Comparing a novel machine learning method to the Friedewald formula and Martin-Hopkins equation for low-density lipoprotein estimation

Gurpreet Singh; Yasin Hussain; Zhuoran Xu; Evan Sholle; Kelly Michalak; Kristina Dolan; Benjamin C. Lee; Alexander R. van Rosendael; Zahra Fatima; Jessica M. Peña; Peter W. F. Wilson; Antonio M. Gotto Jr.; Leslee J. Shaw; Lohendran Baskaran; Subhi J. Al’Aref

doi:10.1371/journal.pone.0239934

Peer Review History

Original SubmissionMarch 12, 2020
21 Jul 2020 Decision Letter - Simeon-Pierre Choukem, Editor PONE-D-20-07240 Comparing a novel Machine Learning method to the Friedewald formula and Martin-Hopkins equation for Low-density Lipoprotein Estimation PLOS ONE Dear Dr. Al'Aref, 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. ACADEMIC EDITOR: The authors should address the issues raised by reviewer 1 regarding additional computations (Kappa statistics and TeA) which would strengthen the value of the results. Please submit your revised manuscript by Sep 04 2020 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: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Simeon-Pierre Choukem Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. Thank you for including your competing interests statement; "he authors have declared that no competing interests exist" We note the following; 1) Author Benjamin C. Lee reports receiving consulting fees from Cleerly Inc. 2) Author Leslee J. Shaw reports having an equity interest in Cleerly Inc. and 3) Author Gurpreet Singh is affiliated to GlaxoSmithKline. We note that one or more of the authors are employed by a commercial company: GlaxoSmithKline Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form. Please also include the following statement within your amended Funding Statement. “The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.” If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement. 2. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc. Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared. Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf. Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests 3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. In your revised cover letter, please address the following prompts: a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. We will update your Data Availability statement on your behalf to reflect the information you provide. 4. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ [Note: HTML markup is below. Please do not edit.] 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: Partly Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes ****** 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 ****** 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 ****** 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: Permit me thank the authors for the huge effort put forth in a subject that is as important as it is relevant. Summarily, this study seeks to derive a novel method that is more ACCURATE in estimating LDL-C using Machine Learning (ML). 1) The study portrays a sound scientific framework, but could be more so if a number of clarifications made: As a limitation, the authors observed that, "direct LDL-C was determined using chemical-based methods, and not with the gold standard BQ method". This raise a question, What adjustment was done that allow the authors to compare a method (FF) that was established based on samples analyzed using the standard BQ method to those of the present study analyzed using a chemical-based method(that has been shown to have inherent inaccuracy when compared to the standard BQ)? https://doi.org/10.1177%2F107424840501000106. Is it possible that the novel formula could be reliable but not necessarily valid? 2) Statistics If the interest is to derive a more ACCURATE method of LDL-C estimation, then it is a distraction to focus a lots of attention on describing the strong positive correlation between the novel and the reference method. Strong correlation does not necessarily mean accuracy. secondly, applying Kappa statistics can help us answer the question on whether an observed agreement is by chance or not by chance. Thirdly, if the observed agreement is not by chance, then assessing allowable total error (TEa) as a benchmark for performance of the novel method will be a great idea. Reviewer #2: I have found the paper original as it attemps to resolve a key problem in the estimation of LDLc in people with cardiovascular diseases and other conditions with similar etiologies. The statistical analyses as well as all the steps of the procedure are appropriate and well described. ****** 6. 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: Yes: Tasha Manases Reviewer #2: Yes:** Pr Jules Clement Nguedia Assob [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0239934.r001
Revision 1
26 Aug 2020 Author Response Reviewer Comments (Reviewer #1) Permit me thank the authors for the huge effort put forth in a subject that is as important as it is relevant. We would like to express our sincere appreciation to reviewer #1 for taking the time to review our manuscript and for providing comments that have helped us improve the manuscript. Summarily, this study seeks to derive a novel method that is more ACCURATE in estimating LDL-C using Machine Learning (ML). 