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
Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Frailty Assessment Unit, Cardiovascular Analytics Group, Hong Kong, Hong Kong SAR, China.
Front Cardiovasc Med. 2022 Jul 8;9:735906. doi: 10.3389/fcvm.2022.735906. eCollection 2022.
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.
新诊断的肺动脉高压患者的心脏代谢和肾脏并发症的长期预后以及死亡率尚不清楚。本研究旨在开发一种可扩展的预测模型，即电子虚弱指数（eFI），以预测不同的不良结局。
这是一项基于人群的队列研究，研究对象为2000年1月1日至2017年12月31日期间在香港公立医院被诊断为肺动脉高压的患者。主要结局为死亡率、心血管并发症、肾脏疾病和糖尿病。采用单变量和多变量Cox回归分析来识别显著的危险因素，并将其输入非参数随机生存森林（RSF）模型以开发eFI。
共纳入2560例患者，平均年龄63.4岁（四分位间距：38.0 - 79.0）。在随访期间，1347例患者死亡，1878例、437例和684例患者分别发生心血管并发症、糖尿病和肾脏疾病。RSF模型确定的年龄、平均再入院次数、抗高血压药物、累计住院时间和总胆红素是预测死亡率的最重要危险因素。包括诊断年龄、平均再入院间隔和累计住院时间在内的因素之间的两两相互作用对于死亡率预测也至关重要。发生全因死亡的患者的eFI值高于存活患者（<0.0001）。eFI≥9.5与死亡风险增加相关[风险比（HR）：1.90；95%置信区间（CI）：1.70 - 2.12；<0.0001]。65岁及以上且eFI≥9.5的患者的累积风险更高。使用相同的截断点，eFI预测10年以上的长期死亡率（HR：1.71；95%CI：1.53 - 1.90；<0.0001）。与多变量Cox回归相比，RSF在结局预测中的精度、召回率、曲线下面积（AUC）和C指数显著更高。
RSF模型识别出了并发症发生和死亡的新危险因素及相互作用。由RSF构建的eFI能够准确预测肺动脉高压患者的并发症和死亡率，尤其是在老年人中。
