Literature DB >> 12527951

Clinical estimation of survival and impact of other prognostic factors on terminally ill cancer patients in Oman.

Medhat Faris1.   

Abstract

A prospective study was carried out in the only tertiary oncology department in Oman to analyse the pattern of various prognostic factors in terminally ill cancer patients and their impact on these patients. Between September 1999 and February 2001, terminally ill cancer patients with solid tumours who fulfilled the eligibility criteria were included. All of them were coded Do Not Resuscitate (DNR). Clinical estimation of survival as well as recording of different symptoms and signs was carried out for each patient. Survival was calculated from the date of the DNR coding to the date of death. A total of 162 patients were included. Mean survival time was 41.5 days (median 10 days). The gastrointestinal tract (GIT) was the most common site of malignant disease, followed by the breast. Univariate analysis of evaluable patients showed that performance status (PS), dry mouth, presence of delirium, anorexia, peripheral oedema, absence of bone metastasis, low lymphocyte count and low albumin level had significant effects on survival. Multiple regression analysis showed that PS and oedema were the only independent predictors of survival. Clinical prediction of survival was correlated with observed survival. Patient's PS, presence of peripheral oedema and clinical estimate of survival are good predictors of survival in terminally ill cancer patients.

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Mesh:

Year:  2002        PMID: 12527951     DOI: 10.1007/s00520-002-0401-0

Source DB:  PubMed          Journal:  Support Care Cancer        ISSN: 0941-4355            Impact factor:   3.603


  7 in total

1.  Outcomes of Cardiopulmonary Resuscitation and Estimation of Healthcare Costs in Potential 'Do Not Resuscitate' Cases.

Authors:  Akhwand S Ahmad; Sayed Mudasser; Muhammad N Khan; Hafiz N H Abdoun
Journal:  Sultan Qaboos Univ Med J       Date:  2016-02-02

2.  How palliative care professionals deal with predicting life expectancy at the end of life: predictors and accuracy.

Authors:  Sara Mandelli; Emma Riva; Mauro Tettamanti; Ugo Lucca; Davide Lombardi; Gianmaria Miolo; Simon Spazzapan; Rita Marson
Journal:  Support Care Cancer       Date:  2020-08-31       Impact factor: 3.603

3.  Predicting survival time in noncurative patients with advanced cancer: a prospective study in China.

Authors:  Jing Cui; Lingjun Zhou; B Wee; Fengping Shen; Xiuqiang Ma; Jijun Zhao
Journal:  J Palliat Med       Date:  2014-04-07       Impact factor: 2.947

4.  A systematically structured review of biomarkers of dying in cancer patients in the last months of life; An exploration of the biology of dying.

Authors:  Victoria Louise Reid; Rachael McDonald; Amara Callistus Nwosu; Stephen R Mason; Chris Probert; John E Ellershaw; Séamus Coyle
Journal:  PLoS One       Date:  2017-04-06       Impact factor: 3.240

5.  Survival Rate of Colorectal Cancer in Eastern Mediterranean Region Countries: A Systematic Review and Meta-Analysis.

Authors:  Hossein-Ali Nikbakht; Soheil Hassanipour; Layla Shojaie; Mohebat Vali; Saber Ghaffari-Fam; Mousa Ghelichi-Ghojogh; Zahra Maleki; Morteza Arab-Zozani; Elham Abdzadeh; Hamed Delam; Hamid Salehiniya; Maryam Shafiee; Salman Mohammadi
Journal:  Cancer Control       Date:  2020 Jan-Dec       Impact factor: 3.302

6.  A computer-assisted model for predicting probability of dying within 7 days of hospice admission in patients with terminal cancer.

Authors:  Jui-Kun Chiang; Yu-Hsiang Cheng; Malcolm Koo; Yee-Hsin Kao; Ching-Yu Chen
Journal:  Jpn J Clin Oncol       Date:  2010-01-22       Impact factor: 3.019

Review 7.  A Systematic Review of Predictions of Survival in Palliative Care: How Accurate Are Clinicians and Who Are the Experts?

Authors:  Nicola White; Fiona Reid; Adam Harris; Priscilla Harries; Patrick Stone
Journal:  PLoS One       Date:  2016-08-25       Impact factor: 3.240

  7 in total

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