Comparative Safety of Antidepressants in Adults with CKD

Depression and anxiety in people with kidney disease: understanding symptom variability, patient experience and preferences for mental health support

BACKGROUND

Depression and anxiety are commonly experienced by people with chronic kidney disease (CKD). This study aimed to evaluate person- and service-level factors associated with depression and anxiety symptoms. We sought to also understand utilisation of mental health treatments and preferences for future psychological support.

METHODS

An online survey recruited participants from six UK kidney services with varying levels of psychosocial provision. The survey was also advertised on social media. Participants completed screening questionnaires for depression and anxiety, alongside questions about mental health history, self-efficacy, treatment and support. The study included adults (18 years or older) living with CKD (stages 3b and above) or those receiving any form of Kidney Replacement Therapy (KRT), including individuals with a functioning kidney transplant. Eligible participants had to complete study measures and be proficient in reading and writing in either English or Welsh, as the survey was administered in these languages. This survey was developed with our Patient and Public Involvement group and was administered from January 2023 until 31st January, 2024 using Qualtrics and RedCap.

RESULTS

Four hundred fifty-eight people completed the survey. Moderate-severe symptoms of depression and anxiety were 37.7% and 26.5%, respectively. Over 50% reported a history of diagnosed depression. In addition to depression, sleep problems and fatigue were identified as future support needs, with over a third indicating a preference for in-centre provision. In case-mix adjusted analysis, there was no variability in depression and anxiety symptoms across centres. Centre location and size were unrelated to symptoms. Age, female gender, current mental health treatments, self-efficacy and perceptions regarding opportunity for support, were associated with symptoms of depression and anxiety. In sub-analysis, there was a negative association between psychosocial staffing levels and depression symptoms.

CONCLUSION

Patient-related factors and behavioural characteristics were related to variation of these symptoms. There was little evidence of symptom variability across centres, although in a small sub-analysis, psychosocial provision showed a weak negative correlation with depression symptoms. Our findings highlight preferences of future needs which could be helpful for designing future research and service provision.

Clinical trial emulation in nephrology

Trial emulation, also known as target trial emulation, has significantly advanced epidemiology and causal inference by providing a robust framework for deriving causal relationships from observational data. This approach aims to reduce biases and confounding factors inherent in observational studies, thereby improving the validity of causal inferences. By designing observational studies to mimic randomized controlled trials (RCTs) as closely as possible, researchers can better control for confounding and bias. Key components of trial emulation include defining a clear time-zero, simulating random assignment using techniques like propensity score matching and inverse probability treatment weighting, assessing group comparability by standardized mean differences and establishing a clear comparison strategy. The increasing availability of large-scale real-world databases, such as research cohorts, patient registries, and hospital records, has driven the popularity of target trial emulation. These data sources offer information on patient outcomes, treatment patterns, and disease progression in real-world settings. By applying the principles of target trial emulation to these rich data sources, researchers can design studies that provide robust causal inferences about the effects of interventions, informing clinical guidelines and regulatory decisions. Despite its advantages, trial emulation faces challenges like data quality, unmeasured confounding, and implementation complexity. Future directions include integrating trial emulation with machine learning techniques and developing methods to address unmeasured confounding. Overall, trial emulation represents a significant advancement in epidemiology, offering a valuable tool for deriving accurate and reliable causal inferences from observational data, ultimately improving public health outcomes.