Literature DB >> 17664480

Survival prediction in terminally ill cancer patients by clinical estimates, laboratory tests, and self-rated anxiety and depression.

Stephan Gripp1, Sibylle Moeller, Edwin Bölke, Gerd Schmitt, Christiane Matuschek, Sonja Asgari, Farzin Asgharzadeh, Stephan Roth, Wilfried Budach, Matthias Franz, Reinhardt Willers.   

Abstract

PURPOSE: To study how survival of palliative cancer patients relates to subjective prediction of survival, objective prognostic factors (PFs), and individual psychological coping. PATIENTS AND METHODS: Survival was estimated according to three categories (< 1 month, 1 to 6 months, and > 6 months) by two physicians (A and B) and the institutional tumor board (C) for 216 patients recently referred for palliative radiotherapy. After 6 months, the accuracy of these estimates was assessed. The prognostic relevance of clinical symptoms, performance status, laboratory tests, and self-reported emotional distress (Hospital Anxiety and Depression Scale) was investigated.
RESULTS: In 61%, 55%, and 63% of the patients, prognoses were correctly estimated by A, B, and C, respectively. kappa statistic showed fair agreement of the estimates, which proved to be overly optimistic. Accuracy of the three estimates did not improve with increasing professional experience. In particular, the survival of 96%, 71%, and 87% of patients who died in less than 1 month was overestimated by A, B, and C, respectively. On univariate analysis, 11 of 27 parameters significantly affected survival, namely performance status, primary cancer, fatigue, dyspnea, use of strong analgesics, brain metastases, leukocytosis, lactate dehydrogenase (LDH), depression, and anxiety. On multivariate analysis, colorectal and breast cancer had a favorable prognosis, whereas brain metastases, Karnofsky performance status less than 50%, strong analgesics, dyspnea, LDH, and leukocytosis were associated with a poor prognosis.
CONCLUSION: This study revealed that physicians' survival estimates were unreliable, especially in the case of patients near death. Self-reported emotional distress and objective PFs may improve the accuracy of survival estimates.

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Year:  2007        PMID: 17664480     DOI: 10.1200/JCO.2006.10.5411

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  76 in total

1.  Prospective comparison of prognostic scores in palliative care cancer populations.

Authors:  Marco Maltoni; Emanuela Scarpi; Cristina Pittureri; Francesca Martini; Luigi Montanari; Elena Amaducci; Stefania Derni; Laura Fabbri; Marta Rosati; Dino Amadori; Oriana Nanni
Journal:  Oncologist       Date:  2012-02-29

2.  Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models.

Authors:  Xinsong Du; Jae Min; Chintan P Shah; Rohit Bishnoi; William R Hogan; Dominick J Lemas
Journal:  Int J Med Inform       Date:  2020-04-15       Impact factor: 4.046

3.  Accuracy and Prognostic Significance of Oncologists' Estimates and Scenarios for Survival Time in Advanced Gastric Cancer.

Authors:  Anuradha Vasista; Martin Stockler; Andrew Martin; Nick Pavlakis; Katrin Sjoquist; David Goldstein; Sanjeev Gill; Vikram Jain; Geoffrey Liu; George Kannourakis; Yeul Hong Kim; Louise Nott; Stephanie Snow; Matthew Burge; Dean Harris; Derek Jonker; Yu Jo Chua; Richard Epstein; Antony Bonaventura; Belinda Kiely
Journal:  Oncologist       Date:  2019-04-01

4.  Can oncologists predict survival for patients with progressive disease after standard chemotherapies?

Authors:  T K Taniyama; K Hashimoto; N Katsumata; A Hirakawa; K Yonemori; M Yunokawa; C Shimizu; K Tamura; M Ando; Y Fujiwara
Journal:  Curr Oncol       Date:  2014-04       Impact factor: 3.677

5.  Patients who die during palliative radiotherapy. Status survey.

Authors:  B Berger; H Ankele; M Bamberg; D Zips
Journal:  Strahlenther Onkol       Date:  2014-01-11       Impact factor: 3.621

6.  The TEACHH model to predict life expectancy in patients presenting for palliative spine radiotherapy: external validation and comparison with alternate models.

Authors:  Maryam Dosani; Scott Tyldesley; Brendan Bakos; Jeremy Hamm; Tim Kong; Sarah Lucas; Jordan Wong; Mitchell Liu; Sarah Hamilton
Journal:  Support Care Cancer       Date:  2018-02-01       Impact factor: 3.603

7.  Cohort study of fatty acid synthase expression and patient survival in colon cancer.

Authors:  Shuji Ogino; Katsuhiko Nosho; Jeffrey A Meyerhardt; Gregory J Kirkner; Andrew T Chan; Takako Kawasaki; Edward L Giovannucci; Massimo Loda; Charles S Fuchs
Journal:  J Clin Oncol       Date:  2008-10-27       Impact factor: 44.544

Review 8.  Translational and basic science opportunities in palliative care and radiation oncology.

Authors:  Mai Anh Huynh; Alexander Spektor
Journal:  Ann Palliat Med       Date:  2019-07

9.  Depression and cancer mortality: a meta-analysis.

Authors:  M Pinquart; P R Duberstein
Journal:  Psychol Med       Date:  2010-01-20       Impact factor: 7.723

10.  The worst is yet to come. Many elderly patients with chronic terminal illnesses will eventually die in the emergency department.

Authors:  Erwin J O Kompanje
Journal:  Intensive Care Med       Date:  2010-03-13       Impact factor: 17.440

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