Development and external validation of a nomogram for predicting preterm birth at < 32 weeks in twin pregnancy

Development and validation of a nomogram for individualized prediction of spontaneous extremely preterm birth at < 28 weeks in twin pregnancy: a retrospective cohort study

PURPOSE

To develop a nomogram to predict spontaneous preterm birth at < 28 weeks in pregnant women with twin pregnancies.

METHODS

We retrospectively studied the medical records of twin-pregnancy women with asymptomatic cervical dilation or cervical shortening between December 2015 to February 2022 in two hospitals. Data from one center was used to develop the model and data from the other was used to evaluate the model.

RESULTS

A total of 270 twin pregnancies were enrolled in the study. We incorporated 4 items (cervical length, cervical dilation, C-reactive protein and the use of cerclage) to build the 28-week nomogram with satisfactory discrimination and calibration when applied to the validation sets. The C index for the 28-week nomogram in the development and external cohort was 0.88 (95% CI, 0.84-0.93) and 0.89 (95% CI, 0.80-0.98), respectively. The nomogram reached a sensitivity of 70.70%, specificity of 97.10%, positive predicted value of 95.61% and negative predicted value of 78.77%. Moreover, the decision curve analysis indicated that the nomogram showed positive clinical benefit.

CONCLUSION

We developed and validated a nomogram with good performance in predicting individual risk of spontaneous preterm birth at < 28 in twin pregnancy.

Development and validation of a prediction model on spontaneous preterm birth in twin pregnancy: a retrospective cohort study

BACKGROUND

This study was conducted to develop and validate an individualized prediction model for spontaneous preterm birth (sPTB) in twin pregnancies.

METHODS

This a retrospective cohort study included 3845 patients who gave birth at the Chongqing Maternal and Child Health Hospital from January 2017 to December 2022. Both univariable and multivariable logistic regression analyses were performed to find factors associated with sPTB. The associations were estimated using the odds ratio (OR) and the 95% confidence interval (CI). Model performance was estimated using sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

RESULTS

A total of 1313 and 564 cases were included in the training and testing sets, respectively. In the training set, univariate and multivariate logistic regression analysis indicated that age ≥ 35 years (OR, 2.28; 95% CI 1.67-3.13), pre-pregnancy underweight (OR, 2.36; 95% CI 1.60-3.47), pre-pregnancy overweight (OR, 1.67; 95% CI 1.09-2.56), and obesity (OR, 10.45; 95% CI, 3.91-27.87), nulliparity (OR, 0.58; 95% CI 0.41-0.82), pre-pregnancy diabetes (OR, 5.81; 95% CI 3.24-10.39), pre-pregnancy hypertension (OR, 2.79; 95% CI 1.44-5.41), and cervical incompetence (OR, 5.12; 95% CI 3.08-8.48) are independent risk factors for sPTB in twin pregnancies. The AUC of the training and validation set was 0.71 (95% CI 0.68-0.74) and 0.68 (95% CI 0.64-0.73), respectively. And then we integrated those risk factors to construct the nomogram.

CONCLUSIONS

The nomogram developed for predicting the risk of sPTB in pregnant women with twins demonstrated good performance. The prediction nomogram serves as a practical tool by including all necessary predictors that are readily accessible to practitioners.

Ultrasonographic cervical length screening at 20-24 weeks of gestation in twin pregnancies for prediction of spontaneous preterm birth: A 10-year Taiwanese cohort

BACKGROUND

Shortened cervical length is one of the primary predictors for spontaneous preterm deliveries in twin pregnancies. However, there is lack of consensus regarding cut-off values. Recent evidence highlights that established cut-offs for cervical length screening might not always apply across different populations. This study aims to present the distribution of cervical length in Taiwanese twin pregnancies and to assess its predictive value for spontaneous preterm birth during mid-trimester screening.

MATERIALS AND METHODS

This retrospective analysis of cervical length screening in Taiwan evaluated 469 twin pregnancies between 20-24 weeks of gestation. Outcome data were obtained directly from the medical records of the delivery hospital. The study explored the predictive value of cervical length screening for spontaneous preterm birth and the characteristics of cervical length distribution in Taiwanese twin pregnancies.

RESULTS

The average gestational age at screening was 22.7 weeks. Cervical length values displayed a non-normal distribution (p-value <0.001). The median, 5th and 95th centiles were 37.5 mm 25.1 mm, and 47.9 mm, respectively. Various cut-off values were assessed using different methods, yielding positive [negative] likelihood ratios for spontaneous preterm births between 32-37 weeks of gestational age (GA) (1.3-30.1 and [0.51-0.92]) and for very preterm births between 28-32 weeks GA (5.6-51.1 and [0.45-0.64]).

CONCLUSIONS

The findings from our analysis of Taiwanese twin pregnancies uphold the moderate predictive potential of cervical length screening, consistent with prior investigations. The presented likelihood ratios for predicting preterm birth at different gestational ages equip clinicians with valuable tools to enhance their diagnostic rationale and resource utilization. By fine-tuning screening parameters according to the spontaneous preterm birth prevalence and clinical priorities of the particular population, healthcare providers can enhance patient care. Our data implies that a cervical length below 20 mm might provide an optimal balance between minimizing false negatives and managing false positives when predicting spontaneous preterm birth.

Estimating the individual singleton preterm birth risk: nomogram establishment and independent validation

Background

To establish and independently validate nomograms for predicting singleton preterm birth (PTB) risk based on a large sample size comprising data from two independent datasets.

Methods

This cohort study used data from 50 states and the District of Columbia in the National Vital Statistics System (NVSS) database between January 2016 and December 2020. Multivariate logistic regression analysis was used to confirm the independent risk factors for PTB. Statistically significant variables were incorporated into the logistic regression models to establish PTB prediction nomograms. The models were developed using the United States (US)-derived data and were independently validated using data from US Territories.

Results

A total of 16,294,529 mother-newborn pairs from the US were included in the training set, and 54,708 mother-newborn pairs from the US Territories were included in the validation set. In all, 4 nomograms were built: 1 to predict PTB probability, and another 3 to predict moderately and late PTB probability, very PTB probability, and extremely PTB probability, respectively. Hypertensive eclampsia and infertility treatment were found to be the top 2 contributors to PTB.

Conclusions

We developed and validated nomograms to predict the individualized probability of PTB, which could be useful to physicians for improved early identification of PTB and in making individualized clinical decisions.