Early Chronic Kidney Disease Research Articles

Enhancing kidney disease prediction with optimized forest and ECG signals data

To improve the early detection of Chronic Kidney Disease (CKD) utilizing electrocardiogram (ECG) data, this study explores the use of the Optimized Forest (Opt-Forest) model. Exploiting the possible relationship between kidney function and ECG data, we investigate Opt-Forest's performance in comparison to popular machine learning (ML) models. We evaluate Opt-Forest and find that it outperforms other options in CKD prediction based on many measures such as classification accuracy (CA), false positive rate (FPR), and true positive rate (TPR). In comparison to previous models, Opt-Forest has superior sensitivity and specificity, with a TPR of 0.787 and a low FPR of 0.174. With an accuracy of 78.68 %, a KS of 0.641, and a low RMSE of 0.174, Opt-Forest also demonstrates robustness in CKD prediction. This study demonstrates the potential of Opt-Forest to improve patient outcomes and medical diagnostics, as well as the usefulness of ECG data in enhancing early CKD diagnosis. Prospective research avenues to advance precision medicine in nephrology involve investigating deep learning methodologies and incorporating patient-specific data.

Urine Biomarkers of Kidney Tubule Health and Risk of Incident CKD in Persons Without Diabetes: The ARIC, MESA, and REGARDS Studies

Rationale & ObjectiveTubulointerstitial damage is a feature of early chronic kidney disease (CKD), but current clinical tests capture it poorly. Urine biomarkers of tubulointerstitial health may identify risk of CKD. Study DesignProspective cohort (ARIC) and case-cohort (MESA and REGARDS). Setting & ParticipantsAdults with eGFR ≥60 mL/min/1.73m2 and without diabetes in the ARIC, REGARDS, and MESA studies. ExposureBaseline urine monocyte chemoattractant protein-1 (MCP-1), alpha-1-microglobulin (α1m), kidney injury molecule-1 (KIM-1), epidermal growth factor (EGF), and chitinase-3-like protein 1 (YKL-40). OutcomeIncident CKD or ESKD (end-stage kidney disease). Analytical ApproachMultivariable Cox proportional hazards regression for each cohort; meta-analysis of results from all three cohorts. Results872 ARIC participants (444 cases of incident CKD), 636 MESA participants (158 cases), and 924 REGARDS participants (488 cases) were sampled. Across cohorts, mean age ranged from 60±10 to 63±8 years and baseline eGFR ranged from 88±13 to 91±14 mL/min/1.73m2. In ARIC, higher concentrations of urine MCP-1, α1m, and KIM-1 were associated with incident CKD. In MESA, higher concentration of urine MCP-1 and lower concentration of EGF was each associated with incident CKD. In REGARDS, none of the biomarkers were associated with incident CKD. In meta-analysis of all three cohorts, each two-fold higher α1m concentration was associated with incident CKD (HR=1.19 [1.08, 1.31]). LimitationsObservational design susceptible to confounding; competing risks during long follow-up period; meta-analysis limited to three cohorts. ConclusionsIn three combined cohorts of adults without prevalent CKD or diabetes, higher urine α1m was independently associated with incident CKD. Four biomarkers were associated with incident CKD in at least one of the cohorts when analyzed individually. Kidney tubule health markers might inform CKD risk independent of eGFR and albuminuria.

