Feasibility of Machine Learning in the Prediction of Short-Term Outcomes Following Anterior Cervical Discectomy and Fusion

Gowd, Anirudh K.; O’Neill, Conor N.; Barghi, Ameen; O’Gara, Tadhg; Carmouche, Jonathan J.

doi:10.1016/j.wneu.2022.09.090

Cited by 8 publications

(6 citation statements)

References 48 publications

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“…However, several publications have explored the predictive performance of ML algorithms concerning postoperative outcomes following ACDF surgery using diverse data sources. For example, Gowd et al employed ML models based on conventional comorbidity indices to compare predictive models for postoperative complications following ACDF surgery [ 21 ]. In this study, the logistic regression algorithm was the best performing for predicting any adverse event (AUROC = 0.73), transfusion (AUROC = 0.90), surgical site infection (AUROC = 0.63), and pneumonia (AUROC = 0.80), while gradient boosting trees was the best performing for predicting extended LOS (AUROC = 0.73).…”

Section: Discussionmentioning

confidence: 99%

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

Karabacak,

Bhimani,

Schupper

et al. 2024

BMC Musculoskelet Disord

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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.

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Section: Discussionmentioning

confidence: 99%

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

Karabacak,

Bhimani,

Schupper

et al. 2024

BMC Musculoskelet Disord

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“…When predicting 90-day readmission, the highest AUC was 0.713 using DNN, with our study reporting a similar AUC of 0.70 when predicting 90-day readmission. Another study by Gowd et al 17 queried the NSQIP database for elective ACDF procedures and tested the predictive value of 6 ML models on postoperative complications and health utilization. The study reported that gradient-boosting trees were the strongest model for extended LOS with an AUC of 0.73.…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning and Deep Learning Algorithms to Predict Postoperative Outcomes Following Anterior Cervical Discectomy and Fusion

Khazanchi

Bajaj²,

Shah³

et al. 2023

Clinical Spine Surgery: A Spine Publication

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Objective: To construct, evaluate, and interpret a series of machine learning models to predict outcomes related to inpatient health care resource utilization for patients undergoing anterior cervical discectomy and fusion (ACDF).Summary of Background Data: Reducing postoperative health care utilization is an important goal for improving the delivery of surgical care and serves as a metric for quality assessment. Recent data has shown marked hospital resource utilization after ACDF surgery, including readmissions, and ED visits. The burden of postoperative health care use presents a potential application of machine learning techniques, which may be capable of accurately identifying at-risk patients using patient-specific predictors.Methods: Patients 18-88 years old who underwent ACDF from 2011 to 2021 at a multisite academic center and had preoperative lab values within 3 months of surgery were included. Outcomes analyzed included 90-day readmissions, postoperative length of stay, and nonhome discharge. Four machine learning models-Extreme Gradient Boosted Trees, Balanced Random Forest, Elastic-Net Penalized Logistic Regression, and a Neural Network-were trained and evaluated through the Area Under the Curve estimates. Feature importance scores were computed for the highest-performing model per outcome through modelspecific metrics.Results: A total of 1026 cases were included in the analysis cohort. All machine learning models were predictive for outcomes of interest, with the Random Forest algorithm consistently demonstrating the strongest average area under the curve performance, with a peak performance of 0.84 for nonhome discharge. Important features varied per outcome, though age, body mass index, American Society of Anesthesiologists classification > 2, and medical comorbidities were highly weighted in the studied outcomes.Conclusions: Machine learning models were successfully applied and predictive of postoperative health utilization after ACDF. Deployment of these tools can assist clinicians in determining high-risk patients.Level of Evidence: III.

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“…54 Imaging studies and patientreported outcomes have also been used to predict outcomes using ML. [57][58][59][60][61][62][63][64] The visual analog scale for leg pain and the Oswestry Disability Index predict the course of neuropathic pain development after lumbar disk herniation is treated with either surgical or conservative therapy. 60 Prognostication of short-and long-term mortalities after surgery for spinal malignancy improves decision-making and end-of-life care.…”

Section: Clinical Outcomesmentioning

confidence: 99%

“…54 Imaging studies and patient-reported outcomes have also been used to predict outcomes using ML. 57-64 The visual analog scale for leg pain and the Oswestry Disability Index predict the course of neuropathic pain development after lumbar disk herniation is treated with either surgical or conservative therapy. 60…”

Section: Postoperative Outcomes and Complicationsmentioning

confidence: 99%

Machine Learning in Spine Surgery: A Narrative Review

Adida,

Legarreta,

Hudson

et al. 2023

Neurosurgery

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Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.

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Feasibility of Machine Learning in the Prediction of Short-Term Outcomes Following Anterior Cervical Discectomy and Fusion

Cited by 8 publications

References 48 publications

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

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

Using Machine Learning and Deep Learning Algorithms to Predict Postoperative Outcomes Following Anterior Cervical Discectomy and Fusion

Machine Learning in Spine Surgery: A Narrative Review

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