Complications as a Mediator of the Perioperative Frailty–Mortality Association
Bibliographic record
Abstract
BACKGROUND: Preoperative frailty is strongly associated with postoperative complications and mortality. However, the pathways between frailty, postoperative complications, and mortality are poorly described. The authors hypothesized that the occurrence of postoperative complications would mediate a substantial proportion of the total effect of frailty on mortality after elective noncardiac surgery. METHODS: Following protocol registration, the authors conducted a retrospective cohort study of intermediate- to high-risk elective noncardiac surgery patients (2016) using National Surgical Quality Improvement Program data. The authors conducted Bayesian mediation analysis of the relationship between preoperative frailty (exposure, using the Risk Analysis Index), serious complications (mediator), and 30-day mortality (outcome), comprehensively adjusting for confounders. The authors estimated the total effect of frailty on mortality (composed of the indirect effect mediated by complications and the remaining direct effect of frailty) and estimated the proportion of the frailty-mortality association mediated by complications. RESULTS: The authors identified 205,051 patients; 1,474 (0.7%) died. Complications occurred in 20,211 (9.9%). A 2 SD increase in frailty score resulted in a total association with mortality equal to an odds ratio of 3.79 (95% credible interval, 2.48 to 5.64), resulting from a direct association (odds ratio, 1.76; 95% credible interval, 1.34 to 2.30) and an indirect association mediated by complications (odds ratio, 2.15; 95% credible interval, 1.58 to 2.96). Complications mediated 57.3% (95% credible interval, 40.8 to 73.8) of the frailty-mortality association. Cardiopulmonary complications were the strongest mediators among complication subtypes. CONCLUSIONS: Complications mediate more than half of the association between frailty and postoperative mortality in elective noncardiac surgery.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".