BMJ Open Diabetes Research & Care · 2020 · 11 citations · 16 references
MedicineInternal MedicineClinical Decision SupportDiabetologyHealth Data ScienceDiabetes ControlDiabetes ManagementDiabetes MellitusGlycemic ControlGlycemic MonitoringInsulin ResistanceDiabetes DiagnosisDiabetes Mellitus ManagementGlycated Hemoglobin MonitoringInsulin Resistance AssessmentMachine Learning For HealthcareMachine Learning Prediction ModelsMetabolic Control AnalysisMicrovascular Complications
INTRODUCTION: The aim of this study was to investigate the factors (clinical, organizational or doctor-related) involved in a timely and effective achievement of metabolic control, with no weight gain, in type 2 diabetes. RESEARCH DESIGN AND METHODS: Overall, 5.5 million of Hab1c and corresponding weight were studied in the Associazione Medici Diabetologi Annals database (2005-2017 data from 1.5 million patients of the Italian diabetes clinics network). Logic learning machine, a specific type of machine learning technique, was used to extract and rank the most relevant variables and to create the best model underlying the achievement of HbA1c<7 and no weight gain. RESULTS: The combined goal was achieved in 37.5% of measurements. High HbA1c and fasting glucose values and slow drop of HbA1c have the greatest relevance and emerge as first, main, obstacles the doctor has to overcome. However, as a second line of negative factors, markers of insulin resistance, microvascular complications, years of observation and proxy of duration of disease appear to be important determinants. Quality of assistance provided by the clinic plays a positive role. Almost all the available oral agents are effective whereas insulin use shows positive impact on glucometabolism but negative on weight containment. We also tried to analyze the contribution of each component of the combined endpoint; we found that weight gain was less frequently the reason for not reaching the endpoint and that HbA1c and weight have different determinants. Of note, use of glucagon-like peptide-1 receptor agonists (GLP1-RA) and glifozins improves weight control. CONCLUSIONS: Treating diabetes as early as possible with the best quality of care, before beta-cell deterioration and microvascular complications occurrence, make it easier to compensate patients. This message is a warning against clinical inertia. All medications play a role in goal achievements but use of GLP1-RAs and glifozins contributes to overweight prevention.
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Machine Learning, Neural and Statistical Classification
Bill Fulkerson, D. Michie, D. J. Spiegelhalter et al. · Technometrics · 1995 · 2.2K citations
Achievement of Goals in U.S. Diabetes Care, 1999–2010
Mohammed K. Ali, Kai McKeever Bullard, Jinan Saaddine et al. · New England Journal of Medicine · 2013 · 986 citations · Full text
Artificial intelligence, bias and clinical safety
Robert Challen, Joshua C. Denny, Martin Pitt et al. · BMJ Quality & Safety · 2019 · 903 citations · Full text
An implementation of logical analysis of data
Endre Boros, P. L. Hammer, Toshihide Ibaraki et al. · IEEE Transactions on Knowledge and Data Engineering · 2000 · 409 citations · Full text
Computing, Data Science, Data Mining +16