact that combination therapy was the only factor remaining in the conditional logistic regression model.


Lastly, for dichotomous outcomes, Fisher exact test was used when the results included a value <5, while χ2 test was used for comparisons involving larger values.

It is worth noting that our χ2 analyses incorporated Yates correction, and this may be why Young and colleagues thought we used Fisher exact test.

Analyzing those dichotomous outcomes using χ2 test without Yates correction results in no changes in statistical significance, although P values are generally lower.

Fisher exact test is recommended when result values are <5; for this reason, we do not agree with using Fisher exact test for analyzing mortality.


This discussion highlights certain limitations inherent in retrospective studies.

We echo the sentiment by Young and colleagues that additional research on this topic is needed.

However, the association identified in our study justifies consideration of intravenous metronidazole with oral vancomycin for critically ill patients with CDI.


The decision analysis model compares 3 treatment strategies for patients with MSSA bacteremia and reported PCN allergy: (1) no allergy evaluation, give vancomycin (Vanc); (2) allergy history-guided treatment: if detailed history excludes anaphy- lactic features, give cefazolin (Hx-Cefaz); and (3) complete allergy evaluation with history-appropriate PCN skin testing: if skin test (ST) negative, give cefazolin (ST-Cefaz).


Model outcomes at 12 weeks include (1) MSSA cure, recur- rence, and death; (2) allergic reactions classified as major or minor and, separately, as potentially iatrogenic; and (3) adverse drug reactions (ADRs; drug toxicity or intolerance).

We defined "potentially iatrogenic" as all allergic reactions from cefazolin in patients with PCN allergy history [38].


We assessed which strategy is optimal for each individual outcome.

We then created a composite outcome where we summed outcomes using the following weights: cure, +50; re- currence of infection, −20; death, −50; major allergic reactions, −10; minor allergic reactions, −2; and ADRs, −6.

A composite score of 50 would indicate a strategy where patients are cured without allergic reactions or ADRs.

We examined key input pa- rameters in a series of 1-way, multiway, and probabilistic sensi- tivity analyses (PSA); we also conducted sensitivity analyses on weights for the composite outcome.


The decision tree (TreeAge Pro 2014, TreeAge Software, Inc, Wil- liamstown, Massachusetts) includes 3 competing strategies for the patient with MSSA bacteremia and reported PCN allergy (Figure 1).

Within each strategy, an alternative strategy may be considered, depending on allergic and adverse outcomes.

We limited the model so that no patient receives >3 sequential anti- biotics.

At the end of a patient's course through the decision tree, he or she is treated with vancomycin, cefazolin, nafcillin, or an alternative non-β-lactam drug (eg, daptomycin).

Model out- comes are defined after 12 weeks, at the rightmost end of the tree.

If a tree course does not have the outcome of interest, the In the Vanc strategy, all patients with MSSA bacteremia and reported PCN allergy receive vancomycin treatment without allergy evaluation.

Vancomycin-treated patients may develop an allergic reaction (major or minor) or an ADR.

Upon an allergic reaction or ADR to vancomycin, clinicians reconsider therapy options, and the patients have an equal chance of get- ting (1) an alternative non-β-lactam drug; (2) a history-driven allergy evaluation where cefazolin is given if the PCN allergy history is not anaphylactic; or (3) a full PCN allergy evaluation with history-appropriate skin testing, with cefazolin given if there is no allergy.

The latter strategies are similar to the Hx- Cefaz and ST-Cefaz branches of the tree, although, because these patients suffered an ADR or allergic reaction to vancomy- cin, they do not receive vancomycin again.


In the Hx-Cefaz strategy, patients with a history of PCN allergy with anaphylactic features receive vancomycin; patients with milder allergy histories receive cefazolin.

Patients may have an al- lergic reaction to cefazolin necessitating a drug change to vanco- mycin.

Patients who do not tolerate cefazolin but did not have an allergic reaction have an equal chance of receiving vancomycin therapy or a full allergy evaluation with PCN skin testing.

The lat- ter strategy is similar to the ST-Cefaz branch, although if these pa- tients are ST negative, they receive another first-line MSSA antimicrobial (nafcillin).

If they are ST positive, they receive van- comycin.

Patients with an anaphylactic history receive vancomy- cin, and may have an allergic reaction or an ADR to vancomycin necessitating a drug change to an alternative non-β-lactam drug.


In the ST-Cefaz arm, patients eligible for skin testing receive PCN skin testing.

Patients ineligible for skin testing are given van- comycin.

Allergy specialists generally define patients ineligible for skin testing as those with anaphylaxis to PCN in the last 10 years, with a history of a Gell and Coombs type II-IV hypersensitivity reaction to PCN, or currently taking medications that interfere with ST results (eg, antihistamines) [17].

For patients skin tested to PCN, those who are ST negative receive cefazolin, those who are ST positive receive vancomycin, and those with a nondiagnostic ST receive history-guided therapy (as in Hx-Cefaz).

In this strat- egy, patients ineligible for ST who develop an allergic reaction to vancomycin have an equal chance of being given cefazolin guided by allergy history alone or given an alternative non-β-lactam drug.


The model uses data from literature sources as input parameters (Table 1).

These data, reported as fractions and frequencies in the primary literature, are converted into probabilities for inclu- sion into the decision tree model at the chance nodes (Figure 1).

We weighted the outcomes from sources based on cohort numbers when >1 source was available for our base case values (Table 1).

Reported probabilities are conditional upon prior outcomes, as indicated by tree structure.


Within treatment efficacy, parameters include the 12-week MSSA cure with vancomycin (62.0%) and cefazolin (85.0%), and the 12-week probability of MSSA recurrence and mortality associated with vancomycin (18.8% and 19.2%, respectively) and cefazolin (9.1% and 5.9%, respectively) [7, 8, 16].

Because only 1 study reports MSSA recurrence (13.2%) and death (0.4%) with an alternative drug, we used vancomycin literature ranges to define cure, recurrence, and death with an alternative, non-β-lactam drug [23].


