A Multimodal Prediction Model for Diagnosing Pulmonary Hypertension in Systemic Sclerosis

Objective: Diagnosis of pulmonary hypertension (PH) in systemic sclerosis (SSc) requires an invasive right heart catheterization (RHC), often based on an elevated estimated pulmonary artery systolic pressure on screening echocardiography. However, because of the poor specificity of echocardiography, a greater number of patients undergo RHC than necessary, exposing patients to potentially avoidable complication risks. The development of improved prediction models for PH in SSc may inform decision-making for RHC in these patients.

Methods: We conducted a retrospective study of 130 patients with SSc; 66 (50.8%) were diagnosed with PH by RHC. We used data from pulmonary function testing, electrocardiography, echocardiography, and computed tomography to identify and compare the performance characteristics of 3 models predicting the presence of PH: 1) random forest, 2) classification and regression tree, and 3) logistic regression. For each model, we generated receiver operating curves and calculated sensitivity and specificity. We internally validated models using a train-test split of the data.

Results: The random forest model performed best with an area under the curve of 0.92 (95% confidence interval [95% CI] 0.83-1.00), sensitivity of 0.95 (95% CI 0.75-1.00), and specificity of 0.80 (95% CI 0.56-0.94). The 2 most important variables in our random forest model were pulmonary artery diameter on chest computed tomography and diffusing capacity for carbon monoxide on pulmonary function testing.

Conclusions: In patients with SSc, a random forest model can aid in the detection of PH with high sensitivity and specificity and may allow for better patient selection for RHC, thereby minimizing patient risk.