Systematic review, meta-analysis and economic modelling of ...

Assessment of cost-effectiveness evidencereinfarction. The assumed gold standard for diagnosis, troponin measured 10 hours after worst symptomsis the most effective, but also the most expensive strategy because patients are admitted to hospital untilresults are available. Presentation biomarkers incur costs and may miss cases due to suboptimal sensitivity(thus worsening outcomes), but save costs by reducing length of hospital stay. We built a model to allowus to analyse the effect of different diagnostic management strategies on these costs and benefits.The decision-analysis model structureThe different diagnostic strategies were applied to a hypothetical cohort of patients attending the ED withsuspected, but not proven, ACS. We assumed that the diagnostic strategy would determine which patientshad MI and that the probability of detecting an MI was determined by the sensitivity of the diagnosticstrategy. We assumed that patients with detected MI would be managed promptly by treatment. Themodel assigned each patient a probability of reinfarction or death depending on their characteristicsand whether or not they had treatment. Each patient then accrued lifetime QALYs and health-care costsaccording to their age, sex, reinfarction and treatment status. Costs were also accrued through measuringbiomarkers, hospital stay for diagnosis, further investigation, treatment and/or reinfarction depending onthe strategy and the patient characteristics. Details of each of these processes are outlined below.PopulationThe population consisted of a hypothetical cohort of patients attending the ED with suspected but notproven ACS, i.e. a history compatible with ACS but no diagnostic ECG changes (ST deviation of > 1 mmor T-wave inversion > 3 mm), and who had no major comorbidities requiring inpatient treatment (such asHF or arrhythmia). We ran the diagnostic phase model separately for patients with and without a knownhistory of CAD. Different characteristics were used for the populations with and without known CAD.Each patient entering the model had the following characteristics defined: age, sex, MI present or not,time delay between onset of worst pain and arrival at hospital, and time of day. We estimated populationcharacteristics using data from a large recent trial of point-of-care markers in patients with suspected butnot proved MI, the RATPAC (Randomised Assessment of Treatment using Panel Assay of Cardiac markers)trial. 153 Table 32 shows the population characteristics used in the model.The arrival time of patients is an important factor when considering the optimal cost-effectiveness strategybecause outside the ED medical staff may be available only at certain times of the day to make dispositiondecisions (e.g. ward rounds at specific times of the day). We analysed the arrival times of 2240 patientsfrom the RATPAC trial 153 to estimate the arrival distribution used in the model and the results are shown inTable 33. Patients in the RATPAC trial 153 presented across six hospitals over a 15-month period, so the tableis intended to demonstrate relative differences in arrival rates at different times of the day, rather thanproviding any meaningful estimate of absolute arrival rates at a particular hospital.The results are also shown in the form of a histogram in Figure 34. It can be seen that between midnightand 7 am, there are small numbers of patients. The patients arrive at a faster rate between 7 am and 9 ambut between 9 am and 2 pm is the peak time, which sees the fastest arrival rate of patients. There is a steadydecrease in the patient arrival rate between 2 pm and 6 pm and the finally, patients arrive in a constant slowstream between 6 pm and midnight.Selection of strategiesWe tested several strategies to explore the trade-off between sensitivity and specificity. Each potentialstrategy was applied to each patient. The strategy determined:1. what tests each patient received and when2. how long each patient spent in hospital3. what treatments each patient received.82NIHR Journals Library