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Development of an Electronic Frailty Index for Predicting Mortality and Complications Analysis in Pulmonary Hypertension Using Random Survival Forest Model

Zhou, Jiandong, Chou, Oscar Hou In, Wong, Ka Hei Gabriel, Lee, Sharen, Leung, Keith Sai Kit, Liu, Tong, Cheung, Bernard Man Yung, Wong, Ian Chi Kei, Tse, Gary, Zhang, Qingpeng and others. (2022) Development of an Electronic Frailty Index for Predicting Mortality and Complications Analysis in Pulmonary Hypertension Using Random Survival Forest Model. Frontiers in Cardiovascular Medicine, 9 . Article Number 735906. ISSN 2297-055X. (doi:10.3389/fcvm.2022.735906) (KAR id:96130)


The long-term prognosis of the cardio-metabolic and renal complications, in addition to mortality in patients with newly diagnosed pulmonary hypertension, are unclear. This study aims to develop a scalable predictive model in the form of an electronic frailty index (eFI) to predict different adverse outcomes. This was a population-based cohort study of patients diagnosed with pulmonary hypertension between January 1st, 2000 and December 31st, 2017, in Hong Kong public hospitals. The primary outcomes were mortality, cardiovascular complications, renal diseases, and diabetes mellitus. The univariable and multivariable Cox regression analyses were applied to identify the significant risk factors, which were fed into the non-parametric random survival forest (RSF) model to develop an eFI. A total of 2,560 patients with a mean age of 63.4 years old (interquartile range: 38.0-79.0) were included. Over a follow-up, 1,347 died and 1,878, 437, and 684 patients developed cardiovascular complications, diabetes mellitus, and renal disease, respectively. The RSF-model-identified age, average readmission, anti-hypertensive drugs, cumulative length of stay, and total bilirubin were among the most important risk factors for predicting mortality. Pair-wise interactions of factors including diagnosis age, average readmission interval, and cumulative hospital stay were also crucial for the mortality prediction. Patients who developed all-cause mortality had higher values of the eFI compared to those who survived ( < 0.0001). An eFI ≥ 9.5 was associated with increased risks of mortality [hazard ratio (HR): 1.90; 95% confidence interval [CI]: 1.70-2.12; < 0.0001]. The cumulative hazards were higher among patients who were 65 years old or above with eFI ≥ 9.5. Using the same cut-off point, the eFI predicted a long-term mortality over 10 years (HR: 1.71; 95% CI: 1.53-1.90; < 0.0001). Compared to the multivariable Cox regression, the precision, recall, area under the curve (AUC), and C-index were significantly higher for RSF in the prediction of outcomes. The RSF models identified the novel risk factors and interactions for the development of complications and mortality. The eFI constructed by RSF accurately predicts the complications and mortality of patients with pulmonary hypertension, especially among the elderly.

Item Type: Article
DOI/Identification number: 10.3389/fcvm.2022.735906
Additional information: ** From PubMed via Jisc Publications Router ** History: received 03-07-2021; accepted 20-04-2022.
Uncontrolled keywords: electronic frailty index, cardiovascular disease, renal complications, pulmonary hypertension, diabetes mellitus, random survival forest (RSF)
Subjects: R Medicine
Divisions: Divisions > Division of Natural Sciences > Kent and Medway Medical School
Funders: National Natural Science Foundation of China (
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 22 Nov 2022 15:26 UTC
Last Modified: 04 Mar 2024 16:07 UTC
Resource URI: (The current URI for this page, for reference purposes)

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