A Nomogram for Predicting the Risk of Pulmonary Hypertension for Patients with Chronic Obstructive Pulmonary Disease.

Background: Pulmonary hypertension (PH) is a life-threatening complication of chronic obstructive pulmonary disease (COPD). Timely diagnosis of PH in COPD patients is vital to achieve proper treatment; however, there is no algorithm to identify those at high risk. We aimed to develop a predictive model for PH in patients with COPD that provides individualized risk estimates. Methods: A total of 527 patients with COPD who were admitted to our hospital between May 2019 and December 2020 were retrospectively enrolled in this study. Using echocardiographic results as a standard, patients were stratified into a moderate- or high-PH probability group and a low-PH probability group. They were randomly grouped into either the training set (n = 368 patients) or validation set (n = 159 patients) in a ratio of 7:3. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to select the feature variables. The characteristic variables selected in the LASSO regression were analyzed using multivariable logistic regression to construct the predictive model. The predictive model was displayed using a nomogram. We used the receiver operating characteristic curve, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance, and internal validation was assessed. Results: The predictive factors included in the prediction model were Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage, emphysema, PaCO Conclusion: This study showed that a simple nomogram could be used to calculate the risk of PH in patients with COPD which can be useful for the individualized clinical management of COPD patients who may be occur with PH. Further studies need to be confirmed by larger sample sizes and validated in the stable COPD population.