Assessment of absolute risk of death after myocardial infarction by use of multiple-risk-factor assessment equations; GISSI-Prevenzione mortality risk chart
Bibliographic record
Abstract
AIMS: To present and discuss a comprehensive and ready to use prediction model of risk of death after myocardial infarction based on the very recently concluded follow-up of the large GISSI-Prevenzione cohort and on the integrated evaluation of different categories of risk factors: those that are non-modifiable, and those related to lifestyles, co-morbidity, background, and other conventional clinical complications produced by the index myocardial infarction. METHODS: The 11-324 men and women recruited in the study within 3 months from their index myocardial infarction have been followed-up to 4 years. The following risk factors have been used in a Cox proportional hazards model: non-modifiable risk factors: age and sex; complications after myocardial infarction: indicators of left ventricular dysfunction (signs or symptoms of acute left ventricular failure during hospitalization, ejection fraction, NYHA class and extent of ventricular asynergy at echocardiography), indicators of electrical instability (number of premature ventricular beats per hour, sustained or repetitive arrhythmias during 24-h Holter monitoring), indicators of residual ischaemia (spontaneous angina pectoris after myocardial infarction, Canadian Angina Classification class, and exercise testing results); cardiovascular risk factors: smoking habits, history of diabetes mellitus and arterial hypertension, systolic and diastolic blood pressure, blood total and HDL cholesterol, triglycerides, fibrinogen, leukocytes count, intermittent claudication, and heart rate. Multiple regression modelling was assessed by receiver operating characteristic (ROC) analysis. Generalizability of the models was assessed through cross validation and bootstrapping techniques. POPULATION AND RESULTS: During the 4 years of follow-up, a total of 1071 patients died. Age and left ventricular dysfunction were the most relevant predictors of death. Because of pharmacological treatments, total blood cholesterol, triglycerides, and blood pressure values were not significantly associated with prognosis. Sex-specific prediction equations were formulated to predict risk of death according to age, simple indicators of left ventricular dysfunction, electrical instability, and residual ischaemia along with the following cardiovascular risk factors: smoking habits, history of diabetes mellitus and arterial hypertension, blood HDL cholesterol, fibrinogen, leukocyte count, intermittent claudication, and heart rate. The predictive models produced on the basis of information available in the routine conditions of clinical care after myocardial infarction provide ready to use and highly discriminant criteria to guide secondary prevention strategies. CONCLUSIONS AND IMPLICATIONS: Besides documenting what should be the preferred and practicable focus of clinical attention for today's patients, the experience of GISSI-Prevenzione suggests that periodically and prospectively collected databases on naturalistic' cohorts could be an important option for updating and verifying the impact of guidelines, which should incorporate the different components of the complex profile of cardiovascular risk. The GISSI Prevenzione risk function is a simple tool to predict risk of death and to improve clinical management of subjects with recent myocardial infarction. The use of predictive risk algorithms can favour the shift from medical logic, based on the treatment of single risk factors, to one centred on the patient as a whole as well as the tailoring of medical interventions according to patients' overall risk.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".