Identification Of Chronic Kidney Disease Research Articles

Chronic kidney disease in patients with heart failure in German primary care practices: the InspeCKD study

Abstract Background Chronic kidney disease (CKD) is, alongside type 2 diabetes mellitus, the most common comorbidity in patients with heart failure (HF), with a prevalence of 40-50%. HF patients should therefore be screened, monitored, and treated according to international Kidney Disease Improving Global Outcomes (KDIGO) conclusions for early identification and intervention of CKD, with annual determination of eGFR to monitor kidney function and annual urine albumin-to-creatinine ratio (UACR) testing as appropriate. Early diagnosis is important as therapeutic options can be offered to slow the progression of CKD and reduce the risk of cardiovascular complications and mortality. Purpose Currently, there are only limited data available regarding screening, diagnosis and treatment of CKD for HF patients in German primary care physician (PCP) practices. Therefore, this analysis of the InspeCKD study was designed to examine the frequency of use of CKD-specific laboratory diagnostics in HF patients in routine PCP care. Methods In the current data analysis, fully anonymized patient data from German PCP practices were evaluated. In accordance with the screening recommendation of the KDIGO guideline, adult patients with diabetes, hypertension and/or cardiovascular disease, including HF, with at least one year of observation period were included in the analysis. Results The overall patient cohort comprised 448.837 patients from 1.244 German PCP practices. Of the 7.9% HF patients, 6.1% participated in a disease management program (DMP) for coronary heart disease. The average age of HF patients was 74 years. The prevalence of diagnosed CKD in HF patients during the observational period (mean: 1.7 years) was 26.8%. Within the subgroup of patients with HF, serum creatinine to estimate GFR was measured at least once in 48.5% of patients. Urine dipstick testing for albuminuria was performed in 7.4% of patients and UACR was measured at least once in 0.3% of patients. In patients with at least one documented measurement, eGFR and UACR were checked 2.2 and 0.9 times, respectively, per patient and year. Of the HF patients with laboratory-confirmed CKD according to KDIGO criteria, 81.8% were not diagnosed with CKD. Conclusion The analysis shows that, given the high prevalence of CKD in HF patients and the current KDIGO recommendations for early identification and intervention of CKD, a substantial proportion of HF patients in German PCP practices did not receive adequate laboratory diagnostics for early diagnosis of CKD. In addition, the majority of patients with laboratory-confirmed CKD were not diagnosed. Early detection of CKD and the use of evidence-based therapeutic options can significantly reduce the risk of CKD progression. In conclusion, there is an urgent need to raise awareness among PCPs about secondary and tertiary prevention of CKD in patients with HF and to include coordinated CKD screening recommendations in HF guidelines.

Ultrasound viscoelastic imaging in the noninvasive quantitative assessment of chronic kidney disease

Background This study aims to evaluate the clinical application value of ultrasound viscoelastic imaging in noninvasive quantitative assessment of chronic kidney disease (CKD). Methods A total of 332 patients with CKD and 190 healthy adults as a control group were prospectively enrolled. Before kidney biopsy, ultrasound viscoelastic imaging was performed to measure the mean stiffness value (Emean), mean viscosity coefficient (Vmean), and mean dispersion coefficient (Dmean) of the renal. CKD patients were divided into three groups based on estimated glomerular filtration rate. The differences in clinic, pathology, ultrasound image parameters between the control and patient groups, or among different CKD groups were compared. The correlation between viscoelastic parameters and pathology were analyzed. Results Emean, Vmean, and Dmean in the control group were less than the CKD group (p < 0.05). In the identification of CKD from control groups, the area under curve of Vmean, Dmean, Emean, and combining the three parameters is 0.90, 0.79, 0.69, 0.91, respectively. Dmean and Vmean were increased with the decline of renal function (p < 0.05). Vmean and Dmean were positively correlated with white blood cell, urea, serum creatinine, and uric acid (p < 0.05). Vmean is positively correlated with interstitial fibrosis and inflammatory cell infiltration grades (p < 0.001). Conclusions Ultrasound viscoelastic imaging has advantages in noninvasive quantitative identification and evaluating renal function of CKD. Emean > 6.61 kPa, Vmean > 1.86 Pa·s, or Dmean > 7.51 m/s/kHz may suggest renal dysfunction. Combining Vmean, Dmean, and Emean can improve the efficiency of identifying CKD.

