{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T07:36:21Z","timestamp":1790753781282,"version":"4.1.0"},"reference-count":40,"publisher":"American Medical Association (AMA)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Older adults with advanced cancer who have high pretreatment symptom severity often experience adverse events during cancer treatments. Unsupervised machine learning may help stratify patients into different risk groups.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To evaluate whether clusters identified from baseline patient-reported symptom severity were associated with adverse outcomes.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This secondary analysis of the Geriatric Assessment Intervention for Reducing Toxicity in Older Patients With Advanced Cancer (GAP70+) Trial (2014-2019) included patients who completed the National Cancer Institute Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) before starting a new cancer treatment regimen and received care at community oncology sites across the United States. An unsupervised machine learning algorithm (k-means with Euclidean distance) clustered patients based on similarities of baseline symptom severities. Clustering variables included severity items of 24 PRO-CTCAE symptoms (range, 0-4; corresponding to none, mild, moderate, severe, and very severe). Total severity score was calculated as the sum of 24 items (range, 0-96). Whether the clusters were associated with unplanned hospitalization, death, and toxic effects was then examined. Analyses were conducted in January and February 2022.<\/jats:p>\n <\/jats:sec>\n \n Exposures<\/jats:title>\n Symptom severity.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n Unplanned hospitalization over 3 months (primary), all-cause mortality over 1 year, and any clinician-rated grade 3 to 5 toxic effect over 3 months.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Of 718 enrolled patients, 706 completed baseline PRO-CTCAE and were included (mean [SD] age, 77.2 [5.5] years, 401 [56.8%] male patients; 51 [7.2%] Black and 619 [87.8%] non-Hispanic White patients; 245 [34.7%] with gastrointestinal cancer; 175 [24.8%] with lung cancer; mean [SD] impaired Geriatric Assessment domains, 4.5 [1.6]). The algorithm classified 310 (43.9%), 295 (41.8%), and 101 (14.3%) into low-, medium-, and high-severity clusters (within-cluster mean [SD] severity scores: low, 6.3 [3.4]; moderate, 16.6 [4.3]; high, 29.8 [7.8];\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001). Controlling for sociodemographic variables, clinical factors, study group, and practice site, compared with patients in the low-severity cluster, those in the moderate-severity cluster were more likely to experience hospitalization (risk ratio, 1.36; 95% CI, 1.01-1.84;\n P<\/jats:italic>\n \u2009=\u2009.046). Moderate- and high-severity clusters were associated with a higher risk of death (moderate: hazard ratio, 1.31; 95% CI, 1.01-1.69;\n P<\/jats:italic>\n \u2009=\u2009.04; high: hazard ratio, 2.00; 95% CI, 1.43-2.78;\n P<\/jats:italic>\n \u2009&amp;lt;\u2009.001), but not toxic effects.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In this study, unsupervised machine learning partitioned patients into distinct symptom severity clusters; patients with higher pretreatment severity were more likely to experience hospitalization and death.<\/jats:p>\n <\/jats:sec>\n \n Trial Registration<\/jats:title>\n \n ClinicalTrials.gov Identifier:\n NCT02054741<\/jats:ext-link>\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2023.4198","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T11:11:39Z","timestamp":1679483499000},"page":"e234198","source":"Crossref","is-referenced-by-count":40,"title":["An Unsupervised Machine Learning Approach to Evaluating the Association of Symptom Clusters With Adverse Outcomes Among Older Adults With Advanced Cancer"],"prefix":"10.1001","volume":"6","author":[{"given":"Huiwen","family":"Xu","sequence":"first","affiliation":[{"name":"School of Public and Population Health, University of Texas Medical Branch, Galveston"},{"name":"Sealy Center on Aging, University of Texas Medical Branch, Galveston"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences, University of Rochester Medical Center, Rochester, New York"},{"name":"James P. 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Wilmot Cancer Institute, Division of Hematology\/Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kah Poh","family":"Loh","sequence":"additional","affiliation":[{"name":"James P. Wilmot Cancer Institute, Division of Hematology\/Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megan","family":"Wells","sequence":"additional","affiliation":[{"name":"James P. 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