A predictive model for dilated cardiomyopathy with pulmonary hypertension. (2nd August 2021)

• Record Type: Journal Article
• Authors: Liang, Jiahua; Zhu, Ruochen; Yang, Yi; Li, Rong; Hong, Chuangxiong; Luo, Chuanjin
• Is Part Of: ESC heart failure. Volume 8:Number 5(2021)
• Page Start: 4255
• Page End: 4264
• Publication Date: 2021-08-02
• Subjects: Dilated cardiomyopathy; Pulmonary hypertension; Predictive model; Echocardiographic; Heart failure -- Periodicals; 616.129005
• DOI: 10.1002/ehf2.13535
• Languages: English
• ISSNs: 2055-5822

Abstract

Abstract: Aims: Dilated cardiomyopathy (DCM) is defined as a serious cardiac disorder caused by the presence of left ventricular dilatation and contractile dysfunction in the absence of severe coronary artery disease and abnormal loading conditions. The incidence of cardiac death is markedly higher in patients with DCM with pulmonary hypertension (PH) than in DCM patients without PH. No previous studies have constructed a predictive model to predict PH in patients with DCM. Methods: Data from 218 DCM patients (68.3% man; mean age 57.33) were collected. Patients were divided into low, intermediate and high PH‐risk groups based on the echocardiographic assessment at the tricuspid regurgitation peak velocity (TRV) in conjunction with the presence of echocardiographic signs from at least two different categories. Basic information, vital signs, comorbidities and biochemical data of each patient were determined. The impact of each parameter on PH probability was analysed by univariable and multivariable analyses, the data from which were employed to establish a predictive model. Finally, the discriminability, calibration ability and clinical efficacy of the model were verified for both the modelling group and the external validation group. Results: We successfully applied a history of chronic obstructive pulmonary disease (COPD) or chronic bronchitis, systolic murmur (SM) at the tricuspid area, SM at the apex and brain natriuretic peptide (BNP) level to establish a model for predicting PH probability in DCM. The model was proven to have high accuracy and good discriminability (area under the receiver operating characteristic curve 0.889), calibration ability and clinical application value. Conclusions: A model for predicting PH probability in patients with DCM was successfully established. The new model is reliable for predicting PH probability in DCM and has good clinical applicability.