A dynamic nomogram for predicting the risk of asthma: Development and validation in a database study.

Area of Science:

• Pulmonary Medicine
• Epidemiology
• Biostatistics

Background:

• Asthma prevalence and incidence are increasing globally, posing a significant public health challenge.
• Accurate risk prediction is crucial for effective asthma management and prevention strategies.

Purpose of the Study:

• To develop and validate a dynamic nomogram for predicting the risk of asthma attacks.
• To identify independent predictors associated with asthma risk.

Main Methods:

• Utilized data from 597 adult subjects with asthma from the National Health and Nutrition Examination Survey (NHANES) database (2013-2018).
• Employed logistic regression to identify predictors and developed a nomogram, validated internally using a training/testing split (4:6 ratio).
• Assessed nomogram performance using Receiver Operating Characteristic (ROC) curve analysis and decision curve analysis.

Main Results:

• Independent predictors for asthma risk included smoking duration (≥40 years), female gender, age at first cigarette, and family history of asthma.
• The dynamic nomogram demonstrated good predictive performance with an Area Under the Curve (AUC) of 0.726 in the training set and 0.702 in the testing set.
• Calibration curves showed good fit, and decision curve analysis confirmed clinical utility.

Conclusions:

• The developed dynamic nomogram provides a valuable tool for clinicians to assess individual asthma attack probability.
• This tool can aid in improving asthma treatment strategies and patient prognosis.