Dataset Information

Toward a Multivariate Prediction Model of Pharmacological Treatment for Women With Gestational Diabetes Mellitus: Algorithm Development and Validation.

ABSTRACT:

Background

Successful management of gestational diabetes mellitus (GDM) reduces the risk of morbidity in women and newborns. A woman's blood glucose readings and risk factors are used by clinical staff to make decisions regarding the initiation of pharmacological treatment in women with GDM. Mobile health (mHealth) solutions allow the real-time follow-up of women with GDM and allow timely treatment and management. Machine learning offers the opportunity to quickly analyze large quantities of data to automatically flag women at risk of requiring pharmacological treatment.

Objective

The aim of this study is to assess whether data collected through an mHealth system can be analyzed to automatically evaluate the switch to pharmacological treatment from diet-based management of GDM.

Methods

We collected data from 3029 patients to design a machine learning model that can identify when a woman with GDM needs to switch to medications (insulin or metformin) by analyzing the data related to blood glucose and other risk factors.

Results

Through the analysis of 411,785 blood glucose readings, we designed a machine learning model that can predict the timing of initiation of pharmacological treatment. After 100 experimental repetitions, we obtained an average area under the receiver operating characteristic curve of 0.80 (SD 0.02) and an algorithm that allows the flexibility of setting the operating point rather than relying on a static heuristic method, which is currently used in clinical practice.

Conclusions

Using real-time data collected via an mHealth system may further improve the timeliness of the intervention and potentially improve patient care. Further real-time clinical testing will enable the validation of our algorithm using real-world data.

SUBMITTER: Velardo C

PROVIDER: S-EPMC7991989 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundNon-pharmacological interventions are the first line of Gestational diabetes mellitus (GDM) management. Community-based interventions are cheaper, more accessible, with higher patient satisfaction.ObjectivesTo systematically review community-based non-pharmacological interventions and evaluate their effectiveness for GDM.Search strategyTwelve bibliographic databases and reference list of related studies from inception until January 2022.Selection criteriaAll primary studies of community-based non-pharmacological interventions for GDM reported in English which investigated any behavioural or clinical outcome(s).Data collection and analysisData were extracted using modified Cochrane's data extraction template. Studies were evaluated using Cochrane Collaboration's risk of bias tool. Narrative synthesis was used to summarise findings. This study is registered with PROSPERO (CRD42021257634).Main resultsTwenty-seven studies involving 6,242 pregnant women with GDM investigated self-management programmes, medical nutrition/diet therapy, exercise/physical activity, combined diet and exercise, calcium plus vitamin D supplementation, and continuous glucose monitoring. Self-management programmes were more effective than routine care in improving self-efficacy, two-hour postprandial blood glucose, and lifestyle behaviours but were as effective as routine care in improving infant birth weight. Self-management programmes were superior to or as effective as usual care in improving fasting blood glucose, blood glucose control, glycated haemoglobin, macrosomia, and preterm delivery. Medical nutrition/diet therapy was more effective than usual care in improving postprandial blood glucose levels. Postprandial blood glucose levels were better improved by regular supervised exercise plus daily brisk walks or a daily walking intervention than routine obstetric care or no treatment. The effects of exercise/physical activity programmes were mostly inconsistent for other outcomes. Diet and exercise were superior to diet alone in reducing maternal weight gain although there were similar outcomes for other pregnancy and foetal outcomes. Limited or conflicting evidence was found for other outcomes and interventions including calcium and vitamin D supplementation and continuous glucose monitoring intervention.ConclusionsCommunity-based non-pharmacological interventions are more effective than placebo; and are more or as effective as usual care. Self-management programmes and medical nutrition/diet therapy had the most promising GDM outcomes.FundingThere was no funding for this study. The study design, data collection, data analysis and interpretation, and writing of this manuscript were not influenced externally by any funder.

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