Prognostic value of myocardial injury biomarker in COVID-19: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background An accurate prognostic tool that can help with risk stratification at first admission is crucial in COVID-19 patients, as severe complications and mortality rate can be reduced with prompt treatment and close follow up. The myocardial injury biomarker, Troponin, is reported to be a potential predictor of mortality in COVID-19 cases. Purpose This study aims to investigate the utility of cardiac troponin level as a prognosis tool in COVID-19 patients. Methods On March 12th, 2021, data was collected from PMC, PubMed, Google Scholar, Science Direct, and Scopus, using combination of keywords associated with myocardial injury biomarker, Troponin, and its prognostic value for mortality in COVID-19 patients. Included publications are original clinical trials, limited to those with English manuscripts, and all were reviewed and evaluated by 5 authors to minimise bias. Inclusion criteria for studies are original clinical trials with a study population of adults (≥18 years old) patients with confirmed SARS-CoV-2 infection through PCR. The quality of each studies included was assessed using the Newcastle-Ottawa Scale (NOS) and data synthesis and statistical analysis were carried out using Review Manager software. Results A total of 19 studies consisting of 13,789 COVID-19 patients were included. All 19 studies were good quality according to the NOS. Studies found that myocardial injury, indicated by the elevation of its biomarker, Troponin, is a strong predictor of in-hospital mortality. Furthermore, elevated troponin level itself is an independent marker of mortality, regardless of its concomitant cardiac disease. Meta-analysis of 9 included studies revealed a significant association between elevated cardiac troponin levels and mortality (HR 4.13; 95% CI 3.42 - 5.00; P<0.001). Based on GRADE, the result of this systematic review and meta-analysis proved to be moderate in quality as there are no inconsistency and variability in results and there are no publication biases. Conclusion Cardiac troponin level is found to be a strong predictor of mortality in COVID-19 patients and therefore should be treated both as an evaluation for cardiac involvement and as an important prognostic marker. Patients with elevated troponin levels within admission should be closely monitored and given prompt treatment to decrease mortality. Funding Acknowledgement Type of funding sources: None. Forest plot figure
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".