title: The prediction of functional decline in older hospitalised patients.
abstract: BACKGROUND
  thirty to sixty per cent of older patients experience functional decline after hospitalisation, associated with an increase in dependence, readmission, nursing home placement and mortality. First step in prevention is the identification of patients at risk.
  
  
  OBJECTIVE
  to develop and validate a prediction model to assess the risk of functional decline in older hospitalised patients.
  
  
  METHODS
  development study: cohort study (n = 492). Validation study: secondary data analysis of a cohort study (n = 484) in an independent population. Both with follow-up after 3 months. Functional decline was defined as a decline of at least one point on the Katz ADL index at follow-up compared with pre-admission status.
  
  
  METHODS
  development study: general internal medicine wards of two university hospitals and one regional hospital. Validation study: general internal wards of an university hospital.
  
  
  METHODS
  patients ≥65 years acutely admitted and hospitalised for at least 48 h.
  
  
  RESULTS
  thirty-five per cent of all patients in the development cohort and 32% in the validation cohort developed functional decline. A four-item model could accurately predict functional decline with an AUC of 0.71. At threshold 2 sensitivity, specificity, positive and negative predictive values were 87, 39, 43 and 85%, respectively. In the validation study, this was, respectively, 0.68, 89, 41, 41 and 89%.
  
  
  CONCLUSIONS
  pre-admission need for assistance in instrumental activities of daily living, use of a walking device, need for assistance in travelling and no education after age 14, are the items of a prediction model to identify older patients at risk for functional decline following hospital admission. The strength of the model is that it relies on four simple questions and this makes it easy to use in clinical practice and easy to administer.
authors.affiliation: Faculty of Health Care, University of Applied Sciences Utrecht, Utrecht, The Netherlands. jita.hoogerduijn@hu.nl
authors.forename: Jita G
authors.initials: JG
authors.lastname: Hoogerduijn
authors.forename: Bianca M
authors.initials: BM
authors.lastname: Buurman
authors.forename: Johanna C
authors.initials: JC
authors.lastname: Korevaar
authors.forename: Diederick E
authors.initials: DE
authors.lastname: Grobbee
authors.forename: Sophia E
authors.initials: SE
authors.lastname: de Rooij
authors.forename: Marieke J
authors.initials: MJ
authors.lastname: Schuurmans
mesh_terms: D000203:Activities of Daily Living; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000523:psychology; D019540:Area Under Curve; D016009:Chi-Square Distribution; D003071:Cognition; D051341:Dependent Ambulation; D004522:Educational Status; D005260:Female; D015577:Geriatric Assessment / Q000379:methods*; D006760:Hospitalization; D006784:Hospitals, Teaching; D006785:Hospitals, University; D006801:Humans; D007297:Inpatients / Q000523:psychology; D016015:Logistic Models; D008297:Male; D009426:Netherlands; D009483:Neuropsychological Tests; D015596:Nutrition Assessment; D009752:Nutritional Status; D011237:Predictive Value of Tests; D011379:Prognosis; D011569:Psychiatric Status Rating Scales; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires; D013997:Time Factors; D014195:Travel