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

Development and validation of a risk prediction model for preterm birth in women with gestational diabetes mellitus

OBJECTIVES

This study aims to develop and validate a prediction model for preterm birth in women with gestational diabetes mellitus (GDM).

DESIGN

We conducted a retrospective study on women with GDM who gave birth at the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, between November 2017 and July 2021. We divided 1879 patients into a development set (n = 1346) and a validation set (n = 533). The development set was used to construct the prediction model for preterm birth using the stepwise logistic regression model. A nomogram and a web calculator were established based on the model. Discrimination and calibration were assessed in both sets.

PATIENTS AND MEASUREMENTS

Patients were women with GDM. Data were collected from medical records. GDM was diagnosed with 75-g oral glucose tolerance test during 24-28 gestational weeks. Preterm birth was definied as gestational age at birth <37 weeks.

RESULTS

The incidence of preterm birth was 9.4%. The predictive model included age, assisted reproductive technology, hypertensive disorders of pregnancy, reproductive system inflammation, intrahepatic cholestasis of pregnancy, high-density lipoprotein, homocysteine, and fasting blood glucose of 75-g oral glucose tolerance test. The area under the receiver operating characteristic curve for the development and validation sets was 0.722 and 0.632, respectively. The model has been adequately calibrated using a calibration curve and the Hosmer-Lemeshow test, demonstrating a correlation between the predicted and observed risk.

CONCLUSION

This study presents a novel, validated risk model for preterm birth in pregnant women with GDM, providing an individualized risk estimation using clinical risk factors in the third trimester of pregnancy.

Use of artificial intelligence in obstetric and gynaecological diagnostics: a protocol for a systematic review and meta-analysis

INTRODUCTION

Emerging developments in applications of artificial intelligence (AI) in healthcare offer the opportunity to improve diagnostic capabilities in obstetrics and gynaecology (O&G), ensuring early detection of pathology, optimal management and improving survival. Consensus on a robust AI healthcare framework is crucial for standardising protocols that promote data privacy and transparency, minimise bias, and ensure patient safety. Here, we describe the study protocol for a systematic review and meta-analysis to evaluate current applications of AI in O&G diagnostics with consideration of reporting standards used and their ethical implications. This protocol is written following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 checklist.

METHODS AND ANALYSIS

The study objective is to explore the current application of AI in O&G diagnostics and assess the reporting standards used in these studies. Electronic bibliographic databases MEDLINE, EMBASE and Cochrane will be searched. Study selection, data extraction and subsequent narrative synthesis and meta-analyses will be carried out following the PRISMA-P guidelines. Included papers will be English-language full-text articles from May 2015 to March 2024, which provide original data, as AI has been redefined in recent literature. Papers must use AI as the predictive method, focusing on improving O&G diagnostic outcomes.We will evaluate the reporting standards including the risk of bias, lack of transparency and consider the ethical implications and potential harm to patients. Outcome measures will involve assessing the included studies against gold-standard criteria for robustness of model development (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis, model predictive performance, model risk of bias and applicability (Prediction model Risk Of Bias Assessment Tool and study reporting (Consolidated Standards of Reporting Trials-AI) guidance.

ETHICS AND DISSEMINATION

Ethical approval is not required for this systematic review. Findings will be shared through peer-reviewed publications. There will be no patient or public involvement in this study.

PROSPERO REGISTRATION NUMBER

CRD42022357024 .

Gestational diabetes mellitus aggravates adverse perinatal outcomes in women with intrahepatic cholestasis of pregnancy

PURPOSE

To evaluate the effect of intrahepatic cholestasis of pregnancy (ICP) with gestational diabetes mellitus (GDM) on perinatal outcomes and establish a prediction model of adverse perinatal outcomes in women with ICP.

