Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes

Temporal validation and updating of a prediction model for the diagnosis of gestational diabetes mellitus

OBJECTIVE

The original Monash gestational diabetes mellitus (GDM) risk prediction in early pregnancy model is internationally externally validated and clinically implemented. We temporally validate and update this model in a contemporary population with a universal screening context and revised diagnostic criteria and ethnicity categories, thereby improving model performance and generalizability.

STUDY DESIGN AND SETTING

The updating dataset comprised of routinely collected health data for singleton pregnancies delivered in Melbourne, Australia from 2016 to 2018. Model predictors included age, body mass index, ethnicity, diabetes family history, GDM history, and poor obstetric outcome history. Model updating methods were recalibration-in-the-large (Model A), intercept and slope re-estimation (Model B), and coefficient revision using logistic regression (Model C1, original ethnicity categories; Model C2, revised ethnicity categories). Analysis included 10-fold cross-validation, assessment of performance measures (c-statistic, calibration-in-the-large, calibration slope, and expected-observed ratio), and a closed-loop testing procedure to compare models' log-likelihood and akaike information criterion scores.

RESULTS

In 26,474 singleton pregnancies (4,756, 18% with GDM), the original model demonstrated reasonable temporal validation (c-statistic = 0.698) but suboptimal calibration (expected-observed ratio = 0.485). Updated model C2 was preferred, with a high c-statistic (0.732) and significantly better performance in closed testing.

CONCLUSION

We demonstrated updating methods to sustain predictive performance in a contemporary population, highlighting the value and versatility of prediction models for guiding risk-stratified GDM care.

Educational Technology on Urinary Incontinence during Pregnancy: Development and Validation of an Online Course for the Brazilian Population

Objective: To describe the development and validation process of an online course on urinary incontinence during pregnancy in Brazil. Materials and methods: This methodological study followed an online course’s literature search, development, and validation steps. A total of 22 specialists participated in the validation step, and the content validity index (CVI) was used. Fifty-one Physical Therapy students (target audience) also participated in the Suitability Assessment of Materials. Results: The synthesis reached in the integrative review provided the basis for the course’s theoretical content, which was regarded as suitable by the specialists regarding its content, language, presentation, stimulation/motivation, and cultural adequacy (CVI = 0.99). The target audience considered the course organized, easily understandable, engaging, and motivational, with a positive response index ranging from 84.3 % to 100 %. Conclusions: The Brazilian version of the online course was considered sufficiently adequate in content and interface quality by both specialists and the target audience.

Development, validation and clinical utility of a risk prediction model for adverse pregnancy outcomes in women with gestational diabetes: The PeRSonal GDM model

BACKGROUND

The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with gestational diabetes mellitus (GDM) would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. We aimed to develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM.

METHODS

A prediction model development and validation study was conducted on data from a observational cohort. Participants included all women with GDM from three metropolitan tertiary teaching hospitals in Melbourne, Australia. The development cohort comprised those who delivered between 1 July 2017 to 30 June 2018 and the validation cohort those who delivered between 1 July 2018 to 31 December 2018. The main outcome was a composite of critically important maternal and perinatal complications (hypertensive disorders of pregnancy, large-for-gestational age neonate, neonatal hypoglycaemia requiring intravenous therapy, shoulder dystocia, perinatal death, neonatal bone fracture and nerve palsy). Model performance was measured in terms of discrimination and calibration and clinical utility evaluated using decision curve analysis.

FINDINGS

The final PeRSonal (Prediction for Risk Stratified care for women with GDM) model included body mass index, maternal age, fasting and 1-hour glucose values (75-g oral glucose tolerance test), gestational age at GDM diagnosis, Southern and Central Asian ethnicity, East Asian ethnicity, nulliparity, past delivery of an large-for-gestational age neonate, past pre-eclampsia, GWG until GDM diagnosis, and family history of diabetes. The composite adverse pregnancy outcome occurred in 27% (476/1747) of women in the development (1747 women) and in 26% (244/955) in the validation (955 women) cohorts. The model showed excellent calibration with slope of 0.99 (95% CI 0.75 to 1.23) and acceptable discrimination (c-statistic 0.68; 95% CI 0.64 to 0.72) when temporally validated. Decision curve analysis demonstrated that the model was useful across a range of predicted probability thresholds between 0.15 and 0.85 for adverse pregnancy outcomes compared to the alternatives of managing all women with GDM as if they will or will not have an adverse pregnancy outcome.

