Hip and knee replacement are two of the most commonly performed operations in the National Health Service (NHS), with over 200,000 performed in the United Kingdom (UK) in 2017. The majority of these operations are performed in older individuals, with an average age of 69 years at the time of surgery. As such, it is not uncommon for individuals to have additional medical conditions such as diabetes or heart disease. It is recognised that some of these conditions can influence the outcome after joint replacement surgery (such as risk of infection or death). Frailty is a concept that considers a patient's medical, psychological and social care needs, with increasing frailty associated with poorer outcomes in previous studies.
One way to determine frailty is by reviewing a patient's medical record. This is the method used by the electronic Frailty Index, using medical records kept by General Practitioners (GPs), where it is now widely used at the request of NHS England. However, hospitals within the UK use a different system to record patient diagnoses, meaning the electronic Frailty Index cannot be used in its current form using hospital data.
To address this, we will map the codes used by the electronic Frailty Index in GP systems to those used in hospital systems, and then assess if the two forms of the electronic Frailty Index give similar results. In addition, we will also assess the concordance between a newly developed index (the Hospital Frailty Risk Score) based on hospital data and the electronic Frailty Index.
Hip and knee replacement are common operations in the NHS, with 200,000 in 2017. 90% of patients have an American Society of Anaesthesiologists (ASA) score of >/=2, indicating most patients have a degree of co-morbidity. Previous research has considered the influence of co-morbidity on outcome after joint replacement using standard measures such as the ASA score and Charlson Comorbidity Index, with increasing disease burdens associated with poorer outcomes. However, there is increasing recognition of the need to consider patient health in a more holistic manner. The concept of frailty has been developed to do this, which considers a patient's physiological, psychological and social care needs.
The electronic Frailty Index (eFI) was developed and validated by Clegg et al., and uses a cumulative deficit model applied to READ code based primary care records to calculate patient frailty. The eFI is now widely integrated in primary care data systems. However, as the eFI depends upon READ codes, it is not currently possible to calculate frailty with this tool using Hospital Episodes Statistics (HES) secondary care data, where information is coded according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10).
This study will map the eFI for use with ICD-10 codes, such that it can be used in future investigation of frailty in the setting of arthroplasty using hospital data. This will require cross-mapping of READ codes used in the eFI to corresponding ICD-10 codes. The eFI will then be applied to linked Clinical Practice Research Datalink (CPRD)/HES records to determine the coefficients of agreement for frailty determined by the two forms of eFI and the distribution of frailty in each dataset. Furthermore, we will compare the distribution of frailty between the ICD-10 mapped eFI, and the recently published ICD-10 based Hospital Frailty Risk Score.
1. Coefficients of agreement between READ and ICD-10 calculated eFI
2. Concordance of frailty levels determined by the ICD-10 based eFI and Hospital Frailty Risk Score
Daniel Prieto-Alhambra - Chief Investigator - University of Oxford
Rob Middleton - Corresponding Applicant - University of Oxford
Antonella Delmestri - Collaborator - University of Oxford
Edward Burn - Collaborator - University of Oxford
Rory Ferguson - Collaborator - University of Oxford
HES Admitted Patient Care