Hautala Arto J, Shavazipour Babooshka, Afsar Bekir, Tulppo Mikko P, Miettinen Kaisa
Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland.
Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland.
Cardiovasc Digit Health J. 2023 May 13;4(4):137-142. doi: 10.1016/j.cvdhj.2023.05.001. eCollection 2023 Aug.
Health care budgets are limited, requiring the optimal use of resources. Machine learning (ML) methods may have an enormous potential for effective use of health care resources.
We assessed the applicability of selected ML tools to evaluate the contribution of known risk markers for prognosis of coronary artery disease to predict health care costs for all reasons in patients with a recent acute coronary syndrome (n = 65, aged 65 ± 9 years) for 1-year follow-up.
Risk markers were assessed at baseline, and health care costs were collected from electronic health registries. The Cross-decomposition algorithms were used to rank the considered risk markers based on their impacts on variances. Then regression analysis was performed to predict costs by entering the first top-ranking risk marker and adding the next-best markers, one by one, to build up altogether 13 predictive models.
The average annual health care costs were €2601 ± €5378 per patient. The Depression Scale showed the highest predictive value (r = 0.395), accounting for 16% of the costs ( = .001). When the next 2 ranked markers (LDL cholesterol, r = 0.230; and left ventricular ejection fraction, r = -0.227, respectively) were added to the model, the predictive value was 24% for the costs ( = .001).
Higher depression score is the primary variable forecasting health care costs in 1-year follow-up among acute coronary syndrome patients. The ML tools may help decision-making when planning optimal utilization of treatment strategies.
医疗保健预算有限，需要优化资源利用。机器学习（ML）方法在有效利用医疗保健资源方面可能具有巨大潜力。
我们评估了所选ML工具的适用性，以评估已知冠心病预后风险标志物对预测近期急性冠状动脉综合征患者（n = 65，年龄65±9岁）1年随访期间所有原因的医疗保健费用的贡献。
在基线时评估风险标志物，并从电子健康记录中收集医疗保健费用。使用交叉分解算法根据风险标志物对方差的影响对其进行排名。然后进行回归分析，通过输入排名第一的风险标志物并逐个添加次优标志物来预测费用，从而总共建立13个预测模型。
每位患者的平均年度医疗保健费用为2601欧元±5378欧元。抑郁量表显示出最高的预测价值（r = 0.395），占费用的16%（P = .001）。当将接下来排名的2个标志物（低密度脂蛋白胆固醇，r = 0.230；左心室射血分数，r = -0.227）添加到模型中时，费用的预测价值为24%（P = .001）。
较高的抑郁评分是急性冠状动脉综合征患者1年随访中预测医疗保健费用的主要变量。ML工具在规划治疗策略的最佳利用时可能有助于决策。
