Clinical and Predictive Value of Computed Tomography Angiography in High-Altitude Pulmonary Hypertension

High-altitude pulmonary hypertension: a comprehensive review of mechanisms and management

High-altitude pulmonary hypertension (HAPH) is characterized by an increase in pulmonary artery pressure due to prolonged exposure to hypoxic environment at high altitudes. The development of HAPH involves various factors such as pressure changes, inflammation, oxidative stress, gene regulation, and signal transduction. The pathophysiological mechanisms of this condition operate at molecular, cellular, and genetic levels. Diagnosis of HAPH often relies on echocardiography, cardiac catheterization, and other methods to assess pulmonary artery pressure and its impact on cardiac function. Treatment options for HAPH encompass both nondrug and drug therapies. While advancements have been made in understanding the pathological mechanisms through research on animal models and clinical trials, there are still limitations to be addressed. Future research should focus on exploring molecular targets, personalized medicine, long-term management strategies, and interdisciplinary approaches. By leveraging advanced technologies like systems biology, omics technology, big data, and artificial intelligence, a comprehensive analysis of HAPH pathogenesis can lead to the identification of new treatment targets and strategies, ultimately enhancing patient quality of life and prognosis. Furthermore, research on health monitoring and preventive measures for populations living at high altitudes should be intensified to reduce the incidence and mortality of HAPH.

A novel clinical prediction scoring system of high-altitude pulmonary hypertension

Background

High-altitude pulmonary hypertension (HAPH) is a common disease in regions of high altitude where performing right heart catheterization (RHC) is challenging. The development of a diagnostic scoring system is crucial for effective disease screening.

Methods

A total of 148 individuals were included in a retrospective analysis, and an additional 42 residents were prospectively enrolled. We conducted a multivariable analysis to identify independent predictors of HAPH. Subsequently, we devised a prediction score based on the retrospective training set to anticipate the occurrence and severity of HAPH. This scoring system was further subjected to validation in the prospective cohort, in which all participants underwent RHC.

Results

This scoring system, referred to as the GENTH score model (Glycated hemoglobin [OR = 4.5], Echocardiography sign [OR = 9.1], New York Heart Association-functional class [OR = 12.5], Total bilirubin [OR = 3.3], and Hematocrit [OR = 3.6]), incorporated five independent risk factors and demonstrated strong predictive accuracy. In the training set, the area under the curve (AUC) values for predicting the occurrence and severity of HAPH were 0.851 and 0.832, respectively, while in the validation set, they were 0.841 and 0.893. In the validation set, GENTH score model cutoff values of ≤18 or >18 points were established for excluding or confirming HAPH, and a threshold of >30 points indicated severe HAPH.

Conclusions

The GENTH score model, combining laboratory and echocardiography indicators, represents an effective tool for distinguishing potential HAPH patients and identifying those with severe HAPH. This scoring system improves the clinical screening of HAPH diseases and offers valuable insights into disease diagnosis and management.