Predicting the risk for hospital-onset Clostridium difficile infection (HO-CDI) at the time of inpatient admission: HO-CDI risk score.
OBJECTIVE
To predict the likelihood of hospital-onset Clostridium difficile infection (HO-CDI) based on patient clinical presentations at admission


METHODS
Retrospective data analysis


METHODS
Six US acute care hospitals


METHODS
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).


CONCLUSIONS
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.
1Clinical Research,Clinical Operation,CareFusion,San Diego,California.
Ying P
YP
Tabak
1Clinical Research,Clinical Operation,CareFusion,San Diego,California.
Richard S
RS
Johannes
1Clinical Research,Clinical Operation,CareFusion,San Diego,California.
Xiaowu
X
Sun
1Clinical Research,Clinical Operation,CareFusion,San Diego,California.
Carlos M
CM
Nunez
4Centers for Disease Control and Prevention,Atlanta,Georgia.
L Clifford
LC
McDonald
D000328:Adult; D000368:Aged; D000900:Anti-Bacterial Agents / Q000009:adverse effects / Q000627:therapeutic use; D002140:California / Q000453:epidemiology; D016360:Clostridioides difficile / Q000302:isolation & purification*; D003428:Cross Infection / Q000517:prevention & control; D004761:Enterocolitis, Pseudomembranous / Q000175:diagnosis / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control; D005260:Female; D006761:Hospitals / Q000706:statistics & numerical data; D006801:Humans; D017053:Infection Control / Q000379:methods*; D007297:Inpatients / Q000706:statistics & numerical data; D008297:Male; D054539:Medication Therapy Management / Q000706:statistics & numerical data; D008875:Middle Aged; D011237:Predictive Value of Tests; D012107:Research Design; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods; D017751:Safety Management / Q000379:methods
D000900:Anti-Bacterial Agents