Robust prediction of nonhome discharge following elective anterior cervical discectomy and fusion using explainable machine learning.

Autor: Geng EA; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., Gal JS; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America.; Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, United States of America., Kim JS; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America. kim.jun3@gmail.com., Martini ML; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., Markowitz J; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., Neifert SN; Department of Neurosurgery, New York University Grossman School of Medicine, New York, United States of America., Tang JE; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., Shah KC; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., White CA; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., Dominy CL; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., Valliani AA; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., Duey AH; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., Li G; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., Zaidat B; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., Bueno B; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America., Caridi JM; Department of Neurosurgery, McGovern Medical School at University of Texas Health, Houston, United States of America., Cho SK; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, United States of America.
Jazyk: angličtina
Zdroj: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society [Eur Spine J] 2023 Jun; Vol. 32 (6), pp. 2149-2156. Date of Electronic Publication: 2023 Feb 28.
DOI: 10.1007/s00586-023-07621-8
Abstrakt: Purpose: Predict nonhome discharge (NHD) following elective anterior cervical discectomy and fusion (ACDF) using an explainable machine learning model.
Methods: 2227 patients undergoing elective ACDF from 2008 to 2019 were identified from a single institutional database. A machine learning model was trained on preoperative variables, including demographics, comorbidity indices, and levels fused. The validation technique was repeated stratified K-Fold cross validation with the area under the receiver operating curve (AUROC) statistic as the performance metric. Shapley Additive Explanation (SHAP) values were calculated to provide further explainability regarding the model's decision making.
Results: The preoperative model performed with an AUROC of 0.83 ± 0.05. SHAP scores revealed the most pertinent risk factors to be age, medicare insurance, and American Society of Anesthesiology (ASA) score. Interaction analysis demonstrated that female patients over 65 with greater fusion levels were more likely to undergo NHD. Likewise, ASA demonstrated positive interaction effects with female sex, levels fused and BMI.
Conclusion: We validated an explainable machine learning model for the prediction of NHD using common preoperative variables. Adding transparency is a key step towards clinical application because it demonstrates that our model's "thinking" aligns with clinical reasoning. Interactive analysis demonstrated that those of age over 65, female sex, higher ASA score, and greater fusion levels were more predisposed to NHD. Age and ASA score were similar in their predictive ability. Machine learning may be used to predict NHD, and can assist surgeons with patient counseling or early discharge planning.
(© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
Externí odkaz: