Utilizing a comprehensive machine learning approach to identify patients at high risk for extended length of stay following spinal deformity surgery in pediatric patients with early onset scoliosis.

IF 1.6 Q3 CLINICAL NEUROLOGY
Spine deformity Pub Date : 2024-09-01 Epub Date: 2024-05-03 DOI:10.1007/s43390-024-00889-w
Michael W Fields, Jay Zaifman, Matan S Malka, Nathan J Lee, Christina C Rymond, Matthew E Simhon, Theodore Quan, Benjamin D Roye, Michael G Vitale
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引用次数: 0

Abstract

Purpose: Early onset scoliosis (EOS) patient diversity makes outcome prediction challenging. Machine learning offers an innovative approach to analyze patient data and predict results, including LOS in pediatric spinal deformity surgery.

Methods: Children under 10 with EOS were chosen from the American College of Surgeon's NSQIP database. Extended LOS, defined as over 5 days, was predicted using feature selection and machine learning in Python. The best model, determined by the area under the curve (AUC), was optimized and used to create a risk calculator for prolonged LOS.

Results: The study included 1587 patients, mostly young (average age: 6.94 ± 2.58 years), with 33.1% experiencing prolonged LOS (n = 526). Most patients were female (59.2%, n = 940), with an average BMI of 17.0 ± 8.7. Factors influencing LOS were operative time, age, BMI, ASA class, levels operated on, etiology, nutritional support, pulmonary and neurologic comorbidities. The gradient boosting model performed best with a test accuracy of 0.723, AUC of 0.630, and a Brier score of 0.189, leading to a patient-specific risk calculator for prolonged LOS.

Conclusions: Machine learning algorithms accurately predict extended LOS across a national patient cohort and characterize key preoperative drivers of increased LOS after PSIF in pediatric patients with EOS.

利用综合机器学习方法,识别早期脊柱侧凸儿科患者脊柱畸形手术后延长住院时间的高风险患者。
目的:早发脊柱侧凸(EOS)患者的多样性使得结果预测具有挑战性。机器学习提供了一种创新方法来分析患者数据并预测结果,包括小儿脊柱畸形手术的LOS:从美国外科医生学会的 NSQIP 数据库中选取了 10 岁以下的 EOS 儿童。使用 Python 中的特征选择和机器学习功能对超过 5 天的延长 LOS 进行预测。根据曲线下面积(AUC)确定的最佳模型得到优化,并用于创建延长 LOS 的风险计算器:该研究共纳入 1587 名患者,其中大部分是年轻人(平均年龄:6.94 ± 2.58 岁),33.1% 的患者经历过长期住院治疗(n = 526)。大多数患者为女性(59.2%,n = 940),平均体重指数为(17.0 ± 8.7)。影响住院时间的因素包括手术时间、年龄、体重指数、ASA分级、手术级别、病因、营养支持、肺部和神经系统合并症。梯度提升模型的测试准确率为0.723,AUC为0.630,Brier评分为0.189,表现最佳,从而产生了一个患者特异性LOS延长风险计算器:结论:机器学习算法能准确预测全国患者队列中的长期住院时间,并描述了导致儿科 EOS 患者 PSIF 术后长期住院时间延长的主要术前因素。
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来源期刊
CiteScore
3.20
自引率
18.80%
发文量
167
期刊介绍: Spine Deformity the official journal of the?Scoliosis Research Society is a peer-refereed publication to disseminate knowledge on basic science and clinical research into the?etiology?biomechanics?treatment?methods and outcomes of all types of?spinal deformities. The international members of the Editorial Board provide a worldwide perspective for the journal's area of interest.The?journal?will enhance the mission of the Society which is to foster the optimal care of all patients with?spine?deformities worldwide. Articles published in?Spine Deformity?are Medline indexed in PubMed.? The journal publishes original articles in the form of clinical and basic research. Spine Deformity will only publish studies that have institutional review board (IRB) or similar ethics committee approval for human and animal studies and have strictly observed these guidelines. The minimum follow-up period for follow-up clinical studies is 24 months.
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