INTERPRETATION

The PeRSonal GDM model comprising of routinely available clinical data shows compelling performance, is transportable across time, and has clinical utility across a range of predicted probabilities. Further external validation of the model to a more disparate population is now needed to assess the generalisability to different centres, community based care and low resource settings, other healthcare systems and to different GDM diagnostic criteria.

FUNDING

This work is supported by the Mothers and Gestational Diabetes in Australia 2 NHMRC funded project #1170847.

Development and validation of a clinical diagnostic model for pregnant women with renal colic in the emergency department in China: a protocol for a retrospective cohort study

INTRODUCTION

Urolithiasis affects many people throughout their lives. Among the maternal population, although the morbidity of acute urolithiasis in pregnant women is unremarkable, it is the leading cause of hospitalisation during pregnancy. There is no effective clinical diagnostic tool to help doctors diagnose diseases. Our primary aim was to develop and validate a clinical prediction model based on statistical methods to predict the probability of having disease in pregnant women who visited the emergency department because of urolithiasis-induced colic.

METHODS AND ANALYSIS

We will use multivariate logistic regression analysis to build a multivariate regression linear model. A receiver operating characteristic curve plot and calibration plot will be used to measure the discrimination value and calibration value of the model, respectively. We will also use least absolute shrinkage and selection operator regression analysis combined with logistic regression analysis to select predictors and construct the multivariate regression model. The model will be simplified to an application that has been reported before, and users will only need to enter their clinical parameters so that risk probability is automatically derived.

ETHICS AND DISSEMINATION

The review and approval documents of the clinical research ethics committee have been received from the ethics committee of our hospital (The Third Affiliated Hospital of Wenzhou Medical University). We will disseminate research findings through presentations at scientific conferences and publication in peer-reviewed journals.

Risk stratification of women with gestational diabetes mellitus using mutually exclusive categories based on the International Association of Diabetes and Pregnancy Study Groups criteria for the development of postpartum dysglycaemia: a retrospective cohort study

OBJECTIVES

Women with gestational diabetes mellitus (GDM) are more predisposed to develop postpartum diabetes mellitus (DM). This study aimed to estimate the relative risk (RR) of postpartum dysglycaemia (prediabetes and DM) using mutually exclusive categories according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria cut-off points in patients with GDM, so as to establish a risk-stratification method for developing GDM management strategies.

DESIGN, SETTING AND PARTICIPANTS

In this retrospective cohort study, 942 women who had been diagnosed with GDM (IADPSG criteria) at 24-28 weeks of gestation from November 2016 to April 2018 underwent a 75 g oral glucose tolerance test (OGTT) at 6-12 weeks postpartum in a tertiary hospital of Singapore. Seven mutually exclusive categories (three one timepoint positive categories (fasting, 1 hour and 2 hours), three two timepoint positive categories (fasting+1 hour, fasting+2 hours and 1 hour+2 hours) and one three timepoint positive category (fasting+1 hour+2 hours)) were derived from the three timepoint antenatal OGTT according to the IADPSG criteria. To calculate the RRs of postpartum dysglyceamia of each mutually exclusive group, logistic regression was applied.

RESULTS

924 mothers with GDM, whose mean age was 32.7±4.7 years, were mainly composed of Chinese (45.4%), Malay (21.7%) and Indian (14.3%) ethnicity. The total prevalence of postnatal dysglycaemia was 16.7% at 6-12 weeks postpartum. Stratifying subjects into seven mutually exclusive categories, the RRs of the one-time, two-time and three-time positive groups of the antenatal OGTT test were 1.0 (Ref.), 2.0 (95% CI=1.3 to 3.1; p=0.001) and 6.7 (95% CI=4.1 to 10.9; p<0.001), respectively, which could be used to categorise patients with GDM into low-risk, intermediate-risk and high-risk group.

CONCLUSIONS

Mutually exclusive categories could be useful for risk stratification and early management of patients with prenatal GDM. It is plausible and can be easily translated into clinical practice.

