Unveiling the Hidden Potential of Simple but Promising Blood Cell Parameters on Acute Myocardial Infarction Prognostication

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript entitled "Unveiling the hidden potential of simple but promising blood cells parameters on Acute myocardial infarction prognostic" by Jercălău et al is interesting. This study is well-designed and authors work is appreciable; however, some issues need to be addressed.

 1) In  Table 2. Correlation between MCV, RDW, MPV, PDW and other laboratory parameters

Please elaborate why the Dependent variable NT-proBNP posses three different coefficient and p values at the same r2 value and independent variable. If the result is from independent patients, it would be better some legends provided to avoid confusion. This also helps to correlate different parameters of the same patients.

The same thing applies to another dependent variable in this table as well as all other tables. 

2) Figure 4. MCV value in patients with NSTEMI Killip class III-IV vs. without Killip class III-IV.

The authors are suggested to add proper units of the variable mentioned in the y axis. The same applies to other graphs also.

33)  Standard error is missing Figure 7 MPV vs count graph

 

4) Line 93: PSEP >300 pg/mL. The authors are suggested to throw light on PSEP.

5)  Line 156: BMI greater than 30 kg/m2 and 79.4% were

         In  m2, Please superscript 2

6) Line 81-82. After applying these exclusion criteria, 98 NSTEMI patients were enrolled in the study.

 

7) In  Table 5. Logistic regression applied to NSTEMI patients based on MCV values 97 observations.

The researchers have enrolled 98 patients but why the observations is otherwise mentioned

8)  In many areas of the manuscript, the authors have focused on the statistical parameters and values which is good. The authors are suggested to equally correlate these mathematical values with the medical outcomes and make them easy and understandable for audiences.

9) Typographical

Line 31-32: In the present day, living in the world of blue-sky concept allows
us to search for new diagnostic alghorythms.

Line 150:  For analysing the cutt-off value of MCV, RDW, MPV and PDW in predicting Killip

   

Line 297: pacient developing Killip class III-IV. According to the logistic regression model, as MCV

 

Line 311:   the other hand, as mentionned previously, MPV serves as an important indicator

    

Line  623: Aditionnally, our study demonstrated a positive         

 

 

 

 

Author Response

 

Response to Reviewer 1

 

The manuscript entitled "Unveiling the hidden potential of simple but promising blood cells parameters on Acute myocardial infarction prognostic" by Jercălău et al is interesting. This study is well-designed and authors work is appreciable; however, some issues need to be addressed.

 

 1) In  Table 2. Correlation between MCV, RDW, MPV, PDW and other laboratory parameters

Please elaborate why the Dependent variable NT-proBNP posses three different coefficient and p values at the same r2 value and independent variable. If the result is from independent patients, it would be better some legends provided to avoid confusion. This also helps to correlate different parameters of the same patients.The same thing applies to another dependent variable in this table as well as all other tables. 

 

Thank you for pointing this out. I apologize for any confusion caused. Regrettably, there was an error in my previous submission where I mistakenly duplicated the correlation between NT-proBNP and MVP. The intended correlation to be presented was between NT-proBNP and RDW, PDW. The correct version can be found in the revised manuscript. Thank you kindly for your understanding.

2) Figure 4. MCV value in patients with NSTEMI Killip class III-IV vs. without Killip class III-IV.

The authors are suggested to add proper units of the variable mentioned in the y axis. The same applies to other graphs also.

 

Thank you for pointing this out. The plot shows the two distributions of X given the two types of occurences. In order to be as suggestive as possible, we came up with three plots to reflect the distributions. The first are boxplots where oy label is, of course, the name of X variable used. The second is a histogram, where oy label shows the counts (this is what histogram does and we could have selected percentages). In the third one, we have approximations of density functions. In this case, we don't need a oy label, other than density perhaps, because  by definition, density plots show where is the mass of "occurence" for X, so the integral over that function will have the value 1 (probability theory). In these cases we don't actually need a special unit of mesurement there. Another note, all the plots are made using programming language so they have the same structure.

3)  Standard error is missing Figure 7 MPV vs count graph

Thank you for pointing this out. Here we just present the distributions from a graphical perspective. We do not estimate something, therefore we didn't provide any Std Error. The variance is also suggested by the plots.

