Development and validation of a nomogram for predicting pulmonary infections after Intracerebral hemorrhage in elderly people

Abstract

ObjectivesThe purpose of this study was to develop and validate a nomogram for the prediction of pulmonary infections in elderly patients with intracerebral hemorrhage (ICH) during hospitalization in the intensive care unit (ICU). MethodsA total of 1183 elderly patients diagnosed with ICH were included from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly grouped into training (n=831) and validation (n=352) cohorts. Candidate predictors were identified using the least absolute shrinkage and selection operator (LASSO) regression. Meanwhile, the variables derived from the LASSO regression were included in the multivariate logistic regression analysis, the variables with P < 0.05 were included in the final model and the nomogram was constructed. The discriminatory ability was assessed by plotting the receiver operating curve (ROC) and calculating the area under the curve (AUC). The Performance of the model was assessed by calibration plots and the Hosmer-Lemeshow goodness-of-fit test (HL test). In addition, clinical decision curves assess the net clinical benefit. ResultsThe nomogram included chronic lung disease, dysphagia, mechanical ventilation, use of antibiotics, Glasgow Coma Scale (GCS), Logical Organ Dysfunction System (LODS), blood oxygen saturation (SpO2), white blood cell count (WBC) and prothrombin time (PT). The AUC of the predictive model was 0.905 (95 % CI: 0.877, 0.764) in the training cohort and 0.888 (95 % CI: 0.754, 0.838) in the validation cohort, which showed satisfactory discriminative ability. Second, the nomogram showed good calibration. Decision curve analysis showed that the predictive nomogram was clinically useful. ConclusionA prediction model for predicting pulmonary infections in elderly ICH patients was constructed. The model can help clinicians to identify high-risk patients as soon as possible and prevent the occurrence of pulmonary infections.