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Development and validation of the predictive models for the QUiPP App v.2: a tool for predicting preterm birth in high-risk asymptomatic women

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Development and validation of the predictive models for the QUiPP App v.2 : a tool for predicting preterm birth in high-risk asymptomatic women. / Watson, Helena A; Seed, Paul T; Carter, Jenny; Hezelgrave, Natasha L; Kuhrt, Katy; Tribe, Rachel M; Shennan, Andrew H.

In: Ultrasound in Obstetrics and Gynecology, 20.07.2019.

Research output: Contribution to journalArticle

Harvard

Watson, HA, Seed, PT, Carter, J, Hezelgrave, NL, Kuhrt, K, Tribe, RM & Shennan, AH 2019, 'Development and validation of the predictive models for the QUiPP App v.2: a tool for predicting preterm birth in high-risk asymptomatic women', Ultrasound in Obstetrics and Gynecology. https://doi.org/10.1002/uog.20401

APA

Watson, H. A., Seed, P. T., Carter, J., Hezelgrave, N. L., Kuhrt, K., Tribe, R. M., & Shennan, A. H. (2019). Development and validation of the predictive models for the QUiPP App v.2: a tool for predicting preterm birth in high-risk asymptomatic women. Ultrasound in Obstetrics and Gynecology. https://doi.org/10.1002/uog.20401

Vancouver

Watson HA, Seed PT, Carter J, Hezelgrave NL, Kuhrt K, Tribe RM et al. Development and validation of the predictive models for the QUiPP App v.2: a tool for predicting preterm birth in high-risk asymptomatic women. Ultrasound in Obstetrics and Gynecology. 2019 Jul 20. https://doi.org/10.1002/uog.20401

Author

Watson, Helena A ; Seed, Paul T ; Carter, Jenny ; Hezelgrave, Natasha L ; Kuhrt, Katy ; Tribe, Rachel M ; Shennan, Andrew H. / Development and validation of the predictive models for the QUiPP App v.2 : a tool for predicting preterm birth in high-risk asymptomatic women. In: Ultrasound in Obstetrics and Gynecology. 2019.

Bibtex Download

@article{fcfc274218a84ce894f81ac6a70fcb6c,
title = "Development and validation of the predictive models for the QUiPP App v.2: a tool for predicting preterm birth in high-risk asymptomatic women",
abstract = "Accurate mid-pregnancy prediction of spontaneous preterm birth (sPTB) is essential to ensure appropriate surveillance of high-risk women. Advancing the QUiPP prototype, QUiPP 2 aimed to provide individualised risk of delivery based on cervical length, quantitative fetal fibronectin (qfFN), or both tests combined, taking into account further risk factors, such as multiple pregnancy. Validation of the QUiPP 2 predictive models using a distinct dataset aims to confirm the accuracy and transportability of QUiPP overall and within certain clinically relevant timeframes.METHODS: This was a prospective secondary analysis of data from 13 UK preterm birth clinics. A total of 1803 (3878 visits) women were included in the training set and 904 women (1400 visits) in the validation set. Survival analysis was used to identify the significant predictors of sPTB and parametric structures for survival models were compared and the best selected. The estimated overall probability of delivery before six clinically important timepoints (30, 34, 37 weeks of pregnancy and within 1, 2 and 4 weeks of testing) was calculated for each woman and analysed as a predictive test for the actual occurrence of each event. This allowed receiver-operating characteristics (ROC) curves to be plotted, and areas under the curve (AUC) calculated. Calibration was performed to measure the agreement between expected and actual outcomes.RESULTS: All algorithms demonstrated high accuracy; AUCs between 0.75 and 0.90 for the use of qfFN and cervical length combined, 0.68 and 0.90 for qfFN and 0.71 and 0.87 for cervical length. The differences between the three algorithms were not statistically significant. Calibration confirmed no significant differences between expected and observed rates of sPTB within 4 weeks and slight over-estimation of risk with the use of cervical length measurement between 22-25+6 weeks' gestation.CONCLUSION: QUiPP app version 2 is a highly accurate prediction tool for prematurity, based on a unique combination of biomarkers, symptoms and statistical algorithm. It can reliably be used in the context of discussing risk. Whilst further work is required to determine its role in identifying women requiring prophylactic interventions, it is a reliable and convenient screening tool for planning follow-up or hospitalisation for high risk women. This article is protected by copyright. All rights reserved.",
author = "Watson, {Helena A} and Seed, {Paul T} and Jenny Carter and Hezelgrave, {Natasha L} and Katy Kuhrt and Tribe, {Rachel M} and Shennan, {Andrew H}",
note = "This article is protected by copyright. All rights reserved.",
year = "2019",
month = "7",
day = "20",
doi = "10.1002/uog.20401",
language = "English",
journal = "Ultrasound in Obstetrics and Gynecology",
issn = "0960-7692",
publisher = "John Wiley and Sons Ltd",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Development and validation of the predictive models for the QUiPP App v.2

T2 - a tool for predicting preterm birth in high-risk asymptomatic women

AU - Watson, Helena A

AU - Seed, Paul T

AU - Carter, Jenny

AU - Hezelgrave, Natasha L

AU - Kuhrt, Katy

AU - Tribe, Rachel M

AU - Shennan, Andrew H

N1 - This article is protected by copyright. All rights reserved.

