Pre-eclampsia prevention by timed birth at term (PREVENT–PE): Protocol for economic evaluation alongside PREVENT-PE trial and intermediate-term decision analytic model

Pre-eclampsia prevention by timed birth at term (PREVENT–PE): Protocol for economic evaluation alongside PREVENT-PE trial and intermediate-term decision analytic model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pre-eclampsia prevention by timed birth at term (PREVENT–PE): Protocol for economic evaluation alongside PREVENT-PE trial and intermediate-term decision analytic model Siddesh Shetty, James Goadsby, Laura A. Magee, Argyro Syngelaki, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5433222/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives : Preeclampsia (PE) occurs most commonly at term, and currently, there is no effective strategy to prevent it. The PE prevention by timed birth at term trial (PREVENT- PE) with embedded economic evaluation aims to provide cost-effectiveness evidence on whether screening for PE risk at 35+0–36+6 weeks’ gestation and offering women risk-based, planned term birth compared to usual care at term, can reduce the incidence of PE, without increasing emergency caesarean sections or neonatal morbidity (i.e., neonatal unit admission for ≥48 hours). Study design : This protocol outlines the methods for within-trial and intermediate-term economic evaluations. Main outcome measures : The economic evaluation will identify, measure and value resources and health outcomes for both mothers at risk of term PE and newborn babies, from the National Health Service (NHS) perspective. A cost-effectiveness analysis within the trial will be undertaken, and the incremental cost per PE case averted will be reported as the main outcome. Costs and health outcomes for the trial duration will be calculated using patient-level data, from case report forms and electronic medical records. A decision model will be developed to assess the cost-utility of the intervention for one year. Transition probabilities, costs, and quality-adjusted life years (QALYs) will be populated using trial data and existing literature. Uncertainty will be assessed using deterministic and probabilistic sensitivity analyses. Subgroup analyses (for example, standalone maternal/newborn perspective and by gestational age for planned birth) will be undertaken to assess heterogeneity in study results, sample size permitting. Cost-effectiveness pre-eclampsia screening economic evaluation protocol Figures Figure 1 Figure 2 Introduction Pre-eclampsia (PE) complicates 1.5-7.7% of pregnancies in England (1) and is associated with increased maternal and perinatal morbidity and mortality (2,3). The incidence of PE grows with gestational age and the majority of PE cases (≈75%) develop at term (2). Though the risk of adverse events is higher with preterm PE, the absolute number of maternal and perinatal adverse events associated with term PE is significant and poses a substantial burden on health system resources (3,4). It is estimated that PE costs the NHS an additional £9,000 per pregnancy (5). PE screening early in pregnancy identifies individuals at high risk, who can benefit from preventive therapy, namely aspirin (1,6), which is effective and cost-saving (7–10). However, such an approach to reduce the incidence of term PE currently does not exist. The Fetal Medicine Foundation 35-36 (FMF36) model is a novel screening strategy that can identify about 75% of women who develop term PE, at a 10% screen-positive rate (11–13) . The FMF36 model incorporates maternal risk factors, mean arterial pressure (MAP), serum placental growth factor (PlGF), and soluble fms-like tyrosine kinase receptor 1 (sFlt-1) to predict the risk of term PE. Whilst screening for PE risk at 35-36 weeks can identify a high proportion of women likely to develop term PE, there is currently no intervention proven to reduce that risk (13) . A recent systematic review of 16 randomised controlled trials (RCTs) observed that in low-risk pregnancies, induction of labour (IOL) at 39-40 weeks (vs. expectant management) may reduce the incidence of the hypertensive disorders of pregnancy (14) . As such, timed birth at term based on PE risk was identified as a potential strategy for term PE prevention (15,16) . The PREVENT-PE trial examines whether the intervention of screening for PE risk at 35-36 weeks using the FMF36 model and planning early term birth for women at increased PE risk, compared with usual care at term, reduces the incidence of subsequent birth with PE, without increasing emergency caesarean birth or neonatal unit admission for ≥48 hours. This economic evaluation examines whether the intervention (vs. usual care at term) is cost-effective. As the PREVENT-PE trial follows-up women and newborns to hospital discharge or 28 days after birth (whichever is earlier), the within-trial analysis will be complemented by a one-year cost-utility analysis (CUA), using a decision analytic model and published literature (17–20), to account for potential health gains and costs beyond the RCT follow-up duration. Methods Study design PREVENT- PE is an open-label RCT recruiting at two sites (King’s College Hospital, London; and Medway Maritime Hospital, Gillingham) in the United Kingdom (UK), that each offer a 35 +0 -36 +6 -week ultrasound scan for the maternity population and provide screening assessment for PE using the FMF36 model. The trial includes women with a single live foetus at 35 +0 to 36 +6 weeks’ gestation, who are able to provide an informed, documented consent. The trial excludes women who are: under 16 years of age, have established PE or a known major foetal abnormality, or are participating in another intervention study that influences the trial. The study plans to recruit up to 8000 women, randomised in a 1:1 ratio, following an adaptive design, with sample size re-estimation accounting for PE incidence rates determined at interim analysis (trial protocol submitted and under review). There are two data collection phases: (i) the screening phase during the ultrasound scan visit at 35 +0 -36 +6 weeks; and (ii) the time from screening until primary hospital discharge home or 28 days after birth (whichever comes first). Data are collected from the electronic medical record (EMR) onto trial case report forms (CRFs), or by directly obtaining EMR data from the participating hospitals. Intervention and control arm The intervention involves screening for PE risk using the FMF36 model. This combines maternal demographics and medical history, MAP, and maternal serum PlGF and sFlt-1, to provide a personalised risk of PE (Table 1). Following this, women at high risk of term PE are offered planned initiation of birth at 37, 38, 39, or 40 weeks, by labour induction (as per local protocol) or elective CS (if indicated or desired by the woman) within the first two days of the gestational week. Women with a low risk of term PE will be managed as per local hospital protocol. Table 1 presents the PE risk groups, and corresponding plan of birth offered in each trial arm. Table 1. Trial arms for planned early term birth. Trial arm Group PE risk Gestational age for planned birth* Intervention A ≥1 in 2 37 +0-2 B 1in 3 to 1 in 5 38 +0-2 C 1 in 6 to 1 in 20 39 +0-2 D 1 in 21 to 1 in 50 40 +0-2 E <1 in 50 41 + (per local policy) Usual care at term ≈3 in 100 41 + (per local policy) * Planned timing of birth unless clinical need arises. The control arm (usual care) involves awaiting spontaneous onset of labour or development of a clinical need for birth, following national guidelines on the timing of birth, in general (NG207 for inducing labour) or for specific maternal or foetal conditions (e.g., NG133 for hypertension in pregnancy) (1,21). Within-trial economic evaluation The within-trial cost-effectiveness analysis will adopt an NHS perspective. The main health outcome will be the ‘number of PE cases’ reported, and the cost-effectiveness results will be presented as ‘incremental cost per PE case averted’. The time horizon will be the duration of trial follow-up (i.e., primary hospital discharge home or 28 days after birth, whichever is earlier). Costing: identification, measurement, and valuation of resources 1) Identification of resource use Cost components include setting-up the intervention (e.g. personnel training) and utilisation of health services for both trial arms. Health professionals delivering the intervention undergo an online training module, to achieve FMF36 model competency (22). Screening with the FMF36 model requires resources for: counselling of women by an obstetrician-registrar; a midwife to measure MAP using a validated automated device (Microlife WatchBP Home BP monitor); and blood sampling for measurement of serum sFlt-1 and PlGF concentrations, using an automated device (BRAHMS KRYPTOR compact PLUS, Thermo Fisher Scientific, Hennigsdorf, Germany). In both arms, following randomisation, women may seek care antenatally, at the time of birth, or postnatally until primary discharge home. The newborn may receive postnatal care until primary discharge to home. Both CRF and EMR sources will be used to capture health service use, including hospital outpatient attendances, hospital admissions, Accident and Emergency (A&E) room visits including associated healthcare resources utilised in the form of medications, diagnostics and monitoring for both mothers and the baby. Patient-level EMR data for example will provide information on resources used for each type of delivery and postnatal care offered to the baby. Resources common to both arms (for example, taking maternal history at the screening visit) and those related to research (for example, recording data on the CRFs) will not be costed. Figure 1 lists the costing components for the study. 