A machine learning model for predicting worsening renal function using one‐year time series data in patients with type 2 diabetes
ABSTRACT 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...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Journal of Diabetes Investigation |
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| Online Access: | https://doi.org/10.1111/jdi.14309 |
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| _version_ | 1846097194027843584 |
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| author | Mari Watanabe Shu Meguro Kaiken Kimura Michiaki Furukoshi Tsuyoshi Masuda Makoto Enomoto Hiroshi Itoh |
| author_facet | Mari Watanabe Shu Meguro Kaiken Kimura Michiaki Furukoshi Tsuyoshi Masuda Makoto Enomoto Hiroshi Itoh |
| author_sort | Mari Watanabe |
| collection | DOAJ |
| description | ABSTRACT 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 m2. 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 m2 and are likely to benefit clinically from immediate treatment intensification. |
| format | Article |
| id | doaj-art-be8e764550124d70a3a94fa783be0c92 |
| institution | Kabale University |
| issn | 2040-1116 2040-1124 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Investigation |
| spelling | doaj-art-be8e764550124d70a3a94fa783be0c922025-01-02T04:44:54ZengWileyJournal of Diabetes Investigation2040-11162040-11242025-01-01161939910.1111/jdi.14309A machine learning model for predicting worsening renal function using one‐year time series data in patients with type 2 diabetesMari Watanabe0Shu Meguro1Kaiken Kimura2Michiaki Furukoshi3Tsuyoshi Masuda4Makoto Enomoto5Hiroshi Itoh6Division of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine Keio University School of Medicine Tokyo JapanDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine Keio University School of Medicine Tokyo JapanAsahi Kasei Corporation Tokyo JapanAsahi Kasei Corporation Tokyo JapanAsahi Kasei Corporation Tokyo JapanAsahi Kasei Corporation Tokyo JapanDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine Keio University School of Medicine Tokyo JapanABSTRACT 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 m2. 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 m2 and are likely to benefit clinically from immediate treatment intensification.https://doi.org/10.1111/jdi.14309Diabetic nephropathiesMachine learningPrognosis |
| spellingShingle | Mari Watanabe Shu Meguro Kaiken Kimura Michiaki Furukoshi Tsuyoshi Masuda Makoto Enomoto Hiroshi Itoh A machine learning model for predicting worsening renal function using one‐year time series data in patients with type 2 diabetes Journal of Diabetes Investigation Diabetic nephropathies Machine learning Prognosis |
| title | A machine learning model for predicting worsening renal function using one‐year time series data in patients with type 2 diabetes |
| title_full | A machine learning model for predicting worsening renal function using one‐year time series data in patients with type 2 diabetes |
| title_fullStr | A machine learning model for predicting worsening renal function using one‐year time series data in patients with type 2 diabetes |
| title_full_unstemmed | A machine learning model for predicting worsening renal function using one‐year time series data in patients with type 2 diabetes |
| title_short | A machine learning model for predicting worsening renal function using one‐year time series data in patients with type 2 diabetes |
| title_sort | machine learning model for predicting worsening renal function using one year time series data in patients with type 2 diabetes |
| topic | Diabetic nephropathies Machine learning Prognosis |
| url | https://doi.org/10.1111/jdi.14309 |
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