Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data

Ravizza, Stefan; Huschto, Tony; Адамов, А. К.; Böhm, Lars; Büsser, Alexander; Flöther, Frederik F.; Hinzmann, Rolf; König, Helena; McAhren, Scott M.; Robertson, Daniel H.; Schleyer, Titus; Schneidinger, Bernd; Petrich, Wolfgang

doi:10.1038/s41591-018-0239-8

Cited by 133 publications

(92 citation statements)

References 28 publications

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“…In addition, regarding utilization of ANN and CNN methods, Kolachalama et al [76] recently provided a perspicacity into the association of pathological fibrosis identified from histologic images with clinical phenotypes for patients with CKD, helping the diagnostics and prognostics of these phenotypes. Subsequently, there has been an increasing number of AI studies, with great emphasis on the usage of nephrology and transplantation [85,[87][88][89]. Inspired by the idea of mimicking the biological structure of human brains, deep learning is a subfield of machine learning based on ANN [74].…”

Section: Using Electronic Health Record Data In Nephrologymentioning

confidence: 99%

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Thongprayoon

Kaewput

Kovvuru

et al. 2020

JCM

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Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.

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Section: Using Electronic Health Record Data In Nephrologymentioning

confidence: 99%

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Thongprayoon

Kaewput

Kovvuru

et al. 2020

JCM

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“…This study found that African Americans have much higher rates of CKD-related medical problems than Caucasians for all five CKD stages, and prominent markers leading to ESRD were high glucose, high systolic BP, obesity, alcohol/drug use, and low hematocrit. In 2019, Ravizza et al [86] carried out a direct comparison of algorithms derived from real-world data (RWD) and clinical data for quantifying the risk of CKD as a long-term complication of diabetes. After teaching the Roche/IBM model using seven prioritized features, including age, body mass index, and glomerular filtration rate, creatinine, albumin, glucose, and hemoglobin, the AUC of the prediction algorithm was 0.7937.…”

Section: Diabetic Kidney Disease (Dkd)mentioning

confidence: 99%

Role of Artificial Intelligence in Kidney Disease

Yuan¹,

Zhang²,

Deng³

et al. 2020

Int. J. Med. Sci.

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Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.

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“…A recent study explored the use of RWD to identify individuals with diabetes who are at risk for developing CKD in the near future. 42 The analysis compared an algorithm derived from patient records from 417,912 individuals with newly diagnosed T1D or T2D from the IBM Explorys database with algorithms derived from four large clinical trials. The Roche/IBM algorithm targeted seven features-age, BMI, glomerular filtration rate, HbA1c, glucose, and concentrations of creatinine and albumin-as important predictors of risk based on data-driven and medical selection.…”

Section: Assessing the Risk For Ckdmentioning

confidence: 99%

12th Roche Diabetes Care Network Meeting: April 11–13, 2019, Copenhagen, Denmark

Parkin

Zepezauer²,

Hinzmann³

2020

Diabetes Technology & Therapeutics

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A panel of international experts in the field of diabetes and diabetes technology met in Copenhagen, Denmark, for the 12th Roche Diabetes Care Network Meeting. The goal of these meetings is to share current knowledge, facilitate new collaborations, and encourage further research projects that can improve the lives of people with diabetes. Specific areas of interest included use of telemedicine and mobile health technologies, behavior change, patient-centered care, and multifactorial approaches to addressing all metabolic abnormalities associated with diabetes. The 2019 meeting covered a comprehensive scientific program and four keynote lectures.

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Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data

Cited by 133 publications

References 28 publications

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Role of Artificial Intelligence in Kidney Disease

12th Roche Diabetes Care Network Meeting: April 11–13, 2019, Copenhagen, Denmark

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