Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2014
Risk estimation for recurrent Clostridium difficile infection based on clinical factors.
Клиническое исследование III фазыСтатьяРандомизированное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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D'Agostino Ralph B, Collins Sylva H, Pencina Karol M, Kean Yin, Gorbach Sherwood
Аннотация
BACKGROUND: The incidence of Clostridium difficile infection (CDI) has risen dramatically during the last decade. Although patients respond well to medical therapy such as vancomycin, 20%-30% of patients treated suffer a recurrence of CDI.
METHODS: We developed a simple/practical scoring rule (logistic regression model) for recurrent CDI using data from 2 large phase 3 clinical trials. Seventy-seven baseline CDI factors were classified: demographics, comorbidity, medications, vital signs, laboratory tests, severity, and symptoms. Predictors with the highest discrimination in each class (using receiver operating characteristics curve) were selected. For the final model, stepwise selection was performed. Discrimination, calibration, and internal validation were used to assess the model.
RESULTS: The final model with a simple scoring rule was developed. It includes 4 independent risk factors that are readily available when the patient makes initial contact: age (<75 vs ≥75 years), number of unformed bowel movements during previous 24 hours (<10 vs ≥10), serum creatinine leves (<1.2 mg/dL vs ≥1.2 mg/dL) and prior episode of CDI (yes vs no). In addition, the model includes choice of treatment (vancomycin or fidaxomicin).
CONCLUSIONS: The prediction model for recurrence may be useful for treatment decision.
CLINICAL TRIALS REGISTRATION: NCT00314951 and NCT00468728.
METHODS: We developed a simple/practical scoring rule (logistic regression model) for recurrent CDI using data from 2 large phase 3 clinical trials. Seventy-seven baseline CDI factors were classified: demographics, comorbidity, medications, vital signs, laboratory tests, severity, and symptoms. Predictors with the highest discrimination in each class (using receiver operating characteristics curve) were selected. For the final model, stepwise selection was performed. Discrimination, calibration, and internal validation were used to assess the model.
RESULTS: The final model with a simple scoring rule was developed. It includes 4 independent risk factors that are readily available when the patient makes initial contact: age (<75 vs ≥75 years), number of unformed bowel movements during previous 24 hours (<10 vs ≥10), serum creatinine leves (<1.2 mg/dL vs ≥1.2 mg/dL) and prior episode of CDI (yes vs no). In addition, the model includes choice of treatment (vancomycin or fidaxomicin).
CONCLUSIONS: The prediction model for recurrence may be useful for treatment decision.
CLINICAL TRIALS REGISTRATION: NCT00314951 and NCT00468728.