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.
Background: 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. However, no previous studies have constructed a predictive model to predict PH in patients with DCM.Methods: Data from 218 DCM patients were collected. The diagnostic criterion for PH by echocardiography was a pulmonary artery systolic pressure (PASP) ≥ 40 mmHg. Basic information, vital signs, comorbidities and biochemical data of each patient were determined. The impact of each parameter on PH 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 in DCM. The model was proven to have high accuracy and good discriminability, calibration ability, and clinical application value.Conclusions: A model for predicting PH in patients with DCM was successfully established. The new model is reliable for predicting DCM with PH and has good clinical applicability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.