A systematic review of prediction models for spontaneous preterm birth in singleton asymptomatic pregnant women with risk factors.

Chunmei Yan; Qiuyu Yang; Richeng Li: Conceived and designed the experiments. Chunmei Yan; Qiuyu Yang; Aijun Yang; Yu Fu; Jieneng Wang; Ying Li; Qianji Cheng; Shasha Hu: Performed the experiments; Analyzed and interpreted the data. Chunmei Yan; Qiuyu Yang: Wrote the paper.

Project Supported by the 10.13039/100017943 Gansu Natural Science Foundation (22JR5RA366).

Our systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 13 ]. The study protocol was registered in PROSPERO (CRD42022329721). We systematically searched seven databases, including PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, VIP Database, China Biology Medicine Disk, and Wanfang Data, for articles published up to September 27, 2021. The search strategy is presented in Table S1 . In addition, we manually searched for the references of eligible studies and relevant systematic reviews. Studies were eligible for this systematic review based on the following inclusion criteria: (1) developing and/or validating a prediction model for spontaneous preterm birth; (2) studies focused on singleton asymptomatic pregnant women with risk factors for preterm birth who did not have symptoms of threatened preterm labour and abortion but had at least one risk factor for spontaneous preterm birth, such as previous spontaneous preterm birth, previous late miscarriage, previous cervical surgery, cervical length measuring 35 years, and assisted reproductive technology (ART) in the current pregnancy; and (3) the prediction model included 2 or more predictors. Reviews, conference abstracts, and letters were also excluded. The six reviewers (YCM, YQY, LRC, LY, CQJ, and YAJ) were divided into three groups, each responsible for screening 1/3 of the literature. Each group independently screened the titles and abstracts of the identified articles and selected the articles for full-text review. Disagreements were resolved by a third reviewer (HSS). Each group independently extracted data from eligible articles based on a critical appraisal and data extraction checklist for systematic reviews of prediction modelling studies (CHARMS) checklist [ 14 ]. From each eligible study, the first author, publication year, country, data source, study design, data collection period, age, number of participants, number of events, risk factors for spontaneous preterm birth, number of predictors retained in the final model, the definition of spontaneous preterm birth, modelling method, handling of continuous predictors and missing data, selection of predictors, and model performance measures were extracted. Six reviewers (YCM, YQY, LY, CQJ, FY, and WJN) were divided into three groups, with each group responsible for 1/3 of the included studies. Each group was independently assessed for risk of bias in the included studies using the PROBAST tool [ 15 ]. Disagreements were resolved by a third reviewer (HSS). The PROBAST tool consists of 20 signalling questions across four domains: participants, predictors, outcomes, and analysis. Signalling questions were answered with yes, probably yes, no, probably no, or no information. The risk of bias in each domain was rated as low, high, or unclear risk of bias. The overall assessment of the risk of bias was rated as low risk if all domains were judged to be low risk, high risk if at least one domain was judged to be high risk, unclear risk if at least one domain was judged to be at unclear risk, and all other domains were judged to be low risk. The results were summarised and reported using descriptive statistics. If more than one model for the same predicted outcome was used in a study, we chose the maximum C-statistic or AUC (used to describe the discriminatory ability of the model) to represent that outcome. If more than one predicted outcome (e.g., spontaneous delivery at <37 and < 34 weeks) was included in the study, we presented the results for each outcome separately. Meta-analyses were not performed as the included studies were heterogeneous.

