Development and validation of a predictive model for the progression of diabetic kidney disease to kidney failure.
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ABSTRACT:
SUBMITTER: Cheng Y
PROVIDER: S-EPMC7946054 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Among diabetics, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality, and progression of their underlying disease. Finerenone is a novel, non-steroidal, selective mineralocorticoid-receptor antagonist which has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD), while revealing only a low risk of hyperkalemia. However, the effect of finerenone on renal and CV outcomes has not been investigated in long-term trials yet. METHODS:The Finerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney Disease -(FIDELIO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important renal and CV outcomes in T2D patients with CKD. FIDELIO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 5.5 years. FIDELIO-DKD randomized 5,734 patients with an estimated glomerular filtration rate (eGFR) ?25-<75 mL/min/1.73 m2 and albuminuria (urinary albumin-to-creatinine ratio ?30-?5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of primary outcome (overall two-sided significance level ? = 0.05), the composite of time to first occurrence of kidney failure, a sustained decrease of eGFR ?40% from baseline over at least 4 weeks, or renal death. CONCLUSION:FIDELIO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of renal and CV events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen.
| S-EPMC6888890 | biostudies-literature
Project description:IntroductionThe Canagliflozin and Renal Endpoints in Diabetes with Established Nephropathy Clinical Evaluation (CREDENCE) study showed that compared with placebo, canagliflozin 100 mg significantly reduced the risk of major cardiovascular events and adverse renal outcomes in patients with diabetic kidney disease (DKD). We developed a simulation model that can be used to estimate the long-term health and economic consequences of DKD treatment interventions for patients matching the CREDENCE study population.MethodsThe CREDENCE Economic Model of DKD (CREDEM-DKD) was developed using patient-level data from CREDENCE (which recruited patients with estimated glomerular filtration rate 30 to < 90 mL/min/1.73 m2, urinary albumin to creatinine ratio > 300-5000 mg/g, and taking the maximum tolerated dose of a renin-angiotensin-aldosterone system inhibitor). Risk prediction equations were fit for start of maintenance dialysis, doubling of serum creatinine, hospitalization for heart failure, nonfatal myocardial infarction, nonfatal stroke, and all-cause mortality. A micro-simulation model was constructed using these risk equations combined with user-definable kidney transplant event risks. Internal validation was performed by loading the model to replicate the CREDENCE study and comparing predictions with trial Kaplan-Meier estimate curves. External validation was performed by loading the model to replicate a subgroup of the CANagliflozin cardioVascular Assessment Study (CANVAS) Program with patient characteristics that would have qualified for inclusion in CREDENCE.ResultsRisk prediction equations generally fit well and exhibited good concordance, especially for the placebo arm. In the canagliflozin arm, modest underprediction was observed for myocardial infarction, along with overprediction of dialysis, doubling of serum creatinine, and all-cause mortality. Discrimination was strong (0.85) for the renal outcomes, but weaker for the macrovascular outcomes and all-cause mortality (0.60-0.68). The model performed well in internal and external validation exercises.ConclusionCREDEM-DKD is an important new tool in the evaluation of treatment interventions in the DKD population.Trial registrationClinicalTrials.gov identifier, NCT02065791.
| S-EPMC7547928 | biostudies-literature
Project description:BackgroundAnemia is common and is associated with impaired clinical outcomes in diabetic chronic kidney disease (CKD). It may be explained by reduced erythropoietin (EPO) synthesis, but recent data suggest that EPO-resistance and diminished iron availability due to inflammation contribute significantly. In this cohort study, we evaluated the impact of hepcidin-25--the key hormone of iron-metabolism--on clinical outcomes in diabetic patients with CKD along with endogenous EPO levels.Methods249 diabetic patients with CKD of any stage, excluding end-stage renal disease (ESRD), were enrolled (2003-2005), if they were not on EPO-stimulating agent and iron therapy. Hepcidin-25 levels were measured by radioimmunoassay. The association of hepcidin-25 at baseline with clinical variables was investigated using linear regression models. All-cause mortality and a composite endpoint of CKD progression (ESRD or doubling of serum creatinine) were analyzed by Cox proportional hazards models.ResultsPatients (age 67 yrs, 53% male, GFR 51 ml/min, hemoglobin 131 g/L, EPO 13.5 U/L, hepcidin-25 62.0 ng/ml) were followed for a median time of 4.2 yrs. Forty-nine patients died (19.7%) and forty (16.1%) patients reached the composite endpoint. Elevated hepcidin levels were independently associated with higher ferritin-levels, lower EPO-levels and impaired kidney function (all p<0.05). Hepcidin was related to mortality, along with its interaction with EPO, older age, greater proteinuria and elevated CRP (all p<0.05). Hepcidin was also predictive for progression of CKD, aside from baseline GFR, proteinuria, low albumin- and hemoglobin-levels and a history of CVD (all p<0.05).ConclusionsWe found hepcidin-25 to be associated with EPO and impaired kidney function in diabetic CKD. Elevated hepcidin-25 and EPO-levels were independent predictors of mortality, while hepcidin-25 was also predictive for progression of CKD. Both hepcidin-25 and EPO may represent important prognostic factors of clinical outcome and have the potential to further define "high risk" populations in CKD.
