Minimally acceptable criteria and required sample size for an accuracy study of non-invasive prenatal testing of sickle cell disease in screen positive women in England: results of a decision tree model

BACKGROUND: The screening pathway for sickle cell disease (SCD) in England starts with a carrier status blood test for the pregnant woman. Following a positive result, the test is offered to the biological father. Where both results are positive, further invasive testing is offered to assess the risk of SCD to the fetus. When the father is unavailable, the timely offer of further testing may be delayed and even missed, leaving the pregnant woman underinformed and limiting reproductive choice. Non-invasive prenatal testing (NIPT) presents an alternative to paternal testing and diagnostic accuracy studies of NIPT in SCD screening are required to inform utility and application. We explored the minimally acceptable sensitivity and specificity to inform such an accuracy study of NIPT. METHODS: A decision tree model was produced to identify the minimally acceptable sensitivity and specificity of NIPT. Stakeholder engagement identified a positive predictive value (PPV) equal to the paternal carrier blood test and two sensitivity/specificity scenarios: Scenario 1 should result in < 10 false negative diagnoses per year; Scenario 2 should result in ≤ 2 false negative diagnoses per year. Subsequently, the minimally acceptable sensitivity and specificity were used to calculate the sample size for a hypothetical accuracy study of NIPT in each scenario. RESULTS: Scenario 1 led to a minimally acceptable sensitivity of 96.0% and specificity of 88.5% and corresponding negative predictive value (NPV) of 99.81%, PPV of 25.51% and accuracy of 88.80%. Utilising an expected prevalence of SCD of 3.94%, the resulting sample size would include 315,824 total pregnancies. Scenario 2 led to a minimally acceptable sensitivity of 99.0% and specificity of 88.0% and corresponding NPV of 99.95%, PPV of 25.29% and accuracy of 88.43%. The resulting sample size would include 65,509 overall pregnancies. CONCLUSIONS: While the sample size calculated for each scenario is unfeasible if considering a prospective cohort study, approaches are presented to achieve realistic sample sizes. Increasing the sensitivity and specificity levels might be considered if this is likely to be achievable based on available studies, albeit limited in volume. Consideration could be given to study design mitigations that are consistent with guidance on accuracy studies in low prevalence settings.