{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T08:25:00Z","timestamp":1787041500796,"version":"build-2736575974"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000030","name":"Centers for Disease Control and Prevention","doi-asserted-by":"publisher","award":["75D30124C18824"],"award-info":[{"award-number":["75D30124C18824"]}],"id":[{"id":"10.13039\/100000030","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000030","name":"Centers for Disease Control and Prevention","doi-asserted-by":"publisher","award":["75D30123C18329"],"award-info":[{"award-number":["75D30123C18329"]}],"id":[{"id":"10.13039\/100000030","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000030","name":"Centers for Disease Control and Prevention","doi-asserted-by":"publisher","award":["75D30124C18824"],"award-info":[{"award-number":["75D30124C18824"]}],"id":[{"id":"10.13039\/100000030","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000030","name":"Centers for Disease Control and Prevention","doi-asserted-by":"publisher","award":["75D30124C19955"],"award-info":[{"award-number":["75D30124C19955"]}],"id":[{"id":"10.13039\/100000030","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000030","name":"Centers for Disease Control and Prevention","doi-asserted-by":"publisher","award":["75D30124C18869"],"award-info":[{"award-number":["75D30124C18869"]}],"id":[{"id":"10.13039\/100000030","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Electronic health records provide opportunities for improving Lyme disease surveillance by collecting detailed clinical information and timely, scalable disease incidence estimation. Medical record review can improve understanding of clinical manifestations, how likely and possible cases compare, and potential misclassification of possible cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The SubLyme Network is a collaboration among 5 healthcare systems, a coordinating center, and the Centers for Disease Control and Prevention. The sites each conducted stratified random sampling of 500 potential acute Lyme disease episodes for medical record review from patients with a diagnosis, test order, and\/or appropriate antibiotic in 2022 or 2023. Reviewers abstracted data, including from free text notes, to classify patients as likely or possible Lyme disease and by clinical manifestation. Multivariable logistic regression was used to identify differences between possible compared to likely Lyme disease cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 2500 reviews, 880 patients were categorized as having likely or possible new onset, active Lyme disease. The patient proportion identified as having a specific clinical manifestation ranged from 61% to 76% across sites. Of likely and possible cases, 55.9%, 7.8%, 1.3%, and 3.1% were diagnosed with erythema migrans, Lyme arthritis, Lyme carditis, and neuroborreliosis, respectively. Possible cases differed from likely cases in having more nonspecific symptoms, non-Lyme disease diagnoses, provider uncertainty, and undocumented provider decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Medical record review is expected to be more accurate than computable phenotypes for Lyme disease identification and staging, yet likely and possible cases were challenging to distinguish, and a sizable proportion could not have clinical manifestation assigned. Findings provide direction for computable phenotype refinement to improve case identification. 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