Development and Validation of a Model Predicting Short Survival (Death Within 30 Days) After Palliative Radiotherapy
- KENT ANGELO1,
- JAN NORUM1,2,3,
- ASTRID DALHAUG1,4,
- ADAM PAWINSKI4,
- GRO AANDAHL4,
- ELLINOR HAUKLAND4,
- KIRSTEN ENGLJÄHRINGER4 and
- CARSTEN NIEDER1,4⇑
+ Author Affiliations
- Correspondence to: Carsten Nieder, MD, Department of Oncology and Palliative Medicine, Nordland Hospital, 8092 Bodø, Norway. Tel: +47 75578449, Fax: +47 75534975, e-mail: carsten.nieder@nlsh.no
Abstract
The present study aimed to develop a
predictive model that would allow for reduced utilization of palliative
radiotherapy
(PRT) during the final 30 days of life in patients
with incurable cancer.
We performed uni- and multivariate analyses of
factors
predicting PRT during the final 30 days of life for
all PRT courses administered at a dedicated PRT facility between
20.06.2007
and 31.12.2009.
We also developed a predictive
model by recursive partitioning analysis (RPA), followed by independent
validation
of its performance in patients treated during 2010
and 2011. We analyzed 579 PRT courses.
Median survival was 6.3 months.
In 53 cases (9%) PRT was administered during the
final 30 days of life. RPA resulted in a model consisting of six
parameters
(lung or bladder cancer, Eastern Cooperative
Oncology Group performance status of 3-4, low hemoglobin, opioid
analgesic use,
steroid use, known progressive disease outside PRT
volume), which correctly identified 75% of PRT courses administered
during
the final 30 days of life.
Maximum survival of
patients fulfilling all criteria was 69 days. Death within 40 days
occurred
in 83% of patients. In the independent validation
data set, similar results were obtained: 74% (30 days), 84% (40 days),
while
maximum survival was 92 days.
As demonstrated here
and in other recent studies, assigning the right patient to the right
palliative
approach is challenging. We suggest that patients
with lung or bladder cancer and the adverse features mentioned above are
at high risk of dying shortly after initiation of
PRT.
Our model might support decision-making (best supportive care
versus
PRT) and is the first decision aid specifically
addressing PRT near end of life.
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