Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion

Abstract

Background: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility.

Methods: We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables' relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application.

Results: The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively.

Conclusions: Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF.

Keywords: ACDF; Artificial intelligence; Machine learning; Outcome prediction; Personalized medicine; Precision medicine; Spine surgery; Web application.

Fig. 1

Methodology flowchart

Fig. 2

Patient selection flowchart

Fig. 3

Algorithms’ radar plots for the outcomes (A) prolonged length of stay, (B) non-home discharges, (C) 30-day readmissions, and (D) major complications

Fig. 4

Algorithms’ receiver operating characteristics for the outcomes (A) prolonged length of stay, (B) non-home discharges, (C) 30-day readmissions, and (D) major complications

Fig. 5

Algorithms’ precision-recall curves for the outcome (A) prolonged length of stay, (B) non-home discharges, (C) 30-day readmissions, and (D) major complications

Fig. 6

The 15 most important features and their mean SHAP values for the model predicting the outcome (A) prolonged length of stay with the Random Forest algorithm, (B) non-home discharges with the Random Forest algorithm, (C) 30-day readmissions with the Random Forest algorithm, and (D) major complications with the Random Forest algorithm