Literature DB >> 20085908

Development of a predicting tool for survival of terminally ill cancer patients.

Ichinosuke Hyodo1, Tatsuya Morita, Isamu Adachi, Yasuo Shima, Akitaka Yoshizawa, Kazuaki Hiraga.   

Abstract

OBJECTIVE: To develop a predicting tool for survival of terminally ill cancer patients.
METHODS: This prospective, multicenter study was composed of two cohorts of samples: development and test. In the development sample of terminally ill cancer patients, 32 candidate predictors were studied to develop a new tool, Japan Palliative Oncology Study-Prognostic Index using the Cox proportional hazard model. Then the test sample was studied to validate Japan Palliative Oncology Study-Prognostic Index and compared it with the conventional predicting tools, such as palliative prognostic score and simplified palliative prognostic index.
RESULTS: Five significant predictors, physician's clinical prediction of survival, consciousness, pleural effusion, white blood cell count and lymphocyte % were derived from the analysis of 201 patients, and Japan Palliative Oncology Study-Prognostic Index was developed using these predictors. It could divide patients into three risk groups: low (A), intermediate (B) and high (C). Median survival times for Groups A, B and C were 51, 35 and 16 days, respectively. Survival probability for more than 30 days for Groups A, B and C in the development sample was 78%, 61% and 16%, respectively. Japan Palliative Oncology Study-Prognostic Index was studied in subsequent 208 patients for the test sample, and constant results (median survival times for Groups A, B and C; 67, 31 and 10 days, and survival probability for more than 30 days for Groups A, B and C; 81, 48 and 11%) were obtained. Palliative prognostic score can also predict three risk groups well, but simplified palliative prognostic index could not discriminate low risk from intermediate risk group.
CONCLUSION: Japan Palliative Oncology Study-Prognostic Index, a tool to predict survival, has been developed. Its reliability should be confirmed further in the future study, comparing with palliative prognostic score.

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Year:  2010        PMID: 20085908     DOI: 10.1093/jjco/hyp182

Source DB:  PubMed          Journal:  Jpn J Clin Oncol        ISSN: 0368-2811            Impact factor:   3.019


  14 in total

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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.  Prognostic value of parameters derived from white blood cell and differential counts in patients receiving palliative radiotherapy.

Authors:  Tetsuo Saito; Ryo Toya; Tomohiko Matsuyama; Akiko Semba; Keiya Matsuyama; Natsuo Oya
Journal:  Mol Clin Oncol       Date:  2016-07-22

3.  Prediction of survival in terminally ill cancer patients at the time of terminal cancer diagnosis.

Authors:  Yu Jung Kim; Su-Jung Kim; June Koo Lee; Won-Suk Choi; Jin Hyun Park; Hee Jun Kim; Sung Hoon Sim; Keun-Wook Lee; Se-Hoon Lee; Jee Hyun Kim; Dong-Wan Kim; Jong Seok Lee; Yung-Jue Bang; Dae Seog Heo
Journal:  J Cancer Res Clin Oncol       Date:  2014-05-04       Impact factor: 4.553

Review 4.  Dealing with prognostic uncertainty: the role of prognostic models and websites for patients with advanced cancer.

Authors:  David Hui; John P Maxwell; Carlos Eduardo Paiva
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5.  Usefulness of the Palliative Prognostic Index in patients with lung cancer.

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Journal:  Med Oncol       Date:  2014-08-10       Impact factor: 3.064

Review 6.  Prognostication of Survival in Patients With Advanced Cancer: Predicting the Unpredictable?

Authors:  David Hui
Journal:  Cancer Control       Date:  2015-10       Impact factor: 3.302

7.  Prognostic laboratory score to predict 14-day mortality in terminally ill patients with respiratory malignancy.

Authors:  Mari Tanaka; Natsuko Kawai; Norihiro Yuasa
Journal:  Int J Clin Oncol       Date:  2022-01-23       Impact factor: 3.402

8.  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

9.  Development of a prognostic score using the complete blood cell count for survival prediction in unselected critically ill patients.

Authors:  Fang Chongliang; Li Yuzhong; Shi Qian; Liu Xiliang; Liu Hui
Journal:  Biomed Res Int       Date:  2013-02-28       Impact factor: 3.411

10.  Rough set theory based prognostic classification models for hospice referral.

Authors:  Eleazar Gil-Herrera; Garrick Aden-Buie; Ali Yalcin; Athanasios Tsalatsanis; Laura E Barnes; Benjamin Djulbegovic
Journal:  BMC Med Inform Decis Mak       Date:  2015-11-25       Impact factor: 2.796

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