Performance of the First-Trimester Cervical Consistency Index to Predict Preterm Birth.

Background/Objectives: Preterm birth (PTB) remains a significant global health challenge. Previous attempts to predict preterm birth in the first trimester using cervical length have been contradictory. The cervical consistency index (CCI) was introduced to quantify early cervical changes and has shown promise across various clinical scenarios in the mid-trimester, though testing in the first trimester is lacking. This study aims to assess the cervical consistency index performance in predicting preterm birth during the first trimester of pregnancy. Methods: In this prospective cohort study, focused exclusively on research, women with singleton pregnancies, both with and without a history of spontaneous preterm birth (sPTB), were included. The primary outcome was sPTB before 37 weeks, with a secondary outcome of sPTB before 34 weeks. CCI measurements were taken between 11+0 to 13+6 weeks of gestation. Receiver operating characteristic (ROC) curves were generated, and sensitivity and specificity were calculated for the optimal cut-off and for the 5th, 10th, and 15th percentile. Intraobserver and interobserver agreements were assessed using the intraclass correlation coefficient (ICC). Results: Among the 667 patients analyzed, the rates of sPTB before 37 and 34 weeks were 9.2% (61/667) and 1.8% (12/667), respectively. The detection rates (DRs) for CCI predicting PTB before 37 and 34 weeks were 19.7% (12/61) and 33.3% (4/12). Negative predictive values were 91.8% (546/595) and 98.7% (588/596), while the areas under the curve (AUC) for sPTB before 37 and 34 weeks were 0.62 (95% CI: 0.54-0.69) and 0.80 (95% CI: 0.71-0.89), respectively. Of the 61 patients with preterm birth, 13 (21.3%) had a preterm birth history; in this group, the CCI percentile 10th identified 39% (5/13). Intraobserver ICC was 0.862 (95% CI: 0.769-0.920), and interobserver ICC was 0.833 (95% CI: 0.722-0.902). Conclusions: This study suggests that utilizing CCI in the first trimester of pregnancy could serve as a valuable tool for predicting preterm birth before 34 weeks of gestation, demonstrating robust intraobserver and interobserver reliability.

Vaginal matrix metalloproteinase-9 (MMP-9) as a potential early predictor of preterm birth.

OBJECTIVES: To evaluate the differences in vaginal matrix metalloproteinases (MMP) and tissue inhibitors of metalloproteinases (TIMPs) in pregnant patients with a history of prior preterm birth compared with controls. METHODS: A prospective cohort pilot study recruited patients during prenatal care with history of prior spontaneous preterm birth (high-risk group) or no history of preterm birth (low-risk/controls). Inclusion criteria were singleton gestation at 11-16 weeks and between 18 and 55 years of age. Exclusion criteria were diabetes mellitus, hypertension, diseases affecting the immune response or acute vaginitis. A vaginal wash was performed at time of enrollment, and patients were followed through delivery. Samples were analyzed using semi-quantitative analysis of MMPS and TIMPS. The study was approved by the IRB and a p-value <0.05 was considered significant. RESULTS: A total of 48 pregnant patients were recruited: 16 with a history of preterm birth (high-risk group) and 32 with no history of preterm birth (low-risk group/controls). Groups were similar in age, race, BMI, and delivery mode. The high-risk group had more multiparous women (100 vs. 68.8 %; p=0.02), a greater preterm birth rate (31.2 vs. 6.3 %; p=0.02), and a lower birth weight (2,885 ± 898 g vs. 3,480 ± 473 g; p=0.02). Levels of vaginal MMP-9 were greater in high-risk patients than low-risk patients (74.9 % ± 27.0 vs. 49.4 % ± 31.1; p=0.01). When dividing the cohort into patients that had a spontaneous preterm birth (7/48, 14.6 %) vs. those with a term delivery (41/48, 85.4 %), the vaginal MMP-9 remained elevated in the cohort that experienced a preterm birth (85.46 %+19.79 vs. 53.20 %+31.47; p=0.01). There were no differences in the other MMPS and in TIMPs between high and low-risk groups. CONCLUSIONS: There was an increase in vaginal MMP-9 during early pregnancy in those at high risk for preterm birth and in those who delivered preterm, regardless of prior pregnancy outcome. Vaginal MMP-9 may have potential as a marker of increased risk of preterm birth.

