A machine learning algorithm for the prediction of complications incorporated in electronic medical records improves type 2 diabetes care

IF 7.4 3区医学 Q1 ENDOCRINOLOGY & METABOLISM

Diabetes research and clinical practice Pub Date : 2025-09-13 DOI:10.1016/j.diabres.2025.112900

Antonio Nicolucci , Giacomo Vespasiani , Domenico Mannino , Giuseppina T. Russo , Giuseppe Lucisano , Maria Chiara Rossi , Paola Ponzani , Salvatore De Cosmo , Graziano Di Cianni , Cristina Lencioni , Luca Romeo , Michele Bernardini , Emanuele Frontoni , Riccardo Candido , the EGOAL - AMD Annals Study Group

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引用次数: 0

Abstract

Aims

Early identification of patients with type 2 diabetes (T2D) at high risk for complications may help reduce clinical inertia and improve care quality. This study assessed the clinical impact of integrating a machine learning-based prediction tool into electronic medical records (EMRs) in Italian diabetes clinics.

Methods

A validated algorithm estimating the 5-year risk of six major diabetes complications was embedded in the EMRs of 38 centers. A pre-post comparison over 12 months was conducted between patients whose risk score was generated (test group) and those eligible but not assessed (control group).

Results

Among 138,558 eligible patients, 20,314 (14.7 %) had at least one score generated. Compared to controls, test group patients showed significantly greater improvements in HbA1c ≤7.0 % (+9.0 % vs. +4.5 %), LDL-C <70 mg/dL (+27.9 % vs. +20.7 %), and BMI <25 kg/m² (+16.5 % vs. +11.0 %), with larger reductions in HbA1c >8.0 % (–18.4 % vs. –10.1 %). They also more frequently initiated antihypertensive, lipid-lowering, and cardio-renal protective therapies.

Conclusions

Embedding an AI-based prediction tool in routine clinical practice improved several quality indicators and therapeutic decisions. Its real-world application shows promise in overcoming clinical inertia and promoting personalized diabetes management.

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一种用于预测电子医疗记录并发症的机器学习算法改善了2型糖尿病的护理。

目的：早期发现并发症高危的2型糖尿病（T2D）患者，有助于减少临床惰性，提高护理质量。本研究评估了在意大利糖尿病诊所将基于机器学习的预测工具整合到电子医疗记录（emr）中的临床影响。方法：在38个中心的电子病历中嵌入一种经过验证的算法，估计6种主要糖尿病并发症的5年风险。在产生风险评分的患者（试验组）和符合条件但未评估的患者（对照组）之间进行了超过12 个月的前后比较。结果：在138,558例符合条件的患者中，20,314例（14.7 %）至少产生了一个评分。与对照组相比，试验组患者在HbA1c≤7.0 %（+9.0 % vs +4.5 %）、LDL-C 2（+16.5 % vs +11.0 %）、HbA1c≤8.0 %（-18.4 % vs -10.1 %）方面有更大的改善。他们也更频繁地开始抗高血压、降脂和心肾保护治疗。结论：在常规临床实践中嵌入基于人工智能的预测工具可改善若干质量指标和治疗决策。它的实际应用显示出克服临床惰性和促进个性化糖尿病管理的希望。

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

Diabetes research and clinical practice 医学-内分泌学与代谢

CiteScore

10.30

自引率

3.90%

发文量

862

审稿时长

32 days

期刊介绍： Diabetes Research and Clinical Practice is an international journal for health-care providers and clinically oriented researchers that publishes high-quality original research articles and expert reviews in diabetes and related areas. The role of the journal is to provide a venue for dissemination of knowledge and discussion of topics related to diabetes clinical research and patient care. Topics of focus include translational science, genetics, immunology, nutrition, psychosocial research, epidemiology, prevention, socio-economic research, complications, new treatments, technologies and therapy.