Complexity Prediction Instrument to detect ‘complex cases’ in respiratory wards: instrument development
Article first published online: 20 SEP 2008
© 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd
Journal of Advanced Nursing
Volume 64, Issue 1, pages 96–103, October 2008
How to Cite
Lobo, E., Rabanaque, M.-J., De Jonge, P., Barcones, M.-F., Cazcarra, R.-A., Huyse, F. J. and Lobo, A. (2008), Complexity Prediction Instrument to detect ‘complex cases’ in respiratory wards: instrument development. Journal of Advanced Nursing, 64: 96–103. doi: 10.1111/j.1365-2648.2008.04756.x
- Issue published online: 20 SEP 2008
- Article first published online: 20 SEP 2008
- Accepted for publication 27 May 2008
- case management;
- Complexity Prediction Instrument;
- length of stay;
- nursing care complexity;
- Spanish version
Title. Complexity Prediction Instrument to detect ‘complex cases’ in respiratory wards: instrument development.
Aim. This paper is a report of a study to test the hypothesis that the Spanish version of the Complexity Prediction Instrument is a reliable and valid measure of complexity of patients with respiratory disease and to identify the frequency of positive indicators of potential complexity.
Background. Respiratory patients are often disabled and severely ill, with co-morbid physical conditions and associated psychosocial problems and need complex nursing care.
Method. Trained nurses assessed 299 consecutive adult patients admitted to a respiratory service in Spain from May 2003 until June 2004 with the new, Spanish version of the instrument. Criterion-related validity was tested by studying its ability to predict complexity of care in terms of: severity of illness, scored using the Cumulative Illness Rating Scale; length of hospital stay; ‘multiple consultations’ during admission; and ‘multiple specialists’ after discharge.
Findings. The hypothesis was supported: patients rating above the standard cut-off point on the Complexity Prediction Instrument scored statistically significantly higher on most of the measures of care complexity studied. Linear regression models showed that the tool was associated with ‘length of hospital stay’, and predicted both ‘multiple consultations’ and ‘multiple specialists’, after controlling for potential confounders. The proportion of ‘probable complex cases’ was 59·5%. Five positive indicators of potential complexity had a frequency higher than 50%.
Conclusion. The Complexity Prediction Instrument is reliable and valid in a new clinical area, respiratory disease. It may be used by nurses for the early prediction of complexity of care. International comparisons may be facilitated with this new Spanish version.