Systematic Review of Prediction Models for Preterm Birth Using CHARMS

Kim, Jeung Im; Lee, Joo Yun

doi:10.1177/10998004211025641

Cited by 10 publications

(9 citation statements)

References 49 publications

(165 reference statements)

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“…A single predictor may be weak in predicting preterm birth, while a better prediction can be obtained by combining a predictive model of multiple predictors [ 9 ]. Kim et al conducted a systematic review summarizing current predictive models for predicting the risk of preterm birth [ 10 ]. The area under the receiver operating characteristic curve (AUC) for predicting preterm birth in these studies varied from 62 to 80%, and the effect of prediction was related to the number of predictors, populations, and the period of data (the first trimester, second trimester, etc.)…”

Section: Introductionmentioning

confidence: 99%

Maternal preterm birth prediction in the United States: a case-control database study

Guo

et al. 2022

BMC Pediatr

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Background Preterm birth is serious public health worldwide, and early prediction of preterm birth in pregnant women may provide assistance for timely intervention and reduction of preterm birth. This study aimed to develop a preterm birth prediction model that is readily available and convenient for clinical application. Methods Data used in this case-control study were extracted from the National Vital Statistics System (NVSS) database between 2018 and 2019. Univariate and multivariate logistic regression analyses were utilized to find factors associated with preterm birth. Odds ratio (OR) and 95% confidence interval (CI) were used as effect measures. The area under the curve (AUC), accuracy, sensitivity, and specificity were utilized as model performance evaluation metrics. Results Data from 3,006,989 pregnant women in 2019 and 3,039,922 pregnant women in 2018 were used for the model establishment and external validation, respectively. Of these 3,006,989 pregnant women, 324,700 (10.8%) had a preterm birth. Higher education level of pregnant women [bachelor (OR = 0.82; 95%CI, 0.81–0.84); master or above (OR = 0.82; 95%CI, 0.81–0.83)], pre-pregnancy overweight (OR = 0.96; 95%CI, 0.95–0.98) and obesity (OR = 0.94; 95%CI, 0.93–0.96), and prenatal care (OR = 0.48; 95%CI, 0.47–0.50) were associated with a reduced risk of preterm birth, while age ≥ 35 years (OR = 1.27; 95%CI, 1.26–1.29), black race (OR = 1.26; 95%CI, 1.23–1.29), pre-pregnancy underweight (OR = 1.26; 95%CI, 1.22–1.30), pregnancy smoking (OR = 1.27; 95%CI, 1.24–1.30), pre-pregnancy diabetes (OR = 2.08; 95%CI, 1.99–2.16), pre-pregnancy hypertension (OR = 2.22; 95%CI, 2.16–2.29), previous preterm birth (OR = 2.95; 95%CI, 2.88–3.01), and plurality (OR = 12.99; 95%CI, 12.73–13.24) were related to an increased risk of preterm birth. The AUC and accuracy of the prediction model in the testing set were 0.688 (95%CI, 0.686–0.689) and 0.762 (95%CI, 0.762–0.763), respectively. In addition, a nomogram based on information on pregnant women and their spouses was established to predict the risk of preterm birth in pregnant women. Conclusions The nomogram for predicting the risk of preterm birth in pregnant women had a good performance and the relevant predictors are readily available clinically, which may provide a simple tool for the prediction of preterm birth.

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Section: Introductionmentioning

confidence: 99%

Maternal preterm birth prediction in the United States: a case-control database study

Guo

et al. 2022

BMC Pediatr

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“… 21 Previous systematic reviews from other research teams showed that there are high ROB and suboptimal reporting quality in prediction models in oral health and preterm birth. 22 , 23 To date, there has been limited data for evaluating the quality of existing IPF prognostic models; therefore, in this cross-sectional study, we assessed IPF prognostic models with PROBAST and TPRIPOD checklists, aiming to identify the ROB and reporting quality of these studies and highlight the strengths and limitations of the methodologies of IPF prognostic models.…”

