Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease

Background The exact risk assessment is crucial for the management of connective tissue disease-associated interstitial lung disease (CTD-ILD) patients. In the present study, we develop a nomogram to predict 3‑ and 5-year mortality by using machine learning approach and test the ILD-GAP model in Chinese CTD-ILD patients. Methods CTD-ILD patients who were diagnosed and treated at the First Affiliated Hospital of Zhengzhou University were enrolled based on a prior well-designed criterion between February 2011 and July 2018. Cox regression with the least absolute shrinkage and selection operator (LASSO) was used to screen out the predictors and generate a nomogram. Internal validation was performed using bootstrap resampling. Then, the nomogram and ILD-GAP model were assessed via likelihood ratio testing, Harrell’s C index, integrated discrimination improvement (IDI), the net reclassification improvement (NRI) and decision curve analysis. Results A total of 675 consecutive CTD-ILD patients were enrolled in this study, during the median follow-up period of 50 (interquartile range, 38–65) months, 158 patients died (mortality rate 23.4%). After feature selection, 9 variables were identified: age, rheumatoid arthritis, lung diffusing capacity for carbon monoxide, right ventricular diameter, right atrial area, honeycombing, immunosuppressive agents, aspartate transaminase and albumin. A predictive nomogram was generated by integrating these variables, which provided better mortality estimates than ILD-GAP model based on the likelihood ratio testing, Harrell’s C index (0.767 and 0.652 respectively) and calibration plots. Application of the nomogram resulted in an improved IDI (3- and 5-year, 0.137 and 0.136 respectively) and NRI (3- and 5-year, 0.294 and 0.325 respectively) compared with ILD-GAP model. In addition, the nomogram was more clinically useful revealed by decision curve analysis. Conclusions The results from our study prove that the ILD-GAP model may exhibit an inapplicable role in predicting mortality risk in Chinese CTD-ILD patients. The nomogram we developed performed well in predicting 3‑ and 5-year mortality risk of Chinese CTD-ILD patients, but further studies and external validation will be required to determine the clinical usefulness of the nomogram.

Aortic Root Dimensions and Pulse Wave Velocity in Young Competitive Athletes

The assessment of aortic root dimensions is a cornerstone in cardiac pre-participation screening as dilation can result in severe cardiac events. Moreover, it can be a hint for an underlying connective tissue disease, which needs individualized sports counseling. This study examines the prevalence of aortic root dilatation in a cohort and its relationship to arterial stiffness as an early marker of cardiovascular risk due to vascular aging. From May 2012 to March 2018, we examined 281 young male athletes (14.7 ± 2.1 years) for their aortic root dimension. Moreover, we noninvasively assessed arterial stiffness parameter during pre-participation screening. Mean aortic diameter was 25.9 ± 3.1 mm and 18 of the 281 (6.4%) athletes had aortic root dilation without other clinical evidence of connective tissue disease. After adjusting for BSA, there was no association of aortic root diameter to pulse wave velocity (p = −0.054 r = 0.368) nor to central blood pressure (p = −0.029 r = 0.634). Thus, although a significant proportion of young athletes had aortic root dilatation, which certainly needs regular follow up, no correlation with arterial stiffness was found. It could be suggested that a dilated aortic root in young athletes does not alter pulse waveform and pulse reflection, and thus there is no increased cardiovascular risk in those subjects.