{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T17:06:00Z","timestamp":1751648760228,"version":"3.28.0"},"reference-count":41,"publisher":"Georg Thieme Verlag KG","issue":"03","funder":[{"name":"National Institutes of Health\/National Cancer Institute Cancer Center Support","award":["P30 CA008748"],"award-info":[{"award-number":["P30 CA008748"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Objectives\u2003While clinical practice guidelines recommend that oncologists discuss goals of care with patients who have advanced cancer, it is estimated that less than 20% of individuals admitted to the hospital with high-risk cancers have end-of-life discussions with their providers. While there has been interest in developing models for mortality prediction to trigger such discussions, few studies have compared how such models compare with clinical judgment to determine a patient's mortality risk.<\/jats:p><jats:p>\n          Methods\u2003This study is a prospective analysis of 1,069 solid tumor medical oncology hospital admissions (n\u2009=\u2009911 unique patients) from February 7 to June 7, 2022, at Memorial Sloan Kettering Cancer Center. Electronic surveys were sent to hospitalists, advanced practice providers, and medical oncologists the first afternoon following a hospital admission and they were asked to estimate the probability that the patient would die within 45 days. Provider estimates of mortality were compared with those from a predictive model developed using a supervised machine learning methodology, and incorporated routine laboratory, demographic, biometric, and admission data. Area under the receiver operating characteristic curve (AUC), calibration and decision curves were compared between clinician estimates and the model predictions.<\/jats:p><jats:p>\n          Results\u2003Within 45 days following hospital admission, 229 (25%) of 911 patients died. The model performed better than the clinician estimates (AUC 0.834 vs. 0.753, p\u2009&lt;\u20090.0001). Integrating clinician predictions with the model's estimates further increased the AUC to 0.853 (p\u2009&lt;\u20090.0001). Clinicians overestimated risk whereas the model was extremely well-calibrated. The model demonstrated net benefit over a wide range of threshold probabilities.<\/jats:p><jats:p>\n          Conclusion\u2003The inpatient prognosis at admission model is a robust tool to assist clinical providers in evaluating mortality risk, and it has recently been implemented in the electronic medical record at our institution to improve end-of-life care planning for hospitalized cancer patients.<\/jats:p>","DOI":"10.1055\/s-0044-1787185","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T23:24:43Z","timestamp":1719444283000},"page":"489-500","source":"Crossref","is-referenced-by-count":3,"title":["Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients"],"prefix":"10.1055","volume":"15","author":[{"given":"Adrianna Z.","family":"Herskovits","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States"}]},{"given":"Tiffanny","family":"Newman","sequence":"additional","affiliation":[{"name":"Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States"}]},{"given":"Kevin","family":"Nicholas","sequence":"additional","affiliation":[{"name":"Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States"}]},{"given":"Cesar F.","family":"Colorado-Jimenez","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States"}]},{"given":"Claire E.","family":"Perry","sequence":"additional","affiliation":[{"name":"Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States"}]},{"given":"Alisa","family":"Valentino","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States"}]},{"given":"Isaac","family":"Wagner","sequence":"additional","affiliation":[{"name":"Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York, United States"}]},{"given":"Barbara","family":"Egan","sequence":"additional","affiliation":[{"name":"Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States"}]},{"given":"Dmitriy","family":"Gorenshteyn","sequence":"additional","affiliation":[{"name":"Thirty Madison, New York, New York, United States"}]},{"given":"Andrew J.","family":"Vickers","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States"}]},{"given":"Melissa S.","family":"Pessin","sequence":"additional","affiliation":[{"name":"Department of Pathology, University of Chicago, Chicago, Illinois, 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