Frailty and pre-frailty in cardiac surgery: a systematic review and meta-analysis of 66,448 patients
Bibliographic record
Abstract
BACKGROUND: The burden of frailty on cardiac surgical outcomes is incompletely understood. Here we perform a systematic review and meta-analysis of studies comparing frail versus pre-frail versus non-frail patients following cardiac surgery. METHODS: We searched MEDLINE and EMBASE databases until July 2018 for studies comparing cardiac surgery outcomes in "frail", "pre-frail" and "non-frail" patients. Data was extracted in duplicate. Primary outcome was operative mortality. RESULTS: There were 19 observational studies with 66,448 patients. Frail patients were more likely female (risk ratio [RR]1.7; 95%CI:1.5-1.9), older (mean difference: 2.4; 95%CI:1.3-3.5 years older) with greater comorbidities and higher STS-PROM. Frailty (RR2.35; 95%CI:1.57-3.51; p < 0.0001) and pre-frailty (RR2.03; 95%CI:1.52-2.70; p < 0.00001) were associated with increased operative mortality compared with non-frail patients. Frailty was also associated with greater risk of prolonged hospital stay (RR1.83; 95%CI:1.61-2.08; p < 0.0001) and intermediate care facility discharge (RR2.71; 95%CI:1.45-5.05; p = 0.002). Frail (Hazard Ratio [HR]3.27; 95%CI:1.93-5.55; p < 0.0001) and pre-frail patients (HR2.30; 95%CI:1.29-4.09; p = 0.005) had worse mid-term mortality (median follow-up 1 years [range 0.5-4 years]). After adjustment for baseline imbalances, frailty was still associated with greater operative mortality (odds ratio [OR]1.97; 95%CI:1.51-2.57; p < 0.00001), intermediate care facility discharge (OR4.61; 95%CI:2.78-7.66; p < 0.00001) and midterm mortality (HR1.37; 95%CI:1.03-1.83; p = 0.03). CONCLUSION: In patients undergoing cardiac surgery, frailty and pre-frailty were associated with 2-fold and 1.5-fold greater adjusted operative mortality, respectively, greater adjusted perioperative complications and frailty was associated with almost 5-fold risk of non-home discharge. Burden of frailty and pre-frailty on cardiac surgical outcomes.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".