Dynamic-Nomogram-Asthma

Dynamic-Nomogram-Asthma predicts individual risk of asthma attacks in adults using National Health and Nutrition Examination Survey (NHANES) 2013–2018 data and statistical modeling to provide personalized risk assessments.

Key Features:

• Data Source and Sample Size: Based on National Health and Nutrition Examination Survey (NHANES) data from 2013 to 2018 comprising 597 subjects meeting specified criteria including age, smoking history, and familial asthma background.
• Predictors Identified: Independent predictors identified include smoking for 40 years or more, female gender, age at first cigarette use, and having a close relative with asthma.
• Statistical Modeling: Utilizes simple and multiple logistic regression analyses to develop the predictive model and construct the nomogram.
• Model Development and Validation: Data were split into training and testing sets with a 4:6 ratio for model development and validation.
• Performance Metrics: Performance was evaluated using Receiver Operating Characteristic (ROC) curves with Area Under the Curve (AUC) values of 0.726 (training) and 0.702 (testing) and reported high sensitivity.
• Calibration and Clinical Utility: Calibration curves assessed agreement between predicted and observed outcomes and decision curve analysis evaluated clinical utility.

Scientific Applications:

• Clinical risk stratification: Provides personalized probability estimates of asthma attacks to support clinician decision-making and management strategies.
• Epidemiological investigation: Integrates demographic, behavioral, and familial variables from NHANES to quantify associations and identify risk factors for asthma.

Methodology:

Simple and multiple logistic regression, dataset splitting into training and testing sets at a 4:6 ratio, Receiver Operating Characteristic (ROC) curve analysis with AUC reporting, calibration curves, and decision curve analysis.