Dataset Information

A predictive model of macrosomic birth based upon real-world clinical data from pregnant women.

ABSTRACT:

Background

Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction.

Methods

In the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software.

Results

We compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908-0.927) and 0.910 (95% CI, 0.894-0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model.

Conclusions

Our model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.

SUBMITTER: Jing G

PROVIDER: S-EPMC9386989 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

A predictive model of macrosomic birth based upon real-world clinical data from pregnant women.

Jing Gao G Huwei Shi S Chao Chen C Lei Chen C Ping Wang W Zhongzhou Xiao X Sen Yang Y Jiayuan Chen C Ruiyao Chen C Lu Lu L Shuqing Luo L Kaixiang Yang Y Jie Xu X Weiwei Cheng C

BMC pregnancy and childbirth 20220818 1

<h4>Background</h4>Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction.<h4>Methods</h4>In the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delive ...[more]

PMID: 35982421

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Project description:ObjectiveOne primary consideration when developing predictive models is downstream effects on future model performance. We conduct experiments to quantify the effects of experimental design choices, namely cohort selection and internal validation methods, on (estimated) real-world model performance.Materials and methodsFour years of hospitalizations are used to develop a 1-year mortality prediction model (composite of death or initiation of hospice care). Two common methods to select appropriate patient visits from their encounter history (backwards-from-outcome and forwards-from-admission) are combined with 2 testing cohorts (random and temporal validation). Two models are trained under otherwise identical conditions, and their performances compared. Operating thresholds are selected in each test set and applied to a "real-world" cohort of labeled admissions from another, unused year.ResultsBackwards-from-outcome cohort selection retains 25% of candidate admissions (n = 23 579), whereas forwards-from-admission selection includes many more (n = 92 148). Both selection methods produce similar performances when applied to a random test set. However, when applied to the temporally defined "real-world" set, forwards-from-admission yields higher areas under the ROC and precision recall curves (88.3% and 56.5% vs. 83.2% and 41.6%).DiscussionA backwards-from-outcome experiment manipulates raw training data, simplifying the experiment. This manipulated data no longer resembles real-world data, resulting in optimistic estimates of test set performance, especially at high precision. In contrast, a forwards-from-admission experiment with a temporally separated test set consistently and conservatively estimates real-world performance.ConclusionExperimental design choices impose bias upon selected cohorts. A forwards-from-admission experiment, validated temporally, can conservatively estimate real-world performance.Lay summaryThe routine care of patients stands to benefit greatly from assistive technologies, including data-driven risk assessment. Already, many different machine learning and artificial intelligence applications are being developed from complex electronic health record data. To overcome challenges that arise from such data, researchers often start with simple experimental approaches to test their work. One key component is how patients (and their healthcare visits) are selected for the study from the pool of all patients seen. Another is how the group of patients used to create the risk estimator differs from the group used to evaluate how well it works. These choices complicate how the experimental setting compares to the real-world application to patients. For example, different selection approaches that depend on each patient's future outcome can simplify the experiment but are impractical upon implementation as these data are unavailable. We show that this kind of "backwards" experiment optimistically estimates how well the model performs. Instead, our results advocate for experiments that select patients in a "forwards" manner and "temporal" validation that approximates training on past data and implementing on future data. More robust results help gauge the clinical utility of recent works and aid decision-making before implementation into practice.

Dataset Information

A predictive model of macrosomic birth based upon real-world clinical data from pregnant women.

Background

Methods

Results

Conclusions

Publications

A predictive model of macrosomic birth based upon real-world clinical data from pregnant women.

Similar Datasets

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