{"doi":"10.1093/cid/ciad748","title":"Correspondence to Hasegawa et al. “Diagnostic Accuracy of Hospital Antibiograms in Predicting the Risk of Antimicrobial Resistance in Enterobacteriaceae Isolates: A Nationwide Multicenter Evaluation at the Veterans Health Administration”","abstract":null,"journal":"Clinical Infectious Diseases","year":2024,"id":611470,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1573694,"name":"Nina M Haste","orcid":null,"position":1,"is_corresponding":false},{"id":227849,"name":"Shira R. Abeles","orcid":"0000-0003-3635-2019","position":2,"is_corresponding":false},{"id":1190410,"name":"Michael B. Doud","orcid":"0000-0002-8172-6342","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Correspondence to Hasegawa et al. “Diagnostic Accuracy of Hospital Antibiograms in Predicting the Risk of Antimicrobial Resistance in Enterobacteriaceae Isolates: A Nationwide Multicenter Evaluation at the Veterans Health Administration”","abstract":"To the Editor—We read with great interest the report by Hasegawa et al [1] on their recent study examining the accuracy of antibiograms in predicting antimicrobial susceptibility, and we appreciate their call for a critical assessment of the role and utility of antibiograms. We agree with the authors that antibiograms are unable to predict susceptibility for individual isolates, and we note that as susceptibility rates decrease, predictive models based solely on those rates will necessarily become less accurate—an unfortunate mathematical reality. However, instead of calling for a dismissal of antibiograms owing to their lack of predictive power, we call for revisiting what antibiograms can and should become, and we underscore the importance of integrating patient-specific factors that should drive clinical decision making. There are significant technical challenges in compiling and analyzing antibiograms within and across institutions, including nonuniform uptake of Clinical and Laboratory Standards Institute (CLSI)–recommended methods for data compilation, CLSI breakpoint and interpretation changes over time, variation in laboratory uptake of assays, and laboratory-specific data suppression or cascading rules [2, 3]. However, overcoming these technical challenges is only half the battle. Traditional antibiograms tell the story of the institution, not the individual patients seeking care there. Institutional antibiograms make sense for nursing facilities and long-term acute care hospitals for longitudinal monitoring of resistance patterns. Acute care facilities, on the other hand, treat populations with diverse environmental and medical exposure histories, making use of institutional data to make inferences about individual patients myopic. Antibiograms stratified by various criteria (level of care, inpatient unit, duration of stay, etc)—as recommended by Infectious Diseases Society of America/Society for Healthcare Epidemiology of America guidelines [4]—may help guide patient-contextualized empiric therapy, but we note that when clinical practice guidelines promote use of local antibiograms, they emphasize the importance of patient-specific factors in determining an empiric antibiotic choice [5–7]. Methods for making antibiograms need to be realigned for the patient-contextualized insights we wish to gain from them. Commonly appreciated healthcare exposures, as well as underappreciated and understudied nonhealthcare exposures, can shape colonization with resistant organisms. For example, methicillin-resistant Staphylococcus aureus colonization is associated with living near conventional livestock farms [8], and there are associations between antibiotic resistance and air quality [9]. Fine-scale analysis of geographic distributions of resistance may even enable the incorporation of neighborhood-level data [10]. We suggest that evaluating susceptibility patterns with a patient-oriented lens attuned to all relevant exposures, rather than an institutional lens lacking patient-specific features, will be more meaningful both in understanding drivers of antimicrobial resistance and in improving predictive models. As we proceed further into the “big data” era in medicine, we are optimistic that the type of commendable work in cultivating large and rich data sets exemplified by Hasegawa et al [1] can be complemented with increasingly sophisticated predictive models incorporating patient-specific factors, along with more rigorous analyses of spatial and temporal trends in antimicrobial resistance. We hope the progression of this work can deliver the touted potential of precision medicine to the antibiotic stewardship realm and improve patient outcomes.","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38059520","pmcid":null,"openalex_id":"https://openalex.org/W4389452541","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2023,"count":1}],"oa_status":"closed","license":"https://academic.oup.com/pages/standard-publication-reuse-rights","oa_locations":[{"url":"https://academic.oup.com/cid/advance-article-pdf/doi/10.1093/cid/ciad748/54760072/ciad748.pdf","host_type":"publisher"},{"url":"https://academic.oup.com/cid/article-pdf/79/1/281/58599865/ciad748.pdf","host_type":"publisher"},{"url":"http://dx.doi.org/10.1093/cid/ciad748","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38059520","host_type":"repository"}],"fields_of_study":["Antibiotic Use and Resistance","Bacterial Identification and Susceptibility Testing","Neutropenia and Cancer Infections"],"mesh_terms":["Anti-Bacterial Agents","Enterobacteriaceae","Enterobacteriaceae Infections","Hospitals","Humans","Microbial Sensitivity Tests","United States","United States Department of Veterans Affairs","Drug Resistance, Bacterial"],"keywords":["Medicine","Family medicine","Public health","MEDLINE","Library science","Law","Political science","Pathology"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-01T19:48:57.107641Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}