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Tumor Discovery Prognostication in palliative cancer care

small sample size and study context, with a single-center hospitalizations (13%) the patient died. With emerging
33
design and a highly selected patient population, represent therapies, more oncology patients are expected to live
other factors affecting the risk of false results, incorrect longer but also face the risk of undergoing a rapid and
associations, and limited external validity. 37,38 Moreover, unpredicted decline. 29,53 This emphasizes the importance
the current study included patients from two different of available contextual information in the prognostication
care trajectories with significantly different prognoses, and process.
both sample size and survival variability affect statistical Functional performance typically dwindles as cancer
power. 39 patients are approaching death, and ECOG PS is strongly

4.3. Comparison with previous work associated with survival in patients with late-stage
disease. PS is incorporated into existing tools and
8,54
Prognostic information is important at several levels for evaluated for its prognostic capacity across different clinical
patients, their families, and health care providers. For settings. 6,7,14,40,55-58 The ESMO clinical practice guideline on
6
patients and their next of kin, it may guide and facilitate prognostic evaluation in patients with advanced cancer
realistic future planning when time is limited, addressing during the last months of life also recommends the use of
both opportunities and limitations. For health care PS for prognostication purposes. However, variability in
6
providers, prognostic information may represent a valuable individual survival within each ECOG PS group has been
tool for optimizing resource utilization and ensuring demonstrated in populations with both shorter and longer
quality of care. Therefore, previous research conducted median survival than reported in the current study. 13,54
6
to improve the accuracy and precision of prognostication These findings, combined with the current results,
methods and to describe associations with quality of life is underscore the challenges of using group data to treat or
highly relevant. 23,40-43 prognosticate individuals. Nevertheless, communicating
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Clinicians should use their clinical experience to predict prognosis based on declined functional performance may
the survival of patients with advanced cancer, and it is represent an easily understandable starting point for both
suggested that they supplement their judgment with input shared decision-making and advance care planning. 6
from multiple professionals. In addition, over the years, Inflammation and tumor progression are closely
6
it has become increasingly evident that combining several linked, and from a prognostic perspective, CRP and
factors improves prognostication accuracy. 6,23,40,44 The albumin have been studied in thousands of patients. 18,60
ESMO Clinical Practice Guideline on prognostic evaluation These two measures constitute the components of the
in patients with advanced cancer in the last months of mGPS, with increased CRP levels recognized as a reliable
life also endorses this practice. After the development negative prognostic factor, particularly when accompanied
6
of the Palliative Performance Scale, which is a modified by decreased albumin levels. 6,18,40 The ESMO clinical
Karnofsky Performance scale, more complex scoring practice guideline on prognostic evaluation in patients
systems and predictor models have been developed. 45-49 To with advanced cancer during the last months of life also
varying degrees, comprehensive tools such as the palliative advises the inclusion of systemic inflammation markers
prognostic index, the palliative prognostic score, the Feliu in the prognostic assessment. Nevertheless, as observed
6
prognostic nomogram, and the prognosis in palliative care for ECOG PS categories, a large individual variability in
study incorporate knowledge based on patient-reported survival exists within each mGPS group. Furthermore,
information, laboratory findings, as well as physical median survival values for groups do not allow precise
examination and evaluations. The goal was neither to predictions for individual patients. Additionally, palliative
7,48
validate established approaches nor to suggest new ones; cancer patients face a high risk of serious infections, and
the focus was solely to underline that prognostication alternative explanations for the presence of systemic
implies dealing with uncertainties that must be considered inflammatory biomarkers should also be considered. 61
from a clinical point of view. 1,31,50,51 Properly addressing Patients with advanced cancer may experience a
these uncertainties includes gauging the patients’ baseline multitude of symptoms. Many of these symptoms are
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understanding of their prognosis and gathering knowledge clustered, with four common groupings identified: Anxiety-
about the type of information they would like to discuss. 6 depression, nausea-vomiting, nausea-appetite loss, and
Patients with advanced cancer may suffer from sudden fatigue-dyspnea-drowsiness-pain. Both the presence and
62
and unexpected worsening and clinical crises, often severity of certain symptoms, as well as overall symptom
regarded as oncological emergencies. In the dataset intensity scores, have been shown to negatively correlate
52
on which the current study is based, 302 out of 451 to survival. 58,63 However, when interpreting results on
admissions (67%) were due to emergencies, and during 57 symptom burden and survival, it is important to consider

Volume 4 Issue 3 (2025) 52 doi: 10.36922/td.8576