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Palliative and Supportive Care
cambridge.org/pax
Review Article
Cite this article:
Palliative and
Supportive Care 23
Keywords:
Corresponding author:
Abstract
Objectives. To evaluate the prognostic utility of Palliative Prognostic Index (PPI) scores in
predicting the death of adults with advanced cancer.
Methods. A systematic review and meta-analysis were conducted. Six databases were searched
for articles published from inception till 16 February 2024. Observational studies reporting
time-to-event outcomes of PPI scores used in any setting, timing and score cutos were eligible.
Participants were adults with advanced cancer residing in any setting. Random eects meta-
analysis was used to pool hazard, risk, or odds ratios. Findings were narratively synthesized
when meta-analysis was not possible.
Results. Twenty-three studies (n=11,235 patients) were included. All meta-analyses found
that higher PPI scores or risk categories were signicantly associated with death and, simi-
larly, in most narratively synthesized studies. PPI >6 vs PPI ≤4 (pooled adjusted HR =5.42,
95% condence intervals [CI] 2.01–14.59, p=0.0009; pooled unadjusted HR =5.05, 95%
CI 4.10–6.17, p<0.00001), 4 <PPI ≤6 vs PPI ≤4 (pooled adjusted HR =2.04, 95% CI
1.30–3.21, p=0.002), PPI ≥6 vs PPI <6 (pooled adjusted HR =2.52, 95% CI 1.39–4.58,
p=0.005), PPI ≤4 vs PPI >6 for predicting inpatient death (unadjusted RR =3.48, 95% CI
2.46–4.91, p<0.00001), and PPI as a continuous variable (pooled unadjusted HR =1.30, 95%
CI 1.22–1.38, p<0.00001) were signicant predictors for mortality. Changes in PPI scores may
also be useful as a prognostic factor.
Signicance of results. A higher PPI score is likely an independent prognostic factor for an
increased risk of death, but more research is needed to validate the risk groups as dened by
the original development study. Meta-analysis results need to be interpreted cautiously, as only
2–4 studies were included in each analysis. Clinicians and researchers may nd this useful for
guiding decision-making regarding the suitability of curative and/or palliative treatments and
clinical trial design.
Introduction
Cancer patients and their families seek prognostic information to guide decision-making and
emotionally prepare for end-of-life (Chu et al. 2020). Although physician survival prediction
is widely utilized, it could be unreliable and unduly optimistic (Chu et al. 2020). To qualify for
specialized care and guide treatment decisions, an accurate prognosis is necessary (Chu et al.
2019; Kutzko et al. 2022). Tools like the Palliative Prognostic Index (PPI) oer standardized
estimates to address the limitations of clinician prediction. Other validated tools for advanced
cancer patients include the Palliative Prognostic Score (Yoong et al. 2024), the Suprise Question
(van Lummel et al. 2022), and the Prognosis in Palliative Care tool and the Objective Prognostic
Score (Lee et al. 2021).
e European Association of Palliative Care (Maltoni et al. 2005) and the European Society
for Medical Oncology identied the PPI as a key tool for predicting survival in advanced
cancer patients (Stone et al. 2023). Developing using data from a Japanese inpatient hospice
(Morita et al. 1999), the PPI score ranges from 0 to 15 and includes assessments of the Palliative
Performance Scale, edema, dyspnea, and delirium (Morita et al. 2001), with higher scores
indicating shorter survival.
e PPI has been validated in various cancer settings, such as hospices (Kim et al. 2014;
Subramaniam et al. 2013), palliative care units (Gerber et al. 2021; Miyagi et al. 2021),
https://doi.org/10.1017/S1478951525000021 Published online by Cambridge University Press
et al.
community (Hamano et al. 2014), and hematology wards (Lee
et al. 2022; Ohno et al. 2017). Palliative care nurses in the commu-
nity hospitals easily integrated it into admission routines (Belanger
et al. 2015). A web-based prognostic calculator that included PPI
increased doctors’ condence and willingness to discuss prognosis
with patients and ability to tailor treatments according to progno-
sis (Hui et al. 2024). Additionally, healthcare professionals in aged
care teams found it easy to use and not burdensome, with most
recommending it to colleagues (Gerber et al. 2023). e PPI was
particularly useful for uncertain prognoses, promoting end-of-life
discussions and early recognition of dying. However, its challenges
included distinguishing between acute and terminal delirium and
when edema should be rated as present (Gerber et al. 2023).
e original study’s survival analysis divided patients into 3
groups: PPI ≤2, 2 <PPI ≤4, and PPI >4. Log-rank analyses
showed that PPI could dierentiate survival across these groups
(Morita et al. 1999). Validation studies typically presented log-
rank tests and Kaplan–Meier curves but not hazard ratios (HR),
odds ratios (OR), or risk ratios (RR). While Kaplan–Meier curves
reveal crude survival dierences among risk groups, they lack eect
measures with 95% condence intervals (CI) that adjust for other
variables (Stel et al. 2011). Furthermore, validation studies did not
always adhere to the original model’s risk group denitions, which
may account for instances where survival dierences were not sig-
nicant (Palomar-Muñoz et al. 2018; Trejo-Ayala et al. 2018; Yoon
et al. 2014).
