重症心力衰竭患者的生存率及其影响因素

Prognosis and effective factors for refractory heart failure

目的 :评价重症心力衰竭患者的死亡率及其影响因素。方法 :入选 1997年 1月至 2 0 0 0年12月我院心内科住院的NYHAⅢ～Ⅳ级心力衰竭患者 ,记录心力衰竭发生时间、年龄、性别等人口学因素、病因、超声心动图测定的左心室射血分数 (LVEF)、实验室检查结果 ,随访至 2 0 0 3年 9月 ,观察终点为全因死亡 ,用寿命表法统计 1年、3年和 5年死亡率 ,Kaplan- Meier法绘制心力衰竭的生存曲线 ,用单因素和多因素Cox回归模型分析以上临床指标对预后的影响。结果 :共 15 0例 ,其中男性 10 0例 ,女性 5 0例 ,平均年龄 (6 1. 88± 12 . 4 )岁 ,在平均 6 2个月的随访中共死亡 6 2例 ,自心力衰竭发生起 1年、3年和 5年累积死亡率分别为 2 0 . 37%、33. 6 0 %和 4 7. 83% ,生存时间的中位数为 4 7个月。多因素Cox回归显示 ,老年患者预后较差 [相对危险度RR 2. 0 5 8,95 %可信区间 (CI) 1 .0 96～ 3 96. 7,P =0 .0 2 5 ],入院时收缩压较高的患者 (RR 0 . 983,95 %CI 0 . 96 9～ 0 .997,P =0. 0 19)和LVEF≥ 4 0 %的患者预后较好 (RR 0 . 4 13,95 %CI0 . 2 2 1～ 0 . 772 ,P =0 . 0 0 6 )。结论 :重症心力衰竭患者的长期预后较差 ,年龄、入院收缩压和LVEF是影响预后的独立预测因素。

Objective：To assess the mortality of patients with refractory heart failure and involving risk factors. We studied 150 patients admitted with the diagnosis of class Ⅲ-Ⅳ NYHA heart failure from January 1997 to December 2000. Record indexes included symptom time, age, gender, etiology, left ventricular ejection fraction, lab results. Follow-up was done in September 2003. The end point was death. Life stables method was used to evaluated lyear, 3 year and 5year cumulative survival rates. The Cox regression model was used to assess the association of variables with survival, and Kaplan-Meier cumulative survival plot were constructed to estimate the influence of risk of factors. Result： Average age of 150 patients was 61.88 years, with 100 male and 50 female. There were 62 deaths over follow up of mean 62 months at the end Sep.2003. The median survival was 47 months. Overall, 1-year, 3-year and 5- year cumulative mortality rates were 20.37% , 33.60% and 47.83% . Age （RR 2.085,95% CI 1.096 - 3.967, P = 0.025）, systolic blood pressure（RR 0.983,95% CI 0.969 - 0.997, P = 0.019） and LVEF （RR 0.413, 95 %CI 0.221 - 0.772, P = 0.006） were independent risk factors for mortality in heart failure. Conclusion： In patients with Ⅲ-Ⅳ decompensated heart failure, mortality is very high. Simple clinical features can identify a group of patients at especially high risk of death.