{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T02:59:11Z","timestamp":1790045951228,"version":"4.0.1"},"update-to":[{"DOI":"10.12688\/wellcomeopenres.15477.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T00:00:00Z","timestamp":1592956800000}}],"reference-count":48,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T00:00:00Z","timestamp":1592956800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/P014658\/1"],"award-info":[{"award-number":["MR\/P014658\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011856","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/N013638\/1"],"award-info":[{"award-number":["MR\/N013638\/1"]}],"id":[{"id":"10.13039\/501100011856","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CGIAR","award":["CRP21-0B3-2017"],"award-info":[{"award-number":["CRP21-0B3-2017"]}]},{"name":"Wellcome Trust","award":["201344\/Z\/16\/Z"],"award-info":[{"award-number":["201344\/Z\/16\/Z"]}]}],"content-domain":{"domain":["wellcomeopenresearch.org"],"crossmark-restriction":false},"short-container-title":["Wellcome Open Res"],"abstract":"<ns4:p>\n                    <ns4:bold>Background:<\/ns4:bold>\n                    Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level.\n                  <\/ns4:p>\n                  <ns4:p>\n                    <ns4:bold>Methods:<\/ns4:bold>\n                    To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset. We integrated these with data on aetiology of common infection syndromes, existing empiric prescribing guidelines, and pricing and availability of antibiotics.\n                  <\/ns4:p>\n                  <ns4:p>\n                    <ns4:bold>Results:<\/ns4:bold>\n                    \u00a0The ATLAS dataset covered many more bacterial species (287) and antibiotics (52) than other datasets (ranges = 8-11 and 16-32 respectively), but had a similar number of samples per country per year. Using these data, we were able to make empiric prescribing recommendations for bloodstream infection, pneumonia and cellulitis\/skin abscess in up to 44 countries. There was insufficient data to make national-level recommendations for the other six syndromes investigated. Results are presented in an interactive web app, where users can visualise underlying resistance proportions to first-line empiric antibiotics for infection syndromes and countries of interest.\n                  <\/ns4:p>\n                  <ns4:p>\n                    <ns4:bold>Conclusions:<\/ns4:bold>\n                    We found that whilst the creation of a composite resistance index for empiric antibiotic therapy was technically feasible, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets are largely limited to bloodstream infection specimens and cannot directly inform treatment of other syndromes. With improving availability of international AMR data and better understanding of infection aetiology, this approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data\n                  <\/ns4:p>","DOI":"10.12688\/wellcomeopenres.15477.2","type":"journal-article","created":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T11:20:11Z","timestamp":1592997611000},"page":"140","update-policy":"https:\/\/doi.org\/10.12688\/wellcomeopenres.crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Feasibility of informing syndrome-level empiric antibiotic recommendations using publicly available antibiotic resistance datasets"],"prefix":"10.12688","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4761-001X","authenticated-orcid":false,"given":"Quentin J.","family":"Leclerc","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4122-6631","authenticated-orcid":false,"given":"Nichola R.","family":"Naylor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander M.","family":"Aiken","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7882-2325","authenticated-orcid":false,"given":"Francesc","family":"Coll","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7263-9896","authenticated-orcid":false,"given":"Gwenan M.","family":"Knight","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2020,6,24]]},"reference":[{"key":"ref-1","doi-asserted-by":"publisher","first-page":"e619-e629","DOI":"10.1016\/S2214-109X(18)30186-4","article-title":"Antimicrobial consumption and resistance in adult hospital inpatients in 53 countries: results of an internet-based global point prevalence survey.","volume":"6","author":"A Versporten","year":"2018","journal-title":"Lancet Glob Health."