A Review On Early Detection Of Chronic Kidney Disease

Early detection of Chronic Kidney Disease (CKD) is critical for timely intervention and effective treatment. Deep learning algorithms have demonstrated promise in medical applications, including disease detection. In this study, we propose a deep learning-based system for early CKD detection using the Chronic Kidney Disease dataset from Kaggle. Additionally, we incorporate the Grasshopper Optimization Algorithm (GOA) for feature selection to enhance the system's performance and interpretability. Our system employs a convolutional neural network (CNN) architecture to analyze clinical and laboratory attributes from the CKD dataset, obtained from Kaggle, consisting of 4,000 instances with 25 attributes. These attributes encompass patient demographics, blood tests, and medical history, providing a comprehensive representation of CKD-related factors. To improve the system's performance, we integrate the GOA for feature selection. The GOA is a nature-inspired metaheuristic optimization algorithm that mimics the foraging behavior of grasshoppers. It aims to identify the most relevant attributes associated with CKD from the dataset. By selecting a subset of informative features, we enhance the model's predictive accuracy and reduce overfitting. During the training phase, the CNN learns to automatically extract relevant features and patterns associated with CKD from the selected attributes. Additionally, data preprocessing techniques such as normalization and feature scaling are applied to further improve the model's performance and generalizability. To evaluate the system's performance, we conduct experiments using a separate test dataset comprising 1,000 instances from the CKD dataset. The incorporation of the GOA for feature selection in our deep learning system not only improves its performance but also enhances interpretability. By identifying the most relevant attributes associated with CKD, we focus on key biomarkers and risk factors, enhancing the system's accuracy and providing valuable insights into the disease. Our research showcases the potential of deep learning algorithms, coupled with GOA-based feature selection, for early CKD detection. By leveraging the Kaggle CKD dataset and incorporating the GOA, we contribute to improving the accuracy and applicability of the system in real-world clinical settings. To handle Big data we are proposing to implement this problem on Pyspark one of the Big data computational environments for effective learning. In this platform, we can dynamically scale the infrastructure as per the demand of the data. Ultimately, our work aims to advance the early detection and management of CKD, leading to improved patient outcomes and more effective healthcare interventions.

Determinants of early chronic kidney disease in patients with recently diagnosed type 2 diabetes mellitus: a retrospective study from the Taiwan Diabetes Registry

BackgroundWe tried to identify the risk factor associate with early chronic kidney disease (CKD) in recently diagnosed type 2 diabetes mellitus patients by utilizing real-world data from Taiwan Diabetes Registry.Materials and methodsPatients with type 2 diabetes mellitus recently diagnosed within 1 year. We divided the study participants into control group and early CKD group. Early CKD was defined as either CKD stage G1 with albuminuria, CKD stage G2 with albuminuria, or CKD stage G3a regardless of albuminuria (Urine-albumin to creatinine ratio (UACR) ≥ 3 mg/mmol). Control group was defined as CKD G1 or CKD G2 without albuminuria. Logistic regression analyses were used to compare differences in clinical characteristics between the subgroups. Linear regression models were employed to examine the factors predicting estimated glomerular filtration rate (eGFR) and UACR.ResultsTotal 2217 patients with recently diagnosed type 2 diabetes mellitus were included. 1545 patients were assigned to control group and 618 patients were assigned to the early CKD group. Age (odds ratio (OR) 1.215, 95% confidence interval [CI] 1.122–1.316), systolic blood pressure (OR 1.203, 95% CI 1.117–1.296), glycated hemoglobin (OR 1.074, 95% CI 1.023–1.129) and triglyceride (OR 2.18, 95% CI 1.485–3.199) were found to be significant risk factors. Further, presence of bidirectional association between UACR and eGFR was found.ConclusionsWe reported factors associated with early CKD in recently diagnosed type 2 diabetes mellitus patients. Variables that associated with eGFR and UACR were identified respectively, included a mutual influence between UACR and eGFR.