The chance of allergic reaction to vancomycin was 3.0%, of which 8.3% of reactions were considered major and 91.7% con- sidered minor [39, 40].

In the Hx-Cefaz strategy, data from an internal database informed the proportion of patients whose reported reaction to PCN had anaphylactic features (5.6%), defined by reported reactions of bronchospasm, wheezing, ana- phylaxis, hypotension, and angioedema [53].

The proportion of patients without an anaphylactic history who react to cefazolin was 2.2%, derived from cohort data and mathematical compu- tation [17, 30, 32, 41-44].

In the ST-Cefaz strategy, we defined 9.1% of patients as ST ineligible [20, 21]; among those eligible, 95.6% were ST negative, 1.2% were ST positive, and 3.2% had a nondiagnostic ST [20, 21, 24, 25, 45].

After a negative ST, 1.6% of patients react to cefazolin [20-22, 45].

In both the Hx-Cefaz and ST-Cefaz strategies, once an allergic reaction occurs, 4.7% can be considered major (95.3% are minor); 0.2% of major reactions result in death [26, 36, 46-49, 54].

ADR rate was 5.3% with van- comycin and 4.4% with cefazolin [40, 50-52].


We conducted sensitivity analyses using probability ranges from the primary literature (Table 1).

We varied parameters individually (1-way sensitivity analysis) for each outcome and identified influ- ential parameters.

We then varied these key inputs simultaneously in a series of multiway sensitivity analyses.

Finally, we used litera- ture estimates to create distributions for the key input parameters to conduct PSA, whereby 100 000 patients travel through the decision tree sampling different probabilities from the defined distributions (Supplementary Table 1).

We conducted a PSA for all 3 strategies on all outcomes.

Because results for the Hx-Cefaz and ST-Cefaz groups were similar, we subsequently ran the PSA for Vanc com- pared with a sample allergy evaluation strategy (Hx-Cefaz).


The Vanc strategy results in 67.3% chance of cure, 14.8% chance of recurrence, and 17.9% chance of death (Table 2).

Total MSSA and Reported Penicillin Allergy • CID 2015:61 (1 September) • 743 allergic reactions are 3.0%, with 0.3% of patients experiencing a major reaction and 0.8% experiencing a potentially iatrogenic reaction.

ADRs are present in 5.2% of vancomycin-treated pa- tients.

Hx-Cefaz results in better clinical outcomes than Vanc, including a much higher chance of cure (83.4%), as well as a substantially lower chance of recurrence (9.3%), and less than half the 12-week mortality (7.3%).

Fewer patients in Hx-Cefaz have allergic reactions (2.4%) and ADRs (4.6%) than in Vanc (3.0% and 5.2%, respectively).

While major allergic reactions (0.1%) are less frequent in Hx-Cefaz, more patients experience a reaction that could be considered iatrogenic (2.1%) than in the Vanc strategy.


Because the allergy evaluation strategies offer similar outcomes, for simplicity, we focused sensitivity analyses on Vanc com- pared to a sample allergy strategy, Hx-Cefaz.


With variation of input pa- rameters within all reasonable ranges from reported literature, Vanc consistently has inferior clinical outcomes of MSSA cure, recurrence, death, and the composite outcome.

The input parameters with the greatest effect on the value of the composite outcome are the probability of death and recurrence with vancomycin (Figure 2).

However, varying these inputs within reported literature ranges still leads to Vanc giving the least desired outcomes.


When examining individual outcomes, iatrogenic allergic reac- tions, although generally infrequent in all strategies (<2.1%), are always minimized with Vanc.

If <1.7% of patients (base case 3.0%) have an allergic reaction to vancomycin, then Vanc results in the fewest total allergic reactions.

If >5% of patients experience an ADR from cefazolin (base case 4.4%), then Vanc becomes optimal over allergy evaluation in terms of minimizing ADRs.


identified in 1-way sensitivity analyses lead to different conclu- sions about the optimal allergy strategy.


Using distributions for influential input parameters (Supplementary Table 1), ST- Cefaz was preferred to Hx-Cefaz to maximize cure 99.5% of the time, minimize recurrence 90.4% of the time, minimize death 99.7% of the time, minimize total allergic reactions 83.8% of the time, and maximize the composite outcome 99.8% of the time.

Hx-Cefaz was preferred to ST-Cefaz to min- imize ADRs 96.1% of the time.


One-Way Sensitivity Analyses.

Although outcomes between Hx-Cefaz and ST-Cefaz were quite close, variations of most pa- rameters within literature-reported ranges continued to mod- estly favor ST-Cefaz.

MSSA cure, recurrence, and death were sensitive to the proportion of patients with a positive ST, with Hx-Cefaz becoming optimal within the range examined.

Con- sidering total allergic reactions, the optimal strategy is most sen- sitive to the proportion of patients with a nonanaphylactic PCN allergy history who react to cefazolin.

Specifically, Hx-Cefaz be- comes optimal if <1.4% (base case, 2.2% [range, 0.02%-5.6%]) of patients with a nonanaphylactic PCN allergy history react to cefazolin.

Considering ADRs, ST-Cefaz becomes optimal if the probability of an ADR from cefazolin is <2.9% (base case, 4.4% [range, 0%-7.0%]).

The composite outcome favored ST-Cefaz, but was also sensitive to the probability of a positive ST; Hx- Cefaz becomes optimal when >8.5% (base case, 1.2% [range, 0%-10.0%]) of patients are ST positive.


Using a decision analysis model, we project that patients with MSSA bacteremia and reported PCN allergy will have inferior outcomes if treated with vancomycin rather than having their PCN allergy addressed.

Both the base case analysis and sensitiv- ity analyses favored a full evaluation with PCN skin testing to optimize outcomes compared with basing therapy on PCN al- lergy history alone; however, differences were small and sensi- tive to uncertain input parameters.


