1307. Derivation and Validation of a Clinical Prediction Rule for Complications of Clostridium difficile Infection Using a Multicenter Prospective Cohort
Session: Poster Abstract Session: HAI: C. difficile Risk Assessment and Prevention
Friday, October 6, 2017
Room: Poster Hall CD
Background: Clostridium difficileinfection (CDI) outbreaks were associated with increase in unfavourable outcomes. Identifying and predicting risk of developing complications (cCDI) early in the course of illness could improve clinical decision-making. We developed and validated a prediction rule for cCDI.

Methods: Adult inpatients with confirmed CDI in 10 Canadian hospitals were enrolled and followed for 90 days. Data within 48h of CDI diagnosis were collected: demographics, underlying illnesses, past medical and drug history, clinical signs, blood tests, and strain ribotype. cCDI was defined as one or more of: colonic perforation, toxic megacolon, colectomy, need of vasopressors, ICU admission due to CDI, or if CDI contributed to 30-day death. Predictors’ selection was supported by experts’ opinion suggesting 17 clinical criteria. Cross-validation technique was used (2:1 ratio) and multivariable logistic regression for predictive modelling in the derivation subset. The optimal model was assessed by area under ROC curve (AUC) and prediction error (PE). A predictive score was built by assigning points proportional to adjusted risk estimates.

Results: Among 1380 patients enrolled, 1050 were used for predictive modelling (median age 70 years and one third infected by ribotype 027 strains). Cases were split into training (n=700) and validation sets (n=350). A cCDI occured in 8% and 6.6% respectively. The optimal model with a PE of 5% and an AUC of 0.84 in the validation set included WCC (< 4, 12-19.9, or ≥20x109/L), BUN≥11 mmol/L, serum albumin <25 g/L, heart rate > 90/min, and respiratory rate >20/min. A predictive score of min 0 and max 13 points was derived. A score ≥7 points was associated with 70% cases of cCDI, showed 68% sensitivity (95%CI, 55-80) in the derivation set and 70% (51-88) in the validation set, a specificity of 73% (69-76) and 76% (72-81) respectively, 17% PPV (9-25), and 97% NPV (95-99) in both sets.

Conclusion: Using a large multicenter prospective cohort and robust modelling approach, we derived a predictive score that included easily available measures at the bedside. The score showed acceptable performance. Further validation is needed on cohorts with different characterstics (non-outbreak setting, higher rate of cCDI). Other approaches such as combination of biomarkers could be more predictive of cCDI.

Claire Nour Abou Chakra, PhD1, Allison Mcgeer, MD, MSc2, Annie-Claude Labbé, MD3, Andrew E. Simor, MD, FRCPC, FACP4, Wayne Gold, MD5, Matthew P. Muller, MD, PhD, FRCPC6, Jeff Powis, MD, MSc, FRCPC7, Kevin Katz, MD, CM, MSc, FRCPC8, Suzanne Cadarette, PhD9, Jacques Pépin, M.D.1, Julian R. Garneau, MSc1 and Louis Valiquette, MD, MSc, FRCPC1,10, (1)Microbiology and Infectious Disease, Université de Sherbrooke, Sherbrooke, QC, Canada, (2)Infection Control, Sinai Health System, Toronto, ON, Canada, (3)Microbiology, CIUSSS de l’est-de-l’île-de-Montréal, Montreal, QC, Canada, (4)Microbiology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada, (5)Toronto General Hospital, Toronto, ON, Canada, (6)Medicine, St.Michael's Hospital, Toronto, ON, Canada, (7)Michael Garron Hospital, Toronto, ON, Canada, (8)Department of Infection Control, North York General Hospital, Toronto, ON, Canada, (9)University of Toronto, Toronto, ON, Canada, (10)Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada


C. N. Abou Chakra, None

A. Mcgeer, None

A. C. Labbé, None

A. E. Simor, None

W. Gold, None

M. P. Muller, None

J. Powis, Merck: Grant Investigator , Research grant
GSK: Grant Investigator , Research grant
Roche: Grant Investigator , Research grant
Synthetic Biologicals: Investigator , Research grant

K. Katz, None

S. Cadarette, None

J. Pépin, None

J. R. Garneau, None

L. Valiquette, None

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