Geriatrics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Via Paradisa, 2, 56124, Pisa, Italy.
Department of Neurobiology, Care Sciences and Society, Department of Geriatrics Aging Research Center, Karolinska Institutet, Stockholm University, Stockholm, Sweden.
Aging Clin Exp Res. 2023 Dec;35(12):2919-2928. doi: 10.1007/s40520-023-02566-w. Epub 2023 Oct 18.
Machine-learning techniques have been recently utilized to predict the probability of unfavorable outcomes among elderly patients suffering from heart failure (HF); yet none has integrated an assessment for frailty and comorbidity. This research seeks to determine which machine-learning-based phenogroups that incorporate frailty and comorbidity are most strongly correlated with death or readmission at hospital for HF within six months following discharge from hospital.
In this single-center, prospective study of a tertiary care center, we included all patients aged 65 and older discharged for acute decompensated heart failure. Random forest analysis and a Cox multivariable regression were performed to determine the predictors of the composite endpoint. By k-means and hierarchical clustering, those predictors were utilized to phenomapping the cohort in four different clusters.
A total of 571 patients were included in the study. Cluster analysis identified four different clusters according to frailty, burden of comorbidities and BNP. As compared with Cluster 4, we found an increased 6-month risk of poor outcomes patients in Cluster 1 (very frail and comorbid; HR 3.53 [95% CI 2.30-5.39]), Cluster 2 (pre-frail with low levels of BNP; HR 2.59 [95% CI 1.66-4.07], and in Cluster 3 (pre-frail and comorbid with high levels of BNP; HR 3.75 [95% CI 2.25-6.27])).
In older patients discharged for ADHF, the cluster analysis identified four distinct phenotypes according to frailty degree, comorbidity, and BNP levels. Further studies are warranted to validate these phenogroups and to guide an appropriate selection of personalized, model of care.
机器学习技术最近已被用于预测老年心力衰竭（HF）患者不良结局的概率；但尚未将脆弱性和合并症评估纳入其中。本研究旨在确定纳入脆弱性和合并症的基于机器学习的表型群，与出院后 6 个月内 HF 死亡或再入院的相关性最强。
在这项单中心、前瞻性的三级保健中心研究中，我们纳入了所有因急性失代偿性心力衰竭出院的年龄在 65 岁及以上的患者。进行随机森林分析和 Cox 多变量回归，以确定复合终点的预测因素。通过 K-均值和层次聚类，利用这些预测因素对队列进行表型映射，分为四个不同的簇。
共有 571 名患者纳入研究。聚类分析根据脆弱性、合并症负担和 BNP 确定了四个不同的簇。与 Cluster 4 相比，我们发现 Cluster 1（非常脆弱且合并症多；HR 3.53 [95% CI 2.30-5.39]）、Cluster 2（衰弱前且 BNP 水平低；HR 2.59 [95% CI 1.66-4.07]）和 Cluster 3（衰弱前且合并症多且 BNP 水平高；HR 3.75 [95% CI 2.25-6.27]）的患者 6 个月不良结局风险增加。
在因 ADHF 出院的老年患者中，聚类分析根据脆弱程度、合并症和 BNP 水平确定了四个不同的表型。需要进一步研究来验证这些表型群，并指导选择适当的个性化护理模式。
