Physician prediction of 1-year mortality in the cardiac catheterization laboratory: comparison to a validated risk score
Bibliographic record
Abstract
BACKGROUND: Physician perception of procedural risk and clinical outcome can affect revascularization decision making. Public reporting of percutaneous coronary intervention outcomes accentuates the need for accuracy in risk prediction in order to avoid a treatment paradox of undertreating the highest risk patients. Our study compares a validated risk score to physician prediction (PP) of 1-year mortality based on clinical impression at the time of invasive angiography. METHODS AND RESULTS: We performed a cohort study between August 2015 and May 2018 to determine the discriminative accuracy of interventional cardiologists on one-year mortality of the treated patient. PP of one-year mortality was compared to the New York State Percutaneous Coronary Intervention Reporting System (NYPCIRS) score in predicting mortality. Three thousand seven hundred ninety-two patients were followed with a median follow-up period of 14.4 months (interquartile range 12.4-18.1 months) and 165 patients (4.4%) died within one-year. PP of mortality was associated with one-year mortality with a hazard ratio of 8.78 (95% confidence interval 5.24-14.71, P < 0.0001). Clinical presentation in the form of cardiogenic shock, return of spontaneous circulation, and liver and renal dysfunction were associated with PP. Diagnostic accuracy and specificity were improved in PP compared to NYPCIRS. The combination of PP to NYPCIRS improved the overall c-statistic and diagnostic yield. CONCLUSION: PP appears to be especially specific and accurate for prediction of mortality compared to NYPCIRS though it lacks sensitivity. Furthermore, the combination of PP with NYPCIRS improved the c-statistic and diagnostic yield. Overall, the utility of PP with an objective risk score improves the diagnostic accuracy of mortality prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".