Prediction of pulmonary hypertension in older adults based on vital capacity and systolic pulmonary artery pressure

Supplementation with Tex261 provides a possible preventive treatment for hypoxic pulmonary artery hypertension

Objectives: Pulmonary artery hypertension (PAH) is a serious disease for which there is no effective treatment. Its pathogenesis is complex and has not yet been clarified. Tex261 is a protein-coding gene whose functional enrichment nodes include the transporter activity of COP II. However, the role of Tex261 in PAH remains unknown. Methods: Sugen5416/Hypoxic PAH models were established, and pulmonary arteries (PAs) were isolated for proteomic sequencing. The binding sites between Hif-1α and Tex261 were verified by dual-luciferase reporter gene assay. Cell proliferation was detected by MTS and EdU assays. For determination of the preventive and therapeutic effects of Tex261, intratracheal instillation of adeno-associated virus (AVV6) with Tex261 vectors was performed. Results: Tex261 was screened according to the proteomic sequencing data. Hif-1α inhibited Tex261 promoter activity under hypoxia. Decreased Tex261 expression promoted PASMC proliferation. Tex261 regulated Sec23 via the Ndrg1-mediated Akt pathway. Tex261 overexpression improved the pressure and vessel remodeling of PAs induced by Sugen5416/hypoxia. Conclusion: Hypoxia suppressed Tex261 expression through Hif-1α activation. The decreased Tex261 could promote Ndrg1 and depress Akt activity and then inhibit Sec23 activity, which leads to cell proliferation and vessel remodeling. Elevated Tex261 has some preventive and therapeutic effects on rats with PAH.

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

Background

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.

Methods

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.

Results

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 (P < 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; P < 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; P < 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.

Conclusion

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.