Enhanced identification of women at risk for preterm birth via quantitative ultrasound: a prospective cohort study.

BACKGROUND: Historically, clinicians have relied on medical risk factors and clinical symptoms for preterm birth risk assessment. In nulliparous women, clinicians may rely solely on reported symptoms to assess for the risk of preterm birth. The routine use of ultrasound during pregnancy offers the opportunity to incorporate quantitative ultrasound scanning of the cervix to potentially improve assessment of preterm birth risk. OBJECTIVE: This study aimed to investigate the efficiency of quantitative ultrasound measurements at relatively early stages of pregnancy to enhance identification of women who might be at risk for spontaneous preterm birth. STUDY DESIGN: A prospective cohort study of pregnant women was conducted with volunteer participants receiving care from the University of Illinois Hospital in Chicago, Illinois. Participants received a standard clinical screening followed by 2 research screenings conducted at 20±2 and 24±2 weeks. Quantitative ultrasound scans were performed during research screenings by registered diagnostic medical sonographers using a standard cervical length approach. Quantitative ultrasound features were computed from calibrated raw radiofrequency backscattered signals. Full-term birth outcomes and spontaneous preterm birth outcomes were included in the analysis. Medically indicated preterm births were excluded from the analysis. Using data from each visit, logistic regression with Akaike information criterion feature selection was conducted to derive predictive models for each time frame based on historical clinical and quantitative ultrasound features. Model evaluations included a likelihood ratio test of quantitative ultrasound features, cross-validated receiver operating characteristic curve analysis, sensitivity, and specificity. RESULTS: On the basis of historical clinical features alone, the best predictive model had an estimated receiver operating characteristic area under the curve of 0.56±0.03. By the time frame of Visit 1, a predictive model using both historical clinical and quantitative ultrasound features provided a modest improvement in the area under the curve (0.63±0.03) relative to that of the predictive model using only historical clinical features. By the time frame of Visit 2, the predictive model using historical clinical and quantitative ultrasound features provided significant improvement (likelihood ratio test, P<.01), with an area under the curve of 0.69±0.03. CONCLUSION: Accurate identification of women at risk for spontaneous preterm birth solely through historical clinical features has been proven to be difficult. In this study, a history of preterm birth was the most significant historical clinical predictor of preterm birth risk, but the historical clinical predictive model performance was not statistically significantly better than the no-skill level. According to our study results, including quantitative ultrasound yields a statistically significant improvement in risk prediction as the pregnancy progresses.

Outperformance of interleukin-6 over placental alpha microglobulin-1 in predicting preterm delivery in symptomatic women.

