PONE-D-22-07622Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-carePLOS ONE

Dear Dr. Burton

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Sanjeev Bhavnani MD is a scientific advisor to Corvista Health and Blumio; consultant to Bristol Meyers Squibb, Pfizer, and Infineon Semiconductor; data safety monitoring board chair at Proteus Digital; has received research support from Scripps Clinic and the Qualcomm Foundation and is member of the healthcare innovation advisory boards at the American College of Cardiology, American Society of Echocardiography, and BIOCOM (all non-profit institutions with all positions voluntary).

Jeremiah P. Depta MD reports consulting fees from Edwards Lifesciences LLC, Boston Scientific, V wave Medical Ltd and Abbot.

Brett Bennett MD reports payment or honoraria for lecture from Philips.

Horace R. Gillins BS, Ian Shadforth EngD, Emmanuel Lange, Abhinav Doomra MScAC, Mohammad Firouzi MSc, Farhad Fathieh PhD, Timothy Burton BComp, Ali Khosousi PhD, Shyam Ramchandami PhD and William E. Sanders Jr. MD report employment by CorVista Health, and stock options in the same.

Frank Smart MD reports grants or contracts from Abbot (GUIDE HF clinical trial), NIH / Ohio State (DCM genetic study), Duke Clinical Research (Transform HF), CorVista Health (Pulmonary Hypertension clinical trial), and participation on a Data Safety Monitoring Board or Advisory Board (Abbott Medical; GUIDE-HF Steering committee).

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Additional Editor Comments:

This paper has been reviewed. A number of comments have been raised that require responses and revision.

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Reviewers' comments:

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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: Partly

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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: No

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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: In this manuscript, the authors aimed to verify if phase space ML analysis with orthogonal voltage gradient and photoplethysmography as features can be used to predict elevated left ventricular end diastolic pressure. Overall, the manuscript is well-structured and clearly written. Below I list some comments for this manuscript.

1. The authors can provide more clear description for the definition of the indices used as features (perhaps provide some figure illustrations).

2. Minor comment: since this is a multi-center study, the authors can provide descriptions about the chosen centers. For example, why choosing these centers? Any special subject characteristic for each center?

Reviewer #2: Introduction:

1. It would be nice to include some literature reviews on any previous studies predicting LVEDP using non-invasive measurement, if any.

Results:

1. Are all the results shown in the results section using the ensembled model?

Discussion:

1. The discussion should focus on the results presented in this study, and the literature reviews should be moved to the introduction.

2. In the limitation, the authors mentioned that study subjects taking diuretics might affect the specificity. Have the authors tried to remove those subjects and rerun the model to test that hypothesis?

3. The authors should add the study populations are patients likely to have CAD to the limitation.

4. The authors should also add the possibility of overfitting to the limitation unless they can justify the large number of features they used in the study.

Methods:

1. The study population are patients that are likely to have CAD, which seems to be a biased dataset to predict LVEDP. If the aim is being able to predict the LVEDP values for just patients who are likely to have CAD, then this would be acceptable, but if the aim is to predict LVEDP for a wider range of populations, then it would make sense to include other patients irrelevant to having CAD.

2. Line 336: Is it without CVD or CAD? If it is CVD, then which diseases are considered here? As CVD is a very wide category.

3. Is the validation group also patients likely to have CAD?

4. What type of catheter was used to measure LVEDP, specs?

5. Line 353: it says the following 4 categories, but there are 5 listed

6. Line 368: what are the symptoms are considered here for HF

7. From Line 414 – 420, please give some specific numbers for the cut-offs, e.g. high-frequency noise above XXX Hz?? Maximum measurable value XXX?

8. Could you give a bit more details on how the features are calculated? I imagine you have both OVG and PPG signals with multiple cardiac cycles. Do you calculate the features cycle by cycle? If so, how do you get the final subject level features based on the cycle features? Do you calculate the features over the whole recorded signal? If so, what is the time window you use for each recording? If the time window is constant, then how many seconds? If not, please justify.

9. Could you provide a list of features used in the appendix? And how they are calculated?

10. Is the outlier detection for the cycle level features or in-between subjects as well?

11. The rationale for choosing the 13 machine-learning models is unclear and how the hyper-parameters were chosen is also unclear. The authors claim that the ensemble outputs are intended to outperform any single model, but would they be able to provide some results showing that?

12. In terms of training the model, what type of training was used? Cross-validation?

13. Can the author provide some information about what they did to prevent overfitting the models?

14. Can the author provide some details on how the 95% CIs were calculated?

General question:

1. How do you avoid overfitting the models?

2. Have you investigated the correlations between features?

Figures:

Figure 8: could you add what each colour and lines represent?

Figure 9: This figure is not very clear. Personally, I don’t think it is necessary to have a figure to describe the random forest, as it is a very well known method. I think it is best to describe what setup was used in the Random Forest, such as bootstrapping=True, how many estimators, any limits for the number of trees, leaves etc. Same for the figure of XGBoost in the appendix.

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Reviewer #1: No

Reviewer #2: No

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PONE-D-22-07622R1Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-carePLOS ONE

Dear Sir,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Kindly note the comments of the reviewer to assist in the further revision needed for your manuscript, Please ensure that your decision is justified on PLOS ONE’s publication criteria and not, for example, on novelty or perceived impact.

For Lab, Study and Registered Report Protocols: These article types are not expected to include results but may include pilot data. 

==============================

Please submit your revised manuscript by Oct 27 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Chim C. Lang

Academic Editor

PLOS ONE

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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 #1: All comments have been addressed

Reviewer #2: (No Response)

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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 #1: Yes

Reviewer #2: Partly

**********

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

Reviewer #1: Yes

Reviewer #2: 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 #1: Yes

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 #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. 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: (No Response)

Reviewer #2: 1. About overfitting

a. Generally, a good ratio between sample size and parameters to avoid overfitting is somewhere above 10 to 1. This means for an average of 149 features, you would need about 1490 subjects. If I am not mistaken, the maximum number included in this study is 684 post exclusions. It is true that the tree-based methods (Random Forest and XGB) and the elastic net can help reduce the risk of overfitting, but it does not mean you will not overfit because you are using these models. I have seen many overfitting cases when people use those models. I do see the authors trying to manage overfitting by limiting the maximum depth, but there are other parameters as well that need to be looked into.

b. One of my additional questions here is how did you choose the hyperparameters for your tree-based models? What type of tunning was performed?

c. Similarly, what type of tunning was conducted for the elastic net model? What makes you choose 0.003 or 0.01 for alpha?

d. Also, an AUC of 0.81 from the validation does not prove that overfitting did not occur since it is just one data point. One can easily argue if there is no overfitting, you could have an AUC of 0.95.

e. In summary, it is still not very clear how the hyperparameters were chosen for each model. And it is unfair for the authors to conclude that the overfitting did not occur based on an AUC of 0.81 from validation. Also, please remove the word “high-performing”, as a word like “high” or “low” is subjective and not scientific.

2. Regarding the type of catheter, the authors have replied to Comment #10 that the choice of the catheter was left to the discretion of each cardiologist, which means the device for measuring the ground truth can be different between time and the medical centre. Have the authors analysed the potential impact of this?

3. Is there a reason that the authors can only provide a list of the families of features used in the study instead of a list containing the actual features?

4. What type of cross-validation did the authors use? Can they specify it in the paper?

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Reviewer #1: No

Reviewer #2: No

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Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care

PONE-D-22-07622R2

Dear Dr

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