Cardiac Arrhythmia Service, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114-2696, USA.
Europace. 2009 Dec;11(12):1647-53. doi: 10.1093/europace/eup250. Epub 2009 Sep 13.
This retrospective analysis sought to develop and validate a model using the measured diagnostic variables in cardiac resynchronization therapy (CRT) devices to predict mortality.
Data used in this analysis came from two CRT studies: Cardiac Resynchronization Therapy Registry Evaluating Patient Response with RENEWAL Family Devices (CRT RENEWAL) (n = 436) and Heart Failure-Heart Rate Variability (HF-HRV) (n = 838). Patients from CRT RENEWAL were used to create a model for risk of death using logistic regression and to create a scoring system that could be used to predict mortality. Results of both the logistic regression and the clinical risk score were validated in a cohort of patients from the HF-HRV study. Diagnostics significantly improved over time post-CRT implant (all P < 0.001) and were correlated with a trend of decreased risk of death. The regression model classified CRT RENEWAL patients into low (2.8%), moderate (6.9%), and high (13.8%) risk of death based on tertiles of their model predicted risk. The clinical risk score classified CRT RENEWAL patients into low (2.8%), moderate (10.1%), and high (13.4%) risk of death based on tertiles of their score. When both the regression model and the clinical risk score were applied to the HF-HRV study, each was able to classify patients into appropriate levels of risk.
Device diagnostics may be used to create models that predict the risk of death.
本回顾性分析旨在开发和验证一种模型,该模型使用心脏再同步治疗(CRT)设备中的测量诊断变量来预测死亡率。
本分析中使用的数据来自两项 CRT 研究:心脏再同步治疗注册评估患者对 RENEWAL 系列设备的反应(CRT RENEWAL)(n = 436)和心力衰竭-心率变异性(HF-HRV)(n = 838)。CRT RENEWAL 的患者用于使用逻辑回归创建死亡风险模型,并创建一个评分系统,用于预测死亡率。逻辑回归和临床风险评分的结果均在 HF-HRV 研究的患者队列中得到验证。诊断在 CRT 植入后随时间显著改善(均 P < 0.001),并与死亡率降低的趋势相关。回归模型根据模型预测风险的三分位数将 CRT RENEWAL 患者分为低(2.8%)、中(6.9%)和高(13.8%)死亡风险。临床风险评分根据评分的三分位数将 CRT RENEWAL 患者分为低(2.8%)、中(10.1%)和高(13.4%)死亡风险。当回归模型和临床风险评分都应用于 HF-HRV 研究时,每个模型都能够将患者分类到适当的风险水平。
设备诊断可能用于创建预测死亡率的模型。