Chronic Kidney Disease Classification Research Articles

Renal rehabilitation learning in Japanese physical therapy schools: a fact-finding study

BackgroundThe scope and content of pregraduate education in renal rehabilitation, a comprehensive program for patients with chronic kidney disease (CKD), at training institutions remain unclear. Therefore, this cross-sectional study aimed to evaluate the state of renal rehabilitation education at physical therapy schools in Japan via a survey.MethodsQuestionnaire forms were distributed to instructors responsible for physical therapy across 277 physical therapy schools in Japan. The questionnaire comprised items that assessed the opinions of the respondents on the necessity of learning renal rehabilitation during their student days and after graduation and the content covered at institutions that offered classes on renal rehabilitation. The participating institutions were divided into Renal Rehab Education and No Renal Rehab Education groups. Fisher’s exact and chi-squared tests were used to compare variables between the groups.ResultsAmong the 154 schools that responded to the survey between 14 June 2023 and 31 August 2023 (response rate 55.6%), 123 schools (79.9%) were included in the Renal Rehab Education group. The percentage of classes on rehabilitation for patients with endocrine/metabolic diseases, digestive system diseases, and cancer was higher in the Renal Rehab Education group than that in the No Renal Rehab Education group (P = 0.02, P < 0.001, and P = 0.003, respectively). The distribution of the perceived need to learn renal rehabilitation during student days and after graduation differed significantly between the Renal Rehab Education and No Renal Rehab Education groups (P < 0.001 and P = 0.004, respectively). Exercise therapy for patients with CKD (83.7%); definition, diagnosis, severity classification, and testing for CKD (81.3%); general remarks on renal rehabilitation (80.5%); pathophysiology of CKD (78.9%); and renal replacement therapy (69.9%) were the most frequently implemented contents. Prioritization of other fields (45.2%), insufficient class hours (41.9%), and lack of specialized faculty (38.7%) were identified as reasons for not implementing renal rehabilitation courses in the No Renal Rehab Education group; however, 77.4% of schools in this group aimed to commence courses in the future.ConclusionsApproximately 80% of physiotherapy schools offered renal rehabilitation courses. Awareness campaigns must be conducted to facilitate the widespread implementation of renal rehabilitation.

The New Creatinine-Based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 Equation: Potential Impact on Screening for Chronic Kidney Disease in an Asymptomatic Black African Population.

In 2021, a new Chronic Kidney Disease Epidemiology (CKD-EPI) Collaboration equation was introduced that excluded race correction. We set out to compare estimated glomerular filtration rate (eGFR) determined using the creatinine-based CKD-EPI 2009 and 2021 equations and the reclassification of chronic kidney disease (CKD) eGFR staging to explore the potential ramifications of adopting the 2021 equation on reported eGFR and CKD staging. We analyzed secondary data previously utilized to determine reference intervals among Black African individuals residing in urban towns in Kenya. Serum creatinine was measured using a standardized modified Jaffé kinetic method on a Beckman AU5800 analyzer. Glomerular filtration rate (GFR) was estimated using both the 2009 and 2021 CKD-EPI creatinine equations. Classification of CKD based on eGFR was performed using the Kidney Disease: Improving Global Outcomes (KDIGO) practice guidelines. Using 533 study samples, the median eGFR was highest when determined using the race-corrected CKD-EPI 2009 equation. The CKD-EPI 2021 equation yielded a median eGFR that was similar to the non-race-corrected CKD-EPI 2009 equation. The race-corrected CKD-EPI 2009 equation classified 93.6% of participants into CKD stage G1 compared with 85.6% by the CKD-EPI 2021 equation. The CKD-EPI 2021 equation classified 14.3% of participants into CKD stage G2 compared to 6.4% by the race-corrected CKD-EPI 2009 equation. The CKD-EPI 2021 equation gave a comparable eGFR to the non-race-corrected CKD-EPI 2009 equation and its implementation in laboratories reporting eGFR in Kenya will help in identifying patients with an appropriate decrease in renal function.

Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

Baseline and usual triglyceride-glucose index and the risk of chronic kidney disease: a prospective cohort study

Triglyceride-glucose (TyG) index is an emerging marker of adverse cardiometabolic conditions such as cardiovascular disease and type 2 diabetes. The long-term relevance of TyG index to chronic kidney disease (CKD) is uncertain. We aimed to assess the association of TyG index with CKD risk and its utility in risk prediction in a prospective study. The TyG index was calculated using fasting triglycerides and fasting plasma glucose (FPG) levels measured in 2362 men aged 42–61 years with normal kidney function using the formula: Ln (fasting triglycerides [mg/dL] × FPG [mg/dL]/2). Multivariable adjusted hazard ratios (HRs) (95% confidence intervals, CIs) were estimated for CKD. Correction for within-person variability was made using data from repeat measurements of triglycerides and FPG taken 11 years after baseline. Over a median follow-up duration of 17.5 years, 223 CKD cases were recorded. The age-adjusted regression dilution ratio for the TyG index was 0.54 (95% CI, 0.48–0.60). The risk of CKD increased continuously with increasing TyG index across the range 9.3 to 11.6 (p value for nonlinearity<.001). In analysis adjusted for established risk factors, a unit higher TyG index was associated with an increased risk of CKD (HR 1.59, 95% CI 1.24–2.05). Comparing extreme tertiles of the TyG index, the corresponding adjusted HR (95% CI) for CKD was 1.61 (1.15–2.27). Addition of the TyG index to a CKD risk prediction model containing established risk factors improved risk discrimination and reclassification (p value for difference in −2 log likelihood<.001; NRI=47.66%, p=.014; IDI=0.0164, p<.001). Higher TyG index is associated with an increased risk of CKD and improves the prediction and classification of CKD beyond established risk factors. Using single baseline estimations of the TyG index to investigate its association with CKD risk could considerably under-estimate the true association.

A Hybrid Deep Learning Technique for Feature Selection and Classification of Chronic Kidney Disease

A Hybrid Deep Learning Technique for Feature Selection and Classification of Chronic Kidney Disease

A review on the classification and current treatment of Chronic Kidney Disease

Introduction:&#x0D; Chronic Kidney Disease (CKD) is a chronic condition which is characterized by the gradual loss of kidney function over time leading to End-Stage Renal Disease (ESRD). Role of kidneys is very vital to maintain homeostasis of the system. CKD is classified into five stages based on the estimated Glomerular Filtration Rate (eGFR). CKD is a long-term condition which cannot be completely cured. Treatment focuses on managing underlying conditions , slowing disease progression and addressing complications. &#x0D; Aim of the study: &#x0D; Purpose of this study is to encapsulate available knowledge about classification and therapeutic options for patients with Chronic Kidney Disease and End-Stage Kidney Disease. Both old and new treatment methods have been summarized in the following publication.&#x0D; Material and methods: &#x0D; Literature available in the PubMed database was reviewed using the following keywords:&#x0D; “Chronic kidney disease”, “ACE inhibitors” , “Angiotensin II Type 1 Receptor Blockers", “Diabetic Kidney Disease” ,”End-Stage Kidney Disease”, “Dapagliflozin”, &#x0D; Conclusions:&#x0D; Chronic Kidney Disease is a condition which is progressive and affects more than 10% of the general population worldwide totaling over 800 million people. There are many causes of chronic kidney disease; therefore, it is important to focus on slowing the progression of the disease, minimizing complications and modifying risk factors.

