Predictive model for depression risk in CKD patients using NHANES data (2005-2018)

Area of Science:

• Nephrology
• Psychiatry
• Biostatistics

Background:

• Chronic kidney disease (CKD) is associated with an increased prevalence of depression.
• Accurate prediction of depression risk is crucial for timely intervention in CKD patients.

Purpose of the Study:

• To develop and validate a nomogram model for forecasting the likelihood of depression in individuals with CKD.
• To identify key predictive factors associated with depression in the CKD population.

Main Methods:

• Utilized data from 3858 CKD patients from the National Health and Nutrition Examination Survey (2005-2018).
• Employed Least Absolute Shrinkage and Selection Operator (LASSO) regression for variable selection.
• Constructed a predictive nomogram using multivariable logistic regression and validated its performance.

Main Results:

• The nomogram incorporated 8 significant factors: age, gender, BMI, smoking status, sleep duration, poverty-to-income ratio, cardiovascular disease, and diabetes mellitus.
• The model demonstrated strong discriminative ability with an AUC of 0.735 (training set) and 0.781 (validation set).
• The nomogram showed good calibration and clinical utility in both datasets.

Conclusions:

• The developed nomogram effectively predicts depression risk in CKD patients.
• This tool can assist healthcare providers in identifying at-risk individuals and optimizing management strategies.
• Early assessment of depression risk can lead to improved patient outcomes in chronic kidney disease.