Clinical trials

The primary aim will be to understand whether a digital breathing biofeedback system can improve the outcomes of physiotherapist guided breathing retraining.

One in three adults over 65 fall annually, with one in five remaining on the floor for greater than one hour, which is referred to as a long lie. Pressure on the National Health Service has resulted in extended stays in the Emergency Department (ED) (sometimes longer than 12 hours) and prolonged ambulance response times. This impacts the older adults who have fallen and remain on the floor. This project aims to develop a risk prediction model (RPM) for use within the ED to understand which older adults (60 years or older) who fall over and remain on the floor for longer than one hour ("long lie") and develop rhabdomyolysis (a serious condition where muscle breaks down and releases substances into the blood that can damage the kidneys) will develop poor outcomes and need admission to hospital for treatment and which patients can be safely discharged home. Aim: To develop a RPM to identify which older adults who have a fall and a long lie and attend the ED develop poor outcomes such as Acute kidney Injury (AKI) \[kidneys suddenly stop working properly\], needing kidney replacement therapy (KRT) \[a treatment that helps kidneys that aren't working properly do their job of cleaning the blood\] and mortality \[death\]. Objectives: 1. Abstract patient level data (e.g. biochemical, demographic, situational, medical history, medication history) from medical records combined with outcomes to understand which variables lead to poor outcomes such as AKI, needing KRT and mortality. 2. Analyse the data using a statistical package (Statistical Package for Social Sciences \[SPSS\]) to develop a RPM with good discriminative abilities \[how well the score can tell high-risk from low-risk patients\]. 3. Demonstrate the ability of the RPM to identify which patients need admission to hospital with treatment and which patients can be safely discharged home.