Early detection of chronic kidney disease in low-income and middle-income countries: development and validation of a point-of-care screening strategy for India

Effectiveness of a Disease Management Program (DMP) in controlling the progression of Chronic Kidney Disease among hypertensives and diabetics

Background

The occurrence rate of stage 5 chronic kidney disease (CKD) will be 151 per million population in India in the coming years. Comorbidities like diabetes mellitus and hypertension are the usual triggers of CKD. Hence this study aimed to control the progression of CKD and to note the effectiveness of a structured education program that would help in the prevention of complications related to diabetes and hypertension.

Methods

This quasi-experimental study was conducted among 88 participants who had hypertension, diabetes mellitus, or both for five or more years. The study objective was to find the effect of a Disease Management Program on delaying progression of CKD in patients with hypertension or diabetes mellitus.The baseline data were obtained from demographic proforma, and the clinical data collected were the blood pressure, serum creatinine, and random blood sugar (RBS) of the participants. The management of hypertension and diabetes mellitus was taught to them. In the fourth and the eighth month, blood pressure and blood sugar were reassessed. At one-year blood pressure, blood sugar, and serum creatinine were tested. Baseline and one-year follow-up blood pressure, blood sugar, and estimated Glomerular Filtration Rate were compared. Descriptive statistics and "Wilcoxon signed-rank test" were used to analyze the data.

Results

In one year, the mean systolic blood pressure reduced by six mm of Hg and mean blood sugar by 24 mg/dl. The prevalence of CKD stage three and above (< 60 ml/min/m2) was nine (10.22%). The median decline in eGFR was 5 ml/min/m2 (Z= 5.925, P< 0.001).

Conclusion

The Disease Management Program led to improvements in blood pressure and diabetes control and median progression of CKD was estimated at five ml/min/m2/year.

Sustainable Development Goals: Challenges and the Role of the International Society of Nephrology in Improving Global Kidney Health

The United Nations 2030 agenda for sustainable development includes 17 sustainable development goals (SDGs) that represent a universal call to end poverty and protect the planet, and are intended to guide government and private sector policies for international cooperation and optimal mobilization of resources. At the core of their achievement is reducing mortality by improving the global burden of noncommunicable diseases (NCDs), the leading causes of death and disability worldwide. CKD is the only NCD with a consistently rising age-adjusted mortality rate and is rising steadily up the list of the causes of lives lost globally. Kidney disease is strongly affected by social determinants of health, with a strong interplay between CKD incidence and progression and other NCDs and SDGs. Tackling the shared CKD and NCD risk factors will help with progress toward the SDGs and vice versa . Challenges to global kidney health include both preexisting socioeconomic factors and natural and human-induced disasters, many of which are intended to be addressed through actions proposed in the sustainable development agenda. Opportunities to address these challenges include public health policies focused on integrated kidney care, kidney disease surveillance, building strategic partnerships, building workforce capacity, harnessing technology and virtual platforms, advocacy/public awareness campaigns, translational and implementation research, and environmentally sustainable kidney care.

Comparison Between Statistical Model and Machine Learning Methods for Predicting the Risk of Renal Function Decline Using Routine Clinical Data in Health Screening

Purpose

Using machine learning method to predict and judge unknown data offers opportunity to improve accuracy by exploring complex interactions between risk factors. Therefore, we evaluate the performance of machine learning (ML) algorithms and to compare them with logistic regression for predicting the risk of renal function decline (RFD) using routine clinical data.

Patients and Methods

This retrospective cohort study includes datasets from 2166 subjects, aged 35–74 years old, provided by an adult health screening follow-up program between 2010 and 2020. Seven different ML models were considered – random forest, gradient boosting, multilayer perceptron, support vector machine, K-nearest neighbors, adaptive boosting, and decision tree - and were compared with standard logistic regression. There were 24 independent variables, and the baseline estimate glomerular filtration rate (eGFR) was used as the predictive variable.

Results

A total of 2166 participants (mean age 49.2±11.2 years old, 63.3% males) were enrolled and randomly divided into a training set (n=1732) and a test set (n=434). The area under receiver operating characteristic curve (AUROC) for detecting RFD corresponding to the different models were above 0.85 during the training phase. The gradient boosting algorithms exhibited the best average prediction accuracy (AUROC: 0.914) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the RFD prediction performance, compared to logistic regression model (AUROC:0.882), except the K-nearest neighbors and decision tree algorithms (AUROC:0.854 and 0.824, respectively). However, the improvement differences with logistic regression were small (less than 4%) and nonsignificant.

Conclusion

Our results indicate that the proposed health screening dataset-based RFD prediction model using ML algorithms is readily applicable, produces validated results. But logistic regression yields as good performance as ML models to predict the risk of RFD with simple clinical predictors.