Feature Selection using Information Gain on the K-Nearest Neighbor (KNN) and Modified K-Nearest Neighbor (MKNN) Methods for Chronic Kidney Disease Classification

Purpose: Kidneys has an important role in the human excretory system. Unhealthy kidneys can affect kidney function. It is important to know the symptoms of chronic kidney disease. One data mining technique that can be applied is the classification technique to determine whether a person has chronic kidney disease or not based on the symptoms (attributes) obtained from medical records. The symptoms of chronic kidney disease obtained amount to 24 symptoms or attributes,Methods/Study design/approach: In this research, the classification of chronic kidney disease is performed using the information gain feature selection method and the KNN and MKNN classification methods. The number of data used is 400 data with 2 classes, namely chronic kidney disease (CKD) and non-chronic kidney disease (non-CKD).Result/Findings: Based on the test results, it was found that the hemo (Hemoglobin) attribute has the highest information gain value, which is 0.6255. The best accuracy for the KNN classification method is 96.61%, and for the MKNN method, it is 98%. Novelty/Originality/Value: The purpose of information gain feature selection is to choose features or attributes that significantly influence chronic kidney disease. Keywords: Chronic Kidney Disease, Information Gain, KNN, MKNN

Questionable Validity of Creatinine-Based eGFR in Elderly Patients but Cystatin C Is Helpful in First-Line Diagnostics.

The recommended chronic kidney disease (CKD) first-line diagnostic test is based on the creatinine-derived (estimated) glomerular filtration rate (eGFR). Cystatin C use may provide a better assessment. We compared creatinine- and cystatin C-derived eGFR determination as the first-line diagnostic test for 112 hospital patients aged > 60 years (median = 76 years). The patients were judged to not have CKD (no-CKD group) according to the first-line diagnostic recommendations (n = 61, eGFR (CKD Epidemiology Collaboration (CKD-EPI)) ≥ 60 mL/min/1.73 m2, total urine protein < 150 mg/g creatinine, urinary red/white blood cells not increased) or classified to be at risk for kidney insufficiency due to aortic valve dysfunction (at-risk group; n = 51). The accuracy of the eGFR values was evaluated retrospectively with the final case diagnoses. The eGFR (Caucasian, Asian, pediatric, and adult formula (CAPA)) was found to be linearly correlated to the eGFR (CKD-EPI) (R2 = 0.5, slope = 0.69, p < 0.0001). In 93/112 (>80%) cases, the eGFR (CAPA) yielded lower values (on average ≈-20%). In 55/112 (49%) cases, the cystatin C-derived CKD stage was lower. CKD reclassification from no-CKD to a kidney-insufficient state (i.e., CKD1/2 to CKD3a/b or 4) or reclassification to a more severe kidney insufficiency (i.e., CKD3a → 3b/4 or 3b → 4) was found in 41/112 (37%) cases. A worse CKD classification (no-CKD → kidney-insufficient) based on the eGFR (CAPA) was plausible in 30% of cases in light of the final case diagnoses. In elderly patients (>60 years), renal function appears to be systematically overestimated by the creatinine-based eGFR (CKD-EPI), indicating that, for this group, the cystatin C-based eGFR (CAPA) should be used as the first-line diagnostic test.

Classification of Chronic Kidney Disease based on health care records using machine learning with Support Vector Machine

Chronic Kidney Disease (CKD) is a global health concern with a rising prevalence that necessitates early and accurate diagnosis for effective management. This study proposes the application of Machine Learning (ML), specifically Support Vector Machine (SVM), to classify CKD based on health care records. Leveraging a comprehensive dataset of patient health records, including clinical and demographic information, the research aims to develop a predictive model that can assist in the timely identification of individuals at risk of CKD. The methodology involves preprocessing the health care records, extracting relevant features, and implementing the SVM algorithm for classification. The dataset is divided into training and testing sets to evaluate the model's performance. The SVM classification model that was developed after going through the data preprocessing process produced results that were good enough to be able to classify whether a patient was diagnosed with CKD or not with an accuracy level of 98% and a total of 400 lines of data and 25 features.

