Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-11T07:11:27.777Z Has data issue: false hasContentIssue false
  • English
  • Français

Risk factors of sudden unexpected death in patients with advanced cancer near the end of life

Published online by Cambridge University Press:  05 October 2021

Tomohiko Taniyama ,
Rie Tokutani  and
Shuji Hiramoto Open the ORCID record for Shuji Hiramoto [Opens in a new window]
Tomohiko Taniyama
Affiliation:
Department of Clinical Oncology and Palliative Medicine, Mitsubishi Kyoto Hospital, Kyoto, Japan
Rie Tokutani
Affiliation:
Department of Internal Medicine, Palliative Care and Clinical Oncology, Peace Home Care Clinic, Otsu, Japan
Shuji Hiramoto*
Affiliation:
Department of Internal Medicine, Palliative Care and Clinical Oncology, Peace Home Care Clinic, Otsu, Japan
*
Author for correspondence: Shuji Hiramoto, Department of Internal Medicine, Palliative Care and Clinical Oncology, Peace Home Care Clinic, Oiwakecho16-21, Otsu, Japan. E-mail: otomari1rx.8@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

The definition of sudden unexpected death (SUD) in patients with advanced cancer near the end of life (EOL) was unclear.

Methods

This study was conducted as a single-center retrospective analysis. We analyzed 1,282 patients who died of advanced cancer from August 2011 to August 2019 retrospectively. We divided into patients who died within 24 h after the acute change of general condition or others and analyzed risk factors by a multiple logistics method. The reason for SUD was found, the reason is detected by using an electronic medical record retrospectively. The risk factors in SUD were analyzed using age, sex, EOL symptom and treatment, the primary site of cancer, metastatic site of cancer, comorbidly, chemotherapy, and Eastern Cooperative Oncology Group Performance Status. The primary endpoint was to identify the frequency and risk factors of SUD in patients with advanced cancer near the EOL.

Results

As a background, the median age is 73 years old, 690 males, 592 females, 227 gastroesophageal cancers, 250 biliary pancreatic cancers, 54 hepatocellular carcinomas, 189 colorectal cancer, 251 lung cancers, 71 breast cancers, 58 urological malignancies, 60 gynecological malignancies, 47 head and neck cancer, 31 hematological malignancies, and 22 sarcomas. The number of patients who died suddenly was 93 (7.2%) at EOL. In a multivariate analysis, Age (ORs 0.619), sex (ORs 1.700), patients with EOL delirium (ORs 0.483), nausea and vomiting (ORs 2.263), 1L or more infusion (ORs 3.479), EOL opioids (ORs 0.465), EOL sedations (ORs 0.339), and with cardiac comorbidity (ORs 0.345) were independent risk factors.

Conclusions

The frequency of patients who died suddenly was 7.2% (n = 93) at EOL. Age, sex, EOL symptom, EOL treatment, and cardiac comorbidity were independent risk factors in patients with advanced cancer near the EOL. Information on these risk factors is useful to explaining their EOL in advance.

Keywords

Advanced cancerEnd of lifeRisk factorsSudden unexpected death
Type
Original Article
Information
Palliative & Supportive Care , Volume 20 , Issue 6 , December 2022 , pp. 818 - 822
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Baba, M, Hiramoto, S, Morita, T, et al. (2015) Survival prediction for advanced cancer patients in the real world: A comparison of the Palliative Prognostic Score, Delirium-Palliative Prognostic Score, Palliative Prognostic Index and modified Prognosis in Palliative Care Study predictor model. European Journal of Cancer 51(12), 1618–1629.Google ScholarPubMed
Bridget, G, Vahghan, K, Patrick, C, et al. (2011) Development of Prognosis in Palliative Care Study (PiPS) predictor models to improve prognostication in advanced cancer: Prospective cohort study. BMJ 25, 343.Google Scholar
Bruera, S, David, H, Bruera, E, et al. (2015) Frequency and factors associated with unexpected death in an acute palliative care unit: Expect the unexpected. Journal of Pain and Symptom Management 49, 822–827.CrossRefGoogle Scholar
Hamano, Y, Hiramoto, S, Morita, T, et al. (2018) A combination of routine laboratory findings and vital signs can predict survival of advanced cancer patients without physician evaluation: A fractional polynomial model. European Journal of Cancer 105, 50–60.CrossRefGoogle ScholarPubMed
Hiramoto, S, Yoshioka, A, Inoue, A, et al. (2019) Prognostic factors in patients who received end-of-life chemotherapy for advanced cancer. International Journal of Clinical Oncology 24(4), 454.CrossRefGoogle ScholarPubMed
Hiramoto, S, Yoshioka, A, Inoue, A, et al. (2021) Effects of molecular targeting agents and immune-checkpoint inhibitors in patients with advanced cancer who are near the end of life. Palliative and Supportive Care, 1–6.Google ScholarPubMed
Inoue, SK, van Dyck, CH, Alessi, CA, et al. (1990) Clarifying confusion: The confusion assessment method. A new method for detection of delirium. Annals of Internal Medicine 113(12), 941–948.CrossRefGoogle Scholar
Lunney, JR, Joanne, L, Christopher, H, et al. (2002) Profiles of older medicare decedents. Journal of the American Geriatrics Society 50, 1108–1112.CrossRefGoogle ScholarPubMed
Lunney, JR, Janne, L, Daniel, JF, et al. (2003) Patterns of functional decline at the end of life. JAMA 289, 2387–2392.CrossRefGoogle ScholarPubMed
Maeda, I, Kikuchi, A, Kinoshita, H, et al. (2016) Effect of continuous deep sedation on survival in patients with advanced cancer (J-proval): A propensity score-weighted analysis of a prospective cohort study. The Lancet Oncology 17(1), 115–122.CrossRefGoogle ScholarPubMed
Maltoni, M, Nannni, O, Pirovano, M, et al. (1999) A new palliative prognostic score: A first step for the staging of terminally ill cancer patients. Italian Multicenter and Study Group on Palliative Care. Journal of Pain and Symptom Management 17(4), 231–239.CrossRefGoogle Scholar
Morita, T, Tsunoda, J, Inoue, S, et al. (1999a) The Palliative Prognostic Index: A scoring system for survival prediction of terminally ill cancer patients. Supportive Care in Cancer 7(3), 128–133.CrossRefGoogle Scholar
Morita, T, Tsunoda, J, Inoue, S, et al. (1999b) Accuracy of clinical prediction of survival for terminally ill cancer patients. Gan to Kagaku Ryoho 26, 131–136 (in Japanese).Google Scholar
Morita, T, Bito, S, Uchitomi, Y, et al. (2005) Development of a clinical guideline for palliative sedation therapy using the Delphi method. Journal of Palliative Medicine 8(4), 716–729.CrossRefGoogle ScholarPubMed
Nauck, F and Alt-Epping, B (2008) Crises in palliative care – A comprehensive approach. The Lancet Oncology 9(11), 1086–1091.CrossRefGoogle ScholarPubMed
Tsuneto, S, Ikenaga, M, Hosoi, J, et al. (1996) Research for terminal cancer patients. The Japanese Journal of Terminal Care 6, 482–490 (in Japanese).Google Scholar
Uneno, Y, Hiramoto, S, Muto, M, et al. (2017) Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study. PLoS ONE 12(8).CrossRefGoogle ScholarPubMed