Zang Chengxi, Hou Yu, Schenck Edward J, Xu Zhenxing, Zhang Yongkang, Xu Jie, Bian Jiang, Morozyuk Dmitry, Khullar Dhruv, Nordvig Anna S, Shenkman Elizabeth A, Rothman Russell L, Block Jason P, Lyman Kristin, Zhang Yiye, Varma Jay, Weiner Mark G, Carton Thomas W, Wang Fei, Kaushal Rainu
Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Division of Pulmonary and Critical Care Medicine, Weill Cornell Department of Medicine, New York, NY, USA.
Commun Med (Lond). 2024 Jul 11;4(1):130. doi: 10.1038/s43856-024-00549-0.
BACKGROUND: SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection. These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems. Limited studies have investigated the predictability of Long COVID development and its associated risk factors.
METHODS: In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (1.4 million patients from New York) and OneFlorida+ (0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development. Both SARS-CoV-2-infected and non-infected adults were analysed during the period of March 2020 to November 2021. Factors associated with Long COVID risk were identified by removing background associations and correcting for multiple tests.
RESULTS: We observed complex association patterns between baseline factors and a variety of Long COVID conditions, and we highlight that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities (e.g., cancer and cirrhosis) are likely associated with increased risk of developing Long COVID. Several Long COVID conditions, e.g., dementia, malnutrition, chronic obstructive pulmonary disease, heart failure, PASC diagnosis U099, and acute kidney failure are well predicted (C-index > 0.8). Moderately predictable conditions include atelectasis, pulmonary embolism, diabetes, pulmonary fibrosis, and thromboembolic disease (C-index 0.7-0.8). Less predictable conditions include fatigue, anxiety, sleep disorders, and depression (C-index around 0.6).
CONCLUSIONS: This observational study suggests that association patterns between investigated factors and Long COVID are complex, and the predictability of different Long COVID conditions varies. However, machine learning-based predictive models can help in identifying patients who are at risk of developing a variety of Long COVID conditions.
背景：新型冠状病毒2型（SARS-CoV-2）感染患者在急性感染后的一段时间内可能会出现新的病症。这些病症即SARS-CoV-2感染的急性后遗症（PASC，或长新冠），涉及多种器官系统。仅有有限的研究调查了长新冠发生的可预测性及其相关风险因素。 方法：在这项回顾性队列研究中，我们使用了来自两个大型PCORnet临床研究网络（INSIGHT，约140万名来自纽约的患者；以及OneFlorida+，约70万名来自佛罗里达的患者）的电子医疗记录，以确定与长新冠相关的因素，并开发基于机器学习的模型来预测长新冠的发生。在2020年3月至2021年11月期间，对SARS-CoV-2感染和未感染的成年人进行了分析。通过消除背景关联并校正多重检验，确定了与长新冠风险相关的因素。 结果：我们观察到基线因素与多种长新冠病症之间存在复杂的关联模式，并且我们强调，严重的急性SARS-CoV-2感染、体重过轻以及患有基线合并症（如癌症和肝硬化）可能与长新冠发生风险增加相关。几种长新冠病症，如痴呆、营养不良、慢性阻塞性肺疾病、心力衰竭、PASC诊断U099以及急性肾衰竭，具有良好的预测性（C指数>0.8）。中等可预测的病症包括肺不张、肺栓塞、糖尿病、肺纤维化和血栓栓塞性疾病（C指数0.7 - 0.8）。可预测性较低的病症包括疲劳、焦虑、睡眠障碍和抑郁（C指数约为0.6）。 结论：这项观察性研究表明，所调查因素与长新冠之间的关联模式复杂，并且不同长新冠病症的可预测性各不相同。然而，基于机器学习的预测模型有助于识别有发生多种长新冠病症风险的患者。
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