 

4) Line 93: PSEP >300 pg/mL. The authors are suggested to throw light on PSEP.

Thank you for pointing this out. Please, find the revised version in the main text, highlighted in red (line 91).

5)  Line 156: BMI greater than 30 kg/m2 and 79.4% were

         In  m2, Please superscript 2

Thank you for pointing this out. Please, find the revised version in the main text, highlighted in red (line 145).

 

6) Line 81-82. After applying these exclusion criteria, 98 NSTEMI patients were enrolled in the study.

Thank you for pointing this out. Please, find the revised version in the main text, highlighted in red (line 79).

 

7) In  Table 5. Logistic regression applied to NSTEMI patients based on MCV values 97 observations.

The researchers have enrolled 98 patients but why the observations is otherwise mentioned ?

 Thank you for pointing this out. In fact, our study included the enrollment of 98 patients, but in this case, 1 patient did not have the MCV value available for technical reasons related to the laboratory. We believed that is not ethical to replace this value from admission with another later value, considering that later value can be influenced by other  factors related to the patient’s evolution.

 

8)  In many areas of the manuscript, the authors have focused on the statistical parameters and values which is good. The authors are suggested to equally correlate these mathematical values with the medical outcomes and make them easy and understandable for audiences.

Thank you kindly for this observation. I followed your advice and added new medical data correlated to our statistics results. Please find them highlighted in red in the main text. (Line 296-300;407-409;535-540; 743-755).

 

9) Typographical

Line 31-32: In the present day, living in the world of blue-sky concept allows
us to search for new diagnostic alghorythms.

Line 150:  For analysing the cutt-off value of MCV, RDW, MPV and PDW in predicting Killip

   

Line 297: pacient developing Killip class III-IV. According to the logistic regression model, as MCV

 

Line 311:   the other hand, as mentionned previously, MPV serves as an important indicator

    

Line  623: Aditionnally, our study demonstrated a positive         

  Thank you for pointing this out.Please, find the revised version in the main text, highlighted in red (Line-32;139;286;302;620.)

 

 

 

Reviewer 2 Report

Comments and Suggestions for Authors

The article under review attempts to shed light on the prognostic significance of blood parameters such as MCV, RDW, MPV, and PDW in patients with non-ST-elevation myocardial infarction (NSTEMI). While the premise is intriguing and could potentially add value to existing risk stratification models, the study is marred by significant shortcomings that cannot be overlooked.

Firstly, the study's single-center design severely limits the generalizability of the findings. The small sample size further exacerbates this issue, as it undermines the statistical power of the study, making it challenging to draw firm conclusions or to apply these findings broadly across diverse patient populations.

Another critical concern is the lack of correlation found between the new markers and the established GRACE score. This disconnect raises questions about the clinical applicability of these markers and whether they can truly enhance or refine the current standards of risk assessment in NSTEMI patients.

Moreover, the study's methodology and analysis lack depth in certain areas. For instance, there is insufficient discussion on how confounding variables were addressed, which is crucial for ensuring the validity of the findings. The potential interactions between the studied parameters and other clinical or demographic factors are not adequately explored, leaving a gap in understanding the full implications of these markers in the clinical setting.

Most alarmingly, the article has been flagged for plagiarism, with a significant portion of the content not being original. This is a grave academic misconduct that undermines the integrity of the research and the credibility of the authors. Plagiarism not only disrespects the original creators of the content but also deceives the readers and the scientific community about the novelty and authenticity of the research presented.

In conclusion, while the study aims to contribute to the field of cardiology by identifying new prognostic markers for NSTEMI, the numerous methodological flaws, limited scope, and particularly the issue of plagiarism cast a long shadow over its findings and conclusions. It is imperative that scientific research maintains the highest standards of integrity and rigor, and unfortunately, this article falls short of those standards. Further, more robust and ethically conducted research is necessary to validate the potential prognostic value of these blood parameters in NSTEMI patients.

Author Response

Comment 1: Firstly, the study's single-center design severely limits the generalizability of the findings. The small sample size further exacerbates this issue, as it undermines the statistical power of the study, making it challenging to draw firm conclusions or to apply these findings broadly across diverse patient populations.

