title: Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications.
abstract: OBJECTIVE
  To develop methods for visual analysis of temporal phenotype data available through electronic health records (EHR).
  
  
  METHODS
  24 580 adults from the multiparameter intelligent monitoring in intensive care V.6 (MIMIC II) EHR database of critically ill patients were analyzed, with significant temporal associations visualized as a map of associations between hospital length of stay (LOS) and ICD-9-CM codes. An expanded phenotype, using ICD-9-CM, microbiology, and computerized physician order entry data, was defined for hospital-acquired Clostridium difficile (HA-CDI). LOS, estimated costs, 30-day post-discharge mortality, and antecedent medication provider order entry were evaluated for HA-CDI cases compared to randomly selected controls.
  
  
  RESULTS
  Temporal phenome analysis revealed 191 significant codes (p value, adjusted for false discovery rate, ≤0.05). HA-CDI was identified in 414 cases, and was associated with longer median LOS, 20 versus 9 days, and adjusted HR 0.33 (95% CI 0.28 to 0.39). This prolongation carries an estimated annual incremental cost increase of US$1.2-2.0 billion in the USA alone.
  
  
  CONCLUSIONS
  Comprehensive EHR data have made large-scale phenome-based analysis feasible. Time-dependent pathological disease states have dynamic phenomic evolution, which may be captured through visual analytical approaches. Although MIMIC II is a single institutional retrospective database, our approach should be portable to other EHR data sources, including prospective 'learning healthcare systems'. For example, interventions to prevent HA-CDI could be dynamically evaluated using the same techniques.
  
  
  CONCLUSIONS
  The new visual analytical method described in this paper led directly to the identification of numerous hospital-acquired conditions, which could be further explored through an expanded phenotype definition.
authors.affiliation: Department of Medicine, Division of Hematology and Oncology, Vanderbilt University, Nashville, Tennessee, USA.
authors.forename: Jeremy L
authors.initials: JL
authors.lastname: Warner
authors.forename: Amin
authors.initials: A
authors.lastname: Zollanvari
authors.forename: Quan
authors.initials: Q
authors.lastname: Ding
authors.forename: Peijin
authors.initials: P
authors.lastname: Zhang
authors.forename: Graham M
authors.initials: GM
authors.lastname: Snyder
authors.forename: Gil
authors.initials: G
authors.lastname: Alterovitz
mesh_terms: D000328:Adult; D016638:Critical Illness; D003428:Cross Infection / Q000175:diagnosis*; D057286:Electronic Health Records; D006801:Humans; D007049:Iatrogenic Disease; D007362:Intensive Care Units / Q000458:organization & administration; D038801:International Classification of Diseases; D010641:Phenotype; D013995:Time
keywords: Computer Graphics; Computing Methodologies; Data Mining; Electronic Health Records; Phenotype
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