Improving survival prognostication in patients with metastatic cancer through clinical judgment Journal Article


Authors: Kao, J.; Zucker, A.; Urso, M.; Karwowski, P.; Jain, N.; Jain, S.; Bustamante, G.; Lugo, D.; Rowland, L.
Article Title: Improving survival prognostication in patients with metastatic cancer through clinical judgment
Abstract: Background/Aim: NEAT is a validated prognostic model that calculates survival estimates based on the number of active tumors, ECOG performance status, albumin, and primary tumor site. Since models are imperfect, we hypothesized that experienced clinicians could predict the survival of patients with metastatic cancer better than a validated prognostic model alone, thereby quantifying the previously unmeasured value of clinical judgment. Patients and Methods: This prospective, single-institution cohort study conducted at a large community hospital recruited 73 patients with metastatic cancer referred to radiation oncology between October 2016 and December 2017. The consulting nurse and physician were prospectively surveyed on whether the patient would survive a longer or shorter duration than the calculated NEAT survival estimates. The accuracy of predictions between groups was assessed using the McNemar's chi-squared test. Results: The median survival for enrolled patients was 9.2 months. Nursing and physician predictions were similarly accurate (61.6% vs. 60.3%, p=0.85). The accuracy of confident clinical predictions was similar to less confident predictions (64.2% vs. 58.2%, p=0.46). Radiation dose intensity was informed by predicted survival, and median survival was significantly higher in patients receiving an EQD2≥40 (17 months vs. 2 months, p<0.001). Conclusion: Experienced clinicians, both nurses and oncologists, have insight that modestly supplements the accuracy of a validated model to predict survival in patients with advanced cancer. © 2022 International Institute of Anticancer Research. All rights reserved.
Keywords: aged; aged, 80 and over; middle aged; mortality; comparative study; radiation dose; prospective study; prospective studies; neoplasm; neoplasms; palliative care; metastasis; risk factors; psychology; attitude to health; pathology; risk factor; time factors; risk assessment; radiation dosage; artificial intelligence; neoplasm metastasis; predictive value of tests; nursing staff, hospital; health knowledge, attitudes, practice; predictive value; radiation oncologists; decision support techniques; decision support system; nursing staff; time factor; proof of concept; clinical reasoning; very elderly; humans; prognosis; human; male; female; radiation oncologist; proof of concept study
Journal Title: Anticancer Research
Volume: 42
Issue: 3
ISSN: 0250-7005
Publisher: International Institute of Anticancer Research  
Date Published: 2022-03-01
Start Page: 1397
End Page: 1401
Language: English
DOI: 10.21873/anticanres.15609
PUBMED: 35220232
PROVIDER: scopus
DOI/URL:
Notes: Article -- Export Date: 1 April 2022 -- Source: Scopus
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  1. Debra R. Lugo
    10 Lugo