Section: Introductionmentioning

confidence: 99%

Bias and Reporting Quality of Clinical Prognostic Models for Idiopathic Pulmonary Fibrosis: A Cross-Sectional Study

Di¹,

Li²,

Yang³

et al. 2022

RMHP

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Objective This study aims to evaluate the risk of bias (ROB) and reporting quality of idiopathic pulmonary fibrosis (IPF) prediction models by assessing characteristics of these models. Methods The development and/or validation of IPF prognostic models were identified via an electronic search of PubMed, Embase, and Web of Science (from inception to 12 August, 2021). Two researchers independently assessed the risk of bias (ROB) and reporting quality of IPF prediction models based on the Prediction model Risk Of Bias Assessment Tool (PROBAST) and Transparent Reporting of a multivariable prognostic model for Individual Prognosis or Diagnosis (TRIPOD) checklist. Results Twenty prognostic model studies for IPF were included, including 7 (35%) model development and external validation studies, 8 (40%) development studies, and 5 (25%) external validation studies. According to PROBAST, all studies were appraised with high ROB, because of deficient reporting in the domains of participants (45.0%) and analysis (67.3%), and at least 55% studies were susceptible to 4 of 20 sources of bias. For the reporting quality, none of them completely adhered to the TRIPOD checklist, with the lowest mean reporting score for the methods and results domains (46.6% and 44.7%). For specific items, eight sub-items had a reporting rate ≥80% and adhered to the TRIPOD checklist, and nine sub-items had a very poor reporting rate, less than 30%. Conclusion Studies adhering to PROBAST and TRIPOD checklists are recommended in the future. The reproducibility and transparency can be improved when studies completely adhere to PROBAST and TRIPOD checklists.

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“…Initial applications of ML techniques have assisted experts in better predicting PTB [28,30]. Studies have continuously used updated ML techniques on a more diverse obstetric population to extract generalizable PTB predictors [18,[31][32][33]. Such efforts proved to have the potential to significantly benefit women's and children health by identifying significant risk factors [34].…”

Section: Introductionmentioning

confidence: 99%

Identifying the Early Signs of Preterm Birth from U.S. Birth Records Using Machine Learning Techniques

2022

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Preterm birth (PTB) is the leading cause of infant mortality in the U.S. and globally. The goal of this study is to increase understanding of PTB risk factors that are present early in pregnancy by leveraging statistical and machine learning (ML) techniques on big data. The 2016 U.S. birth records were obtained and combined with two other area-level datasets, the Area Health Resources File and the County Health Ranking. Then, we applied logistic regression with elastic net regularization, random forest, and gradient boosting machines to study a cohort of 3.6 million singleton deliveries to identify generalizable PTB risk factors. The response variable is preterm birth, which includes spontaneous and indicated PTB, and we performed a binary classification. Our results show that the most important predictors of preterm birth are gestational and chronic hypertension, interval since last live birth, and history of a previous preterm birth, which explains 10.92, 5.98, and 5.63% of the predictive power, respectively. Parents' education is one of the influential variables in predicting PTB, explaining 7.89% of the predictive power. The relative importance of race declines when parents are more educated or have received adequate prenatal care. The gradient boosting machines outperformed with an AUC of 0.75 (sensitivity: 0.64, specificity: 0.73) for the validation dataset. In this study, we compare our results with seminal and most related studies to demonstrate the superiority of our results. The application of ML techniques improved the performance measures in the prediction of preterm birth. The results emphasize the importance of socioeconomic factors such as parental education as one of the most important indicators of preterm birth. More research is needed on these mechanisms through which socioeconomic factors affect biological responses.

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Systematic Review of Prediction Models for Preterm Birth Using CHARMS

Cited by 10 publications

References 49 publications

Maternal preterm birth prediction in the United States: a case-control database study

Maternal preterm birth prediction in the United States: a case-control database study

Bias and Reporting Quality of Clinical Prognostic Models for Idiopathic Pulmonary Fibrosis: A Cross-Sectional Study

Identifying the Early Signs of Preterm Birth from U.S. Birth Records Using Machine Learning Techniques

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