PONE-D-24-03036Development and validation of a cardiovascular risk prediction model for Sri LankansPLOS ONE

Dear Dr. Mettananda,

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==============================This study has been reviewed by two of the eminent reviewers. They find your work interesting, however they have raised multiple concern on the present version of the manuscript. Therefore, a revised version addressing all the concernns of reviewers is needed. 

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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

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

Reviewer #1: Yes

Reviewer #2: 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

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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

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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: This article on the development and validation of a cardiovascular risk prediction model for the Sri Lankan population provides a comprehensive research report, including the development, validation, and assessment of the model's practicality. Here are some comments on various aspects of the article:

Importance and Background of the Research:

The article well articulates the importance of developing a specific cardiovascular disease risk prediction model for Sri Lankans, particularly considering that the currently used models are not tailored to the Sri Lankan population, which may lead to inaccurate risk assessments.

Research Design and Methods:

The research design is sound, utilizing a large cohort dataset and a decade-long follow-up, which enhances the predictive power and reliability of the model. The application of machine learning methods is advanced, and the article provides a detailed description of data management, model development, and statistical analysis processes. Modern data processing techniques, such as SMOTE for dealing with class imbalance and stratified K-fold cross-validation for model performance evaluation, were used.

Results:

The results section clearly presents the comparison of the three models with the WHO risk charts, showing significant improvements in predictive performance. Notably, the SLCVD score demonstrated better predictive ability than the WHO risk charts in external validation, enhancing the practical value of the model.

Conclusions and Applications:

The conclusion clearly outlines the advantages of the SLCVD score and emphasizes its convenience in practical applications, especially with the development of an online platform that makes this tool more widely accessible to Sri Lankans.

Discussion:

The discussion delves into the advantages and potential practicality of the SLCVD score while also honestly mentioning the model's limitations and directions for future improvements. For instance, although the SLCVD score performs well in prediction, the actual clinical application effect still needs further research to confirm.

Overall, this article provides a cardiovascular disease risk scoring tool of significant clinical relevance for the Sri Lankan population. The research methods are rigorous, the results are compelling, and it has the potential for a significant impact on improving cardiovascular disease prevention and management in Sri Lanka. Future research might consider validating the SLCVD score in a broader population and exploring its application in other settings. While this article does considerable work in developing and validating the predictive model and provides detailed methods and results, there are still some potential shortcomings and issues that might affect the interpretation of its conclusions and the general applicability of the model. Here are my thoughts:

1. Whether the research can represent the entire Sri Lankan population is a question. Is the sample sufficiently diverse, including different socioeconomic statuses, educational levels, and geographical locations, which may affect cardiovascular risk and health outcomes?

2. Although the model has been externally validated in another cohort, this validation cohort is hospital-based, which might differ from community residents. This could limit the model's applicability to the general population.

3. Risk Factor Selection: Has the model considered all relevant cardiovascular disease risk factors? Some potential risk factors, such as family history, dietary habits, and physical activity levels, may not have been included.

4. Clinical Relevance of Results: While the model's statistical performance indicators such as AUC and F1 scores are high, these do not always translate directly into effectiveness in clinical practice. Further research is needed to assess the model's application in actual clinical settings.

5. Effectiveness of Interventions: The article does not mention whether the model can predict which interventions are most effective at reducing an individual's risk of cardiovascular disease.

To fully understand the impact of these potential issues, further research and analysis might be necessary. Moreover, the article should detail these limitations in the discussion section and consider how they might affect the interpretation of the research findings.

Reviewer #2: In the current study, the authors aimed to develop a prediction model specifically for the Sri Lankan population. For this, data from the RHS cohort is used to create 3 machine learning models. The most simple model is compared to the WHO CVD risk charts, as this is most applicable to clinical practice, on which I agree. The model seems to be slightly more accurate than the WHO CVD risk charts in a small external validation set. I do have a few concerns on the study:

- How representative is the derivation data? Does this have national coverage, or is this specific to a region, possibly a higher educated, more urban university region of the country? What is the response rate of the cohort?

- In the data, individuals that could not be traced are excluded. I can imagine that his is often individuals that have died. Please add some information about whether this may have caused selection and how many individuals were unable to be found.

- To account for missing data a complex approach is used. Why not just use ‘regular’ multiple imputation? Please elaborate. Please also refer to methodological papers showing the validity of this approach.

- An oversampling approach is done to correct for class imbalance. Please verify that the correction is not a worse problem than the imbalance itself, see for example https://academic.oup.com/jamia/article/29/9/1525/6605096 . Please discuss this.

- A complicated machine learning approach is used in a quite small dataset, which always leads to the risk of overfitting. What was done to minimize this? It can be considered to use a regular regression-based technique (Cox model, or penalized Cox model such LASSO/RIDGE) for comparison.

- External validation is done in a set of individuals admitted to a hospital. How representative is this cohort? It is written like it is constructed as a kind of case-control cohort. Please elaborate on the exact recruitment mechanism.

- Please clarify the calculation of WHO risks: where the charts used or was the underlying algorithm applied? And was this the version with lab or without lab variables?

- The link supplied to the online calculator doesn’t work for me. However, I think it is great the authors have developed such a calculator.

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Reviewer #1: No

Reviewer #2: No

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Development and validation of a cardiovascular risk prediction model for Sri Lankans using machine learning

PONE-D-24-03036R1

Dear Dr. Mettananda,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Gyaneshwer Chaubey

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

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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

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

Reviewer #2: Yes

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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: No

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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: Yes

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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: Thank you for addressing my concerns. Especially the data is better described now. I still haven't got the calculator working, but I expect the authors will work on that.

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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

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