},{"key":"ref-2","doi-asserted-by":"publisher","first-page":"1106-1117","DOI":"10.1093\/jac\/dkv418","article-title":"The Worldwide Antibiotic Resistance and Prescribing in European Children (ARPEC) point prevalence survey: developing hospital-quality indicators of antibiotic prescribing for children.","volume":"71","author":"A Versporten","year":"2016","journal-title":"J Antimicrob Chemother."},{"key":"ref-3","doi-asserted-by":"publisher","first-page":"847-856","DOI":"10.1016\/S1473-3099(16)00065-7","article-title":"Current evidence on hospital antimicrobial stewardship objectives: a systematic review and meta-analysis.","volume":"16","author":"E Schuts","year":"2016","journal-title":"Lancet Infect Dis."},{"key":"ref-4","doi-asserted-by":"publisher","first-page":"4851-4863","DOI":"10.1128\/AAC.00627-10","article-title":"Systematic review and meta-analysis of the efficacy of appropriate empiric antibiotic therapy for sepsis.","volume":"54","author":"M Paul","year":"2010","journal-title":"Antimicrob Agents Chemother."},{"key":"ref-5","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1186\/1471-2334-12-268","article-title":"Inadequate treatment of ventilator-associated and hospital-acquired pneumonia: risk factors and impact on outcomes.","volume":"12","author":"N Piskin","year":"2012","journal-title":"BMC Infect Dis."},{"key":"ref-6","first-page":"21-4","article-title":"Comparison of the appropriate use of antibiotics based on clinical guidelines between physicians in-training versus practicing physicians.","volume":"105","author":"F Fern\u00e1ndez Gonz\u00e1lez","year":"2013","journal-title":"Bol Asoc Med P R."},{"key":"ref-7","doi-asserted-by":"publisher","first-page":"286-293","DOI":"10.1093\/jac\/dku336","article-title":"Using an index-based approach to assess the population-level appropriateness of empirical antibiotic therapy.","volume":"70","author":"M Ciccolini","year":"2015","journal-title":"J Antimicrob Chemother."},{"key":"ref-8","first-page":"1-23","article-title":"Summary of antimicrobial prescribing guidance - 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The aim is to provide a &quot;clinically-oriented presentation of resistance rates&quot; from available AMR surveillance datasets. The main conclusion is that while this is technically possible for some syndromes, the datasets are not yet adequate.&nbsp;I enjoyed reading the manuscript and anticipate that it will be read with interest by other researchers interested in this general approach. Thanks to the authors for carrying out this work!&nbsp;<\/i><\/b>Thank you for your comments and review, much appreciated. We have left your comments in bold and italics below, while our answers are written in plain text. <b><i>Positive points:<\/i><\/b> <b><i>An impressive integration with existing datasets beyond the central dataset (ATLAS).&nbsp;<\/i><\/b> <b><i>A range of sources of data have been synthesised \u2013 not just resistance rates but also information on syndromes, guidelines, etc.&nbsp;<\/i><\/b> <b><i>Clear presentation of methodology and results.&nbsp;<\/i><\/b> <b><i>Online interactive app so readers can experiment themselves and get a feel for the approach.&nbsp;<\/i><\/b> <b><i>Reproducibility: I could successfully clone the github repository and run the app myself in under a minute.&nbsp;<\/i><\/b> <b><i>I have selected &quot;Approved with reservations&quot; as I do have some suggestions for improvement, which I hope the authors will consider.&nbsp;<u>Major points<\/u><\/i><\/b> <b><i>Infection syndromes:<\/i><\/b> <b><i>I think a strict definition of what is meant by \u201cinfection syndrome\u201d should be given, as readers may not be that familiar with the term (I admit I wasn\u2019t totally clear what was meant without looking at the examples). With the caveat that I am not a clinician, I think it is underplayed that the infection syndromes looked at are often not known to be due to bacteria. For example, upper respiratory tract infections \u2013 the majority are viral (the Further Methods state 90%). And for bacteraemia, patients can present only with fever, at which point a decision has to be made about treatment. It seems to me it would be less common to have a scenario where a patient has a known bacterial infection syndrome but nothing is known beyond that. This may be my own ignorance; I\u2019d welcome any supporting evidence about how often this is the case in practice.I know the authors are aware of this problem, but I think it should be stated explicitly that the problem considered here is: \u201can ideal world