PREDICTING THE OCCURRENCE OF MAJOR ADVERSE CARDIAC EVENTS WITHIN 30 DAYS AFTER A PATIENT'S VASCULAR SURGERY: AN INDIVIDUAL PATIENT-DATA META-ANALYSIS
Bibliographic record
Abstract
Introduction Major adverse cardiac events (MACE) – which include cardiac death and non-fatal myocardial infarction – are severe harmful outcomes that commonly arise after elective non-cardiac vascular surgeries. Current preoperative risk prediction models are not as effective in predicting post-operative outcomes. This talk will discuss the key results of an individual patient-data meta-analysis, based on data from six cohort studies of patients undergoing vascular surgery. Objectives We aimed to determine a prediction model that dichotomizes patients into high and low risk categories of MACE within 30 days after noncardiac vascular surgery. Approach This is an application of the minimum p-value method (MPM) to determine the optimal cut-off points for: (i) B-type naturietic peptide (BNP) and (ii) N-terminal pro B-type natriuretic peptide (NTproBNP) in predicting MACE within 30 days after non-cardiac vascular surgery. Elevated concentrations of these hormones are secreted into the blood in response to heart failure. We compare results from MPM with those based on the receiver operating characteristic (ROC) curve approach using logistic regression; develop and validate the prediction rule for MACE; and assess the robustness of the results under different statistical models. Results The ROC curve approach (applied by Rodseth and colleagues) identified 116pg/mL and 277.5pg/mL as the optimal thresholds for BNP and NTproBNP, respectively. The minimum p-value method dichotomized these covariates as BNP: 115.57pg/mL (p<0.0001) and NTproBNP: 241.7pg/mL (p=0.0001). Our logistic regression analysis identified MINP_thrshld, the indicator variable of our MPM results for BNP and NTproBNP, as a stronger covariate than our ROC curve results. Our final prediction model contained variables MINP_thrshld, the type of surgery, and diabetes mellitus. Internal validation was performed using bootstrapping while mixed effects logistic regression and generalized estimating equations were performed for sensitivity analysis. Although our model was validated using 1000 samples, it was not robust against methods that accounted for clustering effects. Conclusion As current preoperative risk stratification models are not as effective in predicting post-operative outcomes for vascular surgery patients, clinicians are at an advantage in using this model for the ease and accuracy that it provides. Further exploration into clustering effects is needed for determining the best model.
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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 | 0.034 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.063 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".