2) Measurement of resource use FMF36 model Resource utilisation for FMF36 model setup will be measured using observation, administrative records, and inputs from research/clinical staff. Data collection will measure the number of sFlt-1 and PlGF tests conducted in the study. Health service utilisation The CRF records data on FMF36 model screening, maternal and neonatal birth outcomes, medications, and investigations (23). This health service utilisation information from the CRF will be used as a supplement if EMR data such as Healthcare Resource Group (HRG) and treatment function codes (TFCs) for any inpatient or outpatient consultations, for both mothers and babies during trial follow-up is unavailable. HRG codes reflect resource utilization by accounting for the type of admission, complexity of care, complications, and length of stay (24–26). TFCs record the specialised service within which the patient is treated. Figure 1 identifies the costing components within the trial whereas Table 2 reports the availability of resource use measurement from either the CRF or EMR, and valuation sources for all costing sub-categories for the within trial evaluation. Table 2. Costs categories, sources of data for measurement and valuation. Cost category Source of resource use data Source of unit cost CRF EMR Screening using FMF36 competing-risk model a) sFlt-1, PlGF test b) MAP measurement - Human resource (midwife) - Medical/non-medical equipment (MAP device, chair) - Room area c) FMF36 model set-up - Software - Training (online) Human resource (doctor, midwife) CRF Research record Research record Research record Research record Research record N/A N/A N/A N/A N/A N/A NICE committee paper (27) NHS supply chain (28) PSSRU (29) ERIC (30) FMF website (free) (31) PSSRU (29) Health service utilization a) Antenatal management - Outpatient visit - Hospital admission (ICU, HDU, ward) - Accident & Emergency room visit - Blood tests (CBC) - Biochemical tests (dipstick) - Foetal assessment (U/S, CTG, etc . ) - Medications (anti-HTN) b) Delivery - Initiation of birth - Mode of birth - Medications (MgSO4) c) Postnatal management – maternal - Hospital admission (ICU, ward) - Blood tests (CBC) - Biochemical tests (urine) - Any other investigations or management (chest x-ray, CT scan, etc) d) Postnatal management – baby - Hospital admission (ICU, HDU, ward) - Blood tests (Blood gas) - Urine tests (culture, urine) - Any other investigations or management (chest x-ray, CT scan, etc) Not available Not available Not available Not available Not available Not available CRF CRF CRF CRF CRF CRF CRF Not available CRF CRF CRF Not available HRG HRG HRG HRG HRG HRG HRG HRG HRG HRG HRG HRG HRG HRG HRG HRG HRG HRG NHS NCC data (32) NHS NCC data (32) NHS NCC data (32) NHS NCC data (32) NHS NCC data (32) NHS NCC data (32) NHS Prescription cost analysis (33) NHS NCC data (32) NHS NCC data (32) NHS Prescription cost analysis (33) NHS NCC data (32) NHS NCC data (32) NHS NCC data (32) NHS NCC data (32) NHS NCC data (32) NHS NCC data (32) NHS NCC data (32) NHS NCC data (32) (Notes: CBC – Complete Blood Count, CRF – Case Report Form, CTG – Cardiotocography, EMR – Electronic medical record, ERIC - Estates Returns Information Collection, FMF36 – Fetal Medicine Foundation 35 to 36 week competing-risk model, HDU – High Dependency Unit, HRG – Healthcare Resource Group, HTN – Hypertension, ICU – Intensive care unit, MAP – Mean Arterial Pressure, MgSO4 – Magnesium Sulphate, NCC – National Cost Collection, NHS - National Health Service, NICE – National Institute for Health and Care Excellence, N/A – Not applicable, PlGF – placental growth factor, PSSRU - Personal Social Services Research Unit, sFlt-1 - soluble fms‑like tyrosine kinase-1, U/S – Ultrasonography) 3) Valuation of resource use Costs will be obtained by multiplying the quantity of resources by respective unit costs. Unit costs will reflect national costs where possible, or local costs if national costs are unavailable (28,29,32–34). For example, the cost of an automated MAP device will be obtained from local hospital administrative records, as the only available source of cost. Human resource time will be costed using national unit costs from the Personal Social Services Research Unit (PSSRU) – Unit Costs of Health and Social Care (for example, MAP measurement will be costed using a £48 per hour cost for agenda for change Band 5 i.e. staff nurse/entry level midwife from PSSRU 2022) (29). The software required for FMF36 model set-up is freely available online (31). Training costs will account for two hours of time spent by personnel (registrars) in completing the FMF course, using PSSRU costs (22). The economic cost of blood-sampling for sFlt1 and PlGF testing using the BRAHMS KRYPTOR kit, has recently been published by the National Institute for Health and Care Excellence (NICE) (27). Room area where screening takes place will be valued using the Estates Return Information Collection (ERIC) data published by the NHS (30). Procedures and health service use HRGs will be valued using the NHS National Cost Collection (NCC) data (32). For example, for a normal birth, with epidural or induction and complication score of 1 classifying into HRG currency ‘NZ31B’, an average cost of £3,606 will be used from NHS NCC 21-22 (32). Medications on the CRF will be costed based on the average expected course as prescribed by British National Formulary (BNF). Resources will be valued using the latest available data year, and any unit cost not in the corresponding year will be adjusted using the NHS cost inflation index (29). All capital items (e.g., automated MAP device) will be annualised using the recommended 3.5% discount rate (35,36). 4) Computation of total costs In the intervention arm, total cost will be computed at the maternal/newborn dyad level, by summing the cost of FMF36 competing risk model set-up and screening, along with health service utilisation during the antenatal, birth, and postnatal periods. For the usual care arm, the cost per dyad will reflect only health services utilised. Different apportioning rules will be used to calculate per patient costs for different components. For example, the methods and apportioning rules used to cost MAP measurement and FMF36 model set-up are explained in Tables S1 and S2 (S1 Appendix). Health outcome for cost-effectiveness analysis The primary outcome for the trial is delivery with PE, as defined by the International Society for the Study of Hypertension in Pregnancy (ISSHP) 2021 (37). The number of PE cases in the intervention and control arms will be measured and recorded in the CRFs at primary discharge home. The number of PE cases averted (i.e. the difference in total number of PE cases between the two trial arms) will be used in the cost-effectiveness analysis. Statistical Analyses 1) Handling missing data Before commencing analysis, data quality checks will be conducted and any duplicates, missing, or extreme values will be checked against original records or available data sources and documented. Any partial or complete duplication of observations will be checked and removed or corrected, as appropriate. The pattern and amount of missing data, between the intervention and control arms, by study variables, will be assessed. If data are missing at random (MAR), multiple imputation using Markov Chain Monte Carlo methods, accounting for clustering, will be applied (38). When the data are MAR, multiple imputation can lead to consistent, asymptotically efficient, and asymptotically normal estimates (39). If missing data is not MAR, modified multiple imputation will be used (40). The impact of imputation will be presented in sensitivity analyses 2) Statistical analysis An intention-to-treat principle will be used in statistical analyses. Resource use, costs, and outcomes (PE cases) will be summarised across trial arms, using descriptive statistics (frequency, percentage, mean, median, standard deviation, minimum [including % zeros] and maximum values). Univariate analyses will compare means and variability (95% confidence intervals) in study variables between intervention and control arms. To adjust for any baseline imbalance (e.g. age, ethnicity, index of multiple deprivation, BMI, comorbidities, obstetric history, baseline risk of PE) (41,42). after randomisation, and for clustering of patients within sites, multilevel regression models will be estimated for total costs and health outcomes. The PE outcome model will be estimated using logistic regression with PE defined as a binary variable (Yes/No). The cost model will be estimated using a generalised linear model (GLM) with a gamma distribution and a log link to accommodate the non-normal distribution of costs. 