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Stock SJ, Horne M, Bruijn M, White H, Heggie R, Wotherspoon L, Boyd K, Aucott L, Morris RK, Dorling J, Jackson L, Chandiramani M, David A, Khalil A, Shennan A, Baaren GJV, Hodgetts-Morton V, Lavender T, Schuit E, Harper-Clarke S, Mol B, Riley RD, Norman J, Norrie J. A prognostic model, including quantitative fetal fibronectin, to predict preterm labour: the QUIDS meta-analysis and prospective cohort study. Health Technol Assess 2021; 25:1-168. [PMID: 34498576 DOI: 10.3310/hta25520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND The diagnosis of preterm labour is challenging. False-positive diagnoses are common and result in unnecessary, potentially harmful treatments (e.g. tocolytics, antenatal corticosteroids and magnesium sulphate) and costly hospital admissions. Measurement of fetal fibronectin in vaginal fluid is a biochemical test that can indicate impending preterm birth. OBJECTIVES To develop an externally validated prognostic model using quantitative fetal fibronectin concentration, in combination with clinical risk factors, for the prediction of spontaneous preterm birth and to assess its cost-effectiveness. DESIGN The study comprised (1) a qualitative study to establish the decisional needs of pregnant women and their caregivers, (2) an individual participant data meta-analysis of existing studies to develop a prognostic model for spontaneous preterm birth within 7 days in women with symptoms of preterm labour based on quantitative fetal fibronectin and clinical risk factors, (3) external validation of the prognostic model in a prospective cohort study across 26 UK centres, (4) a model-based economic evaluation comparing the prognostic model with qualitative fetal fibronectin, and quantitative fetal fibronectin with cervical length measurement, in terms of cost per QALY gained and (5) a qualitative assessment of the acceptability of quantitative fetal fibronectin. DATA SOURCES/SETTING The model was developed using data from five European prospective cohort studies of quantitative fetal fibronectin. The UK prospective cohort study was carried out across 26 UK centres. PARTICIPANTS Pregnant women at 22+0-34+6 weeks' gestation with signs and symptoms of preterm labour. HEALTH TECHNOLOGY BEING ASSESSED Quantitative fetal fibronectin. MAIN OUTCOME MEASURES Spontaneous preterm birth within 7 days. RESULTS The individual participant data meta-analysis included 1783 women and 139 events of spontaneous preterm birth within 7 days (event rate 7.8%). The prognostic model that was developed included quantitative fetal fibronectin, smoking, ethnicity, nulliparity and multiple pregnancy. The model was externally validated in a cohort of 2837 women, with 83 events of spontaneous preterm birth within 7 days (event rate 2.93%), an area under the curve of 0.89 (95% confidence interval 0.84 to 0.93), a calibration slope of 1.22 and a Nagelkerke R 2 of 0.34. The economic analysis found that the prognostic model was cost-effective compared with using qualitative fetal fibronectin at a threshold for hospital admission and treatment of ≥ 2% risk of preterm birth within 7 days. LIMITATIONS The outcome proportion (spontaneous preterm birth within 7 days of test) was 2.9% in the validation study. This is in line with other studies, but having slightly fewer than 100 events is a limitation in model validation. CONCLUSIONS A prognostic model that included quantitative fetal fibronectin and clinical risk factors showed excellent performance in the prediction of spontaneous preterm birth within 7 days of test, was cost-effective and can be used to inform a decision support tool to help guide management decisions for women with threatened preterm labour. FUTURE WORK The prognostic model will be embedded in electronic maternity records and a mobile telephone application, enabling ongoing data collection for further refinement and validation of the model. STUDY REGISTRATION This study is registered as PROSPERO CRD42015027590 and Current Controlled Trials ISRCTN41598423. FUNDING This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 52. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Sarah J Stock
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Margaret Horne
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Merel Bruijn
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Helen White
- Division of Nursing, Midwifery and Social Work, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Robert Heggie
- Health Economics and Health Technology Assessment, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Lisa Wotherspoon
- Medical Research Council Centre for Reproductive Health, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Kathleen Boyd
- Health Economics and Health Technology Assessment, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Lorna Aucott
- Health Services Research Unit, University of Aberdeen, Aberdeen, UK
| | - Rachel K Morris
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Jon Dorling
- Department of Neonatology, IWK Health Centre, Halifax, NS, Canada