Despite our finding that no allergy evaluation and treatment with vancomycin is inferior, patients with MSSA bacteremia and reported PCN allergy commonly receive vancomycin [11, 23].

This could reflect providers' concerns about iatrogenic allergic reactions and limited PCN allergy knowledge [18, 55].

Indeed, the only model outcome that suggested benefit to van- comycin was minimizing potential iatrogenic allergic reactions.

Given our findings, the practice of giving vancomycin to pa- tients with MSSA bacteremia and a reported PCN allergy with- out investigating the allergy should not be standard, regardless of the availability of allergy specialists or PCN skin testing.

A brief bedside allergy history that excludes anaphylactic features can guide cefazolin use and result in better outcomes than choosing vancomycin.


Perhaps counterintuitively, our model projects that the use of vancomycin would result in more allergic reactions than the use of cefazolin through allergy history-guided therapy or skin test- ing-guided therapy.

Allergy to vancomycin includes immediate reactions as well as delayed reactions (eg, morbilliform erup- tions, drug rash with eosinophilia, and systemic symptoms syn- drome) [17, 56-58].

Notably, our projection of increased allergic reactions with vancomycin excluded the common infusion re- action "red man syndrome" [17, 39, 59].


Our model projects that ST-Cefaz results in the most favor- able outcomes.

However, the differences were small, and litera- ture-reported variations suggest that data may be inadequate to definitively determine which allergy evaluation strategy is op- timal.

Whereas much of the allergy skin testing and challenge literature is derived from outpatients, those with MSSA bacter- emia are usually inpatients [24, 26, 36].

Because we may see different skin testing eligibility and results among inpatients, more robust inpatient data would be of value.

A key parameter in determining the optimal allergy strategy is the proportion of patients without an anaphylactic PCN allergy history who react to cefazolin; this parameter was based on sparse literature and mathematical calculation.

Last, because of the declining rate of reported ST positivity in the literature, we deliberately used re- cent allergy data to inform skin testing inputs [20, 21, 24, 26, 45, 51].

The optimal allergy strategy was sensitive to this parameter within reported ranges, and older data that used more skin- testing reagents reported higher rates of ST positivity [31].


While the model suggests that Hx-Cefaz may be as good as ST-Cefaz, ST-Cefaz is likely preferable for avoiding iatrogenic al- lergic reactions that can result in patient dissatisfaction.

PCN skin testing is currently performed using Pre-Pen ( penicilloyl- poly-lysine) and dilutions of PCN.

The negative predictive value with these reagents is at least 95%; the procedure can safely be performed on general medical floors, and results are available within 1 hour [17, 21, 26, 45].

With proper training, PCN skin testing can be performed by registered nurses, which allows for reduced costs of providing the service [22].


Although data limitations and variability of input parameters in this analysis made it challenging to project which allergy strat- egy was optimal, either strategy is substantially better than Vanc.

This conclusion is insensitive to parameters and despite evalua- tions of Vanc in a "best case for Vanc" scenario.

We did not con- sider that patients with PCN allergy may have other drug allergies that could impact treatment.

We justify exclusion of nafcillin be- cause cefazolin is considered to be equally effective, better tolerat- ed, and less expensive than nafcillin for MSSA bacteremia [16, 52, 60-62].

We additionally justify exclusion of other alternative agents (eg, daptomycin) as initial therapy because of limited com- parative infectious outcome data in MSSA bacteremia [63-66].

However, given vancomycin's poor efficacy and tolerability in MSSA bacteremia, future comparisons of β-lactams to alternative agents will be important to consider.

Finally, although we limited our outcome assessment to 12 weeks, we believe this strengthens our conclusion that the allergy must be addressed, as recurrences are more likely with vancomycin than cefazolin [7-11, 15, 16, 23].


Compared to treatment with vancomycin, we find that patients with MSSA bacteremia and reported PCN allergy have improved outcomes when treated with cefazolin, either by an allergy history-guided treatment or full allergy evaluation with skin testing.

Although full allergy evaluation is likely preferred over history alone, more data from inpatients are needed.


Reported allergy to beta-lactam antibiotics is common and often leads to unnecessary avoidance in patients who could tolerate these antibiotics.

We prospectively evaluated the impact of these reported allergies on clinical outcomes.

Among 507 patients, 95 (19%) reported beta-lactam allergy; preferred therapy was a beta-lactam in 72 (76%).

When beta-lactam therapy was preferred, 25 (35%) did not receive preferred therapy due to their report of allergy even though 13 (52%) reported non-severe prior reactions.

After adjustment for confounders, patients who did not receive preferred beta-lactam therapy were at greater risk of adverse events (adjusted odds ratio [aOR], 3.1; 95% confidence interval [CI], 1.28–7.89) compared with those without reported allergy.

In contrast, patients who received preferred beta-lactam therapy had a similar risk of adverse events compared with patients not reporting allergy (aOR, 1.33; 95% CI, .62–2.87).

Avoidance of preferred beta-lactam therapy in patients who report allergy is associated with an increased risk of adverse events.

Development of inpatient programs aimed at accurately identifying beta-lactam allergies to safely promote beta-lactam administration among these patients is warranted.


Reported allergy to beta-lactam antibiotics is common and often leads to unnecessary avoidance in patients who could tolerate beta-lactam therapy.

In fact, the majority of reported prior reactions are misclassified as allergies; moreover, many individuals with true prior immunoglobulin (Ig) E–mediated reactions have loss of hypersensitivity over time [1, 2].

Carrying a label of beta-lactam allergy is not inconsequential.

Patients with reported allergies may be treated with alternative agents that can be clinically inferior [3] and pose increased risks of harm [4, 5].

Furthermore, alternative agents frequently have a broader spectrum of activity and may contribute to the emergence and spread of drug-resistant organisms [5].

Correctly confirming or refuting allergies to enable receipt of beta-lactam therapy, when indicated, is an important function of antibiotic stewardship [6–10].

Beta-lactam antibiotic agents are recommended first-line therapies for many infectious syndromes [6–9].