METHODS

This multicenter retrospective cohort study included the clinical data of 2,178 pregnant women with ICP, including 1,788 women with ICP and 390 co-occurrence ICP and GDM. The data of all subjects were collected from hospital electronic medical records. Univariate and multivariate logistic regression analysis were used to compare the incidence of perinatal outcomes between ICP with GDM group and ICP alone group.

RESULTS

Baseline characteristics of the population revealed that maternal age (p < 0.001), pregestational weight (p = 0.01), pre-pregnancy BMI (p < 0.001), gestational weight gain (p < 0.001), assisted reproductive technology (ART) (p < 0.001), and total bile acid concentration (p = 0.024) may be risk factors for ICP with GDM. Furthermore, ICP with GDM demonstrated a higher association with both polyhydramnios (OR 2.66) and preterm labor (OR 1.67) compared to ICP alone. Further subgroup analysis based on the severity of ICP showed that elevated total bile acid concentrations were closely associated with an increased risk of preterm labour, meconium-stained amniotic fluid, and low birth weight in both ICP alone and ICP with GDM groups. ICP with GDM further worsened these outcomes, especially in women with severe ICP. The nomogram prediction model effectively predicted the occurrence of preterm labour in the ICP population.

CONCLUSIONS

ICP with GDM may result in more adverse pregnancy outcomes, which are associated with bile acid concentrations.

Development and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural Ethiopia

Preterm birth is one of the most common obstetric complications in low- and middle-income countries, where access to advanced diagnostic tests and imaging is limited. Therefore, we developed and validated a simplified risk prediction tool to predict preterm birth based on easily applicable and routinely collected characteristics of pregnant women in the primary care setting. We used a logistic regression model to develop a model based on the data collected from 481 pregnant women. Model accuracy was evaluated through discrimination (measured by the area under the Receiver Operating Characteristic curve; AUC) and calibration (via calibration graphs and the Hosmer-Lemeshow goodness of fit test). Internal validation was performed using a bootstrapping technique. A simplified risk score was developed, and the cut-off point was determined using the "Youden index" to classify pregnant women into high or low risk for preterm birth. The incidence of preterm birth was 19.5% (95% CI:16.2, 23.3) of pregnancies. The final prediction model incorporated mid-upper arm circumference, gravidity, history of abortion, antenatal care, comorbidity, intimate partner violence, and anemia as predictors of preeclampsia. The AUC of the model was 0.687 (95% CI: 0.62, 0.75). The calibration plot demonstrated a good calibration with a p-value of 0.713 for the Hosmer-Lemeshow goodness of fit test. The model can identify pregnant women at high risk of preterm birth. It is applicable in daily clinical practice and could contribute to the improvement of the health of women and newborns in primary care settings with limited resources. Healthcare providers in rural areas could use this prediction model to improve clinical decision-making and reduce obstetrics complications.

Prevention of spontaneous preterm delivery – an update on where we are today

Spontaneous preterm birth (delivery before 37 completed weeks) is the single most important cause of perinatal morbidity and mortality. The rate is increasing world-wide with a great disparity between low, middle and high income countries. It has been estimated that the cost of neonatal care for preterm babies is more than 4 times that of a term neonate admitted into the neonatal care. Furthermore, there are high costs associated with long-term morbidity in those who survive the neonatal period. Interventions to stop delivery once preterm labor starts are largely ineffective hence the best approach to reducing the rate and consequences is prevention. This is either primary (reducing or minimizing factors associated with preterm birth prior to and during pregnancy) or secondary - identification and amelioration (if possible) of factors in pregnancy that are associated with preterm labor. In the first category are optimizing maternal weight, promoting healthy nutrition, smoking cessation, birth spacing, avoidance of adolescent pregnancies and screening for and controlling various medical disorders as well as infections prior to pregnancy. Strategies in pregnancy, include early booking for prenatal care, screening and managing medical disorders and their complications, and identifying predisposing factors to preterm labor such as shortening of the cervix and timely instituting progesterone prophylaxis or cervical cerclage where appropriate. The use of biomarkers such as oncofetal fibronectin, placental alpha-macroglobulin-1 and IGFBP-1 where cervical screening is not available or to diagnosis PPROM would identify those that require close monitoring and allow the institution of antibiotics especially where infection is considered a predisposing factor. Irrespective of the approach to prevention, timing the administration of corticosteroids and where necessary tocolysis and magnesium sulfate are associated with an improved outcome. The role of genetics, infections and probiotics and how these emerging dimensions help in the diagnosis of preterm birth and consequently prevention are exciting and hopefully may identify sub-populations for targeted strategies.