BACKGROUND: Threatened preterm labor is the major cause of hospital admission during the second half of pregnancy. An early diagnosis is crucial for adopting pharmacologic measures to reduce perinatal mortality and morbidity. Current diagnostic criteria are based on symptoms and short cervical length. However, there is a high false-positive rate when using these criteria, which implies overtreatment, causing unnecessary side effects and an avoidable economic burden. OBJECTIVE: This study aimed to compare the use of placental alpha microglobulin-1 and interleukin-6 as vaginal biomarkers combined with cervical length and other maternal characteristics to improve the prediction of preterm delivery in symptomatic women. STUDY DESIGN: A prospective observational study was conducted in women with singleton pregnancies complicated by threatened preterm labor with intact membranes at 24+0 to 34+6 weeks of gestation. A total of 136 women were included in this study. Vaginal fluid was collected with a swab for placental alpha microglobulin-1 determination using the PartoSure test, interleukin-6 was assessed by electrochemiluminescence immunoassay, cervical length was measured by transvaginal ultrasound, and obstetrical variables and newborn details were retrieved from clinical records. These characteristics were used to fit univariate binary logistic regression models to predict time to delivery <7 days, time to delivery <14 days, gestational age at delivery ≤34 weeks, and gestational age at delivery ≤37 weeks, and multivariate binary logistic regression models were fitted with imbalanced and balanced data. Performance of models was assessed by their F2-scores and other metrics, and the association of their variables with a risk or a protective factor was studied. RESULTS: A total of 136 women were recruited, of whom 8 were lost to follow-up and 7 were excluded. Of the remaining 121 patients, 22 had a time to delivery <7 days and 31 had a time to delivery <14 days, and 30 deliveries occurred with a gestational age at delivery ≤34 weeks and 55 with a gestational age at delivery ≤37 weeks. Univariate binary logistic regression models fitted with the log transformation of interleukin-6 showed the greatest F2-scores in most studies, which outperformed those of models fitted with placental alpha microglobulin-1 (log[interleukin-6] vs placental alpha microglobulin-1 in time to delivery <7 days: 0.38 vs 0.30; time to delivery <14 days: 0.58 vs 0.29; gestational age at delivery ≤34 weeks: 0.56 vs 0.29; gestational age at delivery ≤37 weeks: 0.61 vs 0.16). Multivariate logistic regression models fitted with imbalanced data sets outperformed most univariate models (F2-score in time to delivery <7 days: 0.63; time to delivery <14 days: 0.54; gestational age at delivery ≤34 weeks: 0.62; gestational age at delivery ≤37 weeks: 0.73). The performance of prediction of multivariate models was drastically improved when data sets were balanced, and was maximum for time to delivery <7 days (F2-score: 0.88±0.2; positive predictive value: 0.86±0.02; negative predictive value: 0.89±0.03). CONCLUSION: A multivariate assessment including interleukin-6 may lead to more targeted treatment, thus reducing unnecessary hospitalization and avoiding unnecessary maternal-fetal treatment.

Longitudinal change in cervical length following vaginal or abdominal cervical cerclage: a randomized comparison.

