The Fast-track Centre for Hip and Knee Replacement Database

This is a prospective study-registry on preoperative patient characteristics and postoperative complications in patients having fast-track hip and knee replacement surgery in 8 Danish dedicated arthroplasty departments from all five health regions in Denmark. The registry consists of detailed patient and physician reported preoperative characteristics and including prescribed medication and lab results. Follow-up is based on electronical medical records by dedicated nurses with physician backup and includes Clavien-Dindo and Comprehensive Complication Index scoring. All patients having day-surgery also completes a patient reported questionaire on health-care utilization and return to work by day 30. Finally, a machine-learning algorithm for identification of "high-risk" patients based on he preoperative data is included.

The study registry is intended for further detailed research on postoperative morbidity, feasibility and safety of day surgery and functional outcomes after fast-track hip and knee replacement in 8 dedicated Danish arthroplasty wards across all five health care regions in Denmark. The work is a continuation of the Lundbeck Foundation Centre Database on Hip and Knee Replacement also registered in ClinicalTrials but ceased enrolling in august 2017. The aim of the registry is to provide standardized and comprehensive information on preoperative patient characteristics and postoperative complications, patient reported outcome measures and health-care utilization in fast-track hip and knee replacement within a socialized health-care system. Each department have dedicated research personel assuring registration and follow-up supervised by a datamanager, and with database-support from the Danish health Region of Southern Denmark. All departments have similar established fast-track protocols as standard of care, but with focus on dividing patients into day-surgery, main fast-track group and high-risk cohorts. Preoperative data includes information on prescribed medication, Clinical Frailty Score using CFS v.1, evaluation of Pain Catastrophizing Scale as well as laboratory testing of hemoglobin, kidney function, Hb1Ac in diabetics and C-reactive protein level. On the basis of the collected preoperative data a machine-learning algorithm for identifying the "high-risk" patients is being refined and with planned induction in the database.