Assessment of absolute risk of death after myocardial infarction by use of multiple-risk-factor assessment equations; GISSI-Prevenzione mortality risk chart
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».