Objective To construct and validate a risk prediction model for chronic critical illness after sepsis in elderly patients, and to provide reference for early identification and intervention by healthcare professionals.MethodsA total of 5,527 elderly sepsis patients from the MIMIC Ⅳ database were used as the training cohort. Lasso regression and logistic regression were employed to identify the influencing factors of chronic critical illness and construct nomogram prediction model. The model′s discrimination, clinical applicability, and calibration were evaluated using receiver operating characteristic (ROC) curve, decision curve analysis, and calibration curve. Data of 134 elderly sepsis patients from a tertiary hospital in Hefei were retrospectively collected for external validation.ResultsAge, respiratory rate within 24 hours of ICU admission, body temperature, hematocrit, red blood cell distribution width, blood urea nitrogen, blood lactate, activated partial thromboplastin time, mechanical ventilation, and antibiotic use were identified as influencing factors for the development of chronic critical illness in elderly sepsis patients (all P<0.05). The nomogram prediction model constructed based on these factors achieved an area under the ROC curve of 0.733 (95%CI:0.722-0.755) in the training cohort and 0.817 (95%CI:0.746-0.887) in the external validation cohort. Decision curve analysis indicated that the model had good clinical applicability when the threshold probability was between 8% and 99%. The calibration curve showed that the predicted probabilities of the model were close to the actual probabilities in both the training cohort and the external validation cohort. ConclusionThe 10 factor nomogram risk prediction model is valuable for identifying elderly sepsis patients at risk of developing chronic critical illness.