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Background: Current predictive machine learning techniques for spontaneous preterm birth heavily rely on a history of previous preterm birth and/or costly techniques such as fetal fibronectin and ultrasound measurement of cervical length to the disadvantage of those considered at low risk and/or those who have no access to more expensive screening tools. Aims and objectives: We aimed to develop a predictive model for spontaneous preterm delivery < 37 weeks using socio-demographic and clinical data readily available at booking -an approach which could be suitable for all women regardless of their previous obstetric history. Methods: We developed a logistic regression model using seven feature variables derived from maternal socio-demographic and obstetric history from a preterm birth (n = 917) and a matched full-term (n = 100) cohort in 2018 and 2020 at a tertiary obstetric unit in the UK. A three-fold cross-validation technique was applied with subsets for data training and testing in Python® (version 3.8) using the most predictive factors. The model performance was then compared to the previously published predictive algorithms. Results: The retrospective model showed good predictive accuracy with an AUC of 0.76 (95% CI: 0.71–0.83) for spontaneous preterm birth, with a sensitivity and specificity of 0.71 (95% CI: 0.66–0.76) and 0.78 (95% CI: 0.63–0.88) respectively based on seven variables: maternal age, BMI, ethnicity, smoking, gestational type, substance misuse and parity/obstetric history. Conclusion: Pending further validation, our observations suggest that key maternal demographic features, incorporated into a traditional mathematical model, have promising predictive utility for spontaneous preterm birth in pregnant women in our region without the need for cervical length and/or fetal fibronectin. [ABSTRACT FROM AUTHOR]

The global challenge of preterm birth persists with little or no progress being made to reduce its prevalence or mitigate its consequences, especially in low‐resource settings where health systems are less well developed. Improved delivery of respectful person‐centered care employing effective care models delivered by skilled healthcare professionals is essential for addressing these needs. These FIGO good practice recommendations provide an overview of the evidence regarding the effectiveness of the various care models for preventing and managing preterm birth across global contexts. We also highlight that continuity of care within existing, context‐appropriate care models (such as midwifery‐led care and group care), in primary as well as secondary care, is pivotal to delivering high quality care across the pregnancy continuum—prior to conception, through pregnancy and birth, and preparation for a subsequent pregnancy—to improve care to prevent and manage preterm birth. Synopsis: To mitigate preterm birth and its consequences, maternity models of care in all contexts should prioritize consistency and continuity of care and carer(s). [ABSTRACT FROM AUTHOR]

Introduction: Inflammation-induced remodelling of gestational tissues that underpins spontaneous preterm birth (sPTB, delivery < 37 weeks' gestation) may vary by race and context. To explore relationships between markers of these pathological processes, we (a) characterised the cervicovaginal fluid (CVF) cytokine profiles of pregnant South African women at risk of PTB; (b) determined CVF matrix-metalloproteinase-9 (MMP-9) and its regulator tissue inhibitor of metalloproteinase-1 (TIMP-1); and (c) explored the predictive potential of these markers for sPTB. Method of study: The concentrations of 10 inflammatory cytokines and MMP-9 and TIMP-1 were determined by ELISA in CVF samples from 47 non-labouring women at high risk of PTB. We studied CVF sampled at three gestational time points (GTPs): GTP1 (20-22 weeks, n = 37), GTP2 (26-28 weeks, n = 40), and GTP3 (34-36 weeks, n = 29) and analysed for changes in protein concentrations and predictive capacities (area under the ROC curve (AUC) and 95% confidence interval (CI)) for sPTB. Results: There were 11 (GTP1), 13 (GTP2), and 6 (GTP3) women who delivered preterm within 85.3 ± 25.9, 51.3 ± 15.3, and 11.8 ± 7.5 (mean ± SD) days after assessment, respectively. At GTP1, IL-8 was higher (4-fold, p = 0.02), whereas GM-CSF was lower (~1.4-fold, p = 0.03) in the preterm compared with term women with an average AUC = 0.73. At GTP2, IL-1β (18-fold, p < 0.0001), IL-8 (4-fold, p = 0.03), MMP-9 (17-fold, p = 0.0007), MMP-9/TIMP-1 ratio (9-fold, p = 0.004), and MMP-9/GM-CSF ratio (87-fold, p = 0.005) were higher in preterm compared with term women with an average AUC = 0.80. By contrast, IL-10 was associated with term delivery with an AUC (95% CI) = 0.75 (0.55-0.90). At GTP3, IL-1β (58-fold, p = 0.0003), IL-8 (12-fold, p = 0.002), MMP-9 (296-fold, p = 0.03), and TIMP-1 (35-fold, p = 0.01) were higher in preterm compared with term women with an average AUC = 0.85. Elevated IL-1β was associated with delivery within 14 days of assessment with AUC = 0.85 (0.67-0.96). Overall, elevated MMP-9 at GTP3 had the highest (13.3) positive likelihood ratio for distinguishing women at risk of sPTB. Lastly, a positive correlation between MMP-9 and TIMP-1 at all GTPs (r = 0.61, p < 0.01) for women delivering at term was only observed at GTP1 for those who delivered preterm (r = 0.70, p < 0.03). Conclusions: In this cohort, sPTB is associated with gestation-dependent increase in pro-inflammatory cytokines, decreased IL-10 and GM-CSF, and dysregulated MMP-9-TIMP-1 interaction. Levels of cytokine (especially IL-1β) and ECM remodelling proteins rise significantly in the final 2 weeks before the onset of labour when sPTB is imminent. The signalling mechanisms for these ECM remodelling observations remain to be elucidated. [ABSTRACT FROM AUTHOR]