3) Presentation of cost-effectiveness results The difference in costs divided by difference in outcomes (number of PE cases) will give the incremental cost per PE case averted (i.e. the incremental cost-effectiveness ratio [ICER] for the evaluation). If the intervention dominates usual care (i.e. lower costs, lower PE cases), the intervention will be considered cost-effective. Similarly, if the intervention is dominated by usual care (i.e. higher costs, higher PE cases), it will be considered not cost-effective. As there is no recommended willingness to pay (WTP) threshold value for cases of PE averted, the results will be compared against available literature (43,44). For example, it was observed that for a PE screening intervention in the Netherlands, majority scenarios became cost-effective from a threshold of €50,000 per PE case averted (43). A cost-effectiveness acceptability curve (CEAC) will be developed to illustrate the probability of cost-effectiveness at varying WTP thresholds. 4) Handling uncertainty One-way sensitivity analysis will assess the impact of assumptions on results, such as the FMF36 model set-up costing assumption. Sampling uncertainty around deterministic results will be characterised by developing 95% CI limits for ICER values, based on the convergence of non-parametric bootstrapping results, along with cost-effectiveness plane (CEP) and CEAC (45,46). The cost-effectiveness findings will be assessed for heterogeneity across subgroups such as the first trimester risk of preterm preeclampsia, previous pregnancy history (nulliparous, multiparous with no PE, multiparous with PE) and aspirin started before 16 weeks (Yes/No) that are likely to lead to differential costs or outcomes. Further subgroups may be considered following findings of any clinically relevant differences. 5) Intermediate-term cost-utility model Evidence suggests that PE may have lasting health consequences for mothers and babies (47). PE is a potential risk factor for cardiovascular and cerebrovascular conditions among mothers, and neurodevelopmental disorders in their offspring (47,48). However, the mechanism that explains this association is a matter of debate, with shared risk factors for PE and cardiovascular disease (e.g., hypertension) potentially confounding the association (49,50). Screening for PE using the FMF36 model may influence the development of PE and associated multi-organ complications, in mothers and babies, lasting beyond the short trial follow-up duration (51). This justifies development of a decision model to assess the potential economic implications of the intervention (versus control) beyond primary hospital discharge home or 28 days of postpartum. In defining the time horizon for the proposed model, we reviewed available cost-effectiveness literature (27,44,52). An intermediate-term, one-year time horizon, was chosen to meaningfully capture differences in costs and health consequences beyond the trial duration instead of simply attaching lifetime costs from literature or accounting only for mortality outcomes (27,53,54), while minimising uncertainty due to data limitations and extrapolation of relevant treatment effects . A cost-utility analysis from the NHS perspective will be undertaken. Given the paucity of economic evaluations for term PE (35,45,53), a new decision tree model will be developed (Figure 2), by adapting the structure used by a recent NICE evaluation for PLGF-based testing in relevant population i.e. up to 36 +6 weeks of pregnancy (24). The proposed decision model aims to capture the development of PE, management of birth outcomes in mothers, and PE-related adverse outcomes in mothers and babies until one year after birth. Maternal adverse outcomes will be captured as: hospital stay or admission to intensive care unit (ICU) following childbirth, and PE-related morbidity, including mental health consequences for those having an ICU admission. Similarly, adverse outcomes for babies will be captured as admission to the neonatal care unit, hospital stay following birth, and PE-related morbidity (respiratory, neurological, etc.) until one year after birth for those with an NICU admission. This is the initial model concept which will be refined depending on available supporting data. Both trial data and published literature will be used to populate the model, including probabilities for health states, costs, and health outcomes (Quality-Adjusted Life Years [QALYs]). The primary summary measure for the CUA will be the incremental cost per QALY gained for the maternal/newborn dyad. An additive approach will be used to sum individual maternal and neonatal QALYs (55). The outcome measure will be compared against recommended threshold value of £20,000 to £30,000 per QALY (35). Additionally, the cost-per per life-year gained for the dyad will be reported. Uncertainty will be assessed using deterministic and probabilistic sensitivity analysis and subgroup analyses. One-way sensitivity analysis will assess the impact of changing base case input values on the cost-effectiveness and will be presented using a Tornado diagram that identifies the most influential parameters. Probabilistic sensitivity analysis, using Monte Carlo simulations, will address the joint uncertainty across parameters, and will be presented using the CEP and CEAC. To assess heterogeneity, the model will be re-run with subgroups, as defined by the within-trial CEA and further depending on availability of subgroup-specific input parameters. Discussion PREVENT-PE is a prospective RCT aimed at testing whether screening for PE risk at 35 + 0–36 + 6 weeks’ gestation and offering women risk-based, planned term birth can reduce the incidence of PE, without increasing emergency caesarean or neonatal morbidity (i.e., neonatal unit admission for ≥ 48 hours). This study has the potential to enhance efficient use of resources, enable better policy decisions and thus improve pregnancy outcomes through evidence-based strategies. The economic evaluation aims to understand whether the intervention is cost-effective by the 28-day follow-up period of the trial and over a one-year period, with a view to providing guidance to policymakers. Strengths of this study include the first evaluation of FMF36 risk model for term PE using both prospectively collected primary data for a trial-based cost-effectiveness analysis, and the intermediate-term (one-year) model-based cost-utility analysis. The economic evaluation utilises patient-level data from a large RCT, offering enhanced accuracy of cost and outcome estimates, and minimising the effect of confounders. Finally, the large sample size allows for analysis of subgroups of interest. There are study limitations. First, the study population is being recruited from only two sites, one in south London (King’s College Hospital) and the other in south-east England (Medway Maritime Hospital), which may limit the generalisability of study findings to other settings or countries. Similarly, recruitment is limited to singleton pregnancies, reducing generalisability to multiple pregnancies. Second, the lack of a recognised WTP threshold value per PE case averted recommendations based on cost-effectiveness for NICE ( 35 ), although we plan to develop an intermediate-term cost-utility model. Third, QALYs for the CUA will be derived from published literature, as relevant data is not collected in the trial, and we don’t yet know how sensitive conclusions will be to the published data. Fourth, the study is confined to the NHS perspective, excluding indirect and social care costs, out-of-pocket expenses, and other societal impacts. Finally, it needs to be acknowledged that the 35–36-week ultrasonography scan was offered in both arms which is not routine NHS practise and may have cost-effectiveness and implementation implications that are not assessed currently. Conclusion This study will provide the first evidence about the cost-effectiveness of PE risk-based timing of birth at term, for mothers and their babies. The analysis will identify a range of sub-populations for which the intervention may yield the highest value for money. By addressing both short- and intermediate-term economic implications, this study aims to offer a comprehensive understanding of the cost-effectiveness profile of FMF36 competing risks model-based planned term birth for prevention of term PE. We anticipate that these results will be of relevance to women, families, maternity care-providers, and policymakers. Declarations Ethics approval (London–Dulwich, National Research Ethics Service Committee, 22/LO/0794) and trial registration (ISRCTN 41632964) are in place. Declaration of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Evidence | Hypertension in pregnancy: diagnosis and management | Guidance | NICE Magee LA, Wright D, Syngelaki A, Von Dadelszen P, Akolekar R, Wright A et al Preeclampsia Prevention by Timed Birth at Term. 