Currently, infectious diseases consultants make great efforts to verify details of reported prior allergies to beta-lactam agents in order to determine whether or not they can be safely prescribed.

When history suggests a significant IgE-mediated allergy, patients often receive nonpreferred therapy because it is judged to be the safest option for the patient.

Whether or not the risk of allergy from beta-lactam use in some patients reporting allergy is outweighed by the toxicity or potentially inferior efficacy associated with alternate therapies has not been systematically evaluated.

We hypothesized that receipt of alternative therapy when a beta-lactam agent is the preferred therapy would lead to worse clinical outcomes.

To test this hypothesis, we conducted a trainee-led prospective cohort study to determine the burden and clinical impact of reported beta-lactam allergy on patients seen by infectious diseases consultation services at 3 academic hospitals.


Drug allergy is reported by 20%–40% of patients in the United States [1, 2].

Beta-lactam antibiotics, which include penicillins, cephalosporins, and carbapenems, are the most commonly implicated [1, 2].

Penicillin allergy prevalence estimates range from 8% to 25% across ambulatory and hospitalized populations [2–6].

With another 2% of patients reporting allergy to cephalosporin antibiotics, reported beta-lactam allergy impacts a substantial portion of our patients [7–9].

Patients who report a history of penicillin allergy may be treated with alternative antibiotics, including vancomycin, clindamycin, aztreonam, and fluoroquinolones, when they present with infections [3, 4, 10].

Some of these alternative antibiotics are used in patients reporting allergy histories despite a beta-lactam being the preferred antimicrobial agent [10].

Alternative agents may be less effective for specific infections, which can lead to higher rates of treatment failure [6, 10, 11].

Alternative agents expose patients to increased risk of toxicity, resulting in higher rates of drug discontinuation and adverse drug reactions [11, 12].

Use of alternatives to beta-lactams may additionally increase patients’ risk of infection with resistant organisms, including methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococci, and may increase the risk of Clostridium difficile colitis [4].

The vast majority, close to 95%, of patients who report an allergy to beta-lactam antibiotics are not truly allergic [7, 8, 13].

The large difference between reported and true beta-lactam hypersensitivity can be attributed to the assessment and documentation of the allergy history and the natural history of immune-mediated hypersensitivity reactions.

With the former, an intolerance or side effect may be considered an allergy or the original reaction may have been misclassified by a medical provider as an allergy (eg, viral exanthems).

With the latter, previous hypersensitivity reactions do not uniformly imply a future hypersensitivity reaction.

The most common penicillin hypersensitivity reaction is a benign T cell-mediated maculopapular rash that may not recur with subsequent reexposure [7, 14], and immunoglobulin (Ig)E-mediated penicillin allergies such as urticaria, angioedema, and anaphylaxis wane over time [15].

Thus, in the adult inpatient population, patients reporting beta-lactam allergies who are treated with alternative agents due to concerns for allergy are likely avoiding beta-lactam antibiotics unnecessarily.


In this issue of Clinical Infectious Diseases, MacFadden et al report on a multisite, trainee-led prospective cohort analysis of inpatients who received an infectious diseases consultation [16].

As part of a quality improvement project, infectious diseases trainees prospectively collected their team’s assessment of the patient allergy history and optimal antibiotic.

They then retrospectively gathered patient outcomes, including readmissions for the same infection, acute kidney injury, C. difficile infection, and drug-related adverse events.

Additional confounding variables used for adjusted analyses were gathered either prospectively or retrospectively.

The authors found that among patients with a reported beta-lactam allergy, 3 in 4 should be treated with a beta-lactam antibiotic.

Of the patients with beta-lactam allergy history where a beta-lactam should have been used, 65% received the beta-lactam and 35% did not.

The data additionally suggest that the decision of whether to administer a beta-lactam antibiotic was related to the allergy history; those with severe reaction histories were significantly less likely to receive a beta-lactam, as were those patients whose allergy histories were consistent with an IgE-mediated reaction.

The primary outcome analyzed by MacFadden and colleagues was a composite outcome termed “adverse events,” which included readmissions for the same infection, acute kidney injury, C. difficile infection, and drug-related adverse events.

The crude and adjusted analyses found that patients with reported beta-lactam allergy who did not get beta-lactam had more than a 3-fold increase in odds of adverse events compared with those without beta-lactam allergy (crude odds ratio [OR], 3.43; 95% confidence interval [CI], 1.48, 7.96 and adjusted OR [aOR], 3.18; 95% CI, 1.28, 7.89).

The model adjusted for age, sex, hospital, referring service, length of stay, duration of antibiotic therapy, Charlson comorbidity index, and whether or not the patient was immunocompromised.

While the analysis required a composite outcome for power, the authors’ findings were demonstrably driven by readmission and adverse drug events.

Interestingly, they also found that patients with a reported beta-lactam allergy who were nonetheless given a beta-lactam did not have any significant observed difference in adverse events (aOR, 1.33; 95% CI, .62, 2.87).


To our knowledge, the authors are the first to report on a multisite, prospective analysis that begins to define the true impact of reported beta-lactam allergies on clinically important patient outcomes.

Prior studies have demonstrated that patients with more complete or detailed penicillin allergy histories are more likely to be rechallenged with the same or similar medication in the future [17].

In the current study, all patients had an allergy history taken, but 35% of beta-lactam “allergic” patients were still not given a beta-lactam when indicated.

This suggests that it is important to go beyond the allergy history to an assessment and plan informed by well-validated allergy practice and procedures, such as implementation of the penicillin skin test and/or graded challenges (also known as test doses) under medical observation [5, 13, 14, 18, 19].

In fact, this study included only 1 patient who was ineligible for penicillin skin testing because of a history of severe cutaneous adverse reaction to a beta-lactam antibiotic.

Prior data would support that at least 22 of the 24 patients with a history of beta-lactam allergy who did not receive a beta-lactam would have tolerated the agent if they were more thoroughly evaluated.

