BACKGROUND & AIMS: Prevention of recurrent Clostridium difficile infection (CDI) is a substantial therapeutic challenge. A previous prospective study of 63 patients with CDI identified risk factors associated with recurrence. This study aimed to develop a prediction rule for recurrent CDI using the above derivation cohort and prospectively evaluate the performance of this rule in an independent validation cohort. METHODS: The clinical prediction rule was developed by multivariate logistic regression analysis and included the following variables: age>65 years, severe or fulminant illness (by the Horn index), and additional antibiotic use after CDI therapy. A second rule combined data on serum concentrations of immunoglobulin G (IgG) against toxin A with the clinical predictors. Both rules were then evaluated prospectively in an independent cohort of 89 patients with CDI. RESULTS: The clinical prediction rule discriminated between patients with and without recurrent CDI, with an area under the curve of the receiver-operating-characteristic curve of 0.83 (95% confidence interval [CI]: 0.70-0.95) in the derivation cohort and 0.80 (95% CI: 0.67-0.92) in the validation cohort. The rule correctly classified 77.3% (95% CI: 62.2%-88.5%) and 71.9% (95% CI: 59.2%-82.4%) of patients in the derivation and validation cohorts, respectively. The combined rule performed well in the derivation cohort but not in the validation cohort (area under the curve of the receiver-operating-characteristic curve, 0.89 vs 0.62; diagnostic accuracy, 93.8% vs 69.2%, respectively). CONCLUSIONS: We prospectively derived and validated a clinical prediction rule for recurrent CDI that is simple, reliable, and accurate and can be used to identify high-risk patients most likely to benefit from measures to prevent recurrence.
BACKGROUND & AIMS: Prevention of recurrent Clostridium difficileinfection (CDI) is a substantial therapeutic challenge. A previous prospective study of 63 patients with CDI identified risk factors associated with recurrence. This study aimed to develop a prediction rule for recurrent CDI using the above derivation cohort and prospectively evaluate the performance of this rule in an independent validation cohort. METHODS: The clinical prediction rule was developed by multivariate logistic regression analysis and included the following variables: age>65 years, severe or fulminant illness (by the Horn index), and additional antibiotic use after CDI therapy. A second rule combined data on serum concentrations of immunoglobulin G (IgG) against toxin A with the clinical predictors. Both rules were then evaluated prospectively in an independent cohort of 89 patients with CDI. RESULTS: The clinical prediction rule discriminated between patients with and without recurrent CDI, with an area under the curve of the receiver-operating-characteristic curve of 0.83 (95% confidence interval [CI]: 0.70-0.95) in the derivation cohort and 0.80 (95% CI: 0.67-0.92) in the validation cohort. The rule correctly classified 77.3% (95% CI: 62.2%-88.5%) and 71.9% (95% CI: 59.2%-82.4%) of patients in the derivation and validation cohorts, respectively. The combined rule performed well in the derivation cohort but not in the validation cohort (area under the curve of the receiver-operating-characteristic curve, 0.89 vs 0.62; diagnostic accuracy, 93.8% vs 69.2%, respectively). CONCLUSIONS: We prospectively derived and validated a clinical prediction rule for recurrent CDI that is simple, reliable, and accurate and can be used to identify high-risk patients most likely to benefit from measures to prevent recurrence.
Authors: Jun Huang; Ciarán P Kelly; Kyriaki Bakirtzi; Javier A Villafuerte Gálvez; Dena Lyras; Steven J Mileto; Sarah Larcombe; Hua Xu; Xiaotong Yang; Kelsey S Shields; Weishu Zhu; Yi Zhang; Jeffrey D Goldsmith; Ishan J Patel; Joshua Hansen; Meijin Huang; Seppo Yla-Herttuala; Alan C Moss; Daniel Paredes-Sabja; Charalabos Pothoulakis; Yatrik M Shah; Jianping Wang; Xinhua Chen Journal: Nat Microbiol Date: 2018-12-03 Impact factor: 17.745
Authors: Kristina Oresic Bender; Megan Garland; Jessica A Ferreyra; Andrew J Hryckowian; Matthew A Child; Aaron W Puri; David E Solow-Cordero; Steven K Higginbottom; Ehud Segal; Niaz Banaei; Aimee Shen; Justin L Sonnenburg; Matthew Bogyo Journal: Sci Transl Med Date: 2015-09-23 Impact factor: 17.956
Authors: Parambir S Dulai; Brigid S Boland; Siddharth Singh; Khadija Chaudrey; Jenna L Koliani-Pace; Gursimran Kochhar; Malav P Parikh; Eugenia Shmidt; Justin Hartke; Prianka Chilukuri; Joseph Meserve; Diana Whitehead; Robert Hirten; Adam C Winters; Leah G Katta; Farhad Peerani; Neeraj Narula; Keith Sultan; Arun Swaminath; Matthew Bohm; Dana Lukin; David Hudesman; John T Chang; Jesus Rivera-Nieves; Vipul Jairath; G Y Zou; Brian G Feagan; Bo Shen; Corey A Siegel; Edward V Loftus; Sunanda Kane; Bruce E Sands; Jean-Frederic Colombel; William J Sandborn; Karen Lasch; Charlie Cao Journal: Gastroenterology Date: 2018-05-30 Impact factor: 22.682
Authors: Kelly R Reveles; Eric M Mortensen; Jim M Koeller; Kenneth A Lawson; Mary Jo V Pugh; Sarah A Rumbellow; Jacqueline R Argamany; Christopher R Frei Journal: Pharmacotherapy Date: 2018-02-22 Impact factor: 4.705