Early prediction of chronic kidney disease based on ensemble of deep learning models and optimizers

Recent studies have proven that data analytics may assist in predicting events before they occur, which may impact the outcome of current situations. In the medical sector, it has been utilized for predicting the likelihood of getting a health condition such as chronic kidney disease (CKD). This paper aims at developing a CKD prediction framework, which forecasts CKD occurrence over a specific time using deep learning and deep ensemble learning approaches. While a great deal of research focuses on disease detection, few studies contribute to disease prediction before it may occur. However, the performance of previous work was not competitive. This paper tackles the under-explored area of early CKD prediction through a high-performing deep learning and ensemble framework. We bridge the gap between existing detection methods and preventive interventions by: developing and comparing deep learning models like CNN, LSTM, and LSTM-BLSTM for 6–12 month CKD prediction; addressing data imbalance, feature selection, and optimizer optimization; and building an ensemble model combining the best individual models (CNN-Adamax, LSTM-Adam, and LSTM-BLSTM-Adamax). Our framework achieves significantly higher accuracy (98% and 97% for 6 and 12 months) than previous work, paving the way for earlier diagnosis and improved patient outcomes.

Characterization of renal (dys)function in rat models of early and advanced chronic kidney disease

Characterization of renal (dys)function in rat models of early and advanced chronic kidney disease

Real-world evidence of constipation and laxative use in the Korean population with chronic kidney disease from a common data model

Constipation is a highly prevalent gastrointestinal disorder in patients with chronic kidney disease (CKD). However, our understanding of its epidemiology and management in CKD is limited. We aimed to explore real-world data on constipation and laxative use in patients with CKD in a nationwide population-based cohort from the Korean Health Insurance Review and Assessment-National Patient Sample database. This study analyzed retrospective health claims data in Korea from 2012 to 2017 that were transformed into the Observational Medical Outcomes Partnership Common Data Model. The pooled proportion of constipation diagnoses was 30.5% in all patients with CKD and 15.9%, 16.5%, 17.4%, 29.9%, and 43.3% in patients with CKD stages 1–5, respectively, suggesting a higher prevalence in advanced CKD. Patients receiving peritoneal dialysis or hemodialysis had the highest prevalence of constipation, while transplant recipients showed a prevalence comparable to that of patients with early CKD. Patients with CKD had a significantly higher risk of constipation than age- and sex-matched non-CKD individuals (range of odds ratio [OR]:1.66–1.90). Laxative prescribing patterns differed by CKD severity. Osmotic agents were prescribed in more than half of patients with advanced CKD, while magnesium salts and bulking agents were prescribed less frequently. The CKD patients with constipation were more likely to be prescribed constipation-inducing medications, including antipsychotic and neurological medications. Our findings provide real-world constipation and laxative prescription status in the Korean CKD population, revealing a significantly higher risk of constipation and different laxative prescribing patterns in patients with CKD.

The estimated mediating roles of anemia-related variables in the association between kidney function and mortality: a National Health and Nutrition Examination Survey (NHANES) study

Anemia is a common complication of chronic kidney disease (CKD), impacting long-term outcomes such as mortality and morbidity. Analyzing NHANES data from 1999 through 2016 for adults aged ≥ 20 years, we assessed the mediating effects of anemia biomarkers (hemoglobin, hematocrit, red cell distribution width [RDW], and mean corpuscular hemoglobin concentration [MCHC]) on CKD-related outcomes by using hazard ratios from a biomarker-adjusted model. Of 44,099 participants, 7463 experienced all-cause death. Cox proportional hazard models revealed a higher all-cause mortality risk in the > 45 years and CKD groups than in the early CKD group. Hemoglobin, hematocrit and MCHC were inversely related to all-cause mortality; RDW was related to mortality. Single mediation analysis showed greater mediating effects of anemia indicators on CKD and mortality in the elderly (> 65 years) population than those in the general population. In the multimediation analysis, the combined mediating effect of anemia was higher in the CKD population than in the general population. This study showed a proportional increase in the mediating effect of anemia with CKD stage, suggesting potential therapeutic avenues. However, further exploration of other mediating factors on kidney outcomes is necessary.

