The role of pre-existing chronic disease in cardiac complications from SARS-CoV-2 infection: A systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT Importance SARS-CoV-2 is associated with multiple direct and indirect effects to the heart. It is not yet well defined whether patient groups at increased risk of severe respiratory disease due to SARS-CoV-2 infection also experience a heightened incidence of cardiac complications. Objective We sought to analyse the role of pre-existing chronic disease (chronic respiratory illness, cardiovascular disease (CVD), hypertension and diabetes mellitus) in the development of cardiac complications from SARS-CoV-2. Data Sources We retrospectively investigated published (including pre-prints), publicly released, de-identified, data made available between Dec 1, 2019, and May 11, 2020. Information was accessed from PubMed, Embase, medRxiv and SSRN. Study Selection 379 full-text articles were reviewed and 321 excluded for lack of original research, irrelevance to outcome, inappropriate cohort, or small patient numbers (case reports of <10 patients). Data were extracted from two studies and the remaining 56 contacted to request appropriate data, to which three responded with data contributions. A final of five studies were included. Data Extraction and Synthesis This systematic review was conducted based on PRISMA and MOOSE statements. Included studies were critically appraised using Newcastle Ottawa Quality Assessment Scale (NOS). Data were extracted independently by multiple observers. A fixed-effects model was selected for the meta-analysis based on relatively low heterogeneity between the studies ( I 2 <50%). Main Outcome and Measures Cardiac complications were determined via blood levels of cardiac biomarkers above the 99 th percentile of the upper reference limit, abnormalities in electrocardiography, and/or abnormalities in echocardiography. Results SARS-CoV-2-infected patients who developed cardiac complications were, on average, 10 years older than those that did not. Pooled analyses showed the development of cardiac complications from SARS-CoV-2 was significantly increased in patients with underlying chronic respiratory illness (OR 2.88[1.45,5.71]), CVD (OR 5.12[3.09,8.48]), hypertension (OR 4.37[2.99,6.39]) and diabetes mellitus (OR 2.61[1.67,4.09]). Conclusions and Relevance Older age and pre-existing chronic respiratory illness, CVD, hypertension, and diabetes mellitus may represent prognostic factors for the development of additional cardiac complications in COVID-19, highlighting the need for a multidisciplinary approach to chronic disease patient management and providing justification for a larger scale observational study.
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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 | 0.024 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.046 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".