These compelling data add to the accumulating body of evidence that supports the evaluation of beta-lactam allergy to improve patient outcomes.

Penicillin allergy evaluation, rooted in a complete and accurate allergy history and combined, as indicated, by allergy procedures, is a practice encouraged by the Centers for Disease Control and Prevention [20], the American Board of Internal Medicine/American Academy of Allergy, Asthma, and Immunology [21], and the Infectious Diseases Society of America/Society for Healthcare Epidemiology of America [22].

Over the years, a number of hospitals have created and implemented a variety of quality improvement initiatives aimed at addressing this very problem [5, 18, 23–25].

Programs have used different personnel (general providers, allergists/immunologists, pharmacists, infectious diseases experts, or a combination of these) and have implemented different allergy tools, such as taking a comprehensive allergy history, using skin testing alone, and providing guidelines that use both history and allergy procedures.

However, the most efficient and effective method for addressing reported beta-lactam allergies among inpatients remains unknown.


The authors performed this study as the initial step of their quality and process improvement course as Canadian trainees, where accreditation includes quality improvement and patient safety as core competencies [26].

Similarly, the Accreditation Council for Graduate Medical Education in the United States has integrated quality improvement into post-graduate training programs, requiring programs to ensure trainee participation in quality improvement and patient safety programs.

Indeed, trainees are asked to use quality improvement methods to systematically assess a current practice and to implement changes with the goal of improvement [27].

We commend MacFadden and colleagues for taking on an important clinical question, applying appropriate methods, and reporting their results to the larger community.


eDNA analysis in the context of monitoring aquatic and semi--aquatic species via species--specific DNA 54 from water bodies is still in its relative infancy.

Because of this there is much still to be done in terms 55 of optimising and improving eDNA analysis.


Our review intended to discuss the state of both the 56 field sampling and laboratory analysis of these samples and to suggest areas which required 57 improvement, we also discussed innovative ideas for how eDNA can be applied to conservation and 58 management.


It was not our intention to produce an exhaustive review of every aspect of the field, 59 we aimed instead to produce a review with specific emphasis on environmental water sampling, the 60 PCR analysis itself, and then the specific areas where this technique has been applied: eDNA 61 persistence; biomass estimation; species composition; and invasive and rare or threatened species; 62 along with a comparison of eDNA versus traditional methods.


There was no intention to neglect 63 what are deemed to be important areas of this type of work such as the use of modelling for 64 sampling design.


In this respect we welcome the endeavour of Roussel et al. ( 2015, hereafter 65 'Roussel') in providing comment on our review paper (Rees et al. 2014b).

We respond to the five 66 main points below.


The first point made by Roussel states that it is 'premature to imply that eDNA will be an efficient 70 tool for detection all aquatic animal species'.

We agree with this statement and also that from 71 Ficetola et al. ( 2008) who stated that eDNA is 'useful for studying secretive aquatic or semi--aquatic 72 species, which release DNA into the environment through mucus, faeces, urine and remains'.

In our 73 review we do not imply that eDNA can be used for 'all' aquatic species we merely point out that: 1) 74 eDNA analysis has been used as an accurate indicator of species presence in a range of 75 environments; 2) that techniques such as eDNA analysis are of great interest in the management and 76 conservation of vulnerable species and would be of great use in these areas; and 3) that eDNA is 77 particularly useful for those species which are difficult to detect using conventional methods.

We go 78 on to state that the success rate of eDNA analysis is not always 100% illustrating that it is not always 79 an efficient tool for the detection of aquatic animal species.


Roussel rightly chose one of their own publications (Treguier et al. 2014), which was not available at 81 the time of writing Rees et al. ( 2014b), as an example of eDNA being an inefficient tool on its own for 82 the detection of the red--swap crayfish, Procambarus clarkia.

They showed that when crayfish 83 abundance was estimated to be low by conventional trapping, eDNA detection efficiency was poor 84 illustrating a limitation of eDNA detection for this species/habitat combination.

However, Roussel 85 did not mention that this same study found that eDNA had a better overall detection efficiency than 86 conventional trapping (73% versus 65% respectively) and that experimental evidenced suggested 87 that false positives were unlikely in this case so would not account for the higher detection 88 efficiency.

This result would suggest that in this case eDNA was more efficient than traditional 89 survey.

A further example of an invertebrate which has been successfully detected using eDNA 90 analysis is the tadpole shrimp, Lepidurus apus (Thomsen et al. 2012) which was detected at 100% 91 success rate in 10 ponds known to contain this species.

Both studies illustrate that detection of 92 invertebrate species is possible and that with continuing developments in PCR technologies and 93 sampling methods the detection efficiency of invertebrate species could be improved making eDNA 94 a more efficient tool for their detection.


In Rees et al. ( 2014b) we stated that 'The probability of detection will vary for the chosen target 96 species and will be dependent on the taxa, the density of organisms, and also the type of water 97 body.' therefore, we completely agree with Roussel that when studying a new target species, 98 preliminary tests to estimate the detectability of the target DNA should be a pre--requisite for 99 surveying.

We would go further and suggest that any such development of a new method for eDNA 100 detection should be compared directly with any established "gold standard" method for the 101 detection of the species/habitat under investigation.

As mentioned in the original review, given the 102 developmental stage of applying eDNA to ecological surveys of aquatic animals it is vital that new 103 emerging methodologies are correlated directly with the established survey methods for a particular 104 species/habitat type, enabling a robust comparison and assessment of new eDNA survey methods.


The second point made by Roussel is again correct in that it is often perceived that eDNA analysis is 107 more efficient that traditional surveys.

As we stated in Rees et al. ( 2014b) eDNA has 'the potential of 108 greater sensitivity over traditional survey methods' we do not state that it is more efficient.

eDNA 109 analysis has been shown to be more efficient in a few studies (Dejean et al. 2012; Takahara, 110 Minamoto & Doi 2013) and latterly (Treguier et al. 2014) but our review points out that the eDNA 111 success rate was not always 100% when the species was known to be present (Thomsen et al. 2012) 112 illustrating that it is not always more efficient.


