Can we identify individuals at risk to develop multiple myeloma? A machine learning‐based predictive model

• Published in: British journal of haematology Vol. 207; no. 2; pp. 387 - 394
• Main Authors: Mittelman, Moshe, Israel, Ariel, Oster, Howard S., Leshchinsky, Michael, Ben‐Shlomo, Yatir, Kepten, Eldad, Dolberg, Osnat Jarchowsky, Balicer, Ran, Shaham, Galit
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.08.2025; John Wiley and Sons Inc
• Subjects: Adult; Aged; Bilirubin; Case-Control Studies; computer modelling; Creatinine; disease prediction; Electronic Health Records; Electronic medical records; Female; Ferritin; Globulins; gradient boosted; High density lipoprotein; Humans; Learning algorithms; Leukocytes (basophilic); Leukocytes (neutrophilic); logistic regression; Low density lipoprotein; Lymphocytes; Machine Learning; Male; Middle Aged; Monocytes; Multiple myeloma; Multiple Myeloma - blood; Multiple Myeloma - diagnosis; Multiple Myeloma - epidemiology; Multiple Myeloma - etiology; Original Paper; Prediction models; Risk Assessment; Risk Factors; Statins; Triglycerides; Uric acid; gradient boosted; multiple myeloma; disease prediction; computer modelling; logistic regression
• ISSN: 0007-1048; 1365-2141; 1365-2141
• DOI: 10.1111/bjh.20136

Summary Multiple myeloma evolves unnoticed over years, and when diagnosed, organ damage is common. Electronic health records (EHR) can help in developing predictive models identifying ‘healthy’ people at risk. MM patients from Clalit Health Services (2002–2019) were matched with healthy controls. Stage I: EHR from 5 years prior to MM diagnosis were reviewed and >200 parameters were compared (patients vs. controls). Stage II: Establishing xgboost model predicting 5 year 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‐C; LDL‐C; triglycerides). EHR from the pre‐MM 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‐diabetics and statins. Using labs from future MM (n = 19 129) and controls (n = 382 580, 20:1), a predictive model was developed (ROC AUC = 0.836). The simple LR model provided individual risk prediction for MM within 5 years (AUC = 0.72). Two models with machine learning predict the risk of myeloma in ‘healthy’ individuals within 5 years. The models can be used in practice. Individuals who may develop multiple myeloma within 5 years. Stage I (left) identifies patient and control groups and variables that differ between them. Stage II (middle) develops a complex SGBOOST model to predict future MM patients. Stage III (right) develops a simplified model.