Big Data and Machine Learning Based Early Chronic Kidney Disease Prediction

A chronic kidney disease, sometimes called a chronic renal disease, is characterized by a gradual decline in kidney purpose or abnormal kidney purpose which continues for months or even years. Patients with a domestic past of chronic kidney disease (CKD), high BP, or other kidney-related conditions are often the first to have chronic kidney disease (CKD) identified during screenings. Consequently, effective illness prevention and therapy rely on early prediction. Methods from the field of machine learning, including XGBoost, KNN, Decision Tree, and Random Forest, are being considered for use in this CKD project. The final product uses the fewest characteristics possible to determine whether the patient has chronic kidney disease (CKD).

Cardiac transcriptomic changes induced by early CKD in mice reveal novel pathways involved in the pathogenesis of Cardiorenal syndrome type 4

BackgroundCardiorenal syndrome (CRS) type 4 is prevalent among the chronic kidney disease (CKD) population, with many patients dying from cardiovascular complications. However, limited data regarding cardiac transcriptional changes induced early by CKD is available. MethodsWe used a murine unilateral ureteral obstruction (UUO) model to evaluate renal damage, cardiac remodeling, and transcriptional regulation at 21 days post-surgery through histological analysis, RT-qPCR, RNA-seq, and bioinformatics. ResultsUUO leads to significant kidney injury, low uremia, and pathological cardiac remodeling, evidenced by increased collagen deposition and smooth muscle alpha-actin 2 expression. RNA-seq analysis identified 76 differentially expressed genes (DEGs) in UUO hearts. Upregulated DEGs were significantly enriched in cell cycle and cell division pathways, immune responses, cardiac repair, inflammation, proliferation, oxidative stress, and apoptosis. Gene Set Enrichment Analysis further revealed mitochondrial oxidative bioenergetic pathways, autophagy, and peroxisomal pathways are downregulated in UUO hearts. Vimentin was also identified as an UUO-upregulated transcript. ConclusionsOur results emphasize the relevance of extensive transcriptional changes, mitochondrial dysfunction, homeostasis deregulation, fatty-acid metabolism alterations, and vimentin upregulation in CRS type 4 development.

Biomedical Evaluation of Early Chronic Kidney Disease in the Air Force: Building a Predictive Model from the Taiwan Military Health Service.

Chronic kidney disease (CKD) is one of the most common diseases worldwide. The increasing prevalence and incidence of CKD have contributed to the critical problem of high medical costs. Due to stressful environments, aircrew members may have a high risk of renal dysfunction. A better strategy to prevent CKD progression in Air Force personnel would be to diagnosis CKD at an early stage. Since few studies have been conducted in Taiwan to examine the long-term trends in early CKD in Air Force aircrew members, this study is highly important. We investigated the prevalence of CKD and established a predictive model of disease variation among aircrew members. In this retrospective study, we included all subjects who had received physical examinations at a military hospital from 2004 to 2010 and who could be tracked for four years. The Abbreviated Modification of Diet in Renal Disease Formula (aMDRD) was used to estimate the glomerular filtration rate (GFR) and was combined with the National Kidney Foundation/ Kidney Disease Outcomes Quality Initiative (NKF-K/DOQI) to identify CKD patients. A total of 212 aircrew members were assessed. The results showed that the prevalence of CKD was 3.8%, 9.4%, 9.0%, and 9.4% in each of the four years. According to the logistic regression analysis, abnormal urobilinogen levels, ketones, and white blood cell (WBC) counts in urine and a positive urine occult blood test increased the risk of CKD. A positive urine occult blood test can be used to predict the future risk of CKD. Moreover, the generalized estimating equation (GEE) model showed that a greater risk of CKD with increased examination time, age and seniority had a negative effect. In conclusion, abnormal urobilinogen levels, ketones, and urine WBC counts in urine as well as a positive urine occult blood test might serve as independent predictors for CKD. In the future, we can focus not only on annual physical examinations but also on simple and accurate examinations, such as urine occult blood testing, to determine the risk of CKD and prevent its progression in our aircrew members.

A comprehensive analysis of albuminuria in canine chronic kidney disease.