Chronic Kidney Disease and Frailty: Insights from a Cross-Sectional Study in Older Adults

Abstract Background Chronic Kidney Disease (CKD) and frailty are prevalent conditions among older adults, contributing to increased healthcare utilisation and adverse outcomes. The aim of this study was to examine CKD prevalence and associated factors in older adults attending a Community Specialist Team (CST) for older persons for Comprehensive Geriatric Assessment (CGA). Methods A cross-sectional study was conducted of all adults aged ≥65 years who had a CGA from October 2023 to March 2024. Variables recorded on an Excel spreadsheet included age, gender, Clinical Frailty Scale (CFS) score, Modified Barthel Index (mBI), presence of probable sarcopenia, Malnutrition Screening Tool (MST) and Dementia Screening Interview (AD8) outcomes. CKD was defined as estimated Glomerular Filtration Rate (eGFR) &amp;lt;60ml/min/1.73m2. The presence of frailty was defined as CFS ≥4. Functional impairment was defined by mBI&amp;lt;15 and MST ≥2 defined malnutrition risk. Regression analysis was used to identify associations. Results Ninety-one patients, 59 (64.8%) female and 32 (35.2%) male, with a mean age of 81.83 (SD=7.19) years were included. The prevalence of frailty was 90% (n=82) and of CKD was 53.8% (n=49). Age was a significant predictor of CKD and with each additional year, the odds of having CKD increased by 10% (p=0.004). Twelve (24.5%) of the CKD group had MST ≥2 compared with 17 (40.5%) without CKD (p=0.120). The odds of having probable sarcopenia was non-significantly higher (OR=1.09, p=0.8444) in CKD compared to without CKD. The odds of a positive AD8 was also non-significantly higher (OR=1.24, p=0.605) in CKD compared with no CKD. There was no difference in prevalence of functional impairment between the 2 groups (p=0.466). Conclusion CKD is common chronic condition in frail older persons referred to a CST. It is frequently unrecognised. Identification of CKD through CGA should facilitate an integrated interdisciplinary approach to care which may contribute to better overall health and quality of life.

Socioeconomic Inequalities Worsen the Risk of Death in CKD: A Population-Based Cohort Study in Italy.

Few studies have explored the impact of socioeconomic position on chronic kidney disease (CKD). This study aims to fill this gap using a large Italian cohort of CKD patients. We analyzed a cohort of incident CKD cases from the Lazio regional Health Information System from 1 January 2012 to 31 December 2021. We used the deprivation index (DI), a 5-category census-block indicator that integrates several dimensions of disadvantage. The outcomes were mortality and end-stage kidney disease (ESKD). We characterized the health status of patients in the two years before CKD identification and followed each subject from the index date to the end of follow-up, i.e., the date of the outcome, of emigration, or 31 December 2022, whichever came first. We used Cox proportional hazard models to investigate the association between DI and outcomes (HR, 95% CI). From 2012 to 2021, 127,457 new cases (55.9% men) were diagnosed. The average age was 72.2 (± 13.7) for men and 74.4 (± 14.8) for women. During an average follow-up of 4.3 years (± 3.2), 57,158 patients (45%) died, and 5,994 developed ESKD (5%). The age-adjusted association between DI and mortality was higher in men than in women (p-value interaction = 0.02), HRs for the extreme categories of DI (very high vs. very low) were 1.16, 95% CI 1.12-1.21 for men, and 1.08, 95% CI 1.04-1.13, for women). There was no evidence of association between DI and ESKD. In this population, socioeconomic disadvantage is associated with a higher risk of death but not of ESKD in CKD patients.

Updated guidelines for chronic kidney disease.