BACKGROUND: Cervical cerclage has been shown to reduce the risk of recurrent spontaneous preterm birth in a high-risk patient population; however, the mechanism is not well understood. Transabdominal cerclage is superior to low and high vaginal cerclage in reducing early spontaneous preterm birth and fetal loss in women with previous failed vaginal cerclage. Cervical length measurements are commonly used to monitor high-risk women and may explain the mechanism of success. OBJECTIVE: This study aimed to evaluate the rate of change in longitudinal cervical length after randomized placement of low transvaginal, high transvaginal, or transabdominal cerclage in women with a previous failed vaginal cerclage. STUDY DESIGN: This was a planned analysis of longitudinal transvaginal ultrasound cervical length measurements from patients enrolled in the Vaginal Randomised Intervention of Cerclage trial, a randomized controlled trial comparing transabdominal cerclage or high transvaginal cerclage with low transvaginal cerclage. Cervical length measurements at specific gestational ages were compared over time and between groups, using generalized estimating equations fitted using the maximum-likelihood random-effects estimator. In addition, cervical length measurements were compared in women with transabdominal cerclage placed before and during pregnancy. The diagnostic accuracy of cervical length as a predictor of spontaneous preterm birth at <32 weeks of gestation was explored. RESULTS: This study included 78 women who underwent longitudinal cervical length assessment (70% of the analyzed cohort) with a history of failed cerclage, of whom 25 (32%) were randomized to low transvaginal cerclage, 26 (33%) to high transvaginal cerclage, and 27 (35%) to transabdominal cerclage. Abdominal cerclage was superior to low (P=.008) and high (P=.001) vaginal cerclage at maintaining cervical length over the surveillance period (14 to 26 weeks of gestation) (+0.08 mm/week, 95% confidence interval, -0.40 to 0.22; P=.580). On average, the cervical length was 1.8 mm longer by the end of the 12-week surveillance period in women with transabdominal cerclage (+1.8 mm; 95% confidence interval, -7.89 to 4.30; P=.564). High vaginal cerclage was no better than low cervical cerclage in the prevention of cervical shortening; the cervix shortened by 13.2 mm over 12 weeks in those with low vaginal cerclage (95% confidence interval, -21.7 to -4.7; P=.002) and by 20 mm over 12 weeks in those with high vaginal cerclage (95% confidence interval, -33.1 to -7.4; P=.002). Preconception transabdominal cerclage resulted in a longer cervix than those performed during pregnancy; this difference was significant after 22 weeks of gestation (48.5 mm vs 39.6 mm; P=.039). Overall, cervical length was an excellent predictor of spontaneous preterm birth at <32 weeks of gestation (receiver operating characteristic curve, 0.92; 95% confidence interval, 0.82-1.00). CONCLUSION: In women with a previous failed cervical cerclage, in the next pregnancy, the cervical length in women treated with vaginal cerclage funneled and shortened over time, whereas there was preservation of cervical length in women who receive transabdominal cerclage. Cervical length remained longer in transabdominal procedures performed before pregnancy than in transabdominal procedures performed during pregnancy. Overall, cervical length was an excellent predictor of spontaneous preterm birth in our cohort. Our findings may explain the mechanism of benefit for transabdominal cerclage, with its high placement better maintaining the structural integrity of the cervix at the level of the internal os.

End-to-end learning with interpretation on electrohysterography data to predict preterm birth.

Prediction of preterm birth is a difficult task for clinicians. By examining an electrohysterogram, electrical activity of the uterus that can lead to preterm birth can be detected. Since signals associated with uterine activity are difficult to interpret for clinicians without a background in signal processing, machine learning may be a viable solution. We are the first to employ Deep Learning models, a long-short term memory and temporal convolutional network model, on electrohysterography data using the Term-Preterm Electrohysterogram database. We show that end-to-end learning achieves an AUC score of 0.58, which is comparable to machine learning models that use handcrafted features. Moreover, we evaluate the effect of adding clinical data to the model and conclude that adding the available clinical data to electrohysterography data does not result in a gain in performance. Also, we propose an interpretability framework for time series classification that is well-suited to use in case of limited data, as opposed to existing methods that require large amounts of data. Clinicians with extensive work experience as gynaecologist used our framework to provide insights on how to link our results to clinical practice and stress that in order to decrease the number of false positives, a dataset with patients at high risk of preterm birth should be collected. All code is made publicly available.

Characterization and separation of preterm and term spontaneous, induced, and cesarean EHG records.

To improve the understanding of the underlying physiological processes that lead to preterm birth, and different term delivery modes, we quantitatively characterized and assessed the separability of the sets of early (23rd week) and later (31st week) recorded, preterm and term spontaneous, induced, cesarean, and induced-cesarean electrohysterogram (EHG) records using several of the most widely used non-linear features extracted from the EHG signals. Linearly modeled temporal trends of the means of the median frequencies (MFs), and of the means of the peak amplitudes (PAs) of the normalized power spectra of the EHG signals, along pregnancy (from early to later recorded records), derived from a variety of frequency bands, revealed that for the preterm group of records, in comparison to all other term delivery groups, the frequency spectrum of the frequency band B0L (0.08-0.3 Hz) shifts toward higher frequencies, and that the spectrum of the newly identified frequency band B0L' (0.125-0.575 Hz), which approximately matches the Fast Wave Low band, becomes stronger. The most promising features to separate between the later preterm group and all other later term delivery groups appear to be MF (p=1.1⋅10-5) in the band B0L of the horizontal signal S3, and PA (p=2.4⋅10-8) in the band B0L' (S3). Moreover, the PA in the band B0L' (S3) showed the highest power to individually separate between the later preterm group and any other later term delivery group. Furthermore, the results suggest that in preterm pregnancies the resting maternal heart rate decreases between the 23rd and 31st week of gestation.

