Document Type
Article
Publication Date
8-1-2024
Abstract
Background: Screening for depression can be challenging among hemodialysis patients due to the overlap of depressive symptoms with dialysis or kidney disease related symptoms. The aim of this study was to understand these overlapping symptoms and develop a depression screening tool for better clinical assessment of depressive symptoms in dialysis patients. Methods: We surveyed 1,085 dialysis patients between March 1, 2018 and February 28, 2023 at 15 dialysis facilities in Northeast Ohio with the 9-item patient health questionnaire (PHQ-9) and kidney disease quality of life (KDQOL) instrument. To evaluate overlap across questionnaire items, we used structural equation modeling (SEM). We predicted and transformed factor scores to create a hemodialysis-adjusted PHQ-9 (hdPHQ-9). In exploratory analysis (N = 173), we evaluated the performance of the hdPHQ-9 relative to the PHQ-9 that also received a Mini-International Neuropsychiatric Interview. Results: Our study sample included a high percentage of Black patients (74.6%) and 157 (14.5%) survey participants screened positive for depression (PHQ-9 ≥ 10). The magnitude of overlap was small for (respectively, PHQ-9 item with KDQOLTM item) fatigue with washed out, guilt with burden on family, appetite with nausea and movement with lightheaded. The hdPHQ-9 showed reasonably high sensitivity (0.81 with 95% confidence interval [CI] 0.58, 0.95) and specificity (0.84 with 95% CI 0.77, 0.89); however, this was not a significant improvement from the PHQ-9. Conclusion: There is little overlap between depressive symptoms and dialysis or kidney disease symptoms. The PHQ-9 was found to be an appropriate depression screening instrument for dialysis patients.
Keywords
depression screening, differential item functioning, hemodialysis, multiple indicator multiple cause modeling, structural equation modeling
Language
English
Publication Title
Renal Failure
Grant
R01DK112905
Rights
© 2024 The Author(s). This is an Open Access work distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Gunzler DD, Dolata J, Figueroa M, Kauffman K, Pencak J, Sajatovic M, Sehgal AR. Using latent variables to improve the management of depression among hemodialysis patients. Ren Fail. 2024 Dec;46(2):2350767. doi: 10.1080/0886022X.2024.2350767. Epub 2024 Aug 1. PMID: 39091090; PMCID: PMC11299459.