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Limited Knowledge of Chronic Kidney Disease among Type 2 Diabetes Mellitus Patients in India.

ABSTRACT: Diabetes and hypertension are the two major causes of chronic kidney disease (CKD). Epidemiological studies have found poor knowledge about the CKD among the general population. Hence, this study aimed to assess the awareness of CKD among type 2 diabetes mellitus (T2DM) patients in India. Patients with confirmed T2DM were included in the study. Patients receiving dialysis or with a history of a kidney transplant were excluded. A validated questionnaire was used to assess knowledge about CKD. Demographic characteristics were presented using descriptive statistics and trends in groups were calculated using the chi-square test. Statistical analysis was performed using SAS v9.4. A total of 323 patients completed the study. The mean age of the patients was 56 ± 11.25 years, and 51.7% were female. Only 33.43% of the patients correctly identified diabetes and hypertension as risk factors for CKD, while 44.27% were aware of the kidney's function. Statistically significant associations were observed between kidney disease knowledge and education status (p = 0.004), socioeconomic status (p = 0.000), and income status (p = 0.003). No association was observed between the knowledge about CKD and age, gender, hypertension stages, CKD stages, duration of diabetes as well as hypertension and co-morbidities. Based on the results of this study, we found poor knowledge of CKD among Indian T2DM patients. The government should start a CKD awareness programme to deal with this rising co-morbid condition.

SUBMITTER: Hussain S

PROVIDER: S-EPMC6518175 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Limited Knowledge of Chronic Kidney Disease among Type 2 Diabetes Mellitus Patients in India.

Hussain Salman S Habib Anwar A Najmi Abul Kalam AK

International journal of environmental research and public health 20190423 8

Diabetes and hypertension are the two major causes of chronic kidney disease (CKD). Epidemiological studies have found poor knowledge about the CKD among the general population. Hence, this study aimed to assess the awareness of CKD among type 2 diabetes mellitus (T2DM) patients in India. Patients with confirmed T2DM were included in the study. Patients receiving dialysis or with a history of a kidney transplant were excluded. A validated questionnaire was used to assess knowledge about CKD. Dem ...[more]

PMID: 31018581

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Project description:Type 1 diabetes mellitus (T1DM) patients are a significant threat to chronic kidney disease (CKD) development during their life. However, there is always a high chance of delay in CKD detection because CKD can be asymptomatic, and T1DM patients bypass traditional CKD tests during their routine checkups. This study aims to develop and validate a prediction model and nomogram of CKD in T1DM patients using readily available routine checkup data for early CKD detection. This research utilized 1375 T1DM patients' sixteen years of longitudinal data from multi-center Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials conducted at 28 sites in the USA and Canada and considered 17 routinely available features. Three feature ranking algorithms, extreme gradient boosting (XGB), random forest (RF), and extremely randomized trees classifier (ERT), were applied to create three feature ranking lists, and logistic regression analyses were performed to develop CKD prediction models using these ranked feature lists to identify the best performing top-ranked features combination. Finally, the most significant features were selected to develop a multivariate logistic regression-based CKD prediction model for T1DM patients. This model was evaluated using sensitivity, specificity, accuracy, precision, and F1 score on train and test data. A nomogram of the final model was further generated for easy application in clinical practices. Hypertension, duration of diabetes, drinking habit, triglycerides, ACE inhibitors, low-density lipoprotein (LDL) cholesterol, age, and smoking habit were the top-8 features ranked by the XGB model and identified as the most important features for predicting CKD in T1DM patients. These eight features were selected to develop the final prediction model using multivariate logistic regression, which showed 90.04% and 88.59% accuracy in internal and test data validation. The proposed model showed excellent performance and can be used for CKD identification in T1DM patients during routine checkups.