| | - Lesley Jackson
- Department of Neonatology, Queen Elizabeth Hospital, Glasgow, UK
| | - Manju Chandiramani
- Department of Obstetrics and Gynaecology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Anna David
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK
| | - Asma Khalil
- Department of Fetal Medicine, St George's Hospital, St George's, University of London, London, UK
| | - Andrew Shennan
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Gert-Jan van Baaren
- Department of Obstetrics and Gynaecology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | | | - Tina Lavender
- Division of Nursing, Midwifery and Social Work, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Ben Mol
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, VIC, Australia
| | - Richard D Riley
- Centre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele University, Keele, UK
| | - Jane Norman
- Medical Research Council Centre for Reproductive Health, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - John Norrie
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
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Stock SJ, Horne M, Bruijn M, White H, Boyd KA, Heggie R, Wotherspoon L, Aucott L, Morris RK, Dorling J, Jackson L, Chandiramani M, David AL, Khalil A, Shennan A, van Baaren GJ, Hodgetts-Morton V, Lavender T, Schuit E, Harper-Clarke S, Mol BW, Riley RD, Norman JE, Norrie J. Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis. PLoS Med 2021; 18:e1003686. [PMID: 34228732 PMCID: PMC8259998 DOI: 10.1371/journal.pmed.1003686] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/07/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Timely interventions in women presenting with preterm labour can substantially improve health outcomes for preterm babies. However, establishing such a diagnosis is very challenging, as signs and symptoms of preterm labour are common and can be nonspecific. We aimed to develop and externally validate a risk prediction model using concentration of vaginal fluid fetal fibronectin (quantitative fFN), in combination with clinical risk factors, for the prediction of spontaneous preterm birth and assessed its cost-effectiveness. METHODS AND FINDINGS Pregnant women included in the analyses were 22+0 to 34+6 weeks gestation with signs and symptoms of preterm labour. The primary outcome was spontaneous preterm birth within 7 days of quantitative fFN test. The risk prediction model was developed and internally validated in an individual participant data (IPD) meta-analysis of 5 European prospective cohort studies (2009 to 2016; 1,783 women; mean age 29.7 years; median BMI 24.8 kg/m2; 67.6% White; 11.7% smokers; 51.8% nulliparous; 10.4% with multiple pregnancy; 139 [7.8%] with spontaneous preterm birth within 7 days). The model was then externally validated in a prospective cohort study in 26 United Kingdom centres (2016 to 2018; 2,924 women; mean age 28.2 years; median BMI 25.4 kg/m2; 88.2% White; 21% smokers; 35.2% nulliparous; 3.5% with multiple pregnancy; 85 [2.9%] with spontaneous preterm birth within 7 days). The developed risk prediction model for spontaneous preterm birth within 7 days included quantitative fFN, current smoking, not White ethnicity, nulliparity, and multiple pregnancy. After internal validation, the optimism adjusted area under the curve was 0.89 (95% CI 0.86 to 0.92), and the optimism adjusted Nagelkerke R2 was 35% (95% CI 33% to 37%). On external validation in the prospective UK cohort population, the area under the curve was 0.89 (95% CI 0.84 to 0.94), and Nagelkerke R2 of 36% (95% CI: 34% to 38%). Recalibration of the model's intercept was required to ensure overall calibration-in-the-large. A calibration curve suggested close agreement between predicted and observed risks in the range of predictions 0% to 10%, but some miscalibration (underprediction) at higher risks (slope 1.24 (95% CI 1.23 to 1.26)). Despite any miscalibration, the net benefit of the model was higher than "treat all" or "treat none" strategies for thresholds up to about 15% risk. The economic analysis found the prognostic model was cost effective, compared to using qualitative fFN, at a threshold for hospital admission and treatment of ≥2% risk of preterm birth within 7 days. Study limitations include the limited number of participants who are not White and levels of missing data for certain variables in the development dataset. CONCLUSIONS In this study, we found that a risk prediction model including vaginal fFN concentration and clinical risk factors showed promising performance in the prediction of spontaneous preterm birth within 7 days of test and has potential to inform management decisions for women with threatened preterm labour. Further evaluation of the risk prediction model in clinical practice is required to determine whether the risk prediction model improves clinical outcomes if used in practice. TRIAL REGISTRATION The study was approved by the West of Scotland Research Ethics Committee (16/WS/0068). The study was registered with ISRCTN Registry (ISRCTN 41598423) and NIHR Portfolio (CPMS: 31277).