The search identified 10299 studies from seven databases. A total of 8573 records were excluded after title and abstract screening, and 93 studies were eligible for full-text review. Based on the selection criteria, 12 articles on 12 developmental models were included [ [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] ]. A flowchart of the study selection process is shown in Fig. 1 . A list of excluded studies is provided in Table S2 . Fig. 1 Selection of studies for inclusion in review. Fig. 1 Selection of studies for inclusion in review. The characteristics of the included studies are summarised in Table 1 . The 12 included studies were published between 2003 and 2021, mainly in Europe and East Asia. More than half of the studies (n = 9) were retrospective cohort studies, and three prospective cohort studies were included. The risk factors for spontaneous preterm birth in asymptomatic pregnant women in the included studies are shown in Table 1 . The most common risk factors were previous miscarriage, preterm birth, and a short cervix. Seven studies [ 19 , 21 , 22 , [24] , [25] , [26] , [27] ] constructed prediction models for specific pregnant women (e.g., cervical insufficiency, short cervix, history of cervical conization, and cervical cerclage). Table 1 Characteristics of included studies (n = 12). Table 1 Study Country Multicenter Recruitment dates Study design Sample size (Total/case) Age (Mean years) High-risk factor for spontaneous preterm birth Predicted outcome (Weeks) Gioan, 2018 16 France Yes Jul 2007 to Apr 2012 Prospective cohort 764/220 29.4 Short cervix (a cervical length <25 mm measured by transvaginal ultrasound) and/or an obstetric history: history of preterm birth and/or late miscarriage spontaneous expulsion of a pregnancy ≥14 and < 22 weeks <37 Fuchs, 2012 17 France No Jan 1994 to Dec 2006 Retrospective cohort 85/37 31.5 Previous second-trimester pregnancy losses, previous preterm births, in utero exposure to diethylstilbestrol (e.g. when the pregnant women’ mother was pregnant with them) or surgery for a uterine malformation <32 Kuhrt, 2016 18 UK Yes Oct 2010 to Jul 2014 Prospective cohort 624/94 33 Previous spontaneous preterm birth or previous preterm prelabor rupture of membranes <37 weeks, previous late miscarriage (16–23 + 6 weeks), previous cervical surgery or cervical length measuring <25 mm in the current pregnancy <37 Lee, 2016 19 Korea No Sep 2004 to Apr 2014 Retrospective cohort 57/37 31.7 Cervical insufficiency <34 Odibo, 2003 20 USA No 1996 to 2002 Retrospective cohort 256/51 30.5 One or more spontaneous preterm delivery (14–34 weeks), two or more dilatation and curettages for voluntary first-trimester abortion, Mullerian anomaly, cone biopsy, and diethylstilbestrol exposure <32 Park, 2020 21 Korea No Sep 2004 to Feb 2015 Retrospective cohort 80/39 31.6 Premature cervical dilation or a short cervix (≤25 mm) <32 Rawashdeh, 2020 22 Australia No Jan 2003 to Dec 2014 Retrospective cohort 274/26 15–51 a Cervical cerclage <37 Vogel, 2007 23 Denmark No Over a 2-year period b Retrospective cohort 62/20 24 At least one prior spontaneous birth (16–30 weeks), short cervical length (≤25 mm) <35 Yoo, 2017 24 Korea No Sep 2009 to Dec 2015 Retrospective cohort 62/25 32.2 Cervical insufficiency or a short cervix (≤25 mm) <32 Boelig, 2020 25 USA Yes Jan 2012 to Dec 2018 Retrospective cohort 108/29 29.3 Short cervix (≤20 mm) <34 Lou, 2018 26 China No Jan 2008 to Mar 2018 Retrospective cohort 118/44 30.3 Cervical conization ≧28 and <37 Anumba, 2021 27 UK No Jan 2014 to Aug 2016 Prospective cohort 365/43 30.1 History of