| S-EPMC4404250 | biostudies-literature
Project description:ObjectivesThis study aims to identify risk factors for acute kidney injury (AKI) in patients with ureterolithiasis and to develop a predictive model for early AKI detection in this population.MethodsA retrospective analysis was conducted on data from 1,016 patients with ureterolithiasis who presented to our outpatient emergency department between January 2021 and December 2022. Using multifactorial logistic regression, we identified independent risk factors for AKI and constructed a nomogram to predict AKI risk. The predictive model's efficacy was assessed through the area under the ROC curve, calibration curves, Hosmer-Lemeshow (HL) test, and decision curve analysis (DCA).ResultsAKI was diagnosed in 18.7% of the patients. Independent risk factors identified included age, fever, diabetes, hyperuricemia, bilateral calculi, functional solitary kidney, self-medication, and prehospital delay. The nomogram demonstrated excellent discriminatory capabilities, with AUCs of 0.818 (95% CI, 0.775-0.861) for the modeling set and 0.782 (95% CI, 0.708-0.856) for the validation set. Both calibration curve and HL test results confirmed strong concordance between the model's predictions and actual observations. DCA highlighted the model's significant clinical utility.ConclusionsThe predictive model developed in this study provides clinicians with a valuable tool for early identification and management of patients at high risk for AKI, thereby potentially enhancing patient outcomes.
| S-EPMC11346321 | biostudies-literature
Project description:Anemia, which might contribute to pathogenesis of kidney dysfunction, is a common finding in patients with type 2 diabetes. The aim of this study was to investigate if hemoglobin concentration is associated with the degree of change in urinary albumin-creatinine ratio or the development of albuminuria in patients with type 2 diabetes.We measured hemoglobin concentration in 470 (296 men and 174 women) consecutive type 2 diabetic patients without albuminuria. We performed a follow-up study to assess the progression or development of albuminuria, the interval of which was 3.0 years. Then we evaluated relationships between hemoglobin concentration and albuminuria, using multivariate linear regression analyses and logistic regression analyses.Eighty four patients developed albuminuria during follow-up duration. In multivariate analyses, hemoglobin concentration was negatively associated with a change in urinary albumin-creatinine ratio in men (ß = -0.259, P = 0.0002) and women (ß = -0.194, P = 0.030). Moreover, multivariate adjusted odds ratio associated with 1 g/L in hemoglobin for the development of albuminuria was 0.93 (95% confidence interval; 0.89-0.96) in men and 0.94 (95% confidence interval; 0.88-0.99) in women, respectively. And, multivariate analyses revealed that adjusted odds ratios for the development of albuminuria were 4.78 (95% confidence interval; 1.65-13.91) in men and 4.62 (95% confidence interval; 1.34-16.68) in women with anemia (hemoglobin < 130 g/L for men and < 120 g/L for women), which were higher than those without anemia.Low hemoglobin concentration could be a predictor for the progression and development of albuminuria in patients with type 2 diabetes.
