{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T14:20:22Z","timestamp":1788877222775,"version":"build-2803163510"},"reference-count":28,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T00:00:00Z","timestamp":1638489600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006306","name":"Sigrid Jus\u00e9liuksen S\u00e4\u00e4ti\u00f6","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006306","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The existing risk prediction models for chemotherapy\u2010induced febrile neutropenia (FN) do not necessarily apply to real\u2010life patients in different healthcare systems and the external validation of these models are often lacking. Our study evaluates whether a machine learning\u2010based risk prediction model could outperform the previously introduced models, especially when validated against real\u2010world patient data from another institution not used for model training.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using Turku University Hospital electronic medical records, we identified all patients who received chemotherapy for non\u2010hematological cancer between the years 2010 and 2017 (<jats:italic>N<\/jats:italic>\u00a0=\u00a05879). An experimental surrogate endpoint was first\u2010cycle neutropenic infection (NI), defined as grade IV neutropenia with serum C\u2010reactive protein &gt;10\u00a0mg\/l. For predicting the risk of NI, a penalized regression model (Lasso) was developed. The model was externally validated in an independent dataset (<jats:italic>N<\/jats:italic>\u00a0=\u00a04594) from Tampere University Hospital.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Lasso model accurately predicted NI risk with good accuracy (AUROC 0.84). In the validation cohort, the Lasso model outperformed two previously introduced, widely approved models, with AUROC 0.75. The variables selected by Lasso included granulocyte colony\u2010stimulating factor (G\u2010CSF) use, cancer type, pre\u2010treatment neutrophil and thrombocyte count, intravenous treatment regimen, and the planned dose intensity. The same model predicted also FN, with AUROC 0.77, supporting the validity of NI as an endpoint.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our study demonstrates that real\u2010world NI risk prediction can be improved with machine learning and that every difference in patient or treatment characteristics can have a significant impact on model performance. Here we outline a novel, externally validated approach which may hold potential to facilitate more targeted use of G\u2010CSFs in the future.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cam4.4465","type":"journal-article","created":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T10:33:31Z","timestamp":1638527611000},"page":"654-663","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Improved risk prediction of chemotherapy\u2010induced neutropenia\u2014model development and validation with real\u2010world data"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1777-4259","authenticated-orcid":false,"given":"Mikko S.","family":"Ven\u00e4l\u00e4inen","sequence":"first","affiliation":[{"name":"Turku Bioscience Centre University of Turku and \u00c5bo Akademi University  Turku Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eetu","family":"Heerv\u00e4","sequence":"additional","affiliation":[{"name":"Department of Oncology Turku University Hospital and FICAN West  Turku Finland"},{"name":"University of Turku  Turku Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Outi","family":"Hirvonen","sequence":"additional","affiliation":[{"name":"Department of Oncology Turku University Hospital and FICAN West  Turku Finland"},{"name":"Department of Clinical Oncology University of Turku  Turku Finland"},{"name":"Palliative Center Turku University Hospital  Turku Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sohrab","family":"Saraei","sequence":"additional","affiliation":[{"name":"Turku Bioscience Centre University of Turku and \u00c5bo Akademi University  Turku 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