BMJ open · 2025
Development and validation of a prediction model for in-hospital mortality in patients with intra-abdominal sepsis: a dual-database study using MIMIC-IV and eICU databases.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Zhang Jianjun, Chen Yuhong, Zhao Cong-Cong, Wang Jinrong, Hu Zhen-Jie
Аннотация
OBJECTIVES: To develop and validate a predictive model for assessing in-hospital mortality in patients with intra-abdominal sepsis (IAS), a leading cause of sepsis.
DESIGN: Secondary analysis of two retrospective critical care databases.
SETTING: Data extracted from the Intensive Care Medicine Information Marketplace IV (MIMIC-IV) and the eICU Collaborative Research Database.
PARTICIPANTS: Patients with IAS from MIMIC-IV (2008-2019; 1300 patients, 264 deaths) for model training and internal validation, and eICU (2014-2015; 149 patients, 33 deaths) for external validation.
INTERVENTIONS: Clinical data were used for constructing a predictive model. Variable selection was performed using least absolute shrinkage and selection operator regression, followed by model development with multivariable logistic regression. The model was visualised as a nomogram.
PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was in-hospital mortality. Secondary outcomes were model performance metrics, including the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis and clinical impact curves.
RESULTS: Six predictors (lactate, age, activated partial thromboplastin time, blood urea nitrogen, total bilirubin and platelets) were identified. The predictive model showed good performance with an AUC of 0.795 (95% CI 0.758 to 0.831) in the training set (n=910) and 0.846 (95% CI 0.772 to 0.919) in the external validation set (n=149).
CONCLUSION: A robust predictive model was developed to estimate the risk of in-hospital mortality in patients with IAS. This tool may assist clinicians in enhancing patient management and decision-making.
DESIGN: Secondary analysis of two retrospective critical care databases.
SETTING: Data extracted from the Intensive Care Medicine Information Marketplace IV (MIMIC-IV) and the eICU Collaborative Research Database.
PARTICIPANTS: Patients with IAS from MIMIC-IV (2008-2019; 1300 patients, 264 deaths) for model training and internal validation, and eICU (2014-2015; 149 patients, 33 deaths) for external validation.
INTERVENTIONS: Clinical data were used for constructing a predictive model. Variable selection was performed using least absolute shrinkage and selection operator regression, followed by model development with multivariable logistic regression. The model was visualised as a nomogram.
PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was in-hospital mortality. Secondary outcomes were model performance metrics, including the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis and clinical impact curves.
RESULTS: Six predictors (lactate, age, activated partial thromboplastin time, blood urea nitrogen, total bilirubin and platelets) were identified. The predictive model showed good performance with an AUC of 0.795 (95% CI 0.758 to 0.831) in the training set (n=910) and 0.846 (95% CI 0.772 to 0.919) in the external validation set (n=149).
CONCLUSION: A robust predictive model was developed to estimate the risk of in-hospital mortality in patients with IAS. This tool may assist clinicians in enhancing patient management and decision-making.