Roussel is again correct to state that the abundancies of the species in question is not always stated 114 within the literature and that it is therefore 'difficult to understand how the method might perform 115 across a gradient of target species density' an important point which does add to our review.

We 116 agree that each survey method (eDNA versus traditional) is imperfect and we devoted a whole sub-- 117 section in Rees et al. ( 2014b) to 'eDNA versus traditional survey methods' where we compared the 118 use of eDNA analysis and traditional survey methods.

We agree that there should be more 119 comparisons between eDNA and traditional census methods, so much so that this is exactly what we 120 did in a recent study on great crested newts, Triturus cristatus in the UK (Rees et al. 2014a).

Likewise 121 we agree that it would be very useful for method comparison if species abundance were included in 122 these studies and feel that this is an important point which has not necessarily been mentioned 123 elsewhere.


The third point made by Roussel concentrates on the reporting of experimental details, and false 126 positives.

We agree with the comments made on reporting of experimental details and in fact the 127 point should be made here that consideration of the MIQE guidelines (Bustin et al. 2009) (Minimum 128 Information for Publication of Quantitative Real--Time PCR Experiments) should be done before 129 publication of any study involving real--time PCR a point which both our Rees et al. ( 2014b) review 130 and Roussel neglected to make.

The background to these guidelines is that there is very little 131 consensus on 'how best to perform and interpret real time PCR (qPCR) experiments' and that this 132 problem is exacerbated by the fact that little experimental information is included in published 133 studies.

Not only do these guidelines encourage the inclusion of the complete 134 reaction/thermocycling conditions and limits of detection as suggested should be included in 135 publications by Roussel, but it also covers many other items that should be reported to allow for 136 useful comparison or replication of experiments (provided in the form of a useful checklist).

Also 137 encouraged is the correct use of nomenclature which can often be confused in publications.

In terms of false positives Roussel makes the point that we also made in that false positives 'occur 139 when there is no target DNA in the sample'.

Roussel is correct that the term 'false positive' should be 140 carefully and unambiguously defined and of course in terms of positive PCR signals you need to 141 consider the possibility, as was stated the review, that 'there is target DNA in the sample but no 142 viable/live organisms in the system'.

During the peer--review process of our review paper (Rees et al. 143 2014b) we were asked to be careful about the use of the wording 'false positive'.

We therefore 144 suggested that a 'false positive' could also be termed a 'false detection'.

Perhaps our definitions 145 should have been made more clearly to state that a 'false positive' occurs when there is no target 146 DNA in the sample and a 'false detection' occurs when there is target DNA in the sample but no 147 viable/live organisms in the system.

It will be important for future publications to stipulate whether 148 they are referring to a 'false positive' or a possible 'false detection'.

Roussel goes on to state that 149 'false detections' need more attention if we want to understand what an eDNA detection 150 represents.

We would like to emphasise the point that in some species that are transiently present 151 in water and for example move between ponds, an eDNA detection would still be valid whether the 152 species was present or not at the time of sampling especially when trying to define species 153 distribution.

Of course, as suggested by Roussel consideration should be made to the fact that DNA 154 in a water sample could originate from predator faeces or dead animals which we also stated in Rees 155 et al (2014b).

We agree with Roussel that care should be taken to avoid the inclusion of sedimentary 156 material when taking water samples as sediment could contain eDNA from past site occupancy 157 rather than recent (dependent on eDNA degradation rates) or present occupancy.

We would also 158 point out that in our experience the presence of sediment can act to inhibit the PCR reactions.

The 159 use of an inhibition control (Biggs et al. 2014) can help to monitor for this and other inhibitors which 160 may be present in a sample.

In fact, the inclusion of an inhibition control is now carried out as 161 standard in our laboratories for every sample being tested.


The fourth point made by Roussel relates to sampling design, and we agree that rigorous sampling 164 design is important and that tools such as site occupancy models for example the program 165 PRESENCE version 6.4 [available from http://www.mbr--pwrc.usgs.gov/software/presence.html 166 (MacKenzie et al. 2002)] can be used.

Roussel stated that in Rees et al. ( 2014b) we failed to 167 recognise that there are two sources of imperfect detection: at the field level and in the laboratory.

168 We would dispute this comment as we have a large section devoted to sampling in which we discuss 169 the numerous different methods employed for sampling in the literature.

We discuss how groups 170 have improved the sampling coverage of the water systems and noted that methods for sample 171 collection will vary depending on the type of water body.

Additionally we recognised that the 172 probability of detection will depend on several factors including the taxa, the density of organisms, 173 and the type of water body.

We also note that there is considerable variation in the processing of 174 the water samples prior to the extraction step for which we state that 'a direct comparison of these 175 methods to monitor the effects of different processing methods on eDNA extraction and detection 176 would be useful'.

This implies that there are sources of imperfect detection at the field level, 177 although we may not have stated so in so many words, the citations in Roussel that list the studies 178 (Schmidt et al. 2013; Ficetola et al. 2014) which illustrate this imperfect detection are useful and 179 should be considered when designing sampling regimes.


The fifth point made by Roussel deals with the application of eDNA detection to running water, an 182 area in which there has been few studies (Roussel indicates 9 papers have been published on this 183 subject) .

We chose to discuss the sampling regimes used so far and concluded that 'methods for 184 sample collection will vary' depending on the environment which is to be studied.

We did not discuss 185 the issue of downstream transportation of DNA as there was little information on this within the 186 published studies.

Roussel is correct that the implications of discharge variation and dendritic 187 organization will need to be considered when studying these environments along with DNA 188 degradation (which was discussed within Rees et al. ( 2014b)).

This again goes back to the point that 189 Roussel made about the importance of the study design and the use of modelling approaches such 190 that all these factors are taken into consideration prior to study commencement.