sPTB <37 a = Values were given as the ranges; b = Not reported recruitment dates; sPTB = spontaneous preterm birth; mm = millimeter. Characteristics of included studies (n = 12). a = Values were given as the ranges; b = Not reported recruitment dates; sPTB = spontaneous preterm birth; mm = millimeter. Table 2 summarises the modelling methods and model performance. Most models (n = 9) were developed using a logistic regression analysis. The area under the curve (AUC) of the prediction model is shown in Fig. 2 . Of the 12 models, 11 had AUC ranging from 0.75 to 0.95. Moreover, five studies used the Hosmer-Lemeshow test to report calibration, and one study used a calibration scatter plot. The AUCs of the seven prediction models were greater than 0.85 [ [17] , [18] , [19] , [20] , [21] , 24 , 26 ], and the sensitivity and specificity of the five models were good. In addition, only seven models provided calculation formulae. Of these models with an AUC >0.85, only five provided full formulas to calculate the probability of spontaneous preterm birth. Table 2 Modelling method and model performance of prediction models. Table 2 Study Modelling method Full model presented Discrimination (AUC) Calibration Classification metrics Gioan, 2018 16 Logistic regression Yes 0.77 (95% CI 0.72–0.81) Good calibration Se: 0.74 (95% CI 0.63–0.84); Sp: 0.73 (95% CI 0.67–0.78); PLR: 2.7 (95% CI 2.20–3.50); NLR: 0.35 (95% CI 0.20–0.50); PPV: 0.45 (95% CI 0.36–0.53); NPV: 0.91 (95% CI 0.86–0.94) Fuchs, 2012 17 Logistic regression Yes 0.88 (95% CI 0.81–0.95) NR NR Kuhrt, 2016 18 Parametric survival model Yes <37weeks: 0.78; <34weeks: 0.83; <30weeks: 0.88 NR <37weeks: Se:0.78 (95% CI 0.68–0.85); Sp: 0.64 (95% CI 0.59–0.68); PLR: 2.20 (95% CI 1.80–2.50); NLR: 0.40 (95% CI 0.20–0.50); PPV: 0.28 (95% CI 0.22–0.33); NPV: 0.94 (95% CI 0.91–0.96) <34weeks: Se: 0.78 (95% CI 0.65–0.89); Sp: 0.80 (95% CI 0.76–0.83); PLR: 3.50 (95% CI 2.90–4.30); NLR: 0.30 (95% CI 0.20–0.50); PPV: 0.24 (95% CI 0.17–0.31); NPV: 0.97 (95% CI 0.96–0.99) <30weeks: Se: 0.63 (95% CI 0.42–0.81); Sp: 0.90 (95% CI 0.88–0.93); PLR: 6.60 (95% CI 4.50–9.70); NLR: 0.40 (95% CI 0.30–0.70); PPV: 0.23 (95% CI 0.14–0.34); NPV: 0.98 (95% CI 0.97–0.99) Lee, 2016 19 Logistic regression No 0.95 (95% CI 0.89–1.00) 0.37* Se: 0.92 (95% CI 0.78–0.98); Sp: 0.90 (95% CI 0.68–0.99); PLR: 9.19 (95% CI 2.50–34.30); NLR: 0.09 (95% CI 0.03–0.30) Odibo, 2003 20 Logistic regression No 0.91 NR Se: 0.80; Sp: 0.96; PPV: 0.82; NPV: 0.95 Park, 2020 21 Logistic regression Yes 0.90 (95% CI 0.83–0.97) 0.28* Se: 0.89 (95% CI 0. 76–0.97); Sp: 0.80 (95% CI 0.64–0.91); PLR: 4.50 (95% CI 2.40–8.40); PLR: 0.10 (95% CI 0.10–0.30) Rawashdeh, 2020 22 LWL, GP, K*, LR, RF No RF a ：0.75 Calibration scatter plot NR Vogel, 2007 23 Generalized linear No NR NR Se: 0.69; Sp: 0.95; PPV: 0.82; NPV: 0.91; PLR: 14.2 Yoo, 2017 24 Logistic regression Yes 0.91 (95%CI 0.83–0.99) 0.31* Se: 0.96 (95% CI 0.79–0.99); Sp: 0.76 (95% CI 0.59–0.88); PLR: 3.95 (95% CI 3.20–4.80); PLR: 0.05 (95% CI 0.01–0.40) Boelig, 2020 25 Logistic regression Yes 0.76 (95%CI 0.67–0.86) NR Se: 0.79; Sp: 0.75; PPV: 0.54; NPV: 0.91 Lou, 2018 26 Logistic regression Yes Training set: 0.93 (95%CI 0.87–0.99) Testing set: 0.94 (95% CI 0.86–1.00) 0.993* Training set: Se: 0.92; Sp: 0.82; PPV: 0.69; NPV: 0.96 Testing set: Se: 0.93; Sp: 0.90; PPV: 0.81; NPV: 0.96 Anumba, 2021 27 Logistic regression No 0.80 (95% CI 0.72–0.87) NR Se: 0.80 (95% CI 0.44–0.98); Sp: 0.85 (95% CI 0.77–0.92) AUC=Area Under Curve, NR=Not report; RF=Random forest; Se = sensitivity; Sp = specificity; PLR = positive likelihood ratio; NLR = negative likelihood ratio; PPV = positive predictive value; NPV = negative predictive value; 95% CI = 95% confidence interval; a = correlation coefficient, * = p-value of Hosmer-Lemeshow goodness of fit test. Fig. 2 AUCs of prediction models for spontaneous preterm birth. Fig. 2 Modelling method and model performance of prediction models. AUC=Area Under Curve, NR=Not report; RF=Random forest; Se = sensitivity; Sp = specificity; PLR = positive likelihood ratio; NLR = negative likelihood ratio; PPV = positive predictive value; NPV = negative predictive value; 95% CI = 95% confidence interval; a = correlation coefficient, * = p-value of Hosmer-Lemeshow goodness of fit test. AUCs of prediction models for spontaneous preterm birth. Table 3 lists the predictors included in the models. The number of predictors in each model varied from 2 to 8. While some common variables were included in most studies, such as cervical length, history of preterm birth, and cervical dilatation, many other variables were included in only one or a few studies. Cervical length was the most consistent predictor of spontaneous preterm birth. Table 3 Predictors included in the prediction models for spontaneous preterm birth. Table 3 Predictors Study a Gioan, 2018 16 Fuchs, 2012 17 Kuhrt, 2016 18 Lee, 2016 19 Odibo, 2003 20 Park, 2020 21 Vogel, 2007 23 Yoo, 2017 24 Boelig, 2020 25 Lou, 2018 26 Anumba, 2021 27 Gestational age ✓ ✓ Maternal age ✓ ✓ Smoking during pregnancy ✓ History of cone biopsy ✓ History of preterm birth ✓ ✓ ✓ History of miscarriage ✓ ✓ Daily walk time ✓ Cervical dilatation ✓ ✓ ✓ ✓ Cervical length ✓ ✓ ✓ ✓ ✓ ✓ ✓ Membranes bulging into the vagina ✓ Infection b ✓ Emergency cerclage ✓ AF MMP-1 ✓ AF MMP-8 ✓ Plasma IL-6 ✓ C3a levels ✓ TNF-α ✓ sIL-6Rα ✓ Bacterial vaginosis ✓ Use of corticosteroid ✓ Cervicovaginal fluid VDBP ✓ fFN concentration ✓ ✓ PROM ✓ ✓ Cervical ESI ✓ a = one of the included studies (Rawashdeh, 2020) did not report predictor; b = WBC≥13600 × 10 6  L −1 and/or C-reactive protein >15 mg L −1 ; √ = variable included in each model; AF = amniotic fluid; MMP = matrix metalloproteinase; IL-6 = interleukin-6; TNF-α = tumor necrosis factor-alpha; sIL-6Rα = soluble IL-6 receptor alpha; VDBP = vitamin D binding protein; fFN = fetal fibronectin; PROM = premature rupture of membranes; ESI = electrical impedance spectroscop Predictors included in the prediction models for spontaneous preterm birth. a = one of the included studies (Rawashdeh, 2020) did not report predictor; b = WBC≥13600 × 10 6  L −1 and/or C-reactive protein >15 mg L −1 ; √ = variable included in each model; AF = amniotic fluid; MMP = matrix metalloproteinase; IL-6 = interleukin-6; TNF-α = tumor necrosis factor-alpha; sIL-6Rα = soluble IL-6 receptor alpha; VDBP = vitamin D binding protein; fFN = fetal fibronectin; PROM = premature rupture of membranes; ESI = electrical impedance spectroscop Eleven studies had a high risk of bias. Details of the risk of bias assessment are shown in Fig. 3 . All studies were at low risk of bias for the outcome and participant domains, 25% of studies (n = 4) were at low risk of bias for the predictor domain, and 75% of studies (n = 8) had an unclear risk of bias because there was no information on whether predictors were assessed without knowledge of the outcome. A total of 91.7% of the studies (n = 11) had a high risk of bias in the analysis domain, mainly because of the small sample size with events per predictor and the lack of adjustment for overfitting. Fig. 3 Risk of bias assessment of included studies. Fig. 3 Risk of bias assessment of included studies.