Chronic kidney disease (CKD) is an increasingly common condition worldwide that leads to substantial mortality. Research in the past decade has informed significant advances in preventing CKD or delaying its progression, mainly through new medications. This progress, however, requires adequate screening and identification of early CKD to truly transform patient outcomes. Revised guidelines from the Kidney Disease: Improving Global Outcomes consortium of experts aim to help clinicians incorporate these advances into practice. This article summarizes key updates from the guidelines regarding the evaluation and management of CKD that can lead to better patient outcomes.

A novel deer hunting optimized cat boost approach for interactive medical decision-making system

The need for precise and effective medical decision-making mechanisms is driving constant change in the healthcare sector. In this regard, algorithms based on machine learning are becoming prominent due to their potential to help medical practitioners diagnose and cure a wide range of illnesses. All over the world, one significant public health issue is chronic kidney disease (CKD), which requires prompt and precise diagnosis to manage and cure. A new Deer Hunting Optimized CatBoost (DHO-CB) method for CKD seriousness prediction is presented in this research. Our approach takes advantage of CatBoost's built-in flexibility and resilience by incorporating an optimization framework inspired by deer hunting. This framework dynamically modifies model parameters to traverse intricate medical data environments. The standard CKD dataset is used to verify the suggested DHO-CB approach, and Python is the primary language used in this study. In terms of kappa coefficient (92.6%), f1-score metrics (97.7%), accuracy (97.2%), and specificity (95.5%), the effectiveness of the suggested DHO-CB approach is examined. Based on this analyzed data, our suggested DHO-CB approach outperforms other current approaches in CKD prediction as well as severity assessment. The suggested DHO-CB offers a viable path toward the creation of complex yet approachable medical decision support systems, which will promote increased accuracy and effectiveness in the identification and treatment of CKDs.

Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study.

The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

Chronic Kidney Disease Prediction Using Machine Learning

Abstract: Chronic kidney disease (CKD) is a progressive condition in which the kidneys lose their ability to function effectively over time. Individuals with hypertension, diabetes, or a family history of CKD are at increased risk, emphasizing the importance of early detection for effective intervention and management. Recent research has focused on employing machine learning techniques, including Ant Colony Optimization (ACO) and Support Vector Machine (SVM) classifiers, to predict CKD presence using a minimal set of features. This study aims to optimize predictive accuracy through advanced machine learning methodologies, facilitating timely and targeted healthcare interventions for at-risk individuals. By analyzing relevant clinical data, predictive models developed in this research offer promising avenues for early identification of CKD, enabling proactive disease management strategies. The integration of machine learning techniques in CKD prediction not only enhances predictive accuracy but also contributes to advancing personalized healthcare. Early detection allows for the implementation of preventive measures and personalized treatment plans, ultimately improving patient outcomes and reducing the burden on healthcare systems. The proposed methodology seeks to optimize predictive accuracy, aiding in timely and targeted healthcare interventions.

Prevention of Chronic Kidney Disease and Its Complications in Older Adults.

In an era marked by a global demographic shift towards an aging society, there is a heightened prevalence of chronic kidney disease (CKD) among older adults. The burden of CKD spans from kidney-related complications to impacting psychological well-being, giving rise to depressive symptoms and caregiver burnout. This article delves into CKD prevention strategies within the context of aging, contributing to the discourse by exploring its multifaceted aspects. The prevention of CKD in the older adults necessitates a comprehensive approach. Primary prevention is centered on the modification of risk factors, acknowledging the intricate interplay of various comorbidities. Secondary prevention focuses on early CKD identification. Tertiary prevention aims to address factors contributing to CKD progression and complications, emphasizing the importance of timely interventions. This comprehensive strategy aims to enhance the quality of life for individuals affected by CKD, decelerating the deterioration of functional status. By addressing CKD at multiple levels, this approach seeks to effectively and compassionately care for the aging population.

Can remote testing for chronic kidney disease improve outcomes without widening existing health inequalities? A collaborative quality improvement project in primary care.

