Two-sided univariate χ2 tests were used to compare outcomes of patients with an eosinophil count greater than 0 cells/μL (n = 610) and patients with an eosinophil count of 0 cells/μL (n = 454). For all 4 outcomes, P < .05 between groups. Error bars represent 95% CIs.
eAppendix. Description of Multiple Imputation Approach
eTable. Odds Ratios Associated With Pairwise Comparisons Between Groupings of Eosinophil Counts
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Kulaylat AS, Buonomo EL, Scully KW, et al. Development and Validation of a Prediction Model for Mortality and Adverse Outcomes Among Patients With Peripheral Eosinopenia on Admission for Clostridium difficile Infection. JAMA Surg. 2018;153(12):1127–1133. doi:10.1001/jamasurg.2018.3174
Does peripheral eosinopenia at the time of admission for Clostridium difficile infection predict higher inpatient mortality and other disease-related adverse outcomes?
In this cohort study, peripheral eosinopenia at the time of admission for C difficile infection predicted higher inpatient mortality. The predictive value of eosinopenia for this outcome remains for patients presenting with normal vital signs.
In animal models, peripheral eosinopenia is a biologically plausible predictive factor for adverse outcomes, and human data from this study indicate that this frequent addition to an admission complete blood cell count is an inexpensive, widely available risk index in the treatment of C difficile infection.
Recent evidence from an animal model suggests that peripheral loss of eosinophils in Clostridium difficile infection (CDI) is associated with severe disease. The ability to identify high-risk patients with CDI as early as the time of admission could improve outcomes by guiding management decisions.
To construct a model using clinical indices readily available at the time of hospital admission, including peripheral eosinophil counts, to predict inpatient mortality in patients with CDI.
Design, Setting, and Participants
In a cohort study, a total of 2065 patients admitted for CDI through the emergency department of 2 tertiary referral centers from January 1, 2005, to December 31, 2015, formed a training and a validation cohort. The sample was stratified by admission eosinophil count (0.0 cells/μL or >0.0 cells/μL), and multivariable logistic regression was used to construct a predictive model for inpatient mortality as well as other disease-related outcomes.
Main Outcomes and Measures
Inpatient mortality was the primary outcome. Secondary outcomes included the need for a monitored care setting, need for vasopressors, and rates of inpatient colectomy.
Of the 2065 patients in the study, 1092 (52.9%) were women and patients had a mean (SD) age of 63.4 (18.4) years. Those with an undetectable eosinophil count at admission had increased in-hospital mortality in both the training (odds ratio [OR], 2.01; 95% CI, 1.08-3.73; P = .03) and validation (OR, 2.26; 95% CI, 1.33-3.83; P = .002) cohorts in both univariable and multivariable analysis. Undetectable eosinophil counts were also associated with indicators of severe sepsis, such as admission to monitored care settings (OR, 1.40; 95% CI, 1.06-1.86), the need for vasopressors (OR, 2.08; 95% CI, 1.32-3.28), and emergency total colectomy (OR, 2.56; 95% CI, 1.12-5.87). Other significant predictors of mortality at admission included increasing comorbidity burden (for each 1-unit increase: OR, 1.13; 95% CI, 1.05-1.22) and lower systolic blood pressures (for each 1-mm Hg increase: OR, 0.99; 95% CI, 0.98-1.00). In a subgroup analysis of patients presenting without initial tachycardia or hypotension, only patients with undetectable admission eosinophil counts, but not those with an elevated white blood cell count, had significantly increased odds of inpatient mortality (OR, 5.76; 95% CI, 1.99-16.64).
Conclusions and Relevance
This study describes a simple, widely available, inexpensive model predicting CDI severity and mortality to identify at-risk patients at the time of admission.
Clostridium difficile infection (CDI) is the most common nosocomial infection in the United States1 that is associated with mortality rates as high as 22% at 60 days after initial infection and 36% at 6 months among institutionalized patients 65 years or older.2 The incidence of severe and even life-threatening forms of CDI are common3 among strains of this bacteria known to have virulent potential. These virulent strains are not only capable of producing the large clostridial toxins—toxin A and toxin B, which serve as virulence factors4—but many will produce a third, antigenically distinct toxin known as binary toxin. Animal and human studies5-7 suggest that binary toxin–producing strains are more frequently associated with severe colitis and higher rates of disease-related mortality. Recently, 2 studies7,8 using a mouse model of CDI further described the association between eosinophils and CDI. Quiz Ref IDThese preclinical investigations suggest that eosinophilia protects against CDI mortality and that binary toxin induces a peripheral eosinopenia associated with higher CDI mortality.