Predictive Modeling of Chronic Kidney Disease: An Ensemble ML Approach

Globally, Chronic kidney disease (CKD) is becoming a significant threat to public health, As Effective management and treatment of CKD depend heavily on early detection. In this study, we propose an in depth approach for CKD detection through stacking of machine learning. We utilized a hospital dataset with 25 features to develop prediction models for the classification of chronic kidney disease. The dataset is intended for a classification challenge and contains multivariate data. After that, the data was divided into training and testing sets using an 80-20 split, making it possible to assess the performance of the model. Many machine learning models were used, however one stacked model that included the Random Forest Classifier, Gradient Boosting Machine (RG), Convolutional Neural Network (CNN), and Decision Tree received special attention. The suitability of these models for the CKD classification assignment led to their selection. After hyperparameter tuning was done to maximize the models' performance, the models were assessed using metrics including AUC, accuracy, F1 score, precision, and recall on the testing dataset. Our research showed that the layered RG model produced very good outcomes. Later, hyperparameter tuning was done to maximize the models' performance, the models were assessed using metrics including AUC, accuracy, F1 score, precision, and recall on the testing dataset. Our results showed that the layered RG model produced very good outcomes. These results demonstrate how machine learning can be used to diagnose chronic kidney disease (CKD) early and have positive effects on healthcare by providing a way to improve patient outcomes and healthcare management.

An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application

Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model’s performance. A data balancing technique is also employed during preprocessing to address data imbalance issue that is commonly encountered in clinical contexts. Finally, for reliable CKD classification, an ensemble characteristics-based classifier is encouraged. The effectiveness of our approach is rigorously validated and assessed on multiple datasets, and the clinical relevancy of the strategy is evaluated on the real-world therapeutic data collected from Bangladeshi patients. The study establishes the dominance of adaptive boosting, logistic regression, and passive aggressive ML classifiers with 96.48% accuracy in forecasting unseen therapeutic CKD data, particularly in early-stage cases. Furthermore, the effectiveness of the FSR technique in reducing the prediction time significantly is revealed. The outstanding performance of the proposed model demonstrates its effectiveness in addressing the complexity of healthcare CKD data by incorporating the FS and FSR techniques. This highlights its potential as a promising computer-aided diagnosis tool for doctors, enabling early interventions and improving patient outcomes.

Incidence of major cardiovascular outcomes in patients with chronic kidney disease by severity: a United States population-based study using electronic medical records

Abstract Background and Aims Data on the incidence of cardiovascular (CV) events by stage of chronic kidney disease (CKD) and the effect of previous CV history on future events in CKD patients (pts) is limited. We aimed to analyze a large US electronic medical record (EMR) database and evaluate incidence of new major adverse CV events (MACE) per CKD stage over a 5-year follow-up. Methods A retrospective cohort study was conducted using IBM® Explorys EMR data (&amp;gt;53 million patients) from Jan 2010 to Dec 2019. Two cohorts of CKD patients were created: Cohort 1 based on diagnostic codes (n = 810,987 pts), and Cohort 2 on KDIGO screening/staging criteria (n = 953,396). Sub-cohorts were created for each CKD stage (1-5). CKD classification was not mutually exclusive (individual pts could contribute to multiple main and sub-cohorts). Pts in Cohort 1 were required to have an ICD 9/10 code for a specific CKD stage while pts in Cohort 2 required two consecutives abnormal eGFR, uACR/uPCR results within 3-18 months to determine KDIGO CKD stage. Pts were required to have at least 12-months lookback period. Index date was the date of diagnosis or second abnormal test. We followed up each patient individually until the occurrence of a composite of MACE including death, coronary artery disease (CAD) events, any stroke, hospitalization for heart failure, or loss to follow-up, or for up to 5 years. Results Most patients in Cohort 1 (DxC) and 2 (KDIGO) were Caucasian and had stage 3 CKD at baseline. Mean age was 70y and 71y in Cohort 1 and 2 respectively, with 49% and 40% males. The prevalence of pre-existing CV diseases increased with CKD stage. The proportion of pts from Cohort 2 receiving a diagnosis of CKD at least once during their study period increased from 34% in stage 1 to 86% and 83% in stage 4 and 5, respectively. The 5-year incidence of the composite of MACE increased with stage in both cohorts: 40.3% to 66.0% in Cohort 1 and 25.1% to 65.8% in Cohort 2. These incidences were higher in pts with a CV history (Table 1). DxC pts tended to have a higher incidence of MACE, especially in the early stages. Death was the most frequent event at 5y in both cohorts across all stages (27.8% and 19.7% respectively at stage 3). It occurred in half of pts with a prior history of CV disease in both Cohorts in stages 4-5 CKD. Stroke was the most frequent event in the early stages (21.6% and 19.5% respectively at stage 3) and CAD events the most frequent in the late stages (27.6% and 29.5% respectively at stage 5). Conclusion CKD pts have a high risk of MACE at 5 years, with incidence 2-3 times higher in patients with pre-existing CV disease. There are differences in MACE incidences between CKD definitions, that were higher in coded diagnoses vs. lab results, especially in stages 1-3, warranting further investigation. Aggressive treatment of CV disease and risk factors should be considered for all CKD pts due to high 5-year MACE rates, regardless of CKD stage.Table 1.

