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. 2024 Feb 3:69:102456.
doi: 10.1016/j.eclinm.2024.102456. eCollection 2024 Mar.

Predictors of non-recovery from fatigue and cognitive deficits after COVID-19: a prospective, longitudinal, population-based study

Tim J Hartung  1 , Thomas Bahmer  2   3 , Irina Chaplinskaya-Sobol  4 , Jürgen Deckert  5 , Matthias Endres  1   6   7   8   9 , Katrin Franzpötter  10 , Johanna Geritz  11 , Karl G Haeusler  12 , Grit Hein  5 , Peter U Heuschmann  12   13   14   15 , Sina M Hopff  16 , Anna Horn  13   14 , Thomas Keil  17 , Michael Krawczak  18 , Lilian Krist  17 , Wolfgang Lieb  10 , Corina Maetzler  11 , Felipe A Montellano  13   19   20 , Caroline Morbach  21 , Christian Neumann  11 , Carolin Nürnberger  13   14 , Anne-Kathrin Russ  18 , Lena Schmidbauer  13   14 , Sein Schmidt  22 , Stefan Schreiber  2 , Flo Steigerwald  1 , Stefan Störk  19   23 , Thomas Zoller  24 , Walter Maetzler  11 , Carsten Finke  1 ; NAPKON Study Group
Affiliations

Predictors of non-recovery from fatigue and cognitive deficits after COVID-19: a prospective, longitudinal, population-based study

Tim J Hartung et al. EClinicalMedicine. .

Abstract

Background: Despite the high prevalence and major disability associated with fatigue and cognitive deficits after SARS-CoV-2 infection, little is known about long-term trajectories of these sequelae. We aimed to assess long-term trajectories of these conditions and to identify risk factors for non-recovery.

Methods: We analyzed longitudinal data from the population-based COVIDOM/NAPKON-POP cohort in Germany. Participants with confirmed SARS-CoV-2 infection were assessed at least 6 months (baseline) and again at least 18 months (follow-up) after infection using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue) Scale (cutoff ≤ 30) and the Montreal Cognitive Assessment (MoCA, cutoff ≤ 25). Predictors of recovery from fatigue or cognitive deficits between assessments were identified through univariate and multivariable logistic regression models. The COVIDOM study is registered at the German registry for clinical studies (DRKS00023742) and at ClinicalTrials.gov (NCT04679584).

Findings: Between 15 November 2020 and 9 May 2023, a total of 3038 participants were assessed at baseline (median 9 months after infection) and 83% responded to invitations for follow-up (median 26 months after infection). At baseline, 21% (95% confidence interval (CI) [20%, 23%]) had fatigue and 23% (95% CI [22%, 25%]) had cognitive deficits according to cutoff scores on the FACIT-Fatigue or MoCA. Participants with clinically relevant fatigue (at baseline) showed significant improvement in fatigue scores at follow-up (Hedges' g [95% CI] = 0.73 [0.60, 0.87]) and 46% (95% CI [41%, 50%]) had recovered from fatigue. Participants with cognitive deficits showed a significant improvement in cognitive scores (g [95% CI] = 1.12 [0.90, 1.33]) and 57% (95% CI [50%, 64%]) had recovered from cognitive deficits. Patients with fatigue exhibiting a higher depressive symptom burden and/or headache at baseline were significantly less likely to recover. Significant risk factors for cognitive non-recovery were male sex, older age and <12 years of school education. Importantly, SARS-CoV-2 reinfection had no significant impact on recovery from fatigue or cognitive deficits.

Interpretation: Fatigue and cognitive deficits are common sequelae after SARS-CoV-2 infection. These syndromes improved over time and about half of the patients recovered within two years. The identified risk factors for non-recovery from fatigue and cognitive deficits could play an important role in shaping targeted strategies for treatment and prevention.

Funding: Funded by the German Federal Ministry of Education and Research (BMBF; grant number 01KX2121) and German Research Foundation (DFG) Excellence Cluster "Position Medicine in Information".

Keywords: COVID-19; Cognitive dysfunction; Fatigue; Longitudinal studies; Post-acute COVID-19 syndrome.