For the most part we agree with the comments of Roussel and in several instances the comments 194 made do not correspond to what was said in our review paper but may correspond to the other 195 papers as analysed by Roussel.

We note that many of the points raised were already covered in our 196 original review paper although there are some additional valid considerations, which are discussed 197 above.

Additionally, we dispute the fact that we neglected to discuss the potential for errors in the 198 field sampling although we did not included discussion on the use of modelling during the study 199 design phase, which was an important omission.


We feel that our original conclusions are valid: 200 eDNA analysis is fast becoming an important tool in the study of aquatic species; alternative 201 methods of PCR or novel methodologies could be used to provide rapid on--site detections; the fact 202 that there does not appear to be 'a single study which could be used as a template for 203 standardization of this technique' due to the very different natures of the environments being 204 studied, the taxa, the density of organisms, and the disparity in the way samples are scored as 205 positive; the fact that next generation sequencing of eDNA samples could become a powerful tool 206 for monitoring species diversity; and finally that the technique has been used as an 'accurate 207 indicator of the presence of species in a range of aquatic environments'.


In agreement with the conclusions of Roussel, we feel that eDNA analysis is a 'promising method', 209 that more investigations are required, and as with any method users should be aware of potential 210 weaknesses within its use.

Roussel concludes that non--detection of target species DNA does not 211 mean the absence of that species, nor does a positive detection mean that the species is present.

212 This is of course true for the reasons already discussed above.

However, it is also the case that 213 traditional field survey methods are not always accurate when recording the absence of a particular 214 species, particularly in cases where a secretive or elusive species is being surveyed.

This points to the 215 fact that neither field survey nor eDNA analysis is perfect, which supports a statement made in the 216 original review and is our concluding remark here, that 'eDNA should not be used to replace or 217 disregard the knowledge and expertise of experienced field ecologists and taxon specialists, but 218 should become an important tool to enhance limited conservation resources'.


There is a growing literature on the detection of species-specific DNA fragments in water to reveal animals’ presence in aquatic environments [1].

Analysis of Rees et al. ( Journal of Applied Ecology, 2014, 51, 1450) and additional publications highlight the downside of the method and suggest that some conclusions have to be toned down [2].

Non-detection of species-specific DNA fragments in a water sample does not automatically imply the absence of the target species.

A positive signal does not necessarily mean the species is present because the environmental DNA (eDNA) could have been transported or preserved after an animal’s death [3].

Detection of aquatic invertebrates is weakly documented.

The effect of species abundance on detection efficiency is not always established [4].

Before successfully becoming an efficient tool to assist proper management policies for monitoring of aquatic species, eDNA surveys must implement sampling strategies that allow estimating detection rates and scaling up information from water samples to larger spatial areas [5].

The effects of discharge variations and dendritic organization in running waters require particular consideration [6].


Environmental DNA (eDNA) analysis, that is the detection of species-specific DNA fragments in the environment, is developing as a tool for surveying animal species in aquatic ecosystems [7].

Initial evaluations by Ficetola et al. ( 2008) suggest that eDNA analysis is more efficient for revealing the presence of a target species at low population densities than conventional census methods [8].

Accordingly, eDNA is often considered an effective tool for the detection of recently introduced invasive species, and elusive and endangered species [9].

A review article on eDNA was recently published in Journal of Applied Ecology (Rees et al. 2014), but we think it neglected important ideas and caveats [10].

Our objective was to identify some persistent misunderstandings about eDNA and to further inform potential eDNA users of practical drawbacks for the detection of aquatic animals [11].


First, the existing literature focuses mostly on vertebrate species (84% – primarily fish and amphibians) [12], and it seems premature to imply that eDNA will be an efficient tool for detecting all aquatic animal species [13].

For instance, using the eDNA method alone was a deficient strategy for detecting a highly invasive crayfish, the red swamp crayfish Procambarus clarkii, in a large range of freshwater ponds [14].

In this work, eDNA detection efficiency was poor when crayfish abundance – estimated by conventional trapping – was estimated to be low, perhaps because DNA was too sparse to be detected [15].

As initially indicated by Ficetola et al. ( 2008), the detection of eDNA in water samples is ‘useful for studying secretive aquatic or semi-aquatic species, which release DNA into the environment through mucus, faeces, urine and remains’ [16].

If detection efficiency varies between aquatic species, notably between vertebrates and invertebrates, then preliminary tests to estimate the detectability of DNA of a new target species under a range of semi-natural conditions should be a prerequisite for surveying [17].


Secondly, the idea that eDNA is more efficient than traditional surveys, which is fast becoming commonplace in the literature, rests on a few comparative studies only [18].

These studies typically reported positive eDNA detection in habitats where the target species was already known to be present [19].

Furthermore, the abundance of the target species is not always reported [20].

Consequently, it is difficult to understand how the method might perform across a gradient of target species density [21].

One advantage to surveying by eDNA is that it is easy to deploy at large spatial scales [22].

This does not, however, automatically imply it is more efficient than traditional methods; such confusion is often perceived in published articles [23].

Each survey method is imperfect, but one advantage of methods involving the capture of the target species is that they are not affected by false-positive detection, which introduces additional complexity in occupancy models using eDNA survey results [24].

We appeal for more comparisons between eDNA and traditional census methods in future studies [25].

Routine reporting of the target species’ abundance in studies using eDNA analysis will help practitioners to understand detection rates and evaluate the strength of the eDNA method compared to traditional census methods in natural settings [26].


Thirdly, the lower detection limit for the target eDNA sequences can be easily estimated from a dilution series of known amounts of DNA and PCR replicates per concentration [27].

This information, however, is usually missing from most published studies, the number of PCR cycles being set – presumably – arbitrarily [28].

Among studies that have reported this information, the minimum number of PCR amplifications leading to a positive detection of eDNA at the lowest concentration varies strongly, emphasizing that it should be reported routinely to allow comparisons of detection efficiency between studies [29].

Furthermore, the term ‘false positive’ should be used carefully and, above all, be defined unambiguously [30].