e only review on the prognostic utility of PPI pooled HR
and did not dierentiate between adjusted and unadjusted eect
sizes (Liu et al. 2018), making it dicult to conrm an inde-
pendent association between PPI scores and survival. A previous
review on prognostic tools, including PPI, also highlighted incon-
sistent reporting of HR and 95% CI among the studies, preventing
a meta-analysis (Simmons et al. 2017). We previously conducted
a meta-analysis evaluating the PPI’s performance in terms of dis-
crimination and calibration for predicting cancer patients’ survival
(Yoong et al. 2023). Building on the previous review’s ndings, this
systematic review and meta-analysis aimed to evaluate the utility
of PPI as a prognostic tool for advanced cancer patients (i.e. locally
advanced, metastatic, or incurable cancers). is review focuses on
advanced cancer patients who face an increased need to plan for
end-of-life decisions, including treatment, palliation and personal
matters. Compared toother predictive tools, the PPI oers a simple,
standardized assessment that is easy for clinicians to use without
extensive training or complex technology. Its evidence-based scor-
ing system ensures quick and eective assessments. e ndings
from this review aim to provide cliniciansw ith the best information
to support patients and their families.
Methods
is review was reported according to the Preferred Reporting
Items for Systematic Reviews and Meta-Analyses (PRISMA) guide-
lines (Table S1) (Page et al. 2021). Its protocol was registered in
PROSPERO (CRD42023475009).
Eligibility criteria
e inclusion criteria were as follows: (1) adults (≥18 years old)
with advanced cancer of any type or those receiving palliative
care; (2) studies reporting the association of PPI with death (HR,
OR, RR, and 95% CI, including both adjusted or unadjusted
eect sizes); (3) studies conducted in any setting, at any time
and using any PPI cutos; (4) both prospective or retrospective
studies (including peer-reviewed articles, dissertations/theses, and
preprints); and (5) studies published in English, as the authors are
uent in English only.
Studies were excluded if they involved (1) adults without cancer
(unless the noncancer participants were few, and ≥80% of the par-
ticipants had cancer); (2) other versions of PPI, such as Functional
PPI; (3) study designs other than those specied (e.g. experimental
studies, reviews, letters to the editor); or (4) studies that only pre-
sented Kaplan–Meier curves, log-rank ratios or other descriptive
analyses without reporting eect sizes.
Search strategy
We searched PubMed, ScienceDirect, Embase, Web of Science,
CINAHL, ProQuest, and Google Scholar for relevant articles pub-
lished from inception to 16 February 2024 (Tables S2–S7) and
reviewed the reference lists of relevant studies and reviews. First, we
searched PubMed using keywords and Medical Subject Headings
such as “palliative prognostic index,” “palliative care,” and “can-
cer.” Second, other databases were searched with similar terms.
Finally, Google Scholar and ProQuest were used to locate grey lit-
erature. e initial search results were uploaded to Rayyan, and
aer removing duplicates, SQY and DW identied potential stud-
ies by reviewing titles and abstracts. ey independently assessed
full-text articles for eligibility, with any discrepancies resolved by
HZ.
Data extraction
Five studies were used to design and pilot test a standardized data
extraction form. SQY extracted the data, which was then veried by
DW and HZ. e extracted data included authors, country, study
design, participant characteristics, and prognostic eect measures
(e.g. HR, RR, OR). Any disagreements were resolved through
discussion until a consensus was reached.
Quality appraisal
e risk of bias was assessed independently by SQY and DP using
the Quality in Prognosis Studies tool, which evaluates 6 domains:
(1) study participation, (2) study attrition, (3) prognostic factor
measurement, (4) outcome measurement, (5) study confounding,
and (6) statistical analysis and reporting. Each domain was rated
as high, moderate, or low risk of bias (Grooten et al. 2019; Hayden
et al. 2013). A study was considered “low risk of bias” if all 6
domains, or 1 moderate domain, showed low bias. It was consid-
ered “high risk of bias” if at least 1 domain was rated high or 3
domains were rated moderate. Studies with intermediate ratings
were classied as “moderate risk of bias” (Grooten et al. 2019).
Any discrepancies were resolved through discussion. Figures were
generated using robvis (McGuinness and Higgins 2021).
e modied Grading of Recommendations, Assessment,
Development, and Evaluations (GRADE) framework for prog-
nostic factor reviews was used to assess the overall certainty of
evidence (Huguet et al. 2013). It evaluated 6 factors: investigation
phase, study limitations, inconsistency, indirectness, imprecision,
and publication bias. Evidence with a moderate or large eect size,
or an exposure-response gradient, could lead to an upgrade in
the quality of evidence (Huguet et al. 2013). Studies with Phase
3 explanatory outcomes were initially rated as high-quality evi-
dence (Huguet et al. 2013; Kent et al. 2020). Outcomes based on
https://doi.org/10.1017/S1478951525000021 Published online by Cambridge University Press
Palliative and Supportive Care
Figure 1.
at least 2 studies included in the meta-analyses were rated as high,
moderate, low, or very low quality. Justications were provided
in the “Evidence Prole” tables using the GRADEproGDT so-
ware (GRADE handbook 2013; McMaster University and Evidence
Prime Inc 2022).
