Pre-operative stress testing in the evaluation of patients undergoing non-cardiac surgery: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pre-operative stress testing is widely used to evaluate patients for non-cardiac surgeries. However, its value in predicting peri-operative mortality is uncertain. The objective of this study is to assess the type and quality of available evidence in a comprehensive and statistically rigorous evaluation regarding the effectiveness of pre-operative stress testing in reducing 30-day post -operative mortality following non -cardiac surgery. METHODS: The databases of MEDLINE, EMBASE, and CENTRAL databases (from inception to January 27, 2016) were searched for all studies in English. We included studies with pre-operative stress testing prior to 10 different non-cardiac surgery among adults and excluded studies with sample size<15. The data on study characteristics, methodology and outcomes were extracted independently by two observers and checked by two other observers. The primary outcome was 30-day mortality. We performed random effects meta-analysis to estimate relative risk (RR) and 95% confidence intervals (95% CI) in two-group comparison and pooled the rates for stress test alone. Heterogeneity was assessed using I2 and methodological quality of studies using Newcastle-Ottawa Quality Assessment Scale. The predefined protocol was registered in PROSPERO #CRD42016049212. RESULTS: From 1807 abstracts, 79 studies were eligible (297,534 patients): 40 had information on 30-day mortality, of which 6 studies compared stress test versus no stress test. The risk of 30-day mortality was not significant in the comparison of stress testing versus none (RR: 0.79, 95% CI = 0.35-1.80) along with weak evidence for heterogeneity. For the studies that evaluated stress testing without a comparison group, the pooled rates are 1.98% (95% CI = 1.25-2.85) with a high heterogeneity. There was evidence of potential publication bias and small study effects. CONCLUSIONS: Despite substantial interest and research over the past 40 years to predict 30-day mortality risk among patients undergoing non-cardiac surgery, the current body of evidence is insufficient to derive a definitive conclusion as to whether stress testing leads to reduced peri-operative mortality.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".