Nathan J Lee, Lawrence G Lenke, Varun Arvind, Ted Shi, Alexandra C Dionne, Chidebelum Nnake, Mitchell Yeary, Michael Fields, Matt Simhon, Anastasia Ferraro, Matthew Cooney, Erik Lewerenz, Justin L Reyes, Steven G Roth, Chun Wai Hung, Justin K Scheer, Thomas Zervos, Earl D Thuet, Joseph M Lombardi, Zeeshan M Sardar, Ronald A Lehman, Benjamin D Roye, Michael G Vitale, Fthimnir M Hassan
{"title":"一种新的术前评分系统,可以准确预测脊柱畸形手术中脊髓水平的术中神经监测数据丢失:一种机器学习方法。","authors":"Nathan J Lee, Lawrence G Lenke, Varun Arvind, Ted Shi, Alexandra C Dionne, Chidebelum Nnake, Mitchell Yeary, Michael Fields, Matt Simhon, Anastasia Ferraro, Matthew Cooney, Erik Lewerenz, Justin L Reyes, Steven G Roth, Chun Wai Hung, Justin K Scheer, Thomas Zervos, Earl D Thuet, Joseph M Lombardi, Zeeshan M Sardar, Ronald A Lehman, Benjamin D Roye, Michael G Vitale, Fthimnir M Hassan","doi":"10.2106/JBJS.24.00386","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists.</p><p><strong>Methods: </strong>A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed. Patients were randomly allocated to training (75% of patients) and testing (25% of patients) groups. Feature score weights were derived by rounding up the regression coefficients from the multivariable logistic regression model. Variables in the final scoring calculator were automatically selected through the ML process to optimize predictive performance.</p><p><strong>Results: </strong>Eight features were included in the scoring system: sagittal deformity angular ratio (sDAR) of ≥15 (score = 2), type-3 spinal cord shape (score = 2), conus level below L2 (score = 2), cervical upper instrumented vertebra (score = 2), preoperative upright largest thoracic Cobb angle of ≥75° (score = 2), preoperative lower-extremity motor deficit (score = 2), preoperative upright largest thoracic kyphosis of ≥80° (score = 1), and total deformity angular ratio (tDAR) of ≥25 (score = 1). Higher cumulative scores were associated with increased rates of cord-level IONM data loss: patients with a cumulative score of ≤2 had a cord-level IONM data loss rate of 0.9%, whereas those with a score of ≥7 had a loss rate of 86%. When evaluated in the testing group, the scoring system achieved an accuracy of 93%, a sensitivity of 75%, a specificity of 94%, and an AUC (area under the receiver operating characteristic curve) of 0.898.</p><p><strong>Conclusions: </strong>This is the first study to provide an ML-derived preoperative scoring system that predicts cord-level IONM data loss during pediatric and adult spinal deformity surgery with >90% accuracy.</p><p><strong>Level of evidence: </strong>Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.</p>","PeriodicalId":15273,"journal":{"name":"Journal of Bone and Joint Surgery, American Volume","volume":" ","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Preoperative Scoring System to Accurately Predict Cord-Level Intraoperative Neuromonitoring Data Loss During Spinal Deformity Surgery: A Machine-Learning Approach.\",\"authors\":\"Nathan J Lee, Lawrence G Lenke, Varun Arvind, Ted Shi, Alexandra C Dionne, Chidebelum Nnake, Mitchell Yeary, Michael Fields, Matt Simhon, Anastasia Ferraro, Matthew Cooney, Erik Lewerenz, Justin L Reyes, Steven G Roth, Chun Wai Hung, Justin K Scheer, Thomas Zervos, Earl D Thuet, Joseph M Lombardi, Zeeshan M Sardar, Ronald A Lehman, Benjamin D Roye, Michael G Vitale, Fthimnir M Hassan\",\"doi\":\"10.2106/JBJS.24.00386\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists.</p><p><strong>Methods: </strong>A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed. Patients were randomly allocated to training (75% of patients) and testing (25% of patients) groups. Feature score weights were derived by rounding up the regression coefficients from the multivariable logistic regression model. Variables in the final scoring calculator were automatically selected through the ML process to optimize predictive performance.</p><p><strong>Results: </strong>Eight features were included in the scoring system: sagittal deformity angular ratio (sDAR) of ≥15 (score = 2), type-3 spinal cord shape (score = 2), conus level below L2 (score = 2), cervical upper instrumented vertebra (score = 2), preoperative upright largest thoracic Cobb angle of ≥75° (score = 2), preoperative lower-extremity motor deficit (score = 2), preoperative upright largest thoracic kyphosis of ≥80° (score = 1), and total deformity angular ratio (tDAR) of ≥25 (score = 1). Higher cumulative scores were associated with increased rates of cord-level IONM data loss: patients with a cumulative score of ≤2 had a cord-level IONM data loss rate of 0.9%, whereas those with a score of ≥7 had a loss rate of 86%. When evaluated in the testing group, the scoring system achieved an accuracy of 93%, a sensitivity of 75%, a specificity of 94%, and an AUC (area under the receiver operating characteristic curve) of 0.898.</p><p><strong>Conclusions: </strong>This is the first study to provide an ML-derived preoperative scoring system that predicts cord-level IONM data loss during pediatric and adult spinal deformity surgery with >90% accuracy.</p><p><strong>Level of evidence: </strong>Prognostic Level III. 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A Novel Preoperative Scoring System to Accurately Predict Cord-Level Intraoperative Neuromonitoring Data Loss During Spinal Deformity Surgery: A Machine-Learning Approach.
Background: An accurate knowledge of a patient's risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists.
Methods: A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed. Patients were randomly allocated to training (75% of patients) and testing (25% of patients) groups. Feature score weights were derived by rounding up the regression coefficients from the multivariable logistic regression model. Variables in the final scoring calculator were automatically selected through the ML process to optimize predictive performance.
Results: Eight features were included in the scoring system: sagittal deformity angular ratio (sDAR) of ≥15 (score = 2), type-3 spinal cord shape (score = 2), conus level below L2 (score = 2), cervical upper instrumented vertebra (score = 2), preoperative upright largest thoracic Cobb angle of ≥75° (score = 2), preoperative lower-extremity motor deficit (score = 2), preoperative upright largest thoracic kyphosis of ≥80° (score = 1), and total deformity angular ratio (tDAR) of ≥25 (score = 1). Higher cumulative scores were associated with increased rates of cord-level IONM data loss: patients with a cumulative score of ≤2 had a cord-level IONM data loss rate of 0.9%, whereas those with a score of ≥7 had a loss rate of 86%. When evaluated in the testing group, the scoring system achieved an accuracy of 93%, a sensitivity of 75%, a specificity of 94%, and an AUC (area under the receiver operating characteristic curve) of 0.898.
Conclusions: This is the first study to provide an ML-derived preoperative scoring system that predicts cord-level IONM data loss during pediatric and adult spinal deformity surgery with >90% accuracy.
Level of evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
期刊介绍:
The Journal of Bone & Joint Surgery (JBJS) has been the most valued source of information for orthopaedic surgeons and researchers for over 125 years and is the gold standard in peer-reviewed scientific information in the field. A core journal and essential reading for general as well as specialist orthopaedic surgeons worldwide, The Journal publishes evidence-based research to enhance the quality of care for orthopaedic patients. Standards of excellence and high quality are maintained in everything we do, from the science of the content published to the customer service we provide. JBJS is an independent, non-profit journal.