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轻度 SARS-CoV-2 感染后新冠后状况的预测因素。

Predictors of the post-COVID condition following mild SARS-CoV-2 infection.

机构信息

Cluster for Health Services Research, Norwegian Institute of Public Health, Oslo, Norway.

Department of Health Management and Health Economics, University of Oslo, Oslo, Norway.

出版信息

Nat Commun. 2023 Sep 20;14(1):5839. doi: 10.1038/s41467-023-41541-x.

Abstract

Whereas the nature of the post-COVID condition following mild acute COVID-19 is increasingly well described in the literature, knowledge of its risk factors, and whether it can be predicted, remains limited. This study, conducted in Norway, uses individual-level register data from 214,667 SARS-CoV-2 infected individuals covering a range of demographic, socioeconomic factors, as well as cause-specific healthcare utilization in the years prior to infection to assess the risk of post-COVID complaints ≥3 months after testing positive. We find that the risk of post-COVID was higher among individuals who prior to infection had been diagnosed with psychological (OR = 2.12, 95% CI 1.84-2.44), respiratory (OR = 2.03, 95% CI 1.78-2.32), or general and unspecified health problems (OR = 1.78, 95% CI 1.52-2.09). To assess the predictability of post-COVID after mild initial disease, we use machine learning methods and find that pre-infection characteristics, combined with information on the SARS-CoV-2 virus type and vaccine status, to a considerable extent (AUC = 0.79, 95% CI 0.75-0.81) could predict the occurrence of post-COVID complaints in our sample.

摘要

虽然轻度急性 COVID-19 后新冠后遗症(post-COVID condition)的性质在文献中越来越多地被描述,但对其危险因素及其是否可预测的了解仍然有限。本研究在挪威进行,使用了来自 214667 名 SARS-CoV-2 感染者的个体水平登记数据,这些数据涵盖了一系列人口统计学、社会经济学因素,以及感染前特定于病因的医疗保健利用情况,以评估阳性检测后 3 个月以上出现新冠后遗症的风险。我们发现,在感染前被诊断患有心理(OR=2.12,95%CI 1.84-2.44)、呼吸(OR=2.03,95%CI 1.78-2.32)或一般和未指定健康问题(OR=1.78,95%CI 1.52-2.09)的个体中,新冠后遗症的风险更高。为了评估轻度初始疾病后新冠后遗症的可预测性,我们使用机器学习方法发现,感染前的特征,结合 SARS-CoV-2 病毒类型和疫苗接种状况的信息,在很大程度上(AUC=0.79,95%CI 0.75-0.81)可以预测我们样本中新冠后遗症的发生。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/935d/10511472/72aaa6359cc2/41467_2023_41541_Fig1_HTML.jpg

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