34
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Machine‐learning–based early prediction of end‐stage renal disease in patients with diabetic kidney disease using clinical trials data

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Aim

          To predict end‐stage renal disease (ESRD) in patients with type 2 diabetes by using machine‐learning models with multiple baseline demographic and clinical characteristics.

          Materials and methods

          In total, 11 789 patients with type 2 diabetes and nephropathy from three clinical trials, RENAAL (n = 1513), IDNT (n = 1715) and ALTITUDE (n = 8561), were used in this study. Eighteen baseline demographic and clinical characteristics were used as predictors to train machine‐learning models to predict ESRD (doubling of serum creatinine and/or ESRD). We used the area under the receiver operator curve (AUC) to assess the prediction performance of models and compared this with traditional Cox proportional hazard regression and kidney failure risk equation models.

          Results

          The feed forward neural network model predicted ESRD with an AUC of 0.82 (0.76‐0.87), 0.81 (0.75‐0.86) and 0.84 (0.79‐0.90) in the RENAAL, IDNT and ALTITUDE trials, respectively. The feed forward neural network model selected urinary albumin to creatinine ratio, serum albumin, uric acid and serum creatinine as important predictors and obtained a state‐of‐the‐art performance for predicting long‐term ESRD.

          Conclusions

          Despite large inter‐patient variability, non‐linear machine‐learning models can be used to predict long‐term ESRD in patients with type 2 diabetes and nephropathy using baseline demographic and clinical characteristics. The proposed method has the potential to create accurate and multiple outcome prediction automated models to identify high‐risk patients who could benefit from therapy in clinical practice.

          Related collections

          Most cited references21

          • Record: found
          • Abstract: found
          • Article: not found

          SMOTE: Synthetic Minority Over-sampling Technique

          An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.

            Serum creatinine concentration is widely used as an index of renal function, but this concentration is affected by factors other than glomerular filtration rate (GFR). To develop an equation to predict GFR from serum creatinine concentration and other factors. Cross-sectional study of GFR, creatinine clearance, serum creatinine concentration, and demographic and clinical characteristics in patients with chronic renal disease. 1628 patients enrolled in the baseline period of the Modification of Diet in Renal Disease (MDRD) Study, of whom 1070 were randomly selected as the training sample; the remaining 558 patients constituted the validation sample. The prediction equation was developed by stepwise regression applied to the training sample. The equation was then tested and compared with other prediction equations in the validation sample. To simplify prediction of GFR, the equation included only demographic and serum variables. Independent factors associated with a lower GFR included a higher serum creatinine concentration, older age, female sex, nonblack ethnicity, higher serum urea nitrogen levels, and lower serum albumin levels (P < 0.001 for all factors). The multiple regression model explained 90.3% of the variance in the logarithm of GFR in the validation sample. Measured creatinine clearance overestimated GFR by 19%, and creatinine clearance predicted by the Cockcroft-Gault formula overestimated GFR by 16%. After adjustment for this overestimation, the percentage of variance of the logarithm of GFR predicted by measured creatinine clearance or the Cockcroft-Gault formula was 86.6% and 84.2%, respectively. The equation developed from the MDRD Study provided a more accurate estimate of GFR in our study group than measured creatinine clearance or other commonly used equations.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Effects of Losartan on Renal and Cardiovascular Outcomes in Patients with Type 2 Diabetes and Nephropathy

              Diabetic nephropathy is the leading cause of end-stage renal disease. Interruption of the renin-angiotensin system slows the progression of renal disease in patients with type 1 diabetes, but similar data are not available for patients with type 2, the most common form of diabetes. We assessed the role of the angiotensin-II-receptor antagonist losartan in patients with type 2 diabetes and nephropathy. A total of 1513 patients were enrolled in this randomized, double-blind study comparing losartan (50 to 100 mg once daily) with placebo, both taken in addition to conventional antihypertensive treatment (calcium-channel antagonists, diuretics, alpha-blockers, beta-blockers, and centrally acting agents), for a mean of 3.4 years. The primary outcome was the composite of a doubling of the base-line serum creatinine concentration, end-stage renal disease, or death. Secondary end points included a composite of morbidity and mortality from cardiovascular causes, proteinuria, and the rate of progression of renal disease. A total of 327 patients in the losartan group reached the primary end point, as compared with 359 in the placebo group (risk reduction, 16 percent; P=0.02). Losartan reduced the incidence of a doubling of the serum creatinine concentration (risk reduction, 25 percent; P=0.006) and end-stage renal disease (risk reduction, 28 percent; P=0.002) but had no effect on the rate of death. The benefit exceeded that attributable to changes in blood pressure. The composite of morbidity and mortality from cardiovascular causes was similar in the two groups, although the rate of first hospitalization for heart failure was significantly lower with losartan (risk reduction, 32 percent; P=0.005). The level of proteinuria declined by 35 percent with losartan (P<0.001 for the comparison with placebo). Losartan conferred significant renal benefits in patients with type 2 diabetes and nephropathy, and it was generally well tolerated.
                Bookmark

                Author and article information

                Contributors
                sbn1984@gmail.com
                Journal
                Diabetes Obes Metab
                Diabetes Obes Metab
                10.1111/(ISSN)1463-1326
                DOM
                Diabetes, Obesity & Metabolism
                Blackwell Publishing Ltd (Oxford, UK )
                1462-8902
                1463-1326
                22 September 2020
                December 2020
                : 22
                : 12 ( doiID: 10.1111/dom.v22.12 )
                : 2479-2486
                Affiliations
                [ 1 ] Department of Clinical Pharmacy & Pharmacology University of Groningen, University Medical Center Groningen Groningen the Netherlands
                [ 2 ] University of Michigan Ann Arbor Michigan USA
                [ 3 ] The George Institute for Global Health Sydney Australia
                Author notes
                [*] [* ] Correspondence

                Sunil Belur Nagaraj, PhD, Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, De Brug 1D – 1‐ 019 9700AD, Groningen, the Netherlands.

                Email: sbn1984@ 123456gmail.com

                Author information
                https://orcid.org/0000-0002-6409-4101
                https://orcid.org/0000-0003-3340-2893
                https://orcid.org/0000-0002-3126-3730
                Article
                DOM14178
                10.1111/dom.14178
                7756814
                32844582
                c36eb7da-c57a-41a7-b661-5a07380d3075
                © 2020 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 04 June 2020
                : 18 August 2020
                : 23 August 2020
                Page count
                Figures: 4, Tables: 1, Pages: 8, Words: 5140
                Funding
                Funded by: Innovative Medicines Initiative 2 Joint Undertaking
                Award ID: 115974
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                December 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.6 mode:remove_FC converted:23.12.2020

                Endocrinology & Diabetes
                clinical trial, cohort study, diabetes complications, diabetic nephropathy, type 2 diabetes

                Comments

                Comment on this article