Albuminuria, an important marker of decreased kidney function in chronic kidney disease (CKD), is not routinely used for CKD detection or proteinuria appearance. Its relationships with biochemical parameters and blood pressure in dogs are poorly understood. This study aimed to evaluate the relationship of albuminuria with various CKD markers, its correlation with the urinary protein to creatinine ratio (UPC), and hypertension in dogs with early stages of CKD. It also sought to determine the usability of the urinary albumin to creatinine ratio (UAC) for CKD screening. The study reviewed records of 102 dogs, categorising them into four groups based on disease status. UAC and UPC ratio, biochemistry and haematology variables, age, and systolic blood pressure were determined. The Pearson's correlation coefficient between log-transformed values of UPC and UAC was r = 0.902 (95% CI: 0.87 to 0.93). Median UAC ratio values were 2.1mg/g for the Healthy control group (n = 17), 54.2mg/g for early stages CKD (n = 42), 5.8mg/g for Acute sick control (n = 30), and 104mg/g for Chronic sick control (n = 13). Thresholding UAC ratio as an indicator for impaired kidney function with the threshold of 10mg/g (established based on the receiver operating characteristic curve) had a sensitivity 81.8%, specificity of 89.4%, positive predictive value (PPV) 90%, and negative predictive value (NPV) 80.1%. The correlation of UAC with biochemistry and haematology variables was statistically significant; for SDMA (μg/L), it was r = 0.566 and for other variables, it was weak to moderate. UAC was markedly elevated in cases of severe hypertension. UAC ratio was significantly different among dogs with impaired and not impaired kidney function. The correlation strength for the UAC and UPC ratios was high. UAC ratio may be a promising marker for proteinuria analysis in dogs with CKD or other kidney function alterations.

Untargeted metabolomic profiling of serum from client-owned cats with early and late-stage chronic kidney disease

Evaluation of the metabolome could discover novel biomarkers of disease. To date, characterization of the serum metabolome of client-owned cats with chronic kidney disease (CKD), which shares numerous pathophysiological similarities to human CKD, has not been reported. CKD is a leading cause of feline morbidity and mortality, which can be lessened with early detection and appropriate treatment. Consequently, there is an urgent need for early-CKD biomarkers. The goal of this cross-sectional, prospective study was to characterize the global, non-targeted serum metabolome of cats with early versus late-stage CKD compared to healthy cats. Analysis revealed distinct separation of the serum metabolome between healthy cats, early-stage and late-stage CKD. Differentially abundant lipid and amino acid metabolites were the primary contributors to these differences and included metabolites central to the metabolism of fatty acids, essential amino acids and uremic toxins. Correlation of multiple lipid and amino acid metabolites with clinical metadata important to CKD monitoring and patient treatment (e.g. creatinine, muscle condition score) further illustrates the relevance of exploring these metabolite classes further for their capacity to serve as biomarkers of early CKD detection in both feline and human populations.

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.

Metabolomics study reveals blood biomarkers for early diagnosis of chronic kidney disease and IgA nephropathy: A retrospective cross-sectional study

Background and aimsChronic kidney disease (CKD) causes low quality of life and alarming morbidity and mortality. The crucial to retard CKD progression is to diagnose early for timely treatment. IgA nephropathy (IgAN) is a typical CKD and the most common glomerulonephritis. Both CKD and IgAN lack accurate and sensitive blood biomarkers for early diagnosis. Here we report the potential of plasma biomarkers for early diagnosis of CKD and IgAN. Materials and methodsPlasma levels of metabolites derived from tryptophan were quantified with an LC-MS/MS-based metabolomics for two cohorts. Based on the predictive probability of each metabolite, multivariate models including logistic regression and random forest were used to establish the early diagnostic biomarkers for CKD and IgAN. ResultsThe plasma melatonin diagnosed early CKD (stages Ⅰ−Ⅱ) with an accuracy exceeding 95%, and a panel of melatonin and tryptophan achieved a remarkable 100% accuracy in diagnosing early CKD. Furthermore, indole-3-lactic acid had an excellent ability to distinguish IgAN among CKD patients. Based on the CKD screening and IgAN diagnosis primarily contributed by melatonin and indole-3-lactic acid, early IgAN could be diagnosed with an accuracy of over 85%. ConclusionsThis study provides promising plasma biomarkers for early diagnosis of CKD and IgAN.