Development and validation of a prediction model on spontaneous preterm birth in twin pregnancy: a retrospective cohort study

BACKGROUND

This study was conducted to develop and validate an individualized prediction model for spontaneous preterm birth (sPTB) in twin pregnancies.

METHODS

This a retrospective cohort study included 3845 patients who gave birth at the Chongqing Maternal and Child Health Hospital from January 2017 to December 2022. Both univariable and multivariable logistic regression analyses were performed to find factors associated with sPTB. The associations were estimated using the odds ratio (OR) and the 95% confidence interval (CI). Model performance was estimated using sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

RESULTS

A total of 1313 and 564 cases were included in the training and testing sets, respectively. In the training set, univariate and multivariate logistic regression analysis indicated that age ≥ 35 years (OR, 2.28; 95% CI 1.67-3.13), pre-pregnancy underweight (OR, 2.36; 95% CI 1.60-3.47), pre-pregnancy overweight (OR, 1.67; 95% CI 1.09-2.56), and obesity (OR, 10.45; 95% CI, 3.91-27.87), nulliparity (OR, 0.58; 95% CI 0.41-0.82), pre-pregnancy diabetes (OR, 5.81; 95% CI 3.24-10.39), pre-pregnancy hypertension (OR, 2.79; 95% CI 1.44-5.41), and cervical incompetence (OR, 5.12; 95% CI 3.08-8.48) are independent risk factors for sPTB in twin pregnancies. The AUC of the training and validation set was 0.71 (95% CI 0.68-0.74) and 0.68 (95% CI 0.64-0.73), respectively. And then we integrated those risk factors to construct the nomogram.

CONCLUSIONS

The nomogram developed for predicting the risk of sPTB in pregnant women with twins demonstrated good performance. The prediction nomogram serves as a practical tool by including all necessary predictors that are readily accessible to practitioners.

Prediction of Preterm Delivery Using Serum Ischemia Modified Albumin, Biglycan, and Decorin Levels in Women with Threatened Preterm Labor

OBJECTIVE

 The serum ischemia modified albumin (IMA), biglycan, and decorin levels of pregnant women who were hospitalized for threatened preterm labor were measured.

METHODS

 Fifty-one consecutive pregnant women with a single pregnancy between the 24th and 36th weeks with a diagnosis of threatened preterm labor were included in the present prospective cohort study.

RESULTS

 As a result of multivariate logistic regression analysis for predicting preterm delivery within 24 hours, 48 hours, 7 days, 14 days, ≤ 35 gestational weeks, and ≤ 37 gestational weeks after admission, area under the curve (AUC) (95% confidence interval [CI[) values were 0.95 (0.89-1.00), 0.93 (0.86-0.99), 0.91 (0.83-0.98), 0.92 (0.85-0.99), 0.82 (0.69-0.96), and 0.89 (0.80-0.98), respectively. In the present study, IMA and biglycan levels were found to be higher and decorin levels lower in women admitted to the hospital with threatened preterm labor and who gave preterm birth within 48 hours compared with those who gave birth after 48 hours.

CONCLUSION

 In pregnant women admitted to the hospital with threatened preterm labor, the prediction preterm delivery of the combined model created by adding IMA, decorin, and biglycan in addition to the TVS CL measurement was higher than the TVS CL measurement alone.

