{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:26:31Z","timestamp":1786976791769,"version":"build-2736575974"},"reference-count":44,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["American J Hematol"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Immunoglobulin light chain (\n                    <jats:styled-content style=\"fixed-case\">AL<\/jats:styled-content>\n                    ) amyloidosis is a multisystem disease with varied treatment options and disease\u2010related outcomes. Current staging systems rely on a limited number of cardiac, renal, and plasma cell dyscrasia biomarkers. To improve prognostication for all\u2010cause mortality and end\u2010stage kidney disease (\n                    <jats:styled-content style=\"fixed-case\">ESKD<\/jats:styled-content>\n                    ), we applied unsupervised machine learning using a comprehensive set of clinical and laboratory parameters. Our study cohort comprised 2067 patients with newly diagnosed, biopsy\u2010proven\n                    <jats:styled-content style=\"fixed-case\">AL<\/jats:styled-content>\n                    amyloidosis from the Boston University Amyloidosis Center. Variables included 31 clinical symptoms and 28 baseline laboratory values. Our clustering algorithm identified three subgroups of\n                    <jats:styled-content style=\"fixed-case\">AL<\/jats:styled-content>\n                    amyloidosis (low\u2010risk, intermediate\u2010risk, and high\u2010risk) with distinct clinical phenotypes and median overall survival (\n                    <jats:styled-content style=\"fixed-case\">OS<\/jats:styled-content>\n                    ) estimates of 6.1, 3.7, and 1.2\u2009years, respectively. The 10\u2010year adjusted cumulative incidences of all\u2010cause mortality were 66.8% (95%\n                    <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>\n                    63.4\u201370.1), 75.4% (95%\n                    <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>\n                    72.1\u201378.6), and 90.6% (95%\n                    <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>\n                    87.4\u201393.3) for low, intermediate, and high\u2010risk subgroups. The 10\u2010year adjusted cumulative incidences of end\u2010stage kidney disease (\n                    <jats:styled-content style=\"fixed-case\">ESKD<\/jats:styled-content>\n                    ) were 20.4% (95%\n                    <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>\n                    6.1\u201324.5), 37.6% (95%\n                    <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>\n                    31.8\u201343.8), and 6.7% (95%\n                    <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>\n                    2.8\u201311.3) for low\u2010risk, intermediate\u2010risk, and high\u2010risk subgroups. Finally, we trained a classifier for external validation with high cross\u2010validation accuracy (85% [95%\n                    <jats:styled-content style=\"fixed-case\">CI<\/jats:styled-content>\n                    83\u201386]) using a subset of easily obtainable clinical parameters. This marks an initial stride toward integrating precision medicine into risk stratification of\n                    <jats:styled-content style=\"fixed-case\">AL<\/jats:styled-content>\n                    amyloidosis for both all\u2010cause mortality and\n                    <jats:styled-content style=\"fixed-case\">ESKD<\/jats:styled-content>\n                    .\n                  <\/jats:p>","DOI":"10.1002\/ajh.27472","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T03:13:18Z","timestamp":1726024398000},"page":"2140-2151","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Machine\u2010learning based subgroups of\n                    <scp>AL<\/scp>\n                    amyloidosis and cumulative incidence of mortality and end stage kidney disease"],"prefix":"10.1002","volume":"99","author":[{"given":"Shankara K.","family":"Anand","sequence":"first","affiliation":[{"name":"Amyloidosis Center Boston University Chobanian &amp; Avedisian School of Medicine and Boston Medical Center  Boston Massachusetts USA"},{"name":"Department of Medicine Stanford School of Medicine  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Staron","sequence":"additional","affiliation":[{"name":"Amyloidosis Center Boston University Chobanian &amp; Avedisian School of Medicine and Boston Medical Center  Boston Massachusetts USA"},{"name":"Section of Hematology and Medical Oncology, Department of Medicine Boston University Chobanian &amp; Avedisian School of Medicine and Boston Medical Center  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisa M.","family":"Mendelson","sequence":"additional","affiliation":[{"name":"Amyloidosis Center Boston University Chobanian &amp; Avedisian School of Medicine and Boston Medical 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