Abstract 19137: Chronic Kidney Disease is an Important Predictor of 2-Year Mortality After Chronic Limb Ischemia Repair Using Either Endovascular or Bypass Graft Intervention

Introduction: Chronic kidney disease (CKD) and end-stage renal disease (ESRD) are associated with worse outcomes after both endovascular (PVI) and bypass graft intervention (OI) of critical limb ischemia (CLI). Research Question: Do surgical outcomes of CLI repair differ by approach and CKD severity? Methods: Patients undergoing elective CLI intervention with preoperative renal function data captured by the Vascular Quality Initiative between Jan 2003 and Dec 2018 were analyzed. Patients were stratified by CKD class: normal/mild (CKD stage 1-2), moderate (CKD stage 3a), moderate-severe (CKD stage 3b), severe (CKD stage 4-5), and dialysis. Primary outcomes were 30-day, 1-year, and 2-year mortality, and incidence of major amputation. Predictors of survival were identified by Cox multivariate regression. Results: Out of 18,399 patients who underwent elective CLI treatment, 13,366 had PVI and 5033 had OI. Table 1 demonstrates the distribution of patients and primary outcomes. On Kaplan Meier analysis, 2-year survival favored the normal/moderate CKD cohort after both PVI and OI. On Cox regression analysis, when compared to normal/ mild CKD, CKD Class 3b (HR 1.405, p&lt;0.001) and Class 4 (HR 1.712, p&lt;0.001), and Class 5 (HR, 1.649, p = 0.070) were associated with worse 2-year mortality after OI. When compared to normal/mild CKD, CKD Class 3b (HR, 1.427 [1.2-1.7], p&lt;0.001), Class 4 (HR, 2.256 [1.77-2.87], p&lt;0.001), and Class 5 (HR, 1.210 [.59-2.5], p = 0.603) were associated with worse 2-year mortality. 2-year amputation rates did not demonstrate statistically significant differences among CKD classes. Conclusion: CKD severity is an important predictor of 2-year mortality after CLI repair regardless of approach but does not predict 2-year major amputation rates. Overall, PVI is associated with lower mortality and major amputation.

Abstract 13981: Computed Tomography Angiography Measures of Carotid Artery Plaque Burden in Patients With Atherosclerotic Carotid Artery Disease Stratified by Status of Chronic Kidney Disease

