title: Comparison of Approaches for Heart Failure Case Identification From Electronic Health Record Data.
abstract: OBJECTIVE
  Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation.
  
  
  OBJECTIVE
  To develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital.
  
  
  METHODS
  We performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least 1 of 3 characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide level of 500 pg/mL or higher; (3) logistic regression of 30 clinically relevant structured data elements; (4) machine-learning approach using unstructured notes; and (5) machine-learning approach using structured and unstructured data.
  
  
  METHODS
  Heart failure diagnosis based on discharge diagnosis and physician review of sampled medical records.
  
  
  RESULTS
  A total of 47 119 hospitalizations were included in this study (mean [SD] age, 60.9 [18.15] years; 23 952 female [50.8%], 5258 black/African American [11.2%], and 3667 Hispanic/Latino [7.8%] patients). Of these hospitalizations, 6549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of a PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating characteristic curves of 0.953, 0.969, and 0.974, respectively. With a PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively.
  
  
  CONCLUSIONS
  The problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.
authors.affiliation: Department of Population Health, New York University School of Medicine, New York2Department of Medicine, New York University School of Medicine, New York.
authors.forename: Saul
authors.initials: S
authors.lastname: Blecker
authors.affiliation: Department of Medicine, New York University School of Medicine, New York.
authors.forename: Stuart D
authors.initials: SD
authors.lastname: Katz
authors.affiliation: Department of Population Health, New York University School of Medicine, New York2Department of Medicine, New York University School of Medicine, New York.
authors.forename: Leora I
authors.initials: LI
authors.lastname: Horwitz
authors.affiliation: Department of Information Systems, NewYork-Presbyterian Hospital, New York.
authors.forename: Gilad
authors.initials: G
authors.lastname: Kuperman
authors.affiliation: Department of Population Health, New York University School of Medicine, New York.
authors.forename: Hannah
authors.initials: H
authors.lastname: Park
authors.affiliation: Department of Medicine, New York University School of Medicine, New York.
authors.forename: Alex
authors.initials: A
authors.lastname: Gold
authors.affiliation: Department of Computer Science, New York University, New York.
authors.forename: David
authors.initials: D
authors.lastname: Sontag