Thank you for pointing this out. Indeed, the single-center design of the study, along with the small cohort of patients, were two of the most significant limitations in our research endeavor. However, these constraints were duly recognized and deliberated upon in the limitations section of our article. While acknowledging these shortcomings, we perceive our study as establishing a cornerstone for further literature that should encompass larger patient cohorts from diverse centers to confirm the reliability of our findings.

 

Comment 2: Another critical concern is the lack of correlation found between the new markers and the established GRACE score. This disconnect raises questions about the clinical applicability of these markers and whether they can truly enhance or refine the current standards of risk assessment in NSTEMI patients.

Thank you kindly for this remark. The new predictive markers proposed by us are correlated with heart failure after acute myocardial infarction andrenal dysfunction, outcomes included in the GRACE score. Nevertheless, these new markers not only anticipate in-hospital mortality within 30 days,but also forecast the length of hospital stay, the likelihood of a patient requiring cardiac surgery, and the presence of triple vessel disease – all of which are endpoints not covered by the GRACE score. The predictive markers we have proposed serve as surrogate indicators of the pathophysiological processes occurring during acute coronary syndrome, going beyond the clinical and biological aspects encompassed by the GRACE score. These observations may elucidate the absence of a correlation between these markers and the aforementioned score. Ultimately, it is essential that studies involving larger, multicenter cohorts of patients are conducted to either confirm the lack of correlation or, conversely, establish whether the relatively small patient cohort in our study influenced this outcome. There is no doubt that the GRACE score has guided the management of patients in a competent direction, but it is our belief that risk scores ought to involve permanent updates and attentive perusal. The current risk scores used in NSTEMI, including the GRACE score, were developed in the years 2007 to 2019, and therefore may not fully capture the complexity of the disease and the potential impact of newer markers.

Thank you very much for your suggestion. I followed your advice and added these possible explanations in the main text (see line 743-755 highlighted in red).

Comment 3: Moreover, the study's methodology and analysis lack depth in certain areas. For instance, there is insufficient discussion on how confounding variables were addressed, which is crucial for ensuring the validity of the findings.

Thank you for pointing this out. In this study we aim to measure as accurately as possible the observed relationship between some variables. Since we are limited by the lack of data, we do not analyze causally but only based on the observed variables. But, it is a good suggestion for future study.

Comment 4: Most alarmingly, the article has been flagged for plagiarism, with a significant portion of the content not being original. This is a grave academic misconduct that undermines the integrity of the research and the credibility of the authors. Plagiarism not only disrespects the original creators of the content but also deceives the readers and the scientific community about the novelty and authenticity of the research presented.

 

Thank you kindly for this observation. We confirm, once again, that our article is original. A number of sentences have now been rephrased, after careful consideration of the iThenticate report (please see all the sentences rephrased in the main text, highlighted in yellow). Furthermore, as per the report, some words included in the tables were flagged as overlapping. To prevent this misunderstanding, we removed non-essential data from the tables. Please find all the modifications, highlighted in yellow, in the revised version.

 

 

 

 

Reviewer 3 Report

Comments and Suggestions for Authors

Jercalau te al. present a study to explore the use of blood cell parameters as a prognostic for acute myocardial infarction. The following points need to be addressed:

1) Line 44 and the materials and methods section need appropriate citations.

2) Did the authors study the correlation of the parameters as being an exponential relationship rather than a linear relationship?

3) Can the authors come up with an equation involving all the relevant parameters to be used as prognosis for myocardial infarction?

Author Response

1) Line 44 and the materials and methods section need appropriate citations.

Thank you for pointing this out. I followed your suggestion. Please find the modifications in the revised version.

 2) Did the authors study the correlation of the parameters as being an exponential relationship rather than a linear relationship?

Thank you for pointing this out. For some variables we used logistic regression and for others we used linear. Since we didn't have so much data to approximate a non-linear function, we followed the classical framework to model these types of relationships. In addition, by using logistic or linear regression, we gain interpretability.

3) Can the authors come up with an equation involving all the relevant parameters to be used as prognosis for myocardial infarction?

Thank you for pointing this out. In this study we did not combine multiple factors into one model because this brings another specific issue with this dataset, like multicollinearity or potentially overfitted model. We were focused on factorial models because they generalize better (given a relatively small sample) and because we had direct interpretability of some factors.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript has been significantly improved.