Predicting Bone Health Using Machine Learning in Patients undergoing Spinal Reconstruction Surgery
Yong Shen 1, Zeeshan M Sardar 1, Herbert Chase 2, Josephine R Coury 1, Meghan Cerpa 1, Lawrence G Lenke 1
PMID: 36302158 DOI: 10.1097/BRS.0000000000004511
Study design: Retrospective study of data collected prospectively.
Objective: The goal of this study is to create a predictive model of preoperative bone health status in adult patients undergoing adult spinal reconstructive (ASR) surgery using machine learning (ML).
Summary of background data: Despite understanding that bone health impacts spine surgery outcomes, spine surgeons lack the tools to risk stratify patients preoperatively to determine who should undergo bone health screening. An ML approach mines patterns in data to determine the risk for poor bone health in ASR patients.
Materials and methods: Two hundred and eleven subjects over the age of 30 with dual energy X-ray absorptiometry scans, who underwent spinal reconstructive surgery were reviewed. Data was collected by manual and automated collection from the electronic health records. The Weka software was used to develop predictive models for multiclass classification of healthy, osteopenia, and osteoporosis (OPO) bone status. Bone status was labeled according to the World Health Organization (WHO) criteria using dual energy X-ray absorptiometry T scores. The accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated. The model was evaluated on a test set of unseen data for generalizability.
Results: The prevalence of OPO was 23.22% and osteopenia was 52.61%. The random forest model achieved optimal performance with an average sensitivity of 0.81, specificity of 0.95, and AUC of 0.96 on the training set. The model yielded an averaged sensitivity of 0.64, specificity of 0.78, and AUC of 0.69 on the test set. The model was best at predicting OPO in patients. Numerous patient features exhibited predictive value, such as body mass index, insurance type, serum sodium level, serum creatinine level, history of bariatric surgery, and the use of medications such as selective serotonin reuptake inhibitors.
Conclusion: Predicting bone health status in ASR patients is possible with an ML approach. Additionally, data mining using ML can find unrecognized risk factors for bone health in ASR surgery patients.
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Conflict of interest statement
The authors report no conflicts of interest.