PONE-D-21-35154Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetesPLOS ONE

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 [PS and YW are part funded by Medical Research Council, UK Grant number:  MR/R020981/1 Funder: MRC- UK URL: https://mrc.ukri.org/funding/

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: I Don't Know

Reviewer #4: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript authored by Nishanthi Periyathambi et al aimed to identify the factors associated with non attendance of postpartum glucose test by developing a machine leaning algorithm using available information on a cohort of gestational diabetes mellitus women (607). Their algorithm was able to predict which individuals are unlikely to attend postpartum glucose testing. The study is particularly interesting because it gives an opportunity to the health care system to adapt their strategy to improve the follow up of these individuals using simple tools.

The manuscript is well written and the data are supporting the conclusion. There is minor comments to improve the paper:

1/ It would be very helpful to further extend the discussion to include what other factors the current model is not taking in consideration and which may be very interesting to improve the accuracy of the prediction (My first thought goes to the proximity to a testing center, the flexibility in having an appointment, the nature of jobs that these individuals have...etc . How this can be addressed in future studies,...etc.

2/ The font is not homogenous in the discussion section and need to be adjusted.

Reviewer #2: In this manuscript by Nishanthi Periyathambi et al., describe factors who did not attend gestational type 2 diabetes (T2D) screening in the postpartum period using machine learning algorithms. This study is aimed to identify patient characteristics who did not attend the immediate ppGT and assess their subsequent T2D risk. Authors built a predictive model using machine learning algorithms and proposed factors associated with non-attendance of immediate postpartum glucose test. Further authors think, improved personalised education may improve postpartum glucose screening. Overall factors associated with the gestational T2D, are interesting. I have a few questions on this study, authors have to address these following questions.

Major comments

- Authors should highlight significant values in tables with asterisk marks or underline etc., for younger, unmarried, multiparous, BMI and smoking during pregnancy.

- Authors should describe the details of model analysis in the methods section. Readers benefited by including the S1 file which mentioned "Stepwise 1 description of the Machine learning" into methods section.

- Results sections of the study were not well explained, what is the concept diagram used in this study, how does it generate helpful outcomes of the figure S2 for next steps. The area under ROC was 0.72. How does this value help to decide the factors associated with non attendance of glucose tests? The outcome results of decision curve analysis is not explained in detail. How does decision curve analysis can be used to find factors or glucose levels in women who did not attend gestational type 2 diabetes (T2D) screening in the postpartum period.

- Authors should provide any previous examples or references for the machine learning (ML) used in this study. How does this ML help in the example disease predictive models?

- Authors mention in line 148 “After getting assurance of acceptable performance of this method” how does the assurance of method was confirmed by authors in this study, please explain features that help to get confidence of the model.

Minor comments

- Authors should report how hospitals can be improved the personalised education for postpartum glucose screening using this study, whether this can be applied to hospitals in many countries or specific to a country. How it can be improved could be described in discussion or if any country has better screening for gestational type 2 diabetes (T2D) in the postpartum period, additional details of these elements could be helpful to improve the study or personalised education.

- Add a short form for the area under the ROC as AUROC for figure2 legend or results section describe figure2.

- Label x and y axis in fig S5.

Reviewer #3: Reviewer’s comment

In the study titled ‘Machine learning prediction of nonattendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes,’ the authors Periyathambi et al. have attempted to find the characteristics/habits of the women who did not attend postpartum glucose testing (ppGT) despite the reminder from the hospital staff. This is a retrospective cohort study of all 607 GDM women who were ppGT due between January 2016 and December 2019 at the George Eliot Hospital NHS Trust, UK. This study indicates that the women who did not attend ppGT were younger, multiparous, obese, and continued smoking during pregnancy. Most of these characteristics of individuals are comparable with their previous study (Venkararaman et al., 2015). The authors have used machine learning (Python version 3.7) for predicting the type of women who were more likely to have nonattendance for ppGT. This study has many limitations such as small sample size, uniformity in reminding the individuals, etc. However, this preliminary study can encourage researchers to do a study on a larger sample size in the future. Following are a few suggestions that might improve the article.

1. The authors may explain how different machine learning results are compared to other analyses such as manual analysis.

2. The in-person consultation with hospital staff at the time of discharging from the hospital after delivery can play an important role in whether the woman will attend ppGT or not. Also, the amount of importance given for the consultation may vary among hospital staff.

3. The authors may consider including a future direction such as ‘A study with larger sample size would give better clarity on machine learning for nonattendance for ppGT.

Reviewer #4: The manuscript by Periyathambi et al, titled " Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes" studies the social and physiological factors associated with non-attendance of immediate postpartum glucose test using machine learning algorithm in women with gestational diabetes mellitus.

The study also presents correlation between non-attendance and higher risk of conversion to type 2 diabetes mellitus.

The study is well designed and executed and the import of the report is evident in its findings. However, I would like the authors to discuss the effectiveness of their machine learning model and its predictive power in a different socio-economic setting – for example in under-developed or developing societies. This would certainly enhance the quality and relevance of the manuscript.

Overall, the authors did an excellent job in organizing their workflow, and the manuscript is easily understandable even for non-expert readers. I applaud the authors for their commendable effort.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Anil Mathew Tharappel

Reviewer #4: Yes: Madhurima Dhara

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Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes

PONE-D-21-35154R1

Dear Dr. Saravanan,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: No

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: All the comments were addressed by the authors.

Reviewer #3: In the revised article, titled 'Machine learning prediction of non-attendance to postpartum glucose screening and

subsequent risk of type 2 diabetes following gestational diabetes' the authors response and changes made are satisfactory.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No