Surgical Treatment is Still Recommended for Patients Over 75 Years with IA NSCLC: A Predictive Model Based on Surveillance, Epidemiology and End Results Database

Background

To determine the populations who suitable for surgical treatment in elderly patients (age ≥ 75 y) with IA stage.

Methods

The clinical data of NSCLC patients diagnosed from 2010 to 2015 were collected from the SEER database and divided into surgery group (SG) and no-surgery groups (NSG). The confounders were balanced and differences in survival were compared between groups using PSM (Propensity score matching, PSM). Cox regression analysis was used to screen the independent factors that affect the Cancer-specific survival (CSS). The surgery group was defined as the patients who surgery-benefit and surgery-no benefit according to the median CSS of the no-surgery group, and then randomly divided into training and validation groups. A surgical benefit prediction model was constructed in the training and validation group. Finally, the model is evaluated using a variety of methods.

Results

A total of 7297 patients were included. Before PSM (SG: n = 3630; NSG: n = 3665) and after PSM (SG: n = 1725, NSG: n = 1725) confirmed that the CSS of the surgery group was longer than the no-surgery group (before PSM: 82 vs. 31 months, P < .0001; after PSM: 55 vs. 39 months, P < .0001). Independent prognostic factors included age, gender, race, marrital, tumor grade, histology, and surgery. In the surgery cohort after PSM, 1005 patients (58.27%) who survived for more than 39 months were defined as surgery beneficiaries, and the 720 patients (41.73%) were defined surgery-no beneficiaries. The surgery group was divided into training group 1207 (70%) and validation group 518 (30%). Independent prognostic factors were used to construct a prediction model. In training group (AUC = .678) and validation group (AUC = .622). Calibration curve and decision curve prove that the model has better performance.

Conclusions

This predictive model can well identify elderly patients with stage IA NSCLC who would benefit from surgery, thus providing a basis for clinical treatment decisions.

An Update on Screening Strategies for Gestational Diabetes Mellitus: A Narrative Review

Gestational diabetes mellitus (GDM) is a frequent medical complication during pregnancy. Screening and diagnostic practices for GDM are inconsistent across the world. This narrative review includes data from 87 observational studies and randomized controlled trials (RCTs), and aims to give an overview of the current evidence on screening strategies and diagnostic criteria for GDM. Screening in early pregnancy remains controversial and studies show conflicting results on the benefit of screening and treatment of GDM in early pregnancy. Implementing the one-step "International Association of Diabetes and Pregnancy Study Groups" (IADPSG) screening strategy at 24-28 weeks often leads to a substantial increase in the prevalence of GDM, without conclusive evidence regarding the benefits on pregnancy outcomes compared to a two-step screening strategy with a glucose challenge test (GCT). In addition, RCTs are needed to investigate the impact of treatment of GDM diagnosed with IADPSG criteria on long-term maternal and childhood outcomes. Selective screening using a risk-factor-based approach could be helpful in simplifying the screening algorithm but carries the risk of missing significant proportions of GDM cases. A two-step screening method with a 50g GCT and subsequently a 75g oral glucose tolerance test (OGTT) with IADPSG could be an alternative to reduce the need for an OGTT. However, to have an acceptable sensitivity to screen for GDM with the IADPSG criteria, the threshold of the GCT should be lowered from 7.8 to 7.2 mmol/L. A pragmatic approach to screen for GDM can be implemented during the COVID-19 pandemic, using fasting plasma glucose (FPG), HbA1c or even random plasma glucose (RPG) to reduce the number of OGTTs needed. However, usual guidelines and care should be resumed as soon as the COVID pandemic is controlled.

The Need for Personalized Risk-Stratified Approaches to Treatment for Gestational Diabetes: A Narrative Review

Gestational diabetes mellitus (GDM) is common and is associated with an increased risk of adverse pregnancy outcomes. However, the prevailing one-size-fits-all approach that treats all women with GDM as having equivalent risk needs revision, given the clinical heterogeneity of GDM, the limitations of a population-based approach to risk, and the need to move beyond a glucocentric focus to address other intersecting risk factors. To address these challenges, we propose using a clinical prediction model for adverse pregnancy outcomes to guide risk-stratified approaches to treatment tailored to the individual needs of women with GDM. This will allow preventative and therapeutic interventions to be delivered to those who will maximally benefit, sparing expense, and harm for those at a lower risk.