%0 Observational Study
%T Clinical Prognostic Factors During the Last One Month of Life in Terminally Ill Cancer Patients: A Retrospective Observational Study.
%A Sun H
%A Li H
%A Zhang Z
%A Yan L
%J J Coll Physicians Surg Pak
%V 33
%N 1
%D Jan 2023
%M 36597227
%F 1.022
%R 10.29271/jcpsp.2023.01.10
%X <B>OBJECTIVE: </B>To explore the trajectory of clinical symptoms and biomarkers in the last four weeks of life in terminally ill cancer patients.<BR><B>METHODS: </B>Observational study.<BR><B>METHODS: </B>Department of Oncology, Shijingshan hospital, Shijingshan Teaching Hospital of Capital Medical University, Beijing, China, between January 2017 and January 2020.<BR><B>METHODS: </B>This study evaluated 173 terminally ill cancer patients. Seventeen symptoms and fifteen biomarkers were identified. For sequential analysis, the authors divided the final four weeks of life into four time periods from the date of death. Ordinal multiple logistic regression analysis was used to explore the association between the changes in clinical parameters and the risk of death in a given period. Changes in clinical parameters across different time periods were evaluated using the Wilcoxon signed rank test.<BR><B>RESULTS: </B>Abnormal consciousness; elevated ECOG (Eastern Cooperative Oncology Group) scores, neutrophil-to-lymphocyte ratio (NLR), blood urea nitrogen (BUN) to creatinine ratio, C-reactive protein (CRP)-to-albumin ratio; and decreased platelet (PLT) counts were independent factors (p<0.05) for predicting closer death in the final month of life. All parameters above showed significant changes over time in the last month, although the starting time points for these changes varied.<BR><B>CONCLUSIONS: </B>Abnormal consciousness, elevated ECOG scores, NLR, BUN-to-creatinine ratio, CRP-to-albumin ratio, and decreased PLT counts are potentially useful markers for approaching death in terminally ill cancer patients. These findings are valuable for understanding the biology of death in terminally ill cancer patients. And to some extent, they may help clinicians recognise that a patient will die in the near future.<BR><B>BACKGROUND: </B>Cancer, Ordinal regression analysis, Death, Terminal illness, Biomarkers.