A current knowledge gap about CDI relates to a lack of validated indicators for the disease course that are clinically available as well as reliable enough to guide care decisions. Despite evidence-based consensus guidelines regarding the management of CDI,9 these guidelines focus on timely management once patients develop hemodynamic changes and/or significant abnormalities in laboratory test results. The ability to identify high-risk patients with CDI prior to these clinical changes would potentially improve patient outcomes by allowing for such interventions as admission to a monitored care setting and earlier surgical consultation. Our hypothesis was that peripheral eosinopenia at hospital admission for CDI would be associated with higher odds of mortality and other adverse events. We developed a clinical model to predict inpatient mortality in a large cohort of patients from an academic institution, with this model subsequently validated by using a separate large cohort of patients from a second academic institution. The goal was to use well-established, inexpensive, easily obtainable clinical and laboratory indices, including peripheral eosinophil counts, to identify patients with CDI who were at higher risk for mortality and other adverse events as early as the time of admission.
By using the cost accounting database of 2 academic institutions during an 11-year period (January 1, 2005-December 31, 2015), patients with a positive result of a C difficile nucleic acid amplification test or enzyme-linked immunosorbent assay associated with a hospital admission were identified. Only patients with C difficile testing performed within 24 hours of hospital admission were included to exclude patients who had prolonged hospitalizations for alternative primary diagnoses. Patients were excluded if they were younger than 18 years or if they did not have total white blood cell (WBC) counts, eosinophil counts, or serum creatinine levels measured within 24 hours of hospital admission. If multiple admissions for CDI were identified for a patient, only the first admission associated with CDI was included in this study to eliminate data pertaining to episodes of recurrent disease. This study was approved by the institutional review boards of both the Pennsylvania State University Milton S. Hershey Medical Center (PSU), Hershey, and the University of Virginia Health System (UVA), Charlottesville. Owing to the study’s retrospective nature, the requirement for informed consent was waived by the institutional review boards.
Demographic data collected on admitted patients included age, sex, and race/ethnicity. Comorbidity burden was characterized according to the Charlson Comorbidity Index by using International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes.10 At PSU, use of mechanical ventilation was identified with Charge Description Master code 511861. At UVA, mechanical ventilation was identified through either ICD-9 codes (96.7X), an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) code (5A1935Z), or Current Procedural Terminology codes (94002, 94003, 94656, and 94657). Vital signs at the time of presentation to the hospital were available for most patients (n = 1675), allowing identification of initial hypotension (systolic blood pressure, <90 mm Hg) and tachycardia (heart rate, >100 beats per minute). Laboratory analytes measured at the time of admission included WBC count, eosinophil count, and serum creatinine level.
The primary study outcome was inpatient mortality. Secondary outcomes included the need for admission to a monitored care setting (intermediate or intensive care), vasopressors, and total abdominal colectomy during the index hospitalization.
At both institutions, a range of eosinophil counts between and inclusive of 0.0 cells/μL and 6000.0 cells/μL were reported, with the smallest nonzero values being 10.0 cells/μL at both institutions (to convert to cells ×109 per liter, multiply by 0.001). An eosinophil count reported as 0.0 cells/μL was the cutoff used to stratify the cohort. Binary and categorical variables were compared between the 2 groups using χ2 tests, and continuous data were analyzed using 2-tailed unpaired t tests. A logistic regression model was then developed using patients from PSU as the training cohort, modeling inpatient mortality as a function of the available patient covariates and admission laboratory analyte results, including the markers of severity most commonly reported in the published literature,11,12 which also reflect clinical practice. In addition, an interaction term between low eosinophil count and high WBC count was created and tested within the initial model but was not found to be significant and was therefore excluded from the final model. We used multiple imputation to account for missing values for systolic blood pressure and heart rate. We created 20 multiply imputed data sets and performed logistic regression on each of the data sets (eAppendix in the Supplement). Coefficients were pooled using the methods suggested by Rubin.13 These same covariates were then validated in a separate data set of patients from UVA, keeping patients at each institution separate for the construction of training and validation cohorts to avoid confounding influences from unaccounted institutional variables or differences in C difficile strains between these institutions at different time points. Secondary outcomes were compared between cohorts with a similar method that used initial univariate comparisons and subsequent multivariable logistic regression analysis and incorporated the same covariates from the mortality model.
