A machine learning model for predicting worsening renal function using one‐year time series data in patients with type 2 diabetes
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<sup>2</sup> and are likely to benefit clinically from immediate treatment intensification.
Authors
- Mari Watanabe (ORCID: https://orcid.org/0009-0002-6869-0389)
- Kaiken Kimura
- Makoto Enomoto (ORCID: https://orcid.org/0009-0000-2014-0420)
- Michiaki Furukoshi
- Hiroshi Itoh (ORCID: https://orcid.org/0000-0003-2514-4919)
- Shu Meguro (ORCID: https://orcid.org/0000-0003-3528-128X)
- Tsuyoshi Masuda
Institutions
- Keio University (JP)
- Asahi Kasei (Japan) (JP)
Publication Details
- Journal
- Journal of Diabetes Investigation
- Published
- 2024-11-13
- DOI
- https://doi.org/10.1111/jdi.14309
- Citations
- 3
- Primary Topic
- Chronic Kidney Disease and Diabetes
- Type
- article
- Field-Weighted Citation Impact
- 1.02
Funders
- Eli Lilly and Company
- AstraZeneca
- Abbott Japan
- Asahi Kasei Pharma Corporation
- Kaken Pharmaceutical
- Keio University
- Eisai
- Eli Lilly Japan
- Daiichi Sankyo Europe