Development and validation of a Prediction Model for Chronic Thromboembolic Pulmonary Disease - Guixiang Liu; Jing Wen; Chunyi Lv; Mingjie Liu; Min Li; Kexia Fang; Jianwen Fei; Nannan Zhang; Xuehua Li; Huarui Wang; Yuanyuan Sun; Ling Zhu (Respiratory Research)
Abstract Background Acute pulmonary embolism (APE) is a critical disease with a high mortality rate, some of the surviving patients may develop chronic thromboembolic pulmonary disease (CTEPD), which affects the patient’s prognosis. However, the research on the early diagnosis of CTEPD is limited. This study aimed to establish a prediction model for earlier identification of CTEPD. Methods This prospective study included 464 consecutive patients with APE confirmed between January 2020 and September 2023, at 7 centers from China. After follow-up for at least 3 months, the patients were divided into the CTEPD and non-CTEPD groups based on symptoms and computed tomography pulmonary angiography (CTPA) or pulmonary ventilation perfusion (V/Q) scans showing residual thrombosis. The independent risk factors for CTEPD were identified via univariate and multivariate logistic regression analyses. Next, a nomogram of predictive model was established, and validation was completed via decision curve analysis (DCA) and receiver operating characteristic curve analysis. Result In total, 130 (28%) patients presented with CTEPD, 17% (22/130) of CTEPD patients developed chronic thromboembolic pulmonary hypertension (CTEPH). Based on the multivariate analysis, a time interval from symptoms onset to diagnosis (time-to-diagnosis) ≥ 15 days (95% confidence interval [CI]: 3.392–14.972, p < 0.001), recurrent pulmonary embolism (RPE) (95%CI: 1.560–17.300, p = 0.007), right ventricular dysfunction (RVD) (95%CI: 1.042–6.437, p = 0.040), central embolus (95%CI: 1.776–7.383, p < 0.001) and residual pulmonary vascular obstruction (RPVO) > 10% (95%CI: 4.884–21.449, p < 0.001) were identified as the independent predictors of CTEPD. Then, A prediction model with a C-index of 0.895 (95% CI 0.863–0.927) was established for high-risk patients. The nomogram had an excellent predictive performance for earlier identification of CTEPD, with an area under the curve of 0.908 (95%CI: 0.875–0.941) in the training cohort and 0.875 (95%CI: 0.803–0.947) in the validation cohort. Conclusion The current study established and validated a reliable nomogram for predicting CTEPD, which would assist clinicians identify the high-risk patients for CTEPD earlier.