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Research

PREDICTING LENGTH OF STAY IN ARTHROPLASTY ENHANCED RECOVERY PROGRAMMES

European Orthopaedic Research Society (EORS) 2015, Annual Conference, 2–4 September 2015. Part 2.



Abstract

Background

Predicting length of stay (LOS) is key to providing a cost effective and efficient arthroplasty service in an era of increasing financial constraint. Previous studies predicting LOS have not considered enhanced recovery protocols in elective hip and knee arthroplasty. Our study aims to identify patient variables in the pre and peri-operative period to predict increased LOS on patients enrolled into the standardised Chichester and Worthing Enhanced Recovery Programme (CWERP).

Methods

All patients undergoing elective hip and knee arthroplasty were enrolled into CWERP using standardised anaesthetic, surgical and analgesic protocols. A data analyst prospectively collated data over 6months from anaesthetic charts and daily ward review from 663 patients between Dec 2012 and June 2013.

An independent statistician undertook statistical analysis (program R, version 3.1.1). 80% of the 6months consecutive data (530 patients) were analysed, and predictive variables identified. These variables were tested against the remaining 20% of data (133 patients) predicting a LOS greater or less than our median of 4 days.

Results

663 patients were enrolled into CWERP over this period, 54% in hip arthroplasty. Statistical analysis was performed using Chi-squared test for association between actual and predicted (dichotomised) LOS being significant (p<0.0000000017). In the initial 80% (530 patients), this identified the following statistically significant variables in predicting LOS > 4 days: Age > 80 yrs, ASA 4, failure to mobilise on day of surgery, urinary catheterisation and need for blood transfusion. The statistical model when applied to the remaining 20% (133 patients) correctly categorised LOS in 101 (76%) of the patients.

Conclusions

Identifying patients who fulfil our variables in the preoperative period affords better planning, maximising resources, bed efficiency and discharge planning. This also provides opportunities for financial remuneration for higher risk patients.

Level of Evidence

4