Introduction: Atherosclerotic carotid artery disease (ACAD) is associated with an increased risk of cerebrovascular mortality and morbidity. We have analyzed associations of computed tomography angiography (CTA) measures of carotid artery (CA) plaque burden with chronic kidney disease (CKD). Hypothesis: CTA-based measures of CA plaque burden in patients with ACAD differ across stages of CKD. Methods: A total of 63 ACAD patients (13 without CKD, 50 with CKD), who completed CTA imaging and had demographic data were included in a retrospective analysis of patients from the Michael E. DeBakey Veterans Affairs Medical Center (MEDVAMC), Houston, Texas. CA plaque burden measures include the normalized wall index (NWI), wall area (WA), and lumen area (LA) at three distinct CA locations: CA bifurcation (BF), maximum total vessel cross-sectional area (MTVA) of the internal CA (ICA), and ICA peak stenosis (PS). CTA measures and patient demographics were compared across three CKD classes (no disease: n=13; CKD stages 1 and 2: n=31; and CKD stage 3 and above; n=19). Results: Baseline characteristics differed among CKD groups for the estimated glomerular filtration rate (p=&lt;0.0001) and the body mass index (p=0.046). Inter-observer variability, assessed by intraclass correlation (ICC) analysis, was excellent for CA lumen area and wall area (0.964 (confidence interval [CI]: 0.946, 0.976); 0.865 (CI: 0.804, 0.908): n=100 CTA slices, both). The mean NWI differed significantly among the CKD groups for two of the three CA locations (BF: p=0.018, MTVA: p=0.01; PS: p=0.057). Similarly, lumen area significantly differed between CKD categories (BF: p=0.045, MTVA: p=0.013; PS: p=0.017) but not the wall area. Conclusion: CTA-based measures of carotid plaque burden in ACAD patients are highly reproducible and differed across CKD groups. Imaging-based measures of CA plaque burden may be of interest in stratifying risk assessment of ACAD patients with concomitant CKD.

Abstract 14014: Inclusion of Chronic Kidney Disease Measures Improves Heart Failure Risk Prediction: The Atherosclerosis Risk in Communities (ARIC) Study

Introduction: Although chronic kidney disease (CKD) is closely linked to hypertension and diabetes, and is an independent risk factor for heart failure (HF), most HF risk prediction algorithms do not include CKD measures as covariates. Hypothesis: We hypothesized that the addition of estimated glomerular function (eGFR), using creatinine (Cr) and cystatin C (CysC), and urine albumin to creatinine ratio (UACR) to the ARIC HF Risk Score will improve risk discrimination for HF. Methods: We performed a prospective analysis of participants at ARIC Visit 4 (1996-1998) to assess changes in risk prediction after incorporating CKD measures. eGFR was estimated by both Cr and by Cr-CysC using CKD-EPI 2021 race-free equations. Using eGFR and UACR, we categorized CKD into four risk progression levels per the Kidney Disease Improving Global Outcomes (KDIGO) heat map. We assessed changes in 10-year risk discrimination of the ARIC HF Risk Score with the addition of eGFR-Cr, eGFR-Cr-CysC, and with KDIGO CKD categories using eGFR-Cr+UACR and eGFR-Cr-CysC+UACR. Analyses were performed in the overall population and stratified by hypertension and diabetes. Results: Among 10,277 participants (mean age 63.2 years, 56% women, 21.4% Black, 15.5% diabetes, 40.8% hypertension), 845 people developed HF over 10 years. Risk discrimination progressively improved by adding eGFR-Cr, eGFR-Cr-CysC, and KDIGO categories with eGFR-Cr+UACR and eGFR-Cr-CysC+UACR to the ARIC HF Risk Score (C-statistic base model, 0.783 [95% CI, 0.768, 0.798] versus 0.796 [95% CI, 0.781, 0.810] with KIDIGO categories using eGFR-Cr-CysC+UACR). Greater improvements in risk discrimination were observed among those with diabetes and hypertension than in those without these conditions (Table). Conclusion: Incorporating eGFR and UACR in prediction algorithms improves HF risk discrimination and can help inform HF screening and prevention efforts, particularly among individuals with diabetes and hypertension.