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.

Burden of chronic kidney disease and its risk-attributable burden in 137 low-and middle-income countries, 1990-2019: results from the global burden of disease study 2019

BACKGROUND

Chronic kidney disease (CKD) is a global public health concern, but its disease burden and risk-attributable burden in CKD has been poorly studied in low - and middle-income countries (LMICs). This study aimed to estimate CKD burden and risk-attributable burden in LMICs from 1990 to 2019.

METHODS

Data were collected from the Global Burden of Disease (GBD) Study 2019, which measure CKD burden using the years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs) and calculate percentage contributions of risk factors to age-standardized CKD DALY using population attributable fraction (PAF) from 1990 to 2019. Trends of disease burden between 1990 and 2019 were evaluated using average annual percent change (AAPC). The 95% uncertainty interval (UI) were calculated and reported for YLDs, YLLs, DALYs and PAF.

RESULTS

In 2019, LICs had the highest age-standardized DALY rate at 692.25 per 100,000 people (95%UI: 605.14 to 785.67), followed by Lower MICs (684.72% (95%UI: 623.56 to 746.12)), Upper MICs (447.55% (95%UI: 405.38 to 493.01)). The age-standardized YLL rate was much higher than the YLD rate in various income regions. From 1990 to 2019, the age-standardized DALY rate showed a 13.70% reduction in LICs (AAPC = -0.5, 95%UI: - 0.6 to - 0.5, P < 0.001), 3.72% increment in Lower MICs (AAPC = 0.2, 95%UI: 0.0 to 0.3, P < 0.05). Age-standardized YLD rate was higher in females than in males, whereas age-standardized rates of YLL and DALY of CKD were all higher in males than in females in globally and LMICs. Additionally, the YLD, YLL and DALY rates of CKD increased with age, which were higher in aged≥70 years in various income regions. In 2019, high systolic blood pressure, high fasting plasma glucose, and high body-mass index remained the major causes attributable age-standardized CKD DALY. From 1990 to 2019, there were upward trends in the PAF of age-standardized DALY contributions of high fasting plasma glucose, high systolic blood pressure, and high body-mass index in Global, LICs, Lower MICs and Upper MICs. The greatest increase in the PAF was high body-mass index, especially in Lower MICs (AAPC = 2.7, 95%UI: 2.7 to 2.8, P < 0.001). The PAF of age-standardized DALY for high systolic blood pressure increased the most in Upper MICs (AAPC = 0.6, 95%UI: 0.6 to 0.7, P < 0.001).

CONCLUSIONS

CKD burden remains high in various income regions, especially in LICs and Lower MICs. More effective and targeted preventive policies and interventions aimed at mitigating preventable CKD burden and addressing risk factors are urgently needed, particularly in geographies with high or increasing burden.

Sustainable Development Goals relevant to kidney health: an update on progress

Globally, more than 5 million people die annually from lack of access to critical treatments for kidney disease — by 2040, chronic kidney disease is projected to be the fifth leading cause of death worldwide. Kidney diseases are particularly challenging to tackle because they are pathologically diverse and are often asymptomatic. As such, kidney disease is often diagnosed late, and the global burden of kidney disease continues to be underappreciated. When kidney disease is not detected and treated early, patient care requires specialized resources that drive up cost, place many people at risk of catastrophic health expenditure and pose high opportunity costs for health systems. Prevention of kidney disease is highly cost-effective but requires a multisectoral holistic approach. Each Sustainable Development Goal (SDG) has the potential to impact kidney disease risk or improve early diagnosis and treatment, and thus reduce the need for high-cost care. All countries have agreed to strive to achieve the SDGs, but progress is disjointed and uneven among and within countries. The six SDG Transformations framework can be used to examine SDGs with relevance to kidney health that require attention and reveal inter-linkages among the SDGs that should accelerate progress.

Working towards sustainable development is essential to tackle the rise in the global burden of non-communicable diseases, including kidney disease. Five years after the Sustainable Development Goal agenda was set, this Review examines the progress thus far, highlighting future challenges and opportunities, and explores the implications for kidney disease.

Each Sustainable Development Goal (SDG) has the potential to improve kidney health and prevent kidney disease by improving the general health and well-being of individuals and societies, and by protecting the environment.

Achievement of each SDG is interrelated to the achievement of multiple other SDGs; therefore, a multisectoral approach is required.

The global burden of kidney disease has been relatively underestimated because of a lack of data.

Structural violence and the social determinants of health have an important impact on kidney disease risk.

Kidney disease is the leading global cause of catastrophic health expenditure, in part because of the high costs of kidney replacement therapy.

Achievement of universal health coverage is the minimum requirement to ensure sustainable and affordable access to early detection and quality treatment of kidney disease and/or its risk factors, which should translate to a reduction in the burden of kidney failure in the future.

