Diagnostic prediction models of stratifying chronic heart failure patients based on the underlying disease

Aim. To develop classification criteria for stratifying congestive heart failure (CHF) patients based on the underlying disease.
Methods. 61 patients with CHF were recruited in a study. All patients were assigned to three groups according to the underlying disease: patients with coronary artery disease (CAD) (n = 29), patients with arterial hypertension (AH) (n = 19), and those present with dilated cardiomyopathy (DCM) (n = 13). Patients underwent routine clinical examination. Biochemical and inflammatory markers (IL-6, its soluble receptor sIL-6R, and sgp130) were measured in all patients. The Mann-Whitney U test, the Kruskal-Wallis H test, the Pearson χ² test, and Fischer exact test were used to analyze the selected variables. Discriminant analysis was used for generating prediction models. The quality of the models was evaluated with the ROC analysis.
Results. Statistically significant variables identified by the pairwise comparison of patients with CAD and AH, CAD and DCM, AH and DCM were included in the discriminant analysis along with clinically valid parameters. Clinical prediction models of stratifying patients to different etiological groups were based on these parameters. The optimal cut-off values were determined for each model. The area under the ROC curve (AUC) was used to evaluate the quality of the model. The AUC value for CAD and AH groups was 1, for AH and DCM - 72±0.024, and for CAD and DCM - 0.907±0.053.
Conclusion. Diagnostic prediction models were developed using the discriminant analysis. These models allow stratifying CHF patients according to the underlying disease (CAD, AH, and DCM). The ROC curves have confirmed the good classifying quality of the models.

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