False positives, stricto sensu, occur when a positive signal is obtained, while the sample does not contain any DNA of the target species, which is truly an artefact of the detection process [31].

When interpreting positive signals, however, one should always consider that eDNA might be amplified even though the species is absent from the water body where the sample was taken [32].

These situations can be due to peculiarities of the sampled environment (for instance in running waters, see below) and might be informative about ecological processes that these surveys are hitherto not designed to sample [33].

Notably, molecular detection can reveal trophic interactions among species [34] when, for example, remains of the target species are deposited in predator faeces when the predator was visiting a site where the prey is absent [35].

Moreover, sedimentary eDNA provides longer-lasting signals of the past site occupancy by aquatic species [36].

Thus, resuspension and transport of sedimentary eDNA can complicate the reading of the target species’ presence [37].

That DNA molecules could be present in water samples, while the species is absent certainly deserves more attention, particularly if we hope to understand exactly what an eDNA detection represents [38].


eDNA surveys must implement sampling strategies that allow estimating detection rates [39].

Enhancing detectability will have practical implications, most notably for the costs of field sampling and laboratory molecular analyses [40].

Enhancing detectability cannot, however, replace rigorous sampling design [41].

Rees et al. ( 2014) discussed the number of PCR replicates that are required to detect target DNA, but neglected to recognize that there are two sources of imperfect detection: at the level of the field and the laboratory [42].

Schmidt et al. ( 2013) showed that for the amphibian chytrid fungus Batrachochytrium dendrobatidis, the main source of imperfect detection was at the sampling step in the field, not at the amplification step in the laboratory [43].

Their study and others further demonstrated that simulation tools can help decide the best sampling strategy [44].


The current literature dealing with running waters is still ambiguous about its effect on the downstream transportation of DNA [45].

While DNA could be detected only a few metres downstream of a site where salamanders were introduced, Deiner & Altermatt (2014) detected the DNA of daphnia and mussels in stream water at nearly 20 km downstream of the alpine lake in which they were present [46].

Clearly, explaining such variations is required if one wants to understand the significance of a positive or negative eDNA signal from a running stream survey [47].

Rees et al. ( 2014) highlighted that eDNA degradation rate can affect detection rates in aquatic ecosystems, but it must also be mentioned that discharge variations and dendritic organization further complicate the eDNA detection rates in running waters [48].

The transport of eDNA downstream and its dilution at each stream confluence are inherent processes that need to be properly accounted for if eDNA is to be a satisfactory survey method for running waters [49].

Scaling up results from local water samples is clearly challenging, and modelling approaches should be encouraged [50].

Such models should consider detection rates together with eDNA persistence in spatially explicit dendritic systems [51].


To conclude, eDNA is a promising method but one should be aware of its weaknesses before adopting it as a tool for surveying endangered or invasive aquatic species [52].

Based on current knowledge and the limited case studies in the literature, the non-detection of species-specific DNA fragments in water samples does not automatically imply the absence of the target species, nor does a positive signal necessarily mean the species is present [53].

DNA concentrations in the water sample may be beneath the detection threshold because the target species releases only a small amount of DNA or because of rapid DNA degradation or dilution in the environment [54].

Conversely, DNA may be detected even though the species is absent because DNA can be transported by running waters or animal movements or it might be excreted by a passing predator of the target species [55].

More investigations are required to understand how well the eDNA method will work for aquatic invertebrate species, to evaluate the effect of species abundance on detection efficiency and to upscale species detection from local water samples to larger spatial areas, such as drainage basins [56].

These are necessary conditions for achieving an efficient survey tool to serve the development of proper management policies for monitoring aquatic species and their ecosystems [57].


In Rees et al. ( 2014b), we reviewed the current status of environmental DNA (eDNA) to monitor aquatic populations.

Our aim was to focus on discussion of methodologies used, application of eDNA analysis as a survey tool in ecology, and to include some innovative ideas for using eDNA in conservation and management.

Roussel et al. ( 2015) claim that analysis of Rees et al. ( 2014b) and other publications highlights the downsides of the method, and they suggest that some conclusions should be toned down.

Many of their arguments were covered in our original paper (Rees et al., 2014b); however, they make the point that modelling approaches should be encouraged, and we fully agree with this suggestion.

Roussel et al. ( 2015) also claim that we neglected to recognize that there are two sources of imperfect detection (at the field level and at the laboratory level).

We feel that our review paper implies this point.

Roussel et al. ( 2015) reiterate many of the points made in the original paper but do cover some additional areas that improve the debate on the use of environmental DNA (eDNA).

Both the comment (Roussel et al., 2015) and our rebuttal clearly highlight that detailed laboratory protocols and rigorous field sampling design are crucial factors which require sufficient reporting in the literature to allow for experimental comparison and replication.

Any development of a new method for eDNA detection should be compared directly with established ‘gold standard’ methods for the detection of the species or habitat under investigation.

None of the issues raised in Roussel et al. ( 2015) would alter our main conclusions.


Environmental DNA (eDNA) analysis in the context of monitoring aquatic and semi-aquatic species via species-specific DNA from water bodies is still in its relative infancy.

Because of this, there is much still to be done in terms of optimizing and improving eDNA analysis.

Our review intended to discuss the state of both the field sampling and laboratory analysis of these samples and to suggest areas which required improvement.

We also discussed innovative ideas for how eDNA can be applied to conservation and management.

It was not our intention to produce an exhaustive review of every aspect of the field; we aimed instead to produce a review with specific emphasis on environmental water sampling, the PCR analysis itself, and the specific applications of this technique – biomass estimation, species composition, and detection of invasive and rare or threatened species – along with a comparison of eDNA versus traditional methods.

There was no intention to neglect what are deemed to be important areas of this type of work such as the use of modelling for sampling design.

In this respect, we welcome the endeavour of Roussel et al. ( 2015, hereafter ‘Roussel’) in providing comment on our review paper (Rees et al. 2014b).

We respond to the five main points below.