Prediction model for shortterm mortality after palliative radiotherapy for patients having advanced cancer: a cohort study from routine electronic medical data Lee, Shing Fung Luk, Hollis Wong, Aray Kwan Ng, Chuk Chi Sing Wong, Frank Luque Fernández, Miguel Ángel 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. 2024-10-03T08:13:30Z 2024-10-03T08:13:30Z 2020-04-01 journal article Shing Fung, L. et. al. Sci Rep 10, 5779 (2020). [https://doi.org/10.1038/s41598-020-62826-x] https://hdl.handle.net/10481/95460 10.1038/s41598-020-62826-x eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Springer Nature