The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study.

BACKGROUND: Globally, the preterm birth rate has tended to increase over time. Ultrasonography cervical-length assessment is considered to be the most effective screening method for preterm birth, but routine, universal cervical-length screening remains controversial because of its cost. OBJECTIVE: We used obstetric data to analyze and assess the risk of preterm birth. A machine learning model based on time-series technology was used to analyze regular, repeated obstetric examination records during pregnancy to improve the performance of the preterm birth screening model. METHODS: This study attempts to use continuous electronic medical record (EMR) data from pregnant women to construct a preterm birth prediction classifier based on long short-term memory (LSTM) networks. Clinical data were collected from 5187 pregnant Chinese women who gave birth with natural vaginal delivery. The data included more than 25,000 obstetric EMRs from the early trimester to 28 weeks of gestation. The area under the curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the prediction model. RESULTS: Compared with a traditional cross-sectional study, the LSTM model in this time-series study had better overall prediction ability and a lower misdiagnosis rate at the same detection rate. Accuracy was 0.739, sensitivity was 0.407, specificity was 0.982, and the AUC was 0.651. Important-feature identification indicated that blood pressure, blood glucose, lipids, uric acid, and other metabolic factors were important factors related to preterm birth. CONCLUSIONS: The results of this study will be helpful to the formulation of guidelines for the prevention and treatment of preterm birth, and will help clinicians make correct decisions during obstetric examinations. The time-series model has advantages for preterm birth prediction.

The web-based application "QUiPP v.2" for the prediction of preterm birth in symptomatic women is not yet ready for worldwide clinical use: ten reflections on development, validation and use.

PURPOSE: In this correspondence, we highlight general and domain-specific caveats in the development and validation of prediction models. METHODS: Development and use of the "QUiPP" application, a tool for preterm birth prediction which is supported by the United Kingdom National Health Service, is scrutinised and commented on. RESULTS: We highlight and elaborate ten points which may be perceived to be unclear or potentially misleading. CONCLUSION: While the QUiPP application has high potential, it lacks transparency (on certain aspects related to model development) and proper validation. This precludes transportability to settings with other treatment policies and to other countries where the app has been made publicly available.

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals.

One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.

Serum Copeptin Levels in Threatened Preterm Labor.

AIM: We investigated maternal copeptin level's usefulness in prediction of preterm birth. Materials and methods: The study was comprised of 97 pregnant women hospitalized for threatened preterm labor and 35 healthy pregnant women without preterm labor. Serum copeptin were compared with likelihood of threatened preterm labor timing of delivery and time interval to delivery. Result: Copeptin level of threatened preterm labor group was higher than of control group [7.76(0.39-35.62) ng/mL, 6.23(1.64-36.88) ng/mL, respectively, p = .04]. Copeptin levels of women did not differ according to preterm or term birth [7.76(0.69-35.62) ng/mL, 6.73(0.39-36.88) ng/mL, respectively, p = .22). Quartiles of copeptin levels were not associated with risk status or preterm birth. Conclusions: Serum copeptin is higher in threatened preterm labor. It does not differentiate those with threatened preterm labor verses preterm birth.

Two-stage nomogram models in mid-gestation for predicting the risk of spontaneous preterm birth in twin pregnancy.

PURPOSE: This study aimed to develop two-stage nomogram models to predict individual risk of preterm birth at < 34 weeks of gestation in twin pregnancies by incorporating clinical characteristics at mid-gestation. METHODS: We used a case-control study design of women with twin pregnancies followed up in a tertiary medical centre from January 2018 to March 2019. Maternal demographic characteristics and transvaginal cervical length data were extracted. The nomogram models were constructed with independent variables determined by multivariate logistic regression analyses. The risk score was calculated based on the nomogram models. RESULTS: In total, 65 twin preterm birth cases (< 34 weeks) and 244 controls met the inclusion criteria. Based on univariate and multivariate logistic regression analyses, we built two-stage nomogram prediction models with satisfactory discrimination and calibration when applied to the validation sets (first-stage [22-24 weeks] prediction model, C-index: 0.805 and 0.870, respectively; second-stage [26-28 weeks] prediction model, C-index: 0.847 and 0.908, respectively). Restricted cubic splines graphically showed the risk of preterm birth among individuals with increased risk scores. Moreover, the decision curve analysis indicated that both prediction models show positive clinical benefit. CONCLUSION: We developed and validated two-stage nomogram models at mid-gestation to predict the individual probability of preterm birth at < 34 weeks in twin pregnancy.

Assessing Velocity and Directionality of Uterine Electrical Activity for Preterm Birth Prediction Using EHG Surface Records.

The aim of the present study was to assess the capability of conduction velocity amplitudes and directions of propagation of electrohysterogram (EHG) waves to better distinguish between preterm and term EHG surface records. Using short-time cross-correlation between pairs of bipolar EHG signals (upper and lower, left and right), the conduction velocities and their directions were estimated using preterm and term EHG records of the publicly available Term-Preterm EHG DataSet with Tocogram (TPEHGT DS) and for different frequency bands below and above 1.0 Hz, where contractions and the influence of the maternal heart rate on the uterus, respectively, are expected. No significant or preferred continuous direction of propagation was found in any of the non-contraction (dummy) or contraction intervals; however, on average, a significantly lower percentage of velocity vectors was found in the vertical direction, and significantly higher in the horizontal direction, for preterm dummy intervals above 1.0 Hz. The newly defined features-the percentages of velocities in the vertical and horizontal directions, in combination with the sample entropy of the EHG signal recorded in the vertical direction, obtained from dummy intervals above 1.0 Hz-showed the highest classification accuracy of 86.8% (AUC=90.3%) in distinguishing between preterm and term EHG records of the TPEHGT DS.

Intraobserver and interobserver variability in first-trimester transvaginal ultrasound cervical length.

Objectives: To determine intraobserver and interobserver variability in the measurement of different cervical length (CL) components at the first trimester (endocervical canal and isthmus), describe the optimum measurement method (single line or two lines) and establish a normality curve of first trimester CL in our population.Methods: Women who attended the first-trimester US scan, between 11.0 and 13.6 weeks of gestation at Vall d'Hebron Universitary Hospital, Barcelona, Spain were included. Inclusion criteria were singleton pregnancies in women over 18 years of age, no gestational complications, uterine malformations or uterine surgery. Lengths of the endocervical canal and uterine isthmus were measured using two methods.Results: Both methods for endocervical canal measurement, single line and two lines, showed low intraobserver variability between examiners, with no statistical differences in the majority of measurements. A correct correlation existed between examiners using the single-line two-line measurements, with a concordance correlation coefficient of 0.76.Conclusions: Cervical length in the first trimester was reproducible for the same physician and between different physicians; however, it is essential to ensure examiners receive adequate training in the technique.

Analysis of big data for prediction of provider-initiated preterm birth and spontaneous premature deliveries and ranking the predictive features.