where one was confident that an infection was bacterial but did not know the species or the resistance profile\u201d (I think that\u2019s a reasonable formulation?). The effect of including the proportion expected to be non-bacterial and therefore unaffected by antibiotics would be to reduce the likelihood of prescribing antibiotics. The high proportion of recommendations to treat with antibiotics beyond first-line antibiotics is driven by this exclusively bacterial assumption (and the resistance datasets used). This should be emphasised.To be clear, I\u2019m not suggesting redoing all this work taking into account the proportion of patients presenting with symptoms X\/Y\/Z who have a bacterial vs. viral infection. The work here stands in its own right. However, the major problem in antibiotic prescribing is the decision to prescribe antibiotics *at all* rather than deciding whether to prescribe first\/second\/third-line antibiotics. I think this needs to be mentioned up front; at present it comes very late as the fourth limitation in the discussion. In practice this is what most readers will think of when thinking about antibiotic stewardship and how prescribing apps might help.<\/i><\/b> Thank you for this comment. We have now added a clearer definition of \u201cinfection syndrome\u201d in the second paragraph of the Introduction. In addition, we have added a statement in the last paragraph of the Introduction to clearly mention that we are assuming that we are in a scenario where an antibiotic has to be prescribed, and we therefore aim to answer the question of which antibiotic should be prescribed.&nbsp; <b><i>Accuracy<\/i><\/b> <i><b>I appreciate this is always put in quotes to emphasise the restricted sense of \u201caccurate\u201d that is meant, but I still think this is the wrong term. I suggest changing all uses of it and avoiding the word entirely. \u201cAccurate\u201d suggests that the ground truth or right answer can be derived from the ResistanceMap or ECDC dataset, and that because the ATLAS point estimate lies outside the 95% confidence interval from these datasets it is \u201cinaccurate\u201d, which I think is misleading. The right answer for the \u2018true\u2019 prevalence of resistance isn\u2019t known in this circumstance, and in fact resistance prevalence is context-dependent on the sampling (community vs. hospital etc.). I\u2019d suspect variation in sampling is the driver, as suggested, and also that the ATLAS dataset for some countries might include only a few sites. What is being defined is not accuracy, but agreement between datasets. So, \u201cconcordance\u201d or \u201cagreement\u201d would be a better term.<\/b><\/i> Thank you for this suggestion to improve clarity; we have now replaced \u201caccuracy\u201d by \u201cagreement\u201d throughout the manuscript. <i><b>I would also be interested to see a plot using data presented in Supplementary Figure 1, but adding the agreement against the number of samples that went into producing the ATLAS point estimate. If the problem driving low agreement is not enough samples in the ATLAS dataset, then more samples would produce more agreement with ECDC. If the problem is differences in sampling (e.g. bias towards resistant organisms in ATLAS due to specific clinical setting) then more samples in ATLAS would produce&nbsp;less&nbsp;agreement with ECDC. It might not be as clear-cut as I\u2019m assuming, but at the moment this isn\u2019t clear. It seems like an important distinction to shed light on.<\/b><\/i>Thank you for this suggestion. We have now divided Supplementary Figure 1 into two sections; A) is the old Supplementary Figure 1, and B) is a new plot showing agreement against number of ATLAS samples used to calculate that agreement. One sample is one isolate, for one bacteria, one antibiotic, and one country. This new plot suggests that more ATLAS samples produce more agreement with ECDC and ResistanceMap, however this does not seem clear-cut as the increase in agreement is arguably not significant past 30,000 samples. Therefore, this suggests that while increasing sample size can increase agreement, there is still a difference between the datasets, which could be due to differences in sampling. We now discuss this in the Extended Data \u2013 Further Results section, and have added a sentence on the difference in sampling between the datasets in the discussion section in the main text.