A machine learning model for predicting worsening renal function using one-year time series data in patients with type 2 diabetes.

IF 3.2 3区 医学
Mari Watanabe, Shu Meguro, Kaiken Kimura, Michiaki Furukoshi, Tsuyoshi Masuda, Makoto Enomoto, Hiroshi Itoh
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引用次数: 0

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.

利用一年时间序列数据预测 2 型糖尿病患者肾功能恶化的机器学习模型。
背景和目的:为了预防糖尿病肾病引起的终末期肾病,我们利用机器学习创建了高危患者预测模型:为了预防糖尿病肾病导致的终末期肾病,我们利用机器学习建立了一个高危患者预测模型:参考点是每位患者的估计肾小球滤过率(eGFR)首次低于 60 mL/min/1.73 m2 的时间。输入期从参考点到一年前。主要终点是在 3 年评估期内,eGFR 从输入期平均值下降 50%。我们利用输入期内各种变量的时间序列数据创建了患者主要终点的预测模型。在 2,533 名患者中,1,409 名患者有参考点,31 名患者有输入期和评估期的记录并达到了主要终点,317 名患者没有达到主要终点。当最小 eGFR、最大和最小 eGFR 之差以及最大和最小尿蛋白值被纳入特征时,预测模型的曲线下面积(AUC)达到峰值(0.81):结论:考虑尿蛋白和 eGFR 水平的可变成分可提高预后预测的准确性。结论:考虑尿蛋白和 eGFR 水平的可变成分可提高预后预测的准确性,该模型可帮助我们识别 eGFR 超过 60 mL/min/1.73 m2 的肾功能相对保留的患者,这些患者有可能从立即加强治疗中获益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Diabetes Investigation
Journal of Diabetes Investigation Medicine-Internal Medicine
自引率
9.40%
发文量
218
期刊介绍: Journal of Diabetes Investigation is your core diabetes journal from Asia; the official journal of the Asian Association for the Study of Diabetes (AASD). The journal publishes original research, country reports, commentaries, reviews, mini-reviews, case reports, letters, as well as editorials and news. Embracing clinical and experimental research in diabetes and related areas, the Journal of Diabetes Investigation includes aspects of prevention, treatment, as well as molecular aspects and pathophysiology. Translational research focused on the exchange of ideas between clinicians and researchers is also welcome. Journal of Diabetes Investigation is indexed by Science Citation Index Expanded (SCIE).
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