In the UK, chronic kidney disease (CKD) is a prevalent, silent and strong predictor of cardiovascular disease. Identification of CKD is poor in primary care, particularly in minority ethnic and socio-economically deprived groups. To investigate feasibility of remote ACR testing to improve the detection and management of CKD in underserved groups. 13 591 tests were sent out across South East London. Individuals with diabetes and no ACR in the past year were offered a remote ACR test to complete at home with a smartphone using a validated app (Healthy.io). We extracted data on demographics, medical comorbidities and medication. Analyses (Stata) describe who completed the test. Twenty-seven practices agreed to participate. Analyses of 6082 tests sent show the test completion rate was 46.8%. Adjusted odds ratios demonstrated that people were less likely to complete testing if over 70 years (OR 0.71, 95% CI 0.57 to 0.89) and over 80 (OR 0.43, CI 95% 0.33 to 0.56) compared to <40 years old; people from CORE20 groups (most deprived quintile) were also less likely to complete testing (OR 0.68, 95% CI 0.61 to 0.76) and those with missing data and those with no recorded healthcare interactions within the last 5 years were also less likely to complete testing. Remote ACR testing presents an opportunity to diagnose early CKD but there is still inequity in who completes testing. Engagement with stakeholders is needed to explore innovative ways to implement remote ACR testing to achieve equitable CKD screening.

#2617 Impact of CKD screening in high‑risk populations and guideline-directed therapy on RRT, CV events, and mortality in Europe: an IMPACT CKD analysis

Abstract Background and Aims Despite the rising and substantial burden of chronic kidney disease (CKD), there is a lack of recognition of CKD as a health priority in Europe. This contributes to underdiagnosis of CKD despite the potential for early detection and effective intervention to delay progression to late-stages (associated with costly and resource-intensive renal replacement therapy [RRT; i.e., dialysis and transplantation]). Further, diagnosed patients are undertreated when compared to current and upcoming CKD guidelines, leading to increased risk for progression to RRT and cardiovascular (CV) or other events. Early detection and intervention in high-risk populations, such as those with diabetes mellitus (DM) and hypertension (HTN) have shown cost-effectiveness; however, the broader implications of these strategies on CKD progression and clinical outcomes in a European context remains underexplored. Our study aims to illustrate the clinical benefit of targeted screening followed by an optimal compliance to guideline-directed treatment use to provide insight into potential CKD policies across Europe. Method Four country populations (Germany, Netherlands, Spain, United Kingdom [UK]) were simulated for 10-years (baseline: 2022; simulated years: 2023-2032) using the validated IMPACT CKD model to compare two scenarios: targeted screening for people with DM and/or HTN followed by 90% compliance to guideline-directed therapy versus current practice (i.e., underdiagnosis without screening and low treatment rates). Annual targeted screening was modelled using estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (UACR) testing. Initiation of therapies for people with diagnosed CKD was based on Kidney Disease Improving Global Outcomes guidelines. A 90% compliance to guideline-directed therapies was assumed to approximate maximum clinical benefit. Current practice was modelled based on the observed diagnosed rate without screening and the observed treatment rate in each country. The incremental population initiated on recommended therapies were modelled to experience a multiplicative treatment effect on GFR decline, CV events, and acute kidney injury (AKI) events. The model projected CKD and RRT prevalence, incidence of CV and AKI events with results shown for year 10 (2032), as well as cumulative all-cause mortality over the simulated 10-years. Results Results compare the high-risk population screening followed by guideline-directed treatment scenario to continuation of current practices for the four countries (Fig. 1). The identification of undiagnosed CKD, as well as lower rates of progression due to guideline-directed treatment was associated with a small rise in the number of total CKD patients, with increases in stage 1-2 by 3.5% to 5.0%, and stage 3-5 by 0.2% to 1.2%. There was a reduction in the number of undiagnosed CKD stage 1-2 patients by 49.2% to 71.6%, and stage 3-5 by 60.2% to 69.8%. The largest reductions were predicted for CV events (44.6% to 49.1%) followed by dialysis (22.6% to 41.9%). The strategy resulted in a decrease in cumulative 10-year all-cause mortality between 4.5% to 9.1% in CKD patients. Conclusion The study predicted significant clinical benefits from targeted CKD screening followed by guideline-directed interventions across all four European countries. Notably, this approach was forecasted to reduce undiagnosed CKD cases, dialysis, CV events, and mortality. These findings underscore the potential of acting earlier on CKD to mitigate the future CKD burden.

