A computer-assisted model for predicting probability of dying within 7 days of hospice admission in patients with terminal cancer
Journal
Japanese Journal of Clinical Oncology
Journal Volume
40
Journal Issue
5
Pages
449-455
Date Issued
2010
Author(s)
Abstract
Objective: The 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. Methods: We 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. Results: We estimated the probability dying within 7 days of hospice admission using the logistic function, P = Exp(βx)/[1 + Exp(βx)]. 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 2 probability of dying within 7 days)] = 26.52 + 0.77 × (male = 1, female = 0) + 0.59 × (cancer, liver = 1, others = 0) + 0.82 × (ECOG score) + 0.59 × (jaundice, yes = 1, no = 0) + 0.54 × (Grade 3 edema = 1, others = 0) + 0.95 × (fever, yes = 1, no = 0) + 0.07 × (respiratory rate, as per minute) + 0.01 × (heart rate, as per minute) 2 0.92 × (intervention tube = 1, no = 0) 2 0.37 × (mean muscle power). Conclusions: We proposed a computer-assisted estimated probability formula for predicting dying within 7 days of hospice admission in terminal cancer patients. ? 2010 The Author(s).
Subjects
Advanced cancer; Computer-assisted estimated probability; Hospice; Palliative; Survival
SDGs
Other Subjects
accuracy; adult; advanced cancer; aged; article; breathing rate; cancer grading; cancer patient; cancer survival; clinical feature; computer aided design; computer analysis; controlled study; demography; edema; female; fever; heart rate; hospice patient; human; intermethod comparison; jaundice; major clinical study; male; prediction; probability; process development; process model; receiver operating characteristic; statistical model; terminal disease; computer assisted diagnosis; hospice care; hospitalization; middle aged; neoplasm; palliative therapy; patient care planning; prognosis; prospective study; survival; Taiwan; Adult; Aged; Diagnosis, Computer-Assisted; Female; Hospice Care; Humans; Logistic Models; Male; Middle Aged; Neoplasms; Palliative Care; Patient Care Planning; Prognosis; Prospective Studies; ROC Curve; Severity of Illness Index; Survival Analysis; Taiwan
Type
journal article