&nbsp; <b><i>Comparison with other datasets<\/i><\/b> <i><b>I agree that ECDC data is merged into ResistanceMap and the resistance rates for those countries are therefore the same. Therefore, I\u2019m a bit confused why a comparison is made of ATLAS vs. ECDC&nbsp;and&nbsp;ATLAS vs. ResistanceMap without apparently removing ECDC as far as I could tell? Fundamentally the ECDC data is inside ResistanceMap. It seems to me that the important comparisons would be ATLAS vs. ECDC and ATLAS vs. (ResistanceMap \u2013 ECDC). I think for this reason there\u2019s currently double-counting going on in Supplementary Figure 1, 2, 3 which could be rectified. If I misunderstood and this has already been removed, apologies!<\/b><\/i> Thank you for this suggestion. Our aim behind this comparison was to see how well the ATLAS dataset agreed with other AMR surveillance datasets, therefore we made all the comparisons against the full datasets (i.e. without removing the ECDC data from ResistanceMap). We believe this is the valid comparison to make as this will be the one that others are doing too: how do results from ATLAS analysis compare to results from ResistanceMap analysis rather than subsetting the latter database and comparing.We agree that a more in-depth comparison between the AMR surveillance datasets would be interesting, however this would require a much larger analysis to be correctly examined. We therefore think that this is outside the scope of this paper as it would represent a different research question. <i><u><b>Minor points<\/b><\/u><b>I have ordered these by section.&nbsp;<u>Introduction<\/u><\/b><\/i> <b><i>Para 1: First sentence, suggest replacing \u201cbeing known\u201d by \u201cdetermined by tests\u201d (as highlights that this can only be really known by some sort of test\/procedure outside normal clinical assessment, and also that sometimes tests are inconclusive).<\/i><\/b> Thank you for this suggestion, we have made the recommended change to \u201cdetermined by diagnostic tests\u201d. <b><i>Para 2: \u201cinfection syndrome\u201d --&gt; \u201cinfection syndromes\u201d.<\/i><\/b> Thank you for spotting this mistake, we have now corrected it. <b><i>Para 3: suggest providing example(s) of a bacterial infection syndrome where the aetiology varies by season.<\/i><\/b> We have now added the example of pneumonia as a bacterial infection syndrome where aetiology varies by season. <b><i>Para 4: think the usual phrase is \u201cresource-limited\u201d rather than \u201cresource constrained\u201d (I could be wrong)<\/i><\/b> We have made the recommended change to \u201cresource-limited\u201d. <b><i>Para 5: \u201cwhich have limited healthcare budgets and difficulties to access medicines\u201d --&gt; \u201cwhich can have limited healthcare budgets and access to medicines\u201d<\/i><\/b> &nbsp;We have rephrased this section as recommended. <b><i>Para 6: \u201cinfection syndromes\u201d need to be defined earlier. Suggest in Para 2.<\/i><\/b> We have now added a definition of \u201cinfection syndromes\u201d in paragraph 2 of the Introduction. <i>&nbsp;<u><b>Methods<\/b><\/u><\/i> <b><i>Para 2: \u201cThis dataset was made public in 2017\u201d \u2013 I accept this was when the ATLAS website went live, but the dataset was made browsable, not downloadable. Worth explaining that the whole dataset as csv was actually released in 2018 to participants in Data Reuse Prize.<\/i><\/b> <b><i>\u200b\u200b\u200b\u200b\u200b\u200b\u200b<\/i><\/b>Thank you for this clarification, we have added a sentence in paragraph 2 of the Methods to explain this. <b><i>Para 5, Surveillance data comparison: see major point about changing \u201caccuracy\u201d (and change elsewhere as well).<\/i><\/b> \u201caccuracy\u201d has now been replaced by \u201cagreement\u201d throughout the manuscript.&nbsp; <b><i>(a) \u201cCommon infection syndromes\u201d: I\u2019m not sure if all these syndromes are identifiable with simple clinical examination and\/or radiology, so just want to check this point i.e. what\u2019s meant by \u201cclinical examination\u201d. As I mentioned, as far as I\u2019m aware patients can present only with fever for bacteraemia, so I\u2019m not sure how it can be easily identified. Also, the syndromes are assumed to be independent of each other i.e. patients present with only one. This is a limitation as in practice they can be linked e.g. bacterial meningitis develops from initial bacteraemia. Or e.g. NICE guidelines highlight link between hospital-acquired pneumonia and bacteraemia. &nbsp;<\/i><\/b> Infection syndromes cover a wide range of possibilities and you are right that bacteraemia is notoriously difficult to pin down characteristic features for \u2013 fever is really the only reliably present element. Clinical examination essentially encompasses all the elements of medical examination. This would start with taking temperature and performing basic observations through to examining different parts of the body to look for localized evidence of infection.