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Sep 9 [cited 2024 Feb 6];35(20):3482. /pmc/articles/PMC4981911/ Missing Data | SAGE Publications Inc [Internet] [cited 2024 Feb 6]. https://us.sagepub.com/en-us/nam/missing-data/book9419 Moreno-Betancur M, Chavance M, Lawson AB, Lee D, MacNab Y (2016) Sensitivity analysis of incomplete longitudinal data departing from the missing at random assumption: Methodology and application in a clinical trial with drop-outs. Stat Methods Med Res [Internet]. Aug 1 [cited 2024 Mar 6];25(4):1471–89. https://journals.sagepub.com/doi/ 10.1177/0962280213490014?url_ver=Z39.88- 2003픯_id=ori%3Arid%3Acrossref.org픯_dat=cr_pub++0pubmed Chang KJ, Seow KM, Chen KH, Chang KJ, Seow KM, Chen KH, Preeclampsia Recent Advances in Predicting, Preventing, and Managing the Maternal and Fetal Life-Threatening Condition. International Journal of Environmental Research and Public Health 2023, Vol 20, Page 2994 [Internet]. 2023 Feb 8 [cited 2024 Aug 28];20(4):2994. https://www.mdpi.com/1660-4601/20/4/2994/htm Dimitriadis E, Rolnik DL, Zhou W, Estrada-Gutierrez G, Koga K, Francisco RPV et al Pre-eclampsia. Nature Reviews Disease Primers 2023 9:1 [Internet]. 2023 Feb 16 [cited 2024 Aug 28];9(1):1–22. https://www.nature.com/articles/s41572-023-00417-6 Zakiyah N, Tuytten R, Baker PN, Kenny LC, Postma MJ, van Asselt ADI Early cost-effectiveness analysis of screening for preeclampsia in nulliparous women: A modelling approach in European high-income settings. PLoS One [Internet]. 2022 Apr 1 [cited 2024 Aug 9];17(4):e0267313. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0267313 (Quality) OH. First-Trimester Screening Program for the Risk of Pre-eclampsia Using a Multiple-Marker Algorithm: A Health Technology Assessment. Ont Health Technol Assess Ser [Internet] (2023) [cited 2024 May 13];22(5):1. /pmc/articles/PMC10530459/ ESTIMATING CONFIDENCE INTERVALS FOR COST-EFFECTIVENESS RATIOS: AN EXAMPLE FROM, A RANDOMIZED TRIAL - CHAUDHARY – 1996 - Statistics in Medicine - Wiley Online Library [Internet]. [cited 2024 Jan 16]. https://onlinelibrary.wiley.com/doi/ 10.1002/(SICI)1097-0258 (19960715)15:13%3C1447::AID-SIM267%3E3.0.CO;2-V Hatswell AJ, Bullement A, Briggs A, Paulden M, Stevenson MD Probabilistic Sensitivity Analysis in Cost-Effectiveness Models: Determining Model Convergence in Cohort Models. Pharmacoeconomics [Internet]. 2018 Dec 1 [cited 2024 Sep 16];36(12):1421–6. https://link.springer.com/article/ 10.1007/s40273-018-0697-3 Langlois AWR, Park AL, Lentz EJM, Ray JG (2020) Preeclampsia Brings the Risk of Premature Cardiovascular Disease in Women Closer to That of Men. Can J Cardiol [Internet]. Jan 1 [cited 2024 Feb 15];36(1):60–8. https://pubmed.ncbi.nlm.nih.gov/31735430/ Pittara T, Vyrides A, Lamnisos D, Giannakou K Pre-eclampsia and long-term health outcomes for mother and infant: an umbrella review. BJOG [Internet]. 2021 Aug 1 [cited 2024 Feb 15];128(9):1421–30. https://onlinelibrary.wiley.com/doi/full/ 10.1111/1471-0528.16683 Canoy D, Cairns BJ, Balkwill A, Wright FL, Khalil A, Beral V et al (2016) Hypertension in pregnancy and risk of coronary heart disease and stroke: A prospective study in a large UK cohort. Int J Cardiol 222:1012–1018 Leon LJ, McCarthy FP, Direk K, Gonzalez-Izquierdo A, Prieto-Merino D, Casas JP et al Preeclampsia and Cardiovascular Disease in a Large UK Pregnancy Cohort of Linked Electronic Health Records. Circulation [Internet]. 2019 Sep 24 [cited 2024 May 13];140(13):1050–60. https://www.ahajournals.org/doi/abs/ 10.1161/CIRCULATIONAHA.118.038080 Von Dadelszen P, Payne B, Li J, Ansermino JM, Pipkin FB, Côté AM et al (2011) Prediction of adverse maternal outcomes in pre-eclampsia: Development and validation of the fullPIERS model. The Lancet [Internet]. Jan 15 [cited 2024 Jun 6];377(9761):219–27. http://www.thelancet.com/article/S0140673610613517/fulltext Li W, Kim CS, Howell EA, Janevic T, Liu B, Shi L et al (2022) Economic Evaluation of Prenatal and Postpartum Care in Women With Gestational Diabetes and Hypertensive Disorders of Pregnancy: A Systematic Review. Value Health 25(12):2062–2080 Doshi U, Chaiken S, Hersh A, Gibbins KJ, Caughey AB Treating Mild Chronic Hypertension During Pregnancy: A Cost-Effectiveness Analysis. Obstetrics and Gynecology [Internet]. 2024 Apr 1 [cited 2024 Oct 3];143(4):562–9. https://journals.lww.com/greenjournal/fulltext/2024/04000/treating_mild_chronic_hypertension_during.15.aspx Mone F, O’Mahony JF, Tyrrell E, Mulcahy C, McParland P, Breathnach F et al Preeclampsia prevention using routine versus screening test-indicated aspirin in low-risk women a cost-effectiveness analysis. Hypertension [Internet]. 2018 Dec 1 [cited 2024 Oct 3];72(6):1391–6. https://www.ahajournals.org/doi/ 10.1161/HYPERTENSIONAHA.118.11718 Abel L, Dakin H, Cai T, McManus RJ, McNiven A, Rivero-Arias O How are maternal and fetal outcomes incorporated when measuring benefits of interventions in pregnancy? Findings from a systematic review of cost-utility analyses. Health Qual Life Outcomes [Internet]. 2024 Dec 1 [cited 2024 Oct 3];22(1):1–10. https://hqlo.biomedcentral.com/articles/ 10.1186/s12955-024-02293-4 Additional Declarations The authors declare no competing interests. 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soluble fms‑like tyrosine kinase-1)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5433222/v1/82197be56250d2807c8dcc3a.png"},{"id":69086944,"identity":"3857bffd-e9c5-4f98-b779-4facc8aa0576","added_by":"auto","created_at":"2024-11-15 12:53:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44554,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic overview of the proposed decision analytic model\u003c/p\u003e\n\u003cp\u003e(Notes: FMF36 – Fetal Medicine Foundation 35 to 36 week competing-risk model)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5433222/v1/45f1ecf3b189ceb3c83eaeba.png"},{"id":69087222,"identity":"669edd35-aa0e-4106-a06d-b7c627c9b9dd","added_by":"auto","created_at":"2024-11-15 12:53:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":848039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5433222/v1/628b64ba-3190-4090-83bd-0fb86c6c3af8.pdf"},{"id":69086908,"identity":"2426404a-17c2-4ce5-8d0d-c95b49fbb242","added_by":"auto","created_at":"2024-11-15 12:53:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17995,"visible":true,"origin":"","legend":"","description":"","filename":"S1AppendixPREGHTN.docx","url":"https://assets-eu.researchsquare.com/files/rs-5433222/v1/6efdc1a642b16c5f44a15eab.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePre-eclampsia prevention by timed birth at term (PREVENT–PE): Protocol for economic evaluation alongside PREVENT-PE trial and intermediate-term decision analytic model\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePre-eclampsia (PE) complicates 1.5-7.7% of pregnancies in England (1) and is associated with increased maternal and perinatal morbidity and mortality (2,3). The incidence of PE grows with gestational age and the majority of PE cases (\u0026asymp;75%) develop at term (2). Though the risk of adverse events is higher with preterm PE, the absolute number of maternal and perinatal adverse events associated with term PE is significant and poses a substantial burden on health system resources (3,4). It is estimated that PE costs the NHS an additional \u0026pound;9,000 per pregnancy (5). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePE screening early in pregnancy identifies individuals at high risk, who can benefit from preventive therapy, namely aspirin (1,6), which is effective and cost-saving (7\u0026ndash;10). However, such an approach to reduce the incidence of term PE currently does not exist. The Fetal Medicine Foundation 35-36 (FMF36) model is a novel screening strategy that can identify about 75% of women who develop term PE, at a 10% screen-positive rate \u003cspan lang=\"EN-GB\"\u003e(11\u0026ndash;13)\u003c/span\u003e. The FMF36 model incorporates maternal risk factors, mean arterial pressure (MAP), serum placental growth factor (PlGF), and soluble fms-like tyrosine kinase receptor 1 (sFlt-1) to predict the risk of term PE.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhilst screening for PE risk at 35-36 weeks can identify a high proportion of women likely to develop term PE, there is currently no intervention proven to reduce that risk \u003cspan lang=\"EN-GB\"\u003e(13)\u003c/span\u003e. A recent systematic review of 16 randomised controlled trials (RCTs) observed that in low-risk pregnancies, induction of labour (IOL) at 39-40 weeks (vs. expectant management) may reduce the incidence of the hypertensive disorders of pregnancy \u003cspan lang=\"EN-GB\"\u003e(14)\u003c/span\u003e. As such, timed birth at term based on PE risk was identified as a potential strategy for term PE prevention \u003cspan lang=\"EN-GB\"\u003e(15,16)\u003c/span\u003e. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PREVENT-PE trial \u0026nbsp;examines whether the intervention of screening for PE risk at 35-36 weeks using the FMF36 model and planning early term birth for women at increased PE risk, compared with usual care at term, reduces the incidence of subsequent birth with PE, without increasing emergency caesarean birth or neonatal unit admission for \u0026ge;48 hours. This economic evaluation examines whether the intervention (vs. usual care at term) is cost-effective. As the PREVENT-PE trial follows-up women and newborns to hospital discharge or 28 days after birth (whichever is earlier), the within-trial analysis will be complemented by a one-year cost-utility analysis (CUA), using a decision analytic model and published literature (17\u0026ndash;20), to account for potential health gains and costs beyond the RCT follow-up duration.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design\u003c/h2\u003e\n\u003cp\u003ePREVENT- PE is an open-label RCT recruiting at two sites (King\u0026rsquo;s College Hospital, London; and Medway Maritime Hospital, Gillingham) in the United Kingdom (UK), that each offer a 35\u003csup\u003e+0\u003c/sup\u003e-36\u003csup\u003e+6\u003c/sup\u003e-week ultrasound scan for the maternity population and provide screening assessment for PE using the FMF36 model. The trial includes women with a single live foetus at 35\u003csup\u003e+0\u003c/sup\u003e to 36\u003csup\u003e+6\u003c/sup\u003e weeks\u0026rsquo; gestation, who are able to provide an informed, documented consent. The trial excludes women who are: under 16 years of age, have established PE or a known major foetal abnormality, or are participating in another intervention study that influences the trial. The study plans to recruit up to 8000 women, randomised in a 1:1 ratio, following an adaptive design, with sample size re-estimation accounting for PE incidence rates determined at interim analysis (trial protocol submitted and under review). There are two data collection phases: (i) the screening phase during the ultrasound scan visit at 35\u003csup\u003e+0\u003c/sup\u003e-36\u003csup\u003e+6\u003c/sup\u003e weeks; and (ii) the time from screening until primary hospital discharge home or 28 days after birth (whichever comes first). Data are collected from the electronic medical record (EMR) onto trial case report forms (CRFs), or by directly obtaining EMR data from the participating hospitals.\u003c/p\u003e\n\u003ch2\u003eIntervention and control arm\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe intervention involves screening for PE risk using the FMF36 model. This combines maternal demographics and medical history, MAP, and maternal serum PlGF and sFlt-1, to provide a personalised risk of PE (Table 1). Following this, women at high risk of term PE are offered planned initiation of birth at 37, 38, 39, or 40 weeks, by labour induction (as per local protocol) or elective CS (if indicated or desired by the woman) within the first two days of the gestational week. Women with a low risk of term PE will be managed as per local hospital protocol. Table 1 presents the PE risk groups, and corresponding plan of birth offered in each trial arm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Trial arms for planned early term birth.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"621\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTrial arm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGroup\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePE risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGestational age for planned birth*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003eIntervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;1 in 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37\u003csup\u003e+0-2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1in 3 to 1 in 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003csup\u003e+0-2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 in 6 to 1 in 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003csup\u003e+0-2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 in 21 to 1 in 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40\u003csup\u003e+0-2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;1 in 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41\u003csup\u003e+\u0026nbsp;\u003c/sup\u003e(per local policy)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eUsual care at term\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026asymp;3 in 100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41\u003csup\u003e+\u003c/sup\u003e (per local policy)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e* Planned timing of birth unless clinical need arises.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe control arm (usual care) involves awaiting spontaneous onset of labour or development of a clinical need for birth, following national guidelines on the timing of birth, in general (NG207 for inducing labour) or for specific maternal or foetal conditions (e.g., NG133 for hypertension in pregnancy) (1,21).\u003c/p\u003e\n\u003ch2\u003eWithin-trial economic evaluation\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe within-trial cost-effectiveness analysis will adopt an NHS perspective. The main health outcome will be the \u0026lsquo;number of PE cases\u0026rsquo; reported, and the cost-effectiveness results will be presented as \u0026lsquo;incremental cost per PE case averted\u0026rsquo;. The time horizon will be the duration of trial follow-up (i.e., primary hospital discharge home or 28 days after birth, whichever is earlier).\u003c/p\u003e\n\u003ch2\u003eCosting: identification, measurement, and valuation of resources\u003c/h2\u003e\n\u003ch3\u003e1)\u0026nbsp;Identification of resource use\u003c/h3\u003e\n\u003cp\u003eCost components include setting-up the intervention (e.g. personnel training) and utilisation of health services for both trial arms. Health professionals delivering the intervention undergo an online training module, to achieve FMF36 model competency (22). Screening with the FMF36 model requires resources for: counselling of women by an obstetrician-registrar; a midwife to measure MAP using a validated automated device (Microlife WatchBP Home BP monitor); and blood sampling for measurement of serum sFlt-1 and PlGF concentrations, using an automated device (BRAHMS KRYPTOR compact PLUS, Thermo Fisher Scientific, Hennigsdorf, Germany). In both arms, following randomisation, women may seek care antenatally, at the time of birth, or postnatally until primary discharge home. The newborn may receive postnatal care until primary discharge to home.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBoth CRF and EMR sources will be used to capture health service use, including hospital outpatient attendances, hospital admissions, Accident and Emergency (A\u0026amp;E) room visits including associated healthcare resources utilised in the form of medications, diagnostics and monitoring for both mothers and the baby. Patient-level EMR data for example will provide information on resources used for each type of delivery and postnatal care offered to the baby. Resources common to both arms (for example, taking maternal history at the screening visit) and those related to research (for example, recording data on the CRFs) will not be costed. Figure 1 lists the costing components for the study. