Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, Osaka, Japan.
Division of Biomedical Statistics, Department of Integrated Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan.
ESC Heart Fail. 2022 Aug;9(4):2738-2746. doi: 10.1002/ehf2.13928. Epub 2022 Apr 22.
Application of the latent class analysis to acute heart failure with preserved ejection fraction (HFpEF) showed that the heterogeneous acute HFpEF patients can be classified into four distinct phenotypes with different clinical outcomes. This model-based clustering required a total of 32 variables to be included. However, this large number of variables will impair the clinical application of this classification algorithm. This study aimed to identify the minimal number of variables for the development of optimal subphenotyping model.
This study is a post hoc analysis of the PURSUIT-HFpEF study (N = 1095), a prospective, multi-referral centre, observational study of acute HFpEF [UMIN000021831]. We previously applied the latent class analysis to the PURSUIT-HFpEF dataset and established the full 32-variable model for subphenotyping. In this study, we used the Cohen's kappa statistic to investigate the minimal number of discriminatory variables needed to accurately classify the phenogroups in comparison with the full 32-variable model. Cohen's kappa statistic of the top-X number of discriminatory variables compared with the full 32-variable derivation model showed that the models with ≥16 discriminatory variables showed kappa value of >0.8, suggesting that the minimal number of discriminatory variables for the optimal phenotyping model was 16. The 16-variable model consists of C-reactive protein, creatinine, gamma-glutamyl transferase, brain natriuretic peptide, white blood cells, systolic blood pressure, fasting blood sugar, triglyceride, clinical scenario classification, infection-triggered acute decompensated HF, estimated glomerular filtration rate, platelets, neutrophils, GWTG-HF (Get With The Guidelines-Heart Failure) risk score, chronic kidney disease, and CONUT (Controlling Nutritional Status) score. Characteristics and clinical outcomes of the four phenotypes subclassified by the minimal 16-variable model were consistent with those by the full 32-variable model. The four phenotypes were labelled based on their characteristics as 'rhythm trouble', 'ventricular-arterial uncoupling', 'low output and systemic congestion', and 'systemic failure', respectively.
The phenotyping model with top 16 variables showed almost perfect agreement with the full 32-variable model. The minimal model may enhance the future clinical application of this clustering algorithm.
应用潜在类别分析对射血分数保留的急性心力衰竭（HFpEF）进行分析，结果表明，异质性急性 HFpEF 患者可分为具有不同临床结局的四个不同表型。这种基于模型的聚类需要总共纳入 32 个变量。然而，如此多的变量会损害这种分类算法的临床应用。本研究旨在确定开发最佳亚表型模型所需的最小变量数。
这是前瞻性、多转诊中心、急性 HFpEF 观察性研究 PURSUIT-HFpEF 研究（N=1095）的事后分析。我们之前曾应用潜在类别分析对 PURSUIT-HFpEF 数据集进行分析，并建立了完整的 32 变量亚表型模型。在这项研究中，我们使用 Cohen's kappa 统计量来研究与完整的 32 变量推导模型相比，准确分类表型所需的最小判别变量数。与完整的 32 变量推导模型相比，前-X 个判别变量的 Cohen's kappa 统计量表明，具有≥16 个判别变量的模型的 kappa 值>0.8，表明最佳表型模型的最小判别变量数为 16。该 16 变量模型由 C 反应蛋白、肌酐、γ-谷氨酰转移酶、脑利钠肽、白细胞、收缩压、空腹血糖、甘油三酯、临床情况分类、感染诱发的急性失代偿性心力衰竭、估计肾小球滤过率、血小板、中性粒细胞、GWTG-HF（Get With The Guidelines-Heart Failure）风险评分、慢性肾脏病和 CONUT（控制营养状况）评分组成。通过最小 16 变量模型亚分类的四个表型的特征和临床结局与通过完整的 32 变量模型的特征和临床结局一致。根据其特征，这四个表型分别标记为“节律紊乱”、“心室-动脉解耦”、“低输出和全身充血”和“全身衰竭”。
前 16 个变量的表型模型与完整的 32 变量模型几乎完全一致。最小模型可能会增强这种聚类算法的未来临床应用。
