You are currently viewing a new version of our website. To view the old version click .
MDPIMDPI
Search all articles
Journal of Clinical Medicine
Download PDF
  • Systematic Review
  • Open Access

28 June 2023

Risk Scoring Systems for Preterm Birth and Their Performance: A Systematic Review

,
and
1
Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
2
Center for Health Technology and Services Research (CINTESIS@RISE), Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
3
Department of Obstetrics and Gynecology, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
4
Department of Obstetrics and Gynecology, Centro Hospitalar Universitário de São João, 4200-319 Porto, Portugal
J. Clin. Med.2023, 12(13), 4360;https://doi.org/10.3390/jcm12134360
This article belongs to the Section Obstetrics & Gynecology
Version Notes
Order Reprints

Abstract

Introduction: Nowadays, the risk stratification of preterm birth (PTB) and its prediction remain a challenge. Many risk factors associated with PTB have been identified, and risk scoring systems (RSSs) have been developed to face this challenge. The objectives of this systematic review were to identify RSSs for PTB, the variables they consist of, and their performance. Materials and methods: Two databases were searched, and two authors independently performed the screening and eligibility phases. Records studying an RSS, based on specified variables, with an evaluation of the predictive value for PTB, were considered eligible. Reference lists of eligible studies and review articles were also searched. Data from the included studies were extracted. Results: A total of 56 studies were included in this review. The most frequently incorporated variables in the RSS included in this review were maternal age, weight, history of smoking, history of previous PTB, and cervical length. The performance measures varied widely among the studies, with sensitivity ranging between 4.2% and 92.0% and area under the curve (AUC) between 0.59 and 0.95. Conclusions: Despite the recent technological and scientifical evolution with a better understanding of variables related to PTB and the definition of new ultrasonographic parameters and biomarkers associated with PTB, the RSS’s ability to predict PTB remains poor in most situations, thus compromising the integration of a single RSS in clinical practice. The development of new RSSs, the identification of new variables associated with PTB, and the elaboration of a large reference dataset might be a step forward to tackle the problem of PTB.

1. Introduction

According to the World Health Organization (WHO), a birth that occurs before 37 complete weeks of pregnancy is defined as a preterm birth (PTB). Based on gestational age, preterm births can be categorized as extreme preterm (less than 28 weeks), very preterm (28 to 32 weeks), or moderate to late preterm (32 to 37 weeks). Moreover, PTB can be classified into “spontaneous” (spontaneous onset of labor or following preterm premature rupture of membranes (PPROM)) and “indicated” (parturition initiated by the caregivers: induction of labor or elective cesarean for maternal or fetal indications or other non-medical indications) [1,2]. It is estimated that, annually, 15 million babies are born preterm, meaning that more than 1 in 10 babies are born too early [1].
PTB and its complications account for approximately 1 million child deaths each year, making it the leading cause of death in children under 5 years of age [3]. Current, cost-effective interventions could prevent three-quarters of these deaths [1,3]. Preterm-born surviving infants can have major health impairments due to the severe disruption in the normal developmental maturation of organ systems [4,5]. This immaturity of organ systems concomitant with higher levels of oxidative stress plays a role in the development of the preterm newborn main pathologies, examples of which are necrotizing enterocolitis, bronchopulmonary dysplasia, retinopathy of prematurity, intraventricular hemorrhage, and patent ductus arteriosus. Furthermore, preterm newborns are more susceptible to long-term neurodevelopmental impairments such as cerebral palsy, hearing and vision problems, and intellectual disability [6]. From a maternal health perspective, the experience of a PTB can impair the bonding to the baby, with less positive feelings, and lead to depression and anxiety postpartum [7]. In addition to its mortality and morbidity, PTB has also a significant financial impact not only on the families of preterm infants but also on health systems [8].
Certain factors represent potential risks for a spontaneous PTB. These risk factors can be classified as demographical, obstetrical, gynecological, and those related to the current pregnancy [9,10]. Demographical risk factors include maternal age (the higher the maternal age the greater the risk of a PTB [11]), ethnicity (black ethnicity is reported to have a higher risk of preterm birth compared to other ethnicities [12]), smoking, and the use of illicit drugs (increase the risk of PTB [13]), maternal stress, and other social factors (an association between low maternal education and PTB could be established [14]) [9,10]. Obstetrical and gynecological risk factors comprehend interpregnancy latency (increased odds of PTB were reported at less than 6 and 12 months interpregnancy intervals [15]), prior preterm delivery (PTB in a previous pregnancy is a strong risk factor for PTB in a subsequent pregnancy [16]), uterine, cervical and placental conditions (short cervical length, uterine anomalies, placental abruption, and placenta previa are associated with PTB [17,18,19,20]) [9,10]. Current pregnancy-related risk factors are uterine hemorrhage, fetal malformations (fetal malformations, in general, are associated with a greater risk of PTB [21]), multiple gestations (multiple gestations are a strong risk factor for both spontaneous and indicated PTB [22]), maternal and intra-amniotic infections (infectious conditions such as bacterial vaginosis, pyelonephritis, and chorioamnionitis have been linked to PTB [23,24,25]) [9,10].
Many studies have been developed with the purpose of creating a system by adding some of the risk factors mentioned together and evaluating its clinical significance concerning the prediction of PTB. Some other variables have been studied and incorporated in systems to predict PTB: fetal fibronectin found in cervicovaginal secretions [26]; maternal serum biomarkers, such as pregnant associated plasma protein-A (PAPP-A), human chorionic gonadotrophin (hCG), and alpha-fetoprotein (AFP) [27]; ultrasound markers [28]; and peripheral maternal blood microRNA [29].
Establishing risk factors for the prediction of PTB and understanding the relationship between certain (bio)markers and PTB can help identify women at risk allowing them to initiate adequate antenatal care and risk-specific treatment. In addition, studying all these PTB-related variables might give relevant information about possible causes of PTB and provide the opportunity to study particular interventions [10].
Reviews about risk scoring systems (RSSs) for the prediction of PTB have been published [30,31,32,33]. However, only one evaluated the performance of RSS for the prediction of PTB [32]. Accordingly, we conducted a systematic review to identify and compare RSSs developed to stratify the risk a pregnant woman with a PTB has, namely, regarding the variables considered in each RSS and the RSS predictive value of PTB, as well as the type of model used to develop the scoring system.