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2)\u0026nbsp;Measurement of resource use\u003c/h3\u003e\n\u003cp\u003eFMF36 model\u003c/p\u003e\n\u003cp\u003eResource utilisation for FMF36 model setup will be measured using observation, administrative records, and inputs from research/clinical staff. Data collection will measure the number of sFlt-1 and PlGF tests conducted in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHealth service utilisation\u003c/p\u003e\n\u003cp\u003eThe CRF records data on FMF36 model screening, maternal and neonatal birth outcomes, medications, and investigations (23). This health service utilisation information from the CRF will be used as a supplement if EMR data such as Healthcare Resource Group (HRG) and treatment function codes (TFCs) for any inpatient or outpatient consultations, for both mothers and babies during trial follow-up is unavailable. HRG codes reflect resource utilization by accounting for the type of admission, complexity of care, complications, and length of stay (24\u0026ndash;26). TFCs record the specialised service within which the patient is treated. Figure 1 identifies the costing components within the trial whereas Table 2 reports the availability of resource use measurement from either the CRF or EMR, and valuation sources for all costing sub-categories for the within trial evaluation.\u003c/p\u003e\n\u003cp\u003eTable 2. Costs categories, sources of data for measurement and valuation.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCost category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of resource use data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of unit cost\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScreening using FMF36 competing-risk model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ea) \u0026nbsp; sFlt-1, PlGF test\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eb) \u0026nbsp; MAP measurement\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHuman resource (midwife)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMedical/non-medical equipment (MAP device, chair)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eRoom area\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ec) \u0026nbsp; FMF36 model set-up\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSoftware\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTraining (online)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHuman resource (doctor, midwife)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eResearch record\u003c/p\u003e\n \u003cp\u003eResearch record\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eResearch record\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eResearch record\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eResearch record\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNICE committee paper\u0026nbsp;(27)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS supply chain\u0026nbsp;(28)\u003c/p\u003e\n \u003cp\u003ePSSRU\u0026nbsp;(29)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eERIC (30)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFMF website (free)\u0026nbsp;(31)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePSSRU\u0026nbsp;(29)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth service utilization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ea) \u0026nbsp; Antenatal management\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eOutpatient visit\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHospital admission (ICU, HDU, ward)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAccident \u0026amp; Emergency room visit\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBlood tests (CBC)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBiochemical tests (dipstick)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFoetal assessment (U/S, CTG, etc\u003c/strong\u003e.\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMedications (anti-HTN)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eb) \u0026nbsp; Delivery\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInitiation of birth\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMode of birth\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMedications (MgSO4)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ec) \u0026nbsp; Postnatal management \u0026ndash; maternal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHospital admission (ICU, ward)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBlood tests (CBC)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBiochemical tests (urine)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAny other investigations or management (chest x-ray, CT scan, etc)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ed) \u0026nbsp; Postnatal management \u0026ndash; baby\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHospital admission (ICU, HDU, ward)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBlood tests (Blood gas)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eUrine tests (culture, urine)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e- \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAny other investigations or management (chest x-ray, CT scan, etc)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNot available\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNot available\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNot available\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003cp\u003eNot available\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS Prescription cost analysis (33)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003eNHS Prescription cost analysis\u0026nbsp;(33)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHS NCC data\u0026nbsp;(32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(Notes: CBC \u0026ndash; Complete Blood Count, CRF \u0026ndash; Case Report Form, CTG \u0026ndash; Cardiotocography, EMR \u0026ndash; Electronic medical record, ERIC - Estates Returns Information Collection, FMF36 \u0026ndash; Fetal Medicine Foundation 35 to 36 week competing-risk model, HDU \u0026ndash; High Dependency Unit, HRG \u0026ndash; Healthcare Resource Group, HTN \u0026ndash; Hypertension, ICU \u0026ndash; Intensive care unit, MAP \u0026ndash; Mean Arterial Pressure, MgSO4 \u0026ndash; Magnesium Sulphate, NCC \u0026ndash; National Cost Collection, NHS - National Health Service, NICE \u0026ndash; National Institute for Health and Care Excellence, N/A \u0026ndash; Not applicable, PlGF \u0026ndash; placental growth factor, \u0026nbsp;PSSRU - Personal Social Services Research Unit, sFlt-1 - soluble fms‑like tyrosine kinase-1, U/S \u0026ndash; Ultrasonography)\u003c/p\u003e\n\u003ch3\u003e3)\u0026nbsp;Valuation of resource use\u003c/h3\u003e\n\u003cp\u003eCosts will be obtained by multiplying the quantity of resources by respective unit costs. Unit costs will reflect national costs where possible, or local costs if national costs are unavailable (28,29,32\u0026ndash;34). For example, the cost of an automated MAP device will be obtained from local hospital administrative records, as the only available source of cost. Human resource time will be costed using national unit costs from the Personal Social Services Research Unit (PSSRU) \u0026ndash; Unit Costs of Health and Social Care (for example, MAP measurement will be costed using a \u0026pound;48 per hour cost for agenda for change Band 5 i.e. staff nurse/entry level midwife from PSSRU 2022) (29). The software required for FMF36 model set-up is freely available online (31). Training costs will account for two hours of time spent by personnel (registrars) in completing the FMF course, using PSSRU costs (22). The economic cost of blood-sampling for sFlt1 and PlGF testing using the BRAHMS KRYPTOR kit, has recently been published by the National Institute for Health and Care Excellence (NICE) (27). Room area where screening takes place will be valued using the Estates Return Information Collection (ERIC) data published by the NHS (30). Procedures and health service use HRGs will be valued using the NHS National Cost Collection (NCC) data (32). For example, for a normal birth, with epidural or induction and complication score of 1 classifying into HRG currency \u0026lsquo;NZ31B\u0026rsquo;, an average cost of \u0026pound;3,606 will be used from NHS NCC 21-22 (32). Medications on the CRF will be costed based on the \u0026nbsp;average expected course as prescribed by British National Formulary (BNF). \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResources will be valued using the latest available data year, and any unit cost not in the corresponding year will be adjusted using the NHS cost inflation index (29). All capital items (e.g., automated MAP device) will be annualised using the recommended 3.5% discount rate (35,36).