Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-05T08:36:37.712Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting the Risk for Hospital-Onset Clostridium difficile Infection (HO-CDI) at the Time of Inpatient Admission: HO-CDI Risk Score

Published online by Cambridge University Press:  10 March 2015

Ying P. Tabak ,
Richard S. Johannes ,
Xiaowu Sun ,
Carlos M. Nunez  and
L. Clifford McDonald
Ying P. Tabak
Affiliation:
Clinical Research, Clinical Operation, CareFusion, San Diego, California
Richard S. Johannes
Affiliation:
Clinical Research, Clinical Operation, CareFusion, San Diego, California Division of Gastroenterology, Harvard Medical School and Brigham and Women’s Hospital Boston, Massachusetts
Xiaowu Sun
Affiliation:
Clinical Research, Clinical Operation, CareFusion, San Diego, California
Carlos M. Nunez
Affiliation:
Clinical Research, Clinical Operation, CareFusion, San Diego, California The Biomedical Informatics Research Center, San Diego State University, San Diego, California
L. Clifford McDonald*
Affiliation:
Centers for Disease Control and Prevention, Atlanta, Georgia
*
Address all correspondence to L. Clifford McDonald, MD, FACP, Senior Advisor for Science and Integrity, Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, Atlanta, GA 30341-3724 ([email protected]).
Get access Rights & Permissions [Opens in a new window]

Abstract

OBJECTIVE

To predict the likelihood of hospital-onset Clostridium difficile infection (HO-CDI) based on patient clinical presentations at admission

DESIGN

Retrospective data analysis

SETTING

Six US acute care hospitals

PATIENTS

Adult inpatients

METHODS

We used clinical data collected at the time of admission in electronic health record (EHR) systems to develop and validate a HO-CDI predictive model. The outcome measure was HO-CDI cases identified by a nonduplicate positive C. difficile toxin assay result with stool specimens collected >48 hours after inpatient admission. We fit a logistic regression model to predict the risk of HO-CDI. We validated the model using 1,000 bootstrap simulations.

RESULTS

Among 78,080 adult admissions, 323 HO-CDI cases were identified (ie, a rate of 4.1 per 1,000 admissions). The logistic regression model yielded 14 independent predictors, including hospital community onset CDI pressure, patient age ≥65, previous healthcare exposures, CDI in previous admission, admission to the intensive care unit, albumin ≤3 g/dL, creatinine >2.0 mg/dL, bands >32%, platelets ≤150 or >420 109/L, and white blood cell count >11,000 mm3. The model had a c-statistic of 0.78 (95% confidence interval [CI], 0.76–0.81) with good calibration. Among 79% of patients with risk scores of 0–7, 19 HO-CDIs occurred per 10,000 admissions; for patients with risk scores >20, 623 HO-CDIs occurred per 10,000 admissions (P<.0001).

CONCLUSION

Using clinical parameters available at the time of admission, this HO-CDI model demonstrated good predictive ability, and it may have utility as an early risk identification tool for HO-CDI preventive interventions and outcome comparisons.

Infect Control Hosp Epidemiol 2015;00(0):1–7

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 36 , Issue 6 , June 2015 , pp. 695 - 701
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

PREVIOUS PRESENTATION. The preliminary data were presented in part as a poster at the IDWEEK, October, 2012, San Diego, California.

