Literature DB >> 27924554

Development of a Clinical Tool to Predict Home Death of a Discharged Cancer Patient in Japan: a Case-Control Study.

Sakiko Fukui1, Tatsuya Morita2, Kazuhiro Yoshiuchi3.   

Abstract

PURPOSE: The aim of this study was to investigate the predictive value of a clinical tool to predict whether discharged cancer patients die at home, comparing groups of case who died at home and control who died in hospitals or other facilities.
METHOD: We conducted a nationwide case-control study to identify the determinants of home death for a discharged cancer patient. We randomly selected nurses in charge of 2000 home-visit nursing agencies from all 5813 agencies in Japan by referring to the nationwide databases in January 2013. The nurses were asked to report variables of their patients' place of death, patients' and caregivers' clinical statuses, and their preferences for home death. We used logistic regression analysis and developed a clinical tool to accurately predict it and investigated their predictive values.
RESULTS: We identified 466 case and 478 control patients. Five predictive variables of home death were obtained: patients' and caregivers' preferences for home death [OR (95% CI) 2.66 (1.99-3.55)], availability of visiting physicians [2.13 (1.67-2.70)], 24-h contact between physicians and nurses [1.68 (1.30-2.18)], caregivers' experiences of deathwatch at home [1.41 (1.13-1.75)], and patients' insights as to their own prognosis [1.23 (1.02-1.50)]. We calculated the scores predicting home death for each variable (range 6-28). When using a cutoff point of 16, home death was predicted with a sensitivity of 0.72 and a specificity of 0.81 with the Harrell's c-statistic of 0.84.
CONCLUSION: This simple clinical tool for healthcare professionals can help predict whether a discharged patient is likely to die at home.

Entities:  

Keywords:  Death; Neoplasms; Patient discharge; Place; Predictive to

Mesh:

Year:  2017        PMID: 27924554     DOI: 10.1007/s12529-016-9619-y

Source DB:  PubMed          Journal:  Int J Behav Med        ISSN: 1070-5503


  37 in total

1.  When death is imminent: where terminally ill patients with cancer prefer to die and why.

Authors:  Siew Tzuh Tang
Journal:  Cancer Nurs       Date:  2003-06       Impact factor: 2.592

2.  Reliability and validity of the Japanese version of the Support Team Assessment Schedule (STAS-J).

Authors:  Mitsunori Miyashita; Kazuko Matoba; Tomoyo Sasahara; Yoshiyuki Kizawa; Misae Maruguchi; Mayumi Abe; Masako Kawa; Yasuo Shima
Journal:  Palliat Support Care       Date:  2004-12

3.  Barriers to dying at home: the impact of poor co-ordination of community service provision for patients with cancer.

Authors:  Mary O'Brien; Barbara Jack
Journal:  Health Soc Care Community       Date:  2009-12-20

4.  Resampling and cross-validation techniques: a tool to reduce bias caused by model building?

Authors:  M Schumacher; N Holländer; W Sauerbrei
Journal:  Stat Med       Date:  1997-12-30       Impact factor: 2.373

Review 5.  Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

Authors:  F E Harrell; K L Lee; D B Mark
Journal:  Stat Med       Date:  1996-02-28       Impact factor: 2.373

6.  Can this patient be discharged home? Factors associated with at-home death among patients with cancer.

Authors:  Alberto Alonso-Babarro; Eduardo Bruera; María Varela-Cerdeira; María Jesús Boya-Cristia; Rosario Madero; Isabel Torres-Vigil; Javier De Castro; Manuel González-Barón
Journal:  J Clin Oncol       Date:  2011-02-22       Impact factor: 44.544

7.  Diagnostic accuracy of the palliative prognostic score in hospitalized patients with advanced cancer.

Authors:  Paul A Glare; Steffen Eychmueller; Patrick McMahon
Journal:  J Clin Oncol       Date:  2004-12-01       Impact factor: 44.544

Review 8.  Advance Care Planning in palliative care: a systematic literature review of the contextual factors influencing its uptake 2008-2012.

Authors:  Allison Lovell; Patsy Yates
Journal:  Palliat Med       Date:  2014-05-12       Impact factor: 4.762

9.  Development of prognosis in palliative care study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study.

Authors:  Bridget Gwilliam; Vaughan Keeley; Chris Todd; Matthew Gittins; Chris Roberts; Laura Kelly; Stephen Barclay; Patrick C Stone
Journal:  BMJ       Date:  2011-08-25

10.  Five types of home-visit nursing agencies in Japan based on characteristics of service delivery: cluster analysis of three nationwide surveys.

Authors:  Sakiko Fukui; Noriko Yamamoto-Mitani; Junko Fujita
Journal:  BMC Health Serv Res       Date:  2014-12-20       Impact factor: 2.655

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