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Affiliation(s)
- Sarah J. Stock
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- MRC Centre for Reproductive Health, Queen’s Medical Research Institute, University of Edinburgh, United Kingdom
- * E-mail:
| | - Margaret Horne
- MRC Centre for Reproductive Health, Queen’s Medical Research Institute, University of Edinburgh, United Kingdom
| | - Merel Bruijn
- MRC Centre for Reproductive Health, Queen’s Medical Research Institute, University of Edinburgh, United Kingdom
| | - Helen White
- Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom
| | - Kathleen A. Boyd
- Institute of Health and Wellbeing, University of Glasgow, United Kingdom
| | - Robert Heggie
- Institute of Health and Wellbeing, University of Glasgow, United Kingdom
| | - Lisa Wotherspoon
- MRC Centre for Reproductive Health, Queen’s Medical Research Institute, University of Edinburgh, United Kingdom
| | - Lorna Aucott
- Health Services Research Unit, University of Aberdeen, United Kingdom
| | - Rachel K. Morris
- Institute of Applied Health Research, University of Birmingham, United Kingdom
| | - Jon Dorling
- IWK Health Centre, Halifax, Nova Scotia, Canada
| | | | | | - Anna L. David
- Elizabeth Garrett Anderson Institute for Women’s Health, University College London, United Kingdom
| | - Asma Khalil
- Vascular Biology Research Centre, Molecular and Clinical Sciences Research Institute, St George’s University of London, United Kingdom
| | - Andrew Shennan
- Department of Women and Children’s Health, School of Life Course Sciences, Kings College London, United Kingdom
| | - Gert-Jan van Baaren
- Department of Obstetrics and Gynaecology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | | | - Tina Lavender
- Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom
| | - Ewoud Schuit
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | - Ben W. Mol
- Department of Obstetrics & Gynaecology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Australia
| | - Richard D. Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, United Kingdom
| | - Jane E. Norman
- Faculty of Health Sciences, University of Bristol, Bristol, United Kingdom
| | - John Norrie
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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Watson HA, Carlisle N, Seed PT, Carter J, Kuhrt K, Tribe RM, Shennan AH. Evaluating the use of the QUiPP app and its impact on the management of threatened preterm labour: A cluster randomised trial. PLoS Med 2021; 18:e1003689. [PMID: 34228735 PMCID: PMC8291648 DOI: 10.1371/journal.pmed.1003689] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 07/20/2021] [Accepted: 06/08/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Preterm delivery (before 37 weeks of gestation) is the single most important contributor to neonatal death and morbidity, with lifelong repercussions. However, the majority of women who present with preterm labour (PTL) symptoms do not deliver imminently. Accurate prediction of PTL is needed in order ensure correct management of those most at risk of preterm birth (PTB) and to prevent the maternal and fetal risks incurred by unnecessary interventions