Risk factors and preventive interventions for post Covid-19 condition: systematic reviews
Bibliographic record
Abstract
ABSTRACT Background The Covid-19 outbreak has presented many challenges to governments and healthcare systems, including observations of symptoms that persist beyond acute infection labelled as post Covid-19 condition. Objectives To systematically identify and synthesize evidence around pre-existing and clinical risk factors for post Covid-19 condition (occurring ≥12 weeks after positive test/symptom onset) (KQ1), and interventions during the acute and post-acute phases of the illness that could potentially prevent post Covid-19 condition (KQ2). Methods We searched Medline and Embase (Jan 2021-Aug 12 2021 [KQ1], and Jan 2020-Jul 28, 2021 [KQ2]), Clinicaltrials.gov, organizational websites, and reference lists of included studies and relevant systematic reviews. Two investigators independently reviewed abstracts and full-text articles against a priori inclusion criteria, and disagreements were resolved through discussion or by consulting a third reviewer. One investigator abstracted data and assessed risk of bias using design-specific criteria, and a second investigator checked data abstraction and assessments for completeness and accuracy. Meta-analysis was performed when there was sufficient clinical and methodological similarity in an exposure-outcome comparison, based on prespecified variables. We assessed the certainty of evidence using the Grading of Recommendations, Assessment, Development and Evaluation approach (GRADE). A relative effect/association of 0.75-1.49 was considered as “little-to-no”, whereas 0.50-0.74/1.5-1.99 was “small-to-moderate” and <0.50/ ≥2.00 was “large” for fewer/benefit or more/harm, respectively Results From 4,672 (KQ1) and 3,781 (KQ2) citations we included 17 and 18 studies, though 4 studies were included for both KQs. We found small-to-moderate associations between female sex and higher non-recovery, fatigue, and dyspnea (moderate certainty). Severe or critical acute-phase Covid-19 severity (versus not) has probably (moderate certainty) a large association with increased cognitive impairment, a small-to-moderate association with more non-recovery, and a little-to-no association with dyspnea. There may be (low certainty) large associations between hospitalization during the acute illness and increased non-recovery, increased dyspnea, and reduced return to work. There may be small-to-moderate associations between several other risk factors and post Covid-19 condition outcomes, including age ≥60 versus <60 (functional incapacity), non-White people (lower return to work), children age >6 versus <2 years (non-recovery), having ≥1 versus no comorbidities (non-recovery), chronic pulmonary disease (fatigue), rheumatologic disorder (depression/anxiety), and chronic obstructive pulmonary disease or hypertension (cognitive impairment). Several other risk factors had low certainty for little-to-no association with one or more outcomes (e.g. diabetes, cardiovascular disease) or very low certainty. Interventions to prevent post Covid-19 condition included medications (standard and traditional/ayurvedic), stem cell therapy, rehabilitation or similar therapies, and screening/referrals at either acute phase (symptom onset to 4 weeks) or early post-acute phase (4-8 week), with short (12-16 weeks) or longer (>16 weeks) follow-up for outcomes. We are very uncertain about the effects of preventive interventions, mainly due to risk of bias, inconsistency/lack of consistency (single studies), and in some cases imprecision. Conclusions Guidelines in relation to surveillance, screening services, and other services such as access to sickness and disability benefits, might need to focus on females and those with previously severe Covid-19 illness. Interventions targeting fatigue, dyspnea, and cognitive impairment (especially in those who had severe Covid-19) may be good to prioritize for development and evaluation to provide evidence on their effects. Inputs from patients and primary care providers should be taken into account when developing new care pathways and some tailoring to individual needs will likely be paramount. Continuous assessment of the rapidly emerging evidence is important to better shape our understanding as the body of evidence grows. Sufficiently powered prospective trials of preventive interventions are warranted. PROSPERO registration CRD42021270354
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".