2. Materials and Methods

This systematic review was developed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [34]. This review’s protocol was not documented or registered prospectively.
Studies were considered eligible if they studied or developed an RSS, based on specific variables, as to predict PTB and help to stratify the PTB risk, irrespective of the gestational age used as a threshold to define PTB. Studies that did not study or develop an RSS (uni or multivariable) that did not mention the variable(s) used in the RSS or that did not analyze the RSS predictive value were excluded. No restrictions were imposed concerning the studies’ participants and their pregnancy characteristics. Observational studies, for example, cohort, case-control, and cross-sectional studies were included. Editorials, clinical case reports, literature reviews, or incomplete publications (e.g., abstracts only) were excluded. Non-English and non-human published studies were also excluded.
Two databases were searched for studies: PubMed and Web of Science. We searched from inception to the 12 November 2022, the date on which we ran the final search. No time restrictions were imposed on the search. We screened the databases using the following search query: [(“preterm birth” OR “preterm delivery” OR “preterm labor” OR “preterm labour” OR “premature birth” OR “premature delivery” OR “premature labour” OR “premature labor”) AND (“risk” OR “risk factors”) AND (“scoring systems” OR “score*” OR “scoring algorithm”) AND (“validity” OR “validation” OR “assessment” OR “evaluation”)]. This query was built by adding together keywords considered pertinent for this review. Keywords adding no results were excluded from the query, such as “points system”. Search syntax was adapted for each database. No filters were applied to the searches.
The studies retrieved from the PubMed and Web of Science databases searches were exported to a reference manager (EndNote version 20), where duplicates were removed. Screening by title and abstract of the remaining studies was performed independently by two authors. Studies not meeting the inclusion criteria or not consistent with the purpose of this review were removed. Divergences between investigators were resolved by consensus.
After the screening phase, eligibility assessment was performed independently by the same two authors through the reading of the full-text articles, to certify their eligibility, using the inclusion and exclusion criteria defined. Divergences were also resolved by consensus. The reasons for the exclusion of studies both on screening and eligibility phases were registered.
Additionally, a manual search, of possible missing studies in the databases search, was performed in the list of references of the eligible studies, as well as in related review articles.
A structured data extraction form was developed to extract the data from the eligible studies. After testing it with some included studies, it was appropriately perfected. The data were independently extracted by two authors applying the previously developed data extraction form. Disagreements were discussed and solved by consensus. The following variables were collected: (1) study characteristics (year, country, study design, and sample size); (2) participant characteristics (exclusion and inclusion criteria); (3) outcome measure (PTB gestational age criteria considered, PTB type); and (4) scoring system characteristics (risk factors and variables considered, model used, model outcomes and output, performance analysis—reported by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)—evaluation method and gestational age at RSS testing). Ambiguous or absent information was stated as “not reported”.
Methodological quality and study risk of bias were assessed based on the National Institutes of Health study quality assessment tool. This tool consists of a set of questions to evaluate a study’s internal validity and risk of bias. By applying this tool to a study, it is possible to classify them into one of three predefined categories: poor quality, fair quality, or good quality. Depending on the study design, different criteria were applied to assess the risk of bias in each study. Case-control and cohort studies were both assessed regarding the research question, study population, target population, sample size, and statistical analysis. In the case-control studies, it was also analyzed the inclusion/exclusion criteria, case and control definitions, selection of study participants, and exposure measurement. On the other hand, in the cohort studies, the timeframe was also analyzed to observe an effect, levels of exposure, and exposure and outcome measures and assessment. Intervention studies were assessed based on randomization, treatment allocation, blinding, the similarity of groups at baseline, dropout, adherence, outcome measures, power calculation, and intention-to-treat analysis. Study methodological quality was independently rated as good, fair, or poor by two authors. Disagreements were resolved by consensus.

3. Results

The final search of the two databases retrieved 1226 records. PubMed database search retrieved 654 records, whereas Web of Science database search retrieved 572 records. These records were extracted by a reference manager, where 320 duplicates were removed. From the 906 records screened by title and abstract, 829 did not proceed to the eligibility phase. The vast majority of the exclusions at this point were due to not studying PTB or not studying/developing an RSS (n = 409 and n = 402, respectively). Other reasons for article exclusion at this point were the study type (literature reviews, n = 12, and study protocols, n = 2), incomplete publications (n = 3), and non-English record (n = 1). After the screening, out of the 77 articles assessed for eligibility, 34 were excluded owing to not studying PTB (n = 5), not studying/developing an RSS (n = 18), not analyzing the RSS predictive power of PTB (n = 6), and unavailability of the full papers (n = 5). The unavailable papers were not available on the website of the respective journals. Literature reviews and reference lists from included articles were searched, and 16 records considered relevant to our review were identified. Of those 16, 3 articles were not eligible due to not studying/developing an RSS (n = 1) and not analyzing the RSS predictive value of PTB (n = 2). Therefore, in total, 56 studies [35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90] were included in this review. Figure 1 shows the PRISMA flow diagram, demonstrating the study selection process.
Figure 1. PRISMA flow diagram.
Regarding the study design of the studies included in this review, they were mainly observational cohort or case-control. A total of 46 cohort studies were included, and a total of nine case-control studies were included. Only one randomized controlled trial was included. In the study of methodological quality and risk of bias assessment, one study was rated as poor. A total of 28 studies were rated as good, and 27 were rated as fair. The most frequent reasons for studies not to be rated as good were the lack of adjustment for confounding variables, the non-definition of gestational age at the time of testing, and the poor definition of the study population.
The 56 studies included were conducted in a total of 20 different countries from 5 different continents. The countries with the most published studies under the eligibility criteria of this systematic review were the United States of America (USA, n = 20), the United Kingdom (UK, n = 6), and France (n = 5). Europe totals 25 included studies, considering studies from the UK, France, Germany, Italy, Sweden, Croatia, the Netherlands, Spain, Belgium, and Poland. Asia, South America, Oceania, and Africa were the least represented regions with four, two, two, and one published studies, respectively.
With respect to temporal analysis, only 14 studies were published until the year 2000, and among those, only three addressed an RSS that included laboratory variables in addition to variables obtained by clinical history and physical examination. A total of 42 studies included in this review were published in the 21st century, 16 of which were published in the last 5 years.
The study and the participant characteristics extracted from each of the 56 included studies, as well as the study of risk of bias assessment results, are outlined in Supporting Table S1. This table contains the following subset of characteristics defined in the data extraction form: year, country, study design, sample size, inclusion and exclusion criteria, and quality and risk of bias rating.
The most frequently incorporated variables in the RSS included in this review were maternal age, weight/BMI, history of smoking, history of previous PTB, and cervical length. In Figure 2, the variables are grouped in categories, and it is shown their distribution throughout the decades. Table 1 discriminates the variables that integrated the RSS addressed in the included studies. It also shows, by decade and in total, how many studies each variable was part of the RSS.
Figure 2. Distribution of RSS variables categories throughout time.
Table 1. Constituent variables of the RSS addressed in the included studies. Discrimination of each variable incorporation in RSS by decade and in total.
Not all studies included considered the same gestational age at PTB as the outcome. Prediction of PTB before 37 completed weeks of pregnancy was the most frequent outcome with approximately 69% of the studies included defining it as outcome. While some studies defined a single outcome, others did not restrict the outcome to the prediction of PTB before the completion of 37 weeks of pregnancy and studied the RSS prediction of PTB at different GA, such as before 32 (very PTB) and 34 completed weeks of pregnancy. Concerning the PTB classification as spontaneous or indicated, not all studies unanimously studied the same type of PTB. In total, 53% of the included studies focused exclusively on spontaneous PTB, whereas 22% focused on both spontaneous and medically indicated PTB.
RSSs addressed in the studies were built using different methods: univariate analysis (simple cutoff), multivariate models (linear and logistic regression) and, less frequently, machine learning models (artificial neural networks). Overall, considering all the included studies and the performance measures they presented, sensitivity ranged from 4.2% to 92.0%, specificity ranged from 41.5% to 99.3%, PPV ranged from 5.9% to 91.0%, NPV ranged from 69.2% to 100%, and AUC ranged from 0.59 to 0.95.
Table 2 gathers the eligible studies’ outcome measure, focusing on the PTB gestational age criteria considered and PTB type, and the scoring system characteristics, mainly the model used to build the RSS and its output and outcome, the risk factors and variables used, the performance analysis, reported by AUC, sensitivity, specificity, PPV and NPV, the evaluation method, and the gestational age at RSS testing.
Table 2. Outcome measure and RSS characteristics of each included study.

