Cardiovascular morbidity and mortality among persons diagnosed with tuberculosis: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The emerging epidemiological evidence of increased cardiovascular disease (CVD) risk among persons diagnosed with tuberculosis (TB) has not been systematically reviewed to date. Our aim was to review the existing epidemiological evidence for elevated risk of CVD morbidity and mortality among persons diagnosed with TB compared to controls. MATERIALS AND METHODS: EMBASE, MEDLINE, and Cochrane databases were searched (inception to January 2020) for terms related to "tuberculosis" and "cardiovascular diseases". Inclusion criteria: trial, cohort, or case-control study design; patient population included persons diagnosed with TB infection or disease; relative risk (RR) estimate and confidence interval reported for CVD morbidity or mortality compared to suitable controls. Exclusion criteria: no TB or CVD outcome definition; duplicate study; non-English abstract; non-human participants. Two reviewers screened studies, applied ROBINS-I tool to assess risk of bias, and extracted data independently. Random effects meta-analysis estimated a pooled RR of CVD morbidity and mortality for persons diagnosed with TB compared to controls. RESULTS: 6,042 articles were identified, 244 full texts were reviewed, and 16 were included, meta-analyzing subsets of 8 studies' RR estimates. We estimated a pooled RR of 1.51 (95% CI: 1.16-1.97) for major adverse cardiac events among those diagnosed with TB compared to non-TB controls (p = 0.0024). A 'serious' pooled risk of bias was found across studies with between-study heterogeneity (I2 = 75.3%). CONCLUSIONS: TB appears to be a marker for increased CVD risk; however, the literature is limited and is accompanied by serious risk of confounding bias and evidence of publication bias. Further retrospective and prospective studies are needed. Pending this evidence, best practice may be to consider persons diagnosed with TB at higher risk of CVD as a precautionary measure.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.010 | 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) | 0.003 | 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".