In conclusion, we included 12 prediction models for spontaneous preterm births in singleton asymptomatic women with risk factors and found that the accuracy of these models was good. However, these models are not widely used in clinical practice because they lack replicability and transparency. Future studies should transparently report the methodological details of the model construction and validation to ensure replicability and transparency. Furthermore, prediction models should consider more meaningful predictors in future research.

We systematically reviewed prediction models for spontaneous preterm births in singleton asymptomatic pregnant women with risk factors for preterm births. Twelve studies were included in the systematic review. The AUC of the models ranged from 0.75 to 0.95. The most common predictor for most prediction models was cervical length. Overall, most studies had a high risk of bias, with the analysis domain being most commonly rated as having a high risk of bias. A clinical prediction model was originally constructed to predict diseases using a small number of predictors that are easy to collect and inexpensive to detect [ 28 , 29 ]. In this systematic review, we found that cervical length was the most commonly used predictor was cervical length. Other common predictors were a history of preterm birth and cervical dilatation. These predictors are easily available and do not require invasive laboratory tests. However, with the development of the economy and the advancement of technology, the costs of data collection and storage have been greatly reduced, and data analysis technology is improving. Therefore, clinical prediction models should also break through the inherent concept by applying larger amounts of data to serve doctors, patients, and medical decision-makers with more accurate results [ 29 ]. The aetiopathogenesis of spontaneous preterm birth is multifactorial; therefore, holistic generalized prediction models should be constructed to cover all or most of the etiologic mechanisms of preterm birth [ 2 , 30 ]. In our study, some risk factors associated with preterm birth were not included in the final model, such as ART [ [31] , [32] , [33] , [34] ], gestational diabetes mellitus [ 35 , 36 ], gestational hypertension [ 37 , 38 ]. Studies have shown that pregnant women conceived through ART are more likely to have spontaneous preterm birth, which may be related to their older age, endometriosis, polycystic ovary syndrome or other unexplained infertility [ 39 , 40 ]. Previous studies have also indicated that pregnant women with gestational diabetes mellitus have a direct effect on preterm birth, possibly through hyperglycemia-induced endothelial dysfunction, oxidative stress, and impaired vasodilation [ 36 , 41 ]. Studies have shown that the pathophysiological mechanisms linking pregnancy-induced hypertension to preterm birth include inflammation, oxidative stress and endocrine disruption [ [42] , [43] , [44] ]. Researchers often use automatic screening software (such as logistic regression and Cox regression in IBM SPSS) to determine whether the factors should be included [ 29 ]. They performed a univariate analysis of every variable individually or a multivariate analysis based on the results of the univariate analysis. Factors with P values less than 0.1 will be included in the model (here, the P value could be less than 0.05 or 0.2). Notably, this statistical screening method may sometimes exclude factors associated with preterm birth as disqualifying factors. Other factors screening methods included the Akaike information criterion [ 30 ] and clinical experience [ 29 ]. Choosing a better method for identifying risk factors is important for prediction models, and, importantly, there are no standard rules. Therefore, for future studies, we recommend a combination of statistical analysis and a clinical perspective to determine which factors should be considered. Currently, few models of spontaneous preterm birth have been applied in clinical practice [ 45 , 46 ]. This may be attributed to multiple reasons. First, the reporting and methodological quality of the prediction model was unclear [ 47 ]. For example, in our review, approximately half of the models did not provide calculation formulae, indicating that their clinical use would not be possible. Second, clinicians