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e4)\u0026nbsp;Computation of total costs\u003c/h3\u003e\n\u003cp\u003eIn the intervention arm, total cost will be computed at the maternal/newborn dyad level, by summing the cost of FMF36 competing risk model set-up and screening, along with health service utilisation during the antenatal, birth, and postnatal periods. For the usual care arm, the cost per dyad will reflect only health services utilised. Different apportioning rules will be used to calculate per patient costs for different components. For example, the methods and apportioning rules used to cost MAP measurement and FMF36 model set-up are explained in Tables S1 and S2 (S1 Appendix).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eHealth outcome for cost-effectiveness analysis\u003c/h2\u003e\n\u003cp\u003eThe primary outcome for the trial is delivery with PE, as defined by the International Society for the Study of Hypertension in Pregnancy (ISSHP) 2021 (37). The number of PE cases in the intervention and control arms will be measured and recorded in the CRFs at primary discharge home. The number of PE cases averted (i.e. the difference in total number of PE cases between the two trial arms) will be used in the cost-effectiveness analysis.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical Analyses\u003c/h2\u003e\n\u003ch3\u003e1) Handling missing data\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eBefore commencing analysis, data quality checks will be conducted and any duplicates, missing, or extreme values will be checked against original records or available data sources and documented. Any partial or complete duplication of observations will be checked and removed or corrected, as appropriate. The pattern and amount of missing data, between the intervention and control arms, by study variables, will be assessed. If data are missing at random (MAR), multiple imputation using Markov Chain Monte Carlo methods, accounting for clustering, will be applied (38). When the data are MAR, multiple imputation can lead to consistent, asymptotically efficient, and asymptotically normal estimates (39). If missing data is not MAR, modified multiple imputation will be used (40). The impact of imputation will be presented in sensitivity analyses\u003c/p\u003e\n\u003ch3\u003e2) Statistical analysis\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eAn intention-to-treat principle will be used in statistical analyses. Resource use, costs, and outcomes (PE cases) will be summarised across trial arms, using descriptive statistics (frequency, percentage, mean, median, standard deviation, minimum [including % zeros] and maximum values). Univariate analyses will compare means and variability (95% confidence intervals) in study variables between intervention and control arms. To adjust for any baseline imbalance (e.g. age, ethnicity, index of multiple deprivation, BMI, comorbidities, obstetric history, baseline risk of PE) (41,42). after randomisation, and for clustering of patients within sites, multilevel regression models will be estimated for total costs and health outcomes. The PE outcome model will be estimated using logistic regression with PE defined as a binary variable (Yes/No). The cost model will be estimated using a generalised linear model (GLM) with a gamma distribution and a log link to accommodate the non-normal distribution of costs.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e3)\u0026nbsp;Presentation of cost-effectiveness results\u003c/h3\u003e\n\u003cp\u003eThe difference in costs divided by difference in outcomes (number of PE cases) will give the incremental cost per PE case averted (i.e. the incremental cost-effectiveness ratio [ICER] for the evaluation). If the intervention dominates usual care (i.e. lower costs, lower PE cases), the intervention will be considered cost-effective. Similarly, if the intervention is dominated by usual care (i.e. higher costs, higher PE cases), it will be considered not cost-effective. \u0026nbsp;As there is no recommended willingness to pay (WTP) threshold value for cases of PE averted, the results will be compared against available literature (43,44). For example, it was observed that for a PE screening intervention in the Netherlands, majority scenarios became cost-effective from a threshold of \u0026euro;50,000 per PE case averted (43). A cost-effectiveness acceptability curve (CEAC) will be developed to illustrate the probability of cost-effectiveness at varying WTP thresholds.\u003c/p\u003e\n\u003ch3\u003e4)\u0026nbsp;Handling uncertainty\u003c/h3\u003e\n\u003cp\u003eOne-way sensitivity analysis will assess the impact of assumptions on results, such as the FMF36 model set-up costing assumption.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSampling uncertainty around deterministic results will be characterised by developing 95% CI limits for ICER values, based on the convergence of non-parametric bootstrapping results, along with cost-effectiveness plane (CEP) and CEAC (45,46).\u003c/p\u003e\n\u003cp\u003eThe cost-effectiveness findings will be assessed for heterogeneity across subgroups such as the first trimester risk of preterm preeclampsia, previous pregnancy history (nulliparous, multiparous with no PE, multiparous with PE) and aspirin started before 16 weeks (Yes/No) that are likely to lead to differential costs or outcomes. Further subgroups may be considered following findings of any clinically relevant differences.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5) \u0026nbsp;\u0026nbsp;\u003c/em\u003eIntermediate-term cost-utility model\u003c/p\u003e\n\u003cp\u003eEvidence suggests that PE may have lasting health consequences for mothers and babies (47). PE is a potential risk factor for cardiovascular and cerebrovascular conditions among mothers, and neurodevelopmental disorders in their offspring (47,48). However, the mechanism that explains this association is a matter of debate, with shared risk factors for PE and cardiovascular disease (e.g., hypertension) potentially confounding the association (49,50). Screening for PE using the FMF36 model may influence the development of PE and associated multi-organ complications, in mothers and babies, lasting beyond the short trial follow-up duration (51). This justifies development of a decision model to assess the potential economic implications of the intervention (versus control) beyond primary hospital discharge home or 28 days of postpartum. In defining the time horizon for the proposed model, we reviewed available cost-effectiveness literature (27,44,52). An intermediate-term, one-year time horizon, was chosen to meaningfully capture differences in costs and health consequences beyond the trial duration instead of simply attaching lifetime costs from literature or accounting only for mortality outcomes (27,53,54), while minimising uncertainty due to data limitations and extrapolation of relevant treatment effects .\u003c/p\u003e\n\u003cp\u003eA cost-utility analysis from the NHS perspective will be undertaken. Given the paucity of economic evaluations for term PE (35,45,53),\u0026nbsp;a new decision tree model will be developed (Figure 2), by adapting the structure used by a\u0026nbsp;recent NICE evaluation for PLGF-based testing in relevant population i.e. up to 36\u003csup\u003e+6\u003c/sup\u003e weeks of pregnancy (24).\u003c/p\u003e\n\u003cp\u003eThe proposed decision model aims to capture the development of PE, management of birth outcomes in mothers, and PE-related adverse outcomes in mothers and babies until one year after birth. Maternal adverse outcomes will be captured as: hospital stay or admission to intensive care unit (ICU) following childbirth, and PE-related morbidity, including mental health consequences for those having an ICU admission. Similarly, adverse outcomes for babies will be captured as admission to the neonatal care unit, hospital stay following birth, and PE-related morbidity (respiratory, neurological, etc.) until one year after birth for those with an NICU admission. This is the initial model concept which will be refined depending on available supporting data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBoth trial data and published literature will be used to populate the model, including probabilities for health states, costs, and health outcomes (Quality-Adjusted Life Years [QALYs]). The primary summary measure for the CUA will be the incremental cost per QALY gained for the maternal/newborn dyad. An additive approach will be used to sum individual maternal and neonatal QALYs (55). The outcome measure will be compared against recommended threshold value of \u0026pound;20,000 to \u0026pound;30,000 per QALY (35). Additionally, the cost-per per life-year gained for the dyad will be reported.