{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:02:54Z","timestamp":1789948974558,"version":"4.0.1"},"reference-count":33,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Studying patients with bacteremia in real\u2010world data often requires using microbiologic data to confirm bacterial growth and species. When microbiologic data are unavailable, pathogen\u2010specific diagnostic codes may be used to classify the bacteremia type. We developed and validated ICD\u201010\u2010CM\u2010based algorithms for identifying any bacteremia, Gram\u2010negative bacteremia, and Enterobacteriaceae bacteremia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We identified patients \u2265\u200918\u2009years with a hospitalization \u2265\u20092\u2009days to any Duke Antimicrobial Stewardship Outreach Network hospital from January 1, 2021 to May 31, 2024. Blood culture results were the reference standard for classifying true cases of bacteremia. We evaluated 15 candidate algorithms developed based on previously published algorithms, in addition to the bacteremia ICD\u201010\u2010CM code (R78.81) and pathogen\u2010specific ICD\u201010\u2010CM codes. We estimated sensitivity, specificity, positive predictive value (PPV), and negative predictive value with 95% confidence intervals using generalized estimating equations to account for correlated hospitalizations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 907\u2009958 hospital admissions (623\u2009556 patients), algorithms with bacteremia and pathogen\u2010specific codes had high specificity (range: 97.3%\u2013100.0%) and low sensitivity (range: 4.9%\u201358.1%). Algorithms with bacteremia codes and no pathogen codes were more sensitive (range: 67.4%\u201368.6%) and less specific (range: 91.1%\u201391.2%). In subgroup analyses, PPV for any bacteremia was highest among patients alive at discharge, age\u2009\u2265\u200965, and discharged on antibiotics with Gram\u2010negative coverage (68.9% [65.1%\u201372.7%]).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed and internally validated algorithms to identify bacteremia, Gram\u2010negative bacteremia, and Enterobacteriaceae bacteremia in adult patients. Studies seeking to maximize specificity could consider algorithms that include bacteremia and pathogen\u2010specific codes, whereas studies seeking to maximize sensitivity could consider algorithms only requiring a bacteremia code.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/pds.70454","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T10:55:42Z","timestamp":1786618542000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>ICD<\/scp>\n                    \u201010\u2010\n                    <scp>CM<\/scp>\n                    \u2010Based Algorithms to Identify Bacteremia Incorporating Pathogen\u2010Specific Codes: A Validation Study"],"prefix":"10.1002","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4933-7216","authenticated-orcid":false,"given":"Madison G.","family":"Ponder","sequence":"first","affiliation":[{"name":"Department of Epidemiology Gillings School of Global Public Health, University of North Carolina  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4279-8685","authenticated-orcid":false,"given":"Alan C.","family":"Kinlaw","sequence":"additional","affiliation":[{"name":"Division of Pharmaceutical Outcomes and Policy School of Pharmacy, University of North Carolina  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth S. Dodds","family":"Ashley","sequence":"additional","affiliation":[{"name":"Duke Center for Antimicrobial Stewardship and Infection Prevention  Durham North Carolina USA"},{"name":"Duke Antimicrobial Stewardship Outreach Network  Durham North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1108-0689","authenticated-orcid":false,"given":"Jennifer L.","family":"Lund","sequence":"additional","affiliation":[{"name":"Department of Epidemiology Gillings School of Global Public Health, University of North Carolina  Chapel Hill North Carolina USA"},{"name":"Lineberger Comprehensive Cancer Center University of North Carolina  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2445-8473","authenticated-orcid":false,"given":"Emily J.","family":"Ciccone","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, Department of Medicine University of North Carolina School of Medicine  Chapel Hill North Carolina USA"},{"name":"Carolina Antimicrobial Stewardship Program University of North Carolina Medical Center  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3756-7540","authenticated-orcid":false,"given":"Michele Jonsson","family":"Funk","sequence":"additional","affiliation":[{"name":"Department of Epidemiology Gillings School of Global Public Health, University of North Carolina  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"e_1_2_9_2_1","volume-title":"StatPearls","author":"Smith D. 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