Development of a Novel Nomogram for Predicting Placenta Accreta in Patients With Scarred Uterus: A Retrospective Cohort Study

External Validation of a Multivariable Prediction Model for Placenta Accreta Spectrum

BACKGROUND

Placenta accreta spectrum (PAS) is a disorder of abnormal placentation associated with severe postpartum hemorrhage, maternal morbidity, and mortality. Predelivery prediction of this condition is important to determine appropriate delivery location and multidisciplinary planning for operative management. This study aimed to validate a prediction model for PAS developed by Weiniger et al in 2 cohorts who delivered at 2 different United States tertiary centers.

METHODS

Cohort A (Brigham and Women's Hospital; N = 253) included patients with risk factors (prior cesarean delivery and placenta previa) and/or ultrasound features of PAS presenting to a tertiary-care hospital. Cohort B (Columbia University Irving Medical Center; N = 99) consisted of patients referred to a tertiary-care hospital specifically because of ultrasound features of PAS. Using the outcome variable of surgical and/or pathological diagnosis of PAS, discrimination (via c-statistic), calibration (via intercept, slope, and flexible calibration curve), and clinical usefulness (via decision curve analysis) were determined.

RESULTS

The model c-statistics in cohorts A and B were 0.728 (95% confidence interval [CI], 0.662-0.794) and 0.866 (95% CI, 0.754-0.977) signifying acceptable and excellent discrimination, respectively. The calibration intercept (0.537 [95% CI, 0.154-0.980] for cohort A and 3.001 [95% CI, 1.899- 4.335] for B), slopes (0.342 [95% CI, 0.170-0.532] for cohort A and 0.604 [95% CI, -0.166 to 1.221] for B), and flexible calibration curves in each cohort indicated that the model underestimated true PAS risks on average and that there was evidence of overfitting in both validation cohorts. The use of the model compared to a treat-all strategy by decision curve analysis showed a greater net benefit of the model at a threshold probability of >0.25 in cohort A. However, no net benefit of the model over the treat-all strategy was seen in cohort B at any threshold probability.

CONCLUSIONS

The performance of the Weiniger model is variable based on the case-mix of the population with regard to PAS clinical risk factors and ultrasound features, highlighting the importance of spectrum bias when applying this PAS prediction model to distinct populations. The model showed benefit for predicting PAS in populations with substantial case-mix heterogeneity at threshold probability of >25%.

A risk prediction nomogram of endometrial carcinoma and precancerous lesions in postmenopausal women: A retrospective study

This study aimed to develop a risk prediction nomogram for endometrial carcinoma and precancerous lesions in postmenopausal women to provide postmenopausal patients with more information on disease probability, work out personalized medical plans, and reduce unnecessary invasive clinical examinations. We enrolled 340 patients who underwent hysteroscopy at Beijing Maternity Hospital between March 2016 and July 2018. The patients were divided into the low-risk (275 patients) and high-risk (65 patients) groups, according to the results of the pathological examinations. Binary logistic analysis was performed to evaluate the 20 potential risk factors for endometrial cancer and precancerous lesions in postmenopausal women and to screen for certain risk factors using the Statistical Package for the Social Sciences version 26.0. Using R 4.0.3, we built a prediction nomogram that incorporated the selected factors. The discrimination, calibration, and clinical usefulness of the prediction model were assessed using the concordance (C)-index, calibration plot, and decision curve analysis. Internal validation was assessed using bootstrapping validation. Predictors included in the prediction nomogram included obesity, vaginal bleeding, family history of gynecological malignancies, endometrial thickness ≥ 1.15 cm, and color Doppler flow imaging blood flow. The model displayed good discrimination, with a C-index of 0.853, and good calibration. Decision curve analysis showed that the model was clinically useful, with a benefit range of 2% to 93%. A high C-index value of 0.844 could still be reached in the interval validation. Obesity, vaginal bleeding, family history of gynecological malignancies, endometrial thickness ≥ 1.15 cm, and color Doppler flow imaging blood flow were independent risk factors for endometrial cancer and precancerous lesions. Thus, the prediction nomogram can be conveniently used to facilitate individual risk prediction in patients with endometrial cancer and precancerous lesions.

