科研之友
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摘要
AimsTo design and validate a natural language processing algorithm to identify insulin therapy decline from the text of physician notes, and to determine the prevalence of insulin therapy decline and its impact on insulin initiation. MethodsWe designed the algorithm using the publicly available natural language processing platform Canary. We evaluated the accuracy of the algorithm on 1501 randomly selected primary care physicians' notes from the electronic medical record system of a large academic medical centre. Using the validated language model, we then studied the prevalence of insulin therapy decline between 2000 and 2014. ResultsThe algorithm identified documentation of insulin therapy decline with a sensitivity of 100% (95% CI 82.4-100), a positive predictive value of 95% (95% CI 74.4-99.9), and a specificity of 99.9% (95% CI 99.6-100.0). We identified 3295 insulin-naive adults with Type 2 diabetes who were recommended insulin therapy; 984 of them (29.9%) initially declined insulin. People with HbA(1c) 75 mmol/mol (9.0%) were more likely [766/2239 (34.2%)] to have declined insulin than people with HbA(1c) 53-63 mmol/mol (7.0-7.9%) and 64-74 mmol/mol (8.0-8.9%; P < 0.0001). Among the people who initially declined but ultimately started insulin [374/984 (38.0%)], mean time to insulin initiation was 790 days. ConclusionsInsulin therapy decline is common, potentially leading to progression of hyperglycaemia and a delay in achievement of glycaemic control. Further investigation is needed to determine the reasons, risk factors and long-term outcomes of this important clinical phenomenon.
- 出版日期2017-11
- 单位中国医学科学院北京协和医院; 东北大学