| S-EPMC4449165 | biostudies-other
Project description:This study was performed to develop and validate a predictive model for the risk of end-stage renal disease (ESRD) inpatients with diabetic nephropathy (DN) confirmed by renal biopsy. We conducted a retrospective study with 968 patients with T2DM who underwentrenal biopsy for the pathological confirmation of DNat the First Affiliated Hospital of Zhengzhou University from February 2012 to January 2015; the patients were followed until December 2018. The outcome was defined as a fatal or nonfatal ESRD event (peritoneal dialysis or hemodialysis for ESRD, renal transplantation, or death due to chronic renal failure or ESRD). The dataset was randomly split into development (75%) and validation (25%) cohorts. We used stepwise multivariablelogistic regression to identify baseline predictors for model development. The model's performance in the two cohorts, including discrimination and calibration, was evaluated by the C-statistic and the P value of the Hosmer-Lemeshow test. During the 3-year follow-up period, there were 225 outcome events (47.1%) during follow-up. Outcomes occurred in 187 patients (52.2%) in the derivation cohort and 38 patients (31.7%) in the validation cohort. The variables selected in the final multivariable logistic regression after backward selection were pathological grade, Log Urinary Albumin-to-creatinine ratio (Log ACR), cystatin C, estimated glomerular filtration rate (eGFR) and B-type natriuretic peptide (BNP). 4 prediction models were created in a derivation cohort of 478 patients: a clinical model that included cystatin C, eGFR, BNP, Log ACR; a clinical-pathological model and a clinical-medication model, respectively, also contained pathological grade and renin-angiotensin system blocker (RASB) use; and a full model that also contained the pathological grade, RASB use and age. Compared with the clinical model, the clinical-pathological model and the full model had better C statistics (0.865 and 0.866, respectively, vs. 0.864) in the derivation cohort and better C statistics (0.876 and 0.875, respectively, vs. 0.870) in the validation cohort. Among the four models, the clinical-pathological model had the lowest AIC of 332.53 and the best P value of 0.909 of the Hosmer-Lemeshow test. We constructed a nomogram which was a simple calculator to predict the risk ratio of progression to ESRD for patients with DN within 3 years. The clinical-pathological model using routinely available clinical measurements was shown to be accurate and validated method for predicting disease progression in patients with DN. The risk model can be used in clinical practice to improve the quality of risk management and early intervention.
| S-EPMC7020820 | biostudies-literature
Project description:Current equation-based risk stratification algorithms for kidney failure (KF) may have limited applicability in real world settings, where missing information may impede their computation for a large share of patients, hampering one from taking full advantage of the wealth of information collected in electronic health records. To overcome such limitations, we trained and validated the Prognostic Reasoning System for Chronic Kidney Disease (PROGRES-CKD), a novel algorithm predicting end-stage kidney disease (ESKD). PROGRES-CKD is a naïve Bayes classifier predicting ESKD onset within 6 and 24 months in adult, stage 3-to-5 CKD patients. PROGRES-CKD trained on 17,775 CKD patients treated in the Fresenius Medical Care (FMC) NephroCare network. The algorithm was validated in a second independent FMC cohort (n = 6760) and in the German Chronic Kidney Disease (GCKD) study cohort (n = 4058). We contrasted PROGRES-CKD accuracy against the performance of the Kidney Failure Risk Equation (KFRE). Discrimination accuracy in the validation cohorts was excellent for both short-term (stage 4-5 CKD, FMC: AUC = 0.90, 95%CI 0.88-0.91; GCKD: AUC = 0.91, 95% CI 0.86-0.97) and long-term (stage 3-5 CKD, FMC: AUC = 0.85, 95%CI 0.83-0.88; GCKD: AUC = 0.85, 95%CI 0.83-0.88) forecasting horizons. The performance of PROGRES-CKD was non-inferior to KFRE for the 24-month horizon and proved more accurate for the 6-month horizon forecast in both validation cohorts. In the real world setting captured in the FMC validation cohort, PROGRES-CKD was computable for all patients, whereas KFRE could be computed for complete cases only (i.e., 30% and 16% of the cohort in 6- and 24-month horizons). PROGRES-CKD accurately predicts KF onset among CKD patients. Contrary to equation-based scores, PROGRES-CKD extends to patients with incomplete data and allows explicit assessment of prediction robustness in case of missing values. PROGRES-CKD may efficiently assist physicians' prognostic reasoning in real-life applications.
| S-EPMC8656741 | biostudies-literature
Project description:Background and objectivesProtein carbamylation is a consequence of uremia and carbamylated lipoproteins contribute to atherogenesis in CKD. Proteins can also be carbamylated by a urea-independent mechanism, and whether carbamylated lipoproteins contribute to the progression of CKD has not been investigated.Design, setting, participants, & measurementsA case-control study was performed to determine whether there were changes in plasma levels of carbamylated lipoproteins in individuals with type 2 diabetes with eGFR >60 ml/min per 1.73 m2 compared with a group of age- and sex-matched healthy controls. A cohort of 1320 patients with type 2 diabetes with baseline eGFR ≥30 ml/min per 1.73 m2 was longitudinally followed up to evaluate the association between carbamylated lipoproteins and progression of CKD. The primary kidney outcome was defined as doubling of serum creatinine and/or initiation of KRT during follow-up. Plasma carbamylated LDLs and HDLs was measured by ELISA.ResultsIn individuals with diabetes with eGFR >60 ml/min per 1.73 m2, both plasma carbamylated LDL and HDL levels were higher compared with healthy controls (P<0.001). After a mean follow-up of 9 years of the diabetic cohort, individuals in the top quartile of carbamylated LDL (hazard ratio, 2.21; 95% confidence interval, 1.42 to 3.46; P<0.001) and carbamylated HDL (hazard ratio, 4.53; 95% confidence interval, 2.87 to 7.13; P<0.001) had higher risk of deterioration of kidney function compared with those in the lowest quartile. On multivariable Cox regression analysis, plasma carbamylated LDL was no longer associated with kidney outcome after adjusting for baseline eGFR and potential confounding factors. However, the association between plasma carbamylated HDL and kidney outcome remained significant and was independent of HDL cholesterol.ConclusionsPlasma carbamylated HDL but not carbamylated LDL was independently associated with progression of CKD in patients with type 2 diabetes.
