• Jui-Kun Chiang, Yu-Hsiang Cheng, Malcolm Koo, Yee-Hsin Kao, and Ching-Yu Chen.
• Department of Family Medicine, Tainan Municipal Hospital, Tainan 70173, Taiwan.
• Jpn. J. Clin. Oncol. 2010 May 1;40(5):449-55.

ObjectiveThe aim of the present study is to compare the accuracy in using laboratory data or clinical factors, or both, in predicting probability of dying within 7 days of hospice admission in terminal cancer patients.MethodsWe conducted a prospective cohort study of 727 patients with terminal cancer. Three models for predicting the probability of dying within 7 days of hospice admission were developed: (i) demographic data and laboratory data (Model 1); (ii) demographic data and clinical symptoms (Model 2); and (iii) combination of demographic data, laboratory data and clinical symptoms (Model 3). We compared the models by using the area under the receiver operator curve using stepwise multiple logistic regression.ResultsWe estimated the probability dying within 7 days of hospice admission using the logistic function, P = Exp(betax)/[1 + Exp(betax)]. The highest prediction accuracy was observed in Model 3 (82.3%), followed by Model 2 (77.8%) and Model 1 (75.5%). The log[probability of dying within 7 days/(1 - probability of dying within 7 days)] = -6.52 + 0.77 x (male = 1, female = 0) + 0.59 x (cancer, liver = 1, others = 0) + 0.82 x (ECOG score) + 0.59 x (jaundice, yes = 1, no = 0) + 0.54 x (Grade 3 edema = 1, others = 0) + 0.95 x (fever, yes = 1, no = 0) + 0.07 x (respiratory rate, as per minute) + 0.01 x (heart rate, as per minute) - 0.92 x (intervention tube = 1, no = 0) - 0.37 x (mean muscle power).ConclusionsWe proposed a computer-assisted estimated probability formula for predicting dying within 7 days of hospice admission in terminal cancer patients.

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