PONE-D-24-27654Development and Validation of an Interpretable Machine Learning Model for Predicting Left Atrial Thrombus or Spontaneous Echo Contrast in Non-Valvular Atrial Fibrillation PatientsPLOS ONE

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PLOS ONE

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Reviewers' comments:

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Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: I Don't Know

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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5. Review Comments to the Author

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Reviewer #1: This paper aims to develop and validate a predictive model of LAT/SEC risk in NVAF patients using ML methods. This paper included 1078 patients and six independent factors were identified as predictors of outcome. I have the following questions/comments:

1. Risk of stroke in patients with LAT vs. SEC are very different, SEC whilst may represent higher risk for potential development of LAT which may then lead to cardio-embolic stroke but the risk certainly would not be as high as those with established LAT. SEC sometimes may also be cleared when given isoproterenol infusion with increase in heart rates. Therfore these two outcomes should be separately observed and analyzed. And how did you analyze sludge?

2. Baseline mean LVEF 62 and 59 in those detected with LAT/SEC suggesting an overall less comorbid patient selection. What percentage of patients had reduced LVEF and heart failure? Did these patients have higher risk (and different risk factors) for LAT/SEC?

3. Patients selected from cohort undergoing catheter ablation- representing a targeted group, but it wasn't clearly stated if the following high risk groups were excluded from the study or not (they probably should be, or at least state how many patients had these): permanent AF, rheumatic heat disease, severe valve disease (especially mitral stenosis), hypertrophic cardiomyopathy, cardiac amyloidosis, patients who cannot tolerate at least 3 month of OAC post ablation.

4. How many patients were on anticoagulation at time of study and which type? And were they on long-term or only just around time of ablation? And did these factors influence risk of SEC/LAAT?

5. Did any patients undergo cardiac CT as pre imaging to ablation? Were these findings concordant with TEE? Are there any CT factors that can be incorporated into the risk model?

6. It's not quite clear which risk factors were part of which risk models. Are you able to provide maybe in appendix tables of all the parameters incorporated in each risk model and their relative weightings?

7. Add CHADSVASC to table 3 and show how it performed in the test and validation sets for predicting your outcomes compared with other models.

8. Can you suggest how you would use your findings in clinical practice? Which model would you use and in which situation. Clinically for example, if the risk is high according to your score, what further tests would you do, and what changes to management (including bloodthinner strategy) would you do?

Reviewer #2: 1- In the introduction, substitute the word "stands as" (line 21) with "is". Moreover, there is redundancy in the introductory sentence where you mention heart failure and diminishing left ventricular ejection fraction, so please adjust.

2- Also in introduction lines 24-27, merge these two sentences since the message you are sending is increasing AF prevalence with time. Simplify this part to convey your message by saying for example "the prevalence of AF is on the rise, with x numbers in 2010, that rose to x number in 2017 and is projected to double by number in the year 2050.

3- Please specify in the introduction also that left atrial appendage thrombus is part of left atrial thrombi (LAT), because it is not mentioned clearly to the reader in all of the manuscript and LAT might only evoke for some that left atrial mural thrombosis, which is part of LAT but is much less than LAA clots in terms of prevalence.

4- In the sentence discussing the role of TEE, would adopt saying that TEE is semi-invasive by nature, with rare but inherent risks and leads to patient discomfort and necessitates some form of sedation. There is a typo in the last word of this sentence (interpreted), correct to "interpretation".

5- In line 15 of second page, adjust sentence and remove the type "such", so the sentence becomes machine learning to develop predictive models for thrombus formation in AF.

6- Line 18 of the same page, delete the words most crucial feature -> the important variables

7- I congratulate the authors for developing this sophisticated ML algorithm, with the intention to convey a strong message of novelty in the filed since our only validated model to predict thrombosis and stroke risk in NVAF is the CHADVASC score. However, I would like to further explain some of the limitations and issues of this paper but not rebuke its findings. First of all, the CHADSVASC score was designed to predict risk of ischemic stroke in NVAF, but not predict the risk of LAT/SEC. There should be a modification in the objectives of the study, since comparing the predictive ability of the ML model (to detect LAT/SEC) to the CHADSVASC score is reasonable but is not 100% accurate and correct. Moreover, presence of LAT/SEC significantly increases the risk of stroke and is a contraindication for electrical cardioversion in AF, but the risk of having LAT/SEC is not equivalent to risk of developing stroke. This is a striking point that needs to be revised in the paper.

8-Would mention in exclusion criteria that patients with rheumatic HD and VHD were excluded, and those who did not have a TEE were also excluded.

9- Another important criterion that was overlooked was patients with prosthetic heart valves, since there is no mention of this population. Moreover, patients with HCM amyloidosis are excluded from stroke prediction models in AF due to their inherently increased risk and are usually anticoagulated regardless of CHADVASC score. HCM patients were included in the study, and no mention of patients with amyloid. In the latter, if data is missing or not available, please mention for the reader.

10- In terms of echocardiographic parameters, please specify to the reader that TTE data are used for the ML program. Knowing the limitations of LA diameter in 2D, why was not left atrial volume used instead? What is the utility of mitral and tricuspid regurgitation area when compared to other parameters entailing assessment of MR/TR ? What if the patient enrolled did not exhibit any MR/TR ? Please elaborate on this point because the echocardiographic model could have been markedly improved by looking at other parameters, especially since a statistically important predictor was enlarged LAD.

11- Bias control section: lines 19-20, grammatically incorrect, please make required adjustments.

12- In discussion section, the referenced study (ref 16) in the second sentence of this paragraph discussed predictability of the CHADVASC score for LAT (not SEC) only in those younger than 65 years of age. Their results do not extrapolate specifically to your study.

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Reviewer #1: No

Reviewer #2: No

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Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non- valvular atrial fibrillation patients

PONE-D-24-27654R1

Dear Dr. Li,

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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Kind regards,

Tom Wang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thanks for addressing all the reviewer's comments adequately.