References

1. Magill, SS, Edwards, JR, Bamberg, W, et al. Multistate point-prevalence survey of health care-associated infections. N Engl J Med 2014;370:1198–1208.Google Scholar
2. Lucado, J, Gould, C, Elixhauser, A. Clostridium difficile infections (CDI) in hospital stays, 2009. HCUP Statistical Brief #124. Agency for Healthcare Research and Quality website. http://www.hcup-us.ahrq.gov/reports/statbriefs/sb124.pdf. Published January 2012. Accessed January 16, 2015.Google Scholar
3. Murphy, SL, Xu, J, Kochanek, KD. Deaths: final data for 2010. National Vital Statistics Reports website. http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_04.pdf. Published 2013. Accessed January 9, 2014.Google Scholar
4. Dubberke, ER, Wertheimer, AI. Review of current literature on the economic burden of Clostridium difficile infection. Infect Control Hosp Epidemiol 2009;30:57–66.Google Scholar
5. Tabak, YP, Zilberberg, MD, Johannes, RS, Sun, X, McDonald, LC. Attributable burden of hospital-onset Clostridium difficile infection: a propensity score matching study. Infect Control Hosp Epidemiol 2013;34:588–596.CrossRefGoogle ScholarPubMed
6. Dubberke, ER, Yan, Y, Reske, KA, et al. Development and validation of a Clostridium difficile infection risk prediction model. Infect Control Hosp Epidemiol 2011;32:360–366.Google Scholar
7. Brossette, SE, Hacek, DM, Gavin, PJ, et al. A laboratory-based, hospital-wide, electronic marker for nosocomial infection: the future of infection control surveillance? Am J Clin Pathol 2006;125:34–39.Google Scholar
8. Zilberberg, MD, Tabak, YP, Sievert, DM, et al. Using electronic health information to risk-stratify rates of Clostridium difficile infection in US hospitals. Infect Control Hosp Epidemiol 2011;32:649–655.CrossRefGoogle ScholarPubMed
9. Tabak, YP, Sun, X, Nunez, CM, Johannes, RS. Using electronic health record data to develop inpatient mortality predictive model: Acute Laboratory Risk of Mortality Score (ALaRMS). J Am Med Inform Assoc May–Jun 2014;21:455–463.Google Scholar
10. Multidrug-Resistant Organism & Clostridium difficile Infection (MDRO/CDI) Module. Centers for Disease Control and Prevention website. http://www.cdc.gov/nhsn/PDFs/pscManual/12pscMDRO_CDADcurrent.pdf. Published 2015. Accessed January 16, 2015.Google Scholar
11. Tabak, YP, Sun, X, Derby, KG, Kurtz, SG, Johannes, RS. Development and validation of a disease-specific risk adjustment system using automated clinical data. Health Serv Res 2010;45:1815–1835.Google Scholar
12. Tabak, YP, Johannes, RS, Silber, JH. Using automated clinical data for risk adjustment: development and validation of six disease-specific mortality predictive models for pay-for-performance. Med Care 2007;45:789–805.Google Scholar
13. Dudeck, MA, Weiner, LM, Malpiedi, PJ, Edwards, JR, Peterson, KD, Sievert, DM. Risk Adjustment for Healthcare Facility-Onset C. difficile and MRSA Bacteremia Laboratory-identified Event Reporting in NHSN. Centers for Disease Control and Prevention website. http://www.cdc.gov/nhsn/pdfs/mrsacdi/RiskAdjustment-MRSA-CDI.pdf. Published 2013. Accessed Janurary 16, 2015.Google Scholar
14. Dubberke, ER, Reske, KA, Olsen, MA, et al. Evaluation of Clostridium difficile-associated disease pressure as a risk factor for C difficile-associated disease. Arch Intern Med 28 2007;167:1092–1097.CrossRefGoogle Scholar
15. Hosmer, DW, Lemeshow, S. Applied Logistic Regression, 2nd ed. New York: John Wiley & Sons; 2000.Google Scholar
16. Bursac, Z, Gauss, CH, Williams, DK, Hosmer, DW. Purposeful selection of variables in logistic regression. Source Code Biol Med 2008;3:17.Google Scholar
17. Sullivan, LM, Massaro, JM, D’Agostino, RB Sr. Presentation of multivariate data for clinical use: The Framingham Study risk score functions. Stat Med 30 2004;23:1631–1660.Google Scholar
18. Efron, B, Tibshirani, R. An Introduction to the Bootstrap. London: Chapman & Hall, 1993.Google Scholar
19. Kelly, CP, Kyne, L. The host immune response to Clostridium difficile . J Med Microbiol 2011;60:1070–1079.Google Scholar
20. Huse, SM, Dethlefsen, L, Huber, JA, Mark Welch, D, Relman, DA, Sogin, ML. Exploring microbial diversity and taxonomy using SSU rRNA hypervariable tag sequencing. PLoS Genet 2008;4:e1000255.Google Scholar
21. Hensgens, MP, Goorhuis, A, Dekkers, OM, Kuijper, EJ. Time interval of increased risk for Clostridium difficile infection after exposure to antibiotics. J Antimicrob Chemother 2012;67:742–748.Google Scholar