given to the majority. The QUantitative Innovation in Predicting Preterm birth (QUIPP) app aims to support clinical decision-making about women in threatened preterm labour (TPTL) by combining quantitative fetal fibronectin (qfFN) values, cervical length (CL), and significant PTB risk factors to create an individualised percentage risk of delivery. METHODS AND FINDINGS EQUIPTT was a multi-centre cluster randomised controlled trial (RCT) involving 13 maternity units in South and Eastern England (United Kingdom) between March 2018 and February 2019. Pregnant women (n = 1,872) between 23+0 and 34+6 weeks' gestation with symptoms of PTL in the analysis period were assigned to either the intervention (762) or control (1,111). The mean age of the study population was 30.2 (+/- SD 5.93). A total of 56.0% were white, 19.6% were black, 14.2% were Asian, and 10.2% were of other ethnicities. The intervention was the use of the QUiPP app with admission, antenatal corticosteroids (ACSs), and transfer advised for women with a QUiPP risk of delivery >5% within 7 days. Control sites continued with their conventional management of TPTL. Unnecessary management for TPTL was a composite primary outcome defined by the sum of unnecessary admission decisions (admitted and delivery interval >7 days or not admitted and delivery interval ≤7 days) and the number of unnecessary in utero transfer (IUT) decisions/actions (IUT that occurred or were attempted >7 days prior to delivery) and ex utero transfers (EUTs) that should have been in utero (attempted and not attempted). Unnecessary management of TPTL was 11.3% (84/741) at the intervention sites versus 11.5% (126/1094) at control sites (odds ratio [OR] 0.97, 95% confidence interval [CI] 0.66-1.42, p = 0.883). Control sites frequently used qfFN and did not follow UK national guidance, which recommends routine treatment below 30 weeks without testing. Unnecessary management largely consisted of unnecessary admissions which were similar at intervention and control sites (10.7% versus 10.8% of all visits). In terms of adverse outcomes for women in TPTL <36 weeks, 4 women from the intervention sites and 12 from the control sites did not receive recommended management. If the QUiPP percentage risk was used as per protocol, unnecessary management would have been 7.4% (43/578) versus 9.9% (134/1,351) (OR 0.72, 95% CI 0.45-1.16). Our external validation of the QUiPP app confirmed that it was highly predictive of delivery in 7 days; receiver operating curve area was 0.90 (95% CI 0.85-0.95) for symptomatic women. Study limitations included a lack of compliance with national guidance at the control sites and difficulties in implementation of the QUiPP app. CONCLUSIONS This cluster randomised trial did not demonstrate that the use of the QUiPP app reduced unnecessary management of TPTL compared to current management but would safely improve the management recommended by the National Institute for Health and Care Excellence (NICE). Interpretation of qfFN, with or without the QUiPP app, is a safe and accurate method for identifying women most likely to benefit from PTL interventions. TRIAL REGISTRATION ISRCTN Registry ISRCTN17846337.
