Andrew Cabrera, Alexander Bouterse, Michael Nelson, Coleman Dietrich, Jacob Razzouk, Udochukwu Oyoyo, Christopher M Bono, Olumide Danisa
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As such, the purpose of this study was to conduct machine learning (ML) analysis to predict postoperative mortality in patients with AS or DISH using the Nationwide Inpatient Sample Healthcare Cost and Utilization Project (HCUP-NIS) database.</p><p><strong>Methods: </strong>HCUP-NIS was queried to identify adult patients carrying a diagnosis of AS or DISH who were admitted for spinal fractures and underwent subsequent fusion or corpectomy between 2016 and 2018. Predictions of in-hospital mortality in this cohort were then generated by three independent ML algorithms.</p><p><strong>Results: </strong>An in-hospital mortality rate of 5.40% was observed in our selected population, including a rate of 6.35% in patients with AS, 2.81% in patients with DISH, and 8.33% in patients with both diagnoses. Increasing age, hypertension with end-organ complications, spinal cord injury, and cervical spinal fractures each carried considerable predictive importance across the algorithms utilized in our analysis. Predictions were generated with an average area under the curve of 0.758.</p><p><strong>Conclusions: </strong>This study's application of ML algorithms to predict in-hospital mortality among patients with AS or DISH identified a number of clinical risk factors relevant to this outcome.</p><p><strong>Clinical relevance: </strong>These findings may serve to provide physicians with an awareness of risk factors for in-hospital mortality and, subsequently, guide management and shared decision-making among patients with AS or DISH.</p><p><strong>Level of evidence: 4: </strong></p>","PeriodicalId":38486,"journal":{"name":"International Journal of Spine Surgery","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287805/pdf/","citationCount":"0","resultStr":"{\"title\":\"Prediction of In-Hospital Mortality Following Vertebral Fracture Fixation in Patients With Ankylosing Spondylitis or Diffuse Idiopathic Skeletal Hyperostosis: Machine Learning Analysis.\",\"authors\":\"Andrew Cabrera, Alexander Bouterse, Michael Nelson, Coleman Dietrich, Jacob Razzouk, Udochukwu Oyoyo, Christopher M Bono, Olumide Danisa\",\"doi\":\"10.14444/8567\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Ankylosing spondylitis (AS) and diffuse idiopathic skeletal hyperostosis (DISH) are distinct pathological entities that similarly increase the risk of vertebral fractures. 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引用次数: 0
摘要
背景：强直性脊柱炎（AS）和弥漫性特发性骨骼增生症（DISH）是两种不同的病理实体，它们同样会增加椎体骨折的风险。这种骨折在临床上可能会造成严重后果，并经常预示着严重的神经损伤，因此预防这种骨折成为一个关键重点。尤其重要的是，AS 或 DISH 患者的脊椎骨折具有相当高的死亡风险，1 年损伤相关死亡的报告从 24% 到 33% 不等。因此，本研究旨在利用全国住院病人抽样医疗成本和利用项目（HCUP-NIS）数据库进行机器学习（ML）分析，预测AS或DISH患者的术后死亡率。方法：查询HCUP-NIS，以确定2016年至2018年间因脊柱骨折入院并接受后续融合术或椎体后凸切除术的诊断为AS或DISH的成年患者。然后通过三种独立的 ML 算法对该队列的院内死亡率进行预测：在我们选择的人群中观察到的院内死亡率为5.40%，其中AS患者为6.35%，DISH患者为2.81%，两种诊断的患者均为8.33%。在我们的分析中使用的各种算法中，年龄增加、高血压伴内脏并发症、脊髓损伤和颈椎骨折都具有相当重要的预测意义。预测结果的平均曲线下面积为 0.758：本研究应用 ML 算法预测了 AS 或 DISH 患者的院内死亡率，发现了一些与这一结果相关的临床风险因素：这些发现可帮助医生了解院内死亡率的风险因素，进而指导AS或DISH患者的管理和共同决策：4:
Prediction of In-Hospital Mortality Following Vertebral Fracture Fixation in Patients With Ankylosing Spondylitis or Diffuse Idiopathic Skeletal Hyperostosis: Machine Learning Analysis.
Background: Ankylosing spondylitis (AS) and diffuse idiopathic skeletal hyperostosis (DISH) are distinct pathological entities that similarly increase the risk of vertebral fractures. Such fractures can be clinically devastating and frequently portend significant neurological injury, thus making their prevention a critical focus. Of particular significance, spinal fractures in patients with AS or DISH carry a considerable risk of mortality, with reports on 1-year injury-related deaths ranging from 24% to 33%. As such, the purpose of this study was to conduct machine learning (ML) analysis to predict postoperative mortality in patients with AS or DISH using the Nationwide Inpatient Sample Healthcare Cost and Utilization Project (HCUP-NIS) database.
Methods: HCUP-NIS was queried to identify adult patients carrying a diagnosis of AS or DISH who were admitted for spinal fractures and underwent subsequent fusion or corpectomy between 2016 and 2018. Predictions of in-hospital mortality in this cohort were then generated by three independent ML algorithms.
Results: An in-hospital mortality rate of 5.40% was observed in our selected population, including a rate of 6.35% in patients with AS, 2.81% in patients with DISH, and 8.33% in patients with both diagnoses. Increasing age, hypertension with end-organ complications, spinal cord injury, and cervical spinal fractures each carried considerable predictive importance across the algorithms utilized in our analysis. Predictions were generated with an average area under the curve of 0.758.
Conclusions: This study's application of ML algorithms to predict in-hospital mortality among patients with AS or DISH identified a number of clinical risk factors relevant to this outcome.
Clinical relevance: These findings may serve to provide physicians with an awareness of risk factors for in-hospital mortality and, subsequently, guide management and shared decision-making among patients with AS or DISH.
期刊介绍：
The International Journal of Spine Surgery is the official scientific journal of ISASS, the International Intradiscal Therapy Society, the Pittsburgh Spine Summit, and the Büttner-Janz Spinefoundation, and is an official partner of the Southern Neurosurgical Society. The goal of the International Journal of Spine Surgery is to promote and disseminate online the most up-to-date scientific and clinical research into innovations in motion preservation and new spinal surgery technology, including basic science, biologics, and tissue engineering. The Journal is dedicated to educating spine surgeons worldwide by reporting on the scientific basis, indications, surgical techniques, complications, outcomes, and follow-up data for promising spinal procedures.
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