Introduction:

Chronic Kidney Disease (CKD) remains a significant public health concern, affecting an estimated 850 million people globally. With the rapid rise in CKD prevalence and complications, it is projected to become the fifth most common cause of years of life lost by 2040. On world kidney day we look at the socioeconomic factors influencing CKD progression and the potential of AI in predicting acute kidney disease outcomes.

The Socioeconomic Impact on CKD Progression:

Despite the attention given to socioeconomic status in relation to CKD development and progression, health disparities associated with socioeconomic factors have not reduced worldwide. Even in countries with universal health coverage, income-related disparities associated with CKD are prevalent. Lower income levels are associated with faster CKD progression and a higher risk of initiation of Kidney Replacement Therapies (KRTs). This finding provides crucial information for future health care policies, highlighting the need for more active support beyond financial assistance.

Factors and CKD in Japan:

A recent study in Japan, a country with universal health coverage, found a strong association between lower income levels and faster progression of CKD. These lower income individuals also had a higher risk of starting KRT. The disparities in rapid CKD progression and initiation of KRT across income levels were significant. This occurred despite the country’s comprehensive healthcare coverage and minimal financial obstacles to medical services. These findings suggest that income disparity relates not only to financial barriers to medical care, but also to factors such as health literacy, psychosocial stress, and stress-related behavioural risks.

Key Findings from the Japanese Study:

The study revealed that the risk for the lower income group was over 60% higher than that of the highest income group. This was the case even in a country with almost universal health coverage. They observed this negative monotonic association more prominently among males and those without diabetes. These findings indicate a need to target socioeconomically disadvantaged populations. They underscore the importance of interventions across society to minimise social and behavioural risks for low-income individuals.

The Role of AI in Predicting AKI Outcomes:

Acute Kidney Injury (AKI) complicates 13–18% of hospital admissions in the UK. Predicting which inpatient cases progress to require Renal Replacement Therapy (RRT) remains an unaddressed challenge. Recent research has shown the potential of a Recurrent Neural Network (RNN) in predicting AKI. This AI tool could predict AKI up to 48 hours in advance, the risk of an inpatient with AKI requiring RRT therefore being admitted to a hospital or dying.

Conclusion:

In conclusion, there is a substantial association between low-income levels and the development of impaired kidney function. Moreover, this is seen with rapid CKD progression and initiation of KRT. The findings highlight the importance of society-wide interventions aimed at reducing the social and behavioural risks to individuals in the low-income group. Furthermore, AI has shown promise in predicting AKI progression. If this becomes validated, it could improve patient care while streamlining healthcare resource allocation.