Prediction of early postoperative complications and transfusion risk after lumbar spinal stenosis surgery in geriatric patients: machine learning approach based on comprehensive geriatric assessment

Lumbar spinal stenosis is one of the most common surgery-requiring conditions of the spine in the aged population. As elderly patients often present with multiple comorbidities and limited physiological reserve, individualized risk assessment using comprehensive geriatric assessment is crucial for optimizing surgical outcomes. Patients 65 years or older who underwent elective surgery for lumbar spinal stenosis between June 2015 and December 2018 were prospectively enrolled, resulting in 261 eligible patients of age 72.3 ± 4.8 years (male 108, female 153). Twenty-seven experienced complications of Clavien-Dindo grade 2 or higher within 30 days, and 79 received transfusion during hospital stay. The cohort was split into train-validation (n = 208) and test (n = 53) sets. A total of 48 features, including demographics, comorbidity, nutrition, and perioperative status, were collected. Logistic regression, support vector machine (SVM), random forest, XGBoost, and LightGBM were trained using five-fold cross-validation. AUROC and AUPRC were considered the primary performance metrics, and the results were compared with those estimated with ACS-NSQIP scoring system. A set of Compact models incorporating a smaller number of features was also trained, and SHAP analysis was conducted to evaluate the models' interpretability. The reduced number of features did not result in the drop of AUROC and AUPRC for all machine learning algorithms (P > 0.05). when compared to the ACS-NSQIP scoring system, which achieved a test AUROC of 0.38 (95% confidence interval [CI], 0.13-0.73) and 0.22 (95% CI, 0.10-0.36) on the first two tasks, the Compact model showed significantly greater AUROC values nearing or surpassing 0.90. Decision tree-based algorithms demonstrated larger test AUROC than logistic regression and generally agreed on the most influential features for each task. Advanced machine learning models have consistently shown greater performance and interpretability over conventional methodologies, implying their potential for a more individualized risk assessment of the aged population. Not applicable as this research is not a clinical trial.