Cognitive Impairment in Maintenance Haemodialysis Patients: Early Identification and Management

Abstract

Cognitive impairment (CI) has emerged as a pressing concern for patients with chronic kidney disease (CKD), particularly those undergoing maintenance haemodialysis (MHD) (Xu et al. 2024). As these patients experience a significant burden of cognitive deficits, the impact on their quality of life (QOL) and overall prognosis is profound. Research indicates that CI in MHD patients is more prevalent compared to the general population and is often associated with a myriad of risk factors, including advanced age, comorbid conditions like diabetes mellitus (DM) and prolonged dialysis duration (Anees et al. 2024; Chen et al. 2023). We read with great interest the recent article by Cao and colleagues entitled ‘Risk factors and prevalence of cognitive impairment in maintenance haemodialysis patients: A systematic review and meta-analysis of observational studies’ (Cao et al. 2023), which systematically explored the risk factors for CI in MHD patients and to assess its prevalence. The findings demonstrated that a high prevalence of CI in MHD patients. Eleven risk factors for CI in MHD patients were identified, among which more attention should be paid to modifiable factors such as cardiovascular disease risk factors and specific kidney- and dialysis-related factors. We are writing to express my interest and highlight the current understanding of CI in MHD patients, discuss the underlying mechanisms and suggest strategies for early identification and management.

The prevalence of CI among MHD patients has been widely studied. Anees et al. (2024) conducted a prospective follow-up study in Pakistan, revealing a CI prevalence of 82.7% among MHD patients. Significant predictors of CI identified in this study included increasing age, female gender, DM, unemployment and low education levels (Anees et al. 2024). Interestingly, while CI was prevalent, it was not significantly associated with increased mortality within the study period, suggesting that while CI affects QOL, its impact on survival may be less direct or influenced by other factors. In contrast, a study by Chen et al. (2023) reported a lower prevalence of CI at 31.5% among their cohort of MHD patients. This difference in prevalence might be attributed to differences in the study population and the exclusion of patients with severe comorbidities. Chen et al. (2023) identified age, duration of dialysis and smoking as significant predictors of CI. Their study notably constructed a predictive model with a high accuracy (AUC of 84%), which stratified patients into different risk categories for developing CI. This model provides a valuable tool for clinicians to identify patients at high risk and tailor interventions accordingly. The pathophysiology of CI in MHD patients is complex and multifactorial. Uraemic toxins, such as phosphate, fibroblast growth factor 23 (FGF23) and others, have been implicated in the neurodegeneration observed in MHD patients (Anees et al. 2024). These toxins can cross the blood–brain barrier and contribute to cognitive decline. Additionally, haemodialysis itself, particularly when associated with intradialytic hypotension, can lead to repeated episodes of cerebral ischaemia. This ischaemia, over time, can result in structural brain changes, such as cerebral atrophy, which further exacerbates cognitive decline (Chen et al. 2023). Cerebrovascular disease is another significant contributor to CI in MHD patients. The combination of traditional risk factors for cerebrovascular disease (e.g., hypertension and diabetes) and the unique stressors associated with haemodialysis (e.g., fluctuating blood pressure and uraemic toxins) creates a perfect storm for cognitive decline (Chen et al. 2023). Prolonged dialysis vintage and inadequate dialysis adequacy, as measured by spKt/V, have also been linked to worsening cognitive outcomes.

