Covid has created a significant impact on the lives of patients with joint and muscular disease. There
are a significant number of patients who have not presented to General Practice or
hospital services and who are likely to present in the next few years. These "Hidden"
patients are expected to cause a huge burden on health services.
To plan safe and efficient surgery we require a better understanding of the patients
undergoing surgery. There is a need to understand the group of patients that can safely be
operated on and sent home the same day (“day cases”). Furthermore, more than 10%
of all surgery patients will have some sort of complication after an operation.
Some surgeons feel that patients are discharged too early, and some are discharged too late. There is a need
to determine the patient characteristics that would benefit from day case surgery.
This research aims to use available national data to help streamline patient’s experiences and improve understanding of patients undergoing surgery for muscular and joint diseases.
Aims and Objectives:
1. To find out more about UK patients with joint and muscular diseases and those undergoing routine elective procedures and the number of GP visits and hospital visits they have.
2. To understand the pre-operative and post-operative patient features undergoing elective surgery.
3. To understand the complications after daycase and non-daycase elective surgery with emphasis on A&E attendance, readmission and clinic visits.
4. To address the short-term and long-term complications of routine muscular and joint replacement procedures.
Covid has created a significant impact on the lives of musculoskeletal patients. A number of
patients have not presented to General Practice or hospital services and are likely to present in the future. There is a need to determine the participant characteristics that can safely be operated on and sent home the same day (“day cases”).
Study Design: Descriptive, Hypothesis Testing
Population of interest: Patients with any hip and knee symptom presenting to primary care or who have a diagnosis of Hip and Knee Osteoarthritis in primary and secondary care from April 2004- December 2023.
Objectives:
1. To describe the presentation history and progression of osteoarthritis and elective hip and knee replacement surgery patients in the UK.
2. To explore the post-operative complications and healthcare service burden after hip and knee replacement surgery using data from both primary and secondary care.
3. To determine those who are appropriate candidates for day-case hip and knee replacement surgery.
Primary Outcomes: Patient Outcomes (surgery, complications, mortality, reoperation) and Hospital and Community service Burden (e.g. attendance, waiting times, service use).
Methodology:
Using descriptive statistics, we will use linked CPRD data to describe the current musculoskeletal cohort that present to primary care with common knee and hip symptoms and are then referred to hospital. We will describe the time from the initial symptom presentation in primary care to post operative course history. Using a combination of propensity score matching and time-to-event analysis (Cox Proportional Hazards Model), we will determine the short-term and long-term outcomes of patients who have undergone Hip Replacement (compared with Hip Resurfacing) and Total Knee Replacement (compared with Unicompartmental Knee Replacement).
Intended Benefits:
This will provide NHS, surgeons, and policy planners with information on where to focus funding and provide policy to improve this to assess how to reduce waiting list times.
1. Length of Stay.
2. Community and Hospital Burden Outcome measures(ED attendance; Outpatient follow up visits; reoperation; GP visits pre and post operatively).
3. Post-Operative Complications (Mortality; Reoperation/Revision surgery; Major cardiac event; cerebrovascular attack; Venous Thromboembolic event).
Omar Musbahi - Chief Investigator - Imperial College London
Omar Musbahi - Corresponding Applicant - Imperial College London
Alex Bottle - Collaborator - Imperial College London
Mahsa Mazidi - Collaborator - Imperial College London
Mark Cunningham - Collaborator - Imperial College London
HES Accident and Emergency;HES Admitted Patient Care;HES Diagnostic Imaging Dataset;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Rural-Urban Classification