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Development and Validation of a Prediction Model for Mortality and Adverse Outcomes Among Patients With Peripheral Eosinopenia on Admission for Clostridium difficile Infection

Educational Objective To identify to what extent peripheral eosinopenia can predict outcomes with Clostridium difficile infection.
1 Credit CME
Key Points

Question  Does peripheral eosinopenia at the time of admission for Clostridium difficile infection predict higher inpatient mortality and other disease-related adverse outcomes?

Findings  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.

Meaning  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.

Abstract

Importance  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.

Objective  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.

Results  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.

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Article Information

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 (dbstewart@surgery.arizona.edu).

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.

References
1.
Leffler  DA, Lamont  JT.  Clostridium difficile infection.  N Engl J Med. 2015;372(16):1539-1548. doi:10.1056/NEJMra1403772PubMedGoogle ScholarCrossref
2.
Yu  H, Baser  O, Wang  L.  Burden of Clostridium difficile–associated disease among patients residing in nursing homes: a population-based cohort study.  BMC Geriatr. 2016;16(1):193. doi:10.1186/s12877-016-0367-2PubMedGoogle ScholarCrossref
3.
Pépin  J, Saheb  N, Coulombe  MA,  et al.  Emergence of fluoroquinolones as the predominant risk factor for Clostridium difficile–associated diarrhea: a cohort study during an epidemic in Quebec.  Clin Infect Dis. 2005;41(9):1254-1260. doi:10.1086/496986PubMedGoogle ScholarCrossref
4.
Džunková  M, D’Auria  G, Xu  H,  et al.  The monoclonal antitoxin antibodies (actoxumab-bezlotoxumab) treatment facilitates normalization of the gut microbiota of mice with Clostridium difficile infection.  Front Cell Infect Microbiol. 2016;6:119. doi:10.3389/fcimb.2016.00119PubMedGoogle ScholarCrossref
5.
Gerding  DN, Johnson  S, Rupnik  M, Aktories  K.  Clostridium difficile binary toxin CDT: mechanism, epidemiology, and potential clinical importance.  Gut Microbes. 2014;5(1):15-27. doi:10.4161/gmic.26854PubMedGoogle ScholarCrossref
6.
Lim  SK, Stuart  RL, Mackin  KE,  et al.  Emergence of a ribotype 244 strain of Clostridium difficile associated with severe disease and related to the epidemic ribotype 027 strain.  Clin Infect Dis. 2014;58(12):1723-1730. doi:10.1093/cid/ciu203PubMedGoogle ScholarCrossref
7.
Buonomo  EL, Cowardin  CA, Wilson  MG, Saleh  MM, Pramoonjago  P, Petri  WA  Jr.  Microbiota-regulated IL-25 increases eosinophil number to provide protection during Clostridium difficile infection.  Cell Rep. 2016;16(2):432-443. doi:10.1016/j.celrep.2016.06.007PubMedGoogle ScholarCrossref
8.
Cowardin  CA, Buonomo  EL, Saleh  MM,  et al.  The binary toxin CDT enhances Clostridium difficile virulence by suppressing protective colonic eosinophilia.  Nat Microbiol. 2016;1(8):16108. doi:10.1038/nmicrobiol.2016.108PubMedGoogle ScholarCrossref
9.
Ferrada  P, Velopulos  CG, Sultan  S,  et al.  Timing and type of surgical treatment of Clostridium difficile–associated disease: a practice management guideline from the Eastern Association for the Surgery of Trauma.  J Trauma Acute Care Surg. 2014;76(6):1484-1493. doi:10.1097/TA.0000000000000232PubMedGoogle ScholarCrossref
10.
