Frontiers in Neurology · 2023 · 13 citations · 29 references
MedicinePerioperative MedicineClinical Decision SupportPerioperative Outcomes ResearchRisk AssessmentDecision-analytic ModelingPostoperative MorbidityPreoperative AssessmentPreoperative Risk AssessmentSubarachnoid HemorrhageSubarachnoid Hemorrhage ManagementClinical Risk PredictionExternal Validation Of Risk ModelsLogistic RegressionLogistic Regression AnalysisPerioperative Risk Prediction ModelsPostoperative Risk PredictionPrediction Model DevelopmentPredictive Nomograms
Background: Postoperative pneumonia (POP) is a common complication after aneurysmal subarachnoid hemorrhage (aSAH) associated with increased mortality rates, prolonged hospitalization, and high medical costs. It is currently understood that identifying pneumonia early and implementing aggressive treatment can significantly improve patients' outcomes. The primary objective of this study was to explore risk factors and develop a logistic regression model that assesses the risks of POP. Methods: An internal cohort of 613 inpatients with aSAH who underwent surgery at the Neurosurgical Department of First Affiliated Hospital of Wenzhou Medical University was retrospectively analyzed to develop a nomogram for predicting POP. We assessed the discriminative power, accuracy, and clinical validity of the predictions by using the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA). The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Results: = 96/613) of patients had POP. The least absolute shrinkage and selection operator (LASSO) regression analysis identified the Glasgow Coma Scale (GCS), mechanical ventilation time (MVT), albumin, C-reactive protein (CRP), smoking, and delayed cerebral ischemia (DCI) as potential predictors of POP. We then used multivariable logistic regression analysis to evaluate the effects of these predictors and create a final model. Eighty percentage of patients in the internal cohort were randomly assigned to the training set for model development, while the remaining 20% of patients were allocated to the internal validation set. The AUC values for the training, internal, and external validation sets were 0.914, 0.856, and 0.851, and the corresponding Brier scores were 0.084, 0.098, and 0.143, respectively. Conclusion: We found that GCS, MVT, albumin, CRP, smoking, and DCI are independent predictors for the development of POP in patients with aSAH. Overall, our nomogram represents a reliable and convenient approach to predict POP in the patient population.
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Ary L. Goldberger, Luı́s A. Nunes Amaral, Leon Glass et al. · Circulation · 2000 · 14.5K citations · Full text
Valery L. Feigin, Benjamin Stark, Catherine O. Johnson et al. · The Lancet Neurology · 2021 · 7.6K citations · Full text
Marc‐Jacques Dubois, Carlos Orellana-Jimenez, Christian Mélot et al. · Critical Care Medicine · 2006 · 329 citations