Development of a nomogram for severe costal cartilage calcification: a cohort study

There was few research on the causes or factors of costal cartilage calcification. Thus, this study aimed to develop a nomogram to predict the possibility of severe costal cartilage calcification.

The cohort study included 882 patients who underwent computed tomographic (CT) scan of the chest with three-dimensional reconstructions and have completed the questionnaire between June 2020 to October 2021. The potential variables were included age, sex, body mass index (BMI), nation, residence, smoking, drinking, bedtime, exercise, self-pressure, the frequencies of milk, yogurt, carbonated drink, meat, fish, sea food, fruits intake, hypertension, diabetes, thyroid or parathyroid disease and osteoarthritis. We used the least absolute shrinkage and selection operator (LASSO) regression model to identify the optimal predictive features. Multivariable logistic regression analysis was used to build the predicting model. The receiver operating characteristic (ROC) curve and concordance index (C-index) were used to evaluate the accuracy of the nomogram. Moreover, decision curve analysis (DCA) was to estimate the clinical usefulness of the nomogram. Finally, we adopted internal validation of enhanced bootstrap adjustment for optimism to predict the C-index of the model.

The predictive nomogram contained 11 clinical features using LASSO regression, and "Sex" and "Age" were the most important factor due to the most significant proportion. The model showed good discrimination with a C-index of 0.805, and the internal validation was 0.735. DCA (the threshold probability of a patient is 1-77%) and the ROC curve [the area under ROC (AUC) was 0.805, and the sensitivity and specificity were 0.647 and 0.894] demonstrated that the nomogram was possibly clinically helpful.

This is the first study to develop a nomogram to predict severe costal cartilage. Use of this nomogram might help patients prevent calcification by changing living habits.