We will investigate whether the use of WGS-first is cost-effective compared to the standard genetic testing in NDD patients from a societal perspective, based on the underlying rationale that NGS is able to reduce time-to-diagnosis and saves on direct medical consumption (e.g. futile further, potentially invasive, diagnostics and treatments). In this rationale, it is assumed that on average, parents prefer an (earlier) diagnosis and consequently assign value/utility to this knowledge (e.g. for further reproductive choices and/or joining patient support groups). The economic evaluation is based on the general principles of a cost-effectiveness analysis and uses input from the prospective and retrospective data collection. Outcome measures for the economic evaluation on patient level are i) costs (both direct (in and outside health care) and indirect), ii) number of definitive genetic diagnosis, and iii) time to diagnosis.
Both prospective as retrospective input will be used to assess the difference between diagnostic costs for both pathways (costs from first visit until having or not having a diagnosis). Assessment of this difference will be used as input for the cost-effectiveness analysis in 1.5.4.
1.5.2 Impact on medical decision making
To measure the impact of a diagnosis on medical decision making and costs, we will ask health care professionals to report follow-up exams and/or changes made to the standard care. For this analysis, questionnaires will be used that will serve as a guideline to interview neonatologists/clinical geneticists involved in clinical care of the patients enrolled in this study. The main question that will be addressed, for each patient individually, is "has knowledge on the genetic diagnosis changed/influenced clinical care provided to this patient?" That is, it may be expected that for some patients, the genetic diagnosis has led to referral to e.g. cardiologist for surveillance of cardiac performance, whereas without the knowledge of the genetic etiology, this referral may not have taken place. Especially for NICU patients, certain difficult to diagnose acute conditions may lead to withdrawal of further treatment or re-direction of care, whereas other diagnosis may lead to medical interventions. Changes in therapeutic strategies will be monitored and related to the health care costs for each individual patient. The output will be added to the CRFs. In addition to quantitative assessments, we aim to perform structured interviews with medical geneticists and other specialists to outline their viewpoints of approaches in changing patient disease management after diagnosis. Outlined changes will be compared to the collected retrospective dataset to envision whether changes have appeared and whether more guidance in patient management is needed. In addition, for analytical decision modeling, changes in health care resource are used for calculations.
For time to diagnosis a time-to-event analysis will be performed, with definitive diagnosis being the event. NDD patients for whom no definitive diagnosis was made, contribute complete follow up time (two years) to the time of diagnosis as a censored observation. The mean time to definitive diagnosis saved over the duration of interest is the difference in area under the time-to-definitive diagnosis curve (Kaplan-Meier curve) of both the standard and WGS-first pathways.
1.5.4 Decision analytic modeling
Eventually outcomes regarding costs-to-diagnosis (1.5.1) and impact on medical decision making (1.5.2) will be combined in a cost-effectiveness analysis. Outcomes will be depicted as Incremental Cost Effectiveness Ratio’s (ICERs) which express the extra investment (if any) that is needed to achieve an extra diagnosis or a specified shortening of time to diagnosis.
The decision analytic modeling is to find the most cost-effective place for WGS in the diagnostic process on the medium and longer term. These models will allow us to generate scenarios and guidelines when who to offer WGS in a diagnostic setting. At least four scenarios will be considered, as presented in “Toelichting kosteneffectiviteitsanalyse en budgetimpact” respectively, being i) current care, ii) immediate 100% WGSfirst, iii) gradual implementation WGS-first and iv) partial implementation of WGS-first.
1.5.5 Uncertainty analysis
Uncertainty surrounding difference in costs of diagnostic pathways and ICERs will be determined using the bootstrap method or the Fieller method. The impact of uncertainty surrounding deterministic parameters (for example prices) on the differences in total costs and the ICER will be explored using one-way sensitivity analyses on the range of extremes.
Additionally, decision analytic modeling will be used to determine the costs that may be associated with knowledge of the diagnosis. That is, it may for instance be expected that based on knowledge of the genetic diagnosis, patients may be consulted by a cardiologist that otherwise would not have happened.
The VAS satisfaction and GCOS-24 responses from each survey will be transformed into utility and capability scores, respectively (using published values). The relationship between the differences between SDM-Q-9/SDM-Q-Doc scores will be assessed in each care pathway by MANCOVA, student's t-test and Pearson correlations.