title: Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records.
abstract: OBJECTIVE
  To develop and validate automated electronic note search strategies (automated digital algorithm) to identify Charlson comorbidities.
  
  
  METHODS
  The automated digital algorithm was built by a series of programmatic queries applied to an institutional electronic medical record database. The automated digital algorithm was derived from secondary analysis of an observational cohort study of 1447 patients admitted to the intensive care unit from January 1 through December 31, 2006, and validated in an independent cohort of 240 patients. The sensitivity, specificity, and positive and negative predictive values of the automated digital algorithm and International Classification of Diseases, Ninth Revision (ICD-9) codes were compared with comprehensive medical record review (reference standard) for the Charlson comorbidities.
  
  
  RESULTS
  In the derivation cohort, the automated digital algorithm achieved a median sensitivity of 100% (range, 99%-100%) and a median specificity of 99.7% (range, 99%-100%). In the validation cohort, the sensitivity of the automated digital algorithm ranged from 91% to 100%, and the specificity ranged from 98% to 100%. The sensitivity of the ICD-9 codes ranged from 8% for dementia to 100% for leukemia, whereas specificity ranged from 86% for congestive heart failure to 100% for leukemia, dementia, and AIDS.
  
  
  CONCLUSIONS
  Our results suggest that search strategies that use automated electronic search strategies to extract Charlson comorbidities from the clinical notes contained within the electronic medical record are feasible and reliable. Automated digital algorithm outperformed ICD-9 codes in all the Charlson variables except leukemia, with greater sensitivity, specificity, and positive and negative predictive values.
authors.affiliation: Multidisciplinary Epidemiology and Translational Research in Intensive Care, Mayo Clinic, Rochester, MN, USA.
authors.forename: Balwinder
authors.initials: B
authors.lastname: Singh
authors.forename: Amandeep
authors.initials: A
authors.lastname: Singh
authors.forename: Adil
authors.initials: A
authors.lastname: Ahmed
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Wilson
authors.forename: Brian W
authors.initials: BW
authors.lastname: Pickering
authors.forename: Vitaly
authors.initials: V
authors.lastname: Herasevich
authors.forename: Ognjen
authors.initials: O
authors.lastname: Gajic
authors.forename: Guangxi
authors.initials: G
authors.lastname: Li
mesh_terms: D000465:Algorithms; D015897:Comorbidity; D016001:Confidence Intervals; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D038801:International Classification of Diseases