PY - 2019/7/20

Y1 - 2019/7/20

N2 - Accurate mid-pregnancy prediction of spontaneous preterm birth (sPTB) is essential to ensure appropriate surveillance of high-risk women. Advancing the QUiPP prototype, QUiPP 2 aimed to provide individualised risk of delivery based on cervical length, quantitative fetal fibronectin (qfFN), or both tests combined, taking into account further risk factors, such as multiple pregnancy. Validation of the QUiPP 2 predictive models using a distinct dataset aims to confirm the accuracy and transportability of QUiPP overall and within certain clinically relevant timeframes.METHODS: This was a prospective secondary analysis of data from 13 UK preterm birth clinics. A total of 1803 (3878 visits) women were included in the training set and 904 women (1400 visits) in the validation set. Survival analysis was used to identify the significant predictors of sPTB and parametric structures for survival models were compared and the best selected. The estimated overall probability of delivery before six clinically important timepoints (30, 34, 37 weeks of pregnancy and within 1, 2 and 4 weeks of testing) was calculated for each woman and analysed as a predictive test for the actual occurrence of each event. This allowed receiver-operating characteristics (ROC) curves to be plotted, and areas under the curve (AUC) calculated. Calibration was performed to measure the agreement between expected and actual outcomes.RESULTS: All algorithms demonstrated high accuracy; AUCs between 0.75 and 0.90 for the use of qfFN and cervical length combined, 0.68 and 0.90 for qfFN and 0.71 and 0.87 for cervical length. The differences between the three algorithms were not statistically significant. Calibration confirmed no significant differences between expected and observed rates of sPTB within 4 weeks and slight over-estimation of risk with the use of cervical length measurement between 22-25+6 weeks' gestation.CONCLUSION: QUiPP app version 2 is a highly accurate prediction tool for prematurity, based on a unique combination of biomarkers, symptoms and statistical algorithm. It can reliably be used in the context of discussing risk. Whilst further work is required to determine its role in identifying women requiring prophylactic interventions, it is a reliable and convenient screening tool for planning follow-up or hospitalisation for high risk women. This article is protected by copyright. All rights reserved.

AB - Accurate mid-pregnancy prediction of spontaneous preterm birth (sPTB) is essential to ensure appropriate surveillance of high-risk women. Advancing the QUiPP prototype, QUiPP 2 aimed to provide individualised risk of delivery based on cervical length, quantitative fetal fibronectin (qfFN), or both tests combined, taking into account further risk factors, such as multiple pregnancy. Validation of the QUiPP 2 predictive models using a distinct dataset aims to confirm the accuracy and transportability of QUiPP overall and within certain clinically relevant timeframes.METHODS: This was a prospective secondary analysis of data from 13 UK preterm birth clinics. A total of 1803 (3878 visits) women were included in the training set and 904 women (1400 visits) in the validation set. Survival analysis was used to identify the significant predictors of sPTB and parametric structures for survival models were compared and the best selected. The estimated overall probability of delivery before six clinically important timepoints (30, 34, 37 weeks of pregnancy and within 1, 2 and 4 weeks of testing) was calculated for each woman and analysed as a predictive test for the actual occurrence of each event. This allowed receiver-operating characteristics (ROC) curves to be plotted, and areas under the curve (AUC) calculated. Calibration was performed to measure the agreement between expected and actual outcomes.RESULTS: All algorithms demonstrated high accuracy; AUCs between 0.75 and 0.90 for the use of qfFN and cervical length combined, 0.68 and 0.90 for qfFN and 0.71 and 0.87 for cervical length. The differences between the three algorithms were not statistically significant. Calibration confirmed no significant differences between expected and observed rates of sPTB within 4 weeks and slight over-estimation of risk with the use of cervical length measurement between 22-25+6 weeks' gestation.CONCLUSION: QUiPP app version 2 is a highly accurate prediction tool for prematurity, based on a unique combination of biomarkers, symptoms and statistical algorithm. It can reliably be used in the context of discussing risk. Whilst further work is required to determine its role in identifying women requiring prophylactic interventions, it is a reliable and convenient screening tool for planning follow-up or hospitalisation for high risk women. This article is protected by copyright. All rights reserved.

U2 - 10.1002/uog.20401

DO - 10.1002/uog.20401

M3 - Article

C2 - 31325332

JO - Ultrasound in Obstetrics and Gynecology

JF - Ultrasound in Obstetrics and Gynecology

SN - 0960-7692

ER -

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