Use of Machine-Learning Models to Identify Clinical Features Associated With A Future Clinical Worsening Event in Patients With Pulmonary Arterial Hypertension

Hilary DuBrock, Xiaoqin Tang, Gurinderpal Doad, Jenny Lam, Michelle Cho, Karthik Murugadoss, Deeksha Doddahonnaiah, Tyler E. Wagner

https://doi.org/10.1002/pul2.70294 
 

Abstract

Clinical worsening events are increasingly recognized as a meaningful outcome in pulmonary arterial hypertension (PAH). We applied machine-learning models to real-world data to identify clinical features that may predict clinical worsening events in PAH. Data were obtained retrospectively from the electronic health records of adults diagnosed with PAH at Mayo Clinic locations (January 2015–December 2019). Machine-learning LASSO regularized logistic regression models were developed to analyze the association between 100 clinical features and occurrence of clinical worsening events. In total, 455 patients were included (mean age 62.1 years; 59.3% female). Of these, 232 (51.0%) experienced a clinical worsening event after a median (quartile 1, quartile 3) of 10.9 (4.1, 21.4) months. The best-performing model had an area under the curve of 0.78, sensitivity of 78%, and specificity of 59%. The model identified 11 clinical features associated with clinical worsening events in PAH with a non-zero coefficient: baseline age, body mass index (BMI), creatinine level, red cell (erythrocyte) distribution width (RDW), mean pulmonary arterial pressure, pulmonary vascular resistance, sodium concentration, and QT interval, along with dyspnea, number of clinic visits, and other forms of heart disease within a 1-month window before a clinical worsening event. Baseline RDW and BMI, and number of clinic visits within the 1-month window were statistically significantly associated with clinical worsening events. Our study used machine-learning models to identify clinical features associated with risk of clinical worsening events in PAH. Automation of risk prediction could lead to earlier therapeutic intervention to optimize patient outcomes.

Read the full research article