title: Predicting time to nursing home care and death in individuals with Alzheimer disease.
abstract: OBJECTIVE
  To develop and validate an approach that uses clinical features that can be determined in a standard patient visit to estimate the length of time before an individual patient with Alzheimer disease (AD) requires care equivalent to nursing home placement or dies.
  
  
  METHODS
  Prospective cohort study of 236 patients, followed up semiannually for up to 7 years. A second validation cohort of 105 patients was also followed.
  
  
  METHODS
  Three AD research centers.
  
  
  METHODS
  All patients met National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD and had mild dementia at the initial visit.
  
  
  METHODS
  Predictive features, ascertained at the initial visit, were sex, duration of illness, age at onset, modified Mini-Mental State Examination (mMMS) score, and the presence or absence of extrapyramidal signs or psychotic features.
  
  
  METHODS
  (1) Requiring the equivalent of nursing home placement and (2) death.
  
  
  RESULTS
  Prediction algorithms were constructed for the 2 outcomes based on Cox proportional hazard models. For each algorithm, a predictor index is calculated based on the status of each predictive feature at the initial visit. A table that specifies the number of months in which 25%, 50%, and 75% of patients with any specific predictor index value are likely to reach the end point is then consulted. Survival curves for time to need for care equivalent to nursing home placement and for time to death derived from the algorithms for selected predictor indexes fell within the 95% confidence bands of actual survival curves for patients. When the predictor variables from the initial visit for the validation cohort patients were entered into the algorithm, the predicted survival curves for time to death fell within the 95% confidence bands of actual survival curves for the patients.
  
  
  CONCLUSIONS
  The prediction algorithms are a first but promising step toward providing specific prognoses to patients, families, and practitioners. This approach also has clear implications for the design and interpretation of clinical trials in patients with AD.
authors.affiliation: Department of Neurology, Gertrude H. Sergievsky Center, New York, NY 10032, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Stern
authors.forename: M X
authors.initials: MX
authors.lastname: Tang
authors.forename: M S
authors.initials: MS
authors.lastname: Albert
authors.forename: J
authors.initials: J
authors.lastname: Brandt
authors.forename: D M
authors.initials: DM
authors.lastname: Jacobs
authors.forename: K
authors.initials: K
authors.lastname: Bell
authors.forename: K
authors.initials: K
authors.lastname: Marder
authors.forename: M
authors.initials: M
authors.lastname: Sano
authors.forename: D
authors.initials: D
authors.lastname: Devanand
authors.forename: S M
authors.initials: SM
authors.lastname: Albert
authors.forename: F
authors.initials: F
authors.lastname: Bylsma
authors.forename: W Y
authors.initials: WY
authors.lastname: Tsai
mesh_terms: D017668:Age of Onset; D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000401:mortality* / Q000503:physiopathology* / Q000534:rehabilitation; D001057:Apolipoproteins E / Q000378:metabolism; D018450:Disease Progression; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008609:Mental Status Schedule; D008875:Middle Aged; D009735:Nursing Homes; D011379:Prognosis; D016016:Proportional Hazards Models; D011446:Prospective Studies; D016019:Survival Analysis; D013997:Time Factors
chemical_list: D001057:Apolipoproteins E