Classification of chronic kidney disease based on machine learning techniques

&lt;span&gt;In recent years, indescribable suffering from various kidney diseases has been experienced by people all over the world. The situation has been significantly worse because of chronic kidney disease (CKD). Only through an early diagnosis of CKD may kidney disease be hinder in its early stages from progressing. However, it is easier to detect the chronic kidney disease with the aid of machine learning (ML) classifier algorithms sooner than any other existing methods. The present work proposes an approach for potentially predicting CKD infection while considering patient health dataset information into consideration, employing nine distinct ML algorithms; random forest (RF), naïve bayes (NB), support vector machine (SVM), decision tree (DT), logistic regression (LR), extreme gradient boosting (XGB), adaptive boosting (ADB), k-nearest neighbors (KNN), and neural network (NN). Machine learning algorithms had been utilized and conducted in four experiments, then they were compared using five performance measures; F1-score, precision, accuracy, recall and run time are used to evaluate the performance. Results had shown that AdaBoost (ADA) outperformed other techniques with achieved accuracy of 99.17%.&lt;/span&gt;

COVID-19 and Kidney: The Importance of Follow-Up and Long-Term Screening.

Renal involvement and kidney injury are common in COVID-19 patients, and the symptoms are more severe if the patient already has renal impairment. Renal involvement in COVID-19 is multifactorial, and the renal tubule is mainly affected, along with podocyte injury during SARS-CoV-2 infection. Inflammation, complement activation, hypercoagulation, and crosstalk between the kidney and lungs, brain, and heart are contributory factors. Kidney injury during the acute phase, termed acute kidney injury (AKI), may proceed to chronic kidney disease if the patient is discharged with renal impairment. Both AKI and chronic kidney disease (CKD) increase mortality in COVID-19 patients. Further, COVID-19 infection in patients suffering from CKD is more severe and increases the mortality rate. Thus, it is important to address both categories of patients, either developing AKI or CKD after COVID-19 or previously having CKD, with proper management and treatment. This review discusses the pathophysiology involved in AKI and CKD in COVID-19 infection, followed by management and treatment of AKI and CKD. This is followed by a discussion of the importance of screening and treatment of CKD patients infected with COVID-19 and future perspectives to improve treatment in such patients.

Is There Any Effect of Retrograde Intrarenal Stone Surgery on Renal Functions in Childhood?

Purpose In this study, we aimed to demonstrate whether retrograde intrarenal stone surgery (RIRS) performed due to renal stones during the childhood period has any detrimental impact on renal functions. Materials and methods We retrospectively analyzed 121 patients aged 2 to 16 years who underwent RIRS for renal stones between March 2018 and February 2023. Data were available for 41 patients. The estimated glomerular filtration rate (eGFR) was computed using the modification of diet in renal disease (MDRD) formula on the day preceding the surgery and the third month after the surgery. We employed the National Kidney Foundation's chronic kidney disease (CKD) classification to categorize the glomerular filtration rate (GFR) into five groups. Preoperative and postoperative eGFR values of pediatric patients were compared by analyzing changes in CKD groups. Results Of the patients included in the study, 21 (51.2%) were male, while 20 (48.8%) were female children. No significant difference was found between the preoperative median eGFR and the postoperative median eGFR values (p=0.958). In the 3rd month after surgery, it was observed that 5 (12.1%) patients showed an improvement in their CKD stage, while deterioration was noted in 1 (2.4%) patient. Although a negative correlation was observed between the median eGFR change and both the operation time and the stone volume in Pearson correlation analysis, it was determined that this change did not create a significant difference (p=0.213 and p=0.295, respectively). Conclusion Stone surgery conducted with RIRS appears to yield positive outcomes on kidney function in the pediatric population. Nevertheless, being particularly attentive to patients with prolonged operation times and larger stone volumes is essential.

Twenty-four-hour urine oxalate and risk of chronic kidney disease.

