Sickle cell disease (SCD) is a condition that represents a significant unmet medical need. It is one of the most prevalent genetic disorders causing substantial illness and death and is responsible for a high number of hospitalisations annually. SCD patients suffer unpredictable and recurrent complications due to blocked blood flow to organs. This blocked blood flow, combined with haemolytic anaemia, can lead to multi-organ damage and early death.
Although SCD is mostly associated with fewer red blood cells, there is still difficulty in understanding the relationship between haemoglobin (Hb) levels and the risk of clinical outcomes such as stroke, pulmonary hypertension, chronic kidney disease, end stage renal disease, leg ulcer and mortality. it is therefore difficult to predict which patient groups are at a higher risk of these outcomes and who in turn should be targeted for intervention.
This study aims to establish the association of Hb levels on clinical outcomes and healthcare resource use in patients with SCD. We shall also be able to profile SCD patients based on haemoglobin levels, genotypes and medication prescribed and look at how much resources they consume in the health system.
With this study, we should be able to provide an in-depth look into SCD in England, which will allow changes in health policy and treatment patterns, especially targeting patients based on risk factor such as haemoglobin levels, genotypes, medicine prescribed and those who consume a lot of resources in the NHS.
With current advancements in drug research, there is hope that Sickle cell disease (SCD) can be managed by a once daily oral direct-acting haemoglobin modifier for chronic, prophylactic treatment of patients which would in turn reduce the morbidity, mortality and health care costs associated with them. However, there is limited understanding of the most at-risk patients which would affect the impact of targeted treatments if they are to be made available on the NHS. There is also a limited availability of knowledge on the risk of adverse clinical outcomes such as stroke in this group.
This study aims to ascertain the association between haemoglobin (Hb) levels and selected clinical outcomes and healthcare resource use in patients diagnosed with SCD. This will be done by creating a cohort of patients with SCD using an algorithm based on codes. Further sub cohorts will be created out of the main cohort based on Hb levels, genotype, age and medications prescribed.
We shall describe demographic characteristics, occurrence of clinical outcomes including stroke and mortality. Health care resource usage will also be calculated and reported for primary care, inpatient, outpatient and A&E activity including cost. Outcomes will be described as total, means, medians, percentage or rates as appropriate.
In the comparative analysis, we shall look to null hypothesise that there is no relationship between risk of adverse clinical outcomes and SCD. Among patients with and controls without SCD, unadjusted and adjusted odds, odds ratios and mean incidence along with 95% confidence intervals will be calculated for all selected clinical outcomes and death at 1-6 months, >6-12 months and >12 months on comparing each cohort to matched controls. We shall match on age and sex and adjust for any other risk factors that we may identify as having an effect on the dependent variables.
Prevalence of SCD; haemoglobin levels; Demographics (Mean and median age on inclusion, age distribution by decade, sex distribution, deprivation, Charlson co-morbidity score distribution, mean and median follow-up, total and mean admitted time, smoking status, BMI, alcohol consumption), prevalence of VOCs, ACS, Hypertension, Asthma, Stroke, History of leg ulcer, Diabetes, Upper respiratory tract infections, Osteonecrosis, Transfusions in the year prior to index, Gallstones, Chronic pain, Neoplasms benign and malignant, CKD by stage, Sepsis); crude death rate in the cohort; Healthcare resource outcomes (prescriptions issued in primary care, GP appointments in primary care, number of inpatient admissions, inpatient length of stay, inpatient HRG tariffs, number of outpatient appointments, inpatient HRG tariffs, number of A&E attendance, A&E HRG tariffs ); Clinical outcomes (Stroke, PH, CKD, ESRD, Leg ulcer, Composite ACS/pneumonia, mortality)
Jennifer Davidson - Chief Investigator - Health iQ Ltd ( UK ) t/a CorEvitas
Jennifer Davidson - Corresponding Applicant - Health iQ Ltd ( UK ) t/a CorEvitas
Archie Farrer - Collaborator - Health iQ Ltd ( UK ) t/a CorEvitas
Boglarka Kovacs - Collaborator - Health iQ Ltd ( UK ) t/a CorEvitas
Gulsah Akin Unal - Collaborator - Health iQ Ltd ( UK ) t/a CorEvitas
Judith Ruzangi - Collaborator - Health iQ Ltd ( UK ) t/a CorEvitas
Mico Hamlyn - Collaborator - Health iQ Ltd ( UK ) t/a CorEvitas
Shea O'Connell - Collaborator - Health iQ Ltd ( UK ) t/a CorEvitas
Gulsah Akin Unal - Collaborator - Health iQ Ltd ( UK ) t/a CorEvitas
HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation