Telemedical Approaches to Managing Gestational Diabetes Mellitus During COVID-19: Systematic Review
Bibliographic record
Abstract
Background In 2019, a new coronavirus emerged in China, and the disease caused by the virus (COVID-19) was rapidly classified as a pandemic. Pregnant women with gestational diabetes mellitus (GDM) are considered to be at risk for severe COVID-19. In the context of the pandemic, there are serious concerns regarding adverse effects on maternal and neonatal outcomes for women with GDM. Effective treatments for patients with GDM are therefore particularly important. Due to contact restrictions and infection risks, digital approaches such as telemedicine are suitable alternatives. Objective This systematic review aims to summarize currently available evidence on maternal and offspring outcomes of pregnant women with GDM and COVID-19 and to examine telemedical interventions to improve maternal glycemic control during the COVID-19 pandemic. Methods Publications were systematically identified by searching the Cochrane Library, MEDLINE via PubMed, Web of Science Core Collection, Embase, and CINAHL databases for studies published up to March 2021. We sorted the COVID-19 studies by outcome and divided the telemedical intervention studies into web-based and app-based groups. We analyzed case reports (COVID-19) and both randomized and nonrandomized controlled clinical trials (telemedicine). To determine the change in glycated hemoglobin A1c (HbA1c), we pooled appropriate studies and calculated the differences in means, with 95% CIs, for the intervention and control groups at the end of the interventions. Results Regarding COVID-19 studies, we identified 11 case reports, 3 letters, 1 case series, and 1 retrospective single-center study. In total, 41 patients with GDM and COVID-19 were analyzed. The maternal and neonatal outcomes were extremely heterogeneous. We identified adverse outcomes for mother and child through the interaction of GDM and COVID-19, such as cesarean deliveries and low Apgar scores. Furthermore, we selected 9 telemedicine-related articles: 6 were randomized controlled trials, 2 were clinical controlled trials, and 1 was a quasi-experimental design. In total, we analyzed 480 patients with GDM in the intervention groups and 494 in the control groups. Regarding the quality of the 9 telemedical studies, 4 were rated as strong, 4 as moderate, and 1 as weak. Telemedical interventions can contribute to favorable impacts on HbA1c and fasting blood glucose values in the context of the COVID-19 pandemic. Meta-analysis revealed a mean difference in HbA1c of –0.19% (95% CI 0.34% to 0.03%) for all telemedical interventions, –0.138% (95% CI –0.24% to –0.04%) for the web-based interventions, and –0.305% (96% CI –0.88% to 0.27%) for the app-based interventions. Conclusions Telemedicine is an effective approach in the context of COVID-19 and GDM because it enables social distancing and represents optimal care of patients with GDM, especially with regard to glycemic control, which is very important in view of the identified adverse maternal and neonatal outcomes. Further research is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".