{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T12:45:21Z","timestamp":1782132321226,"version":"3.54.5"},"reference-count":90,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,1]]},"abstract":"\n OBJECTIVE<\/jats:title>\n The aim of this study was to develop and compare 4 predictive algorithms, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and neural network (NN), for perioperative outcomes in adult spinal deformity (ASD) surgery. By evaluating these models, the authors sought to explore how linear and nonlinear interactions unique to each outcome influence predictive accuracy, emphasizing the need for outcome-specific model selection.<\/jats:p>\n <\/jats:sec>\n \n METHODS<\/jats:title>\n A retrospective cohort of 7430 patients (mean age of 67 years) who underwent multilevel thoracolumbar deformity correction was identified using the Nationwide Readmissions Database (2016\u20132019). Predictor variables included demographic data, frailty status, comorbidity indices, nutritional status, and hospital characteristics. Outcomes assessed were prolonged hospital length of stay (LOS), nonroutine discharge, top-quartile all-payer cost, 30-day readmission, and posthemorrhagic anemia. Models were trained on 75% of the dataset and tested on the remaining 25%. LR served as the baseline parametric model, while RF and GBM employed ensemble methods to handle nonlinear interactions, and NN used hidden layers optimized via backpropagation. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values, and DeLong\u2019s test was used for statistical comparisons.<\/jats:p>\n <\/jats:sec>\n \n RESULTS<\/jats:title>\n RF achieved the highest AUC for LOS (0.713), while GBM excelled for posthemorrhagic anemia (AUC = 0.717). LR provided consistent moderate accuracy across all outcomes (AUC range 0.556\u20130.690). NN underperformed (AUC range 0.540\u20130.665), likely due to dataset size limitations. Significant differences were observed between models for prediction of LOS and posthemorrhagic anemia (p < 0.05), with RF and GBM performing the best as they capture nonlinear interactions effectively.<\/jats:p>\n <\/jats:sec>\n \n CONCLUSIONS<\/jats:title>\n The results highlight that no single algorithm universally outperforms others across all perioperative outcomes, as each model captures different linear and nonlinear heterogeneities. Careful consideration of the outcome\u2019s unique characteristics is essential when selecting a predictive model for ASD surgery. These findings support the integration of tailored machine learning approaches to optimize patient-specific risk stratification and perioperative care.<\/jats:p>\n <\/jats:sec>","DOI":"10.3171\/2025.3.focus2532","type":"journal-article","created":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T05:55:28Z","timestamp":1748757328000},"page":"E12","source":"Crossref","is-referenced-by-count":2,"title":["Optimizing predictive model performance in adult spinal deformity surgery: a comparative head-to-head analysis of learning models for perioperative complications"],"prefix":"10.3171","volume":"58","author":[{"given":"Shane","family":"Shahrestani","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine","family":"Garcia","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew M.","family":"Miller","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robin","family":"Babadjouni","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andre E.","family":"Boyke","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Quintero-Consuegra","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohin","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, University of Rochester, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Tuchman","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Corey T.","family":"Walker","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California; and"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"issue":"6","key":"b1","series-title":"Asian Spine J","first-page":"886","article-title":"Adult spinal deformity: current concepts and decision-making strategies for management","volume":"14","author":"Kim HJ","year":"2020","unstructured":"Kim HJ, Yang JH, Chang DG, Adult spinal deformity: current concepts and decision-making strategies for management. 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Eur Spine J. 2022;31(3):669\u2013677."},{"key":"b21","series-title":"World Neurosurg","first-page":"e560","article-title":"Integration of chronological age does not improve the performance of a mixed-effect model using comorbidity burden and frailty to predict 90-day readmission after surgery for degenerative scoliosis","volume":"187","author":"Shahrestani S","year":"2024","unstructured":"Shahrestani S, Chung LK, Brown NJ, Integration of chronological age does not improve the performance of a mixed-effect model using comorbidity burden and frailty to predict 90-day readmission after surgery for degenerative scoliosis. 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Spine (Phila Pa 1976). 2019;44(18):E1083\u2013E1091."},{"key":"b24","series-title":"World Neurosurg","first-page":"e548","article-title":"Frailty and health-related quality of life improvement following adult spinal deformity surgery","volume":"112","author":"Reid DBC","year":"2018","unstructured":"Reid DBC, Daniels AH, Ailon T, Frailty and health-related quality of life improvement following adult spinal deformity surgery. World Neurosurg. 2018;112:e548\u2013e554."},{"key":"b25","series-title":"The Johns Hopkins Adjusted Clinical Groups Technical Reference Guide, Version 9.0","year":"2009","unstructured":"The Johns Hopkins Adjusted Clinical Groups Technical Reference Guide, Version 9.0. Johns Hopkins University;2009."},{"issue":"1","key":"b26","series-title":"Med Care","first-page":"8","article-title":"Comorbidity measures for use with administrative data","volume":"36","author":"Elixhauser A","year":"1998","unstructured":"Elixhauser A, Steiner C, Harris DR, Coffey RM. 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Factors predicting hospital stay, operative time, blood loss, and transfusion in patients undergoing revision posterior lumbar spine decompression, fusion, and segmental instrumentation. Spine (Phila Pa 1976). 2002;27(8):818\u2013824."},{"issue":"18","key":"b33","series-title":"J Clin Med","first-page":"4074","article-title":"Machine learning prediction of length of stay in adult spinal deformity patients undergoing posterior spine fusion surgery","volume":"10","author":"Zhang AS","year":"2021","unstructured":"Zhang AS, Veeramani A, Quinn MS, Alsoof D, Kuris EO, Daniels AH. Machine learning prediction of length of stay in adult spinal deformity patients undergoing posterior spine fusion surgery. J Clin Med. 2021;10(18):4074."},{"issue":"1","key":"b34","series-title":"J Insure Med","first-page":"31","article-title":"Random Forest","volume":"47","author":"Rigatti SJ","year":"2017","unstructured":"Rigatti SJ. Random Forest. J Insure Med. 2017;47(1):31\u201339."},{"issue":"3","key":"b35","series-title":"Perspect Clin Res","first-page":"148","article-title":"Common pitfalls in statistical analysis: logistic regression","volume":"8","author":"Ranganathan P","year":"2017","unstructured":"Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: logistic regression. Perspect Clin Res. 2017;8(3):148\u2013151."},{"key":"b36","series-title":"N Am Spine Soc J","first-page":"100142","article-title":"A scoping review of complication prediction models in spinal surgery: an analysis of model development, validation and impact","volume":"11","author":"Canturk TC","year":"2022","unstructured":"Canturk TC, Czikk D, Wai EK, A scoping review of complication prediction models in spinal surgery: an analysis of model development, validation and impact. 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