Several sensitivity and subgroup analyses were also performed to test the robustness of the model. To further characterize the association between eosinophil counts and mortality, the eosinophil count was also examined as a continuous variable and as a categorical variable by stratifying nonzero eosinophil values into quartiles and comparing them with a reference group of higher nonzero eosinophil quartile (counts >220.0 cells/μL). Last, a subgroup analysis was performed to evaluate the findings of the model among 551 patients who presented without hypotension or tachycardia. The area under the receiver operating characteristic curve was used to evaluate the ability of the regression model to predict adverse outcomes. All statistical analyses were performed using Stata statistical software, version 12.1 (StataCorp). All P values were from 2-sided tests, and results were deemed statistically significant at P < .05.
Quiz Ref IDA total of 2428 patients met the inclusion criteria; 363 patients had missing laboratory values (WBC counts or eosinophil counts) and were excluded from the study, leaving a final study cohort of 2065 patients: 1064 patients from PSU and 1001 patients from UVA. Of the study population, 799 (38.7%) presented with an admission eosinophil count of 0 cells/μL and 1266 (61.3%) had measurable admission eosinophil counts. Patients with eosinophil counts of 0 cells/μL had a higher mean (SD) age (64.2 [17.4] vs 61.6 [18.3] years; P = .002), but they otherwise did not differ significantly by sex, race/ethnicity, or comorbidity burden from those with measurable admission eosinophil counts (Table 1). Quiz Ref IDPatients with admission eosinophil counts of 0 cells/μL were more likely than those with measurable admission eosinophil counts to have hypotension (64 [8.0%] vs 58 [4.6%]; P = .001) and tachycardia (254 [31.8%] vs 275 [21.7%]; P < .001) and were more likely to require mechanical ventilation (58 [7.3%] vs 55 [4.3%]; P = .005) and have higher median (interquartile range) admission WBC counts (16 100 [9540-24 500] vs 11 050 [7500-15 700] cells/μL; P < .001).
With the use of data from the PSU cohort (n = 1064), inpatient mortality was first compared between cohorts. Unadjusted comparisons of mortality rates between patients with admission eosinophil counts of 0 cells/μL (n = 454) and those with eosinophil counts greater than 0 cells/μL (n = 610) in the training cohort are shown in the Figure. The results demonstrate that mortality rates were significantly higher among patients admitted with eosinophil counts of 0 cells/μL than among patients with counts greater than 0 cells/μL (46 [10.1%] vs 20 [3.3%]; P < .001).
A multivariable logistic regression model was then constructed using the training cohort. After controlling for other patient characteristics to isolate the effect of eosinophil count, an admission eosinophil count of 0 cells/μL was an independent predictor of inpatient mortality (odds ratio [OR], 2.01; 95% CI, 1.08-3.73) (Table 2). Similarly, a WBC count of 15 000 cells/μL or more (to convert to cells × 109 per liter, multiply by 0.001) was associated with more than twice the odds of inpatient mortality compared with a WBC count less than 15 000 cells/μL (OR, 2.69; 95% CI, 1.44-5.05). Serum creatinine levels were not significantly associated with inpatient mortality (OR, 1.11 per 1-U increase in serum creatinine; 95% CI, 0.97-1.28). Results for other model factors are shown in Table 2. The area under the receiver operating characteristic curve associated with this model for mortality was 0.82, suggesting that this model had good ability to predict the occurrence of mortality.
Table 3 shows the sensitivity, specificity, and accuracy of the model provided in Table 2 in predicting mortality. For example, predicting a patient to experience mortality at a cutoff of the probability of mortality of 10% or greater, 56.1% of deceased patients were correctly predicted to experience mortality, while 87.4% of patients who were not predicted to experience mortality were correctly predicted to survive. At a higher threshold for predicted mortality, 70% or more, only 4.6% of deceased patients were correctly predicted to experience mortality, whereas 99.9% of patients who were not predicted to experience mortality were correctly predicted to survive. Quiz Ref IDBecause the number of patients who experienced mortality was low relative to those who survived, this model has greater than 80% accuracy for any patient whose predicted probability of mortality exceeds approximately 7.5% and an accuracy greater than 90% for any patient whose predicted probability of mortality exceeds 20%.
