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

Discussionuncertainty about these parameters. We were also fortunate to have data from the Mills study 155 toprovide an estimate of the benefit of a positive reference standard test in the diagnostic model. Suchestimates are unusual and modelling the benefit of diagnostic tests often involves relying on expert opinionto estimate treatment effects. We used expert opinion to build the model and develop our assumptionsbut did not need to draw upon expert opinion for parameter estimates.As with any economic analysis, the model involved some important and influential assumptions. Mostof these have been discussed alongside the summary of main findings above, as an appreciation of theirimpact is essential to understanding the model output. An additional assumption in the model is thatmedical decision-making flows in a predictable and consistent manner from the results of diagnostictesting. This obviously may not hold in practice and previous trials 12 have been invaluable in testingassumptions that diagnostic test results will lead to predictable changes in patient care. Further researchis required to test some of the assumptions in our model. For example, we assumed that the implicationof a FP presentation biomarker was limited to the cost of keeping the patient in hospital until a definitive10-hour troponin level was measured. We also assumed that the diagnostic testing strategy did notinfluence the location of patient admission (e.g. use of coronary care) and that patients would bedischarged if tests were negative. These assumptions were justified on the basis of absence of evidence tochallenge them and/or the practical difficulties of incorporating them into the model rather than availableevidence to suggest they are not relevant or influential.We only tested a limited range of potential strategies addressing specific issues in patient management.We typically limited the strategies tested to those with sufficient data to support them. This means thatwe did not test potentially worthwhile strategies with limited data, such as 6-hour strategies, or pragmaticstrategies, such as selecting patients to delayed diagnostic testing or subsequent prognostic testing on thebasis of clinical risk. In particular, we only tested using H-FABP as a prognostic marker in troponin-negativepatients by assuming it would be used to select patients for ICA. A more logical approach might involveusing H-FABP to select patients for CTCA. However, this would involve making an assumption aboutwhether or not the prognostic value of H-FABP and CTCA are independent. We had no data to allow us totest this assumption, yet this interaction is crucial to determining the cost-effectiveness of the combination.A substantial limitation of the prognostic model is that we had no data to directly estimate the benefitof treating positive cases, in the way that we had for the diagnostic model. 155 Therefore, we assumedthat the effect of identifying and treating an increased risk of adverse outcome in the prognostic modelwas the same as the effect of identifying and treating MI in the diagnostic model. This assumption maynot hold and, in combination with the uncertainty about the risk of subsequent adverse events discussedearlier, means that the benefit of identifying positive cases in the prognostic model is extremely uncertain.A further limitation of the prognostic model relates to limitations of the primary data. The heterogeneityin the definition of MACEs and follow-up procedures, and the potential for bias is discussed above, butother limitations of the primary data relate to implementation of the technology. Whereas, biomarkersare mostly quantitative tests with clear diagnostic thresholds, CTCA and exercise ECG require carefulinterpretation. Issues such as interobserver error and the training and expertise required for interpretationhave not been extensively studied, creating more uncertainties about how these technologies will performwhen put into practice.Finally, the model assumed that all benefits from diagnostic testing were accrued as a result of the riskof adverse outcome. However, the testing process may have other benefits that are not captured in ourmodel. Patients may benefit from the reassurance of negative testing or the opportunity to institutelifestyle changes stimulated by positive testing. The evidence for these benefits is limited and debatablebut, if confirmed, could substantially alter the potential cost-effectiveness of diagnostic strategies.114NIHR Journals Library