Key result
Multiple clinical and socioeconomic factors are linked to depression in peritoneal dialysis patients.
Why the study?
This study aimed to analyze the risk factors for depression in peritoneal dialysis patients and develop a predictive nomogram model for assessing these risks.
Observational (n=430)
p-value: p=< 0.05
A nomogram incorporating demographic and clinical factors can accurately predict the risk of depression in peritoneal dialysis patients.
May aid depression risk assessment in peritoneal dialysis; leaves open whether modifying factors improves outcomes.
OBJECTIVE: This study aims to analyze the risk factors for depression in peritoneal dialysis patients and to develop a predictive nomogram model for assessing these risks. METHODS: A total of 326 peritoneal dialysis patients treated between August 2021 and December 2023 were selected as the training set. These patients were categorized into a non-depression group (229 cases) and a depression group (97 cases) based on the presence of depression. Additionally, 104 peritoneal dialysis patients from the same period were selected as the validation set. Clinical data were collected from all patients for analysis. RESULTS: The depression group had higher proportions of female patients, non-employed individuals, those with a per capita monthly income of <2000-yuan, cardiovascular complications, cerebrovascular complications, and sleep disorders compared to the non-depression group. Additionally, the levels of hemoglobin and blood uric acid were lower in the depression group than in the non-depression group (p < 0.05). Gender, work status, per capita monthly income, cardiovascular complications, cerebrovascular complications, and sleep disorders are risk factors for depression in peritoneal dialysis patients (p < 0.05), while hemoglobin and blood uric acid are protective factors for depression in peritoneal dialysis patients (p < 0.05). Calibration curve analysis results showed that the predicted probability was basically consistent with the actual incidence rate. The results of the Decision Curve Analysis (DCA) demonstrated that the nomogram model developed in this study has strong clinical applicability. CONCLUSION: The nomogram model for predicting depression in peritoneal dialysis patients, which incorporates factors such as gender, work status, per capita monthly income, cardiovascular complications, cerebrovascular complications, sleep disorders, hemoglobin levels, and blood uric acid levels, demonstrates excellent calibration and discrimination. Additionally, it has high clinical applicability.
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A 2025 study conducted an observational in Depression in peritoneal dialysis patients (n=430). Clinical and demographic risk factors (e.g., gender, work status, income, comorbidities) vs. Absence of risk factors was evaluated on Presence of depression (p=< 0.05). Clinical factors including gender, work status, income, comorbidities, sleep disorders, hemoglobin, and uric acid were significant predictors of depression in peritoneal dialysis patients (p<0.05).
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