Introduction: Pulmonary embolism (PE) is a common cardiovascular condition with high mortality rates if left untreated. Given the non-specific and varied symptoms of PE, its diagnosis remains challenging and approaches can lend themselves to inefficiencies through over-testing and over-diagnosis. Clinicians rely on a multi-component and sequential approach, including clinical risk assessment, rule-out biomarkers, and diagnostic imaging. This study assessed the potential cost-effectiveness of different diagnostic algorithms. Methods: A cost-utility model was developed with an upfront decision tree capturing the diagnostic accuracy and a Markov cohort model reflecting the lifetime disease progression and clinical utility of each diagnostic strategy. 57 diagnostic strategies were evaluated that were permutations of various clinical risk assessment, rule-out biomarkers and diagnostic imaging modalities. Diagnostic test accuracy was informed by systematic reviews and meta-analyses, and costs (2016 CAD) were obtained from Canadian costing databases to reflect a health-care payer perspective. Separate scenario analyses were conducted on patients contra-indicated for computed tomography (CT) or who are pregnant as this entails a comparison of a different set of diagnostic strategies. Results: Six diagnostic strategies formed the efficiency frontier. Diagnosing patients with PE was generally cost-effective if willingness-to-pay was greater than $1,481 per quality-adjusted-life year (QALY). CT dominated other imaging modality given its greater diagnostic accuracy, lower rates of non-diagnostic findings and lowest overall costs. The use of clinical prediction rules to determine clinical pre-test probability of PE and the application of rule-out test for patients with low-to-moderate risk of PE may be cost-effective while reducing the proportion of patients requiring CT and lowering radiation exposure. At a willingness-to-pay of $50,000 per QALY, the strategy of Wells (2 tier) --> d-dimer --> CT --> CT was the most likely cost-effective diagnostic strategy. However, different diagnostic strategies were considered cost-effective for pregnant patients and those contra-indicated for CT. Conclusion: This study highlighted the value of economic modelling to inform judicious use of resources in achieving a diagnosis for PE. These findings, in conjunction with a recent health technology assessment, may help to inform clinical practice and guidelines. Which strategy would be considered cost-effective reflected ones willingness to trade-off between misdiagnosis and over-diagnosis.

Objectives: The headroom method was introduced for the very early evaluation of the potential value of new technologies. It allows for establishing a ceiling price for technologies to still be cost-effective by combining the maximum effect a technology might yield, the maximum willingness-to-pay (WTP) for this effect, and potential downstream expenses and savings. Although the headroom method is QALY-based, not all innovations are expected to result in QALY gain.

Methods: This study explores the feasibility and usefulness of the headroom method in the evaluation of technologies that are unlikely to result in QALY gain. This will be illustrated with the diagnostic trajectory of complex pediatric neurology (CPN).

Results: Our headroom analysis showed a large room for improvement in the current diagnostic trajectory of CPN in terms of diagnostic yield. Combining this with a maximum WTP value for an additional diagnosis and the potential downstream expenses and savings, resulted in a total headroom of €15,028. This indicates that a new technology in this particular diagnostic trajectory, might be cost-effective as long as its costs do not exceed €15,028.

Conclusions: The headroom method seems a useful tool in the very early evaluation of medical technologies, also in cases when immediate QALY gain is unlikely. It allows for allocating healthcare resources to those technologies that are most promising. It should be kept in mind, however, that the headroom assumes an optimistic scenario, and for that reason cannot guarantee future cost-effectiveness. It might be most useful for ruling out those technologies that are unlikely to be cost-effective.