Characterising long COVID: a living systematic review
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
While it is now apparent clinical sequelae (long COVID) may persist after acute COVID-19, their nature, frequency and aetiology are poorly characterised. This study aims to regularly synthesise evidence on long COVID characteristics, to help inform clinical management, rehabilitation strategies and interventional studies to improve long-term outcomes.
Methods
A living systematic review. Medline, CINAHL (EBSCO), Global Health (Ovid), WHO Global Research on COVID-19 database, LitCovid and Google Scholar were searched till 17 March 2021. Studies including at least 100 people with confirmed or clinically suspected COVID-19 at 12 weeks or more post onset were included. Risk of bias was assessed using the tool produced by Hoy et al . Results were analysed using descriptive statistics and meta-analyses to estimate prevalence.
Results
A total of 39 studies were included: 32 cohort, 6 cross-sectional and 1 case–control. Most showed high or moderate risk of bias. None were set in low-income countries and few included children. Studies reported on 10 951 people (48% female) in 12 countries. Most included previously hospitalised people (78%, 8520/10 951). The longest mean follow-up time was 221.7 (SD: 10.9) days post COVID-19 onset. Over 60 physical and psychological signs and symptoms with wide prevalence were reported, most commonly weakness (41%; 95% CI 25% to 59%), general malaise (33%; 95% CI 15% to 57%), fatigue (31%; 95% CI 24% to 39%), concentration impairment (26%; 95% CI 21% to 32%) and breathlessness (25%; 95% CI 18% to 34%). 37% (95% CI 18% to 60%) of patients reported reduced quality of life; 26% (10/39) of studies presented evidence of reduced pulmonary function.
Conclusion
Long COVID is a complex condition with prolonged heterogeneous symptoms. The nature of studies precludes a precise case definition or risk evaluation. There is an urgent need for prospective, robust, standardised, controlled studies into aetiology, risk factors and biomarkers to characterise long COVID in different at-risk populations and settings.
PROSPERO registration number
CRD42020211131.
Article activity feed
-
-
-
SciScore for 10.1101/2020.12.08.20246025: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Search Strategy: The following databases were searched: Medline and CINAHL (EBSCO), Global Health (Ovid), WHO Global Research Database on covid-19, and LitCOVID from 1st January to 28th September 2020. Medlinesuggested: (MEDLINE, RRID:SCR_002185)Additionally, we searched Google Scholar on 28th September 2020, screening the first 500 titles. Google Scholarsuggested: (Google Scholar, RRID:SCR_008878)[20] Data Extraction: Data extraction was performed using Microsoft Excel. Microsoft…SciScore for 10.1101/2020.12.08.20246025: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Search Strategy: The following databases were searched: Medline and CINAHL (EBSCO), Global Health (Ovid), WHO Global Research Database on covid-19, and LitCOVID from 1st January to 28th September 2020. Medlinesuggested: (MEDLINE, RRID:SCR_002185)Additionally, we searched Google Scholar on 28th September 2020, screening the first 500 titles. Google Scholarsuggested: (Google Scholar, RRID:SCR_008878)[20] Data Extraction: Data extraction was performed using Microsoft Excel. Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)Confidence intervals for the individual studies were estimated using the exact method. [21] The analysis was performed in STATA MP 15 using the metaprop command. STATAsuggested: (Stata, RRID:SCR_012763)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study is not without limitations. The literature on ‘long covid’ is still immature, and most of the incorporated studies were not designed as prevalence studies. Symptoms were mostly reported by a small number of studies and participants, without control subjects, limiting our ability to establish causality. For example, anxiety, depression, and fatigue could have a multifactorial aetiology and be direct results of the viral infection or may be influenced by other factors, including lockdown and media reporting. Furthermore, the studies have considerable heterogeneities due to study designs, settings, populations, follow-up time, and symptom ascertainment methods. In addition, the inconsistent terminology describing symptoms and limited details on pre-existing comorbidities, the severity of covid-19, and treatment methods prevented reliable meta-analysis. This inconsistency and limited reporting partly explain the high degree of variability observed and prevents us from drawing clear estimates of symptom prevalence. Smaller studies were not included in the analysis in order to avoid bias; this together with the limited reporting in the included studies may mean that new, emerging evidence was not detected in this version. ‘Long covid’ is an emerging area of study and we anticipate future updates of this review will address these challenges, provided more robust and consistent methods are used to study ‘long covid’ in the future. Such is the strength of a living systematic...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
SciScore for 10.1101/2020.12.08.20246025: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Search Strategy The following databases were searched: Medline and CINAHL (EBSCO), Global Health ( Medlinesuggested: (MEDLINE, RRID:SCR_002185)Additionally, we searched Google Scholar on 28th September 2020, screening the first 500 titles. Google Scholarsuggested: (Google Scholar, RRID:SCR…SciScore for 10.1101/2020.12.08.20246025: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Search Strategy The following databases were searched: Medline and CINAHL (EBSCO), Global Health ( Medlinesuggested: (MEDLINE, RRID:SCR_002185)Additionally, we searched Google Scholar on 28th September 2020, screening the first 500 titles. Google Scholarsuggested: (Google Scholar, RRID:SCR_008878)[ 20] Data Extraction Data extraction was performed using Microsoft Excel. Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)Confidence intervals for the individual studies were estimated using the exact method. [ 21] The analysis was performed in STATA MP 15 using the metaprop command. STATAsuggested: (Stata, RRID:SCR_012763)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Our study is not without limitations. The literature on ‘long covid’ is still immature, and most of the incorporated studies were not designed as prevalence studies. Symptoms were mostly reported by a small number of studies and participants, without control subjects, limiting our ability to establish causality. For example, anxiety, depression, and fatigue could have a multifactorial aetiology and be direct results of the viral infection or may be influenced by other factors, including lockdown and media reporting. Furthermore, the studies have considerable heterogeneities due to study designs, settings, populations, follow-up time, and symptom ascertainment methods. In addition, the inconsistent terminology describing symptoms and limited details on pre-existing comorbidities, the severity of covid-19, and treatment methods prevented reliable metaanalysis. This inconsistency and limited reporting partly explain the high degree of variability observed and prevents us from drawing clear estimates of symptom prevalence. Smaller studies were not included in the analysis in order to avoid bias; this together with the limited reporting in the included studies may mean that new, emerging evidence was not detected in this version. ‘Long covid’ is an emerging area of study and we anticipate future updates of this review will address these challenges, provided more robust and consistent methods are used to study ‘long covid’ in the future. Such is the strength of a living systematic ...
Results from TrialIdentifier: We found the following clinical trial numbers in your paper:
Identifier Status Title NCT04362150 Active, not recruiting Long-term Impact of Infection With Novel Coronavirus (COVID-... Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.
-