Artificial intelligence in age-related cardiogenic shock prediction: a systematic review and meta-analysis

Popat, Apurva1,*; Pethe, Gauri2,*; Yadav, Sweta1,*; Sharma, Param3,*

1Internal Medicine, Marshfield Clinic Health System, Marshfield, WI, USA

2Rheumatology, Marshfield Clinic Health System, Marshfield, WI, USA

3Cardiology, Marshfield Clinic Health System, Marshfield, WI, USA

*Correspondence to: Apurva Popat, MD, popat.apurva@marshfieldclinic.org; Gauri Pethe, MD, pethe.gauri@marshfieldclinic.org; Sweta Yadav, MD, yadav.sweta@marshfieldclinic.org; Param Sharma, MD, sharma.param@marshfieldclinic.org.

Abstract

Cardiogenic shock is a life-threatening condition that significantly impacts the mortality of patients suffering from acute myocardial infarction and heart failure. Despite advancements in medical care, early identification of cardiogenic shock remains a critical challenge, with delayed diagnosis contributing to poor outcomes. Recent developments in artificial intelligence and machine learninghave provided innovative solutions for predictive medicine, offering new opportunities to enhance early cardiogenic shock detection and intervention. This mini-review highlights the application of machine learning models, particularly in the context of artificial intelligence in age-related cardiogenic shock prediction, for predicting cardiogenic shock in hospitalized patients with acute myocardial infarction and heart failure. A thorough search of the PubMed, Web of Science, Cochrane, and Scopus databases was conducted from inception to November 2, 2023, to find relevant studies. The studies analyzed were drawn from various cohorts, focusing on the predictive accuracy of machine learning algorithms in clinical settings. Six studies were included in our review, including four retrospective studies and two prospective cohort studies. The results revealed that machine learning models achieved a pooled mean area under the curve of 0.808 (95% confidence interval: 0.727, 0.890), indicating reliable performance. The area under the curve in the included studies ranged from 0.77 to 0.91. Machine learning models performed well, with accuracy ranging from 0.88 to 0.93 and sensitivity and specificity of 58%–78% and 88%–93%, respectively. Key predictive variables across studies included age, blood pressure, heart rate, oxygen saturation, and blood glucose—all of which were routinely measured in hospital settings. These findings suggest that machine learning can support clinicians by automating risk assessment and providing early alerts, leading to better patient stratification and timely interventions. The integration of machine learning into clinical workflows can bridge the gap between data analysis and decision-making, potentially reducing the workload on medical professionals and enhancing patient outcomes. The use of machine learning models for early cardiogenic shock prediction represents a promising development in cardiology, harnessing artificial intelligence to improve diagnostic precision and facilitate proactive care. Continued research and validation are necessary to refine these models, optimize data management, and expand their applicability. By overcoming current limitations, machine learning can be seamlessly integrated into healthcare to support early detection and improve survival rates in patients at risk for cardiogenic shock.

人工智能在衰老相关的心源性休克预测中的应用：系统综述和meta分析

摘要

心源性休克是一种危及生命的疾病，严重影响急性心肌梗死和心衰患者的死亡率。尽管医疗保健取得了进步，但早期识别心源性休克仍是一项严峻的挑战，诊断延误会导致不良后果。人工智能和机器学习的最新发展为预测医学提供了创新的解决方案，为加强心源性休克的早期检测和干预提供了新的机遇。这篇综述重点介绍了机器学习模型的应用，尤其是人工智能在年龄相关性心源性休克预测中的应用，以预测急性心肌梗死和高血压住院患者的心源性休克。为了找到相关研究，对PubMed、Web of Science、Cochrane 和 Scopus 数据库进行了全面检索，检索时间从开始检索到 2023 年 11 月 2 日。所分析的研究来自不同的队列，重点关注机器学习算法在临床环境中的预测准确性。最终纳入6项研究，包括4项回顾性研究和2项前瞻性队列研究。结果显示，机器学习模型的集合平均曲线下面积 (AUC) 为 0.808（95% CI：0.727, 0.890），表明其性能可靠。纳入研究的 AUC 在 0.77 到 0.91 之间。机器学习模型表现良好，准确率为 0.88 至 0.93，灵敏度和特异性分别为 58%-78% 和 88%-93%。各项研究中的关键预测变量包括年龄、血压、心率、血氧饱和度和血糖--这些都是在医院环境中常规测量的。这些研究结果表明，人工智能可以通过自动进行风险评估和提供早期警报为临床医生提供支持，从而更好地对患者进行分层和及时干预。将人工智能整合到临床工作流程中，可以缩小数据分析与决策之间的差距，从而减轻医疗专业人员的工作量，提高患者的治疗效果。使用机器学习模型进行早期心源性休克预测是心脏病学的一个有前途的发展方向，它利用人工智能提高了诊断的精确性，促进了前瞻性护理。要完善这些模型、优化数据管理并扩大其适用范围，还需要继续进行研究和验证。通过克服当前的局限性，机器学习可以无缝集成到医疗保健中，以支持早期检测并提高有心源性休克险的老年患者存活率。