Frailty is a common condition in elderly patients with declines in health and difficulties in day-to-day activities such as walking. Approximately 10% of those aged over 65 years have frailty. The recent Covid-19 pandemic has disrupted routine care in the health care system and there are concerns in primary care about what we can do for the most vulnerable patients in the post Covid-19 world and what interventions should be prioritised in order to help these patients. This study will identify opportunities to better target clinical improvement activities for frail multimorbid elderly patients in general practice. Anonymised electronic hearth records of frail elderly patients will be analysed. The outcomes of interest will be death (irrespective of the cause) emergency hospital admissions (and reasons),and infection-related complications, Accident and Emergency Attendance and care home admission. The medicines that were prescribed to frail elderly patients will be evaluated for their associations with increased or decreased risk of these outcomes. Of particular interest will be to look at treatment strategies that vary between general practices and identify those that could suggest potential for improvement. Experts will review the clinically important predictors. These variables will include characteristics such as extent and type of how many medicines a frail person receives and the complexity of a medication regimen. The results of this study will be used to provide feedback to GPs (using a secure internet infrastructure) which can help to identify opportunities to better target clinical improvement activities to frail patients in their practice.
Recent research in primary care found that 23ยท2% of the total population were multimorbid (i.e., were suffering from two or more long-term disorders). There are concerns in primary care about what we can do for the most vulnerable patients in the post Covid-19 world and what interventions should be prioritised in order to help these patients. The overall objective of this study is to identify opportunities to better target clinical improvement activities for frail multimorbid elderly patients and to evaluate the extent of variability in clinical outcomes and treatments and its predictors before and during/after Covid-19. The design will be a cohort study with nested case-control study. The main study population will be patients in participating practices who are 65 years or older and have electronic frailty index score of 0.21. The outcomes of interest will be death (irrespective of the cause), emergency hospital admissions (and reasons), infection-related complications, Accident and Emergency Attendance and care home admission. Indicators of type of clinical care and clinical measurements will be considered as modifiable predictors. A broad set of modifiable predictors will be evaluated in the development cohort and tested in the validation cohort (using Cox proportional hazards models). This will then be followed by expert review for clinically important predictors. The variables will include characteristics such as extent and type of polypharmacy, STOPP criteria (which concern potentially inappropriate prescribing or omissions) and medication regimen complexity index. propensity-matched case-control datasets will be used to identify the predictors that are associated with increased or decreased risks of the outcome of interest. The results of this study will be used to provide feedback to GPs (using a secure internet infrastructure) which can help to identify opportunities to better target clinical improvement activities to frail patients in their practice.
- all-cause mortality
- emergency hospital admissions
- infection-related complications
- Accident and Emergency Attendance
- care home admission
The primary outcomes of interest will be all-cause mortality as recorded in the GP EHRs (Hippisley-Cox & Coupland, 2017), emergency hospital admissions as recorded in the Hospital Episode Statistics (HES) and, for the frail multimorbid antibiotic cohort, infection-related complications. All-cause mortality will be based on the death recording in the GP EHRs (with sensitivity analyses conducted with death certificate records, where available). The definition for emergency hospital admissions will be similar to that used for the prediction tool QAdmissions (Hippisley-Cox & Coupland, 2013). The emergency admission information will be derived from the method of admission field recorded for each hospitalisation including code 21 (accident and emergency), 22 (GP direct to hospital), 23 (GP via a bed bureau); 24 (consultant clinic), 25 (mental health crisis resolution team), and 28 (other means). Only emergency admissions where the admission date and discharge date were both recorded and where the admission date was on or before the discharge date will be included (Hippisley-Cox & Coupland, 2013). The definition for infection-related complications will be similar as used in previous studies, including infection-related hospital admissions as recorded in HES and GP-recorded complications (Mistry et al., 2020; Van Staa et al., 2020). Secondary outcomes of interest will include Accident and Emergency Attendance, care home admission (as recorded in the GP EHRs) and the distribution of admission and discharge ICD diagnoses as recorded in HES.
Tjeerd van Staa - Chief Investigator - University of Manchester
Victoria Palin - Corresponding Applicant - University of Manchester
Alexander Pate - Collaborator - University of Manchester
Ali Fahmi - Collaborator - University of Manchester
Andrew Clegg - Collaborator - University of Leeds
Benjamin Brown - Collaborator - University of Manchester
Darren Ashcroft - Collaborator - University of Manchester
Harriet Cant - Collaborator - University of Manchester
Iain Buchan - Collaborator - University of Manchester
Munir Pirmohamed - Collaborator - University of Liverpool
Xiaomin Zhong - Collaborator - University of Manchester
Ya-Ting Yang - Collaborator - University of Manchester
Yuan Tian - Collaborator - University of Manchester
Yuqi Wang - Collaborator - University of Manchester
Ghita Berrada - Collaborator - University of Manchester
HES Accident and Emergency;HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation