Predicting non-responders to lifestyle intervention in prediabetes: a machine learning approach.

Autor: Foppiani A; International Center for the Assessment of Nutritional Status and the Development of Dietary Intervention Strategies (ICANS-DIS), Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, 20133, Milan, Italy. andrea.foppiani@unimi.it.; IRCCS Istituto Auxologico Italiano, Clinical Nutrition Unit, Department of Endocrine and Metabolic Medicine, 20100, Milan, Italy. andrea.foppiani@unimi.it., De Amicis R; International Center for the Assessment of Nutritional Status and the Development of Dietary Intervention Strategies (ICANS-DIS), Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, 20133, Milan, Italy.; IRCCS Istituto Auxologico Italiano, Obesity Unit and Laboratory of Nutrition and Obesity Research, Department of Endocrine and Metabolic Diseases, 20145, Milan, Italy., Leone A; International Center for the Assessment of Nutritional Status and the Development of Dietary Intervention Strategies (ICANS-DIS), Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, 20133, Milan, Italy.; IRCCS Istituto Auxologico Italiano, Clinical Nutrition Unit, Department of Endocrine and Metabolic Medicine, 20100, Milan, Italy., Sileo F; International Center for the Assessment of Nutritional Status and the Development of Dietary Intervention Strategies (ICANS-DIS), Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, 20133, Milan, Italy.; IRCCS Istituto Auxologico Italiano, Clinical Nutrition Unit, Department of Endocrine and Metabolic Medicine, 20100, Milan, Italy., Mambrini SP; International Center for the Assessment of Nutritional Status and the Development of Dietary Intervention Strategies (ICANS-DIS), Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, 20133, Milan, Italy.; IRCCS Istituto Auxologico Italiano, Laboratory of Metabolic Research, San Giuseppe Hospital, 28824, Piancavallo, Italy., Menichetti F; International Center for the Assessment of Nutritional Status and the Development of Dietary Intervention Strategies (ICANS-DIS), Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, 20133, Milan, Italy., Pozzi G; International Center for the Assessment of Nutritional Status and the Development of Dietary Intervention Strategies (ICANS-DIS), Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, 20133, Milan, Italy., Bertoli S; International Center for the Assessment of Nutritional Status and the Development of Dietary Intervention Strategies (ICANS-DIS), Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, 20133, Milan, Italy.; IRCCS Istituto Auxologico Italiano, Obesity Unit and Laboratory of Nutrition and Obesity Research, Department of Endocrine and Metabolic Diseases, 20145, Milan, Italy., Battezzati A; International Center for the Assessment of Nutritional Status and the Development of Dietary Intervention Strategies (ICANS-DIS), Department of Food, Environmental and Nutritional Sciences (DeFENS), University of Milan, 20133, Milan, Italy.; IRCCS Istituto Auxologico Italiano, Clinical Nutrition Unit, Department of Endocrine and Metabolic Medicine, 20100, Milan, Italy.
Jazyk: angličtina
Zdroj: European journal of clinical nutrition [Eur J Clin Nutr] 2024 Oct 23. Date of Electronic Publication: 2024 Oct 23.
DOI: 10.1038/s41430-024-01495-9
Abstrakt: Background: The clinical care process for people with prediabetes starts with lifestyle intervention, often escalating to more intense treatment due to the low success rate of the first-line intervention. Clinicians lack clear guidelines on which patients would benefit from early treatment with more intensive therapeutic options, so we aimed to develop an algorithm to early identify non-responders to lifestyle intervention for prediabetes.
Method: Several statistical and machine learning algorithms were screened with internal cross-validation on the basis of accuracy and discrimination ability to correctly classify patients that would fail to normalize fasting glycemia within one year of being prescribed a lifestyle intervention, solely based on the first examination measurements.
Result: Of the many screened algorithm, only a random forest model performed with sufficient accuracy to exceed the historical failure rate of patients within our center, with an accuracy of 0.689 (CI 0.669, 0.710) and an AUROC of 0.687 (CI 0.673, 0.701).
Conclusions: This study showcases the ability of machine learning models to provide useful insight in clinical practice leveraging knowledge contained in routinely collected data.
(© 2024. The Author(s).)
Databáze: MEDLINE
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