Identifying right heart failure patients from at-risk individuals using machine learning approaches: Insights from the Right HEart fAilure (RHEA) study. 2024 (unpublished)
Use in patients with clinical manifestations similar to RHF who are PH and at risk of progressing to RHF, to assess the probability of developing RHF.
RHF is difficult to discriminate especially in specific settings such as primary care and emergency department.
This score offers an evidence-based way to identify patients likely to have RHF.
Identifying right heart failure patients from at-risk individuals using machine learning approaches: Insights from the Right HEart fAilure (RHEA) study. 2024 (unpublished)
RHEA Score is derived from the data of RHEA cohort and proposed for the discrimination of RHF from PH. The cohort contains a total of 802 RHF patients, PH patients and normal controls confirmed by right heart catheterization and echocardiography from 32 provinces and areas in China. RHEA Score was developed with a machine learning approach relying on available biomarkers. To get the probability of RHF, only 5 predictors including NT-proBNP, GGT, RDW, AGE and NSE, are needed.
This work was supported by grants from National High Level Hospital Clinical Research Funding (NO. 2023-NHLHCRF-YYPPLC-ZR-05, NO. 2022-NHLHCRF-YXHZ-01, NO. 2022-NHLHCRF-LX-02-0102, 2024-NHLHCRF-PYII-15), National Science and Technology Major Project (2023ZD0502805), Capital’s Funds for Health Improvement and Research (NO. 2024-1-4061), Beijing Nova Program (NO. 20220484171), and National Natural Science Foundation of China.
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Identifying right heart failure patients from at-risk individuals using machine learning approaches: Insights from the Right HEart fAilure (RHEA) study. 2024 (unpublished)