Medical expenditure of women during pregnancy, childbirth and puerperium at the beginning of China's universal two-child policy enactment: a population-based retrospective study

OBJECTIVES

To describe and explore women's medical expenditures during pregnancy, childbirth and puerperium at the beginning of the universal two-child policy enactment in China.

DESIGN

Population-based retrospective study.

SETTING

Dalian, China.

PARTICIPANTS

Under the System of Health Accounts 2011 framework, the macroscopic dataset was obtained from the annual report at the provincial and municipal levels in China. The research sample incorporated 65 535 inpatient and outpatient records matching International Classification of Diseases, 10th Revision codes O00-O99 in Dalian city from 2015 through 2017.

PRIMARY AND SECONDARY OUTCOME MEASURES

The study delineates women's current curative expenditure (CCE) during pregnancy, childbirth and puerperium at the beginning of the universal two-child policy in China. The temporal changes of medical expenditure of women during pregnancy, childbirth and puerperium at the beginning of China's universal two-child policy enactment were assessed. The generalised linear model and structural equation model were used to test the association between medical expenditure and study variables.

RESULTS

Unlike the inverted V-shaped trend in the number of live newborns in Dalian over the 3 studied years, CCE on pregnancy, childbirth and puerperium dipped slightly in 2016 (¥260.29 million) from 2015 (¥263.28 million) and saw a surge in 2017 (¥288.65 million). The ratio of out-of-pocket payment/CCE reduced year by year. There was a rapid increase in CCE in women older than 35 years since 2016. Length of stay mediated the relationship between hospital level, year, age, reimbursement ratio and medical expenditure.

CONCLUSIONS

The rise in CCE on pregnancy, delivery and puerperium lagged 1 year behind the surge of newborns at the beginning of China's universal two-child policy. Length of stay acted as a crucial mediator driving up maternal medical expenditure. Reducing medical expenditure by shortening the length of stay could be a feasible way to effectively address the issue of cost in women during pregnancy, childbirth and puerperium.

Integration of transcriptome and proteome profiles in placenta accreta reveals trophoblast over-migration as the underlying pathogenesis

Background

Placenta accreta (PA) is a major cause of maternal morbidity and mortality in modern obstetrics, few studies have explored the underlying molecular mechanisms.

Methods

In our study, transcriptome and proteome profiling were performed in placental tissues from ten participants including five cases each in the PA and control groups to clarify the pathogenesis of PA.

Results

We identified differential expression of 37,743 transcripts and 160 proteins between the PA and control groups with an overlap rate of 0.09%. The 33 most-significant transcripts and proteins were found and further screened and analyzed. Adhesion-related signature, chemotaxis related signatures and immune related signature were found in the PA group and played a certain role. Sum up two points, three significant indicators, methyl-CpG-binding domain protein 2 (MeCP2), podocin (PODN), and apolipoprotein D (ApoD), which participate in “negative regulation of cell migration”, were downregulated at the mRNA and protein levels in PA group. Furthermore, transwell migration and invasion assay of HTR-8/SVneo cell indicated the all of them impaired the migration and invasion of trophoblast.

Conclusion

A poor correlation was observed between the transcriptome and proteome data and MeCP2, PODN, and ApoD decreased in transcriptome and proteome profiling, resulting in increased migration of trophoblasts in the PA group, which clarify the mechanism of PA and might be the biomarkers or therapy targets in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-021-09336-8.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

BACKGROUND

Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method.

OBJECTIVE

This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians' decision making.

METHODS

Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians' decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2.

RESULTS

Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07).

CONCLUSIONS

Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines.

TRIAL REGISTRATION

PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106.