Development of an electronic frailty index for predicting mortality and complications analysis in pulmonary hypertension using random survival forest model
Journal article
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 and Zhang, Qingpeng 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, p. 735906. https://doi.org/10.3389/fcvm.2022.735906
| Authors | 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 and Zhang, Qingpeng |
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| Abstract | 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. [Abstract copyright: Copyright © 2022 Zhou, Chou, Wong, Lee, Leung, Liu, Cheung, Wong, Tse and Zhang.] |
| Keywords | Electronic frailty index; Cardiovascular disease; Renal complications; Pulmonary hypertension; Diabetes mellitus; Random survival forest (RSF) |
| Year | 2022 |
| Journal | Frontiers in Cardiovascular Medicine |
| Journal citation | 9, p. 735906 |
| Publisher | Frontiers |
| ISSN | 2297-055X |
| Digital Object Identifier (DOI) | https://doi.org/10.3389/fcvm.2022.735906 |
| Official URL | https://www.frontiersin.org/articles/10.3389/fcvm.2022.735906/full |
| Publication dates | |
| Online | 08 Jul 2022 |
| Publication process dates | |
| Accepted | 20 Apr 2022 |
| Deposited | 08 Aug 2022 |
| Publisher's version | License |
| Output status | Published |
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https://repository.canterbury.ac.uk/item/91z9x/development-of-an-electronic-frailty-index-for-predicting-mortality-and-complications-analysis-in-pulmonary-hypertension-using-random-survival-forest-model
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