Rahman Faisal, Finkelstein Noam, Alyakin Anton, Gilotra Nisha A, Trost Jeff, Schulman Steven P, Saria Suchi
Department of Cardiology, Baylor College of Medicine, Houston, Texas.
Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
J Soc Cardiovasc Angiogr Interv. 2022 Apr 22;1(3):100308. doi: 10.1016/j.jscai.2022.100308. eCollection 2022 May-Jun.
Despite technological and treatment advancements over the past 2 decades, cardiogenic shock (CS) mortality has remained between 40% and 60%. Our objective was to develop an algorithm that can continuously monitor heart failure patients and partition them into cohorts of high and low risk for CS.
We retrospectively studied 24,461 patients hospitalized with acute decompensated heart failure, 265 of whom developed CS, in the Johns Hopkins Health System. Our cohort identification approach is based on logistic regression and makes use of vital signs, lab values, and medication administrations recorded during the normal course of care.
Our algorithm identified patients at high risk of CS. Patients in the high-risk cohort had 10.2 times (95% confidence interval, 6.1-17.2) higher prevalence of CS than those in the low-risk cohort. Patients who experienced CS while in the high-risk cohort were first deemed high risk a median of 1.7 days (interquartile range, 0.8-4.6) before CS diagnosis was made by their clinical team. To evaluate , we randomly selected 50 patients designated as high risk who did develop CS and 50 who did not. On review of true positive cases, from the time of model identification as high risk to the eventual diagnosis of CS, 12% of patients had possible inappropriate therapy, and for 50% of patients, more tailored therapy options existed. On review of the false positive cases, 44% of cases were considered at high risk of CS or end-stage cardiomyopathy by their clinical teams or went onto develop other types of shock.
This risk model was able to predict patients at higher risk of CS in a time frame that allowed a change in clinical care. The actionability evaluation demonstrates a possible opportunity to intervene as part of a CS algorithm for escalation of care.
尽管在过去20年里技术和治疗方法取得了进步，但心源性休克（CS）的死亡率仍在40%至60%之间。我们的目标是开发一种算法，能够持续监测心力衰竭患者，并将他们分为CS高风险和低风险队列。
我们对约翰霍普金斯医疗系统中24461例因急性失代偿性心力衰竭住院的患者进行了回顾性研究，其中265例发生了CS。我们的队列识别方法基于逻辑回归，并利用了常规护理过程中记录的生命体征、实验室检查值和用药情况。
我们的算法识别出了CS高风险患者。高风险队列中的患者发生CS的患病率是低风险队列中的10.2倍（95%置信区间，6.1 - 17.2）。在高风险队列中发生CS的患者，在其临床团队做出CS诊断前，中位1.7天（四分位间距，0.8 - 4.6）就首次被判定为高风险。为了进行评估，我们随机选择了50例被判定为高风险且确实发生了CS的患者和50例未发生CS的患者。在审查真阳性病例时，从模型识别为高风险到最终诊断为CS这段时间内，12%的患者可能接受了不恰当的治疗，并且50%的患者存在更具针对性的治疗选择。在审查假阳性病例时，44%的病例被其临床团队认为有CS或终末期心肌病的高风险，或者后来发展为其他类型的休克。
这种风险模型能够在一个允许改变临床护理的时间框架内预测CS高风险患者。可操作性评估表明，作为CS护理升级算法的一部分，存在可能的干预机会。
