Referral decision support in patients with subacute brain injury: evaluation of the Rehabilitation Complexity Scale – Extended

Abstract

Purpose: To test if the Rehabilitation Complexity Scale Extended (RCS-E) can be used as decision support for patient referral to primary rehabilitation as either complex specialized services (CSS) or district specialist services (DSS).

Method: Two independent expert teams analyzed medical records on 299 consecutive patients admitted for CSS or DSS rehabilitation. One team provided a golden standard for the patient referrals, and the other team provided RCS-E scores. Models for predicting referrals from RCS-E scores were developed on data for 149 patients and tested on the remaining 150 patients.

Results: The optimal RCS-E sum score threshold for referral prediction was 11, predicting the golden standard for patient referral with sensitivity 88%, specificity 78% and correct classification rate 81%. Improved referral prediction performance was achieved by using RCS-E item-wise score thresholds (sensitivity 81%, specificity 89%, correct classification rate 87%). The RCS-E sum score range for patients referred CSS and DSS by the item-wise model was, respectively, 0–12 and 2–22 suggesting strong non-linear interaction of the RCS-E items.

Conclusions: We found excellent referral decision support in the RCS-E and the item specific threshold model, when patients with acquired brain injury are to be referred to CSS or DSS as their primary rehabilitation.

Implications for Rehabilitation

• Efficient rehabilitation after acquired brain injury requires rehabilitation settings that meet patient needs.

• Validated tools for referral decision support make the process more transparent.

• Patient rehabilitation complexity can be stratified by the RCS-E with high sensitivity, specificity and predictive value of positive test.

• RCS-E is an excellent tool for referral decision support.

Keywords:

• Acquired brain injury
• decision support
• referral
• rehabilitation complexity scale-extended
• stratification

Acknowledgements

The authors are grateful to Karen Marie Rahbech Mørk, Karina Lausten, Iben Bendixen Juul, Anne Christina Almskou Rasmussen, Marianne Liengaard, Marie Friis Gerdsen, and Karen Jette Jensen for their great teamwork.

Disclosure statement

The authors report no conflicts of interest.