Development and validation of a prognostic nomogram for predicting hypostatic pneumonia risk in large vessel occlusion stroke after endovascular therapy patients

Background: Post-stroke hypostatic pneumonia (HP) significantly impairs neurological recovery and worsens prognosis in patients with acute ischemic stroke with large vessel occlusion (AIS-LVO). This study aimed to develop and validate a prognostic nomogram for predicting hypostatic pneumonia risk following endovascular therapy (EVT) in AIS-LVO patients. Methods: = 124), maintaining a 7:3 ratio. Using least absolute shrinkage and selection operator (LASSO) regression for feature selection followed by multivariable logistic regression, we identified independent predictors for nomogram construction. Model performance was assessed through the receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results: Four independent predictors were identified: admission Glasgow Coma Scale (GCS) score (OR 0.77, 95% CI 0.68-0.86), postoperative 48 h fever (OR 2.77, 95% CI 1.52-5.02), postoperative 48 h neutrophil-to-lymphocyte ratio (NLR) (OR 1.15, 95% CI 1.08-1.22), and ASPECTS (OR 0.74, 95% CI 0.63-0.87). The model had an area under the curve (AUC) of 0.829 (95% CI: 0.781-0.877) in the training cohort and 0.817 (95% CI 0.732-0.903) in the validation cohort, which means it was good at making predictions. Calibration curves revealed good alignment between predicted and observed probabilities in the training cohort. The validation cohort retained satisfactory calibration, with only modest overestimation of risk. DCA and CIC consistently indicated the nomogram's applicability in diverse clinical settings. Conclusion: We developed and validated an effective nomogram incorporating four clinically accessible parameters to predict the risk of hypostatic pneumonia after EVT. This tool may facilitate early high-risk patient identification and guide preventive therapy to improve clinical outcomes.