Total joint replacement surgery is considered as the last effective option for patients with severe joint diseases. Despite improvements in operative environment and surgical techniques, postoperative infection remains one of the most devastating and dreaded complications of TJR (both for hip and knee replacements) with potentially life-threatening consequences. The treatment of prosthetic joint infections (PJI) ranges from antibiotic to replacement of the artificial joint or amputation.
Previous studies investigated factors associated with infection such as age, gender, BMI (body mass index), immune-suppression, prior open surgery, steroid therapy and others. However, there have been no comprehensive systematic analysis of risk factors to quantify the risk of infections and associated patients outcomes after TKA (total knee arthroplasty) and THA (total hip arthroplasty).
Understanding the distribution of PJI (which patients, how often, and how severe) and predicting patient outcomes, such as amputations, revision surgeries or death, may lead to better patient management.
With this in mind, this study aims to use an anonymised primary care database and linked secondary care data to: (i) describe the characteristics of patients that experience PJI; (ii) to develop risk predication equations for the risk of PJI and patient outcomes and (iii) to develop predication equations for the Length of stay in hospital due to PJI.
The main objective of this retrospective cohort study is to utilise the Clinical Practice Research Datalink (CPRD) and linked Hospital Episode Statistics (HES) and Office of National Statistics (ONS) to identify risk factors that predict PJI incidence and associated outcomes (i.e. debridement, revision surgery, amputation or death) and develop risk predication equations for each of them.
Recently published real-world studies have largely examined associations between patients characteristics and clinical PJI incidence in relatively narrowly-defined patient populations. However, there is a relative scarcity of studies that elucidate risk factors for PJI patient outcomes.
Univariate and multivariate methods of statistical analysis will be used to identify risk factors that predict PJI and summary descriptive statistics will be generated characterising patient demographics, clinical and treatment characteristics and health resources use in relation to stratification of risk, incidence and outcomes of PJI.
The resulting set of risk equations will be reflective of current UK patients and practices and will provide real word evidence to clinical and policy decision making.
PJI offsets count
Amputations performed due to PJI
Debridement Surgeries performed due to PJI
Revision Surgeries performed due to PJI
Length of stay in hospital due to PJI
Deaths caused by PJI
Hospitalisations for PJI with admission through A&E
to be included in "Planned use of linked data and benefit to patients in England and Wales":
The results of this CPRD analysis will inform health authorities (i.e. NHS and Trusts) about the risk profile and risk factors for PJI of patients in England and Wales undergoing hip or knee replacement along with the potential impact of surgical procedures enacted in the country. This increase in knowledge about underlying risks to PJI, specific to England and Wales, may provide reasons to update local guidelines and initiate active monitoring of patients at high risk of PJI in order to anticipate/prevent the infection offset.
No HES-A&E linkage is required as the analysis proposed will only need the source of the hospitalisation (admimeth coded in the HES-hospitalisations table available from HES-APC that has already been approved).
Polina Prokopovich - Chief Investigator - Cardiff University
Polina Prokopovich - Corresponding Applicant - Cardiff University
Bismah Bojan - Collaborator - Cardiff University
Hywel M. Jones - Collaborator - Cardiff University
Stefano Perni - Collaborator - Cardiff University
Stephen Austin Jones - Collaborator - Cardiff and Vale University Health Board
HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data