OBJECTIVES: Depression frequently occurs among individuals suffering from chronic kidney disease (CKD), diminishing life quality considerably while accelerating the disease course. This study aims to create a predictive model to identify patients with CKD at high risk for depression. DESIGN: Analysis of cross-sectional data. SETTING: US National Health and Nutrition Examination Survey (2007-2014). PARTICIPANTS: A total of 2303 patients with CKD (weighted=17 422 083) with complete data were included in the analysis. OUTCOME MEASURES: We used the least absolute shrinkage and selection operator regression for variable selection and constructed a weighted logistic regression model through stepwise backward elimination based on minimisation of the Akaike information criterion, visualised with a nomogram. Internal validation was conducted using 1000 bootstrap resamples. Model discrimination was assessed using receiver operating characteristic curves, calibration was evaluated using the Hosmer-Lemeshow test and calibration curves, and net benefits and clinical impact were analysed using decision curve analysis and comparative impact chart curves. RESULTS: The final model included 10 predictors: age, gender, poverty income ratio, body mass index, smoking, sleep time, sleep disorder, chest pain, diabetes and arthritis. The model achieved an area under the curve of 0.776 (95% CI 0.745 to 0.806) with good fit (Hosmer-Lemeshow p=0.805). Interventions within the 0.1-0.6 probability range showed significant benefits. CONCLUSION: We have crafted a predictive model with good discriminative power that could potentially help clinicians identify patients with CKD at high risk for depression, thereby facilitating early intervention and improving the prognosis of these patients.