&nbsp; Few of the infection syndromes have entirely reliable identification in practice. You are right that bacteraemia often co-exists with some of the other syndromes, but apart from that, overlap is relatively rare.We have included further discussion of this in the discussion (paragraph 3). <b><i>\u201cNICE guideline\u201d --&gt; \u201cNICE guidelines\u201d. Also, the link to the NICE guidelines (reference 8) is wrong. It takes me to PHE\u2019s 119-page document \u201cSummary of antimicrobial prescribing guidance \u2013 managing common infections\u201d. I think the correct link should be:&nbsp;<\/i><\/b> <b><i>https:\/\/www.nice.org.uk\/Media\/Default\/About\/what-we-do\/NICE-guidance\/antimicrobial%20guidance\/summary-antimicrobial-prescribing-guidance.pdf<\/i><\/b> <b><i>&nbsp;Is this correct? Assuming these are the correct guidelines, I became slightly confused about how the syndromes were identified. For example, upper respiratory tract infection as defined in Table 2 doesn\u2019t include \u201cacute otitis media\u201d, which is included in this syndrome in the NICE guidelines. The first-choice treatment for acute otitis media is amoxicillin, rather than penicillin V. I have not investigated all the other syndromes but I just want to check why this was excluded? Apologies if I missed this somewhere.<\/i><\/b> Thank you for the corrected link. For \u201cacute otitis media\u201d (AOM), even though this infection is frequently caused by bacteria, there are typically no bacterial samples sent for patients. This is because AOM occurs on the inside of eardrum, hence it is hard to access unless the eardrum is perforated. We confined our work to infection syndromes where there were easily corresponding bacterial isolates. To make recommendations about AOM, one would normally draw inferences from bacteria isolates from other body sites, which we have avoided doing here. &nbsp;Broadly, we agree with the reviewer that this is a limitation of our analysis. We aimed to provide a tool that could be used at the point of prescribing antibiotics to treat an \u201cinfection syndrome\u201d. We therefore make no clear statements about whether these are independent or upon what level of evidence the decision is made. We have added this as a limitation into our discussion section (paragraph 3). <b><i>On this point, I couldn\u2019t find the guidelines for bacteraemia in that NICE link. \u201cBacteraemia\u201d is the presence of bacteria in the blood; \u201csepsis\u201d is probably a better term for the infection syndrome. I noticed that the app uses \u201csepsis\u201d, but the main paper uses \u201cbacteraemia\u201d, suggest to make them consistent. &nbsp;<\/i><\/b> You are correct that there is no mention of bacteraemia in the NICE guidelines \u2013 we felt that we should add this as a recommendation category. The terms sepsis and bacteraemia are often used interchangeably, though they refer to different phenomena. For consistency, we will use the term \u201cbloodstream infection\u201d as the standard description for both the app and the paper.&nbsp; <b><i>(d) \u201cContributing pathogen distribution: syndrome aetiology\u201d: on \u201crapid literature search\u201d, it would be good to know how this review worked in practice e.g. searching PubMed \/ Google Scholar \/ the wider web? One author assigned to a syndrome? One author doing all syndromes? How were results integrated? There\u2019s no problem at all with it being rapid and informal, but a little more info on the process would be great.<\/i><\/b> Thank you for this suggestion. We have now added this information in the main text. <i>&nbsp;<u><b>Results<\/b><\/u><\/i> <b><i>Table 3: the summary table of the recommendations for therapy is based on the arbitrary 15% cut-off. This should be stated in the caption of the table. I think it would be good to present the actual distributions in a supplementary figure (i..e. the percentages for the index), but this isn\u2019t essential. I would also state that only those syndromes with an average above 50% were included (which I infer from the associated text). Column stating \u201cAverage syndrome coverage\u201d \u2013 presume \u201cMean syndrome coverage\u201d is meant instead.