Trajectories of mortality risk among patients with cancer and associated end-of-life utilization

Abstract Machine learning algorithms may address prognostic inaccuracy among clinicians by identifying patients at risk of short-term mortality and facilitating earlier discussions about hospice enrollment, discontinuation of therapy, or other management decisions. In the present study, we used pros...

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Autores principales:	Ravi B. Parikh, Manqing Liu, Eric Li, Runze Li, Jinbo Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/89188aa94de142019bff52b182f175b4
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spelling	oai:doaj.org-article:89188aa94de142019bff52b182f175b42021-12-02T14:33:50ZTrajectories of mortality risk among patients with cancer and associated end-of-life utilization10.1038/s41746-021-00477-62398-6352https://doaj.org/article/89188aa94de142019bff52b182f175b42021-07-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00477-6https://doaj.org/toc/2398-6352Abstract Machine learning algorithms may address prognostic inaccuracy among clinicians by identifying patients at risk of short-term mortality and facilitating earlier discussions about hospice enrollment, discontinuation of therapy, or other management decisions. In the present study, we used prospective predictions from a real-time machine learning prognostic algorithm to identify two trajectories of all-cause mortality risk for decedents with cancer. We show that patients with an unpredictable trajectory, where mortality risk rises only close to death, are significantly less likely to receive guideline-based end-of-life care and may not benefit from the integration of prognostic algorithms in practice.Ravi B. ParikhManqing LiuEric LiRunze LiJinbo ChenNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-5 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Ravi B. Parikh Manqing Liu Eric Li Runze Li Jinbo Chen Trajectories of mortality risk among patients with cancer and associated end-of-life utilization
description	Abstract Machine learning algorithms may address prognostic inaccuracy among clinicians by identifying patients at risk of short-term mortality and facilitating earlier discussions about hospice enrollment, discontinuation of therapy, or other management decisions. In the present study, we used prospective predictions from a real-time machine learning prognostic algorithm to identify two trajectories of all-cause mortality risk for decedents with cancer. We show that patients with an unpredictable trajectory, where mortality risk rises only close to death, are significantly less likely to receive guideline-based end-of-life care and may not benefit from the integration of prognostic algorithms in practice.
format	article
author	Ravi B. Parikh Manqing Liu Eric Li Runze Li Jinbo Chen
author_facet	Ravi B. Parikh Manqing Liu Eric Li Runze Li Jinbo Chen
author_sort	Ravi B. Parikh
title	Trajectories of mortality risk among patients with cancer and associated end-of-life utilization
title_short	Trajectories of mortality risk among patients with cancer and associated end-of-life utilization
title_full	Trajectories of mortality risk among patients with cancer and associated end-of-life utilization
title_fullStr	Trajectories of mortality risk among patients with cancer and associated end-of-life utilization
title_full_unstemmed	Trajectories of mortality risk among patients with cancer and associated end-of-life utilization
title_sort	trajectories of mortality risk among patients with cancer and associated end-of-life utilization
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/89188aa94de142019bff52b182f175b4
work_keys_str_mv	AT ravibparikh trajectoriesofmortalityriskamongpatientswithcancerandassociatedendoflifeutilization AT manqingliu trajectoriesofmortalityriskamongpatientswithcancerandassociatedendoflifeutilization AT ericli trajectoriesofmortalityriskamongpatientswithcancerandassociatedendoflifeutilization AT runzeli trajectoriesofmortalityriskamongpatientswithcancerandassociatedendoflifeutilization AT jinbochen trajectoriesofmortalityriskamongpatientswithcancerandassociatedendoflifeutilization
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Trajectories of mortality risk among patients with cancer and associated end-of-life utilization

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