WNT-dependent interaction between inflammatory fibroblasts and FOLR2+ macrophages promotes fibrosis in chronic kidney disease

Chronic kidney disease (CKD) is a public health problem driven by myofibroblast accumulation, leading to interstitial fibrosis. Heterogeneity is a recently recognized characteristic in kidney fibroblasts in CKD, but the role of different populations is still unclear. Here, we characterize a proinflammatory fibroblast population (named CXCL-iFibro), which corresponds to an early state of myofibroblast differentiation in CKD. We demonstrate that CXCL-iFibro co-localize with macrophages in the kidney and participate in their attraction, accumulation, and switch into FOLR2+ macrophages from early CKD stages on. In vitro, macrophages promote the switch of CXCL-iFibro into ECM-secreting myofibroblasts through a WNT/β-catenin-dependent pathway, thereby suggesting a reciprocal crosstalk between these populations of fibroblasts and macrophages. Finally, the detection of CXCL-iFibro at early stages of CKD is predictive of poor patient prognosis, which shows that the CXCL-iFibro population is an early player in CKD progression and demonstrates the clinical relevance of our findings.

Innovative Urinals with Proteinuria Detection: A Potential Screening Tool for Diabetic Kidney Disease

Chronic kidney disease (CKD) poses a considerable health challenge, particularly in populations with high-risk factorssuch as diabetes and hypertension. This article proposes a novel and cost-effective approach to address this concern byintegrating proteinuria detection technology into urinals. We suggest the installation of these innovative ‘smart’ urinals inhomes of individuals with diabetes, offering a convenient and proactive means of monitoring kidney health. Furthermore,the deployment of such ‘smart’ urinals in public places could serve as accessible screening facilities, enabling early detectionof CKD in a broader population. The potential cost-benefit ratio of this approach is highlighted, emphasizing the long-termeconomic benefits associated with early intervention. We argue that the proposed technology has the potential to revolutionizeCKD screening, providing a valuable tool for individuals at risk and contributing to the global effort to mitigate the impactof CKD on public health. Further research and pilot programs are warranted to explore the feasibility and effectiveness ofthis innovative screening strategy

Ultrasensitive sensing urinary cystatin C via an interface-engineered graphene extended-gate field-effect transistor for non-invasive diagnosis of chronic kidney disease

Early chronic kidney disease (CKD) has strong concealment and lacks an efficient, non-invasive, and lable-free detection platform. Cystatin C (Cys C) in urine is closely related to the progress of CKD (especially at the early stage), which is an ideal endogenous marker to evaluate the impairment of renal function. Thus, the accurate detection of urinary Cys C (u-Cys C) is great significant for early prevention and treatment and delaying the course of the disease of CKD patients. Herein, we developed an extended-gate field-effect transistor (EG-FET) sensor for ultrasensitive detection of u-Cys C, which consists of a monolithic interface-engineered graphene EG electrode array and a commercially available MOSFET. Laser-induced graphene (LIG) loaded with sputtered Au NPs in the presence of adhesive Cr (Au NPs/Cr/LIG) boosts the electrical performance of the EG electrode. Meanwhile, Au NPs also serve as linkers to immobilize papain that can selectively form protein complexes with Cys C. Supported by the synergistic effect of multilevel interface-engineered graphene, our sensor exhibits a good linear correlation within the u-Cys C concentration range of 5 ag/μL to 50 ng/μL with low detection limit of 0.05 ag/μL. Our work makes accurate, specific and rapid detection of u-Cys C feasible and promising for early screening for CKD.

