European journal of clinical nutrition · 2024
Predicting non-responders to lifestyle intervention in prediabetes: a machine learning approach.
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Foppiani Andrea, De Amicis Ramona, Leone Alessandro, Sileo Federica, Mambrini Sara Paola, Menichetti Francesca, Pozzi Giorgia, Bertoli Simona, Battezzati Alberto
Аннотация
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.
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.