Half of Unplanned Readmissions Following One or Two-Level Anterior Cervical Decompression and Fusion Are Unrelated to Surgical Site

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

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.

IDENTIFICATION OF RISK FACTORS ASSOCIATED WITH 30-DAY READMISSION OF PATIENTS SUBMITTED TO ANTERIOR OR POSTERIOR ACCESS CERVICAL SPINE SURGERY

ABSTRACT Aim: To conduct a systematic review of the literature to identify risk factors associated with 30-day readmission of patients submitted to anterior or posterior access cervical spine surgery. Methods: The databases used to select the papers were PubMed, Web of Science, and Cochrane, using the following search strategy: patient AND readmission AND (30 day OR “thirty day” OR 30-day OR thirty-day) AND (spine AND cervical). Results: Initially, 179 papers that satisfied the established search stringwere selected. After reading the titles and abstracts, 46 were excluded from the sample for not effectively discussing the theme proposed for this review. Of the 133 remaining papers, 109 were also excluded after a detailed reading of their content, leaving 24 that were included in the sample for the meta-analysis. Conclusions: The average readmission rate in the studies evaluated was 4.85%. Only the occurrence of infections, as well as the presence of patients classified by the American Society of Anesthesiology (ASA) assessment system with scores greater than III, were causal factors that influenced the readmission of patients. No significant differences were noted when comparing the anterior and posterior surgical access routes. Level of evidence II; Systematic Review of Level II or Level I Studies with discrepant results.