Use of large scale HRQoL datasets to generate individualised predictions and inform patients about the likely benefit of surgery

Purpose: The English NHS has mandated the routine collection of health-related quality of life (HRQoL) data before and after surgery, giving prospective patients information about the likely benefit of surgery. Yet, the information is difficult to access and interpret because it is not presented in a lay-friendly format and does not re ect patients' individual circumstances. We set out a methodology to generate personalised information to help patients make informed decisions. Methods: We used anonymised, pre- and post-operative EuroQol-5D-3L (EQ-5D) data for over 490,000 English NHS patients who underwent primary hip or knee replacement surgery or groin hernia repair between April 2009 and March 2016. We estimated linear regression models to relate changes in EQ-5D utility scores to patients' own assessment of the success of surgery, and calculated from that minimally important differences (MID) for health improvements / deteriorations. Classification tree analysis was used to develop algorithms that sort patients into homogeneous groups that best predict post-operative EQ-5D utility scores. Results: Patients were classified into between 55 (hip replacement) to 60 (hernia repair) homogeneous groups. The classifications explained between 14-27% of variation in post-operative EQ-5D utility score. Conclusions: Patients are heterogeneous in their expected benefit from surgery and decision aids should reflect this. Large administrative datasets on HRQoL can be used to generate the required individualised predictions to inform patients.