title: Towards prevention of acute syndromes: electronic identification of at-risk patients during hospital admission.
abstract: BACKGROUND
  Identifying patients at risk for acute respiratory distress syndrome (ARDS) before their admission to intensive care is crucial to prevention and treatment. The objective of this study is to determine the performance of an automated algorithm for identifying selected ARDS predisposing conditions at the time of hospital admission.
  
  
  METHODS
  This secondary analysis of a prospective cohort study included 3,005 patients admitted to hospital between January 1 and December 31, 2010. The automated algorithm for five ARDS predisposing conditions (sepsis, pneumonia, aspiration, acute pancreatitis, and shock) was developed through a series of queries applied to institutional electronic medical record databases. The automated algorithm was derived and refined in a derivation cohort of 1,562 patients and subsequently validated in an independent cohort of 1,443 patients. The sensitivity, specificity, and positive and negative predictive values of an automated algorithm to identify ARDS risk factors were compared with another two independent data extraction strategies, including manual data extraction and ICD-9 code search. The reference standard was defined as the agreement between the ICD-9 code, automated and manual data extraction.
  
  
  RESULTS
  Compared to the reference standard, the automated algorithm had higher sensitivity than manual data extraction for identifying a case of sepsis (95% vs. 56%), aspiration (63% vs. 42%), acute pancreatitis (100% vs. 70%), pneumonia (93% vs. 62%) and shock (77% vs. 41%) with similar specificity except for sepsis and pneumonia (90% vs. 98% for sepsis and 95% vs. 99% for pneumonia). The PPV for identifying these five acute conditions using the automated algorithm ranged from 65% for pneumonia to 91 % for acute pancreatitis, whereas the NPV for the automated algorithm ranged from 99% to 100%.
  
  
  CONCLUSIONS
  A rule-based electronic data extraction can reliably and accurately identify patients at risk of ARDS at the time of hospital admission.
authors.forename: A
authors.initials: A
authors.lastname: Ahmed
authors.forename: C
authors.initials: C
authors.lastname: Thongprayoon
authors.forename: B W
authors.initials: BW
authors.lastname: Pickering
authors.affiliation: Multidisciplinary Epidemiology and Translational Research in Intensive Care (M.E.T.R.I.C.), Mayo Clinic , Rochester.
authors.forename: A
authors.initials: A
authors.lastname: Akhoundi
authors.forename: G
authors.initials: G
authors.lastname: Wilson
authors.affiliation: Institute for Health Informatics, University of Minnesota , Minneapolis.
authors.forename: D
authors.initials: D
authors.lastname: Pieczkiewicz
authors.forename: V
authors.initials: V
authors.lastname: Herasevich
mesh_terms: D000465:Algorithms; D057286:Electronic Health Records; D005260:Female; D006760:Hospitalization; D006801:Humans; D008297:Male; D008875:Middle Aged; D008910:Minnesota / Q000453:epidemiology; D011237:Predictive Value of Tests; D015995:Prevalence; D011446:Prospective Studies; D015203:Reproducibility of Results; D012128:Respiratory Distress Syndrome / Q000453:epidemiology / Q000517:prevention & control*; D012307:Risk Factors
keywords: ARDS; EMR; Electronic search; risk factor
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