PONE-D-24-22158Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): study protocol for the creation of a risk prediction modelPLOS ONE

Dear Dr. Trautwein,

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PLOS ONE

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[I have read the journal's policy and the authors of this manuscript have the following competing interests: B. Jungwirth and S. Kagerbauer received grants from Löwenstein Medical Innovation (Berlin, Germany). M. Blobner received research support from MSD (Haar, Germany), fees for consultancy or lectures from GE Healthcare (Helsinki, Finland), Grünenthal (Aachen, Germany), and Senzime (Landshut, Germany).].

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3. In the online submission form, you indicated that [Due to German data protection regulations, we are not allowed to publish individual patient data. Access to the full protocol and data can be shared upon reasonable request from the corresponding author after appraisal by the data protection officer. The statistical code and machine learning code will be shared via a GitHub repository.]

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does the manuscript provide a valid rationale for the proposed study, with clearly identified and justified research questions?

The research question outlined is expected to address a valid academic problem or topic and contribute to the base of knowledge in the field.

Reviewer #1: Partly

Reviewer #2: Yes

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2. Is the protocol technically sound and planned in a manner that will lead to a meaningful outcome and allow testing the stated hypotheses?

The manuscript should describe the methods in sufficient detail to prevent undisclosed flexibility in the experimental procedure or analysis pipeline, including sufficient outcome-neutral conditions (e.g. necessary controls, absence of floor or ceiling effects) to test the proposed hypotheses and a statistical power analysis where applicable. As there may be aspects of the methodology and analysis which can only be refined once the work is undertaken, authors should outline potential assumptions and explicitly describe what aspects of the proposed analyses, if any, are exploratory.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Is the methodology feasible and described in sufficient detail to allow the work to be replicable?

Descriptions of methods and materials in the protocol should be reported in sufficient detail for another researcher to reproduce all experiments and analyses. The protocol should describe the appropriate controls, sample size calculations, and replication needed to ensure that the data are robust and reproducible.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors described where all data underlying the findings will be made available when the study is complete?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception, at the time of publication. 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: No

Reviewer #2: 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 #1: Yes

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 and, if applicable, provide comments about issues authors must address before this protocol can be accepted for publication. You may also include additional comments for the author, including concerns about research or publication ethics.

You may also provide optional suggestions and comments to authors that they might find helpful in planning their study.

(Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The process of physicians deciding whether images meet criteria and potentially excluding patients introduces selection bias.

Using mean or modal imputation for missing data is overly simplistic and can introduce bias. More sophisticated methods like multiple imputation may be considered.

What test was used for the sample size calculation?

While AUROC is mentioned, in imbalanced datasets (which are common in medical studies), AUPRC may be more informative.

The use of complex deep learning models with relatively limited data raises concerns about overfitting, especially with no clear regularization strategy mentioned.

There are no plans for cross-validation or external validation.

Given that POPC is likely a relatively rare event, there's no clear strategy for dealing with potential class imbalance in the dataset.

Using Youden's index for cut-off determination may not be optimal, especially if the costs of false positives and false negatives are not equal in this clinical context. How will the risk score be calculated?

Reviewer #2: This experimental protocol proposes developing a predictive model for post-surgical pulmonary complications by integrating standardized lung ultrasound examinations with clinical data and employing machine learning techniques based on neural networks and integrated methods. The approach is highly innovative, particularly for its potential application in the perioperative period—a complex clinical environment. It is convenient and cost-effective, with the benefit of serving as an early warning system in clinical practice. Below are some of my comments:

1. The core element in developing a disease risk prediction model using AI and machine learning is the algorithm itself. Developing such a model requires expertise in machine learning. Please provide more details about the professionals involved in your team, the design of the model, and the software tools utilized.

2. Has the model been externally validated? How do you plan to ensure its broad applicability across diverse clinical settings?

3. Lung ultrasound has limited use in clinical practice due to the interference of air in the lungs. Additionally, the complexity of the ultrasound images is influenced by factors such as probe position and angle. How to ensure the accuracy and reliability of the model considering these challenges?

4. Pulmonary complications can be presented in a variety of ways, with differing severity. Consequently, there will likely be numerous outcome events (endpoints) in this study. Some mild postoperative pulmonary complications may not require intervention, and predicting such cases may not provide substantial clinical value. Does this study aim to focus specifically on identifying the more severe pulmonary complications that necessitate medical intervention?

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Reviewer #1: No

Reviewer #2: No

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Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): study protocol for the creation of a risk prediction model

PONE-D-24-22158R1

Dear Dr. Britta Trautwein,

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,

Silvia Fiorelli

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Congratulations to the authors and thanks to the reviewers for the provided suggestions which really helped improve the quality of the manuscript

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does the manuscript provide a valid rationale for the proposed study, with clearly identified and justified research questions?

The research question outlined is expected to address a valid academic problem or topic and contribute to the base of knowledge in the field.

Reviewer #1: Yes

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2. Is the protocol technically sound and planned in a manner that will lead to a meaningful outcome and allow testing the stated hypotheses?

The manuscript should describe the methods in sufficient detail to prevent undisclosed flexibility in the experimental procedure or analysis pipeline, including sufficient outcome-neutral conditions (e.g. necessary controls, absence of floor or ceiling effects) to test the proposed hypotheses and a statistical power analysis where applicable. As there may be aspects of the methodology and analysis which can only be refined once the work is undertaken, authors should outline potential assumptions and explicitly describe what aspects of the proposed analyses, if any, are exploratory.

Reviewer #1: Yes

**********

3. Is the methodology feasible and described in sufficient detail to allow the work to be replicable?

Descriptions of methods and materials in the protocol should be reported in sufficient detail for another researcher to reproduce all experiments and analyses. The protocol should describe the appropriate controls, sample size calculations, and replication needed to ensure that the data are robust and reproducible.

Reviewer #1: Yes

**********

4. Have the authors described where all data underlying the findings will be made available when the study is complete?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception, at the time of publication. 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

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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 #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above and, if applicable, provide comments about issues authors must address before this protocol can be accepted for publication. You may also include additional comments for the author, including concerns about research or publication ethics.

You may also provide optional suggestions and comments to authors that they might find helpful in planning their study.

(Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: All my concerns are addressed.

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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 #1: No

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