Liver international : official journal of the International Association for the Study of the Liver · 2024
Development and external validation of a model to predict multidrug-resistant bacterial infections in patients with cirrhosis.
СтатьяВалидационное исследованиеМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Marciano Sebastián, Piano Salvatore, Singh Virendra, Caraceni Paolo, Maiwall Rakhi, Alessandria Carlo, Fernandez Javier, Kim Dong Joon, Kim Sung Eun, Soares Elza, Marino Mónica, Vorobioff Julio, Merli Manuela, Elkrief Laure, Vargas Victor, Krag Aleksander, Singh Shivaram, Elizondo Martín, Anders Maria M, Dirchwolf Melisa, Mendizabal Manuel, Lesmana Cosmas R A, Toledo Claudio, Wong Florence, Durand Francois, Gadano Adrián, Giunta Diego H, Angeli Paolo, International Club of Ascites Global Study Group
Аннотация
UNLABELLED: With the increasing rate of infections caused by multidrug-resistant organisms (MDRO), selecting appropriate empiric antibiotics has become challenging. We aimed to develop and externally validate a model for predicting the risk of MDRO infections in patients with cirrhosis.
METHODS: We included patients with cirrhosis and bacterial infections from two prospective studies: a transcontinental study was used for model development and internal validation (n = 1302), and a study from Argentina and Uruguay was used for external validation (n = 472). All predictors were measured at the time of infection. Both culture-positive and culture-negative infections were included. The model was developed using logistic regression with backward stepwise predictor selection. We externally validated the optimism-adjusted model using calibration and discrimination statistics and evaluated its clinical utility.
RESULTS: The prevalence of MDRO infections was 19% and 22% in the development and external validation datasets, respectively. The model's predictors were sex, prior antibiotic use, type and site of infection, MELD-Na, use of vasopressors, acute-on-chronic liver failure, and interaction terms. Upon external validation, the calibration slope was 77 (95% CI .48-1.05), and the area under the ROC curve was .68 (95% CI .61-.73). The application of the model significantly changed the post-test probability of having an MDRO infection, identifying patients with nosocomial infection at very low risk (8%) and patients with community-acquired infections at significant risk (36%).
CONCLUSION: This model achieved adequate performance and could be used to improve the selection of empiric antibiotics, aligning with other antibiotic stewardship program strategies.
METHODS: We included patients with cirrhosis and bacterial infections from two prospective studies: a transcontinental study was used for model development and internal validation (n = 1302), and a study from Argentina and Uruguay was used for external validation (n = 472). All predictors were measured at the time of infection. Both culture-positive and culture-negative infections were included. The model was developed using logistic regression with backward stepwise predictor selection. We externally validated the optimism-adjusted model using calibration and discrimination statistics and evaluated its clinical utility.
RESULTS: The prevalence of MDRO infections was 19% and 22% in the development and external validation datasets, respectively. The model's predictors were sex, prior antibiotic use, type and site of infection, MELD-Na, use of vasopressors, acute-on-chronic liver failure, and interaction terms. Upon external validation, the calibration slope was 77 (95% CI .48-1.05), and the area under the ROC curve was .68 (95% CI .61-.73). The application of the model significantly changed the post-test probability of having an MDRO infection, identifying patients with nosocomial infection at very low risk (8%) and patients with community-acquired infections at significant risk (36%).
CONCLUSION: This model achieved adequate performance and could be used to improve the selection of empiric antibiotics, aligning with other antibiotic stewardship program strategies.