CI has profound implications for the QOL of patients undergoing MHD. Cognitive deficits can severely hinder a patient's ability to adhere to complex treatment regimens, which include strict dietary restrictions, medication schedules and regular dialysis sessions (Anees et al. 2024). This non-adherence not only leads to the progression of CKD but also increases the risk of hospitalisation and mortality due to complications arising from poorly managed health. The study by Anees et al. (2024) highlighted that CI in MHD patients was associated with lower educational levels, higher rates of unemployment, and a greater prevalence of DM, all of which contribute to poorer health outcomes (Anees et al. 2024). The inability to manage one's health effectively due to cognitive deficits leads to a vicious cycle of deteriorating health and increasing dependence on caregivers. This situation underscores the critical need for regular cognitive assessments in this population to identify those at risk and to implement strategies to maintain cognitive function as much as possible. The study by Chen et al. (2023) further emphasised the importance of early identification and intervention. Their predictive model allows for the categorisation of patients into different risk groups for CI, which can guide clinicians in prioritising interventions for those at the highest risk (Chen et al. 2023). For instance, patients identified as high-risk might benefit from more frequent cognitive assessments, adjustments in dialysis protocols to minimise hypotensive episodes and targeted management of comorbid conditions such as diabetes and cardiovascular disease (Chen et al. 2023).

Given the high prevalence and significant impact of CI among MHD patients, it is imperative to incorporate routine cognitive screening into the standard care protocol for these individuals. The Montreal Cognitive Assessment (MoCA), used in Chen et al.'s study (Chen et al. 2023), is a practical tool for this purpose, allowing for the early detection of cognitive decline. Early identification of CI enables healthcare providers to tailor interventions that could potentially slow the progression of cognitive decline and improve overall patient outcomes. Moreover, the predictive model developed by Chen et al. (2023) provides a valuable framework for assessing the risk of CI in MHD patients. This model, which takes into account factors such as age, duration of dialysis and smoking status, should be integrated into clinical practice to help stratify patients based on their risk and to inform treatment decisions. For example, high-risk patients could benefit from modifications to their dialysis regimen, such as more frequent but shorter sessions to reduce the risk of hypotension and cerebral ischaemia. Interventions aimed at reducing the burden of uraemic toxins, improving dialysis adequacy and managing comorbid conditions such as DM and cardiovascular disease are crucial. These interventions could help mitigate the cognitive decline observed in this population (Anees et al. 2024). Additionally, patient education and support systems are essential to ensure that patients understand their treatment plans and adhere to them despite cognitive challenges. This could involve the use of memory aids, simplifying medication regimens and involving caregivers in the management of the patient's health.

CI is a prevalent and serious complication in patients undergoing MHD. The studies by Anees et al. (2024) and Chen et al. (2023) provide valuable insights into the prevalence, predictors and impact of CI in this population. While CI significantly affects the quality of life and treatment adherence in MHD patients, its direct impact on mortality remains a topic of ongoing research. However, the negative implications of CI on patient outcomes necessitate early identification and intervention. Routine cognitive screening, as well as the use of predictive models like the one developed by Chen et al. (2023), should be integrated into clinical practice to enhance the care provided to MHD patients. By identifying patients at high risk for cognitive decline, healthcare providers can implement targeted interventions that may slow the progression of CI and improve the overall health and well-being of MHD patients. Given the high prevalence of CI in MHD patients and its detrimental effects on their quality of life, it is crucial that healthcare providers take proactive steps to address this issue. Regular cognitive assessments, the use of predictive models and the implementation of targeted interventions should become standard practice in the management of MHD patients. By doing so, we can improve not only the cognitive outcomes of these patients but also their overall health and quality of life. Healthcare systems should prioritise training for healthcare providers on the importance of cognitive health in MHD patients and on the use of tools like the MoCA and predictive models to identify at-risk patients. Furthermore, more research is needed to better understand the long-term impact of CI on mortality in this population and to develop effective strategies for prevention and management.

In conclusion, addressing CI in MHD patients requires a multidisciplinary approach that includes regular screening, patient education and targeted interventions. By focusing on cognitive health, we can significantly improve the quality of life and outcomes for patients undergoing MHD.

Xingyun Wan and Wenke Fu: conceptualisation, supervision, validation and writing – review and editing. Wenke Fu: conceptualisation, validation and writing – review and editing. All authors agreed to the final version of the manuscript.

The authors declare no conflicts of interest.