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Conflict of interest statement

TB has received grants from the German Center for Lung Research (DZL), consulting fees from AstraZeneca, GlaxoSmithKline, Pfizer, honoraria from AstraZeneca, GlaxoSmithKline, Novartis, Roche, Chiesi, Boeringer-Ingelheim, Merck, Pfizer, support for attending meetings and/or travel from Chiesi, AstraZeneca, and participated on a Data Safety Monitoring Board or Advisory Board for CoVit-2 (NCT04751604). JD has received grants from the BMBF and participated on a Data Safety Monitoring Board or Advisory Board for the Max-Planck-Institute of Psychiatry, served as a section speaker for DGPPN, as president and treasurer for the DGBP, and as a speaker for NUM. ME has received grants and consulting fees from Bayer and honoraria from Bayer, Pfizer, Amgen, GSK, and Novartis, participated on a Data Safety Monitoring Board or Advisory Board for BMS, Bayer, and Daiichi Sankyo, served on the board of directors for EAN. All of the above were paid to the institution (no personal fees). He is a member of the DGN, ISCBFM, AHA/ASA, ESO, WSO, DZHK (German Centre of Cardiovascular Research) and DZNE (German Center of Neurodegenerative Diseases) and has received PCSK9 inhibitors for mouse studies from Amgen. KGH has received consulting fees from Edwards Lifesciences, Premiere Research Bayer Healthcare, Amarin, Alexion, Daiichi Sankyo. AstraZeneca, and Portola, and honoraria from Bayer Healthcare, Pfizer, Daiichi Sankyo, Bristol-Myers-Squibb, Boehringer Ingelheim, AstraZeneca, Abbott, SUN Pharma, and Novartis. PUH has received grants from the German Ministry of Research and Education, European Union, German Parkinson Society, University Hospital Würzburg, German Heart Foundation, Federal Joint Committee (G-BA) within the Innovationfond, German Research Foundation, Bavarian State, German Cancer Aid, Charité—Universitätsmedizin Berlin (within Mondafis; supported by an unrestricted research grant to the Charité from Bayer), University Göttingen (within FIND-AF randomized; supported by an unrestricted research grant to the University Göttingen from Boehringer-Ingelheim), University Hospital Heidelberg (within RASUNOA-prime; supported by an unrestricted research grant to the University Hospital Heidelberg from Bayer, BMS, Boehringer-Ingelheim, Daiichi Sankyo). All of the above were paid to the respective institutions (no personal fees). He participated on a Data Safety Monitoring Board in publicly funded studies (by German Research Foundation, German Ministry of Research, Foundations). FAM has received funding as part of the UNION-CVD Clinician-Scientist Programme (project number 413657723) by the German Research Foundation. CN has received grants from Internal Medicine Department I, University Hospital Schleswig Holstein, Campus Kiel and honoraria from CAU Kiel. CF has received grants from Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Grant numbers FI 2309/1-1 (Heisenberg Program) FI 2309/2-1 and 327654276 (SFB 1315), and the German Ministry of Education and Research (BMBF), Grant numbers 01GM1908D, 01GM2208C, 13GW0566D, 01GM2102, 01EP2201.

Figures

Fig. 1
Fig. 1
Frequency of fatigue and cognitive deficits by age group at baseline (median 9 months after infection, n = 3038). Error bars represent 95% confidence intervals.
Fig. 2
Fig. 2
Sankey diagram of (A) fatigue (dark blue) and (B) cognitive deficits (CD; purple) at baseline (median 9 months after infection) and follow-up (median 26 months after infection). Between baseline and follow-up, 46% of patients with fatigue and 57% of patients with CD had recovered (yellow). Among patients with no fatigue/CD at baseline, 8% had developed fatigue and 9% had developed CD at follow-up (orange).
Fig. 3
Fig. 3
Longitudinal change in (A) FACIT-Fatigue scores for patients with fatigue at baseline (n = 468), (B) MoCA scores for patients with cognitive deficits at baseline (n = 197).
Fig. 4
Fig. 4
Forest plot of (A) unadjusted univariate and (B) adjusted multivariable logistic regression models for predictors of fatigue recovery (Nagelkerke’s R2 = 0.14). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.
Fig. 5
Fig. 5
Forest plot of (A) unadjusted univariate and (B) adjusted multivariable logistic regression models for predictors of cognitive recovery (Nagelkerke’s R2 = 0.19). ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.
See this image and copyright information in PMC

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