Faculty of Sports and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland.
Front Public Health. 2024 May 28;12:1378349. doi: 10.3389/fpubh.2024.1378349. eCollection 2024.
Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to predict health care costs is not well recognized. Since machine learning (ML) applications are rapidly giving new opportunities to assist health care professionals' work, we used selected ML tools to assess the predictive value of defined risk factors for health care costs during 12-month ECR in patients with CAD.
The data for analysis was available from a total of 71 patients referred to Oulu University Hospital, Finland, due to an acute coronary syndrome (ACS) event (75% men, age 61 ± 12 years, BMI 27 ± 4 kg/m2, ejection fraction 62 ± 8, 89% have beta-blocker medication). Risk factors were assessed at the hospital immediately after the cardiac event, and health care costs for all reasons were collected from patient registers over a year. ECR was programmed in accordance with international guidelines. Risk analysis algorithms (cross-decomposition algorithms) were employed to rank risk factors based on variances in their effects. Regression analysis was used to determine the accounting value of risk factors by entering first the risk factor with the highest degree of explanation into the model. After that, the next most potent risk factor explaining costs was added to the model one by one (13 forecast models in total).
The ECR group used health care services during the year at an average of 1,624 ± 2,139€ per patient. Diabetes exhibited the strongest correlation with health care expenses ( = 0.406), accounting for 16% of the total costs ( < 0.001). When the next two ranked markers (body mass index; = 0.171 and systolic blood pressure; = - 0.162, respectively) were added to the model, the predictive value was 18% for the costs ( = 0.004). The depression scale had the weakest independent explanation rate of all 13 risk factors (explanation value 0.1%, = 0.029, = 0.811).
Presence of diabetes is the primary reason forecasting health care costs in 12-month ECR intervention among ACS patients. The ML tools may help decision-making when planning the optimal allocation of health care resources.
基于运动的心脏康复（ECR）已被证明是有效的、具有成本效益的冠心病（CAD）主要治疗选择。然而，众所周知的 CAD 预后风险因素对预测医疗保健成本的贡献尚未得到充分认识。由于机器学习（ML）应用程序正在迅速为协助医疗保健专业人员的工作提供新的机会，因此我们使用选定的 ML 工具来评估在 CAD 患者的 12 个月 ECR 期间，定义的风险因素对医疗保健成本的预测价值。
分析的数据来自芬兰奥卢大学医院因急性冠状动脉综合征（ACS）事件而转诊的总共 71 名患者（75%为男性，年龄 61 ± 12 岁，BMI 27 ± 4 kg/m2，射血分数 62 ± 8，89%服用β受体阻滞剂）。在心脏事件后立即在医院评估风险因素，并在一年中从患者登记处收集所有原因的医疗保健费用。ECR 根据国际指南进行编程。使用交叉分解算法对风险因素进行排名，根据其影响的差异对风险因素进行排名。回归分析用于通过首先将具有最高解释程度的风险因素输入模型来确定风险因素的核算价值。之后，一个接一个地将下一个最有力的解释成本的风险因素添加到模型中（总共 13 个预测模型）。
ECR 组患者在一年内平均每人使用医疗保健服务 1624 ± 2139 欧元。糖尿病与医疗费用相关性最强（= 0.406），占总费用的 16%（< 0.001）。当添加到模型中的下两个排名标记（体重指数；= 0.171 和收缩压；= - 0.162，分别）时，成本的预测值为 18%（= 0.004）。抑郁量表是所有 13 个风险因素中独立解释率最低的（解释率为 0.1%，= 0.029，= 0.811）。
在 ACS 患者的 12 个月 ECR 干预中，糖尿病的存在是预测医疗保健成本的主要原因。ML 工具在规划最佳医疗保健资源分配时可能有助于决策。