1) The study portrays a sound scientific framework but could be more so if a number of clarifications made: As a limitation, the authors observed that, "direct LDL-C was determined using chemical-based methods, and not with the gold standard BQ method". This raise a question, What adjustment was done that allow the authors to compare a method (FF) that was established based on samples analyzed using the standard BQ method to those of the present study analyzed using a chemical-based method(that has been shown to have inherent inaccuracy when compared to the standard BQ)? https://doi.org/10.1177%2F107424840501000106. Is it possible that the novel formula could be reliable but not necessarily valid? We thank the reviewer for the excellent comment. We completely agree that an inherent limitation to our approach is that the “ground truth” LDL is not the accepted gold standard BQ method, which unfortunately has limited real life utility given the labor-intensive nature and significant associated cost. We sought to utilize real-world data for a proof-of-concept application in order to highlight the fact that machine learning can even improve upon relatively simplistic equations, but that applies to a relevant and daily aspect of patient care. We also wanted to highlight the fact that even though we developed an initial model with machine learning, a machine learning model can be dynamic and can become more accurate as more data is provided for model development. As such, future work is aimed at testing this model in an external validation cohort to further establish the advantage of using the methodology presented in this paper, as well as to use the data to further validate our model (by incorporating larger data, as well as data where the gold-standard LDL is measured using the BQ method). Additionally, the machine learning model used in this paper can be understood as multiple learners working together. On a fundamental level, this should allow the model to learn the underlying principle equation for calculating LDL levels and minimize the error procured by the model by learning a globally optimal equation. To that end, we have highlighted this limitation in the discussion section: 1. Fourthly, direct LDL-C was determined using chemical-based methods, and not with the gold standard BQ method, while analysis was limited to correlation with direct LDL-C while true accuracy was not established. Nevertheless, the next step will be to validate the Weill Cornell model on cohorts with LDL-C measured by BQ. 2. Future research is required in order to validate the Weill Cornell model against LDL-C measured using the reference standard BQ method, with subsequent determination of model accuracy, beyond measures of correlation as shown in the present analysis. 2) If the interest is to derive a more ACCURATE method of LDL-C estimation, then it is a distraction to focus a lots of attention on describing the strong positive correlation between the novel and the reference method. Strong correlation does not necessarily mean accuracy. We thank the reviewer for the comment. For continuous labels, mean absolute errors (MAE) is a good way to measure model accuracy. In this study, we compared the absolute prediction errors between the Weill Cornell model with Friedewald formula and Martin-Hopkins Equation by paired t test. We found that the Weill Cornell model has smaller absolute errors in both comparisons which implies higher accuracy. In addition, we utilized the matric of correlation coefficient to evaluate the model performance. It can represent the correlation extent on one hand. On the other hand, it equals to the square root of R square which can be interpreted as how much variance of the outcome can be explained by the predictor. So overall, we used two metrics to evaluate the model performance (in terms of accuracy as well as correlation). 3) Secondly, applying Kappa statistics can help us answer the question on whether an observed agreement is by chance or not by chance. We thank the reviewer for the comment. Cohen’s kappa coefficient is a good measurement to evaluate inter-rater reliability, but it is typically used for categorical variables. In the present analysis, we treated LDL values as a continuous value (rather than splitting into categories since in real life the clinician is interested in the actual value rather than the range). 4) Thirdly, if the observed agreement is not by chance, then assessing allowable total error (TEa) as a benchmark for performance of the novel method will be a great idea. We thank the reviewer for the comment. Congruent with the previous comment, we would like to clarify that our aim was to evaluate whether our model can predict an actual value rather than an actual class label. Reviewer Comments (Reviewer #2) I have found the paper original as it attempts to resolve a key problem in the estimation of LDLc in people with cardiovascular diseases and other conditions with similar etiologies. The statistical analyses as well as all the steps of the procedure are appropriate and well described. We would like to express our sincere appreciation to reviewer #2 for taking the time to review our manuscript. Again, we greatly appreciate the reviewer’s comments and hope that we have answered each point to his/her satisfaction. Thank you very much for your consideration of this manuscript for publication. Yours Sincerely, Subhi J. Al’Aref, MD, FACC (Corresponding Author) Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0239934.r002
16 Sep 2020 Decision Letter - Simeon-Pierre Choukem, Editor Comparing a novel Machine Learning method to the Friedewald formula and Martin-Hopkins equation for Low-density Lipoprotein Estimation PONE-D-20-07240R1 Dear Dr. Al’Aref, 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. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Simeon-Pierre Choukem Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0239934.r003
Formally Accepted
18 Sep 2020 Acceptance Letter - Simeon-Pierre Choukem, Editor PONE-D-20-07240R1 Comparing a novel Machine Learning method to the Friedewald formula and Martin-Hopkins equation for Low-density Lipoprotein Estimation Dear Dr. Al’Aref: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Simeon-Pierre Choukem Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0239934.r004

Open letter on the publication of peer review reports

PLOS recognizes the benefits of transparency in the peer review process. Therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. Reviewers remain anonymous, unless they choose to reveal their names.

We encourage other journals to join us in this initiative. We hope that our action inspires the community, including researchers, research funders, and research institutions, to recognize the benefits of published peer review reports for all parts of the research system.

Learn more at ASAPbio .