Systematic review of diagnostic and prognostic models of chronic kidney disease in low-income and middle-income countries

The Future of Kidney Care in Low- and Middle-Income Countries: Challenges, Triumphs, and Opportunities

CKD affects about 850 million people worldwide and is projected to be the fifth leading cause of death by 2040. Individuals from low- and middle-income countries (LMICs) bear the bulk of CKD. They face challenges including lack of awareness among the general population, as well as health care providers, unique risk factors such as genetic predispositions, infectious diseases, and environmental toxins, limited availability and affordability of diagnostic tests and medications, and limited access to KRTs. The inadequate health system infrastructure, human resources, and financing mechanisms to support comprehensive and integrated kidney care worsen the situation. Overcoming these challenges needs concerted efforts toward early detection, intervention, and multidisciplinary follow-up, policy, collaboration, advocacy, and financing. To achieve this, there is need for individual governments to include kidney health among the key health priorities and build capacity toward resilient health care systems. Integrating kidney care using the roadmaps of well-established management systems for other chronic diseases, such as HIV, has the potential to expedite the widespread adoption of kidney health. The aim of this article is to provide an overview of the current state and future prospects of kidney care in LMICs, highlighting the main challenges, ongoing efforts, and opportunities for improvement. We present case studies of exemplary efforts from three continents of the world with the highest densities of LMICs and propose potential strategies for a sustainable solution.

Development of minimal resource pre-screening tools for chronic kidney disease in people with type 2 diabetes

Regular chronic kidney disease (CKD) screening can facilitate earlier diagnosis of CKD and preventative action to reduce the risk of CKD progression. People with type 2 diabetes are at a higher risk of developing CKD; hence, it is recommended that they undergo annual screening. However, resources may be limited, particularly in lower-to-middle income countries, and those at the highest risk of having an abnormal CKD screening result should be prioritised for screening. We have developed models to determine which patients are at a high risk of renal impairment. We have shown that, for people with type 2 diabetes and no previous diagnosis of CKD stage 3-5, it is possible to use age, gender, body mass index, duration of type 2 diabetes and blood pressure information to detect those at a higher risk of a reduced glomerular filtration rate. When blood measurements are available, triglyceride and cholesterol measurements can be used to improve the estimate of the risk. Even though risk factors were associated with an increased urine albumin:creatinine ratio, we found no clinical benefit of using the model over a screen-all approach.

Systematic review of diagnostic and prognostic models of chronic kidney disease in low-income and middle-income countries

OBJECTIVE

To summarise available chronic kidney disease (CKD) diagnostic and prognostic models in low-income and middle-income countries (LMICs).

METHOD

Systematic review (Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines). We searched Medline, EMBASE, Global Health (these three through OVID), Scopus and Web of Science from inception to 9 April 2021, 17 April 2021 and 18 April 2021, respectively. We first screened titles and abstracts, and then studied in detail the selected reports; both phases were conducted by two reviewers independently. We followed the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies recommendations and used the Prediction model Risk Of Bias ASsessment Tool for risk of bias assessment.

RESULTS

The search retrieved 14 845 results, 11 reports were studied in detail and 9 (n=61 134) were included in the qualitative analysis. The proportion of women in the study population varied between 24.5% and 76.6%, and the mean age ranged between 41.8 and 57.7 years. Prevalence of undiagnosed CKD ranged between 1.1% and 29.7%. Age, diabetes mellitus and sex were the most common predictors in the diagnostic and prognostic models. Outcome definition varied greatly, mostly consisting of urinary albumin-to-creatinine ratio and estimated glomerular filtration rate. The highest performance metric was the negative predictive value. All studies exhibited high risk of bias, and some had methodological limitations.

CONCLUSION

There is no strong evidence to support the use of a CKD diagnostic or prognostic model throughout LMIC. The development, validation and implementation of risk scores must be a research and public health priority in LMIC to enhance CKD screening to improve timely diagnosis.