Literature DB >> 25062815

PREDICT: a diagnostic accuracy study of a tool for predicting mortality within one year: who should have an advance healthcare directive?

Philip Richardson1, Jaimi Greenslade2, Sulochana Shanmugathasan3, Katherine Doucet3, Neil Widdicombe4, Kevin Chu5, Anthony Brown5.   

Abstract

BACKGROUND: CARING is a screening tool developed to identify patients who have a high likelihood of death in 1 year. AIM: This study sought to validate a modified CARING tool (termed PREDICT) using a population of patients presenting to the Emergency Department. SETTING/PARTICIPANTS: In total, 1000 patients aged over 55 years who were admitted to hospital via the Emergency Department between January and June 2009 were eligible for inclusion in this study.
DESIGN: Data on the six prognostic indicators comprising PREDICT were obtained retrospectively from patient records. One-year mortality data were obtained from the State Death Registry. Weights were applied to each PREDICT criterion, and its final score ranged from 0 to 44. Receiver operator characteristic analyses and diagnostic accuracy statistics were used to assess the accuracy of PREDICT in identifying 1-year mortality.
RESULTS: The sample comprised 976 patients with a median (interquartile range) age of 71 years (62-81 years) and a 1-year mortality of 23.4%. In total, 50% had ≥1 PREDICT criteria with a 1-year mortality of 40.4%. Receiver operator characteristic analysis gave an area under the curve of 0.86 (95% confidence interval: 0.83-0.89). Using a cut-off of 13 points, PREDICT had a 95.3% (95% confidence interval: 93.6-96.6) specificity and 53.9% (95% confidence interval: 47.5-60.3) sensitivity for predicting 1-year mortality. PREDICT was simpler than the CARING criteria and identified 158 patients per 1000 admitted who could benefit from advance care planning.
CONCLUSION: PREDICT was successfully applied to the Australian healthcare system with findings similar to the original CARING study conducted in the United States. This tool could improve end-of-life care by identifying who should have advance care planning or an advance healthcare directive.
© The Author(s) 2014.

Entities:  

Keywords:  Emergency medicine; advance care planning; end of life care; palliative care

Mesh:

Year:  2014        PMID: 25062815     DOI: 10.1177/0269216314540734

Source DB:  PubMed          Journal:  Palliat Med        ISSN: 0269-2163            Impact factor:   4.762


  4 in total

1.  The "Surprise Question" Asked of Emergency Physicians May Predict 12-Month Mortality among Older Emergency Department Patients.

Authors:  Kei Ouchi; Guru Jambaulikar; Naomi R George; Wanlu Xu; Ziad Obermeyer; Emily L Aaronson; Jeremiah D Schuur; Mara A Schonberg; James A Tulsky; Susan D Block
Journal:  J Palliat Med       Date:  2017-08-28       Impact factor: 2.947

2.  Improving palliative care with deep learning.

Authors:  Anand Avati; Kenneth Jung; Stephanie Harman; Lance Downing; Andrew Ng; Nigam H Shah
Journal:  BMC Med Inform Decis Mak       Date:  2018-12-12       Impact factor: 2.796

3.  Can usual gait speed be used as a prognostic factor for early palliative care identification in hospitalized older patients? A prospective study on two different wards.

Authors:  Celine Van de Vyver; Anja Velghe; Hilde Baeyens; Jean-Pierre Baeyens; Julien Dekoninck; Nele Van Den Noortgate; Ruth Piers
Journal:  BMC Geriatr       Date:  2020-11-24       Impact factor: 3.921

4.  Leveraging Advances in Artificial Intelligence to Improve the Quality and Timing of Palliative Care.

Authors:  Paul Windisch; Caroline Hertler; David Blum; Daniel Zwahlen; Robert Förster
Journal:  Cancers (Basel)       Date:  2020-05-03       Impact factor: 6.639

  4 in total

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