Clinical, Operative, and Economic Outcomes of the Point-of-Care Blood Gases in the Nephrology Department of a Third-Level Hospital

CONTEXT.—

Point-of-care testing allows rapid analysis and short turnaround times. To the best of our knowledge, the present study assesses, for the first time, clinical, operative, and economic outcomes of point-of-care blood gas analysis in a nephrology department.

OBJECTIVE.—

To evaluate the impact after implementing blood gas analysis in the nephrology department, considering clinical (differences in blood gas analysis results, critical results), operative (turnaround time, elapsed time between consecutive blood gas analysis, preanalytical errors), and economic (total cost per process) outcomes.

DESIGN.—

A total amount of 3195 venous blood gas analyses from 688 patients of the nephrology department before and after point-of-care blood gas analyzer installation were included. Blood gas analysis results obtained by ABL90 FLEX PLUS were acquired from the laboratory information system. Statistical analyses were performed using SAS 9.3 software.

RESULTS.—

During the point-of-care testing period, there was an increase in blood glucose levels and a decrease in pCO2, lactate, and sodium as well as fewer critical values (especially glucose and lactate). The turnaround time and the mean elapsed time were shorter. By the beginning of this period, the number of preanalytical errors increased; however, no statistically significant differences were found during year-long monitoring. Although there was an increase in the total number of blood gas analysis requests, the total cost per process decreased.

CONCLUSIONS.—

The implementation of a point-of-care blood gas analysis in a nephrology department has a positive impact on clinical, operative, and economic terms of patient care.

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations

BACKGROUND

Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal images, which could add to existing chronic kidney disease screening strategies.

METHODS

We used data from three population-based, multiethnic, cross-sectional studies in Singapore and China. The Singapore Epidemiology of Eye Diseases study (SEED, patients aged ≥40 years) was used to develop (5188 patients) and validate (1297 patients) the DLA. External testing was done on two independent datasets: the Singapore Prospective Study Program (SP2, 3735 patients aged ≥25 years) and the Beijing Eye Study (BES, 1538 patients aged ≥40 years). Chronic kidney disease was defined as estimated glomerular filtration rate less than 60 mL/min per 1·73m². Three models were trained: 1) image DLA; 2) risk factors (RF) including age, sex, ethnicity, diabetes, and hypertension; and 3) hybrid DLA combining image and RF. Model performances were evaluated using the area under the receiver operating characteristic curve (AUC).

FINDINGS

In the SEED validation dataset, the AUC was 0·911 for image DLA (95% CI 0·886 -0·936), 0·916 for RF (0·891-0·941), and 0·938 for hybrid DLA (0·917-0·959). Corresponding estimates in the SP2 testing dataset were 0·733 for image DLA (95% CI 0·696-0·770), 0·829 for RF (0·797-0·861), and 0·810 for hybrid DLA (0·776-0·844); and in the BES testing dataset estimates were 0·835 for image DLA (0·767-0·903), 0·887 for RF (0·828-0·946), and 0·858 for hybrid DLA (0·794-0·922). AUC estimates were similar in subgroups of people with diabetes (image DLA 0·889 [95% CI 0·850-0·928], RF 0·899 [0·862-0·936], hybrid 0·925 [0·893-0·957]) and hypertension (image DLA 0·889 [95% CI 0·860-0·918], RF 0·889 [0·860-0·918], hybrid 0·918 [0·893-0·943]).

INTERPRETATION

A retinal image DLA shows good performance for estimating chronic kidney disease, underlying the feasibility of using retinal photography as an adjunctive or opportunistic screening tool for chronic kidney disease in community populations.

FUNDING

National Medical Research Council, Singapore.

Integrating risk prediction models into chronic kidney disease care

PURPOSE OF REVIEW

Although the concept of risk prediction in chronic kidney disease (CKD) is not new, how to integrate risk prediction models into CKD care remains largely unknown, particularly in the prevention and early management of CKD. The present review presents a timely overview of recent CKD risk prediction models and conceptualizes how these may be integrated into the care of patients with CKD.

RECENT FINDINGS

In recent literature, prediction of time-to-ESKD has been thoroughly validated in multiple international cohorts, new models focused on CKD incidence, morbidity, and mortality have been developed, and ongoing work will determine the impact of integrating risk prediction models into CKD care on patients, nephrologists, and health systems.

SUMMARY

With the availability of new models focused on CKD incidence, the United States Preventive Task Force should reconsider its determination of insufficient evidence for primary screening of CKD, which was due in part to the absence of validated risk models to guide CKD screening. Models predicting CKD morbidity and mortality present a new opportunity to standardize the intensity and frequency of care across nephrology practices.