4. Discussion

The present systematic review focused on the identification, characterization, and comparison of RSSs for the screening of the risk of PTB, with a focus on their performance. The extracted characteristics from each system included those related to study design and sample size; inclusion and exclusion criteria of participants; the predictors, considered model, outputs, and the performance; and the outcome measure and its applicability. To our knowledge, the only published systematic review addressing the performance of RSSs for PTB was published about 20 years ago [33].
The classical interpretation of an RSS may be assumed as a system including two or more predictors, based on which a sum of points produces a final score. However, we did not restrict our review to such interpretation, and thus, considered any system including one or more predictors, with different types of model output (also including probabilities).
The incorporation of clinical analysis results in RSS for PTB, according to the included studies in this review, started around the 90s decade. Since then, with the evolution of ultrasonography, the definition of new cervical ultrasonographic parameters and the discovery of biomarkers associated with PTB, the combined use of medical and obstetric history, maternal and pregnancy characteristics, ultrasonographic evaluation, and PTB biomarkers to develop an accurate RSS, capable of predicting PTB, has gradually increased.
Currently, attending to the fast technical development of ultrasonography, more accurate and reproducible ultrasound-based screening strategies can be performed for the prediction of PTB. Guidelines have been developed in order to provide recommendations and a consensus-based approach on this matter [91]. Although there has not been found a biomarker capable of accurately predicting PTB, many have been associated with it. These biomarkers associated with PTB are thought to be more predictive of PTB when used together in a model, instead of alone [27]. However, despite the undeniable progress in these areas and the development of RSSs combining all these components, their predictive value of PTB is still not as good as expected. The range of the RSS performance measures was wide, partly due to differences in inclusion/exclusion criteria of the studies’ participants and different RSS evaluation frameworks. In particular, the use of the whole sample validation scheme overestimates an RSS performance compared with cross-validation or internal/external validation. Therefore, it is difficult to clearly highlight an RSS for its predictive power and integrate it into the clinical practice.
The comparison between RSS should be carefully performed. With respect to the study design, the identified RSSs were tested in either cohort or case-control studies. The latter usually includes a higher prevalence of PTB in the sample than in the population, which may have, in some cases, led to an overestimation of the RSS predictive value, in terms of sensitivity and PPV. There were large differences in the inclusion and exclusion criteria between the reported studies. The identification of main groups of such criteria is advisable, such as singleton versus multiple pregnancies, the presence/absence of maternal and/or fetal pathologies, or obstetric history. The gestational age at PTB is considered as outcome and the gestational age at which the score was computed are also important factors, which influence the performance comparison between RSS. We did not report the existence of interventions in the course of pregnancy, as they are quite heterogeneous, but it may have a clear influence on the risk of PTB in the course of pregnancy.
One of the limitations of the presented review is related to the inherent possibility of any systematic review missing some relevant papers. However, in addition to the efforts put into the development of the search query, we did also a full search for other papers in the list of references of all papers in the eligibility phase, which led us to an increase of 30% in the papers initially identified in the screening phase. Another limitation might be the fact that we did not include RSSs, which combined PTB with other outcomes. Nevertheless, in our opinion, it does not provide a clear identification of risk factors strictly associated with PTB and avoids the comparison of their performance in terms of PTB.

5. Conclusions

This systematic review provides a characterization of most of the published RSSs for the assessment of the risk for PTB, which is, nowadays, not only one of the major causes of burden related to obstetrical care but also related to complications in the short and long term. This review suggests that the prediction of PTB and the risk stratification through RSSs is poor. Therefore, there is plenty of room for improvement in this field. Future studies should seek to develop new RSS, with good clinical applicability, based on PTB strongly associated variables, and should also seek to identify new variables, such as biomarkers or ultrasonographic parameters, related to PTB that can be combined with other variables to build better-performing RSSs. In order to account for the heterogeneity in PTB etiology and to provide an effective comparison between the available systems, a large reference dataset developed based on the joint efforts of different centers worldwide would be a step forward to tackle the problem of PTB.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm12134360/s1, Table S1: Study and participants characteristics of each included study; quality and risk of bias ratings.

