Metabolic control and treatment regimens in patients with type 1 diabetes in Castilla-La Mancha, 10 years later: The 2020 DIACAM1 study

Sastre, Julia; Pinés, Pedro; Val, Florentino del; Moreno-Fernández, Jesús; López, Javier González; Quiroga, Ivan; Herránz, Sandra; Gallardo, Gema López; Calderón, Dulce; López, José López

doi:10.1016/j.endien.2022.07.006

Cited by 1 publication

(1 citation statement)

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“…It is crucial to note that, in addition to timely treatment for decompensated patients, various other factors, if managed appropriately, can contribute to achieving the metabolic target. Examples include physician factors such as patient education, proper titration, patientcentered care, and follow-up visits, as well as patient factors such as therapy adherence, self-monitoring, and adopting a healthy lifestyle [18][19][20][21][22].…”

Section: Machine Learning Analysis 231 General Criteriamentioning

confidence: 99%

Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic Control

Nicoletta,

Zilich,

Masi

et al. 2024

MAKE

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Background: International guidelines for diabetes care emphasize the urgency of promptly achieving and sustaining adequate glycemic control to reduce the occurrence of micro/macrovascular complications in patients with type 2 diabetes mellitus (T2DM). However, data from the Italian Association of Medical Diabetologists (AMD) Annals reveal that only 47% of T2DM patients reach appropriate glycemic targets, with approximately 30% relying on insulin therapy, either solely or in combination. This artificial intelligence analysis seeks to assess the potential impact of timely insulin initiation in all eligible patients via a “what-if” scenario simulation, leveraging real-world data. Methods: This retrospective cohort study utilized the AMD Annals database, comprising 1,186,247 T2DM patients from 2005 to 2019. Employing the Logic Learning Machine (LLM), we simulated timely insulin use for all eligible patients, estimating its effect on glycemic control after 12 months within a cohort of 85,239 patients. Of these, 20,015 were employed for the machine learning phase and 65,224 for simulation. Results: Within the simulated scenario, the introduction of appropriate insulin therapy led to a noteworthy projected 17% increase in patients meeting the metabolic target after 12 months from therapy initiation within the cohort of 65,224 individuals. The LLM’s projection envisages 32,851 potential patients achieving the target (hemoglobin glycated < 7.5%) after 12 months, compared to 21,453 patients observed in real-world cases. The receiver operating characteristic (ROC) curve analysis for this model demonstrated modest performance, with an area under the curve (AUC) value of 70.4%. Conclusions: This study reaffirms the significance of combatting therapeutic inertia in managing T2DM patients. Early insulinization, when clinically appropriate, markedly enhances patients’ metabolic goals at the 12-month follow-up.

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