To assess whether 24-h urine oxalate (UOx) excretion is a risk factor for incident chronic kidney disease (CKD). This longitudinal observational USA-based study included 426896 individuals aged ≥18years with no CKD at baseline and with at least one UOx, and at least 6months of baseline and 6months of follow-up data. Of these, 11239 (2.6%) had an underlying malabsorptive condition. Incident CKD, defined by relevant International Classification of Diseases codes, was identified from a multi-source data cloud containing individual-level healthcare claims and electronic medical records data. The association between categories of UOx and incident CKD was modeled using logistic regression adjusting for age, sex, race, body mass index, baseline urine calcium, urine citrate, urine volume, tobacco use, hypertension, diabetes, malabsorption and cardiovascular disease. Mean follow-up time was 38.9months (standard deviation 21.7). Compared with individuals with UOx <20mg/24h, the odds of developing incident CKD increased for UOx 20-29mg/24h [multivariable-adjusted odds ratio (MVOR) 1.14 (95% CI 1.07, 1.21)] through 80+mg/24h [MVOR 1.35 (1.21, 1.50)] and was statistically significant for each UOx category. A similar pattern was seen in the subgroup with a malabsorptive condition though the magnitudes of association were larger, with the odds of developing incident CKD increased for UOx 20-29mg/24h [MVOR 1.50 (1.03, 2.20)] through 80+mg/24h [MVOR 2.34 (1.50, 3.63)] as compared with UOx <20mg/24h. The risk of incident CKD increases with increasing 24-h UOx excretion. Future studies should examine whether reducing UOx diminishes the risk of developing CKD.

Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes

Chronic kidney disease (low estimated glomerular filtration rate [eGFR] or albuminuria) affects approximately 14% of adults in the US. To evaluate associations of lower eGFR based on creatinine alone, lower eGFR based on creatinine combined with cystatin C, and more severe albuminuria with adverse kidney outcomes, cardiovascular outcomes, and other health outcomes. Individual-participant data meta-analysis of 27 503 140 individuals from 114 global cohorts (eGFR based on creatinine alone) and 720 736 individuals from 20 cohorts (eGFR based on creatinine and cystatin C) and 9 067 753 individuals from 114 cohorts (albuminuria) from 1980 to 2021. The Chronic Kidney Disease Epidemiology Collaboration 2021 equations for eGFR based on creatinine alone and eGFR based on creatinine and cystatin C; and albuminuria estimated as urine albumin to creatinine ratio (UACR). The risk of kidney failure requiring replacement therapy, all-cause mortality, cardiovascular mortality, acute kidney injury, any hospitalization, coronary heart disease, stroke, heart failure, atrial fibrillation, and peripheral artery disease. The analyses were performed within each cohort and summarized with random-effects meta-analyses. Within the population using eGFR based on creatinine alone (mean age, 54 years [SD, 17 years]; 51% were women; mean follow-up time, 4.8 years [SD, 3.3 years]), the mean eGFR was 90 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the median UACR was 11 mg/g (IQR, 8-16 mg/g). Within the population using eGFR based on creatinine and cystatin C (mean age, 59 years [SD, 12 years]; 53% were women; mean follow-up time, 10.8 years [SD, 4.1 years]), the mean eGFR was 88 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the median UACR was 9 mg/g (IQR, 6-18 mg/g). Lower eGFR (whether based on creatinine alone or based on creatinine and cystatin C) and higher UACR were each significantly associated with higher risk for each of the 10 adverse outcomes, including those in the mildest categories of chronic kidney disease. For example, among people with a UACR less than 10 mg/g, an eGFR of 45 to 59 mL/min/1.73 m2 based on creatinine alone was associated with significantly higher hospitalization rates compared with an eGFR of 90 to 104 mL/min/1.73 m2 (adjusted hazard ratio, 1.3 [95% CI, 1.2-1.3]; 161 vs 79 events per 1000 person-years; excess absolute risk, 22 events per 1000 person-years [95% CI, 19-25 events per 1000 person-years]). In this retrospective analysis of 114 cohorts, lower eGFR based on creatinine alone, lower eGFR based on creatinine and cystatin C, and more severe UACR were each associated with increased rates of 10 adverse outcomes, including adverse kidney outcomes, cardiovascular diseases, and hospitalizations.