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Affiliation(s)
- Helena A. Watson
- Department of Women and Children’s Health, School of Life Course Sciences, King’s College London, St Thomas’ Hospital, London
| | - Naomi Carlisle
- Department of Women and Children’s Health, School of Life Course Sciences, King’s College London, St Thomas’ Hospital, London
| | - Paul T. Seed
- Department of Women and Children’s Health, School of Life Course Sciences, King’s College London, St Thomas’ Hospital, London
| | - Jenny Carter
- Department of Women and Children’s Health, School of Life Course Sciences, King’s College London, St Thomas’ Hospital, London
| | - Katy Kuhrt
- Department of Women and Children’s Health, School of Life Course Sciences, King’s College London, St Thomas’ Hospital, London
| | - Rachel M. Tribe
- Department of Women and Children’s Health, School of Life Course Sciences, King’s College London, St Thomas’ Hospital, London
| | - Andrew H. Shennan
- Department of Women and Children’s Health, School of Life Course Sciences, King’s College London, St Thomas’ Hospital, London
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Stock SJ, Wotherspoon LM, Boyd KA, Morris RK, Dorling J, Jackson L, Chandiramani M, David AL, Khalil A, Shennan A, Hodgetts Morton V, Lavender T, Khan K, Harper-Clarke S, Mol BW, Riley RD, Norrie J, Norman JE. Quantitative fibronectin to help decision-making in women with symptoms of preterm labour (QUIDS) part 1: Individual participant data meta-analysis and health economic analysis. BMJ Open 2018; 8:e020796. [PMID: 29627817 PMCID: PMC5892771 DOI: 10.1136/bmjopen-2017-020796] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION The aim of the QUIDS study is to develop a decision support tool for the management of women with symptoms and signs of preterm labour, based on a validated prognostic model using quantitative fetal fibronectin (qfFN) concentration, in combination with clinical risk factors. METHODS AND ANALYSIS The study will evaluate the Rapid fFN 10Q System (Hologic, Marlborough, Massachusetts) which quantifies fFN in a vaginal swab. In part 1 of the study, we will develop and internally validate a prognostic model using an individual participant data (IPD) meta-analysis of existing studies containing women with symptoms of preterm labour alongside fFN measurements and pregnancy outcome. An economic analysis will be undertaken to assess potential cost-effectiveness of the qfFN prognostic model. The primary endpoint will be the ability of the prognostic model to rule out spontaneous preterm birth within 7 days. Six eligible studies were identified by systematic review of the literature and five agreed to provide their IPD (n=5 studies, 1783 women and 139 events of preterm delivery within 7 days of testing). ETHICS AND DISSEMINATION The study is funded by the National Institute of Healthcare Research Health Technology Assessment (HTA 14/32/01). It has been approved by the West of Scotland Research Ethics Committee (16/WS/0068). PROSPERO REGISTRATION NUMBER CRD42015027590. VERSION Protocol version 2, date 1 November 2016.
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Affiliation(s)
- Sarah J Stock
- Tommy's Centre for Maternal and Fetal Health, University of Edinburgh MRC Centre for Reproductive Health, Queen's Medical Research Institute, Edinburgh, UK
- School of Women's and Infants' Health, University of Western Australia, Crawley, Western Australia, Australia
| | - Lisa M Wotherspoon
- Tommy's Centre for Maternal and Fetal Health, University of Edinburgh MRC Centre for Reproductive Health, Queen's Medical Research Institute, Edinburgh, UK
| | - Kathleen A Boyd
- Health Economics & Health Technology Assessment, Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK
| | - Rachel K Morris
- School of Clinical and Experimental Medicine, University of Birmingham, Birmingham, UK
| | - Jon Dorling
- Neonatal Unit, Queen's Medical Centre, Nottingham, UK
| | - Lesley Jackson
- Neonatal Unit, Royal Hospital for Children Glasgow, Glasgow, UK
| | - Manju Chandiramani
- Imperial College Healthcare NHS Trust, Queen Charlotte's and Chelsea Hospital, London, UK
| | - Anna L David
- Institute for Women's Health, University College London Medical School, London, UK
| | - Asma Khalil
- St. George's Medical School, University of London, London, UK
| | - Andrew Shennan
- Maternal and Fetal Research Unit, King's College London, London, UK
| | | | - Tina Lavender
- Centre for Global Women's Health, School of Nursing, Midwifery and Social Work, University of Manchester, Manchester, UK
| | - Khalid Khan
- Centre for Primary Care and Public Health, Queen Mary University of London, London, UK
| | | | - Ben W Mol
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, Australia
| | - Richard D Riley
- Research Institute for Primary Care and Health Sciences, Keele University, Keele, UK
| | - John Norrie
- Edinburgh Clinical Trials Unit, University of Edinburgh, Edinburgh, UK
| | - Jane E Norman
- Tommy's Centre for Maternal and Fetal Health, University of Edinburgh MRC Centre for Reproductive Health, Queen's Medical Research Institute, Edinburgh, UK
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