{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T14:12:07Z","timestamp":1788444727336,"version":"build-2803163510"},"reference-count":45,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"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":["American J Hematol"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Sickle cell disease (SCD), a monogenic disorder, exhibits variable severity due to genetic and environmental modifiers. Prior studies have proposed hemolytic and vaso\u2010occlusive sub\u2010phenotypes based on limited data. We used latent class analysis (LCA) to identify complication\u2010based sub\u2010phenotypes in a large longitudinal cohort. From California administrative databases (1991\u20132019), we assembled a cohort of 7636 SCD patients (53% female). Disease\u2010related complications (e.g., vaso\u2010occlusive episodes [VOE], acute chest syndrome [ACS], avascular necrosis [AVN], chronic kidney disease [CKD], pulmonary hypertension, stroke, gallbladder disease, sepsis, obstructive lung disease, venous\u2010thromboembolism [VTE], and leg ulcers) were extracted via ICD\u20109\/10 codes. LCA models, stratified by sex, identified classes; we repeated the analysis in a younger sub\u2010cohort (\u2264\u200925\u2009years;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20092210). Males had higher cumulative incidences for recurrent ACS, AVN, gallbladder disease, obstructive lung disease (age 20), and leg ulcers\/CKD (age 40). LCA revealed four classes in both sexes: Vaso\u2010Occlusive (high VOE\/ACS\/AVN; 23% males, 15% females), Hemolytic (high CKD\/pulmonary hypertension\/stroke; 8% males, 13% females), Overlap (both patterns; 8% each), and Low Complications (62%\u201363%). In the younger cohort, classes were Vaso\u2010Occlusive (8%), Hemolytic (3%), ACS\u2010dominant (12%), and Low Complications (78%). Vaso\u2010Occlusive, Hemolytic, and Overlap classes had elevated mortality (HR 1.3\u20132.4,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05) versus Low Complications; in youth, Hemolytic had the worst survival (HR 7.8,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). LCA confirms the presence of vaso\u2010occlusive and hemolytic sub\u2010phenotypes, with low\u2010complication groups predominant. Age\u2010specific differences suggest evolving phenotypes, which can inform future targeted therapies and trials.\n                  <\/jats:p>","DOI":"10.1002\/ajh.70459","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:10:18Z","timestamp":1785417018000},"page":"2581-2591","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of Clinical Sub\u2010Phenotypes of Sickle Cell Disease Using Latent Class Analysis"],"prefix":"10.1002","volume":"101","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6691-816X","authenticated-orcid":false,"given":"Shaina M.","family":"Willen","sequence":"first","affiliation":[{"name":"Divisions of Pulmonary Medicine and Hematology\/Oncology, Department of Pediatrics University of California Davis  Sacramento California USA"},{"name":"Center for Oncology Hematology Outcomes Research and Training, Division of Hematology and Oncology University of California Davis Comprehensive Cancer Center  Sacramento California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5308-9457","authenticated-orcid":false,"given":"Ann M.","family":"Brunson","sequence":"additional","affiliation":[{"name":"Center for Oncology Hematology Outcomes Research and Training, Division of Hematology and Oncology University of California Davis Comprehensive Cancer Center  Sacramento California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3082-9114","authenticated-orcid":false,"given":"Oyebimpe O.","family":"Adesina","sequence":"additional","affiliation":[{"name":"Center for Oncology Hematology Outcomes Research and Training, Division of Hematology and Oncology University of California Davis Comprehensive Cancer Center  Sacramento California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6383-7172","authenticated-orcid":false,"given":"Ted","family":"Wun","sequence":"additional","affiliation":[{"name":"Center for Oncology Hematology Outcomes Research and Training, Division of Hematology and Oncology University of California Davis Comprehensive Cancer Center  Sacramento California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1100\/tsw.2008.157"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.blre.2006.07.001"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/1535370216636726"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/ajh.21572"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1056\/nejm198307213090320"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood\u20102011\u201003\u2010325258"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1172\/JCI89741"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.15585\/mmwr.ss7109a1"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjh.14655"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood.2023023031"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/ajh.26818"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjh.15687"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2019000940"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood\u20102017\u201005\u2010783233"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/ajh.25508"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2017005256"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM199406093302303"},{"key":"e_1_2_10_19_1","unstructured":"G.Lin S.Ying andJ.Gordon \u201cAnalyzing Survival Data With Competing Risks Using SAS Software Presented at: SAS Global Forum 2012 \u201d2012. 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