PURPOSE: High rate of preterm birth (birth before 37 weeks of gestation) in the world, its negative outcomes for pregnant women and newborns necessitate to predict preterm birth and identify its main risk factors. Premature deliveries have been divided into provider-initiated (with medical intervention for early terminating the pregnancy) and spontaneous preterm birth (without any intervention) categories in the previous studies. The main aim of this study is proposing methods for prediction of provider-initiated preterm birth and spontaneous premature deliveries and ranking the predictive features. METHODS: Data from national databank of Maternal and neonatal records (IMAN registry) is used in the study. The collected data have information about more than 1,400,000 deliveries with 112 features. Among them, 116,080 preterm births have occurred (from which 11,799 and 104,281 cases belong to provider-initiated preterm birth and spontaneous premature delivery, respectively). The data can be considered as big data due to its large number of data records, large number of the features and unbalanced distribution of the data between three classes of term, provider-initiated and spontaneous preterm birth. Therefore, we need to analyze data based on big data algorithms. In this paper, Map Reduce-based machine learning algorithms named MR-PB-PFS are proposed for this purpose. Map phase use parallel feature selection and classification methods to score the features. Reduce phase aggregates the feature scores obtained in Map phase and assign final scores to the features. Moreover, the classifiers trained in Map phase are aggregated based on two different ensemble rules in Reduce phase. RESULTS: Experimental results show that the best performance of the proposed models for preterm birth prediction is accuracy of 81% and the area under the receiver operating characteristic curve (AUC) of 68%. Top features for predicting term, provider-initiated preterm and spontaneous premature birth identified in this study are having pregnancy risk factors, having gestational diabetes, having cardiovascular disease, maternal underlying diseases, and mother age. Chronic blood pressure is a high rank feature for preterm birth prediction and father nationality is highly important for discriminating provider-initiated from spontaneous premature delivery. CONCLUSIONS: Identifying the pregnant women with high risk of spontaneous premature or therapeutic preterm delivery in our proposed model can help them to: (1) reduce the probability of premature birth with monitoring and management of the main risk factors and/or (2) educate them to care from the premature newborn. Management and monitoring top features discriminating term, provider-initiated preterm and spontaneous premature birth or their associated factors can reduce preterm labor or its negative outcomes.

Can the optimal cervical length for placing ultrasound-indicated cerclage be identified?

OBJECTIVE: To assess a continuum of cervical length (CL) cut-offs for the efficacy of ultrasound-indicated cerclage in women with previous spontaneous preterm birth (PTB). METHODS: This was a planned secondary analysis of a multicenter randomized clinical trial of ultrasound-indicated cerclage for the prevention of PTB in high-risk women. The efficacy of cerclage for preventing recurrent PTB < 35, < 32 and < 24 weeks' gestation was assessed using multivariable logistic regression analysis. Odds ratios (ORs) and CIs were estimated for a range of CL cut-offs using bootstrap regression. The 2.5(th) and 97.5(th) percentiles of bootstrapped ORs determined the CIs. Results were illustrated using smoothed curves superimposed on estimated ORs by CL cut-off. RESULTS: Of 301 women with a CL < 25 mm, 142 underwent ultrasound-indicated cerclage and 159 did not have cerclage placement. The few cases with CL < 10 mm limited the evaluation to CL cut-offs between < 10 mm and < 25 mm. For PTB < 35 weeks, ORs in women with a cerclage and CL < 25 mm were statistically significantly lower than in those without cerclage, and efficacy was maintained at smaller CL cut-offs. Results were similar for PTB < 32 weeks. For PTB < 24 weeks, results differed, with ORs increasing toward unity (no benefit), with wide CIs, for CL cut-offs between < 10 mm and < 15 mm, attributed to the small number of births < 24 weeks. CONCLUSIONS: The efficacy of ultrasound-indicated cerclage in women with previous spontaneous PTB varies by action point CL cut-off and by PTB gestational age of interest. Cerclage significantly reduces the risk of PTB < 35 and < 32 weeks, at CL cut-offs between < 10 mm and < 25 mm, with the greatest reduction at shorter CL, affirming that women with prior spontaneous PTB and a short CL are appropriate candidates for ultrasound-indicated cerclage. Copyright © 2015 ISUOG. Published by John Wiley & Sons Ltd.