Data sources and definitions
Individual clinical data on 10 634 consecutive people attending six rapid access chest pain clinics were electronically recorded from 2 January 1996 to 31 December 2002 on identical databases, details of which have been reported previously.18 An independent panel deemed 1375 of these patients as appropriate for angiography. Figure 1⇓ shows the origins of the study sample. The study size was determined by the availability of systematically collected comprehensive baseline data in the six clinics using the same electronic record system. The clinics were sited in Blackburn, Burnley, Kingston, Manchester, Newham, and Oldchurch. Clinical data collected at attendance included age, sex, ethnicity, duration of symptoms, character of chest pain, smoking status, history of hypertension, diagnosis of diabetes, resting and exercise electrocardiograms (ECG), pulse rate, systolic blood pressure, drugs, and follow-up plan on discharge. At the end of the consultation the clinician recorded the cause of chest pain (angina or non-cardiac chest pain).
Fig 1 Flow diagram of patients in cohort
Ethnicity
During the consultation the clinician who assessed the patient in the clinic ascribed ethnicity as Asian, white, black, or other. “Asian” was used for patients of Indian, Pakistani, Sri Lankan, and Bangladeshi origin, and we have referred to this category as “south Asian” in this paper. In our analysis we compared white and south Asian patients. In the UK south Asian people are at particularly high risk of coronary artery disease.19 We did not have the power to analyse differences in access and outcomes of other ethnic minority groups (African and Afro-Caribbean) because of their relatively low incidence of coronary disease and small numbers of patients in our cohort.
Deprivation
We used fifths of the 2001 census Townsend index, a score of material deprivation20 based on four variables (unemployment, overcrowding, car ownership, and home ownership). In our analysis we compared the most deprived (highest fifth) with the other four combined.
Appropriateness ratings for angiography
Before undertaking this study, we defined the appropriateness of angiography with two expert panels using a modification of the Rand/UCLA method of systematically combining evidence with expert opinion.21 We identified 13 clinical descriptors that influence the decision to undertake angiography on people with suspected or confirmed angina: age (<40, 40-49, 50-59, 60-74, 75-84), sex, typicality of symptoms, severity of symptoms (Canadian Cardiovascular Society (CCS) class), drugs for symptoms (submaximal, maximal), coronary risk factors (low, medium, high), previous acute coronary syndrome, timing of acute coronary syndrome (within past year, more than a year ago), resting ECG (normal, abnormal), findings on exercise ECG (none, normal, abnormal, very abnormal), previous result of angiography, (abnormal, normal), timing of angiography (within past year, more than a year ago), previous revascularisation. We used clinically meaningful combinations of these factors to define specific clinical indications that we grouped in six broad clinical presentations.
Each panel consisted of five cardiologists, five family physicians, and one cardiothoracic surgeon. Panellists judged appropriateness for angiography on a 9point scale. Scores of 7-9 indicated an appropriate investigation, when benefit from subsequent treatment was judged to outweigh harm. Panellists were invited to base their ratings on evidence from peer reviewed research and were provided with a literature review with evidence tables and graded strength of evidence.22 They were asked to rate purely on the basis of clinical benefit and harm, without consideration of financial or workload constraints. Panel members carried out the first round of rating independently and then had the opportunity to change their ratings in the light of a panel discussion.
We matched indications and their associated ratings to patients from the six clinics, based on the 13 clinical individual clinical characteristics and initial investigations listed above, with the exception of CCS class. Patients were included in our analysis if either panel had deemed their indications appropriate for angiography.
Sample population
Our sample comprised white or south Asian patients with chest pain and no known coronary heart disease presenting to rapid access chest pain clinics and deemed appropriate for coronary angiography by either panel. We matched the patient’s characteristics (age; typical, atypical, or non-cardiac symptoms of chest pain; cardiac risk factors; results of resting and exercise ECGs) to the panels’ ratings to define whether they were appropriate for coronary angiography.
Missing baseline data
In the total cohort of 10 634 patients, 801 had incomplete baseline data (7.5%). We did not include these patients in the main analysis; a sensitivity analysis showed that the rates of coronary deaths and non-fatal events for patients with angina and non-cardiac chest pain with missing baseline data were not significantly different from those with angina and non-cardiac chest pain and complete baseline data.
Follow-up of patients and outcome measures
Over 99% of patients were successfully matched at the Office for National Statistics and the NHS-wide clearing system who informed us of the date and ICD-10 (international classification of diseases, 10th revision) coded cause of death and hospital discharge, respectively. Average follow-up for the cohort was three years, until the end of 2003. Analysis of receipt of angiography was truncated three years after the index clinic visit. Data on use of coronary angiography were obtained from the NHS-wide clearing system. We analysed a single combined end point of death from coronary heart disease (ICD I20-I25) and admission to hospital because of acute coronary syndromes, including acute myocardial infarction (I21-I23) and unstable angina (I20.0-I20.9, I24.0, I24.8 and I24.9). We used the primary discharge diagnosis after hospital admission to define non-fatal events in these analyses. To define a group of patients without major comorbidity we identified those without any hospital admission for non-coronary reasons within a year of the clinic visit.
Analysis
We performed all analyses in STATA version 8.
Receipt of angiography
We fitted univariate and multivariable Cox’s regression models to estimate rates of receipt of angiography by age group, sex, ethnic group, and deprivation. The multivariable models included the clinic in which patients were assessed as a random variable. The proportional hazards assumption of the final Cox model was tested by calculating Schoenfeld residuals. We used severity of symptoms to define appropriateness ratings. As Canadian Cardiovascular Society (CCS) angina class was not recorded in this cohort, we assumed all patients were CCS class I-II (mild symptom severity). We chose this assumption because it would underestimate the need for angiography. In a further analysis we made the opposite assumption, coding the patients as class III-IV.
Coronary events
To measure the effect of underuse of angiography on morbidity and mortality in these demographic subgroups we fitted univariate and multivariable random effects Cox’s regression models to estimate the hazard of non-fatal acute coronary syndrome or death from coronary heart disease within five years of clinic visit according to whether or not patients underwent coronary angiography within three years of the index clinic attendance. To address the possibility of confounding by indication—those who did not undergo the procedure might have had too high a coronary risk—we adjusted for all other demographic variables, secondary prevention medication, and result of exercise ECG. The regression models were fitted for each demographic subgroup separately (for example, men separately), and the hazard ratios presented refer to the risk of an event related to not undergoing angiography. The final model of risk of a coronary event incorporated a propensity score for individuals that, in addition to the above variables, included diabetes, hypertension, hypercholesterolaemia, and duration and character of symptoms. The propensity score is the probability of receiving treatment for a patient with specific prognostic factors and is a scalar summary of all measured confounders.23 We assessed differences in the event rates between subgroups with an interaction test.24
Patients with severe comorbid conditions might not have been referred for angiography, though they fulfilled the panels’ criteria for appropriateness. To control for this possible confounding, in an additional analysis we excluded all patients with admissions for non-coronary conditions (all ICD chapters apart from I for circulatory disease, R00-03 for symptoms and signs involving the circulatory system, and R07.4 for unspecified chest pain) in the year after the index attendance.
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