Concepts in Diabetic Nephropathy: From Pathophysiology to Treatment

Kinaan, Mustafa; Yau, Hanford; Martinez, Suzanne Quinn; Kar, Pran M.

doi:10.15586/jrenhep.2017.17

Cited by 11 publications

(11 citation statements)

References 92 publications

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“…It is perhaps not surprising that patients with diabetes are at higher risk of developing acute kidney injury, given that they are more likely to have an impaired renal function [36,37]. Increased susceptibility to infections such as urinary tract infections, pneumonia and sepsis in patients with diabetes can be caused by the effects of hyperglycaemia on the immune system, such as disrupted chemotaxis, impaired phagocytosis, reduced break-down of phagocytosed organisms and altered adherence of micro-organisms to polymorphonuclear leukocytes and lymphocytes [38].…”

Section: Discussionmentioning

confidence: 99%

Impact of Diabetes on Complications, Long Term Mortality and Recurrence in 608,890 Hospitalised Patients with Stroke

Szlachetka

Pana

Tiamkao

et al. 2020

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Background: Patients with diabetes mellitus (DM) have been found to be at an increased risk of suffering a stroke. However, research on the impact of DM on stroke outcomes is limited. Objectives: We aimed to examine the influence of DM on outcomes in ischaemic (IS) and haemorrhagic stroke (HS) patients. Methods: We included 608,890 consecutive stroke patients from the Thailand national insurance registry. In-hospital mortality, sepsis, pneumonia, acute kidney injury (AKI), urinary tract infection (UTI) and cardiovascular events were evaluated using logistic regressions. Long-term analysis was performed on first-stroke patients with a determined pathology (n = 398,663) using Royston-Parmar models. Median follow-ups were 4.21 and 4.78 years for IS and HS, respectively. All analyses were stratified by stroke sub-type. Results: Mean age (SD) was 64.3 (13.7) years, 44.9% were female with 61% IS, 28% HS and 11% undetermined strokes. DM was associated with in-hospital death, pneumonia, sepsis, AKI and cardiovascular events (odds ratios ranging from 1.13-1.78, p < 0.01) in both stroke types. In IS, DM was associated with long-term mortality and recurrence throughout the follow-up: HR max (99% CI) at t = 4108 days: 1.54 (1.27, 1.86) and HR (99% CI) = 1.27(1.23,1.32), respectively. In HS, HR max (t = 4108 days) for long-term mortality was 2.10 (1.87, 2.37), significant after day 14 post-discharge. HR max (t = 455) for long-term recurrence of HS was 1.29 (1.09, 1.53), significant after day 116 post-discharge. Conclusions: Regardless of stroke type, DM was associated with in-hospital death and complications, long-term mortality and stroke recurrence.

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Section: Discussionmentioning

confidence: 99%

Impact of Diabetes on Complications, Long Term Mortality and Recurrence in 608,890 Hospitalised Patients with Stroke

Szlachetka

Pana

Tiamkao

et al. 2020

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“…Considering KDIGO [2], the national institute for health and care excellence [3], and kidney disease outcomes quality initiative (KDOQI) [4] guidelines, four biomarkers, i.e., creatinine, urea, potassium, and albuminuria, are also considered useful tests for identifying CKD. In addition, CKD progression is influenced by other chronic diseases, such as hypertension and diabetes mellitus (DM), which need to be monitored to prevent the worsening of the patient's health status [5].…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis of Chronic Kidney Disease in Developing Countries: A Comparative Analysis of Machine Learning Techniques

et al. 2020

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The high incidence and prevalence of chronic kidney disease (CKD), often caused by late diagnoses, is a critical public health problem, especially in developing countries such as Brazil. CKD treatment therapies, such as dialysis and kidney transplantation, increase the morbidity and mortality rates, besides the public health costs. This study analyses the usage of machine learning techniques to assist in the early diagnosis of CKD in developing countries. Qualitative and quantitative comparative analyses are, respectively, conducted using a systematic literature review and an experiment with machine learning techniques, with the k-fold cross-validation method based on the Weka software and a CKD dataset. These analyses enable a discussion on the suitability of machine learning techniques for screening for CKD risk, focusing on low-income and hard-to-reach settings of developing countries, due to the specific problems faced by them, e.g., inadequate primary health care. The study results show that the J48 decision tree is a suitable machine learning technique for such screening in developing countries, due to the easy interpretation of its classification results, with 95.00% accuracy, reaching a nearly perfect agreement with an experienced nephrologist's opinion. Conversely, random forest, naive Bayes, support vector machine, multilayer perceptron, and k-nearest neighbor techniques, respectively, yield 93.33%, 88.33%, 76.66%, 75.00%, and 71.67% accuracy, presenting at least moderate agreement with the nephrologist, at the cost of a more difficult interpretation of the classification results. INDEX TERMS Reviews, machine learning, medical diagnosis.

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“…The identified importance of the three model-selected attributes blood urea (bu), sodium (sod), electrolyte, albumin (al), and serum creatinine (sc) are consistent with their use in clinical guidelines such as the Kidney Disease Improved Global Outcomes KDIGO [46] , the national institute for health and care excellence [47] , and Kidney Disease Outcomes Quality Initiative (KDOQI) [48] ; these four biomarkers are considered as useful clinical tests for CKD diagnosis. Hypertension (htn) and diabetes mellitus (dm) are themselves chronic diseases that influence the progression of CKD [47] , [49] , and were both identified as important for the ML models to diagnosis CKD. Age and blood pressure, two cost free attributes, were also considered important by SHAP.…”

Section: Discussionmentioning

confidence: 99%

Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening

Rashed-Al-Mahfuz

Haque

Azad

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Objective: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes. Methods: In this study, we developed machine learning models using selective key pathological categories to identify clinical test attributes that will aid in accurate early diagnosis of CKD. Such an approach will save time and costs for diagnostic screening. We have also evaluated the performance of several classifiers with k-fold cross-validation on optimized datasets derived using these selected clinical test attributes. Results: Our results suggest that the optimized datasets with important attributes perform well in diagnosis of CKD using our proposed machine learning models. Furthermore, we evaluated clinical test attributes based on urine and blood tests along with clinical parameters that have low costs of acquisition. The predictive models with the optimized and pathologically categorized attributes set yielded high levels of CKD diagnosis accuracy with random forest (RF) classifier being the best performing. Conclusions: Our machine learning approach has yielded effective predictive analytics for CKD screening which can be developed as a resource to facilitate improved CKD screening for enhanced and timely treatment plans.

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Concepts in Diabetic Nephropathy: From Pathophysiology to Treatment

Cited by 11 publications

References 92 publications

Impact of Diabetes on Complications, Long Term Mortality and Recurrence in 608,890 Hospitalised Patients with Stroke

Impact of Diabetes on Complications, Long Term Mortality and Recurrence in 608,890 Hospitalised Patients with Stroke

Computer-Aided Diagnosis of Chronic Kidney Disease in Developing Countries: A Comparative Analysis of Machine Learning Techniques

Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening

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