Deyo  RA, Cherkin  DC, Ciol  MA.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.  J Clin Epidemiol. 1992;45(6):613-619. doi:10.1016/0895-4356(92)90133-8PubMedGoogle ScholarCrossref
11.
Chopra  T, Awali  RA, Biedron  C,  et al.  Predictors of Clostridium difficile infection–related mortality among older adults.  Am J Infect Control. 2016;44(11):1219-1223. doi:10.1016/j.ajic.2016.04.231PubMedGoogle ScholarCrossref
12.
Cohen  SH, Gerding  DN, Johnson  S,  et al; Society for Healthcare Epidemiology of America; Infectious Diseases Society of America.  Clinical practice guidelines for Clostridium difficile infection in adults: 2010 update by the Society for Healthcare Epidemiology of America (SHEA) and the Infectious Diseases Society of America (IDSA).  Infect Control Hosp Epidemiol. 2010;31(5):431-455. doi:10.1086/651706PubMedGoogle ScholarCrossref
13.
Rubin  DB, ed.  Multiple Imputation for Nonresponse in Surveys. Hoboken, NJ: John Wiley & Sons Inc; 1987. doi:10.1002/9780470316696
14.
Lewis  BB, Carter  RA, Ling  L,  et al.  Pathogenicity locus, core genome, and accessory gene contributions to Clostridium difficile virulence.  MBio. 2017;8(4):e00885-e17. doi:10.1128/mBio.00885-17PubMedGoogle ScholarCrossref
15.
Rao  K, Micic  D, Natarajan  M,  et al.  Clostridium difficile ribotype 027: relationship to age, detectability of toxins A or B in stool with rapid testing, severe infection, and mortality.  Clin Infect Dis. 2015;61(2):233-241. doi:10.1093/cid/civ254PubMedGoogle ScholarCrossref
16.
Walk  ST, Micic  D, Jain  R,  et al.  Clostridium difficile ribotype does not predict severe infection.  Clin Infect Dis. 2012;55(12):1661-1668. doi:10.1093/cid/cis786PubMedGoogle ScholarCrossref
17.
Bloomfield  MG, Sherwin  JC, Gkrania-Klotsas  E.  Risk factors for mortality in Clostridium difficile infection in the general hospital population: a systematic review.  J Hosp Infect. 2012;82(1):1-12. doi:10.1016/j.jhin.2012.05.008PubMedGoogle ScholarCrossref
18.
Crook  DW, Walker  AS, Kean  Y,  et al; Study 003/004 Teams.  Fidaxomicin versus vancomycin for Clostridium difficile infection: meta-analysis of pivotal randomized controlled trials.  Clin Infect Dis. 2012;55(suppl 2):S93-S103. doi:10.1093/cid/cis499PubMedGoogle ScholarCrossref
19.
Walker  AS, Eyre  DW, Wyllie  DH,  et al; Infections in Oxfordshire Research Database.  Relationship between bacterial strain type, host biomarkers, and mortality in Clostridium difficile infection.  Clin Infect Dis. 2013;56(11):1589-1600. doi:10.1093/cid/cit127PubMedGoogle ScholarCrossref
20.
Buonomo  EL, Petri  WA  Jr.  The microbiota and immune response during Clostridium difficile infection.  Anaerobe. 2016;41:79-84. doi:10.1016/j.anaerobe.2016.05.009PubMedGoogle ScholarCrossref
21.
Garey  KW, Jiang  ZD, Ghantoji  S, Tam  VH, Arora  V, Dupont  HL.  A common polymorphism in the interleukin-8 gene promoter is associated with an increased risk for recurrent Clostridium difficile infection.  Clin Infect Dis. 2010;51(12):1406-1410. doi:10.1086/657398PubMedGoogle ScholarCrossref
22.
Jafari  NV, Kuehne  SA, Bryant  CE,  et al.  Clostridium difficile modulates host innate immunity via toxin-independent and dependent mechanism(s).  PLoS One. 2013;8(7):e69846. doi:10.1371/journal.pone.0069846PubMedGoogle ScholarCrossref
23.
Kulaylat  AS, Kassam  Z, Hollenbeak  CS, Stewart  DB  Sr.  A surgical Clostridium-associated risk of death score predicts mortality after colectomy for Clostridium difficile Dis Colon Rectum. 2017;60(12):1285-1290. doi:10.1097/DCR.0000000000000920PubMedGoogle ScholarCrossref
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