In examining secondary outcomes of interest among the training cohort, patients with admission eosinophil counts of 0 cells/μL more frequently required admission to monitored care settings (OR, 1.40; 95% CI, 1.06-1.86), more frequently required the use of vasopressors for the treatment of septic shock (OR, 2.08; 95% CI, 1.32-3.28), and were more likely to require total colectomy for severe, medically refractory disease (OR, 2.56; 95% CI, 1.12-5.87) when compared with those with admission eosinophil counts greater than 0 cells/μL (Figure).
In the validation cohort of UVA patients (n = 1001), inpatient mortality was significantly higher on univariate analysis when the group with admission eosinophil counts of 0 cells/μL was compared with the group with admission eosinophil counts of more than 0 cells/μL (49 [14.2%] vs 43 [6.6%]; P < .001). As observed in the multivariable model for the training cohort, the odds of mortality were significantly higher when the group with admission eosinophil counts of 0 cells/μL was compared with those with counts greater than 0 cells/μL (OR, 2.26; 95% CI, 1.33-3.83). However, total WBC counts of 15 000 cells/μL or greater did not show a significant association with mortality among the UVA patient cohort (OR, 1.33; 95% CI, 0.78-2.26). The area under the receiver operating characteristic curve fit with UVA data was 0.863.
When the eosinophil count was considered as a continuous variable within the model rather than as a dichotomized variable, there was no significant linear association between eosinophil count and mortality (OR, 1.03; 95% CI, 0.54-1.98). Eosinophil count was then modeled as a categorical variable, with quartiles of nonzero counts stratified into eosinophils 60 cells/μL or less, 61 to 100 cells/μL, 101 to 220 cells/μL, and greater than 220 cells/μL. The only category of eosinophil count that was significantly associated with mortality remained the subgroup with eosinophil counts of 0 cells/μL (OR, 2.91; 95% CI, 1.08-7.86). Odds ratios for all pairwise comparisons are presented in the eTable in the Supplement, with inferences adjusted for multiple comparisons.
In the subgroup analysis of patients who presented without tachycardia or hypotension, patients with eosinophil counts of 0 cells/μL had significantly higher odds of mortality (OR, 5.76; 95% CI, 1.99-16.64), whereas those with a WBC count of 15 000/μL or more did not (OR, 1.62; 95% CI, 0.64-4.10).
Quiz Ref IDThis study found that admission eosinophil counts allow for an immediate assessment of mortality risk at admission that is inexpensive and part of a differential for a standard complete blood count available at any hospital. The absence of eosinophils (as opposed to a range of eosinophils, which may vary slightly between hospital laboratories) is not only an independent predictor of inpatient mortality but also associated with higher odds of severe disease requiring intensive care, vasopressor use, and surgery. These clinical findings correlate well with the recent discovery in a murine model that peripheral blood eosinophils are depleted in connection with binary toxin produced by certain strains of C difficile, likely through accelerated apoptosis rather than reduced eosinopoeisis.8 Although certain ribotypes are known to exhibit increased toxigenicity,14 the data correlating specific ribotypes with worsened clinical outcomes are divergent,5,15,16 and tests for binary toxin detection and ribotyping are not available as a part of clinical care.5 The measurement of eosinophil counts is a widely available measure that serves as a marker for key adverse outcomes, which in turn affects length of stay, cost of care, and mortality.
It is unsurprising that a systematic review17 of risk factors for mortality in CDI did not include the association of eosinophil counts and disease-related adverse events because there is little published in the literature on this topic, to our knowledge. Two previous studies18,19 examining mortality in patients with CDI used a wide variety of laboratory measures (C-reactive protein, alkaline phosphatase, cholesterol, sodium, calcium, and lactate dehydrogenase) as well as eosinophil counts in their analyses. Those studies found that lower eosinophil counts obtained at various time points after hospital admission were associated with higher mortality; however, those studies examined eosinophil counts as a continuous biomarker, which, as we previously noted, may be subject to variations in laboratories between hospitals. Our study also provided a subgroup analysis of patients who initially presented to the hospital without any derangement in vital signs (heart rate >100 beats per minute or systolic blood pressure <90 mm Hg) and demonstrated that eosinophil counts of 0 cells/μL were predictive of inpatient mortality; conversely, the more commonly used indicator of severe disease—WBC count of 15 000 cells/μL or more—was not significantly associated with mortality among this subgroup. Because there are data suggesting that inflammation from the host immune system can be deleterious to the host in situations in which the inflammatory response is overactive,20,21 an undetectable admission eosinophil count may be a more reliable marker for ribotypes of C difficile that promote the patterns of host immune response most associated with adverse outcomes. By contrast, a significantly greater degree of attention has been given to the prominent leukocytosis frequently associated with CDI. The finding that numerous toxin-dependent and toxin-independent mechanisms22 in CDI are a likely cause for this leukocytosis raises issues as to whether the absolute value of the WBC count (as opposed to its trend) is as much a measure of severity of colitis as it is a combination of colitis severity and immune response. This difference may help to explain the finding that, for patients with normal vital signs on admission, eosinophil count correlated with mortality while WBC count did not.
