{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T08:29:00Z","timestamp":1782203340613,"version":"3.54.5"},"reference-count":29,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008897","name":"Janssen Pharmaceutica","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008897","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Br J Haematol"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Multiple myeloma evolves unnoticed over years, and when diagnosed, organ damage is common. Electronic health records (EHR) can help in developing predictive models identifying \u2018healthy\u2019 people at risk. MM patients from Clalit Health Services (2002\u20132019) were matched with healthy controls. Stage I: EHR from 5\u2009years prior to MM diagnosis were reviewed and &gt;200 parameters were compared (patients vs. controls). Stage II: Establishing xgboost model predicting 5\u2009year risk for MM, with validation. Stage III: A simplified logistic regression model for community, requiring 20 variables (Age; Hb; RBC; MCV; RDW; WBC; neutrophils; lymphocytes; monocytes; basophils; glucose; creatinine; total protein; albumin; calcium; uric acid; bilirubin; HDL\u2010C; LDL\u2010C; triglycerides). EHR from the pre\u2010MM period of 4256 patients were compared to controls. Future MM patients had higher ESR, lower Hb, ANC, neutrophil\/lymphocyte ratio, higher globulins and ferritin, more immune deficiencies, MDS and FMF. They took fewer tranquilizers, anti\u2010diabetics and statins. Using labs from future MM (<jats:italic>n<\/jats:italic>\u2009=\u200919\u2009129) and controls (<jats:italic>n<\/jats:italic>\u2009=\u2009382\u2009580, 20:1), a predictive model was developed (ROC AUC = 0.836). The simple LR model provided individual risk prediction for MM within 5\u2009years (AUC\u2009=\u20090.72). Two models with machine learning predict the risk of myeloma in \u2018healthy\u2019 individuals within 5\u2009years. The models can be used in practice.<\/jats:p>","DOI":"10.1111\/bjh.20136","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T02:05:38Z","timestamp":1750125938000},"page":"387-394","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Can we identify individuals at risk to develop multiple myeloma? 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