Strasser Zachary H, Dagliati Arianna, Shakeri Hossein Abad Zahra, Klann Jeffrey G, Wagholikar Kavishwar B, Mesa Rebecca, Visweswaran Shyam, Morris Michele, Luo Yuan, Henderson Darren W, Samayamuthu Malarkodi Jebathilagam, Omenn Gilbert S, Xia Zongqi, Holmes John H, Estiri Hossein, Murphy Shawn N
Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
PLOS Digit Health. 2023 Jul 25;2(7):e0000301. doi: 10.1371/journal.pdig.0000301. eCollection 2023 Jul.
Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC). Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis. In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. These improved definitions were then used for estimating PASC among hospitalized patients. 30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between March 13, 2020 and February 28, 2021. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female. We implemented the distributed representation learning technique to augment PASC definitions. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively. We estimated that 25 percent (CI 95%: 6-48), 11 percent (CI 95%: 6-15), and 13 percent (CI 95%: 8-17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients.
急性新冠肺炎感染后持续数月的身体和心理症状，现被认定为新冠肺炎的急性后遗症（PASC）。用于识别此类患者的精准工具，可提高临床试验招募的筛查能力，提升疾病评估的可靠性，并实现更准确的下游队列分析。在这项回顾性队列研究中，我们分析了三个医疗系统中住院新冠肺炎患者的电子健康记录（EHR），以建立一个流程，用于更好地识别感染严重急性呼吸综合征冠状病毒2（SARS-CoV-2）后出现持续性PASC症状（呼吸困难、疲劳或关节疼痛）的患者。我们实施了由健康结果机器学习（MLHO）驱动的分布式表示学习，以识别可能提示典型诊断代码之外PASC症状的新型EHR特征。MLHO应用基于熵的特征选择和增强算法进行表示挖掘。然后，这些改进的定义被用于估计住院患者中的PASC情况。2020年3月13日至2021年2月28日期间，三个医疗系统中共有30422名住院患者被诊断为新冠肺炎。总体平均年龄为62.3岁（标准差21.0岁），女性有15124名（49.7%）。我们实施了分布式表示学习技术来扩充PASC的定义。这些定义对呼吸困难、疲劳和关节疼痛的阳性预测值分别为0.73、0.74和0.91。我们估计，新冠肺炎确诊后3个月或更长时间，分别有25%（95%置信区间：6%-48%）、11%（95%置信区间：6%-15%）和13%（95%置信区间：8%-17%）的住院新冠肺炎患者会出现呼吸困难、疲劳和关节疼痛。我们展示了一个经过验证的框架，用于在电子健康记录中筛查和识别PASC患者，然后使用该工具估计其在住院新冠肺炎患者中的患病率。
