Heart failure (HF) is a disease that develops secondary to multiple underlying diseases that gradually worsens over time. Treatment of underlying disorders is expected to reduce the progression or delay the onset of HF. The diagnostic and treatment processes recommended by current guidelines for HF are expected to improve symptoms, delay disease progression, and improve the prognosis in patients with HF.
There are significant variations in the delivery of care for HF and its outcomes in the United Kingdom and in other countries. This suggests that the potential to reduce the burden of cardiovascular diseases has not been fully realised. Hence, measuring and standardising the quality of medical care is expected to improve healthcare for patients with HF.
In this study, our goal is to understand the course of HF—from before disease onset to patient death. Further, we aim to measure the clinical processes that occur during this time period and investigate the relationship between outcomes and clinical process indicators at the individual and clinical practice levels. This increases the reliability of measuring the clinical process.
In addition, we will conduct comparative research on disease flow and process indicators using international data (Japanese data). The significance of these indicators can be made more robust by examining their external validity as indicators of the clinical process using data that differs from the medical system and the course of HF. The results of these studies will benefit patients, healthcare providers, and policymakers.
To prevent or delay the development of Heart Failure (HF), it is essential to understand the trajectory of HF and provide appropriate interventions at each step to slow progression. However, current treatment for HF is inadequate, and it is unclear whether treating the patient’s HF is sufficient. Hence, the association between variabilities in treatment and patient outcome must be further studied. Moreover, limited information is available for predicting the development of new-onset HF in patients undergoing treatment for underlying diseases, and the effectiveness of treatment of HF-associated comorbidities in clinical practice is unclear.
Therefore, this study aims to investigate clinical pathways in patients with HF and the effects of therapeutic interventions on disease trajectory using electronic, national hospital-linked primary healthcare databases and compare the results with Japanese datasets.
Specifically, the study will: To use international data,
1. To understand the mode of disease development for HF
2. To investigate the onset of the new disease following HF diagnosis using international data
3. To quantify HF a quality of care according to national and international guidelines, and its association of outcomes.
The primary outcomes of this study will be new-onset HF, hospital admission for HF, and all-cause mortality. We will obtain mortality, social deprivation indices and patient outcome from ONS, IMD and HES Admitted Patient Care dataset. We will use a deep machine learning model and process mining techniques to predict onset of HF and determine the disease trajectory after the diagnosis of HF. We will use Cox proportional hazards regression to assess the association between process performance measures and outcomes.
Investigating the trends and clinical pathways of HF patients and their management will help reduce mortality by targeting specific patient groups for therapy. In addition, determining the predictive factors for new-onset HF helps target high-risk individuals to initiate preventive measures.
The primary outcomes of this study will be new-onset HF, hospital admission for HF, and all-cause mortality. The secondary outcomes will include guideline-directed care implementation rate ratios and subsequent diagnosis of HF (post-index HF diagnosis).
Chris Gale - Chief Investigator - University of Leeds
Kazuhiro Nakao - Corresponding Applicant - University of Leeds
Jianhua Wu - Collaborator - University of Leeds
Ramesh Nadarajah - Collaborator - University of Leeds
Yoko Nakao - Collaborator - University of Leeds
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation