Title: Clinical and demographic characteristics associated with delay in help-seeking behaviour in patients with Acute Coronary Syndrome
Funder: Department of Health and Social Care

Abstract: Patients' delay in seeking emergency help following first signs of Acute Coronary Syndrome (ACS) is a potentially reversible obstacle to timely treatment. Campaigns aimed at reducing delay have rarely been fully evaluated (none from the UK). Those that have are disappointing. Campaigns in the UK are run with little information about the characteristics of those who delay. One study was suggestive of delay amongst 'South Asians' (10). One or two other UK studies indicate that women might be prone to delay. However, the large MINAP database, which records ACS patients from hospitals in England, Wales and Belfast, has not to date been interrogated for factors associated with delay. This cohort study will use 3 years of data from MINAP (240,000 cases) with the following variables: • Delay: Date/time of symptom onset; Date/time of call • Socio-demographic: sex; postcode-level deprivation index (IMD); ethnicity; age • Clinical characteristics: e.g. admission method; location at time of symptoms, medical history; smoking status, initial observations and diagnosis; ECG; discharge diagnosis; site of infarct; procedure performed;. • There is some degree of missing information - particularly in the delay field The outcome of the analysis is delay, categorised as: < 60 minutes; 60-120 minutes; 121-360 minutes; >360 minutes; these bandings are based on treatment effectiveness (37). As we have a categorical outcome variable, a multinomial logistic model (MNLR) will be most appropriate for the analysis but we will use ordered categories. MNLR has two advantages: a) a more parsimonious model and b) increased power. This model allows us to analyse associations between the outcome variable (delay) and the multiple clinical and non-clinical independent variables. Interactions will also be examined; for example, we would seek to find whether any delay based on age is associated also with symptom type. The analysis falls into two stages: 1) Examine the association between delay and the clinical and non-clinical factors and any interactions as set out above. 2) Modelling the missing values included in these datasets to make more efficient inferences and enhance the utility of these data. In this stage, we will assess the monotone patterns and the joint probability of missing across variables. Then we will identify the potential predictors of each variable which need to be modelled. Multiple imputations will be used for this stage. Further analysis will be considered if any nonlinear associations are found between the outcome and dependent variables. In this case, semi-parametric (e.g. generalized additive model) analysis will be considered for further investigation. Computations will be carried out using STATA & IBM SPSS 20 programmes.

Lay abstract: The term 'Acute Coronary Syndrome' (ACS) refers to conditions in which the coronary arteries, which feed the heart muscle with blood, become 'furred-up' and fully or partially blocked. ACS includes myocardial infarction (a heart attack) or unstable angina. Although typically associated with chest pain unrelieved by rest, symptoms vary greatly and people may consult with others. As a result, some people with ACS delay seeking help. This delay is associated with two problems. 1) ACS puts the patient at risk of cardiac arrest (the heart stops beating). This can be reversed with the right treatment but time is of the essence and seeking prompt help is therefore essential. 2) The best treatments for heart attack remove the blockage but again time is of the essence. Over recent years treatment of ACS using well-tested procedures, has resulted in a welcome increase in survival, but to benefit from these advances patients need to access the ‘care pathway’ promptly: seeking professional help by calling an ambulance is now the main potentially reversible delay. Attempts have been made to reduce delay, (for example the British Heart Foundation campaigns) but success, or an increase in false positives, has not been evaluated. At present we do not know the characteristics of those who delay most. There are, for example, indications that women and some people from ethnic minorities have longer delays. The aim of this study is to use the national database of heart attack care (MINAP, the Myocardial Ischaemia National Audit Project) to identify the characteristics associated with a delayed call for help where heart attack is suspected. We will examine non-clinical factors, such as age, sex, social class and ethnicity, and clinical factors, such as the presenting condition and medical history. This project will provide the information to inform and evaluate future campaigns to reduce delayed access to emergency care where a heart attack is suspected. As is usual in this type of dataset there is some missing information which has the potential to systematically change our conclusions. We know that the missing information is not random and will use sophisticated statistical techniques to analyse what is missing and predict how, or if, it affects our conclusions.

Research activity codes: 8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: CARDIOVASCULAR