#487 Treating chronic kidney disease in Danish primary care: results from the observational ATLAS study

Abstract Background and Aims Little is known about the clinical characteristics and medical treatment of individuals with chronic Kidney Disease (CKD) in primary care clinics, including uptake of cardio-renoprotective treatments like renin-angiotensin-system inhibitors (RASi) and sodium glucose co-transporter 2 inhibitors (SGLT2i). Therefore, the aim of this study were to describe the clinical characteristics, comorbidity, and medical treatment in the primary care population with decreased estimated glomerular filtration rate (eGFR) and albuminuria. Additionally, to examine the awareness and habits of primary care physicians (PCP) in regard to CKD and treatment patterns in this high-risk patient population. Method An observational study of current CKD prevalence, treatment patterns and comorbidities in primary care based on patient record data, Data was drawn from 128/211 randomly invited primary care clinics throughout Denmark. A computerized selection identified 12 random individuals with CKD per clinic with ≥2 measurements of eGFR &amp;lt;60 mL/min/1.73 m2 or UACR &amp;gt;30 mg/g within two years. Pre-specified data collected from individual electronic health records included demographics, clinical variables, comorbidities, and relevant prescribed medications. Our analyses was strengthened by 125/128 PCP completing a questionnaire with responses regarding diagnosis, management and treatment of impaired kidney function in a real-world, primary care setting. Results Of the identified CKD study population (N = 1497), 80% had hypertension, 32% diabetes (DM), 13% heart failure (HF), 59% no DM/HF. RASi were prescribed to 65%, statins to 56%, SGTL2is to 14%, and MRAs to 8% of all individuals. Treatment patterns differed between individuals with varying comorbidities, e.g., RASi usage was higher in DM (76%) or HF (74%) vs. no DM/HF (58%), as was statin usage (76% in DM vs. 45% in no DM/HF). SGTL2i usage in CKD without DM/HF was low. Questionnaire results showed that most PCPs identified CKD using threshold eGFR &amp;lt;60 mL/min/1.73 m2 (62%) or UACR &amp;gt;30 mg/g (58%) and 62% reported initiating treatment to retard kidney function decline. 53% of PCPs reported using SGLT2is as second-line treatment for CKD. 38% of PCPs often spoke to patients with an eGFR &amp;lt;60 mL/min/1.73 m2 as having CKD, whilst 38% reported rarely doing so and 19% never did so. The voluntary nature of PCP study participation (accepting an invitation to participate) may have generated bias towards those with an interest in CKD. Conclusion Despite good PCP awareness and wish to use relevant guidelines, a gap exists in implementation of cardio-renoprotective treatment, especially in individuals without DM or HF. Data from both patient files and questionnaires emphasizes a need for clear recommendations to PCPs to optimize early cardio-renal protection in individuals with CKD. Applying the same systematic screening as in individuals with DM to non-diabetic populations could offer a paradigm shift for the identification, diagnosis and monitoring of CKD. Boehringer Ingelheim supported this study, but had no role in the design, analysis or interpretation of the results.

Organization and Structures for Detection and Monitoring of CKD Across World Countries and Regions: Observational Data From a Global Survey

Rationale & ObjectiveEstablished therapeutic interventions effectively mitigate the risk and progression of chronic kidney disease (CKD). Countries and regions have a compelling need for organizational structures that enable early identification of people with CKD who can benefit from these proven interventions. We aimed to report the current global status of CKD detection programs. Study DesignA multinational cross-sectional survey. Setting & ParticipantsStakeholders, including nephrologist leaders, policymakers, and patient advocates from 167 countries, participating in the International Society of Nephrology (ISN) survey from June to September 2022. OutcomesStructures for the detection and monitoring of CKD, including CKD surveillance systems in the form of registries, community-based detection programs, case-finding practices, and availability of measurement tools for risk identification. Analytical ApproachDescriptive statistics. ResultsOf all participating countries, 19% (n=31) reported CKD registries and 25% (n=40) reported implementing CKD detection programs as part of their national policies. There were variations in CKD detection program, with 50% (n=20) using a reactive approach (managing cases as identified) and 50% (n=20) actively pursuing case-finding in at-risk populations. Routine case-finding for CKD in high-risk populations was widespread, particularly for diabetes (n=152; 91%) and hypertension (n=148; 89%). Access to diagnostic tools, estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (UACR), was limited, especially in low-income (LICs) and lower-middle-income (LMICs) countries, at primary (eGFR: LICs 22%, LMICs 39%, UACR: LICs 28%, LMICs 39%) and secondary/tertiary healthcare levels (eGFR: LICs 39%, LMICs 73%, UACR: LICs 44%, LMICs 70%), potentially hindering CKD detection. LimitationsA lack of detailed data prevented an in-depth analysis. ConclusionThis comprehensive survey highlights a global heterogeneity in the organization and structures (surveillance systems, detection programs and tools) for early identification of CKD. Ongoing efforts should be geared toward bridging such disparities to optimally prevent the onset and progression of CKD and its complications.

