European journal of medical research · 2025
Risk factors and prediction model for carbapenem-resistant organism infection in sepsis patients.
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Liu Ronghua, Li Xiang, Yang Jie, Peng Yue, Liu Xiaolu, Tian Chanchan
Аннотация
BACKGROUND: It aimed to identify the key risk factors associated with carbapenem-resistant organism (CRO) infections in septic patients, and subsequently develop a nomogram and assess its predictive accuracy.
METHODS: The study population comprised adult critically ill patients with sepsis, drawn from the MIMIC-IV 2.0 data set. The data were split into a training set and a validation set at a 7:3 ratio. Independent predictors were identified using both univariate and multivariate logistic regression models, followed by the construction of a nomogram. The predictive performance of the model was evaluated using the C-index, receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve, and decision curve.
RESULTS: We enrolled 8814 patients, with 529 (6%) CRO-infected and 8285 (94%) non-CRO-infected. Using risk factors such as age, gender, laboratory values (WBC_max, Creatinine_max, BUN_max, Hemoglobin_min, Sodium_max), and medical conditions (COPD, hypoimmunity, diabetes), along with medications (meropenem, ceftriaxone), we developed a predictive model for CRO infection in septic patients. The model demonstrated good performance, with AUC values of 0.747 for the training set and 0.725 for the validation set. The calibration curve indicates that predicted outcomes align well with observed outcomes. The clinical decision curve results indicate that the nomogram prediction model has a high net benefit, which is clinically beneficial.
CONCLUSIONS: The nomogram we have developed for predicting the risk of CRO infection in sepsis patients is reasonably accurate and reliable.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: The study population comprised adult critically ill patients with sepsis, drawn from the MIMIC-IV 2.0 data set. The data were split into a training set and a validation set at a 7:3 ratio. Independent predictors were identified using both univariate and multivariate logistic regression models, followed by the construction of a nomogram. The predictive performance of the model was evaluated using the C-index, receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve, and decision curve.
RESULTS: We enrolled 8814 patients, with 529 (6%) CRO-infected and 8285 (94%) non-CRO-infected. Using risk factors such as age, gender, laboratory values (WBC_max, Creatinine_max, BUN_max, Hemoglobin_min, Sodium_max), and medical conditions (COPD, hypoimmunity, diabetes), along with medications (meropenem, ceftriaxone), we developed a predictive model for CRO infection in septic patients. The model demonstrated good performance, with AUC values of 0.747 for the training set and 0.725 for the validation set. The calibration curve indicates that predicted outcomes align well with observed outcomes. The clinical decision curve results indicate that the nomogram prediction model has a high net benefit, which is clinically beneficial.
CONCLUSIONS: The nomogram we have developed for predicting the risk of CRO infection in sepsis patients is reasonably accurate and reliable.
CLINICAL TRIAL NUMBER: Not applicable.