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Accuracy of prognosis prediction by PPI in hospice inpatients with cancer: a multi-centre prospective study

Subramaniam, S., Thorns, A., Ridout, Martin S., Thirukkumaran, T., Osborne, T.R. (2013) Accuracy of prognosis prediction by PPI in hospice inpatients with cancer: a multi-centre prospective study. BMJ Supportive & Palliative Care, 3 (3). pp. 324-329. ISSN 2045-435X. E-ISSN 2045-4368. (doi:10.1136/bmjspcare-2012-000239) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:41473)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1136/bmjspcare-2012-000239

Abstract

The Palliative Prognostic Index (PPI) is a prognostication tool for palliative care patients based on clinical indices developed in Japan and further validated by one study in the UK. The aim of this study was to test its prediction accuracy in a large inpatient hospice sample. The admitting doctor in three inpatient hospices calculated the PPI score on admission. Two hundred and sixty-two patients were included in this study. Based on the PPI score, three subgroups were identified. Group 1 corresponded to patients with PPI ?4 and the median survival of 53?days (95% CI 40 to 80?days). Group 2 corresponded to those with PPI >4 and ?6 and the median survival 15?days (95% CI 12 to 26?days) and Group 3 corresponded to patients with PPI >6 and the median survival of 5?days (95% CI 3 to 7?days). In this study, PPI was able to identify patients’ likelihood of dying within 3?weeks with a sensitivity of 64% and specificity of 83%. It was able to identify a 6-week survival chance with a sensitivity of 62% and specificity of 86%. A one-unit increase in PPI score was estimated to increase the hazard for death by a factor of 1.33 (95% CI 1.26 to 1.40), based on fitting a stratified Cox proportional hazards model. The authors conclude that PPI can be used to predict prognosis for patients with advanced cancer.

Item Type: Article
DOI/Identification number: 10.1136/bmjspcare-2012-000239
Uncontrolled keywords: Subramaniam, S., Thorns, A. Ridout, M.S., Thirukkumaran, T. Osborne, T.R.
Subjects: R Medicine > RC Internal medicine > RC254 Neoplasms. Tumors. Oncology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Martin Ridout
Date Deposited: 18 Jun 2014 13:41 UTC
Last Modified: 16 Nov 2021 10:16 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/41473 (The current URI for this page, for reference purposes)

University of Kent Author Information

Ridout, Martin S..

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