Prevalence and risk factors for chronic kidney disease among older adult patients with schizophrenia in Taiwan.

There is an increasing incidence and prevalence of patients with chronic kidney disease (CKD) worldwide. Little is known the prevalence of CKD among older patients with schizophrenia. The purpose of this study was to investigate the prevalence of CKD and its risk factors in older adults with schizophrenia. In this cross-sectional study, a convenience sample of 240 patients with schizophrenia age 50 or older were recruited. In addition to demographic and clinical data, participants' estimated glomerular filtration rate was calculated using the Modification of Diet in Renal Disease equation based on age, sex, ethnicity, and serum creatinine level determined from a blood sample taken from participants. The overall prevalence of CKD was 11.3%. Those with CKD group were older, had a longer duration of psychiatric illness, a higher body mass index (BMI), and diagnoses of hypertension compared to those in the non-CKD group. Independent of other risk factors, older age and BMI were significantly associated with CKD. This study found that the overall prevalence of CKD in older patients with schizophrenia was 11.3%. Risk factors for CKD in this population were older age and higher BMI. In addition to early identification and early treatment of CKD in older patients with schizophrenia, clinicians should actively manage the risk factors identified in this study, such as higher BMI and older age.

Outcome Measures of Clinical Trials in Pediatric Chronic Kidney Disease

Clinical trials of chronic kidney disease (CKD) in children have important implications for the early identification and management of CKD. The selection of clinical trial outcomes is critical for assessing the effectiveness of interventions in pediatric CKD clinical trials. This review systematically examines the spectrum of outcome measures deployed in pediatric CKD clinical trials, which includes clinical and alternative outcomes, patient-reported outcome measures (PROMs), and safety indicators. Alternative outcome measures were stratified into four levels of evidence strength: convincing, probable, suggestive, and inconclusive. Consequently, the selection of outcome measures for pediatric CKD clinical trials mandates careful consideration of both their methodological feasibility and the robustness of their evidence base. Moreover, the burgeoning field of PROMs warrants integration into the design of future pediatric clinical trials to enrich the relevance and impact of research findings.

Chronic Kidney Disease Study in Diabetic Patients: Insights From Primary Care Units in Northern Portugal.