Author Contributions

A.F., H.G. and J.B. conceived and designed the study and developed the search strategy. A.F. and H.G. performed the screening and eligibility phases and extracted the data from all the included articles. The first draft was produced by A.F. and H.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by National Funds through FCT—Fundação para a Ciência e a Tecnologia, I.P., within CINTESIS, R&D Unit (reference UIDB/4255/2020).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in this article.

Acknowledgments

The authors would like to thank the libraries of the Faculty of Medicine, University of Porto and Instituto Português de Oncologia do Porto for making available many full-text articles that would otherwise not be accessed.

Conflicts of Interest

The authors explicitly declare that there are no conflict of interest in connection with this article.

References

  1. Blencowe, H.; Cousens, S.; Oestergaard, M.Z.; Chou, D.; Moller, A.-B.; Narwal, R.; Adler, A.; Garcia, C.V.; Rohde, S.; Say, L.; et al. National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: A systematic analysis and implications. Lancet 2012, 379, 2162–2172. [Google Scholar] [CrossRef] [PubMed]
  2. Goldenberg, R.L.; Gravett, M.G.; Iams, J.; Papageorghiou, A.T.; Waller, S.A.; Kramer, M.; Culhane, J.; Barros, F.; Conde-Agudelo, A.; Bhutta, Z.A.; et al. The preterm birth syndrome: Issues to consider in creating a classification system. Am. J. Obstet. Gynecol. 2011, 206, 113–118. [Google Scholar] [CrossRef] [PubMed]
  3. Liu, L.; Oza, S.; Hogan, D.; Chu, Y.; Perin, J.; Zhu, J.; Lawn, J.E.; Cousens, S.; Mathers, C.; Black, R.E. Global, regional, and national causes of under-5 mortality in 2000–15: An updated systematic analysis with implications for the Sustainable Development Goals. Lancet 2016, 388, 3027–3035. [Google Scholar] [CrossRef] [PubMed]
  4. Luu, T.M.; Mian, M.O.R.; Nuyt, A.M. Long-Term Impact of Preterm Birth: Neurodevelopmental and Physical Health Outcomes. Clin. Perinatol. 2017, 44, 305–314. [Google Scholar] [CrossRef] [PubMed]
  5. Saigal, S.; Doyle, L.W. An overview of mortality and sequelae of preterm birth from infancy to adulthood. Lancet 2008, 371, 261–269. [Google Scholar] [CrossRef]
  6. Falsaperla, R.; Lombardo, F.; Filosco, F.; Romano, C.; Saporito, M.A.N.; Puglisi, F.; Piro, E.; Ruggieri, M.; Pavone, P. Oxidative Stress in Preterm Infants: Overview of Current Evidence and Future Prospects. Pharmaceuticals 2020, 13, 145. [Google Scholar] [CrossRef]
  7. Henderson, J.; Carson, C.; Redshaw, M. Impact of preterm birth on maternal well-being and women’s perceptions of their baby: A population-based survey. BMJ Open 2016, 6, e012676. [Google Scholar] [CrossRef]
  8. Bérard, A.; Le Tiec, M.; De Vera, M. Study of the costs and morbidities of late-preterm birth. Arch. Dis. Child.-Fetal Neonatal Ed. 2012, 97, F329–F334. [Google Scholar] [CrossRef]
  9. Cobo, T.; Kacerovsky, M.; Jacobsson, B. Risk factors for spontaneous preterm delivery. Int. J. Gynecol. Obstet. 2020, 150, 17–23. [Google Scholar] [CrossRef]
  10. Vogel, J.P.; Chawanpaiboon, S.; Moller, A.-B.; Watananirun, K.; Bonet, M.; Lumbiganon, P. The global epidemiology of preterm birth. Best Pr. Res. Clin. Obstet. Gynaecol. 2018, 52, 3–12. [Google Scholar] [CrossRef]
  11. Waldenström, U.; Aasheim, V.; Nilsen, A.B.V.; Rasmussen, S.; Pettersson, H.J.; Schytt, E. Adverse Pregnancy Outcomes Related to Advanced Maternal Age Compared with Smoking and Being Overweight. Obstet. Gynecol. 2014, 123, 104–112. [Google Scholar] [CrossRef] [PubMed]
  12. Liem, S.M.; Mol, B.W.J.; Abu-Hanna, A.; Ravelli, A.C.; Schaaf, J.M. Ethnic and Racial Disparities in the Risk of Preterm Birth: A Systematic Review and Meta-Analysis. Am. J. Perinatol. 2012, 30, 433–450. [Google Scholar] [CrossRef] [PubMed]
  13. Faber, T.; Kumar, A.; Mackenbach, J.P.; Millett, C.; Basu, S.; Sheikh, A.; Been, J.V. Effect of tobacco control policies on perinatal and child health: A systematic review and meta-analysis. Lancet Public Health 2017, 2, e420–e437. [Google Scholar] [CrossRef]
  14. Ruiz, M.; Goldblatt, P.; Morrison, J.; Kukla, L.; Švancara, J.; Riitta-Järvelin, M.; Taanila, A.; Saurel-Cubizolles, M.-J.; Lioret, S.; Bakoula, C.; et al. Mother’s education and the risk of preterm and small for gestational age birth: A DRIVERS meta-analysis of 12 European cohorts. J. Epidemiol. Community Health 2015, 69, 826–833. [Google Scholar] [CrossRef]
  15. Wendt, A.; Gibbs, C.M.; Peters, S.; Hogue, C.J. Impact of Increasing Inter-pregnancy Interval on Maternal and Infant Health. Paediatr. Périnat. Epidemiol. 2012, 26 (Suppl. S1), 239–258. [Google Scholar] [CrossRef]
  16. Kazemier, B.; Buijs, P.; Mignini, L.; Limpens, J.; De Groot, C.; Mol, B.; Connect, E. Impact of obstetric history on the risk of spontaneous preterm birth in singleton and multiple pregnancies: A systematic review. BJOG Int. J. Obstet. Gynaecol. 2014, 121, 1197–1208. [Google Scholar] [CrossRef]
