Familial hypercholesterolemia (FH) is a disease that causes high levels of cholesterol to accumulate in the blood. In most cases, the disease shows no signs or symptoms; however, if left untreated, patients with this disease can get heart disease by the age of 40. Evidence shows that this disease is under-diagnosed; and the medicines that are currently used to treat it are not effective in some people.
A new type of medicine, technically referred to as 'PCSK9 inhibitors', has been launched for the treatment of this disease. This new type of medicine is more effective than currently available medicines; and may potentially be used to treat patients who are not adequately treated.
Several studies have used patient records with the aim of improving the detection of FH. The findings show that it is possible to determine patients with this disease based on their healthcare records.
This study will focus on the association between FH patients' clinical characteristics and the achievement of effective treatment. The aim of the study is to develop a method to predict if a patient would need this new type of medicine.
The objective of this study is to develop and validate a mathematical model that can predict the therapeutic outcome of familial hypercholesterolemia (FH) patients who are taking the currently recommended treatments.
All patients with a diagnosis of FH and data 'acceptable' for research will be extracted from CPRD; FH is represented by the NHS Read Code "C32" and daughter codes. The sample will be refined to include patients with at least two low density lipoprotein cholesterol (LDLC) test records that are more than a year apart of medication usage. The outcome of the study will be the change in LDLC levels as described by NICE guidelines. The outcome variable will be split into two categories. 11 patient characteristics variables will be used as the independent variables for the study. The total sample will be randomly split into a derivation cohort (75%) for developing the model; and a validation cohort (25%). Chi square tests will be carried out to assess the correlation between the variables. Binary logistic regression using the forward stepwise method will be used to produce the predictive model (p<0.05).
The model accuracy will be assessed by the area under receiver operating curve (AUC) value or Harrell's c-statistic. Model calibration will be carried out by comparing the predicted cases with observed cases in the validation cohort. The predicted probabilities will be stratified by deciles.
Effective treatment of elevated cholesterol
- Non-effective treatment of elevated cholesterol
Martin Frisher - Chief Investigator - Keele University
Myron Odingo - Corresponding Applicant - Keele University
Stephen Chapman - Collaborator - Keele University