title: Assessing occurrence of hypoglycemia and its severity from electronic health records of patients with type 2 diabetes mellitus.
abstract: OBJECTIVE
  Accurate measures of hypoglycemia within electronic health records (EHR) can facilitate clinical population management and research. We quantify the occurrence of serious and mild-to-moderate hypoglycemia in a large EHR database in the US, comparing estimates based only on structured data to those from structured data and natural language processing (NLP) of clinical notes.
  
  
  METHODS
  This cohort study included patients with type 2 diabetes identified from January 2009 through March 2014. We compared estimates of occurrence of hypoglycemia derived from diagnostic codes to those recorded within clinical notes and classified via NLP. Measures of hypoglycemia from only structured data (ICD-9 Algorithm), only note mentions (NLP Algorithm), and either structured data or notes (Combined Algorithm) were compared with estimates of the period prevalence, incidence rate, and event rate of hypoglycemia, overall and by seriousness.
  
  
  RESULTS
  Of the 844,683 eligible patients, 119,695 had at least one recorded hypoglycemic event identified with ICD-9 or NLP. The period prevalence of hypoglycemia was 12.4%, 25.1%, and 32.2% for the ICD-9 Algorithm, NLP Algorithm, and Combined Algorithm, respectively. There were 6128 apparent non-serious events utilizing the ICD-9 Algorithm, which increased to 152,987 non-serious events within the Combined Algorithm.
  
  
  CONCLUSIONS
  Ascertainment of events from clinical notes more than doubled the completeness of hypoglycemia capture overall relative to measures from structured data, and increased capture of non-serious events more than 20-fold. The structured data and clinical notes are complementary within the EHR, and both need to be considered in order to fully assess the occurrence of hypoglycemia.
authors.affiliation: Optum Epidemiology, Waltham, MA, USA; Department of Quantitative Health Sciences, UMass Medical School, Worcester, MA, USA. Electronic address: anthony.nunes@optum.com.
authors.forename: Anthony P
authors.initials: AP
authors.lastname: Nunes
authors.affiliation: Optum Epidemiology, Waltham, MA, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Yang
authors.affiliation: Merck & Co., Inc., Kenilworth, NJ, USA.
authors.forename: Larry
authors.initials: L
authors.lastname: Radican
authors.affiliation: Merck & Co., Inc., Kenilworth, NJ, USA.
authors.forename: Samuel S
authors.initials: SS
authors.lastname: Engel
authors.affiliation: Merck & Co., Inc., Kenilworth, NJ, USA.
authors.forename: Karen
authors.initials: K
authors.lastname: Kurtyka
authors.affiliation: Merck & Co., Inc., Kenilworth, NJ, USA.
authors.forename: Kaan
authors.initials: K
authors.lastname: Tunceli
authors.affiliation: Merck & Co., Inc., Kenilworth, NJ, USA.
authors.forename: Shengsheng
authors.initials: S
authors.lastname: Yu
authors.affiliation: Merck & Co., Inc., Kenilworth, NJ, USA.
authors.forename: Kristy
authors.initials: K
authors.lastname: Iglay
authors.affiliation: Optum Epidemiology, Waltham, MA, USA.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Doherty
authors.affiliation: Optum Epidemiology, Waltham, MA, USA; Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, RI, USA.
authors.forename: David D
authors.initials: DD
authors.lastname: Dore
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000187:drug effects* / Q000378:metabolism; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000188:drug therapy*; D057286:Electronic Health Records; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000139:chemically induced / Q000453:epidemiology*; D007004:Hypoglycemic Agents / Q000009:adverse effects*; D008297:Male; D008875:Middle Aged; D015995:Prevalence; D018570:Risk Assessment; D013997:Time Factors; D014481:United States / Q000453:epidemiology
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents
keywords: Electronic health records; Hypoglycemia; Mild hypoglycemia; Natural language processing; Severe hypoglycemia; Type 2 diabetes