CLINICAL TRIAL REGISTRATION

 The present trial was registered at ClinicalTrials.gov, number NCT04451928.

Trends and risk of recurrent preterm birth in pregnancy cohorts in rural Bangladesh, 1990-2019

INTRODUCTION

A history of preterm birth reportedly increases the risk of subsequent preterm birth. This association has primarily been studied in high-income countries and not in low-income settings in transition with rapidly descending preterm birth figures. We evaluated the population-based trends of preterm births and recurrent preterm births and the risk of preterm birth recurrence in the second pregnancy based on prospectively studied pregnancy cohorts over three decades in Matlab, Bangladesh.

METHODS

A population-based cohort included 72 160 live births from 1990 to 2019. We calculated preterm birth and recurrent preterm birth trends. We assessed the odds of preterm birth recurrence based on a subsample of 14 567 women with live-born singletons in their first and second pregnancies. We used logistic regression and presented the associations by OR with a 95% CI.

RESULTS

The proportion of preterm births decreased from 25% in 1990 to 13% in 2019. The recurrent preterm births had a similar, falling pattern from 7.4% to 3.1% across the same period, contributing 27% of the total number of preterm births in the population. The odds of second pregnancy preterm birth were doubled (OR 2.18; 95% CI 1.96 to 2.43) in women with preterm birth compared with the women with term birth in their first pregnancies, remaining similar over the study period. The lower the gestational age at the first birth, the higher the odds of preterm birth in the subsequent pregnancy (test for trend p<0.001).

CONCLUSION

In this rural Bangladeshi setting, recurrent preterm births contributed a sizeable proportion of the total number of preterm births at the population level. The increased risk of recurrence remained similar across three decades when the total proportion of preterm births was reduced from 25% to 13%.

Development and validation of a prognosis risk score model for preterm birth among pregnant women who had antenatal care visit, Northwest, Ethiopia, retrospective follow-up study

BACKGROUND

Prematurity is the leading cause of neonatal morbidity and mortality, specifically in low-resource settings. The majority of prematurity can be prevented if early interventions are implemented for high-risk pregnancies. Developing a prognosis risk score for preterm birth based on easily available predictors could support health professionals as a simple clinical tool in their decision-making. Therefore, the study aims to develop and validate a prognosis risk score model for preterm birth among pregnant women who had antenatal care visit at Debre Markos Comprehensive and Specialized Hospital, Ethiopia.

METHODS

A retrospective follow-up study was conducted among a total of 1,132 pregnant women. Client charts were selected using a simple random sampling technique. Data were extracted using structured checklist prepared in the Kobo Toolbox application and exported to STATA version 14 and R version 4.2.2 for data management and analysis. Stepwise backward multivariable analysis was done. A simplified risk prediction model was developed based on a binary logistic model, and the model's performance was assessed by discrimination power and calibration. The internal validity of the model was evaluated by bootstrapping. Decision Curve Analysis was used to determine the clinical impact of the model.

RESULT

The incidence of preterm birth was 10.9%. The developed risk score model comprised of six predictors that remained in the reduced multivariable logistic regression, including age < 20, late initiation of antenatal care, unplanned pregnancy, recent pregnancy complications, hemoglobin < 11 mg/dl, and multiparty, for a total score of 17. The discriminatory power of the model was 0.931, and the calibration test was p > 0.05. The optimal cut-off for classifying risks as low or high was 4. At this cut point, the sensitivity, specificity and accuracy is 91.0%, 82.1%, and 83.1%, respectively. It was internally validated and has an optimism of 0.003. The model was found to have clinical benefit.

CONCLUSION

The developed risk-score has excellent discrimination performance and clinical benefit. It can be used in the clinical settings by healthcare providers for early detection, timely decision making, and improving care quality.

Prediction of Preterm Birth among Infants with Orofacial Cleft Defects

OBJECTIVE

To develop risk prediction models for preterm birth among infants with orofacial clefts.

