Medical inpatients at risk of extended hospital stay and poor discharge health status: detection with COMPRI and INTERMED

Psychosom Med. Jul-Aug 2003;65(4):534-41. doi: 10.1097/01.psy.0000077504.01963.1b.

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.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Case Management*
  • Cohort Studies
  • Disease Susceptibility
  • Female
  • Follow-Up Studies
  • Health Services Needs and Demand*
  • Health Status*
  • Humans
  • Inpatients / statistics & numerical data*
  • Length of Stay / statistics & numerical data*
  • Male
  • Mass Screening
  • Middle Aged
  • Netherlands
  • Nursing Assessment / methods
  • Patient Discharge / statistics & numerical data*
  • Predictive Value of Tests
  • Prognosis
  • Prospective Studies
  • ROC Curve
  • Risk Assessment
  • Surveys and Questionnaires