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The objective of this statistical meta-analysis was to assess the prevalence of other hematological adverse events (AEs) that occur during or after predominantly first-line treatment with TKIs. Data from seventy peer-reviewed, published studies were included in the analysis. Hematological AEs were assessed as a function of TKI drug type (dasatinib, imatinib, bosutinib, nilotinib) and CML phase (chronic, accelerated, blast). AE prevalence aggregated across all severities and phases was significantly different between each TKI (p &lt; 0.05) for anemia\u2014dasatinib (54.5%), bosutinib (44.0%), imatinib (32.8%), nilotinib (11.2%); neutropenia\u2014dasatinib (51.2%), imatinib (29.8%), bosutinib (14.1%), nilotinib (14.1%); thrombocytopenia\u2014dasatinib (62.2%), imatinib (30.4%), bosutinib (35.3%), nilotinib (22.3%). AE prevalence aggregated across all severities and TKIs was significantly (p &lt; 0.05) different between CML phases for anemia\u2014chronic (28.4%), accelerated (66.9%), blast (55.8%); neutropenia\u2014chronic (26.7%), accelerated (63.8%), blast (36.4%); thrombocytopenia\u2014chronic (33.3%), accelerated (65.6%), blast (37.9%). An odds ratio (OR) with 95% confidence interval was used to compare hematological AE prevalence of each TKI compared to the most common first-line TKI therapy, imatinib. For anemia, dasatinib OR = 1.65, [1.51, 1.83]; bosutinib OR = 1.34, [1.16, 1.54]; nilotinib OR = 0.34, [0.30, 0.39]. For neutropenia, dasatinib OR = 1.72, [1.53, 1.92]; bosutinib OR = 0.47, [0.38, 0.58]; nilotinib OR = 0.47, [0.42, 0.54]. For thrombocytopenia, dasatinib OR = 2.04, [1.82, 2.30]; bosutinib OR = 1.16, [0.97, 1.39]; nilotinib OR = 0.73, [0.65, 0.82]. Nilotinib had the greatest fraction of severe (grade 3\/4) hematological AEs (30%). In conclusion, the overall prevalence of hematological AEs by TKI type was: dasatinib &gt; bosutinib &gt; imatinib &gt; nilotinib. Study limitations include inability to normalize for dosage and treatment duration.<\/jats:p>","DOI":"10.3390\/cancers15174354","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T11:41:18Z","timestamp":1693482078000},"page":"4354","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Hematological Adverse Events with Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia: A Systematic Review with Meta-Analysis"],"prefix":"10.3390","volume":"15","author":[{"given":"Olivia","family":"Kronick","sequence":"first","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-3046","authenticated-orcid":false,"given":"Xinyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nidhi","family":"Mehra","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armon","family":"Varmeziar","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel","family":"Fisher","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Kartchner","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5290-9289","authenticated-orcid":false,"given":"Vamsi","family":"Kota","sequence":"additional","affiliation":[{"name":"Department of Medicine, Hematology and Oncology, Georgia Cancer Center at Augusta University, Augusta, GA 30912, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cassie S.","family":"Mitchell","sequence":"additional","affiliation":[{"name":"Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA"},{"name":"The Machine Learning Center at Georgia Tech, Georgia Institute of Technology, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1002\/ajh.26642","article-title":"Chronic myeloid leukemia: 2022 update on diagnosis, therapy, and monitoring","volume":"97","author":"Jabbour","year":"2022","journal-title":"Am. 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