\u003c/p\u003e\n\u003cp\u003eUncertainty will be assessed using deterministic and probabilistic sensitivity analysis and subgroup analyses. One-way sensitivity analysis will assess the impact of changing base case input values on the cost-effectiveness and will be presented using a Tornado diagram that identifies the most influential parameters. Probabilistic sensitivity analysis, using Monte Carlo simulations, will address the joint uncertainty across parameters, and will be presented using the CEP and CEAC. To assess heterogeneity, the model will be re-run with subgroups, as defined by the within-trial CEA and further depending on availability of subgroup-specific input parameters. \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePREVENT-PE is a prospective RCT aimed at testing whether screening for PE risk at 35\u0026thinsp;+\u0026thinsp;0\u0026ndash;36\u0026thinsp;+\u0026thinsp;6 weeks\u0026rsquo; gestation and offering women risk-based, planned term birth can reduce the incidence of PE, without increasing emergency caesarean or neonatal morbidity (i.e., neonatal unit admission for \u0026ge;\u0026thinsp;48 hours). This study has the potential to enhance efficient use of resources, enable better policy decisions and thus improve pregnancy outcomes through evidence-based strategies. The economic evaluation aims to understand whether the intervention is cost-effective by the 28-day follow-up period of the trial and over a one-year period, with a view to providing guidance to policymakers.\u003c/p\u003e \u003cp\u003eStrengths of this study include the first evaluation of FMF36 risk model for term PE using both prospectively collected primary data for a trial-based cost-effectiveness analysis, and the intermediate-term (one-year) model-based cost-utility analysis. The economic evaluation utilises patient-level data from a large RCT, offering enhanced accuracy of cost and outcome estimates, and minimising the effect of confounders. Finally, the large sample size allows for analysis of subgroups of interest.\u003c/p\u003e \u003cp\u003eThere are study limitations. First, the study population is being recruited from only two sites, one in south London (King\u0026rsquo;s College Hospital) and the other in south-east England (Medway Maritime Hospital), which may limit the generalisability of study findings to other settings or countries. Similarly, recruitment is limited to singleton pregnancies, reducing generalisability to multiple pregnancies. Second, the lack of a recognised WTP threshold value per PE case averted recommendations based on cost-effectiveness for NICE (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), although we plan to develop an intermediate-term cost-utility model. Third, QALYs for the CUA will be derived from published literature, as relevant data is not collected in the trial, and we don\u0026rsquo;t yet know how sensitive conclusions will be to the published data. Fourth, the study is confined to the NHS perspective, excluding indirect and social care costs, out-of-pocket expenses, and other societal impacts. Finally, it needs to be acknowledged that the 35\u0026ndash;36-week ultrasonography scan was offered in both arms which is not routine NHS practise and may have cost-effectiveness and implementation implications that are not assessed currently.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study will provide the first evidence about the cost-effectiveness of PE risk-based timing of birth at term, for mothers and their babies. The analysis will identify a range of sub-populations for which the intervention may yield the highest value for money. By addressing both short- and intermediate-term economic implications, this study aims to offer a comprehensive understanding of the cost-effectiveness profile of FMF36 competing risks model-based planned term birth for prevention of term PE. We anticipate that these results will be of relevance to women, families, maternity care-providers, and policymakers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eEthics approval (London\u0026ndash;Dulwich, National Research Ethics Service Committee, 22/LO/0794) and trial registration (ISRCTN 41632964) are in place.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEvidence | Hypertension in pregnancy: diagnosis and management | Guidance | NICE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagee LA, Wright D, Syngelaki A, Von Dadelszen P, Akolekar R, Wright A et al Preeclampsia Prevention by Timed Birth at Term. Hypertension [Internet]. 2023 May 1 [cited 2023 Sep 21];80(5):969. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/pmc/articles/PMC10112937/\u003c/span\u003e\u003cspan address=\"http:///pmc/articles/PMC10112937/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Dadelszen P, Syngelaki A, Akolekar R, Magee LA, Nicolaides KH Preterm and term pre-eclampsia: Relative burdens of maternal and perinatal complications. BJOG [Internet]. 2023 Apr 1 [cited 2023 Sep 22];130(5):524\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/36562190/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/36562190/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLisonkova S, Bone JN, Muraca GM, Razaz N, Wang LQ, Sabr Y et al (2021) Incidence and risk factors for severe preeclampsia, hemolysis, elevated liver enzymes, and low platelet count syndrome, and eclampsia at preterm and term gestation: a population-based study. Am J Obstet Gynecol. ;225(5):538.e1-538.e19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThousands of women to benefit from pre-eclampsia test - Oxford University Hospitals [Internet]. 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Health Qual Life Outcomes [Internet]. 2024 Dec 1 [cited 2024 Oct 3];22(1):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hqlo.biomedcentral.com/articles/\u003c/span\u003e\u003cspan address=\"https://hqlo.biomedcentral.com/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12955-024-02293-4\u003c/span\u003e\u003cspan address=\"10.1186/s12955-024-02293-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cost-effectiveness, pre-eclampsia, screening, economic evaluation, protocol","lastPublishedDoi":"10.21203/rs.3.rs-5433222/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5433222/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ePreeclampsia (PE) occurs most commonly at term, and currently, there is no effective strategy to prevent it. The PE prevention by timed birth at term trial (PREVENT- PE) with embedded economic evaluation aims to provide cost-effectiveness evidence on whether screening for PE risk at 35+0–36+6 weeks’ gestation and offering women risk-based, planned term birth compared to usual care at term, can reduce the incidence of PE, without increasing emergency caesarean sections or neonatal morbidity (i.e., neonatal unit admission for ≥48 hours).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis protocol outlines the methods for within-trial and intermediate-term economic evaluations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain outcome measures\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe economic evaluation will identify, measure and value resources and health outcomes for both mothers at risk of term PE and newborn babies, from the National Health Service (NHS) perspective. A cost-effectiveness analysis within the trial will be undertaken, and the incremental cost per PE case averted will be reported as the main outcome. Costs and health outcomes for the trial duration will be calculated using patient-level data, from case report forms and electronic medical records. A decision model will be developed to assess the cost-utility of the intervention for one year. Transition probabilities, costs, and quality-adjusted life years (QALYs) will be populated using trial data and existing literature. Uncertainty will be assessed using deterministic and probabilistic sensitivity analyses. Subgroup analyses (for example, standalone maternal/newborn perspective and by gestational age for planned birth) will be undertaken to assess heterogeneity in study results, sample size permitting.\u003c/p\u003e","manuscriptTitle":"Pre-eclampsia prevention by timed birth at term (PREVENT–PE): Protocol for economic evaluation alongside PREVENT-PE trial and intermediate-term decision analytic model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-15 12:53:10","doi":"10.21203/rs.3.rs-5433222/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c570bc09-5f1b-40b0-a0a2-4048d53a59ca","owner":[],"postedDate":"November 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-15T12:53:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-15 12:53:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5433222","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5433222","identity":"rs-5433222","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}