A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy. in International journal of medical informatics / Int J Med Inform. 2024 Oct;190:105550. doi: 10.1016/j.ijmedinf.2024.105550. Epub 2024 Jul 15.

2024
ASL Torino 5

Tipo pubblicazione

Journal Article

Autori/Collaboratori (17)Vedi tutti...

Musacchio N
AMD-AI National Group Coordinator, UOS Integrating Primary and Specialist Care, ASST Nord Milano, Via Filippo Carcano 17, 20149 Milan, Italy.
Zilich R
Mix-x Partner, Via Circonvallazione 5, Ivrea (TO), Italy. Electronic address: rita.zilich@mix-x.com.
Masi D
Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy. Electronic address: davide.masi@uniroma1.it.

et alii...

Abstract

AIMS: This study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with suboptimal blood glucose control despite metformin monotherapy. METHODS: Employing the Logic Learning Machine (LLM), an advanced artificial intelligence system, we scrutinized electronic health records of 1.5 million patients treated in 271 diabetes clinics affiliated with the Italian Association of Medical Diabetologists from 2005 to 2019. Inclusion criteria comprised patients on metformin monotherapy with two consecutive mean HbA1c levels exceeding 7.0%. The cohort was divided into "inertia-NO" (20,067 patients with prompt intensification) and "inertia-YES" (13,029 patients without timely intensification). RESULTS: The LLM model demonstrated robust discriminatory ability among the two groups (ROC-AUC = 0.81, accuracy = 0.71, precision = 0.80, recall = 0.71, F1 score = 0.75). The main novelty of our results is indeed the identification of two main distinct subtypes of therapeutic inertia. The first exhibited a gradual but steady HbA1c increase, while the second featured a moderate, non-uniform rise with substantial fluctuations. CONCLUSIONS: Our analysis sheds light on the significant impact of HbA1c levels over time on therapeutic inertia in patients with T2D, emphasizing the importance of early intervention in the presence of specific HbA1c patterns.

Accesso banca dati bibliografica

PMID : 39059083

DOI : 10.1016/j.ijmedinf.2024.105550

Keywords

Humans; Diabetes Mellitus, Type 2/drug therapy/blood; Metformin/therapeutic use; Glycated Hemoglobin/analysis; Machine Learning; Female; Hypoglycemic Agents/therapeutic use; Male; Middle Aged; Aged; Electronic Health Records; Algorithms; Treatment Failure; Blood Glucose/analysis; Artificial intelligence; HbA1c; Machine learning; Metformin failure; Metformin monotherapy; Therapeutic inertia; Type 2 diabetes;