Critical Patient Severity Classification System predicts outcomes in intensive care unit patients

Abstract

Background: The CPSCS was developed to assess the nursing care demands of patients in intensive care units (ICUs).

Aim: This study aimed to examine the Critical Patient Severity Classification System (CPSCS) score as an independent predictor of patient hospital outcomes.

Design: This study was a secondary analysis.

Methods: Data from 6380 cases were extracted from the electronic medical records in ICUs at a tertiary hospital in Korea during 2010-2012. To examine the association of the CPSCS score with 30-day ICU mortality, the Cox proportional hazards model and Kaplan-Meier survival curves were used, and generalized linear regression models of gamma distribution were developed for ICU length of stay (LOS).

Results: More patients were admitted to surgical ICUs than medical ICUs (4664 versus 1716) during the study period. Medical ICU patients had longer ICU LOS, higher 30-day ICU mortality and a higher mean CPSCS score than surgical ICU patients. Cox analysis indicated that the mid and high CPSCS score groups had 1·687 and 2·913 times higher mortality risk, respectively, than the low CPSCS score group after adjusting for age, sex and primary diagnosis. The CPSCS score significantly predicted ICU mortality in both medical and surgical ICUs. Multivariate generalized linear regression indicated that CPSCS score was a significant predictor of ICU LOS after adjusting for other covariates.

Conclusions: The CPSCS score can be used to efficiently predict ICU mortality and LOS in patients admitted to the medical and surgical ICUs, although only the high CPSCS score group had significantly high mortality than the low CPSCS score group in the medical ICU.

Relevance to clinical practice: The findings of this study contribute to valuable evidence that nursing-related factors have an impact on patient outcomes such as ICU mortality and LOS and that they have implications for hospital management, clinical practice and future research.

Keywords: advanced patient assessment; intensive care nursing.