<\/i><\/b> We now mention the 15% cut-off in the caption, and have corrected the column to indicate \u201cMean syndrome coverage\u201d. In the Extended Data (Further Methods) section we acknowledge that the 15% cut-off is rather conservative compared to the 25% resistance rate recommended by European guidelines for managing hospital-acquired and ventilator-acquired pneumonia ( <i>1<\/i>[QL1]&nbsp;). Hence why we allow the user to vary this in the app. <b><i>(g) \u201cResulting recommendations on the appropriateness of empiric antibiotic therapies\u201dPara 3: bacterial meningitis has an \u201caverage syndrome coverage\u201d of 0.51 in Table 3, so why does a sentence here state it has lower than 50%? Also, suggest stating the syndrome coverages for these syndromes that are not listed in Table 3.<\/i><\/b> <b><i>\u200b\u200b\u200b\u200b\u200b\u200b\u200b<\/i><\/b>Thank you for spotting this mistake, bacterial meningitis should not have been in this sentence. We have now corrected that, and give the syndrome coverage values in the text for the two syndromes with a coverage of less than 50%. <b><i>Figures: in general, please check the resolution of the figures. For example, Figure 3 seems quite low-res to me. For Figure 1: the arrows on the left-hand side aren\u2019t clear, I got a bit confused with the arrows pointing to other arrows. Suggest using a colour key for these arrows instead.<\/i><\/b> Thank you for pointing this out, we have now edited all Figures to be of higher resolution, and have added two types of arrows in Figure 1 to hopefully make this figure clearer. <b><i>Table 2: \u201cInfectious syndrome\u201d --&gt; \u201cInfection syndrome\u201d (first column)<\/i><\/b> We have now corrected this. <i>&nbsp;<u><b>Discussion<\/b><\/u><\/i> <b><i>Para 3: \u201cover-representation of antibiotic-resistant isolates in the ATLAS datasets\u201d \u2013 see previous comment about a plot of ATLAS sample size against agreement with ECDC which might provide some more support for the hypothesis causing this.<\/i><\/b> <b><i>\u200b\u200b\u200b\u200b\u200b\u200b\u200b<\/i><\/b>Thanks again for this comment \u2013 we have now added an extra sentence here to highlight the differences in sampling between the datasets. <b><i>Para 5: \u201cantimicrobial resistance\u201d --&gt; AMR<\/i><\/b> We have corrected this to the recommended abbreviation. <i>&nbsp;<u><b>Conclusion<\/b><\/u><\/i> <b><i>\u201cthe WHO calls for\u201d --&gt; \u201cthe WHO\u2019s call for\u201d. Also provide a reference for this call for \u201cevidence-based prescribing\u201d. I wasn\u2019t sure what was being referred to.<\/i><\/b> Thank you for highlighting this, we have corrected the sentence and added a reference for this call. <i>&nbsp;<u><b>Further methods<\/b><\/u><\/i> <b><i>Section 1: no new MIC thresholds were included, which is acknowledged as a limitation. These would not in principle be difficult to include from EUCAST and could bump up the dataset. For example,&nbsp;E. coli&nbsp;and ceftaroline-avibactam: there are 13,203 isolates with a reported MIC. The EUCAST breakpoint is 0.5 mg\/L. So using my version of the ATLAS dataset, 139\/13,202 isolates (1.05%) are resistant. I accept compiling bug\/drug combinations for breakpoints (and considering both EUCAST and CLSI) would be tedious, and so much data integration has already been done! I\u2019d be happy to approve the paper without this extra work. But it would be good to have some idea of how much doing this would increase sample sizes, which could be done without external data integration.<\/i><\/b> Thank you for raising this interesting point. We did not consider this further data integration when completing this work, however you are undeniably correct when saying that it would increase the number of useful data points in the ATLAS dataset. The issue would come with deciding which guideline to follow and to split our analysis further by EUCAST and CLSI for example would indeed by a large amount of extra work. We did do some of this work by using existing breakpoints in the data for isolates without a classification but an MIC value.We have now added a few sentences at the end of this section to clarify that we did not do this, but that it could potentially be applied to increase the value of the dataset. We also would note that many of the antibiotics where the additional data would be added (eg ceftaroline-avibactam, colistin etc) are very \u201chigh-level\u201d antibiotics that would not feature in empirical guidelines.