Construction of Survival Nomogram for Ventilator-Associated Pneumonia Patients: Based on MIMIC Database.

Abstract

Objective: To construct and validate a predictive nomogram model for the survival of patients with ventilator-associated pneumonia (VAP) to enhance prediction of 28-day survival rate in critically ill patients with VAP. Methods: A total of 1,438 intensive care unit (ICU) patients with VAP were screened through Medical Information Mart for Intensive Care (MIMIC)-IV. On the basis of multi-variable Cox regression analysis data, nomogram performance in predicting survival status of patients with VAP at ICU admission for 7, 14, and 28 days was evaluated using the C-index and area under the curve (AUC). Calibration and decision curve analysis curves were generated to assess clinical value and effectiveness of model, and risk stratification was performed for patients with VAP. Result: Through stepwise regression screening of uni-variable and multi-variable Cox regression models, independent prognostic factors for predicting nomogram were determined, including age, race, body temperature, Sequential Organ Failure Assessment score, anion gap, bicarbonate concentration, partial pressure of carbon dioxide, mean corpuscular hemoglobin, and liver disease. The model had C-index values of 0.748 and 0.628 in the train and test sets, respectively. The receiver operating characteristic curve showed that nomogram had better performance in predicting 28-day survival status in the train set (AUC = 0.74), whereas it decreased in the test set (AUC = 0.66). Calibration and decision curve analysis curve results suggested that nomogram had favorable predictive performance and clinical efficacy. Kaplan-Meier curves showed significant differences in survival between low, medium, and high-risk groups in the total set and training set (log-rank p < 0.05), further validating the effectiveness of the model. Conclusion: The VAP patient admission ICU 7, 14, and 28-day survival prediction nomogram was constructed, contributing to risk stratification and decision-making for such patients. The model is expected to play a positive role in supporting personalized treatment and management of VAP.