Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic Control
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 Me...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Machine Learning and Knowledge Extraction |
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| Online Access: | https://www.mdpi.com/2504-4990/6/1/21 |
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| author | Musacchio Nicoletta Rita Zilich Davide Masi Fabio Baccetti Besmir Nreu Carlo Bruno Giorda Giacomo Guaita Lelio Morviducci Marco Muselli Alessandro Ozzello Federico Pisani Paola Ponzani Antonio Rossi Pierluigi Santin Damiano Verda Graziano Di Cianni Riccardo Candido |
| author_facet | Musacchio Nicoletta Rita Zilich Davide Masi Fabio Baccetti Besmir Nreu Carlo Bruno Giorda Giacomo Guaita Lelio Morviducci Marco Muselli Alessandro Ozzello Federico Pisani Paola Ponzani Antonio Rossi Pierluigi Santin Damiano Verda Graziano Di Cianni Riccardo Candido |
| author_sort | Musacchio Nicoletta |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:03:48Z |
| format | Article |
| id | doaj.art-44633c7022aa45af97d3ca10f733b6ca |
| institution | Directory Open Access Journal |
| issn | 2504-4990 |
| language | English |
| last_indexed | 2024-04-24T18:03:48Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj.art-44633c7022aa45af97d3ca10f733b6ca2024-03-27T13:52:05ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902024-02-016142043410.3390/make6010021Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic ControlMusacchio Nicoletta0Rita Zilich1Davide Masi2Fabio Baccetti3Besmir Nreu4Carlo Bruno Giorda5Giacomo Guaita6Lelio Morviducci7Marco Muselli8Alessandro Ozzello9Federico Pisani10Paola Ponzani11Antonio Rossi12Pierluigi Santin13Damiano Verda14Graziano Di Cianni15Riccardo Candido16AMD-AI National Group Coordinator Coordinator, UOS Integrating Primary and Specialist Care, ASST Nord Milano, Via Filippo Carcano 17, 20149 Milan, ItalyMix-x Partner, Via Circonvallazione 5, 10015 Ivrea, ItalyDepartment of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, ItalyDiabetes and Endocrinology Unit, ASL Nord-West Tuscany, 54100 Massa Carrara, ItalyDiabetology Unit, Careggi Hospital, Largo G.A. Brambilla, 3, 50134 Florence, ItalyDiabetes and Endocrinology Unit, ASL TO5, 10023 Chieri, ItalyDiabetes and Endocrinology Unit, ASL SULCIS, 09016 Sulcis, ItalyDiabates and Nutrition UOC, S. Spirito Hospital—ASL Roma 1, 00913 Rome 00913, ItalyRulex Innovation Labs, Rulex Inc., Via Felice Romani 9/2, 16122 Genoa, ItalyAMD Regional Past President, AI AMD National Group, 10090 Bruino, ItalyMix-x Partner, Via Circonvallazione 5, 10015 Ivrea, ItalyDiabetes and Metabolic Disease Unit ASL 4 Liguria, 16043 Chiavari, ItalyIRCCS Ospedale Galeazzi-Sant’Ambrogio, 20149 Milan, ItalyData Scientist Deimos, 33100 Udine, ItalyRulex Innovation Labs, Rulex Inc., Via Felice Romani 9/2, 16122 Genoa, ItalyAMD Past President, Diabetes and Metabolic Diseases Unit, Nord-West Tuscany, Livorno Hospital, Viale Alfieri 36, 57124 Livorno, ItalyAMD New President, Azienda Sanitaria Universitaria Giuliano Isontina, 34128 Trieste, ItalyBackground: 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.https://www.mdpi.com/2504-4990/6/1/21machine learningartificial intelligencetype 2 diabetestherapeutic inertiainsulin |
| spellingShingle | Musacchio Nicoletta Rita Zilich Davide Masi Fabio Baccetti Besmir Nreu Carlo Bruno Giorda Giacomo Guaita Lelio Morviducci Marco Muselli Alessandro Ozzello Federico Pisani Paola Ponzani Antonio Rossi Pierluigi Santin Damiano Verda Graziano Di Cianni Riccardo Candido Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic Control Machine Learning and Knowledge Extraction machine learning artificial intelligence type 2 diabetes therapeutic inertia insulin |
| title | Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic Control |
| title_full | Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic Control |
| title_fullStr | Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic Control |
| title_full_unstemmed | Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic Control |
| title_short | Overcoming Therapeutic Inertia in Type 2 Diabetes: Exploring Machine Learning-Based Scenario Simulation for Improving Short-Term Glycemic Control |
| title_sort | overcoming therapeutic inertia in type 2 diabetes exploring machine learning based scenario simulation for improving short term glycemic control |
| topic | machine learning artificial intelligence type 2 diabetes therapeutic inertia insulin |
| url | https://www.mdpi.com/2504-4990/6/1/21 |
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