Get access

Pressure ulcer risk assessment: application of logistic analysis

Authors

  • Panos Papanikolaou BSc MA MPhil,

  • Patricia A. Lyne BSc PhD RGN,

  • Emma J. Lycett BSc PhD


Panos Papanikolaou, Nursing, Health and Social Care Research Centre, School of Nursing and Midwifery Studies, University of Wales College of Medicine, Heath Park, Cardiff CF14 4XN, UK.
E-mail: papanikolaoup@cardiff.ac.uk

Abstract

Aim. The aim of this study was to investigate the relative importance of key factors affecting the likelihood of hospital acquired pressure ulcers, thus offering the groundwork for the development of an empirically-based risk assessment scale. It also evaluated the predictive performance of the underlying empirical model and compared its classification ability with the empirically observed ability of the Waterlow risk assessment scale.

Background. Pressure ulcers impose a significant burden on patients and carers and demand substantial resources from the health care system. There is, however, a lack of systematic empirical research on which to base the development of risk assessment measurement tools.

Methods. Multivariate statistical methods were applied to data derived from the records of a cross-sectional sample of around 500 randomly selected inpatient episodes drawn from the population of admissions to a single unit in a large acute hospital during a 2-year period (October 1996 to September 1998). Double-checking of a randomly selected 25% sample of the original records and careful screening out of records with incomplete information or inconsistent values was carried out to ensure a high quality sample. Logit analysis was used to investigate the relative contribution of risk factors, such as continence, skin condition, mobility and inter-hospital transfer to the risk of hospital-acquired pressure ulcer occurrence, whilst cross-validation techniques were employed to check the predictive performance of the model.

Results. The results suggest that a simplified version of the Waterlow risk assessment tool has satisfactory predictive ability and the potential for further development.

Conclusions. Two main conclusions emerged from this study. First, it is both feasible and worthwhile to pursue improvement in the development of risk assessment tools using statistical methods. Second, locally-determined risk factors will need to be incorporated into the construction of future risk assessment scales.

Ancillary