■先前的研究未能对符合二次植入心脏复律除颤器(ICD)植入条件的心力衰竭(HF)患者进行风险分层。我们旨在评估使用常规可用的临床数据进行基于机器学习的表型分析是否可以识别特征和预后不同的亚组。
■共包括389例植入ICD的慢性HF患者,并收集了44个基线变量.使用基于混合数据因子分析(FAMD)的分层k均值聚类进行现象映射。通过比较表型群体中首次适当休克和全因死亡的基线特征和结果,验证了表型定位的实用性。
■在中位随访2.7年的设备询问和5.1年的生存状态,142(36.5%)首次适当电击和113(29.0%)全因死亡。使用FAMD提取的前12个主成分,解释了总变异性的60.5%,被留下来进行表型分析。确定了三个相互排斥的表型群。表型组1包括年龄最大的缺血性心肌病患者;糖尿病比例最高,高血压,和高脂血症;在表型群中具有最有利的心脏结构和功能。Phenogroup2包括最年轻的患者,大多数是非缺血性心肌病患者,具有中等心脏尺寸和功能的人,和最少的合并症。表型群3具有最差的HF进展。Kaplan-Meier曲线显示,在表型组之间,第一次适当的休克(p=0.002)和全因死亡(p<0.001)存在显着差异。在Cox回归中调整药物后,与表型组1相比,表型组2和3显示了适当休克风险的分级增加(风险比[HR]1.54,95%置信区间[CI]1.03-2.28,p=0.033;HR2.21,95%CI1.42-3.43,p<0.001;趋势<0.001)。关于死亡风险,表型组3与风险增加相关(HR2.25,95%CI1.45-3.49,p<0.001)。相比之下,表型群2具有与表型群1相当的风险(p=0.124)。
■基于机器学习的表型分析可以识别临床异质性HF患者采用二级预防性ICD治疗的不同表型亚组。这种新策略可能有助于这些患者的个性化医疗。
UNASSIGNED: Previous studies have failed to implement risk stratification in patients with heart failure (HF) who are eligible for secondary implantable cardioverter-defibrillator (ICD) implantation. We aimed to evaluate whether machine learning-based phenomapping using routinely available clinical data can identify subgroups that differ in characteristics and prognoses.
UNASSIGNED: A total of 389 patients with chronic HF implanted with an ICD were included, and forty-four baseline variables were collected. Phenomapping was performed using hierarchical k-means clustering based on factor analysis of mixed data (FAMD). The utility of phenomapping was validated by comparing the baseline features and outcomes of the first appropriate shock and all-cause death among the phenogroups.
UNASSIGNED: During a median follow-up of 2.7 years for device interrogation and 5.1 years for survival status, 142 (36.5%) first appropriate shocks and 113 (29.0%) all-cause deaths occurred. The first 12 principal components extracted using the FAMD, explaining 60.5% of the total variability, were left for phenomapping. Three mutually exclusive phenogroups were identified. Phenogroup 1 comprised the oldest patients with ischemic cardiomyopathy; had the highest proportion of diabetes mellitus, hypertension, and hyperlipidemia; and had the most favorable cardiac structure and function among the phenogroups. Phenogroup 2 included the youngest patients, mostly those with non-ischemic cardiomyopathy, who had intermediate heart dimensions and function, and the fewest comorbidities. Phenogroup 3 had the worst HF progression. Kaplan-Meier curves revealed significant differences in the first appropriate shock (p = 0.002) and all-cause death (p < 0.001) across the phenogroups. After adjusting for medications in Cox regression, phenogroups 2 and 3 displayed a graded increase in appropriate shock risk (hazard ratio [HR] 1.54, 95% confidence interval [CI] 1.03-2.28, p = 0.033; HR 2.21, 95% CI 1.42-3.43, p < 0.001, respectively; p for trend < 0.001) compared to phenogroup 1. Regarding mortality risk, phenogroup 3 was associated with an increased risk (HR 2.25, 95% CI 1.45-3.49, p < 0.001). In contrast, phenogroup 2 had a risk (p = 0.124) comparable with phenogroup 1.
UNASSIGNED: Machine-learning-based phenomapping can identify distinct phenotype subgroups in patients with clinically heterogeneous HF with secondary prophylactic ICD therapy. This novel strategy may aid personalized medicine for these patients.