Dataset Information

Performance of a postnatal metabolic gestational age algorithm: a retrospective validation study among ethnic subgroups in Canada.

ABSTRACT:

Objectives

Biological modelling of routinely collected newborn screening data has emerged as a novel method for deriving postnatal gestational age estimates. Validation of published models has previously been limited to cohorts largely consisting of infants of white Caucasian ethnicity. In this study, we sought to determine the validity of a published gestational age estimation algorithm among recent immigrants to Canada, where maternal landed immigrant status was used as a surrogate measure of infant ethnicity.

Design

We conducted a retrospective validation study in infants born in Ontario between April 2009 and September 2011.

Setting

Provincial data from Ontario, Canada were obtained from the Institute for Clinical Evaluative Sciences.

Participants

The dataset included 230?034 infants born to non-landed immigrants and 70?098 infants born to immigrant mothers. The five most common countries of maternal origin were India (n=10?038), China (n=7468), Pakistan (n=5824), The Philippines (n=5441) and Vietnam (n=1408). Maternal country of origin was obtained from Citizenship and Immigration Canada's Landed Immigrant Database.

Primary and secondary outcome measures

Performance of a postnatal gestational age algorithm was evaluated across non-immigrant and immigrant populations.

Results

Root mean squared error (RMSE) of 1.05 weeks was observed for infants born to non-immigrant mothers, whereas RMSE ranged from 0.98 to 1.15 weeks among infants born to immigrant mothers. Area under the receiver operating characteristic curve for distinguishing term versus preterm infants (?37?vs?<37 weeks gestational age or >34?vs ?34 weeks gestational age) was 0.958 and 0.986, respectively, in the non-immigrant subgroup and ranged from 0.927 to 0.964 and 0.966 to 0.99 in the immigrant subgroups.

Conclusions

Algorithms for postnatal determination of gestational age may be further refined by development and validation of region or ethnicity-specific models. However, our results provide reassurance that an algorithm developed from Ontario-born infant cohorts performs well across a range of ethnicities and maternal countries of origin without modification.

SUBMITTER: Hawken S

PROVIDER: S-EPMC5589017 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

Performance of a postnatal metabolic gestational age algorithm: a retrospective validation study among ethnic subgroups in Canada.

Hawken Steven S Ducharme Robin R Murphy Malia S Q MSQ Atkinson Katherine M KM Potter Beth K BK Chakraborty Pranesh P Wilson Kumanan K

BMJ open 20170903 9

<h4>Objectives</h4>Biological modelling of routinely collected newborn screening data has emerged as a novel method for deriving postnatal gestational age estimates. Validation of published models has previously been limited to cohorts largely consisting of infants of white Caucasian ethnicity. In this study, we sought to determine the validity of a published gestational age estimation algorithm among recent immigrants to Canada, where maternal landed immigrant status was used as a surrogate mea ...[more]

PMID: 28871012

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Project description:Aims/hypothesisGestational diabetes mellitus (GDM) is a heterogeneous condition. Given such variability among patients, the ability to recognise distinct GDM subgroups using routine clinical variables may guide more personalised treatments. Our main aim was to identify distinct GDM subtypes through cluster analysis using routine clinical variables, and analyse treatment needs and pregnancy outcomes across these subgroups.MethodsIn this cohort study, we analysed datasets from a total of 2682 women with GDM treated at two central European hospitals (1865 participants from Charité University Hospital in Berlin and 817 participants from the Medical University of Vienna), collected between 2015 and 2022. We evaluated various clustering models, including k-means, k-medoids and agglomerative hierarchical clustering. Internal validation techniques were used to guide best model selection, while external validation on independent test sets was used to assess model generalisability. Clinical outcomes such as specific treatment needs and maternal and fetal complications were analysed across the identified clusters.ResultsOur optimal model identified three clusters from routinely available variables, i.e. maternal age, pre-pregnancy BMI (BMIPG) and glucose levels at fasting and 60 and 120 min after the diagnostic OGTT (OGTT0, OGTT60 and OGTT120, respectively). Cluster 1 was characterised by the highest OGTT values and obesity prevalence. Cluster 2 displayed intermediate BMIPG and elevated OGTT0, while cluster 3 consisted mainly of participants with normal BMIPG and high values for OGTT60 and OGTT120. Treatment modalities and clinical outcomes varied among clusters. In particular, cluster 1 participants showed a much higher need for glucose-lowering medications (39.6% of participants, compared with 12.9% and 10.0% in clusters 2 and 3, respectively, p<0.0001). Cluster 1 participants were also at higher risk of delivering large-for-gestational-age infants. Differences in the type of insulin-based treatment between cluster 2 and cluster 3 were observed in the external validation cohort.Conclusions/interpretationOur findings confirm the heterogeneity of GDM. The identification of subgroups (clusters) has the potential to help clinicians define more tailored treatment approaches for improved maternal and neonatal outcomes.

Dataset Information

Performance of a postnatal metabolic gestational age algorithm: a retrospective validation study among ethnic subgroups in Canada.

Objectives

Design

Setting

Participants

Primary and secondary outcome measures

Results

Conclusions

Publications

Performance of a postnatal metabolic gestational age algorithm: a retrospective validation study among ethnic subgroups in Canada.

Similar Datasets

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