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Risk equations for prosthetic joint infections (PJIs) in UK: a retrospective study using the Clinical Practice
Stefano Perni1, Polina Prokopovich2
1Cardiff University, Cardiff, UK.
Insights
A new risk model for prosthetic joint infections (PJIs) was developed using UK electronic health records. This tool can identify patients at high risk for PJI, enabling targeted preventive strategies.
Area of Science:
- Arthroplasty Surgery
- Infectious Diseases
- Health Informatics
Background:
- Prosthetic joint infections (PJIs) are a significant complication of arthroplasty, occurring in approximately 1% of cases.
- Current risk assessment for PJI lacks a UK-specific tool, potentially influenced by varying local policies.
Purpose of the Study:
- To develop a risk quantification model for predicting prosthetic joint infections (PJIs) using electronic health records.
- To create a UK-specific risk scoring system for PJI in hip and knee arthroplasty patients.
Main Methods:
- Utilized large-scale UK electronic health records from Clinical Practice Research Datalink (CPRD) GOLD and AURUM, linked with hospital and mortality data.
- Developed and compared parametric models to quantify PJI risk factors across two distinct databases.
- Pooled data to create a UK population-specific risk equation, simplified using stepwise selection.
Main Results:
- Analysis included over 223,000 hip or knee arthroplasties, with 1249 cases of PJI.
- Despite differing patient characteristics between databases, the impact of risk factors on PJI was statistically consistent.
- A log-normal model derived from pooled data demonstrated good predictive performance (C-statistic >0.7).
Conclusions:
- The developed risk prediction tool can identify patients at higher risk for PJI.
- This tool facilitates targeted interventions, including identifying modifiable risk factors and patients benefiting from close monitoring.
- Implementation can aid the National Health Service in reducing the burden of PJI on resources and patient outcomes.
Background:
Prosthetic joint infections (PJIs) are a serious negative outcome of arthroplasty with incidence of about 1%. Risk of PJI could depend on local treatment policies and guidelines; no UK-specific risk scoring is currently available.
Objective:
To determine a risk quantification model for the development of PJI using electronic health records.
Design:
Records in Clinical Practice Research Datalink (CPRD) GOLD and AURUM of patients undergoing hip or knee arthroplasty between January 2007 and December 2014, with linkage to Hospital Episode Statistics and Office of National Statistics, were obtained. Cohorts' characteristics and risk equations through parametric models were developed and compared between the two databases. Pooled cohort risk equations were determined for the UK population and simplified through stepwise selection.
Results:
After applying the inclusion/exclusion criteria, 174 905 joints (1021 developed PJI) were identified in CPRD AURUM and 48 419 joints (228 developed PJI) in CPRD GOLD. Patients undergoing hip or knee arthroplasty in both databases exhibited different sociodemographic characteristics and medical/drug history. However, the quantification of the impact of such covariates (coefficients of parametric models fitted to the survival curves) on the risk of PJI between the two cohorts was not statistically significant. The log-normal model fitted to the pooled cohorts after stepwise selection had a C-statistic >0.7.
Conclusions:
The risk prediction tool developed here could help prevent PJI through identifying modifiable risk factors pre-surgery and identifying the patients most likely to benefit from close monitoring/preventive actions. As derived from the UK population, such tool will help the National Health Service reduce the impact of PJI on its resources and patient lives.