DESIGN

Data from the Texas Birth Defects Registry for infants with orofacial clefts born between 1999-2014 were used to develop preterm birth predictive models. Logistic regression was used to consider maternal and infant characteristics, and internal validation of the final model was performed using bootstrapping methods. The area under the curve (AUC) statistic was generated to assess model performance, and separate predictive models were built and validated for infants with cleft lip and cleft palate alone. Several secondary analyses were conducted among subgroups of interest.

SETTING

State-wide, population-based Registry data.

PATIENTS/PARTICIPANTS

6774 infants with orofacial clefts born in Texas between 1999-2014.

MAIN OUTCOME MEASURE(S)

Preterm birth among infants with orofacial clefts.

RESULTS

The final predictive model performed modestly, with an optimism-corrected AUC of 0.67 among all infants with orofacial clefts. The optimism-corrected models for cleft lip (with or without cleft palate) and cleft palate alone had similar predictive capability, with AUCs of 0.66 and 0.67, respectively. Secondary analyses had similar results, but the model among infants with delivery prior to 32 weeks demonstrated higher optimism-corrected predictive capability (AUC = 0.74).

CONCLUSIONS

This study provides a first step towards predicting preterm birth risk among infants with orofacial clefts. Identifying pregnancies affected by orofacial clefts at the highest risk for preterm birth may lead to new avenues for improving outcomes among these infants.

Development of risk prediction models for preterm delivery in a rural setting in Ethiopia

Background

Preterm birth complications are the leading causes of death among children under five years. However, the inability to accurately identify pregnancies at high risk of preterm delivery is a key practical challenge, especially in resource-constrained settings with limited availability of biomarkers assessment.

Methods

We evaluated whether risk of preterm delivery can be predicted using available data from a pregnancy and birth cohort in Amhara region, Ethiopia. All participants were enrolled in the cohort between December 2018 and March 2020. The study outcome was preterm delivery, defined as any delivery occurring before week 37 of gestation regardless of vital status of the foetus or neonate. A range of sociodemographic, clinical, environmental, and pregnancy-related factors were considered as potential inputs. We used Cox and accelerated failure time models, alongside decision tree ensembles to predict risk of preterm delivery. We estimated model discrimination using the area-under-the-curve (AUC) and simulated the conditional distributions of cervical length (CL) and foetal fibronectin (FFN) to ascertain whether they could improve model performance.

Results

We included 2493 pregnancies; among them, 138 women were censored due to loss-to-follow-up before delivery. Overall, predictive performance of models was poor. The AUC was highest for the tree ensemble classifier (0.60, 95% confidence interval = 0.57-0.63). When models were calibrated so that 90% of women who experienced a preterm delivery were classified as high risk, at least 75% of those classified as high risk did not experience the outcome. The simulation of CL and FFN distributions did not significantly improve models' performance.

Conclusions

Prediction of preterm delivery remains a major challenge. In resource-limited settings, predicting high-risk deliveries would not only save lives, but also inform resource allocation. It may not be possible to accurately predict risk of preterm delivery without investing in novel technologies to identify genetic factors, immunological biomarkers, or the expression of specific proteins.

CA-125 Early Dynamics to Predict Overall Survival in Women with Newly Diagnosed Advanced Ovarian Cancer Based on Meta-Analysis Data

