Predictors of in-hospital mortality in patients hospitalized for heart failure - Insights from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF)
Predictors of in-hospital mortality in patients hospitalized for heart failure - Insights from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF)
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DOI：
10.1016/j.jacc.2008.04.028
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发表时间：
2008-07-29
影响因子：
24
通讯作者：
Young, James B.
中科院分区：
文献类型：
--
作者：
Abraham, William T.;Fonarow, Gregg C.;Young, James B.
目的本研究的目的是建立一个临床模型，预测在医院死亡率在一个广泛的住院心力衰竭（HF）patientpopulation.Background心力衰竭患者的住院率高，预后差。虽然预测死亡率已被确定在HF临床试验中，住院患者可能会有很大的不同，从试验人群，这样的预测可能会低估死亡率在现实世界population.Methods的OPTIMIZE-HF（有组织的计划，以启动挽救生命的治疗住院患者心力衰竭）是一个注册/性能改善计划，在259 U的HF住院患者。S.医院的45个潜在的预测变量被用于逐步logistic回归模型的住院死亡率。对不符合线性假设的连续变量进行转换。所有显著性变量（p < 0.05）均进入多变量分析。广义估计方程被用来解释调整models.Results的同一家医院内的数据的相关性，48,612例患者入组，平均年龄为73.1岁，52%是女性，74%是白人，46%有缺血性病因。平均左心室射血分数为0.39 ± 0.18。住院死亡1，834例（3.8%）。死亡率的多变量预测因素包括年龄、心率、收缩压（SBP）、钠、肌酐、HF（住院的主要原因）和是否存在左心室收缩功能障碍。一个评分系统的开发，以预测mortals.Conclusions住院HF患者的院内死亡率的风险仍然很高，并增加了患者年龄较大，有低SBP或钠水平，并在入院时心率或肌酐升高。这种风险预测算法的应用可能有助于识别可能受益于积极监测和干预的住院死亡率高风险患者。
Objectives The aim of this study was to develop a clinical model predictive of in-hospital mortality in a broad hospitalized heart failure ( HF) patient population.Background Heart failure patients experience high rates of hospital stays and poor outcomes. Although predictors of mortality have been identified in HF clinical trials, hospitalized patients might differ greatly from trial populations, and such predictors might underestimate mortality in a real-world population.Methods The OPTIMIZE-HF ( Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure) is a registry/performance improvement program for patients hospitalized with HF in 259 U. S. hospitals. Forty-five potential predictor variables were used in a stepwise logistic regression model for in-hospital mortality. Continuous variables that did not meet linearity assumptions were transformed. All significant variables ( p < 0.05) were entered into multivariate analysis. Generalized estimating equations were used to account for the correlation of data within the same hospital in the adjusted models.Results Of 48,612 patients enrolled, mean age was 73.1 years, 52% were women, 74% were Caucasian, and 46% had ischemic etiology. Mean left ventricular ejection fraction was 0.39 +/- 0.18. In-hospital mortality occurred in 1,834 ( 3.8%). Multivariable predictors of mortality included age, heart rate, systolic blood pressure (SBP), sodium, creatinine, HF as primary cause of hospitalization, and presence/absence of left ventricular systolic dysfunction. A scoring system was developed to predict mortality.Conclusions Risk of in-hospital mortality for patients hospitalized with HF remains high and is increased in patients who are older and have low SBP or sodium levels and elevated heart rate or creatinine at admission. Application of this risk- prediction algorithm might help identify patients at high risk for in-hospital mortality who might benefit from aggressive monitoring and intervention.