US Department of Veterans Affairs, Palo Alto Healthcare System, Palo Alto, California, United States of America.
Department of Radiology, Stanford University School of Medicine, Stanford, California, United States of America.
PLoS One. 2020 Jul 27;15(7):e0236554. doi: 10.1371/journal.pone.0236554. eCollection 2020.
The sudden emergence of COVID-19 has brought significant challenges to the care of Veterans. An improved ability to predict a patient's clinical course would facilitate optimal care decisions, resource allocation, family counseling, and strategies for safely easing distancing restrictions. The Care Assessment Need (CAN) score is an existing risk assessment tool within the Veterans Health Administration (VA), and produces a score from 0 to 99, with a higher score correlating to a greater risk. The model was originally designed for the nonacute outpatient setting and is automatically calculated from structured data variables in the electronic health record. This multisite retrospective study of 6591 Veterans diagnosed with COVID-19 from March 2, 2020 to May 26, 2020 was designed to assess the utility of repurposing the CAN score as objective and automated risk assessment tool to promptly enhance clinical decision making for Veterans diagnosed with COVID-19. We performed bivariate analyses on the dichotomized CAN 1-year mortality score (high vs. low risk) and each patient outcome using Chi-square tests of independence. Logistic regression models using the continuous CAN score were fit to assess its predictive power for outcomes of interest. Results demonstrated that a CAN score greater than 50 was significantly associated with the following outcomes after positive COVID-19 test: hospital admission (OR 4.6), prolonged hospital stay (OR 4.5), ICU admission (3.1), prolonged ICU stay (OR 2.9), mechanical ventilation (OR 2.6), and mortality (OR 7.2). Repurposing the CAN score offers an efficient way to risk-stratify COVID-19 Veterans. As a result of the compelling statistical results, and automation, this tool is well positioned for broad use across the VA to enhance clinical decision-making.
COVID-19 的突然出现给退伍军人的护理带来了重大挑战。提高预测患者临床病程的能力将有助于做出最佳护理决策、资源分配、家庭咨询以及安全放宽隔离限制的策略。护理评估需求(CAN)评分是退伍军人健康管理局(VA)内现有的风险评估工具,其评分范围为 0 至 99,得分越高表示风险越大。该模型最初是为非急性门诊环境设计的,并且是根据电子健康记录中的结构化数据变量自动计算得出的。这项针对 6591 名于 2020 年 3 月 2 日至 5 月 26 日被诊断患有 COVID-19 的退伍军人的多站点回顾性研究旨在评估重新利用 CAN 评分作为客观和自动化风险评估工具的效用,以便及时增强对 COVID-19 退伍军人的临床决策。我们使用卡方检验对二分化的 CAN 1 年死亡率评分(高风险与低风险)和每个患者结局进行了双变量分析。使用连续 CAN 评分拟合了逻辑回归模型,以评估其对感兴趣结局的预测能力。结果表明,CAN 评分大于 50 与 COVID-19 检测呈阳性后的以下结局显著相关:住院(OR 4.6)、住院时间延长(OR 4.5)、入住 ICU(OR 3.1)、ICU 住院时间延长(OR 2.9)、机械通气(OR 2.6)和死亡(OR 7.2)。重新利用 CAN 评分提供了一种对 COVID-19 退伍军人进行风险分层的有效方法。由于统计结果具有很强的说服力,并且可以实现自动化,因此该工具非常适合在 VA 内广泛使用,以增强临床决策。