Subclinical hypothyroidism and clinical outcomes after cardiac surgery: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Subclinical hypothyroidism (SCH) is associated with major adverse cardiovascular events. Despite the recognized negative impact of SCH on cardiovascular health, research on cardiac postoperative outcomes with SCH has yielded conflicting results, and patients are not currently treated for SCH before cardiac surgery procedures. Methods We performed a study-level meta-analysis on the impact of SCH on patients undergoing nonurgent cardiac surgery, including coronary artery bypass grafting and valve and aortic surgery. The primary outcome was operative mortality. Secondary outcomes were hospital length of stay (LOS), intensive care unit (ICU) stay, postoperative atrial fibrillation (POAF), intra-aortic balloon pump (IABP) use, renal complications, and long-term all-cause mortality. Results Seven observational studies, with a total of 3445 patients, including 851 [24.7%] diagnosed with SCH and 2594 [75.3%] euthyroid patients) were identified. Compared to euthyroid patients, the patients with SCH had higher rates of operative mortality (odds ratio [OR], 2.57; 95% confidence interval [CI], 1.09-6.04; P = .03), prolonged hospital LOS (standardized mean difference, 0.32; 95% CI, 0.02-0.62; P = .04), a higher rate of renal complications (OR, 2.53; 95% CI, 1.74-3.69; P < .0001), but no significant differences in ICU stay, POAF, or IABP use. At mean follow-up of 49.3 months, the presence of SCH was associated with a higher rate of all-cause mortality (incidence rate ratio, 1.82; 95% CI, 1.18-2.83; P = .02). Conclusions Patients with SCH have higher operative mortality, prolonged hospital LOS, and increased renal complications after cardiac surgery. Achieving and maintaining a euthyroid state prior to and after cardiac surgery procedures might improve outcomes in these patients.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.023 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".