| S-EPMC7057307 | biostudies-literature
Project description:Background: Chronic kidney disease (CKD) is characterized by progressive renal function loss, which may finally lead to end-stage renal disease (ESRD). The study is aimed at identifying crucial genes related to CKD progressive and constructing a disease prediction model to investigate risk factors.Methods: GSE97709 and GSE37171 datasets were downloaded from the GEO database including peripheral blood samples from subjects with CKD, ESRD, and healthy controls. Differential expressed genes (DEGs) were identified and functional enrichment analysis. Machine learning algorithm-based prediction model was constructed to identify crucial functional feature genes related to ESRD.Results: A total of 76 DEGs were screened from CDK vs. normal samples while 10,114 DEGs were identified from ESRD vs. CDK samples. For numerous genes related to ESRD, several GO biological terms and 141 signaling pathways were identified including markedly upregulated olfactory transduction and downregulated platelet activation pathway. The DEGs were clustering in three modules according to WGCNA access, namely, ME1, ME2, and ME3. By construction of the XGBoost model and dataset validation, we screened cohorts of genes associated with progressive CKD, such as FZD10, FOXD4, and FAM215A. FZD10 represented the highest score (F score = 21) in predictive model.Conclusion: Our results demonstrated that FZD10, FOXD4, PPP3R1, and UCP2 might be critical genes in CKD progression.
| S-EPMC7676934 | biostudies-literature
Project description:ObjectiveWe aimed to develop and validate a predictive machine learning (ML) model for cardiac surgery associated with acute kidney injury (CSA-AKI) based on a multicenter randomized control trial (RCT) and a Medical Information Mart for Intensive Care-IV (MIMIC-IV) dataset.MethodsThis was a subanalysis from a completed RCT approved by the Ethics Committee of Fuwai Hospital in Beijing, China (NCT03782350). Data from Fuwai Hospital were randomly assigned, with 80% for the training dataset and 20% for the testing dataset. The data from three other centers were used for the external validation dataset. Furthermore, the MIMIC-IV dataset was also utilized to validate the performance of the predictive model. The area under the receiver operating characteristic curve (ROC-AUC), the precision-recall curve (PR-AUC), and the calibration brier score were applied to evaluate the performance of the traditional logistic regression (LR) and eleven ML algorithms. Additionally, the Shapley Additive Explanations (SHAP) interpreter was used to explain the potential risk factors for CSA-AKI.ResultA total of 6495 eligible patients undergoing cardiopulmonary bypass (CPB) were eventually included in this study, 2416 of whom were from Fuwai Hospital (Beijing), for model development, 562 from three other cardiac centers in China, and 3517 from the MIMICIV dataset, were used, respectively, for external validation. The CatBoostClassifier algorithms outperformed other models, with excellent discrimination and calibration performance for the development, as well as the MIMIC-IV, datasets. In addition, the CatBoostClassifier achieved ROC-AUCs of 0.85, 0.67, and 0.77 and brier scores of 0.14, 0.19, and 0.16 in the testing, external, and MIMIC-IV datasets, respectively. Moreover, the utmost important risk factor, the N-terminal brain sodium peptide (NT-proBNP), was confirmed by the LASSO method in the feature section process. Notably, the SHAP explainer identified that the preoperative blood urea nitrogen level, prothrombin time, serum creatinine level, total bilirubin level, and age were positively correlated with CSA-AKI; preoperative platelets level, systolic and diastolic blood pressure, albumin level, and body weight were negatively associated with CSA-AKI.ConclusionsThe CatBoostClassifier algorithms outperformed other ML models in the discrimination and calibration of CSA-AKI prediction cardiac surgery with CPB, based on a multicenter RCT and MIMIC-IV dataset. Moreover, the preoperative NT-proBNP level was confirmed to be strongly related to CSA-AKI.
| S-EPMC9917969 | biostudies-literature