This study has several limitations. The findings from data between the 2 participating institutions were consistent; although questions regarding generalizability given different strains of C difficile could be raised, the use of training and validation cohorts suggests that the results are generalizable. This study is also retrospective, and prospective studies that better control for potential confounders, such as baseline medication use, immune status, and CDI therapies, are required. A retrospective approach may also introduce issues related to selection bias; our inclusion criteria would tend to favor more severe forms of CDI, as these require inpatient hospitalization. However, since eosinophil counts are not currently used to guide management decisions at either institution, the focus of this study on admission eosinophil counts is unlikely to have introduced bias into our observed results. Prospective studies will be necessary to show that the information provided by our model would lead to improved outcomes in patients with CDI as well as to answer questions regarding whether eosinopenia, or recovery of a detectable eosinophil count, at time points after admission are predictive of disease outcomes. In addition, although the data in the literature regarding the association between ribotyping and CDI severity are conflicting, the lack of availability of ribotyping data as well as any other characterization of the predominant strain of C difficile among patients in this study are important data that were unavailable in the current work, as this analysis is not a part of routine clinical care. Last, although the findings of Cowardin et al8 suggest a biologically plausible mechanism for the observed association between the absence of peripheral eosinophils and increased virulence of C difficile, further studies are required to better elucidate this mechanism in humans.
Results have been recently published for a scoring system predicting postoperative mortality in patients with CDI who require total colectomy.23 Our ongoing research in the area of the microbiome in CDI indicates that a host immune reaction associated with an exaggerated inflammasome response is potentially more predictive of severity than are many of the commonly assessed clinical indices. Peripheral eosinopenia is a potential marker for this exaggerated inflammasome response, and our group is in the process of prospectively evaluating a prognostic score that can be calculated at admission and includes eosinopenia. This future study will not only further evaluate the prognostic value of eosinopenia but will address whether improved prognostication at the time of admission will result in decreased mortality by guiding treatment decisions at the beginning of inpatient care.
Accepted for Publication: May 17, 2018.
Corresponding Author: David B. Stewart Sr, MD, Department of Surgery, University of Arizona, 1501 N Campbell Ave, PO Box 245131, Tucson, AZ 85724 (firstname.lastname@example.org).
Published Online: September 12, 2018. doi:10.1001/jamasurg.2018.3174
Author Contributions: Dr Stewart had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Kulaylat, Buonomo, Hollenbeak, Petri, Stewart.
Acquisition, analysis, or interpretation of data: Kulaylat, Scully, Hollenbeak, Cook, Petri, Stewart.
Drafting of the manuscript: Kulaylat, Hollenbeak, Cook, Petri, Stewart.
Critical revision of the manuscript for important intellectual content: Buonomo, Scully, Hollenbeak, Petri, Stewart.
Statistical analysis: Kulaylat, Buonomo, Hollenbeak, Cook, Stewart.
Obtained funding: Petri.
Supervision: Hollenbeak, Petri.
Conflict of Interest Disclosures: None reported.
Funding/Support: This work was supported in part by grant R01 AI124214 from the National Institutes of Health (Dr Petri). Funding from the National Institutes of Health provided the resources needed for previous publications lending to the study hypothesis for this work and for support for laboratory personnel involved in this current work.
Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional Contributions: Jennie Ma, PhD, Department of Public Health Sciences, University of Virginia, assisted with statistical analysis. Andrew Bible, BS, Penn State College of Medicine, assisted with procuring the data from the Pennsylvania State University Milton S. Hershey Medical Center. Kimberly Walker, Penn State College of Medicine, assisted with manuscript preparation. They were not compensated for their contributions.