An Independent and External Validation of the ACC NCDR Bleeding Risk Score among a National Multi-Site Community Hospital Registry of Cardiac Interventions
Bibliographic record
Abstract
Background: An accurate tool with good discrimination for bleeding would be useful to clinicians for improved management of all their patients. Bleeding risk models have been published but not externally validated in independent clinical datasets. We chose the NCDR PCI score to validate within a large, multi-site community dataset. The aim of the study was to determine the diagnostic utility of this bleeding risk score tool. Methods: This is a large-scale retrospective analysis utilizing American College of Cardiology data from a 37-hospital health system. The central repository of PCI procedures between 6-1-2009 and 6-30-2012 was utilized to validate the NCDR PCI bleeding risk score (BRS) among 4693 patients. The primary endpoint was major bleeding. Discriminant analysis calculating the receiver operating characteristic curve was performed. Results: There were 143 (3.0%) major bleeds. Mean bleeding risk score was 14.7 (range 3 – 42). Incidence of bleeding by risk category: low (0.5%), intermediate (1.7%), and high risk (7.6%). Patients given heparin had 113 (3.7%) major bleeds and those given bivalirudin had 30 (2.1%) major bleeds. Tool accuracy was poor to fair (AUC 0.78 heparin, 0.65 bivalirudin). Overall accuracy was 0.71 (CI: 0.66-0.76). Accuracy did not improve when confined to just the intermediate risk group (AUC 0.58; CI: 0.55-0.67). Conclusion: Bleeding risk tools have low predictive value. Adjustment for anticoagulation use resulted in poor discrimination because bivalirudin differentially biases outcomes toward no bleeding. The current state of bleeding risk tools provides little support for diagnostic utility in regards to major bleeding and therefore have limited clinical applicability.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".