A machine learning model for predicting worsening renal function using one‐year time series data in patients with type 2 diabetes Article Swipe
Mari Watanabe
,
Shu Meguro
,
Kaiken Kimura
,
Michiaki Furukoshi
,
Tsuyoshi Masuda
,
Makoto Enomoto
,
Hiroshi Itoh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1111/jdi.14309
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1111/jdi.14309
Background and Aims To prevent end‐stage renal disease caused by diabetic kidney disease, we created a predictive model for high‐risk patients using machine learning. Methods and Results The reference point was the time at which each patient's estimated glomerular filtration rate (eGFR) first fell below 60 mL/min/1.73 m 2 . The input period spanned the reference point to 1 year prior. The primary endpoint was a 50% decrease in eGFR from the mean of the input period over the 3 year evaluation period. We created predictive models for patients’ primary endpoints using time series data of various variables over the input period. Among 2,533 total patients, 1,409 had reference points, 31 had records for their input and evaluation periods and had reached their primary endpoints, and 317 patients had not. The area under the curve (AUC) of the predictive model peaked (0.81) when the minimum eGFR, the difference between maximum and minimum eGFR, and both maximum and minimum urinary protein values were included in the features. Conclusion The accuracy of prognosis prediction can be improved by considering the variable components of urinary protein and eGFR levels. This model will allow us to identify patients whose renal functions are relatively preserved with eGFR of more than 60 mL/min/1.73 m 2 and are likely to benefit clinically from immediate treatment intensification.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/jdi.14309
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jdi.14309
- OA Status
- gold
- Cited By
- 2
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404363393
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404363393Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1111/jdi.14309Digital Object Identifier
- Title
-
A machine learning model for predicting worsening renal function using one‐year time series data in patients with type 2 diabetesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-13Full publication date if available
- Authors
-
Mari Watanabe, Shu Meguro, Kaiken Kimura, Michiaki Furukoshi, Tsuyoshi Masuda, Makoto Enomoto, Hiroshi ItohList of authors in order
- Landing page
-
https://doi.org/10.1111/jdi.14309Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jdi.14309Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jdi.14309Direct OA link when available
- Concepts
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Medicine, Renal function, Diabetes mellitus, Clinical endpoint, Series (stratigraphy), Type 2 diabetes, Kidney disease, Urology, Time point, Disease, Internal medicine, Endocrinology, Aesthetics, Biology, Clinical trial, Paleontology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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