Predicting life expectancy in patients with metastatic cancer receiving palliative radiotherapy: the TEACHH model

预测接受姑息性放疗的转移性癌症患者的预期寿命:TEACHH 模型

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Abstract

BACKGROUND: Predicting life expectancy (LE) in patients with metastatic cancer who are receiving palliative therapies is a difficult task. The purpose of the current study was to develop a LE prediction model among patients receiving palliative radiotherapy (RT) that identifies those patients with short (< 3 months) and long (> 1 year) LEs. METHODS: The records of 862 patients with metastatic cancer receiving palliative RT at the Dana-Farber/Brigham and Women's Cancer Center between June 2008 and July 2011 were retrospectively reviewed. Cox proportional hazards models were used to evaluate established and potential clinical predictors of LE to construct a model predicting LE of < 3 months and > 1 year. RESULTS: The median survival was 5.6 months. On multivariate analysis, factors found to be significantly associated with a shorter LE were cancer type (lung and other vs breast and prostate), older age (> 60 years vs ≤ 60 years), liver metastases, Eastern Cooperative Oncology Group performance status (2-4 vs 0-1), hospitalizations within 3 months before palliative RT (0 vs ≥ 1), and prior palliative chemotherapy courses (≥ 2 vs 0-1). Patients were divided into 3 groups with distinct median survivals: group A (those with 0-1 risk factors), 19.9 months (95% confidence interval [95% CI, 13.9 months-31.1 months]); group B (those with 2-4 risk factors), 5.0 months (95% CI, 4.3 months -5.6 months); and group C (those with 5-6 risk factors), 1.7 months (95% CI, 1.2 months-2.1 months). CONCLUSIONS: The TEACHH model (type of cancer, Eastern Cooperative Oncology Group performance status, age, prior palliative chemotherapy, prior hospitalizations, and hepatic metastases) divides patients receiving palliative RT into 3 distinct LE groups at clinically informative extremes of the LE spectrum. It holds promise to assist radiation oncologists in tailoring palliative therapies to a patient's LE.

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