Predication Model for short-term mortality after palliative radiotherapy for patients having advanced cancer: cohort study from routine electronic medical data

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Abstract Description
Abstract ID :
HAC5532
Submission Type
Authors (including presenting author) :
Lee SF(1),Luk H(1),Wong A(1),NG CK(1),WONG CS(1),Luque-Fernandez MA(2)(3)
Affiliation :
(1)Tuen Mun Hospital, Department of ClinicalOncology,(2) University of Granada, Department of Non-Communicable Disease and Cancer Epidemiology,(3)London School of Hygiene and Tropical Medicine Non-Communicable Disease Epidemiology Unit
Introduction :
The determination of life expectancy is important for the care of patients with advanced cancer. However, existing tools for prognostic predictions are based on small sample sizes or are not specific such patients.
Objectives :
To develop a predictive score system for 30-day mortality and explore the association between potential predictors identified from routine electronic medical records and 30-day mortality.
Methodology :
Design: Observational cohort study in a regional hospital. 30-day mortality odds ratios and probabilities of the death predictive score system were obtained using multivariable logistic regression model.



Setting/Participants: Patients with metastatic cancer receiving first course palliative radiotherapy from 1 July, 2007 to 31 December, 2017.
Result & Outcome :
Results: Overall, 5,795 patients participated. The median age was 64 (interquartile range: 55–75) years, and 5,291 patients died during follow-up, of whom 995 (17.2%) died within 30 days of radiotherapy commencement. The most important mortality predictors were primary lung cancer (odds ratio: 1.73, 95% confidence interval: 1.47–2.04) and log peripheral blood neutrophil lymphocyte ratio (odds ratio: 1.71, 95% confidence interval: 1.52–1.92). The developed predictive scoring system had 10 predictor variables and 20 points. The cross-validated area under curve was 0.81 (95% confidence interval: 0.79–0.82). The model calibration suggested a reasonably good fit for the model (likelihood-ratio statistic: 2.81, P=0.094), providing an accurate prediction for almost all 30-day mortality probabilities.



Conclusions: The predictive scoring system accurately predicted 30-day mortality among patients with stage IV cancer. Oncologists may use this to tailor palliative therapy for patients. Further research is necessary to determine external generalizability.

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