title: Validation of a Delirium Risk Assessment Using Electronic Medical Record Information.
abstract: OBJECTIVE
  Identifying patients at risk for delirium allows prompt application of prevention, diagnostic, and treatment strategies; but is rarely done. Once delirium develops, patients are more likely to need posthospitalization skilled care. This study developed an a priori electronic prediction rule using independent risk factors identified in a National Center of Clinical Excellence meta-analysis and validated the ability to predict delirium in 2 cohorts.
  
  
  METHODS
  Retrospective analysis followed by prospective validation.
  
  
  METHODS
  Tertiary VA Hospital in New England.
  
  
  METHODS
  A total of 27,625 medical records of hospitalized patients and 246 prospectively enrolled patients admitted to the hospital.
  
  
  METHODS
  The electronic delirium risk prediction rule was created using data obtained from the patient electronic medical record (EMR). The primary outcome, delirium, was identified 2 ways: (1) from the EMR (retrospective cohort) and (2) clinical assessment on enrollment and daily thereafter (prospective participants). We assessed discrimination of the delirium prediction rule with the C-statistic. Secondary outcomes were length of stay and discharge to rehabilitation.
  
  
  RESULTS
  Retrospectively, delirium was identified in 8% of medical records (n = 2343); prospectively, delirium during hospitalization was present in 26% of participants (n = 64). In the retrospective cohort, medical record delirium was identified in 2%, 3%, 11%, and 38% of the low, intermediate, high, and very high-risk groups, respectively (C-statistic = 0.81; 95% confidence interval 0.80-0.82). Prospectively, the electronic prediction rule identified delirium in 15%, 18%, 31%, and 55% of these groups (C-statistic = 0.69; 95% confidence interval 0.61-0.77). Compared with low-risk patients, those at high- or very high delirium risk had increased length of stay (5.7 ± 5.6 vs 3.7 ± 2.7 days; P = .001) and higher rates of discharge to rehabilitation (8.9% vs 20.8%; P = .02).
  
  
  CONCLUSIONS
  Automatic calculation of delirium risk using an EMR algorithm identifies patients at risk for delirium, which creates a critical opportunity for gaining clinical efficiencies and improving delirium identification, including those needing skilled care.
authors.affiliation: Center of Innovation in Long-term Services/Supports, Providence VA Medical Center, Providence, RI; Delirium Patient Safety Center of Inquiry, VA Boston Healthcare System, Boston, MA; Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, MA; Division of Aging, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA. Electronic address: James.Rudolph@va.gov.
authors.forename: James L
authors.initials: JL
authors.lastname: Rudolph
authors.affiliation: Delirium Patient Safety Center of Inquiry, VA Boston Healthcare System, Boston, MA.
authors.forename: Kelly
authors.initials: K
authors.lastname: Doherty
authors.affiliation: Delirium Patient Safety Center of Inquiry, VA Boston Healthcare System, Boston, MA; Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, MA; School of Nursing, Science and Health Professions, Regis College, Weston, MA.
authors.forename: Brittany
authors.initials: B
authors.lastname: Kelly
authors.affiliation: Delirium Patient Safety Center of Inquiry, VA Boston Healthcare System, Boston, MA; Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, MA; Division of Aging, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA.
authors.forename: Jane A
authors.initials: JA
authors.lastname: Driver
authors.affiliation: Delirium Patient Safety Center of Inquiry, VA Boston Healthcare System, Boston, MA; Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, MA.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Archambault
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003693:Delirium / Q000209:etiology*; D057286:Electronic Health Records; D005260:Female; D006787:Hospitals, Veterans; D006801:Humans; D008297:Male; D009511:New England; D061214:Patient Safety; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods
keywords: Delirium; patient safety; patient-centered outcomes research; risk assessment