Identifying patients at increased risk of hypoglycaemia in primary care: Development of a machine learning‐based screening tool

Abstract

Introduction: In primary care, identifying patients with type 2 diabetes (T2D) who are at increased risk of hypoglycaemia is important for the prevention of hypoglycaemic events. We aimed to develop a screening tool based on machine learning to identify such patients using routinely available demographic and medication data. Methods:We used a cohort study design and the Groningen Initiative to ANalyse Type 2 diabetes Treatment (GIANTT) medical record database to develop models for hypoglycaemia risk. The first hypoglycaemic event in the observation period (2007)(2008)(2009)(2010)(2011)(2012)(2013) was the outcome. Demographic and medication data were used as predictor variables to train machine learning models. The performance of the models was compared with a model using additional clinical data using fivefold cross validation with the area under the receiver operator characteristic curve (AUC) as a metric.Results: We included 13,876 T2D patients. The best performing model including only demographic and medication data was logistic regression with least absolute shrinkage and selection operator, with an AUC of 0.71. Ten variables were included (odds ratio): male gender (0.997), age (0.990), total drug count (1.012), glucoselowering drug count (1.039), sulfonylurea use (1.62), insulin use (1.769), pre-mixed insulin use (1.109), insulin count (1.827), insulin duration (1.193), and antidepressant use (1.05). The proposed model obtained a similar performance to the model using additional clinical data. Conclusion:Using demographic and medication data, a model for identifying patients at increased risk of hypoglycaemia was developed using machine learning. This model can be used as a tool in primary care to screen for patients with T2D who may need additional attention to prevent or reduce hypoglycaemic events. K E Y W O R D S artificial intelligence, hypoglycaemia, type 2 diabetesThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review

“…In addition, Fujihara et al [81] have shown that ML (Neural Networks) can aid clinicians to decide, with high prediction accuracy, when to initiate an insulin regimen for their patients with Type 2 diabetes. Crutzen et al [82] employed a collection of algorithms to identify patients with type 2 diabetes at an increased risk of hypoglycaemia in primary care.…”

Aprendizado de máquina nos serviços farmacêuticos: uma revisão integrativa

“…A partir da estratégia de busca, foram encontrados 831 artigos: 246 (30%) no Pubmed, 237 (28%) no Portal BVS, 149 (18%) na Cochrane Library e 199 (24%) no Embase. Após excluir as duplicatas, utilizar os critérios de inclusão e exclusão, e consultar as referências dos artigos selecionados, 5 estudos foram incluídos nesta revisão [14][15][16][17][18] (Figura 1).…”

“…Na Tabela 1 estão apresentados os resultados de acordo com autor, país, idioma, periódico, objetivos e atividade clínica do farmacêutico. Entre as atividades clínicas, os estudos abordaram a validação da prescrição médica (n = 3) 14,17,18 identificação de reações adversas a medicamentos (RAM) (n = 2) 15,16 ambas inseridas nos serviços farmacêuticos de revisão da farmacoterapia e monitorização terapêutica, respectivamente 8 .…”

Process evaluation of a pharmacist‐led intervention aimed at deprescribing and appropriate use of cardiometabolic medication among adult people with type 2 diabetes

“…After the training, the pharmacists received a list of patients with high hypoglycaemia risk scores as estimated with a previously developed algorithm, including information about age, sex, number and types of medication used, 23 generated from the pharmacy information system (NControl) used by Service Apotheek pharmacies. This screening tool was applied to all registered patients in the pharmacy who were ≥45 years old and filled a prescription for insulin and/or a sulfonylurea in the past 4 months.…”