  17. Barros-Silva, J.; Pedrosa, A.C.; Matias, A. Sonographic measurement of cervical length as a predictor of preterm delivery: A systematic review. J. Périnat. Med. 2013, 42, 281–293. [Google Scholar] [CrossRef] [PubMed]
  18. Morgan, T.K. Role of the Placenta in Preterm Birth: A Review. Am. J. Perinatol. 2016, 33, 258–266. [Google Scholar] [CrossRef]
  19. Ananth, C.V.; Berkowitz, G.S.; Savitz, D.A.; Lapinski, R.H. Placental Abruption and Adverse Perinatal Outcomes. JAMA 1999, 282, 1646–1651. [Google Scholar] [CrossRef]
  20. Fox, N.; Roman, A.; Stern, E.M.; Gerber, R.S.; Saltzman, D.H.; Rebarber, A. Type of congenital uterine anomaly and adverse pregnancy outcomes. J. Matern. Neonatal Med. 2013, 27, 949–953. [Google Scholar] [CrossRef]
  21. Brown, W.R. Association of Preterm Birth with Brain Malformations. Pediatr. Res. 2009, 65, 642–646. [Google Scholar] [CrossRef] [PubMed]
  22. Fuchs, F.; Senat, M.-V. Multiple gestations and preterm birth. Semin. Fetal Neonatal Med. 2016, 21, 113–120. [Google Scholar] [CrossRef] [PubMed]
  23. Haahr, T.; Ersbøll, A.S.; Karlsen, M.A.; Svare, J.; Sneider, K.; Hee, L.; Weile, L.K.; Ziobrowska-Bech, A.; Østergaard, C.; Jensen, J.S.; et al. Treatment of bacterial vaginosis in pregnancy in order to reduce the risk of spontaneous preterm delivery-a clinical recommendation. Acta Obstet. Gynecol. Scand. 2016, 95, 850–860. [Google Scholar] [CrossRef]
  24. Galinsky, R.; Polglase, G.R.; Hooper, S.B.; Black, M.J.; Moss, T.J.M. The Consequences of Chorioamnionitis: Preterm Birth and Effects on Development. J. Pregnancy 2013, 2013, 412831. [Google Scholar] [CrossRef] [PubMed]
  25. Cunnington, M.; Kortsalioudaki, C.; Heath, P. Genitourinary pathogens and preterm birth. Curr. Opin. Infect. Dis. 2013, 26, 219–230. [Google Scholar] [CrossRef]
  26. Berghella, V.; Saccone, G. Fetal fibronectin testing for reducing the risk of preterm birth. Cochrane Database Syst. Rev. 2019, 7, CD006843. [Google Scholar] [CrossRef]
  27. Hornaday, K.K.; Wood, E.M.; Slater, D.M. Is there a maternal blood biomarker that can predict spontaneous preterm birth prior to labour onset? A systematic review. PLoS ONE 2022, 17, e0265853. [Google Scholar] [CrossRef]
  28. Kaplan, Z.A.O.; Ozgu-Erdinc, A.S. Prediction of Preterm Birth: Maternal Characteristics, Ultrasound Markers, and Biomarkers: An Updated Overview. J. Pregnancy 2018, 2018, 8367571. [Google Scholar] [CrossRef]
  29. Illarionov, R.A.; Pachuliia, O.V.; Vashukova, E.S.; Tkachenko, A.A.; Maltseva, A.R.; Postnikova, T.B.; Nasykhova, Y.A.; Bespalova, O.N.; Glotov, A.S. Plasma miRNA Profile in High Risk of Preterm Birth during Early and Mid-Pregnancy. Genes 2022, 13, 2018. [Google Scholar] [CrossRef]
  30. Keirse, M.J.N.C. An Evaluation of Formal Risk Scoring for Preterm Birth. Am. J. Perinatol. 1989, 6, 226–233. [Google Scholar] [CrossRef]
  31. Shiono, P.H.; Klebanoff, M.A. A Review of Risk Scoring for Preterm Birth. Clin. Perinatol. 1993, 20, 107–125. [Google Scholar] [CrossRef] [PubMed]
  32. Honest, H.; Bachmann, L.; Sundaram, R.; Gupta, J.; Kleijnen, J.; Khan, K. The accuracy of risk scores in predicting preterm birth—A systematic review. J. Obstet. Gynaecol. 2004, 24, 343–359. [Google Scholar] [CrossRef] [PubMed]
  33. Davey, M.-A.; Watson, L.; Rayner, J.A.; Rowlands, S. Risk-scoring systems for predicting preterm birth with the aim of reducing associated adverse outcomes. Cochrane Database Syst. Rev. 2015, 2015, CD004902. [Google Scholar] [CrossRef]
  34. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef] [PubMed]
  35. Fedrick, J. Antenatal Identification of Women at High Risk of Spontaneous Pre-Term Birth. BJOG: Int. J. Obstet. Gynaecol. 1976, 83, 351–354. [Google Scholar] [CrossRef] [PubMed]
  36. Creasy, R.K.; Gummer, A.B.; Liggins, G.C. System for predicting spontaneous preterm birth. Obstet. Gynecol. 1980, 55, 692–695. [Google Scholar]
  37. Guzick, D.S.; Daikoku, N.H.; Kaltreider, D.F. Predictability of pregnancy outcome in preterm delivery. Obstet. Gynecol. 1984, 63, 645–650. [Google Scholar] [PubMed]
  38. Neilson, J.P.; Verkuyl, A.D.; Crowther, A.C.; Bannerman, C. Preterm labor in twin pregnancies: Prediction by cervical assessment. Obstet. Gynecol. 1988, 72, 719–723. [Google Scholar]
  39. Holbrook, J.R.; Laros, J.R.; Creasy, R.K. Evaluation of a Risk-Scoring System for Prediction of Preterm Labor. Am. J. Perinatol. 1989, 6, 62–68. [Google Scholar] [CrossRef]
  40. Mueller-Heubach, E.; Guzick, D.S. Evaluation of risk scoring in a preterm birth prevention study of indigent patients. Am. J. Obstet. Gynecol. 1989, 160, 829–835. [Google Scholar] [CrossRef]
  41. Owen, J.; Goldenberg, R.L.; Davis, R.O.; Kirk, K.A.; Copper, R.L. Evaluation of a risk scoring system as a predictor of preterm birth in an indigent population. Am. J. Obstet. Gynecol. 1990, 163, 873–879. [Google Scholar] [CrossRef]
