In our retrospective analysis and external validation study, we developed a predictive model of SPTB at < 32 weeks based on maternal characteristics and sonographic cervical measurements to provide an accurate and comprehensive risk estimation, which can serve as an assessement tool to help physicians make judicious treatment decisions about further management of twin pregnancy. Moreover, external validation and restricted cubic splines supported the predictive performance.
The reason we comprehensively considered all the above factors when building the model was that the predictive performance of a single maternal factor or cervix geometry (including length) is not satisfactory, primarily due to poor sensitivity.36–40 The mechanism of SPTB involves various mechanical stimuli (two continuously growing foetuses and the expanding uterus) and biochemical stimuli (inflammatory factors, fetoplacental signals and steroid hormones).41,42 Compared to that in singleton pregnancies, the mechanism of SPTB in twin pregnancies is predominantly determined by overdistension, whereas the role of inflammation and microbiologic invasion of the amniotic cavity (MIAC) is relatively minor.16 Overdistension of the lower uterine segment and smooth muscle stretch in the human cervix provokes proinflammatory cytokine secretion, and research on changes in the cervical microstructure has been published by Vink et al.17,43,44 Jose Villar et al proposed the use of a phenotypic classification system of PTB that does not force any PTB into a predefined phenotype but instead relies on a new conceptual framework in which a maternal clinical phenotype of PTB potentially related to a certain perinatal outcome is characterized by all relevant conditions observed during pregnancy.18
A series of common clinical characteristics, such as age, race, BMI, history of PTB, previous uterine surgeries, and tobacco usage, may indicate the initial states and variations in the structure and function of the cervix, which contributes to the risk of cervical insufficiency.19,20,24,45,46 All these risk factors have interconnected effects and a computational framework for changing and remodelling the cervix. Our study is concordant with existing research indicating that nulliparity, lower prepregnancy BMI, history of PTB or late abortion, chorionicity, cervical funnelling and shorter cervical canal increase the possibility of SPTB in twin pregnancies. However, there is no risk calculation yet for PTB after 32 weeks, which still represents a population with a 10-fold increased risk for perinatal mortality compared to twins at term.47 Our research incorporated maternal characteristics and biophysical tests of both cervical length and funnelling to develop a dynamic nomogram model that may better indicate clinical strategies, such as therapy decision-making and follow-up schedules, and may reduce complications for clinicians related to excessive monitoring and administration resulting from an undefined or inherently subjective risk assessment. Thus, the ability to generate a risk assessment and present it in the form of a percentage for each patient will enable caregivers to schedule more frequent follow-ups or administer targeted interventions, such as antenatal corticosteroids and tocolytic therapy as well as transfer to a tertiary medical centre for patients at higher risk, while reducing overtreatment and unnecessary hospitalization for those at lower risk. On the other hand, in the study design for the negative trials regarding PTB intervention, only a few researchers screened out and followed high-risk twin pregnancies, which may introduce confusion regarding indications for the interventions and result in bias when comparing outcomes.7,10,11,48 To some extent, a lack of good care during surveillance frequently makes the difference in RCTs. It would be interesting in the future to determine whether the use of this tool to assess the indications for interventions and stratify patients according to risk could improve outcomes.
Our study has some limitations. Most importantly, it is limited by its retrospective design. There is a possibility of confounding bias: patients with unmeasured or unobservable factors who were excluded may represent patients at higher risk, so that our study might ignore the most clinically interesting population. Second, the study population in the two centres is limited to our own population (Asian), which limits generalizability to people of different races. For example, in many high resource countries, the risk of PTB is associated with obesity and not underweight.24,49 However, this potential limitation may also be considered a strength. All women included in the study were followed up and treated only in the two tertiary medical centres, which limits the confounding factors associated with the heterogeneity in provider bias, such as clinicians’ experience, and differences in the process of monitoring and management for offering the intervention. Based on the model, researchers in other countries can make use of their own data on demographic characteristics to justify the odds for their population. The last limitation is that because of the incomplete data for cervical length before 20 weeks, our model may poorly predict very early PTBs since we adopted cervical measurements during 20–24 weeks and applied the system relatively late for the high-risk population.50 In the future, we should concentrate on earlier evaluation of our algorithm to prevent early mortality and severe morbidity.
In summary, we developed and validated a dynamic nomogram model to predict the individual probability of early preterm birth; this nonogram better represents the complex aetiology of twin pregnancies and hopefully improves our understanding of the indications for interventions and, therefore, our ability to predict when they will be needed.