Background: Premature birth is defined as birth of before 37 completed weeks' gestation. Not all pregnant women showing symptoms of preterm labour will go on to deliver before 37weeks' gestation. Hence, addition of fetal fibronectin (fFN) testing to the diagnostic workup of women with suspected preterm labour may help to identify those women who do not require active management, and thus avoid unnecessary interventions, hospitalisations and associated costs.
Objective: To assess the clinical effectiveness and cost-effectiveness of rapid fFN testing in predicting preterm birth (PTB) in symptomatic women.
Data sources: Bibliographic databases (including EMBASE, Cochrane Database of Systematic Reviews and Cochrane Central Register of Controlled Trials) were searched from 2000 to September/November 2011. Trial registers were also searched.
Review methods: Systematic review methods followed published guidance; we assessed clinical effectiveness and updated a previous systematic review of test accuracy. Risk of bias was assessed using the Cochrane tool (randomised controlled trials; RCTs) and a modification of QUADAS-2 (diagnostic test accuracy studies; DTAs). Summary risk ratios or weighted mean difference were calculated using randomeffects models. Summary sensitivity and specificity used a bivariate summary receiver operating characteristic model. Heterogeneity was investigated using subgroup and sensitivity analyses. Health economic analysis focused on cost consequences. The time horizon was hospital admission for observation. A main structural assumption was that, compared with usual care, fFN testing doesn't increase adverse events or negative pregnancy outcomes.
Results: Five RCTs and 15 new DTAs were identified. No RCT reported significant effects of fFN testing on maternal or neonatal outcomes. One study reported a subgroup analysis of women with negative fFN test observed >6 hours, which showed a reduction in length of hospital stay where results were known to clinicians. Combining data from new studies and the previous systematic review, the pooled estimates of sensitivity and specificity were: 76.7% and 82.7% for delivery within 7–10 days of testing; 69.1% and 84.4% for delivery <34 weeks' gestation; and 60.8% and 82.3% for delivery <37 weeks' gestation. Estimates were similar across all subgroups sensitivity analyses. The base-case cost analysis resulted in a cost saving of £23.87 for fFN testing compared with usual care. The fFN testing was cost-neutral at an approximate cost of £45. Probabilistic sensitivity analysis gave an incremental cost (saving) of –£25.59 (97.5% confidence interval –£304.96 to £240.06), indicating substantial uncertainty. Sensitivity analyses indicated that admission rate had the largest impact on results.
Conclusions: Fetal fibronectin testing has moderate accuracy for predicting PTB. The main potential role is likely to be reducing health-care resource usage by identifying women not requiring intervention. Evidence from RCTs suggests that fFN does not increase adverse outcomes and may reduce resource use. The base-case analysis showed a modest cost difference in favour of fFN testing, which is largely dependent on whether or not fFN testing reduces hospital admission. Currently, there are no high-quality studies and the existing trials were generally underpowered. Hence, there is a need for high-quality adequately powered trials using appropriate study designs to confirm the findings presented.
Study registration: PROSPERO 2011:CRD42011001468. Available from www.crd.york.ac.uk/PROSPERO/ display_record.asp?ID=CRD42011001468.
Funding: The National Institute for Health Research Health Technology Assessment programme

doi.org/10.3310/hta17400, hdl.handle.net/1765/50072
Health Technology Assessment
Erasmus School of Health Policy & Management (ESHPM)

Deshpande, N. V., van Asselt, A., Tomini, F., Armstrong, N., Allen, A., Noake, C., … Westwood, M. (2013). Rapid fetal fibronectin testing to predict preterm birth in women with symptoms of premature labour: a systematic review and cost analysis. Health Technology Assessment (Vol. 17, pp. 1–160). doi:10.3310/hta17400