  42. Newman, R.B.; Godsey, R.K.; Ellings, J.M.; Campbell, B.A.; Eller, D.P.; Miller, C.M. Quantification of cervical change: Relationship to preterm delivery in the multifetal gestation. Am. J. Obstet. Gynecol. 1991, 165, 264–271. [Google Scholar] [CrossRef] [PubMed]
  43. Guinn, D.; Wigton, T.R.; Owen, J.; Socol, M.L.; Frederiksen, M.C. Prediction of preterm birth in nulliparous patients. Am. J. Obstet. Gynecol. 1994, 171, 1111–1115. [Google Scholar] [CrossRef] [PubMed]
  44. Edenfield, S.M.; Thomas, S.D.; Thompson, O.W.; Marcotte, J.J. Validity of the Creasy risk appraisal instrument for prediction of preterm labor. Nurs. Res. 1995, 44, 76–81. [Google Scholar] [CrossRef] [PubMed]
  45. Mercer, B.; Goldenberg, R.; Das, A.; Moawad, A.; Iams, J.; Meis, P.; Copper, R.; Johnson, F.; Thom, E.; McNellis, D.; et al. The preterm prediction study: A clinical risk assessment system. Am. J. Obstet. Gynecol. 1996, 174, 1885–1895. [Google Scholar] [CrossRef]
  46. Rizzo, G.; Capponi, A.; Arduini, D.; Lorido, C.; Romanini, C. The value of fetal fibronectin in cervical and vaginal secretions and of ultrasonographic examination of the uterine cervix in predicting premature delivery for patients with preterm labor and intact membranes. Am. J. Obstet. Gynecol. 1996, 175, 1146–1151. [Google Scholar] [CrossRef]
  47. Heine, R.; McGregor, J.A.; Dullien, V.K. Accuracy of salivary estriol testing compared to traditional risk factor assessment in predicting preterm birth. Am. J. Obstet. Gynecol. 1999, 180, S214–S218. [Google Scholar] [CrossRef]
  48. McLean, M.; Bisits, A.; Davies, J.; Walters, W.; Hackshaw, A.; De Voss, K.; Smith, R. Predicting risk of preterm delivery by second-trimester measurement of maternal plasma corticotropin-releasing hormone and α-fetoprotein concentrations. Am. J. Obstet. Gynecol. 1999, 181, 207–215. [Google Scholar] [CrossRef]
  49. Gudmundsson, S.; Korszun, P.; Olofsson, P.; Dubiel, M. New score indicating placental vascular resistance. Acta Obstet. Gynecol. Scand. 2003, 82, 807–812. [Google Scholar] [CrossRef]
  50. Tekesin, I.; Hellmeyer, L.; Heller, G.; Römer, A.; Kühnert, M.; Schmidt, S. Evaluation of quantitative ultrasound tissue characterization of the cervix and cervical length in the prediction of premature delivery for patients with spontaneous preterm labor. Am. J. Obstet. Gynecol. 2003, 189, 532–539. [Google Scholar] [CrossRef]
  51. Gurbuz, A.; Karateke, A.; Ozturkmen, M.; Kabaca, C. Human chorionic gonadotropin assay in cervical secretions for accurate diagnosis of preterm labor. Int. J. Gynecol. Obstet. 2003, 85, 132–138. [Google Scholar] [CrossRef] [PubMed]
  52. Maslovitz, S.; Hartoov, J.; Wolman, I.; Jaffa, A.; Lessing, J.B.; Fait, G. Cervical Length in the Early Second Trimester for Detection of Triplet Pregnancies at Risk for Preterm Birth. J. Ultrasound Med. 2004, 23, 1187–1191. [Google Scholar] [CrossRef] [PubMed]
  53. Tekesin, I.; Eberhart, L.H.J.; Schaefer, V.; Wallwiener, D.; Schmidt, S. Evaluation and validation of a new risk score (CLEOPATRA score) to predict the probability of premature delivery for patients with threatened preterm labor. Ultrasound Obstet. Gynecol. 2005, 26, 699–706. [Google Scholar] [CrossRef] [PubMed]
  54. Vayssière, C.; Favre, R.; Audibert, F.; Chauvet, M.P.; Gaucherand, P.; Tardif, D.; Grangé, G.; Novoa, A.; Descamps, P.; Perdu, M.; et al. Cervical assessment at 22 and 27 weeks for the prediction of spontaneous birth before 34 weeks in twin pregnancies: Is transvaginal sonography more accurate than digital examination? Ultrasound Obstet. Gynecol. 2005, 26, 707–712. [Google Scholar] [CrossRef]
  55. Ghosh, G.; Breborowicz, A.; Brązert, M.; Maczkiewicz, M.; Kobelski, M.; Dubiel, M.; Gudmundsson, S. Evaluation of third trimester uterine artery flow velocity indices in relationship to perinatal complications. J. Matern. Neonatal Med. 2006, 19, 551–555. [Google Scholar] [CrossRef]
  56. Grgic, O.; Matijevic, R.; Vasilj, O. Qualitative glandular cervical score as a potential new sonomorphological parameter in screening for preterm delivery. Ultrasound Med. Biol. 2006, 32, 333–338. [Google Scholar] [CrossRef]
  57. Matijevic, R.; Grgic, O.; Vasilj, O. Is sonographic assessment of cervical length better than digital examination in screening for preterm delivery in a low-risk population? Acta Obstet. Gynecol. Scand. 2006, 85, 1342–1347. [Google Scholar] [CrossRef]
  58. To, M.S.; Skentou, C.A.; Royston, P.; Yu, C.K.H.; Nicolaides, K.H. Prediction of patient-specific risk of early preterm delivery using maternal history and sonographic measurement of cervical length: A population-based prospective study. Ultrasound Obstet. Gynecol. 2006, 27, 362–367. [Google Scholar] [CrossRef]
