用机器学习方法预测颈椎手术患者的额外住院日。

IF 1.5 4区 医学 Q3 SURGERY Computer Assisted Surgery Pub Date : 2024-12-01 Epub Date: 2024-06-11 DOI:10.1080/24699322.2024.2345066
Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan
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引用次数: 0

摘要

背景:机器学习(ML)是人工智能(AI)的一个分支,它使用算法分析数据并预测结果,无需大量人工干预。在医疗保健领域,ML 在提高患者预后方面的作用越来越受到关注。本研究的重点是预测颈椎病(CS)患者的额外住院日(AHD),颈椎病是一种影响颈椎的疾病。研究旨在开发一种基于 ML 的提名图模型,通过分析临床和人口统计因素来估算住院时间(LOS)。准确的住院时间预测可实现有效的资源分配、改善患者护理并降低医疗成本:研究选择了接受颈椎手术的 CS 患者,并调查了他们的医疗数据。共招募了 945 名患者,其中男性 570 名,女性 375 名。所有样本的平均住院日为 8.64±3.7 天。LOS 等于或 n = 539)和 LOS > 8.64 天的患者组成 AHD 阳性组(n = 406)。收集到的数据按 7:3 的比例随机分为训练组和验证组。参数包括患者的一般情况、慢性疾病、术前临床评分、术前影像学数据,包括前纵韧带骨化(OALL)、后纵韧带骨化(OPLL)、颈椎不稳和磁共振成像 T2 加权成像高信号(MRI T2WIHS)、手术指标和并发症。研究人员开发了基于 ML 的模型,如 Lasso 回归、随机森林(RF)和支持向量机(SVM)递归特征消除(SVM-RFE),用于预测与 AHD 相关的风险因素。利用上述算法筛选出的变量的交叉点构建了预测患者急性心肌梗死的提名图模型。接受者操作特征曲线(ROC)的曲线下面积(AUC)和 C 指数用于评估提名图的性能。校准曲线和决策曲线分析(DCA)用于测试校准性能和临床实用性:结果:在这些参与者中,有 25 个具有统计学意义的参数被确定为急性心肌缺血风险因素。其中,有九个因素是这三种 ML 算法的交叉因素,并被用于建立一个提名图模型。这些因素包括性别、年龄、体重指数(BMI)、美国脊柱损伤协会(ASIA)评分、磁共振成像 T2 加权成像高信号(MRI T2WIHS)、手术区段、术中出血量、引流量和糖尿病。模型验证后,训练队列的 AUC 为 0.753,验证队列的 AUC 为 0.777。校准曲线显示,提名图预测与实际概率之间的一致性令人满意。C 指数为 0.788(95% 置信区间:0.73214-0.84386)。在决策曲线分析(DCA)中,提名图的阈值概率范围为 1%至 99%(训练队列)和 1%至 75%(验证队列):我们成功建立了一个用于预测颈椎手术患者 AHD 的 ML 模型,展示了该模型在支持临床医生识别 AHD 和改进围手术期治疗策略方面的潜力。
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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods.

Background: Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare.

Methods: The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility.

Results: For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214-0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort).

Conclusion: We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

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来源期刊
Computer Assisted Surgery
Computer Assisted Surgery Medicine-Surgery
CiteScore
2.30
自引率
0.00%
发文量
13
审稿时长
10 weeks
期刊介绍: omputer Assisted Surgery aims to improve patient care by advancing the utilization of computers during treatment; to evaluate the benefits and risks associated with the integration of advanced digital technologies into surgical practice; to disseminate clinical and basic research relevant to stereotactic surgery, minimal access surgery, endoscopy, and surgical robotics; to encourage interdisciplinary collaboration between engineers and physicians in developing new concepts and applications; to educate clinicians about the principles and techniques of computer assisted surgery and therapeutics; and to serve the international scientific community as a medium for the transfer of new information relating to theory, research, and practice in biomedical imaging and the surgical specialties. The scope of Computer Assisted Surgery encompasses all fields within surgery, as well as biomedical imaging and instrumentation, and digital technology employed as an adjunct to imaging in diagnosis, therapeutics, and surgery. Topics featured include frameless as well as conventional stereotactic procedures, surgery guided by intraoperative ultrasound or magnetic resonance imaging, image guided focused irradiation, robotic surgery, and any therapeutic interventions performed with the use of digital imaging technology.
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