Project description:Chronic kidney disease (CKD) is one of the severe side effects of type 1 diabetes mellitus (T1DM). However, the detection and diagnosis of CKD are often delayed because of its asymptomatic nature. In addition, patients often tend to bypass the traditional urine protein (urinary albumin)-based CKD detection test. Even though disease detection using machine learning (ML) is a well-established field of study, it is rarely used to diagnose CKD in T1DM patients. This research aimed to employ and evaluate several ML algorithms to develop models to quickly predict CKD in patients with T1DM using easily available routine checkup data. This study analyzed 16 years of data of 1375 T1DM patients, obtained from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials directed by the National Institute of Diabetes, Digestive, and Kidney Diseases, USA. Three data imputation techniques (RF, KNN, and MICE) and the SMOTETomek resampling technique were used to preprocess the primary dataset. Ten ML algorithms including logistic regression (LR), k-nearest neighbor (KNN), Gaussian naïve Bayes (GNB), support vector machine (SVM), stochastic gradient descent (SGD), decision tree (DT), gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and light gradient-boosted machine (LightGBM) were applied to developed prediction models. Each model included 19 demographic, medical history, behavioral, and biochemical features, and every feature's effect was ranked using three feature ranking techniques (XGB, RF, and Extra Tree). Lastly, each model's ROC, sensitivity (recall), specificity, accuracy, precision, and F-1 score were estimated to find the best-performing model. The RF classifier model exhibited the best performance with 0.96 (±0.01) accuracy, 0.98 (±0.01) sensitivity, and 0.93 (±0.02) specificity. LightGBM performed second best and was quite close to RF with 0.95 (±0.06) accuracy. In addition to these two models, KNN, SVM, DT, GB, and XGB models also achieved more than 90% accuracy.

Project description:INTRODUCTION:Ertugliflozin is a sodium-glucose cotransporter 2 inhibitor in development for type 2 diabetes mellitus (T2DM). The safety and efficacy of ertugliflozin were evaluated over 52 weeks in patients with chronic kidney disease (CKD). METHODS:In this double-blind randomized study (NCT01986855), patients with glycated hemoglobin (A1C) 7.0-10.5% and stage 3 CKD [estimated glomerular filtration rate (eGFR) ≥ 30 to < 60 mL/min/1.73 m2] who were undergoing treatment with standard diabetes therapy (or therapies) including insulin and/or sulfonylureas were randomized to once-daily ertugliflozin 5 mg, 15 mg, or placebo. Patients on metformin underwent a pre-randomization ≥ 10-week wash-off period. The primary endpoint was change from baseline in A1C at week 26 in the overall cohort. Secondary efficacy endpoints were assessed in the stage 3A CKD cohort (eGFR ≥ 45 to < 60 mL/min/1.73 m2) at weeks 26 and 52. Safety was assessed in the overall cohort. RESULTS:468 patients were randomized (baseline mean A1C 8.2%). At week 26, reductions from baseline in A1C were observed across groups in the overall cohort [least squares mean changes (95% confidence interval) - 0.3% (- 0.4, - 0.1), - 0.3% (- 0.4, - 0.1), and - 0.4% (- 0.6, - 0.3) for placebo and for ertugliflozin 5 mg and 15 mg, respectively]. Prohibited use of metformin was identified in ~ 17% of patients and impacted evaluation of the primary endpoint. Greater reductions from baseline in body weight, fasting plasma glucose, and systolic blood pressure were observed with ertugliflozin versus placebo at week 26 (stage 3A CKD cohort). The incidences of urinary tract infections, genital mycotic infections, and hypoglycemia adverse events were not meaningfully different between groups. The incidence of hypovolemia-related adverse events was higher with ertugliflozin relative to placebo. CONCLUSION:Although surreptitious metformin use impacted the primary analysis, reductions in blood glucose and body weight were observed with ertugliflozin in patients with T2DM and stage 3 CKD; ertugliflozin had an acceptable safety profile. FUNDING:Merck Sharp & Dohme Corp. a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA and Pfizer Inc. TRIAL REGISTRATION:Clinicaltrials.gov identifier NCT01986855.

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Limited Knowledge of Chronic Kidney Disease among Type 2 Diabetes Mellitus Patients in India.

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Limited Knowledge of Chronic Kidney Disease among Type 2 Diabetes Mellitus Patients in India.

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