&nbsp; <b><i>Section 3: This paragraph is quite dense and a brief overview in the main paper would be useful, because the index is a key concept. In a talk based on this work by Francesc Coll at a SEDRIC meeting on October 9<sup>th<\/sup>&nbsp;2019, I saw a great diagram, I think of the example given here. I would strongly suggest including this as a figure in the main paper, as it quickly conveys the idea of the index visually.<\/i><\/b> We thank you for this suggestion and have now included a further figure (Figure 3) based on &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; the slide presented. &nbsp; <u><i><b>Further results<\/b><\/i><\/u> <b><i>Section 3: Upper respiratory tract infection has a reference (49) which is missing from the references at the end of this document.<\/i><\/b> Thank you for spotting this mistake. We have corrected this to indicate the correct reference number (17), and have added the reference in the bibliography.&nbsp;[QL1]A. Torres <i>et al.<\/i>, International ERS\/ESICM\/ESCMID\/ALAT guidelines for the management of hospital-acquired pneumonia and ventilator-associated pneumonia. <i>European Respiratory Journal<\/i>. <b>50<\/b>, 1700582 (2017).","URL":"https:\/\/wellcomeopenresearch.org\/articles\/4-140\/v2#referee-comment-4229","order":1,"name":"referee-comment-4229","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.17655.r39218, Marlieke de Kraker, Infection Control Program, Faculty of Medicine, World Health Organization Collaborating Centre on Patient Safety (Infection Control and Improving Practices), University of Geneva Hospitals, Geneva, Switzerland, 30 Jun 2020, version 2, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/4-140\/v2#referee-response-39218","order":2,"name":"referee-response-39218","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.16928.r36746, Ben S. Cooper, Nuffield Department of Medicine, University of Oxford, Oxford, UK, 25 Feb 2020, version 1, 3 approved with reservations","URL":"https:\/\/wellcomeopenresearch.org\/articles\/4-140\/v1#referee-response-36746","order":3,"name":"referee-response-36746","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Quentin Leclerc<\/b>; <i>Posted: 19 Jun 2020<\/i>; <b>This feasibility study represents a valuable addition to the surprisingly small literature on how to make empiric antibiotic recommendations. It is clearly written and well-organised, and the framework presented describes a sensible approach to using existing data-sets. Limitations of the approach, most of which are related to current limitations in publicly available data-sets, are clearly acknowledged. Suggestions for improvements below are mostly relatively minor, though points 5 and 6 below seem important to address.<\/b> Thank you for your comments - we are glad you found it a valuable addition.&nbsp;We have left your comments in bold and italics below, while our answers are written in plain text. <b>Page 4, paragraph 2. \u201cConcordance\u201d would be a better word than \u201caccuracy here\u201d as there is no&nbsp;gold standard surveillance system.&nbsp;\u201cAccuracy\u201d is also used elsewhere in the manuscript.<\/b> Thank you for this suggestion. In light of other reviewer comments, we have decided to replace \u201caccuracy\u201d by \u201cagreement\u201d throughout the manuscript. We hope that this is a satisfactory change. <b>Figure 1. Can the caption explain what the direction of the arrows indicates?<\/b> We have now clarified in the caption that the arrows linking the boxes indicate the process of integrating the different open-access datasets together to generate our index to inform empiric therapy. In addition, following suggestions from other reviewers, we have modified the figure to include two types of arrows to improve readability. <b>Page 5&nbsp;\u201c\u2026which is set to 15% by default\u201d. What is the reason for choosing 15%? Perhaps this point could be considered in the discussion. Surely it would make sense to choose different cut-offs for different syndromes as the trade-off between impact on patient outcomes and future selection for resistance will be different.