Digital Health Interventions for Quality Improvements in Chronic Kidney Disease Primary Care: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Chronic kidney disease (CKD) is a significant public health issue globally. The importance of its timely identification and early intervention is paramount. However, a systematic approach for early CKD management in the primary care setting is currently lacking, receiving less attention compared to upstream risk factors such as diabetes and hypertension. This oversight may lead to a failure in meeting quality-of-care indicators. Digital health interventions (DHIs), which leverage digital tools to enhance healthcare delivery, have shown effectiveness in managing chronic diseases and improving the quality, safety, and efficiency of primary care. Our research aimed to evaluate the effectiveness of DHIs in the care process, focusing on their reach, uptake, and feasibility. In this systematic review and meta-analysis, we searched PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and ClinicalTrials.gov for randomized controlled trials (RCTs) assessing DHIs' effectiveness in CKD patient care among adults in primary care settings. The search, conducted on 30 June 2023, included studies published in English from 1 January 2009. Screening was conducted using Covidence, adhering to Cochrane's guidelines for data extraction. We primarily evaluated changes in care processes (testing, documentation, medication use, etc.) and the use of renin-angiotensin-aldosterone system inhibitors (RAASi), referrals, among others. Multilevel meta-analysis was employed to address within-study clustering, and meta-regression analyzed the impact of study characteristics on heterogeneity in effect sizes. Clinical endpoints were recorded where available. Bias risk was assessed using the Cochrane Risk of Bias 2 tool. Data on reach, uptake, and feasibility were narratively summarized. The study is registered with PROSPERO (CRD42023449098). From 679 records, 12 RCTs were included in the narrative synthesis, and 6 studies (encompassing 7 trials) in the meta-analysis. The trials indicated a -0.85% change (95%CI, -5.82% to 4.11%) in the proportion of patients receiving desired care. This result showed considerable heterogeneity (I2 = 91.9%). One study characteristic (co-intervention, education) correlated with larger effects. Although including co-intervention in multivariable meta-regression was significant, it did not diminish heterogeneity. The reported reach varied and was not high, while the uptake was relatively high. Most studies did not explicitly address feasibility, though some statements implied its evaluation. The current literature on the impact of DHIs in community-based CKD care is limited. The studies suggest a non-significant effect of DHIs on enhancing CKD management in community settings, marked by significant heterogeneity. Future research should focus on rigorous, methodologically sound implementations to better assess the effectiveness of DHIs in the primary care management of CKD.

A novel scenario in the therapeutic management of anemia of chronic kidney disease: placement and use of roxadustat.

Anemia is a frequent and early chronic kidney disease (CKD) complication. Its management is currently based on oral or intravenous iron supplements, erythropoiesis-stimulating agents, and red blood cell transfusions, when the benefits of transfusion outweigh the risks. Anemia in CKD patients is underdiagnosed and undertreated. Current standard of care is associated with challenges and therefore new treatment approacheshave been sought. Hypoxia-inducible factor-prolyl-hydroxylase enzyme inhibitors are a new class of orally administered drugs used to treat anemia associated with CKD. Small-molecule hypoxia-inducible factor-prolyl-hydroxylase inhibitors have a novel mechanism of action that activates the hypoxia-inducible factor (oxygen-sensing) pathway resulting in a coordinated erythropoietic response, leading to increased endogenous erythropoietin production, improved iron absorption and transport, and reduced hepcidin. Roxadustat is the first hypoxia-inducible factor-prolyl-hydroxylase inhibitor approved by the European Medicines Agency (EMA) and reimbursed in Italy by the Italian Medicines Agency (AIFA) for the treatment of adult patients with symptomatic CKD-related anemia. This authorization was based on the outcome of a globally-conducted phase 3 clinical trial program comprising eight pivotal multicenter randomized studies. In the absence of up-to-date guidelines, we performed a critical appraisal of the placement and use of roxadustat in this therapeutic context.