Orthopaedics and Rheumatology Institute, Cleveland Clinic, Cleveland, OH, United States of America.
Center for Spine Health, Cleveland Clinic, Cleveland, OH, United States of America.
PLoS One. 2024 Mar 20;19(3):e0299932. doi: 10.1371/journal.pone.0299932. eCollection 2024.
Hypertension is a widely prevalent disease and uncontrolled hypertension predisposes affected individuals to severe adverse effects. Though the importance of controlling hypertension is clear, the multitude of therapeutic regimens and patient factors that affect the success of blood pressure control makes it difficult to predict the likelihood to predict whether a patient's blood pressure will be controlled. This project endeavors to investigate whether machine learning can accurately predict the control of a patient's hypertension within 12 months of a clinical encounter. To build the machine learning model, a retrospective review of the electronic medical records of 350,008 patients 18 years of age and older between January 1, 2015 and June 1, 2022 was performed to form model training and testing cohorts. The data included in the model included medication combinations, patient laboratory values, vital sign measurements, comorbidities, healthcare encounters, and demographic information. The mean age of the patient population was 65.6 years with 161,283 (46.1%) men and 275,001 (78.6%) white. A sliding time window of data was used to both prohibit data leakage from training sets to test sets and to maximize model performance. This sliding window resulted in using the study data to create 287 predictive models each using 2 years of training data and one week of testing data for a total study duration of five and a half years. Model performance was combined across all models. The primary outcome, prediction of blood pressure control within 12 months demonstrated an area under the curve of 0.76 (95% confidence interval; 0.75-0.76), sensitivity of 61.52% (61.0-62.03%), specificity of 75.69% (75.25-76.13%), positive predictive value of 67.75% (67.51-67.99%), and negative predictive value of 70.49% (70.32-70.66%). An AUC of 0.756 is considered to be moderately good for machine learning models. While the accuracy of this model is promising, it is impossible to state with certainty the clinical relevancy of any clinical support ML model without deploying it in a clinical setting and studying its impact on health outcomes. By also incorporating uncertainty analysis for every prediction, the authors believe that this approach offers the best-known solution to predicting hypertension control and that machine learning may be able to improve the accuracy of hypertension control predictions using patient information already available in the electronic health record. This method can serve as a foundation with further research to strengthen the model accuracy and to help determine clinical relevance.
高血压是一种广泛存在的疾病，未得到控制的高血压会使患者面临严重的不良后果。尽管控制高血压的重要性是显而易见的，但治疗方案的多样性以及影响血压控制成功的患者因素使得预测患者的血压是否能够得到控制变得困难。本项目旨在研究机器学习是否可以准确预测患者在临床就诊后 12 个月内的高血压控制情况。为了构建机器学习模型，对 2015 年 1 月 1 日至 2022 年 6 月 1 日期间 18 岁及以上的 35 万名患者的电子病历进行了回顾性审查，以形成模型训练和测试队列。模型中包含的信息包括药物组合、患者实验室值、生命体征测量值、合并症、医疗保健就诊情况和人口统计学信息。患者人群的平均年龄为 65.6 岁，其中男性 161283 人（46.1%），白人 275001 人（78.6%）。使用滑动时间窗口来防止训练集数据泄露到测试集中，并最大限度地提高模型性能。这种滑动窗口导致使用研究数据创建了 287 个预测模型，每个模型使用 2 年的训练数据和一周的测试数据，总研究时间为五年半。跨所有模型组合了模型性能。主要结局是预测 12 个月内血压控制情况，曲线下面积为 0.76（95%置信区间；0.75-0.76），灵敏度为 61.52%（61.0-62.03%），特异性为 75.69%（75.25-76.13%），阳性预测值为 67.75%（67.51-67.99%），阴性预测值为 70.49%（70.32-70.66%）。AUC 为 0.756 被认为是机器学习模型的中等水平。虽然该模型的准确性很有前景，但如果不在临床环境中部署并研究其对健康结果的影响，就无法确定任何临床支持 ML 模型的临床相关性。通过对每个预测结果进行不确定性分析，作者认为该方法提供了预测高血压控制情况的最佳解决方案，并且机器学习可能能够使用电子健康记录中已经存在的患者信息来提高高血压控制预测的准确性。该方法可以作为进一步研究的基础，以增强模型准确性，并帮助确定临床相关性。