&nbsp;&nbsp;<\/b> Such a cut-off is always going to be a somewhat arbitrary choice \u2013 we used 15% as this value had recently been used in the clinical practice for a similar question for one of us. In other situations, we have found that other cut-off values have been used \u2013 for example 25% for treatment of pneumonia in one recommendation from USA (see reference in text). We agree that different cut-offs would probably selected in different circumstances by different users for different syndromes \u2013 hence the value of having a slider for this parameter in the app. However, in the main text, for simplicity we chose to use just one value (15%). <b>Page 5 \u201cmissing isolates\u201d. I don\u2019t think I understand why the term \u201cmissing isolates\u201d is used. Were the isolates missing or were the records just incomplete?<\/b> Thank you for spotting this; we have corrected this phrase to indicate that the isolates were indeed not missing, but had incomplete records (MIC values were missing the corresponding susceptibility labels). <b>Figure 3: I don\u2019t completely understand the logic of this. If there are no data on the first line therapy wouldn\u2019t it make&nbsp;sense to make no recommendation but to collect more data, rather than immediately&nbsp;going on and recommend the second line therapy.<\/b> Thank you for highlighting this \u2013 there was a mistake in this figure in that we do currently recommend first line therapy if there is no data. In the app recommendations, where relevant data is missing, we have: &quot;Use second line, as resistance to first (but no data on resistance to second)&quot; &quot;Use third line, as resistance to both first and second (but no data on resistance to third)&quot; &quot;Use first line, but no data to inform - consider second or third if data&quot; Where there is resistant to the lower rank therapy, then we recommend use of the next level \u2013 \u201cUse second line\u201d in this app is purely because there is resistance to first line. Figure 3 has now been updated to better reflect the logic we use in our recommendations, and highlight that we assume bacteria are susceptible if there are no data to inform on potential resistance. The aim of our work was to see what recommendations could be made at the empiric prescribing level by deriving a syndrome-level index. Therefore, we are interested in the immediate decision a prescriber would make solely based on this index. You are correct when saying that ideally, more data would have to be collected before making any decision. However, we assume that we are in a situation where a patient requires immediate antibiotic treatment, so we report what the app recommends given this set of data. In line with comments from another reviewer we have included more discussion to highlight our focus on this secondary part of the empiric prescribing process. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <b>Table 3: Some of the recommendations here seem incompatible with Figure 3. e.g the first row for bacteraemia recommends using second line, but says no data on resistance to second line, while in the flow chart (Fig 3) the second line can only be recommended if resistance level is below the cut-off. Similar issues for septic arthritis. It would also be useful to have some indication of the strength of the evidence for the recommendation in the table.&nbsp;<\/b> Thank you for spotting this; as discussed in response to your comment 5), there was an error in Figure 3, which has now been corrected. As for an indication of the strength of the evidence, we have not mentioned this in Table 3 as it would vary between countries, but in the AR.IA App we now indicate the number of isolates used to generate the recommendation as a measure of strength of evidence.&nbsp; <b>Page 9, paragraph 2. Should make it clear that the cut-off of 15% can be changed in the app.<\/b> Thank you for highlighting this. We did already mention that the cut-off could be changed in the app in the \u201cMethods, The AR:IA App creation\u201d section, however we have added another phrase in the paragraph you mentioned to hopefully make it clearer that we were referring to the 15% cut-off.","URL":"https:\/\/wellcomeopenresearch.org\/articles\/4-140\/v2#referee-comment-4231","order":4,"name":"referee-comment-4231","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"Q.L. is funded by a Medical Research Council London Intercollegiate Doctoral Training Program studentship (MR\/N013638\/1). N.N. is funded by is funded by CGIAR Research Program on Agriculture for Nutrition and Health (A4NH) \u2013 (CRP21-0B3-2017). F.C. is funded by a Sir Henry Wellcome Postdoctoral Fellowship (201344\/Z\/16\/Z). G.K. is funded by the Medical Research Council (MR\/P014658\/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":5,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}