may question the accuracy of the models because they may not include well-known predictors [ 45 ]. Third, these models are too complex for daily use in clinical settings. Another important reason is that many models have not been validated in other populations, making their generalisability unclear [ 47 ]. In our review, only the results of one study by Kurht et al. [ 18 ] were translated into an application (QUiPP, Quantitative Instrument for the Prediction of Preterm birth) and applied in clinical practice to help clinicians make clinical decisions. However, this application lacks transparency in certain aspects related to model development and proper validation [ 48 ], which precludes transportation to settings with other treatment policies or other countries. No study had an overall low risk of bias, according to the PROBAST, reflecting some methodological shortcomings in the included studies. The analysis domain was most commonly rated as having a high risk of bias in the included studies, mainly because of the small sample size with events per predictor and the lack of adjustment for overfitting. The limited effective sample sizes likely led to overfitting and underfitting of the model, which yielded biased estimates of the apparent model predictive performance [ 15 ]. This systematic review has several strengths. We conducted a comprehensive search, independently screened the literature by six reviewers, and extracted data on the key characteristics of prediction models for spontaneous preterm birth, including the population, predictors, and predicted outcomes. Additionally, we assessed the quality of the included studies using the PROBAST tool. However, this study had several limitations. One limitation of our study is that we did not perform a meta-analysis because the included studies were heterogeneous. The main sources of heterogeneity may include differences in clinical settings, patient characteristics, and time points used to estimate the risk of spontaneous preterm birth across studies. Additionally, we only included studies that focused on singleton pregnancies with risk factors, but the results from studies on multiple pregnancies or pregnant women without risk factors may be informative. Future studies should explore whether there are significant differences in the results of preterm birth prediction models between pregnant women with and without risk factors.

The World Health Organization defines preterm births as babies born alive before 37 completed gestational weeks [ 1 ], including spontaneous and iatrogenic preterm births. Across countries, the estimated preterm birth rate ranged from 4% to 10.6% in 2020, with an estimated 13.4 million live preterm births [ 1 ]. Approximately two-thirds of all preterm births occur spontaneous preterm birth [ 2 ]. Preterm birth complications are the leading causes of death in children under 5 years of age and were responsible for approximately 900 000 babies dying in 2019 [ 1 , 3 ]. However, many survivors are at greater risk of a range of long-term morbidities or lifetime disabilities, including chronic kidney disease, hypertension, diabetes, ischaemic heart disease, lower sleep quality, learning disabilities, and visual and hearing problems [ 1 , [4] , [5] , [6] , [7] , [8] ]. In summary, preterm births, particularly spontaneous births, are a global problem. Therefore, developing and implementing key interventions to prevent spontaneous preterm birth is essential. More attention should be paid to asymptomatic women with risk factors for preterm birth. Early screening, identification, and prevention in asymptomatic pregnant women with risk factors for preterm birth can help reduce the incidence and mortality of preterm births. Many studies have shown that implementing adequate programs to prevent preterm birth is desirable [ [9] , [10] , [11] , [12] ]. This prediction model is a promising approach for identifying risk factors and estimating the probability of preterm birth. Therefore, this systematic review aimed to review existing prediction models for spontaneous preterm birth, summarise model characteristics, appraise their quality, and identify the best-performing prediction model for clinical decision-making.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data included in article/supplementary material/referenced in article.