Early Risk Stratification for Mechanical Ventilation in Acute Ischemic Stroke: Development and Validation of a Simplified Clinical Score

Abstract

Background and Purpose

Mechanical ventilation (MV) occurs in a substantial subset of acute ischemic stroke (AIS) hospitalizations and is associated with worse outcomes, including prolonged hospital stay. Large national studies evaluating clinical predictors of MV and comparing regression with machine learning–based risk stratification in AIS remain limited. We examined clinical factors associated with MV in AIS and compared logistic regression with gradient-boosting approach for early respiratory risk stratification.

Methods

We analyzed 949,916 AIS hospitalizations from National Inpatient Sample (2016–2022). MV was identified using ICD-10 procedure codes and stratified by duration (<24 h, 24–96 h, >96 h). Demographics, comorbidities, treatment variables, and hemorrhagic transformation were compared between MV and non-MV groups. Multivariable logistic regression identified factors associated with MV. A simplified Ventilation Risk Score (sVRS) was derived from clinically relevant variables. Logistic regression and extreme gradient boosting (XGBoost) were trained on an 80/20 split and validated against a prolonged MV (>24 h) outcome. Model performance was assessed using AUC, Brier score, calibration, and SHAP analyses.

Results

MV occurred in 9.7% of hospitalizations; 83.7% were prolonged (>24 h). MV patients were younger and had higher prevalences of congestive heart failure, COPD, atrial fibrillation, chronic kidney disease, coagulopathy, liver disease, and hemorrhagic transformation (15.7% vs. 4.6%, P < .001). Endovascular thrombectomy was more frequent among MV patients and associated with shorter MV duration. The sVRS stratified patients into low-, intermediate-, and high-risk groups with MV rates of 3.65%, 9.25%, and 28.7%, respectively. Logistic regression achieved AUCs of 0.706 (all MV) and 0.711 (prolonged MV); XGBoost achieved 0.716 and 0.721. SHAP analyses identified coagulopathy, age, liver disease, congestive heart failure, and thrombectomy as top predictors.

Conclusion

MV identifies a clinically distinct AIS subgroup with greater comorbidity burden, higher hemorrhagic transformation rates, and longer hospitalizations. The sVRS provides graded, robust risk stratification supporting its use for early respiratory risk assessment in AIS.