  59. Tan, H.; Wen, S.W.; Chen, X.K.; Demissie, K.; Walker, M. Early prediction of preterm birth for singleton, twin, and triplet pregnancies. Eur. J. Obstet. Gynecol. Reprod. Biol. 2007, 131, 132–137. [Google Scholar] [CrossRef]
  60. Celik, E.; To, M.; Gajewska, K.; Smith, G.C.S.; Nicolaides, K.H. Cervical length and obstetric history predict spontaneous preterm birth: Development and validation of a model to provide individualized risk assessment. Ultrasound Obstet. Gynecol. 2008, 31, 549–554. [Google Scholar] [CrossRef]
  61. Allouche, M.; Huissoud, C.; Guyard-Boileau, B.; Rouzier, R.; Parant, O. Development and validation of nomograms for predicting preterm delivery. Am. J. Obstet. Gynecol. 2011, 204, 242.e1–242.e8. [Google Scholar] [CrossRef]
  62. Beta, J.; Akolekar, R.; Ventura, W.; Syngelaki, A.; Nicolaides, K.H. Prediction of spontaneous preterm delivery from maternal factors, obstetric history and placental perfusion and function at 11–13 weeks. Prenat. Diagn. 2011, 31, 75–83. [Google Scholar] [CrossRef] [PubMed]
  63. Bastek, J.A.; Sammel, M.D.; Srinivas, S.K.; McShea, M.A.; Foreman, M.N.; Elovitz, M.A.; Metlay, J.P. Clinical Prediction Rules for Preterm Birth in Patients Presenting with Preterm Labor. Obstet. Gynecol. 2012, 119, 1119–1128. [Google Scholar] [CrossRef] [PubMed]
  64. Fuchs, F.; Senat, M.-V.; Fernandez, H.; Gervaise, A.; Frydman, R.; Bouyer, J. Predictive score for early preterm birth in decisions about emergency cervical cerclage in singleton pregnancies. Acta Obstet. Gynecol. Scand. 2012, 91, 744–749. [Google Scholar] [CrossRef] [PubMed]
  65. Schaaf, J.M.; Ravelli, A.C.; Mol, B.W.J.; Abu-Hanna, A. Development of a prognostic model for predicting spontaneous singleton preterm birth. Eur. J. Obstet. Gynecol. Reprod. Biol. 2012, 164, 150–155. [Google Scholar] [CrossRef]
  66. Kahyaoglu, S.; Kahyaoglu, I.; Kaymak, O.; Sagnic, S.; Mollamahmutoglu, L.; Danişman, N. Can transvaginal ultrasonographic evaluation of the endocervical glandular area predict preterm labor among patients who received tocolytic therapy for threatened labor: A cross-sectional study. J. Matern. Neonatal Med. 2013, 26, 920–925. [Google Scholar] [CrossRef]
  67. Sananes, N.; Meyer, N.; Gaudineau, A.; Aissi, G.; Boudier, E.; Fritz, G.; Viville, B.; Nisand, I.; Langer, B.; Favre, R. Prediction of spontaneous preterm delivery in the first trimester of pregnancy. Eur. J. Obstet. Gynecol. Reprod. Biol. 2013, 171, 18–22. [Google Scholar] [CrossRef]
  68. Alleman, B.W.; Smith, A.R.; Byers, H.M.; Bedell, B.; Ryckman, K.K.; Murray, J.C.; Borowski, K.S. A proposed method to predict preterm birth using clinical data, standard maternal serum screening, and cholesterol. Am. J. Obstet. Gynecol. 2013, 208, 472.e1–472.e11. [Google Scholar] [CrossRef]
  69. Abbott, D.S.; Hezelgrave, N.L.; Seed, P.T.; Norman, J.E.; David, A.L.; Bennett, P.R.; Girling, J.C.; Chandirimani, M.; Stock, S.J.; Carter, J.; et al. Quantitative Fetal Fibronectin to Predict Preterm Birth in Asymptomatic Women at High Risk. Obstet. Gynecol. 2015, 125, 1168–1176. [Google Scholar] [CrossRef]
  70. Chaiworapongsa, T.; Romero, R.; Whitten, A.E.; Korzeniewski, S.J.; Chaemsaithong, P.; Hernandez-Andrade, E.; Yeo, L.; Hassan, S.S. The use of angiogenic biomarkers in maternal blood to identify which SGA fetuses will require a preterm delivery and mothers who will develop pre-eclampsia. J. Matern. Neonatal Med. 2015, 29, 1214–1228. [Google Scholar] [CrossRef]
  71. Saade, G.R.; Boggess, K.A.; Sullivan, S.A.; Markenson, G.; Iams, J.D.; Coonrod, D.V.; Pereira, L.M.; Esplin, M.S.; Cousins, L.M.; Lam, G.K.; et al. Development and validation of a spontaneous preterm delivery predictor in asymptomatic women. Am. J. Obstet. Gynecol. 2016, 214, 633.e1–633.e24. [Google Scholar] [CrossRef] [PubMed]
  72. Kuhrt, K.; Smout, E.; Hezelgrave, N.; Seed, P.T.; Carter, J.; Shennan, A.H. Development and validation of a tool incorporating cervical length and quantitative fetal fibronectin to predict spontaneous preterm birth in asymptomatic high-risk women. Ultrasound Obstet. Gynecol. 2016, 47, 104–109. [Google Scholar] [CrossRef]
  73. Sepúlveda-Martínez, A.; Díaz, F.; Muñoz, H.; Valdés, E.; Parra-Cordero, M. Second-Trimester Anterior Cervical Angle in a Low-Risk Population as a Marker for Spontaneous Preterm Delivery. Fetal Diagn. Ther. 2016, 41, 220–225. [Google Scholar] [CrossRef] [PubMed]
  74. Winger, E.E.; Reed, J.L.; Ji, X. Early first trimester peripheral blood cell microRNA predicts risk of preterm delivery in pregnant women: Proof of concept. PLoS ONE 2017, 12, e0180124. [Google Scholar] [CrossRef] [PubMed]
  75. Baer, R.J.; McLemore, M.R.; Adler, N.; Oltman, S.P.; Chambers, B.D.; Kuppermann, M.; Pantell, M.S.; Rogers, E.E.; Ryckman, K.K.; Sirota, M.; et al. Pre-pregnancy or first-trimester risk scoring to identify women at high risk of preterm birth. Eur. J. Obstet. Gynecol. Reprod. Biol. 2018, 231, 235–240. [Google Scholar] [CrossRef]
