PONE-D-23-14864Medical Admission Prediction Score (MAPS) - A simple tool to predict medical admissions.PLOS ONE

Dear Dr. Khan,

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Reaz Mahmud, MBBS, FCPS (Medicine), MD (Neurology)

Academic Editor

PLOS ONE

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Additional Editor Comments:

Please add "in the emergency department" at the end of the title. The information presented in lines 77-78 is unnecessary as it is duplicated in lines 79-80. In line 87, the symbol "%" is missing after the sensitivity value. The proper format for in-text citation is [3,44], not as superscript. Instead of "busiest," it is recommended to use "one of the busiest hospitals" in line 132. Please clarify why the age was set to >14, as in most instances, the adult population is defined as >18 years.

After the first citation in the text, please provide the tables. Ensure to follow the journal's requirements for the appropriate font, font size, and spacing in the text, as well as the font size of the headings and subheadings. It is essential to read the journal instructions carefully.

On line 253, the sensitivity value has a typo with the "%" symbol missing. On lines 264-268, it is recommended to avoid duplicating the results in the discussion section and instead provide a summary. Additionally, on line 275, there is a conflict with the term MEWS used.

Please ensure that the conclusion is stated precisely. Please note that volume numbers need to be included for references 6 and 7. When referencing a work with six or more authors, please use "et al." after the sixth author's name. The title of each figure should be positioned below the corresponding figure, and figure legends must be included to explain the content of each figure. Furthermore, figure captions should be inserted in the manuscript's text immediately after the paragraph in which the figure is first mentioned. Lastly, kindly add legends to the table to clarify the different variables.

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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

Reviewer #3: Partly

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: 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: No

Reviewer #2: Yes

Reviewer #3: Yes

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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

Reviewer #3: 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: The authors addressed one of the major problems (overcrowding) in emergency departments by analyzing the data of a large population. The admission prediction model developed in this study seems reasonably helpful in clinical decision making, although physicians should not overly rely on it based on its limited sensitivity and specificity.

Upon reviewing this manuscript, the reviewer found several points which should be considered prior to publication.

First, the authors focused on prediction of hospitalization in the setting of initial ED encounter. However, in ED practice, it may be more meaningful to identify those who are in a potentially life-threatening condition rather than those who need hospitalization. In this view point, it may be worth considering to assess the performance of the scoring system for detection of those who died, required ICU care, or developed complications as well.

Although the admission prediction model should support decision making process, it is not obvious if it really shortens the time until patient admission and solve overcrowding of ED. This should be discussed more.

In real world situations, patients who leave the ED or die in the ED before a decision of admission can be made should also undergo the same clinical evaluation as those who can complete clinical decision making. However, the number of those who had been excluded from the analysis was not shown in the manuscript and it is difficult for readers to judge if this study can be readily generalizable.

In Table 1, “Number of comorbidities” of “Study Cohort” is displayed as “NA.” Are there any reasons for this?

In Table 3, “Regression Coefficient” of reference variables are written as “1.” However, it seems misleading to define the regression coefficients for reference variables as 1 in multivariable logistic regression models. For example, in rows on “Age,” patients “>60 years” have higher odds of admission with OR 1.88 (1.77-2.01) as compared to “≦60 years.” But regression coefficient of “≦60 years” is 1.0 and higher than 0.629 for “>60 years.” Such expressions seem confusing.

The methods of determining the best-suitable cutoff of the scoring system should be elaborated more (such as Youden index or closest to (0, 1) approach etc).

In terms of data availability, the original data or their location could not be found in the manuscript. Instead, “Data Sharing” section says “The datasets used and/or analysed in the current study are available from the corresponding author on reasonable request conditional to the permission from the Medical Research Centre.” If so, it seems more appropriate to answer “No” to “Data Availability” question (because there seems some restriction) and elaborate the reason as needed.

Minor issue:

In line 246, the expression “independently associated” should not be used while talking about univariate analysis.

Reviewer #2: I congratulate researchers for conducting research with large amounts of data. There are some minor additions:

1. In the discussion of line 266, it is better to add the criteria for AUC which is said to have good accuracy (eg AUC > 0.9 has high accuracy) and its source.

2. In conclusion, it should be added that this score can be used in patients aged > 14 years because your research inclusion is > 14 years old

Reviewer #3: The manuscript seeks to provide and easy to used scoring system to guide admission decision making at emergency departments. The result shows a good potential of the developed scoring system. However, it is a little farfetched to conclude that the results have better generalizability considering that about 92% of the population are Asians and Arabs. Below are a few other issues that needs to be addressed to improve the quality of the manuscript.

1. Although the term “Medical Admission Prediction Score (MAPS)” is used in the title, it is not used in the content of the manuscript. In lines 279-289, you rather referred to your model as MEWS

2. In the abstract section, the total number of presentations in the methods and in the results do not match

3. In lines 111 to 112 the authors should clarify if the setting of the report is the USA or global

4. The inclusion and exclusion criteria could be added to the Study population section since they were introduced there

5. The sentence "We abstracted clinical, demographic and laboratory data of the patients from the EMR and the patients were not contacted directly to provide any information" could be modified as "Extracted data from the medical records include clinical, Demographic and laboratory data"

6. The authors should provide some justification on why patients 14 years and below were excluded

7. Lines 169 -171 is already captured in lines 133-137. It therefore not necessary to repeat it

8. How were missing data handled in the analysis?

9. The authors should check line 177 -180 and correct the types of data and variables and the required test used for the analysis. For instance, it states that associations between qualitative data was assessed using chi-square or Fisher's exact test. This is not accurate.

10. The authors should define the groups that are being compared and also indicate the list of variables that were included in the regression models in the statistical analysis section

11. Lines 181 - 190 should be moved to last section of the paragraph for coherence

12. Only one figure is presented so the sentence "Pictorial presentations of the key results were made using appropriate statistical graphs" may not be necessary

13. The sentence in lines 206 -208 need to be restructured appropriately for clarity

14. Avoid beginning sentences with figures. Also be consistent on how the figures are written, some have commas others do not.

15. Delete "inpatient" in line 261

16. Provide some background information on what MEWS is used for, for context in the discussion

17. Delete "to calculate MEWS" in lines 280 as it introduces some confusion as to whether the authors are revising MEWS or comparing their model to MEWS. Otherwise the authors should provide more clarity

18. In the discussion section too much attention is paid to the history of admission within the last 30 days. The other variables in the model were not highlighted

19. The authors need to pay close attention to the mathematical symbols used in the data categorisation as indicated in the tables. Its current categorisation leaves out some values. For instance, age was categorised as <60 years and >60 years. This leaves out all those who are exactly 60 years old. Similar errors are found in the other categories and may have impacted the data analysis and findings. Also, the appropriate sign less than or equal to should be used thus "≥" instead of ">= "

20. Each table should have the definition of all abbreviations used underneath it. For each variable, the authors should also define the test used to compare the groups

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Medical Admission Prediction Score (MAPS); A simple tool to predict medical admissions in the emergency department.

PONE-D-23-14864R1

Dear Dr. Khan,

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,

Reaz Mahmud, MBBS, FCPS (Medicine), MD (Neurology)

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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 #4: All comments have been addressed

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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 #4: Yes

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

Reviewer #1: Yes

Reviewer #4: Yes

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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 #4: 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 #4: 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: The authors addressed all the previously raised comments appropriately. The manuscript now seems acceptable for publication.

Reviewer #4: The authors have diligently addressed all reviewer comments, significantly enhancing the study's quality. The well-executed research, sound analysis, and clear writing make it suitable for publication.

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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

Reviewer #4: No

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