@misc{10481/95460,
year = {2020},
month = {4},
url = {https://hdl.handle.net/10481/95460},
abstract = {We developed a predictive score system for 30-day mortality after palliative radiotherapy by using
predictors from routine electronic medical record. Patients with metastatic cancer receiving first course
palliative radiotherapy from 1 July, 2007 to 31 December, 2017 were identified. 30-day mortality
odds ratios and probabilities of the death predictive score were obtained using multivariable logistic
regression model. Overall, 5,795 patients participated. Median follow-up was 39.6 months (range,
24.5–69.3) for all surviving patients. 5,290 patients died over a median 110 days, 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 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. 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.},
organization = {Miguel Servet I Investigator Award (grant CP17/00206 EU-FEDER) from the National Institute
of Health, Carlos III (ISCIII), Madrid, Spain},
publisher = {Springer Nature},
title = {Prediction model for shortterm mortality after palliative radiotherapy for patients having advanced cancer: a cohort study from routine electronic medical data},
doi = {10.1038/s41598-020-62826-x},
author = {Lee, Shing Fung and Luk, Hollis and Wong, Aray and Kwan Ng, Chuk and Chi Sing Wong, Frank and Luque Fernández, Miguel Ángel},
}