Genetic-guided pharmacotherapy for venous thromboembolism: a systematic and critical review of economic evaluations

Genetic-Guided Pharmacotherapy for Coronary Artery Disease: A Systematic and Critical Review of Economic Evaluations

BACKGROUND

Genetic-guided pharmacotherapy (PGx) is not recommended in clinical guidelines for coronary artery disease (CAD). We aimed to examine the extent and quality of evidence from economic evaluations of PGx in CAD and to identify variables influential in changing conclusions on cost-effectiveness.

METHODS AND RESULTS

From systematic searches across 6 databases, 2 independent reviewers screened, included, and rated the methodological quality of economic evaluations of PGx testing to guide pharmacotherapy for patients with CAD. Of 35 economic evaluations included, most were model-based cost-utility analyses alone, or alongside cost-effectiveness analyses of PGx testing to stratify patients into antiplatelets (25/35), statins (2/35), pain killers (1/35), or angiotensin-converting enzyme inhibitors (1/35) to predict CAD risk (8/35) or to determine the coumadin doses (1/35). To stratify patients into antiplatelets (96/151 comparisons with complete findings of PGx versus non-PGx), PGx was more effective and more costly than non-PGx clopidogrel (28/43) but less costly than non-PGx prasugrel (10/15) and less costly and less effective than non-PGx ticagrelor (22/25). To predict CAD risk (51/151 comparisons), PGx using genetic risk scores was more effective and less costly than clinical risk score (13/17) but more costly than no risk score (16/19) or no treatment (9/9). The remaining comparisons were too few to observe any trend. Mortality risk was the most common variable (47/294) changing conclusions.

CONCLUSIONS

Economic evaluations to date found PGx to stratify patients with CAD into antiplatelets or to predict CAD risk to be cost-effective, but findings varied based on the non-PGx comparators, underscoring the importance of considering local practice in deciding whether to adopt PGx.

A Comparison of the Content and Consistency of Methodological Quality and Transferability Checklists for Reviewing Model-Based Economic Evaluations

OBJECTIVES

The aim of this study was to examine whether and how the content of six checklists (Caro, Consensus on Health Economic Criteria [CHEC]-Extended, European Network of Health Economic Databases [EURONHEED], National Institute for Health and Care Excellence [NICE], Philips, Welte) affect the consistency in findings on methodological quality and transferability, using 10 model-based economic evaluations of genetic-guided pharmacotherapy for venous thromboembolism.

METHODS

Each checklist was categorised by domain (structure, data, consistency, etc.) and type of assessment (presence vs. appropriateness) and was applied to each study by two independent reviewers who agreed on ratings via consensus, and discussion with a third reviewer when necessary. Methodological quality scores and rankings were examined using Spearman correlation tests, with subgroup analyses for domains and types of assessment. We compared overall ratings of transferability qualitatively, including how content may affect what is considered 'transferable'.

RESULTS

The checklists had similar proportions of items judging presence and appropriateness, but varying proportions of items across domains. For methodological quality, ranking consistencies were the highest between CHEC-Extended-Philips, Philips-NICE and NICE-Caro, with similar consistencies for domains and type of assessment. For transferability, NICE and Caro identified the same study, which scored high on EURONHEED, as transferable to the UK, while Welte, which considered methodological quality, identified none as transferable.

CONCLUSIONS

We found that the choice of checklist can affect findings on study quality and decisions about whether study results are transferable, indicating that different checklists may shortlist different sets of studies in formulating policy recommendations, leading to different policy decisions. Our systematic approach for evaluating the content of methodological quality and transferability checklists of economic evaluations can be extended to other checklists.

Genetic-Guided Pharmacotherapy for Atrial Fibrillation: A Systematic and Critical Review of Economic Evaluations

OBJECTIVES

This study aimed to examine the extent and quality of evidence from economic evaluations (EEs) of genetic-guided pharmacotherapy (PGx) for atrial fibrillation (AF) and to identify variables influential in changing base-case conclusions.

METHODS

From systematic searches, we included EEs of existing PGx testing to guide pharmacotherapy for AF, without restrictions on population characteristics or language. Articles excluded were genetic tests used to guide device-based therapy or focused on animals.

RESULTS

We found 18 EEs (46 comparisons), all model-based cost-utility analysis with or without cost-effectiveness analysis mostly from health system's perspectives, of PGx testing to determine coumadin/direct-acting anticoagulant (DOAC) dosing (14 of 18), to stratify patients into coumadin/DOACs (3 of 18), or to increase patients' adherence to coumadin (1 of 18) versus non-PGx. Most PGx to determine coumadin dosing found PGx more costly and more effective than standard or clinical coumadin dosing (19 of 24 comparisons) but less costly and less effective than standard DOAC dosing (14 of 14 comparisons). The remaining comparisons were too few to observe any trend. Of 61 variables influential in changing base-case conclusions, effectiveness of PGx testing was the most common (37%), accounted for in the models using time-based or medication-based approaches or relative risk. The cost of PGx testing has decreased and plateaued over time.

CONCLUSIONS

EEs to date only partially inform decisions on selecting optimal PGx testing for AF, because most evidence focuses on PGx testing to determine coumadin dosing, but less on other purposes. Future EE may refer to the list of influential variables and the approaches used to account for the effect of PGx testing to inform data collection and study design.