Introduction Diabetes mellitus (DM) remains a primary cause of morbidity and mortality, leading to complications such as blindness, kidney failure, and lower limb amputations. Early detection of kidney damage, indicated by microalbuminuria (MA), is crucial for managing DM. Given the impact of these conditions, evaluating the prevalence of chronic kidney disease (CKD) in diabetic populations within primary healthcare is essential. Methodology This was a cross-sectional and observational study. Adults diagnosed with DM type 1 or 2, from five primary care units (PCUs) located in the North of Portugal, were included in this study.Descriptive and correlational statistics were performed using IBM SPSS Statistics for Windows, Version 28.0 (IBM Corp., Armonk, NY).Statistical significance was set to P< 0,05. Logistic regression models were created to identify the factors associated with CKD and DM. Results A sample of 357 diabetic patients was obtained, with 166 (46.5%) females. Of the sample, 250 (70.1%) were aged 65 or older, and the median known duration of DM was 9.36 years. Excess weight or obesity accounted for 79.8%, with a median body mass index of 28.73 kg/m2and hypertension in 284 (79.6%). An estimated glomerular filtration rate (eGFR) less than 60 mL/min was present in 89 (24.9%) and an MA of 30 mg/dL or higher was present in 68 (19.0%). In total, 130 (36.4%) individuals exhibited eGFR and MA consistent with CKD. Among these, 25 (78.1%) had other identifiable causes of CKD besides DM, hypertension, overweight, or obesity. Binary logistic regression models were constructed to find a relationship between CKDwith eGFR < 60 mL/min and MA. A statistically significant association was found between CKD with eGFR < 60 mL/minute and age (odds ratio [OR] = 1.150; P< 0.001), kidney stones (OR = 5.112; P= 0.003), absence of excess weight or obesity (OR = 0.267; P< 0.001). The use of GLP1 agonists showed statistical significance as a predictor (OR = 4.653; P= 0.042) of the presence of MA. Discussion The study investigates the impact of DM and its complications in the surveyed population. While most patients had controlled DM (284, 76.2%), prolonged disease duration correlated with poorer glycemic control, underscoring the need for more effective management strategies in advanced disease stages. Notably, a third of individuals with DM had CKD, with significant implications for therapeutic interventions and heightened risks of renal failure and cardiovascular morbidity. MA was a crucial marker for endothelial injury, with prevalence influenced by DM duration and medication type. However, in many cases, correct identification of CKD was lacking, suggesting under-recognition of renal deterioration in DM. While the study offers valuable insights, its limited sample size and geographic scope warrant cautious interpretation, emphasizing the need for broader, context-specific research to inform comprehensive healthcare strategies. Conclusions In conclusion, this study highlights the significant burden of CKD among diabetic patients, emphasizing the need for proactive screening, personalized management, and accurate diagnosis. Despite limitations, it underscores the importance of early detection and tailored interventions, advocating for improved diabetes care to mitigate renal complications on a broader scale.

Optimizing Care Delivery in Patients with Chronic Kidney Disease in the United States: Proceedings of a Multidisciplinary Roundtable Discussion and Literature Review.

Approximately 37 million individuals in the United States (US) have chronic kidney disease (CKD). Patients with CKD have a substantial morbidity and mortality, which contributes to a huge economic burden to the healthcare system. A limited number of clinical pathways or defined workflows exist for CKD care delivery in the US, primarily due to a lower prioritization of CKD care within health systems compared with other areas (e.g., cardiovascular disease [CVD], cancer screening). CKD is a public health crisis and by the year 2040, CKD will become the fifth leading cause of years of life lost. It is therefore critical to address these challenges to improve outcomes in patients with CKD. The CKD Leaders Network conducted a virtual, 3 h, multidisciplinary roundtable discussion with eight subject-matter experts to better understand key factors impacting CKD care delivery and barriers across the US. A premeeting survey identified topics for discussion covering the screening, diagnosis, risk stratification, and management of CKD across the care continuum. Findings from this roundtable are summarized and presented herein. Universal challenges exist across health systems, including a lack of awareness amongst providers and patients, constrained care team bandwidth, inadequate financial incentives for early CKD identification, non-standardized diagnostic classification and triage processes, and non-centralized patient information. Proposed solutions include highlighting immediate and long-term financial implications linked with failure to identify and address at-risk individuals, identifying and managing early-stage CKD, enhancing efforts to support guideline-based education for providers and patients, and capitalizing on next-generation solutions. Payers and other industry stakeholders have opportunities to contribute to optimal CKD care delivery. Beyond addressing the inadequacies that currently exist, actionable tactics can be implemented into clinical practice to improve clinical outcomes in patients at risk for or diagnosed with CKD in the US.