(1) Background: Cancer antigen 125 (CA-125) is a protein produced by ovarian cancer cells that is used for patients' monitoring. However, the best ways to analyze its decline and prognostic role are poorly quantified. (2) Methods: We leveraged individual patient data from the Gynecologic Cancer Intergroup (GCIG) meta-analysis (N = 5573) to compare different approaches summarizing the early trajectory of CA-125 before the prediction time (called the landmark time) at 3 or 6 months after treatment initiation in order to predict overall survival. These summaries included observed and estimated measures obtained by a linear mixed model (LMM). Their performances were evaluated by 10-fold cross-validation with the Brier score and the area under the ROC (AUC). (3) Results: The estimated value and the last observed value at 3 months were the best measures used to predict overall survival, with an AUC of 0.75 CI 95% [0.70; 0.80] at 24 and 36 months and 0.74 [0.69; 0.80] and 0.75 [0.69; 0.80] at 48 months, respectively, considering that CA-125 over 6 months did not improve the AUC, with 0.74 [0.68; 0.78] at 24 months and 0.71 [0.65; 0.76] at 36 and 48 months. (4) Conclusions: A 3-month surveillance provided reliable individual information on overall survival until 48 months for patients receiving first-line chemotherapy.

Fetal Fibronectin and Cervical Length as Predictors of Spontaneous Onset of Labour and Delivery in Term Pregnancies

(1) Objective: This study aimed to determine whether qualitative fetal fibronectin and transvaginal sonographic measurement of cervical length are effective in predicting delivery in term pregnancies within 5 days of the test. (2) Methods: We examined 268 women with singleton pregnancies presenting themselves at 37+0−40+4 weeks (median 38 weeks + 1 day) of gestation with irregular and painful uterine contractions, intact membranes and cervical dilatation less than 2 cm. All women were admitted to hospital up to 72 h after birth. On admission, a qualitative fetal fibronectin test was performed in cervicovaginal secretions and transvaginal sonographic measurement of cervical length was carried out. The primary outcome measure was delivery within 5 days of presentation. RESULTS: Among the women who delivered within 5 days after admission, 65.2% had positive fFN assessment, 43.5% had cervical length below 26 mm, 52.2% had the age > 32.5 years, 34.8% were nulliparous and 56.5% had gestational age ≥ 275 days. Logistic regression analysis demonstrated that significant contributors to the prediction of delivery within 5 days were fibronectin positivity, cervical length ≤ 26 mm, maternal age > 32.5 years and gestational age ≥ 275 days, with no significant contribution from parity. (3) Conclusions: Qualitative fetal fibronectin test and transvaginal cervical length measurement in term pregnancies are useful tests for predicting spontaneous onset of labour within 5 days. It helps women and healthcare providers to determine the optimum time for hospital admission.

Development and validation of a transcriptomic signature-based model as the predictive, preventive, and personalized medical strategy for preterm birth within 7 days in threatened preterm labor women

Preterm birth (PTB) is the leading cause of neonatal death. The essential strategy to prevent PTB is the accurate identification of threatened preterm labor (TPTL) women who will have PTB in a short time (< 7 days). Here, we aim to propose a clinical model to contribute to the effective prediction, precise prevention, and personalized medical treatment for PTB < 7 days in TPTL women through bioinformatics analysis and prospective cohort studies. In this study, the 1090 key genes involved in PTB < 7 days in the peripheral blood of TPTL women were ascertained using WGCNA. Based on this, the biological basis of immune-inflammatory activation (e.g., IFNγ and TNFα signaling) as well as immune cell disorders (e.g., monocytes and Th17 cells) in PTB < 7 days were revealed. Then, four core genes (JOSD1, IDNK, ZMYM3, and IL1B) that best represent their transcriptomic characteristics were screened by SVM and LASSO algorithm. Therefore, a prediction model with an AUC of 0.907 was constructed, which was validated in a larger population (AUC = 0.783). Moreover, the predictive value (AUC = 0.957) and clinical feasibility of this model were verified through the clinical prospective cohort we established. In conclusion, in the context of Predictive, Preventive, and Personalized Medicine (3PM), we have developed and validated a model to predict PTB < 7 days in TPTL women. This is promising to greatly improve the accuracy of clinical prediction, which would facilitate the personalized management of TPTL women to precisely prevent PTB < 7 days and improve maternal-fetal outcomes.

A prognostic model, including quantitative fetal fibronectin, to predict preterm labour: the QUIDS meta-analysis and prospective cohort study

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.