The present study aimed to compare the predictive acuity of latent class regression (LCR) modelling with: standard generalised linear modelling (GLM); and GLMs that include the membership of subgroups/classes (identified through prior latent class analysis; LCA) as alternative or additional candidate predictors. Using real world demographic and clinical data from 1,802 heart failure patients enrolled in the UK-HEART2 cohort, the study found that univariable GLMs using LCA-generated subgroup/class membership as the sole candidate predictor of survival were inferior to standard multivariable GLMs using the same four covariates as those used in the LCA. The inclusion of the LCA subgroup/class membership together with these four covariates as candidate predictors in a multivariable GLM showed no improvement in predictive acuity. In contrast, LCR modelling resulted in a 10-14% improvement in predictive acuity and provided a range of alternative models from which it would be possible to balance predictive acuity against entropy to select models that were optimally suited to improve the efficient allocation of clinical resources to address the differential risk of the outcome (in this instance, survival). These findings provide proof-of-principle that LCR modelling can improve the predictive acuity of GLMs and enhance the clinical utility of their predictions. These improvements warrant further attention and exploration, including the use of alternative techniques (including machine learning algorithms) that are also capable of generating latent class structure while determining outcome predictions, particularly for use with large and routinely collected clinical datasets, and with binary, count and continuous variables.

中文翻译：

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潜在类别回归改善慢性心力衰竭患者生存预测的预测力和临床效用

本研究旨在比较潜在类别回归（LCR）建模与以下各项的预测敏锐度：标准广义线性建模（GLM）；包括子组/类的成员资格（通过先前的潜在类分析； LCA识别）的GLM作为替代或其他候选预测变量。使用来自UK-HEART2队列的1,802名心力衰竭患者的真实人口统计和临床数据，研究发现，使用LCA生成的亚组/类别成员作为生存的唯一候选预测指标的单变量GLM不如使用相同方法的标准多变量GLM LCA中使用的四个协变量。在多变量GLM中将LCA子组/类别成员以及这四个协变量作为候选预测变量纳入研究表明，预测敏锐度没有改善。相反，LCR建模使预测敏锐度提高了10-14％，并提供了一系列可供选择的模型，从中可以平衡预测敏锐度和熵，从而选择最适合改善临床资源有效分配以解决疾病的模型。结果的差异风险（在这种情况下为生存）。这些发现为LCR建模可以改善GLM的预测敏锐度并增强其预测的临床实用性提供了原理证明。这些改进值得进一步关注和探索，包括使用其他技术（包括机器学习算法），这些技术还可以在确定结果预测的同时生成潜在的类结构，特别是用于大型且常规收集的临床数据集时，