title: Medical inpatients at risk of extended hospital stay and poor discharge health status: detection with COMPRI and INTERMED.
abstract: OBJECTIVE
  To detect the patients in medical wards at risk of extended LOS and poor discharge health status with the use of complexity prediction instrument (COMPRI) and interdisciplinary medicine (INTERMED) instruments.
  
  
  METHODS
  STUDY 1: In a sample of 275 consecutively admitted medical inpatients, a hierarchical cluster analysis on INTERMED variables was performed. The clusters were compared on length of hospital stay (LOS) and Short Form 36 (SF-36) at discharge. STUDY 2: Receiver operating characteristic (ROC) analysis was used to optimal cut-off points for the COMPRI and INTERMED. Patients detected with COMPRI and INTERMED were then compared with undetected patients on LOS and SF-36.
  
  
  RESULTS
  STUDY 1: In concordance with previous findings, a cluster of patients with high biopsychosocial vulnerability was identified with significantly higher scores on LOS (p <.05) and lower scores on SF-36 (p <.001) than patients in other clusters. STUDY 2: A cut-off point for the COMPRI of 5/6 was found to detect patients at risk of long LOS. A cut off score for the INTERMED of 20/21 was found to detect patients at risk of poor discharge health status. Patients detected with COMPRI and INTERMED had a significantly longer LOS (p <.001) and a poorer discharge health status (SF-36 MCS: p <.001; SF-36 PCS: p =.05) than nondetected patients. Of the detected patients, 37% had an extended hospital stay and poor discharge health status; of the nondetected patients, this was only 7%.
  
  
  CONCLUSIONS
  The COMPRI-INTERMED can help to detect complex patients admitted to medical wards within the first days of admission, and rule out those with a small chance of poor outcomes.
authors.affiliation: Department of Psychiatry, Vrije Universiteit Medical Center, Amsterdam, The Netherlands. p.de.Jonge@med.rug.nl
authors.forename: Peter
authors.initials: P
authors.lastname: de Jonge
authors.forename: Iris
authors.initials: I
authors.lastname: Bauer
authors.forename: Frits J
authors.initials: FJ
authors.lastname: Huyse
authors.forename: Corine H M
authors.initials: CH
authors.lastname: Latour
mesh_terms: D019090:Case Management; D015331:Cohort Studies; D004198:Disease Susceptibility; D005260:Female; D005500:Follow-Up Studies; D006301:Health Services Needs and Demand; D006304:Health Status; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data*; D008297:Male; D008403:Mass Screening; D008875:Middle Aged; D009426:Netherlands; D009730:Nursing Assessment / Q000379:methods; D010351:Patient Discharge / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D012372:ROC Curve; D018570:Risk Assessment; D011795:Surveys and Questionnaires