Assessment of absolute risk of death after myocardial infarction by use of multiple-risk-factor assessment equations - GISSI-Prevenzione mortality risk chart
Assessment of absolute risk of death after myocardial infarction by use of multiple-risk-factor assessment equations - GISSI-Prevenzione mortality risk chart
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DOI：
10.1053/euhj.2000.2544
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发表时间：
2001-11-01
影响因子：
39.3
通讯作者：
Valagussa, F
中科院分区：
文献类型：
--
作者：
Marchioli, R;Avanzini, F;Valagussa, F
目的基于最近结束的大型gissi - prevzione队列随访和对不同类别不可改变的危险因素的综合评估，提出并讨论一个全面的、可使用的心肌梗死后死亡风险预测模型。以及那些与生活方式，合并症，背景有关的。及其他常规临床并发症所产生的指标心肌梗死。方法纳入研究的11324例男性和女性在心肌梗死后3个月内随访4年。以下危险因素被用于Cox比例风险模型：不可改变的危险因素：年龄和性别、心肌梗死后并发症、左心室功能障碍指标（住院期间急性左心室衰竭的体征或症状）。射血分数、超声心动图NYHA分级和室性无能程度)、电不稳定指标（每小时室性早搏次数、24小时Holler监测期间持续或重复心律失常）、残留缺血指标（心肌梗死后自发性心绞痛、加拿大心绞痛分级和运动试验结果）；心血管危险因素：吸烟习惯、糖尿病和动脉高血压史、收缩压和舒张压。血液总胆固醇和高密度脂蛋白胆固醇，甘油三酯，纤维蛋白原，白细胞计数，间歇性跛行。还有心率。采用受试者工作特征（ROC）分析评价多元回归模型。通过交叉验证和自举技术评估模型的泛化性。人群与结果在4年的随访中，共有1071例患者死亡。年龄和左心室功能障碍是最相关的死亡预测因素。由于药物治疗，总血胆固醇、甘油三酯和血压值与预后无显著相关性。根据年龄、左心室功能障碍的简单指标，制定了性别特异性预测方程来预测死亡风险。伴有以下心血管危险因素：吸烟习惯、糖尿病和动脉高血压史、血液高密度脂蛋白胆固醇、纤维蛋白原、白细胞计数、间歇性跛行和心率。在心肌梗死后常规临床护理条件下可获得的信息基础上产生的预测模型为指导二级预防策略提供了现成的和高度区分的标准。结论和意义除了记录当今患者临床关注的首选和可行焦点外，gisi - prevzione的经验表明，定期和前瞻性地收集“自然”队列的数据库可能是更新和验证指南影响的重要选择，指南应包含心血管风险复杂概况的不同组成部分。GISSI预防风险函数是预测死亡风险和改善近期心肌梗死患者临床管理的简单工具。预测风险算法的使用有利于从基于单一风险因素治疗的医学逻辑向单一风险因素治疗的转变(C) 2001，欧洲心脏病学会。
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 or ventricular asynergy at echocardiography), indicators of electrical instability (number of premature ventricular beats per hour, sustained or repetitive arrhythmias during 24-h Holler 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 (C) 2001 The European Society of Cardiology.