Long-term trends in Post-COVID severity: a machine learning analysis from the POP/COVIDOM cohort of the German NAPKON Cohort Network.

IF 10 1区医学 Q1 MEDICINE, GENERAL & INTERNAL

EClinicalMedicine Pub Date : 2026-03-10 eCollection Date: 2026-03-01 DOI:10.1016/j.eclinm.2026.103822

Julian Gutzeit, Martin Weiß, Thomas Bahmer, Wolfgang Lieb, Stefan Schreiber, J Janne Vehreschild, Carolin Nürnberger, Sina M Pütz, Ekaterina Heim, Anne-Kathrin Ruß, Astrid Dempfle, Michael Krawczak, Susanne Poick, Anna Schäfer, Caroline Morbach, Clara Lehmann, M Cristina Polidori, Jens-Peter Reese, Thomas Zoller, Lilian Krist, Jan Heyckendorf, Lennart Michel Reinke, Jürgen Deckert, Grit Hein

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引用次数: 0

Abstract

Background: Post-COVID syndrome (PCS) affects many survivors with varying symptom profiles driven by acute disease severity (PCS-S) or individual resilience (PCS-R). While cross-sectional studies have identified risk factors and gender differences, long-term trajectories remain unclear. This study investigates the stability and progression of PCS-S and PCS-R scores after 9, 24 and 36 months from initial diagnosis, identifying key predictive factors stratified by gender.

Methods: We analyzed data from 1526 participants of the German National Pandemic Cohort Network (NAPKON), modeling symptom-based PCS-score trajectories over time with linear mixed-effects models. Data were split into training (n = 944), test (n = 233), and two-site external validation (n = 349) sets. Gender-stratified elastic-net regression used nine-month clinical and psychosocial measures to predict PCS scores at 24 and 36 months. All data were collected between November 2020 and February 2024. The study is registered on ClinicalTrials.gov (NCT04679584) and in the German Registry for Clinical Studies (DRKS00023742).

Findings: PCS-S and PCS-R scores showed small but significant declines between 9 and 36 months (β = -0.054 and -0.065, respectively; p < 0.001), indicating persistent symptom burden despite gradual improvement. Predictive models explained 16.7-52.6% of variance in later PCS severity. Fatigue after 9 months and age predicted later PCS-S; quality of life and depression added predictive value in females. Fatigue and sleep issues predicted PCS-R, with living/employment status relevant in females and cognitive deficits in males.

Interpretation: The severity of PCS subtype manifest after 9 months remains relatively stable over time, with distinct gender-specific predictors shaping symptom progression. Tailored interventions are essential for long-term management of PCS pathways.

Funding: The COVIDOM study is funded by the Network University Medicine as part of the NAPKON.

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covid后严重程度的长期趋势：来自德国NAPKON队列网络的POP/ covid队列的机器学习分析。

背景：covid后综合征（PCS）影响许多幸存者，其症状特征由急性疾病严重程度（PCS- s）或个体恢复力（PCS- r）驱动。虽然横断面研究已经确定了风险因素和性别差异，但长期轨迹仍不清楚。本研究调查了首次诊断后9个月、24个月和36个月后PCS-S和PCS-R评分的稳定性和进展，确定了按性别分层的关键预测因素。方法：我们分析了来自德国国家大流行队列网络（NAPKON）的1526名参与者的数据，用线性混合效应模型建模了基于症状的PCS-score随时间的轨迹。数据被分成训练集（n = 944）、检验集（n = 233）和双站外部验证集（n = 349）。性别分层弹性网回归使用9个月的临床和社会心理测量来预测24和36个月时的PCS评分。所有数据收集于2020年11月至2024年2月之间。该研究已在ClinicalTrials.gov （NCT04679584）和德国临床研究注册中心（DRKS00023742）注册。结果：在9至36个月期间，PCS-S和PCS-R评分出现小幅但显著的下降（β分别= -0.054和-0.065;p < 0.001），表明尽管症状逐渐改善，但症状负担持续存在。预测模型解释了后期PCS严重程度的16.7-52.6%的方差。9个月后的疲劳和年龄预测了以后的PCS-S；生活质量和抑郁增加了女性的预测价值。疲劳和睡眠问题预测PCS-R，女性的生活/就业状况和男性的认知缺陷相关。解释：9个月后表现的PCS亚型的严重程度随着时间的推移保持相对稳定，具有明显的性别特异性预测因素塑造症状进展。量身定制的干预措施对于PCS途径的长期管理至关重要。资助：covid - om研究由网络大学医学资助，作为NAPKON的一部分。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EClinicalMedicine Medicine-Medicine (all)

CiteScore

18.90

自引率

1.30%

发文量

506

审稿时长

22 days

期刊介绍： eClinicalMedicine is a gold open-access clinical journal designed to support frontline health professionals in addressing the complex and rapid health transitions affecting societies globally. The journal aims to assist practitioners in overcoming healthcare challenges across diverse communities, spanning diagnosis, treatment, prevention, and health promotion. Integrating disciplines from various specialties and life stages, it seeks to enhance health systems as fundamental institutions within societies. With a forward-thinking approach, eClinicalMedicine aims to redefine the future of healthcare.