  76. Baños, N.; Perez-Moreno, A.; Julià, C.; Murillo-Bravo, C.; Coronado, D.; Gratacós, E.; Deprest, J.; Palacio, M. Quantitative analysis of cervical texture by ultrasound in mid-pregnancy and association with spontaneous preterm birth. Ultrasound Obstet. Gynecol. 2018, 51, 637–643. [Google Scholar] [CrossRef]
  77. Tsikouras, P.; Anastasopoulos, G.; Maroulis, V.; Bothou, A.; Chalkidou, A.; Deuteraiou, D.; Anthoulaki, X.; Tsatsaris, G.; Bourazan, A.H.; Iatrakis, G.; et al. Comparative Evaluation of Arabin Pessary and Cervical Cerclage for the Prevention of Preterm Labor in Asymptomatic Women with High Risk Factors. Int. J. Environ. Res. Public Health 2018, 15, 791. [Google Scholar] [CrossRef]
  78. Vandewiele, G.; Dehaene, I.; Janssens, O.; Ongenae, F.; De Backere, F.; De Turck, F.; Roelens, K.; Van Hoecke, S.; Demeester, T. Time-to-Birth Prediction Models and the Influence of Expert Opinions; Springer: Berlin/Heidelberg, Germany, 2019; pp. 286–291. [Google Scholar]
  79. Gesthuysen, A.; Hammer, K.; Möllers, M.; Braun, J.; de Murcia, K.O.; Falkenberg, M.K.; Köster, H.A.; Möllmann, U.; Fruscalzo, A.; Bormann, E.; et al. Evaluation of Cervical Elastography Strain Pattern to Predict Preterm Birth. Ultraschall Der Med.-Eur. J. Ultrasound 2019, 41, 397–403. [Google Scholar] [CrossRef]
  80. Guszczynska-Losy, M.; Wirstlein, P.K.; Wender-Ozegowska, E.; Kedzia, M. Evaluation of predictive value of biochemical markers for adverse obstetrics outcomes in pregnancies complicated by cholestasis. Ginekol. Polska 2020, 91, 269–276. [Google Scholar] [CrossRef]
  81. Leneuve-Dorilas, M.; Buekens, P.; Favre, A.; Carles, G.; Louis, A.; Breart, G.; Nacher, M. Predictive factors of preterm delivery in French Guiana for singleton pregnancies: Definition and validation of a predictive score. J. Matern. Neonatal Med. 2018, 33, 1709–1716. [Google Scholar] [CrossRef]
  82. Maia, M.C.; Nomura, R.; Mendonça, F.; Rios, L.; Moron, A. Is cervical length evaluated by transvaginal ultrasonography helpful in detecting true preterm labor? J. Matern. Neonatal Med. 2019, 33, 2902–2908. [Google Scholar] [CrossRef] [PubMed]
  83. Markenson, G.R.; Saade, G.R.; Laurent, L.C.; Heyborne, K.D.; Coonrod, D.V.; Schoen, C.N.; Baxter, J.K.; Haas, D.M.; Longo, S.; Grobman, W.A.; et al. Performance of a proteomic preterm delivery predictor in a large independent prospective cohort. Am. J. Obstet. Gynecol. MFM 2020, 2, 100140. [Google Scholar] [CrossRef] [PubMed]
  84. Winger, E.E.; Reed, J.L.; Ji, X.; Gomez-Lopez, N.; Pacora, P.; Romero, R. MicroRNAs isolated from peripheral blood in the first trimester predict spontaneous preterm birth. PLoS ONE 2020, 15, e0236805. [Google Scholar] [CrossRef]
  85. Patil, A.S.; Grotegut, C.A.; Gaikwad, N.W.; Dowden, S.D.; Haas, D.M. Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation. PLoS ONE 2021, 16, e0243585. [Google Scholar] [CrossRef]
  86. Shields, L.B.; Weymouth, C.; Bramer, K.L.; Robinson, S.; McGee, D.; Richards, L.; Ogle, C.; Shields, C.B. Risk assessment of preterm birth through identification and stratification of pregnancies using a real-time scoring algorithm. SAGE Open Med. 2021, 9, 2050312120986729. [Google Scholar] [CrossRef]
  87. Zhang, J.; Pan, M.; Zhan, W.; Zheng, L.; Jiang, X.; Xue, X. Two-stage nomogram models in mid-gestation for predicting the risk of spontaneous preterm birth in twin pregnancy. Arch. Gynecol. Obstet. 2020, 303, 1439–1449. [Google Scholar] [CrossRef] [PubMed]
  88. Belaghi, R.A.; Beyene, J.; McDonald, S.D. Prediction of preterm birth in nulliparous women using logistic regression and machine learning. PLoS ONE 2021, 16, e0252025. [Google Scholar] [CrossRef]
  89. Merlo, I.; Cantarutti, A.; Allotta, A.; Tavormina, E.E.; Iommi, M.; Pompili, M.; Rea, F.; Agodi, A.; Locatelli, A.; Zanini, R.; et al. Development and Validation of a Novel Pre-Pregnancy Score Predictive of Preterm Birth in Nulliparous Women Using Data from Italian Healthcare Utilization Databases. Healthcare 2022, 10, 1443. [Google Scholar] [CrossRef]
  90. Zhou, G.; Holzman, C.; Heng, Y.J.; Kibschull, M.; Lye, S.J. Maternal blood EBF1-based microRNA transcripts as biomarkers for detecting risk of spontaneous preterm birth: A nested case-control study. J. Matern. Neonatal Med. 2020, 35, 1239–1247. [Google Scholar] [CrossRef]
  91. Coutinho, C.M.; Sotiriadis, A.; Odibo, A.; Khalil, A.; D’Antonio, F.; Feltovich, H.; Salomon, L.J.; Sheehan, P.; Napolitano, R.; Berghella, V.; et al. ISUOGPractice Guidelines: Role of ultrasound in the prediction of spontaneous preterm birth. Ultrasound Obstet. Gynecol. 2022, 60, 435–456. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Download PDF
Intestinal Microbiota Reduction Followed by Fasting Discloses Microbial Triggering of Inflammation in Rheumatoid Arthritis
Previous
Autism Spectrum Disorder in Children with an Early History of Paediatric Acquired Brain Injury
Next