Survival after palliative radiation therapy for cancer: The METSSS model.

BACKGROUND: We propose a predictive model that identifies patients at greatest risk of death after palliative radiotherapy, and subsequently, can help medical professionals choose treatments that better align with patient choice and prognosis. METHODS: The National Cancer Database was queried for recipients of palliative radiotherapy during first course of treatment. Cox regression models and adjusted hazard ratios with 95% confidence intervals were used to evaluate survival predictors. The mortality risk index was calculated using predictors from the estimated Cox regression model, with higher values indicating higher mortality risk. Based on tertile cutpoints, patients were divided into low, medium, and high risk groups. RESULTS: A total of 68,505 patients were included from 2010-2014, median age 65.7 years. Several risk factors were found to predict survival: (1) location of metastases (liver, bone, lung, and brain); (2) age; (3) tumor primary (prostate, breast, lung, other); (4) gender; (5) Charlson-Deyo comorbidity score; and (6) radiotherapy site. The median survival times were 11.66 months, 5.09 months, and 3.28 months in the low (n=22,621), medium (n=22,638), and high risk groups (n=22,611), respectively. A nomogram was created and validated to predict survival, available online, https://tinyurl.com/METSSSmodel. Harrel's C-index was 0.71 and receiver operator characteristic area under the curve was 0.76 at 4 years. CONCLUSION: We created a predictive nomogram for survival of patients receiving palliative radiotherapy during their first course of treatment (named METSSS), based on Metastases location, Elderly (age), Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy.

Prognostication and Proactive Planning in COVID-19.

Accurate prognostication is challenging in the setting of SARS-CoV-2, the virus responsible for COVID-19, due to rapidly changing data, studies that are not generalizable, and lack of morbidity and functional outcomes in survivors. To provide meaningful guidance to patients, existing mortality data must be considered and appropriately applied. Although most people infected with SARS-CoV-2 will recover, mortality increases with age and comorbidity in those who develop severe illness.