Heart Failure (HF) is a condition that is common in older people and is associated with multiple symptoms and conditions, frequent hospital admissions and earlier death. Before they develop HF or afterwards, they often experience symptoms of deteriorating heart function, in addition to symptoms related to their other conditions. This can confuse and complicate the care that they receive. Current evidence indicates that there is often a delay of over 12 months in diagnosis of HF and that half of the hospital admissions experienced by people with HF could be avoided if we could detect their worsening symptoms earlier. We want to understand the range of patient, clinical and care factors that could potentially contribute to delayed diagnosis and avoidable admissions for people with HF. Timely diagnosis and avoidance of admission, offers the real potential for improving the care and outcomes of people with HF.
Using the CPRD, first we will investigate the impact of symptoms, pre-existing morbidities, medication use, routine clinical tests and, healthcare patterns on the delay of HF diagnosis. Second, we will investigate the impact of these patient and care factors and delayed diagnosis on subsequent hospital admissions, frailty and death after HF. Third, we will develop prognostic models for hospital admissions and death in people with HF. The findings from the study will provide the evidence for earlier diagnosis before the onset of severe HF and significant potential to improve the care of patients with HF.
Background: Heart Failure (HF) is associated with the poorest quality of life and a poor prognosis. Current evidence suggests that (i) there are significant time delays in the new diagnosis of HF that might contribute to the poor outcomes after diagnosis and (ii) that symptom deterioration in patients with HF prior to unplanned admission is poorly recognised. Diagnosis itself is often made at an unplanned hospital admission rather than in primary care and evidence shows that up to 50% of unplanned admission in HF patients could be avoided. High levels of comorbidity and the associated care complexity likely contribute to sub-optimal care, but this has not yet been explored in any depth.
Design: Case-control and retrospective cohort design
Methods: In the populations aged 40 years and over, the CPRD Gold and Aurum datasets will be used to identify an incident HF cohort. The outcomes data will be ascertained from linked Hospital Episode Statistics (HES) and ONS death data. In three phases, using logistic regression models, flexible parametric models, longitudinal mixed models, and machine learning approaches, there will be an investigation of patient (age, gender, ethnicity), clinical (chronic comorbid conditions and severity, symptoms) and care factors (multiple medications use, routine tests [bloods, imaging], contact patterns), and how they relate to the delay in the diagnosis of HF and the subsequent outcomes of hospital admissions, frailty and death.
Outcomes: This investigation will determine the key patient, clinical and care factors that contribute to significant delays in the diagnosis of new HF and unplanned admissions in HF patients. Identifying these factors provides new opportunities for earlier interventions and in devising clinical and public health policies which significantly improve the care of the patients with HF.
(i) delay in the diagnosis of HF
(ii) planned and unplanned hospital admissions
(iii) frailty
(iii) all-cause-mortality and HF-specific mortality
Umesh Kadam - Chief Investigator - University of Leicester
Suping Ling - Corresponding Applicant - University of Leicester
Claire Lawson - Collaborator - University of Leicester
Mark Rutherford - Collaborator - University of Leicester
Michael Sweeting - Collaborator - University of Leicester
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation