How to improve prognostic value of popular risk scores used in acute coronary syndrome – A single center experience in a long term follow-up
Bibliographic record
Abstract
BACKGROUND: Despite the availability of several acute coronary syndrome (ACS) prognostic risk scores (RSs), there is no appropriate score for post-discharge risk stratification for patients after ACS. The aim of this study was to improve traditional RSs designed for predicting short-term outcome after ACS through the inclusion of additional prognostic factors critical for long-term prognosis. METHODS: Observational prospective single-center study included 672 consecutive patients admitted for ACS and discharged alive between 2002 and 2004. Multivariate analysis identified additional independent risk factors for long-term mortality, primarily not included in the RSs. Prognostic value of each RS (SIMPLE, TIMI-STEMI, TIMI-UA/NSTEMI, GRACE in-hospital, GRACE post-discharge, ZWOLLE, LLOYD-JONES) with additional risk factors was evaluated with the area under receiver operating characteristics (ROC) curve. RESULTS: Multivariate analysis identified following independent risk factors improving prognostic value of each RS: supraventricular or ventricular arrhythmias during hospitalization (for all six scales), peripheral artery disease, male gender, recurrence of angina pectoris with ischemia on ECG (in the case of five scales), diabetes, heart failure (for four scales), multi-vessel coronary disease, impaired renal function (in three scales) and less frequent indicators: hospital discharge, coronary artery disease, dyslipidemia, resuscitated sudden cardiac arrest. CONCLUSION: Additional clinical parameters initially not included in the description of the ACS risk scores provided independent prognostic value, whereby improved global risk assessment. Taking these factors into consideration may improve risk stratification of ACS patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".