Value for money? Array genomic hybridization for diagnostic testing for genetic causes of intellectual disability

Abstract

Array genomic hybridization (AGH) provides a higher detection rate than does conventional cytogenetic testing when searching for chromosomal imbalance causing intellectual disability (ID). AGH is more costly than conventional cytogenetic testing, and it remains unclear whether AGH provides good value for money. Decision analytic modeling was used to evaluate the trade-off between costs, clinical effectiveness, and benefit of an AGH testing strategy compared to a conventional testing strategy. The trade-off between cost and effectiveness was expressed via the incremental cost-effectiveness ratio. Probabilistic sensitivity analysis was performed via Monte Carlo simulation. The baseline AGH testing strategy led to an average cost increase of $217 (95% CI $172-$261) per patient and an additional 8.2 diagnoses in every 100 tested (0.082; 95% CI 0.044-0.119). The mean incremental cost per additional diagnosis was $2646 (95% CI $1619-$5296). Probabilistic sensitivity analysis demonstrated that there was a 95% probability that AGH would be cost effective if decision makers were willing to pay $4550 for an additional diagnosis. Our model suggests that using AGH instead of conventional karyotyping for most ID patients provides good value for money. Deterministic sensitivity analysis found that employing AGH after first-line cytogenetic testing had proven uninformative did not provide good value for money when compared to using AGH as first-line testing.

Figure 1

Decision Analytic Model Legend: open square, decision node; open circle, transition probability; left open triangle, terminal node; Dx, diagnosis; outcome, cost; effectiveness.

Figure 2

Cost-Effectiveness Acceptability Curve of Maximum Willingness to Pay for an AGH Testing Strategy versus a Conventional Testing Strategy