Comparing the Performance of Machine Learning Models in Predicting the Risk of Chronic Kidney Disease

Background: Chronic kidney disease (CKD) poses a significant health burden worldwide, affecting approximately 10 - 15% of the global population. As one of the leading non-communicable diseases, CKD is a major cause of morbidity and mortality. Early identification of CKD is crucial for reducing its adverse effects on patient health. Prompt detection can significantly lessen the harmful consequences and enhance health outcomes for individuals with CKD. Objectives: This study aimed to evaluate and compare the effectiveness of various machine learning models in predicting the occurrence of CKD. Methods: The study involved the collection of data from a sample of 400 patients. We applied the well-established cross-industry standard process (CRISP) methodology for data mining to analyze the data. As part of this process, we efficiently handled missing data using the mode approach and addressed outliers through the interquartile range (IQR) method. We utilized sophisticated techniques, such as CatBoost (CB), random forest (RF), and artificial neural network (ANN) models to predict outcomes. For evaluation, we used the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). Results: An analysis of 400 patient records in this study identified that variables like serum creatinine, packed cell volume, specific gravity, and hemoglobin were most influential in predicting CKD. The results indicated that the CB and RF models surpassed the ANN in predicting the disease. Ten critical predictors were pinpointed for accurate disease prediction. Conclusions: The ensemble models in this study not only showcased remarkable speed but also demonstrated superior accuracy. These findings suggest the potential of ensemble models as an effective tool for enhancing predictive performance in similar studies.

Development and evaluation of a chronic kidney disease risk prediction model using random forest.

This research aims to advance the detection of Chronic Kidney Disease (CKD) through a novel gene-based predictive model, leveraging recent breakthroughs in gene sequencing. We sourced and merged gene expression profiles of CKD-affected renal tissues from the Gene Expression Omnibus (GEO) database, classifying them into two sets for training and validation in a 7:3 ratio. The training set included 141 CKD and 33 non-CKD specimens, while the validation set had 60 and 14, respectively. The disease risk prediction model was constructed using the training dataset, while the validation dataset confirmed the model's identification capabilities. The development of our predictive model began with evaluating differentially expressed genes (DEGs) between the two groups. We isolated six genes using Lasso and random forest (RF) methods-DUSP1, GADD45B, IFI44L, IFI30, ATF3, and LYZ-which are critical in differentiating CKD from non-CKD tissues. We refined our random forest (RF) model through 10-fold cross-validation, repeated five times, to optimize the mtry parameter. The performance of our model was robust, with an average AUC of 0.979 across the folds, translating to a 91.18% accuracy. Validation tests further confirmed its efficacy, with a 94.59% accuracy and an AUC of 0.990. External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model's reliability. In summary, the study identified critical genetic biomarkers and successfully developed a novel disease risk prediction model for CKD. This model can serve as a valuable tool for CKD disease risk assessment and contribute significantly to CKD identification.

Psychosocial well-being among individuals with chronic kidney disease undergoing hemodialysis treatment and their caregivers: a protocol of a mixed method study in Sri Lanka and Poland.

Chronic Kidney Disease (CKD) can be identified as one of the non-communicable diseases (NCDs) which affect millions of people worldwide, including in Sri Lanka and Poland. The prevalence of CKD has been rising over the last three decades due to the identification of CKD with unknown etiology and the increment of NCDs such as hypertension and diabetes mellitus among the Sri Lankan population. Poland can be identified as a European country that has 4 million patients with CKD, which is the second most common chronic disease in the country. CKD is associated with the physical, economic, psychological, and social burden on patients and their caregivers. The current study is aimed to investigate the psychosocial well-being of CKD patients and their caregivers in Sri Lanka and Poland. The current study is a mixed-method study aimed to investigate the psychosocial well-being of individuals with chronic kidney disease undergoing hemodialysis and their caregivers in Sri Lanka and Poland. Participants in the quantitative part of the project will be individuals with chronic kidney disease undergoing hemodialysis from Sri Lanka (n = 63) and Poland (n = 63) who are currently undergoing hemodialysis treatment. Kidney Disease Quality of Life-Sort Form, Beck Depression Scale, Test of Self-Conscious Affect, The Courtauld Emotional Control Scale, Acceptance of Illness Scale, and a demographic and medical information sheet will be used in both Sri Lankan and Polish samples. Apart from that, the qualitative phase of the study will involve semi-structured interviews with individuals diagnosed with CKD, selected randomly from the initial part of the research, and their close relatives. Notably, the participant count will remain undetermined, as this approach addresses the inherent challenges of exploratory research.