Data analysis
Meta-analysis was conducted using restricted maximum likelihood
in JASP (version 0.19.1) (JASP Team 2024). Cochran’s Qtest and I2
statistic were used to assess heterogeneity, with statistical signi-
cance set at p<0.10. Heterogeneity was classied as unimportant
(I2=0–40%), moderate (I2=30–60%), substantial (I2=50–90%),
or considerable (I2=75–100%) (Higgins et al. 2019). Following
Riley et al. (2019), we pooled adjusted and unadjusted eect mea-
sures, grouped similar categories of eect measures, and treated
continuous eect measures separately. Extracted outcomes were
standardized and reclassied into “high” versus “low” PPI risk
groups, with eect sizes representing the risk of death as posi-
tive numbers. When meta-analysis was not feasible, results were
summarized narratively.
Results
Search results
Figure 1 illustrates the study selection process. e initial search
identied 946 records. Aer removing duplicates, 720 records were
screened by title and abstract, and 74 articles were further assessed
through full-texts review. Ultimately, 23 articles from 21 patient
cohorts were included in this systematic review. Reasons for exclu-
sion are detailed in Table S8.
Characteristics of included studies
Characteristics of the included studies are presented in Table 1.
Studies were published between 2008 and 2023, using prospec-
tive (n=9) (Chen et al. 2018; Fernandes et al. 2021; Hung
et al. 2014; Kao et al. 2014; Lee et al. 2014; Miura et al. 2015;
Palomar-Muñoz et al. 2018; Stone et al. 2008; Subramaniam et al.
2013) or retrospective designs (n=14) (Ahn et al. 2021,2016;
Al-Ansari et al. 2022; Arai et al. 2014; Arkın and Aras 2021;
Chang et al. 2021; Cheng et al. 2012; Chou et al. 2015; Gerber
et al. 2021; Iizuka-Honma et al. 2023; Inomata et al. 2014;
https://doi.org/10.1017/S1478951525000021 Published online by Cambridge University Press
et al.
Table 1.
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Palliative and Supportive Care
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Palliative and Supportive Care
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Palliative and Supportive Care
Kiuchi et al. 2022; Shatri et al. 2021; Trejo-Ayala et al.
2018).
e majority of studies were conducted in Asia and Australia
(n=17) (Ahn et al. 2021,2016; Al-Ansari et al. 2022; Arai et al.
2014; Arkın and Aras 2021; Chang et al. 2021; Cheng et al. 2012;
Chou et al. 2015; Gerber et al. 2021; Hung et al. 2014; Iizuka-
Honma et al. 2023; Inomata et al. 2014; Kao et al. 2014; Kiuchi et al.
2022; Lee et al. 2014; Miura et al. 2015; Shatri et al. 2021), followed
by Europe (n=3) (Palomar-Muñoz et al. 2018; Stone et al. 2008;
Subramaniam et al. 2013) and the Americas (n=3) (Chen et al.
2018; Fernandes et al. 2021; Trejo-Ayala et al. 2018).
e review included 11,235 patients aged 18–100, with sam-
ple sizes ranging from 28 to 4,685. Most studies involved a mix
of primary cancers, while 7 studies focused on a single cancer
type (Arkın and Aras 2021; Chang et al. 2021; Chou et al. 2015;
Iizuka-Honma et al. 2023; Inomata et al. 2014; Kiuchi et al. 2022;
Trejo-Ayala et al. 2018). e majority of studies were conducted in
palliative care settings, with 1 conducted in acute wards (Iizuka-
Honma et al. 2023).
Twenty studies reported HR, with 12 adjusting for covariates
(Ahn et al. 2021,2016; Arai et al. 2014; Chang et al. 2021; Chou et al.
2015; Hung et al. 2014; Inomata et al. 2014; Kao et al. 2014; Kiuchi
et al. 2022; Lee et al. 2014; Miura et al. 2015; Palomar-Muñoz et al.
2018). Most studies treated PPI as a categorical variable, while 5
analyzed it as a continuous variable (Arai et al. 2014; Gerber et al.
2021; Lee et al. 2014; Stone et al. 2008; Subramaniam et al. 2013).
Dichotomous outcomes were extracted or computed from 5 stud-
ies (Al-Ansari et al. 2022; Arkın and Aras 2021; Fernandes et al.
2021; Gerber et al. 2021; Trejo-Ayala et al. 2018), with 2 reporting
adjusted eect sizes (Al-Ansari et al. 2022; Gerber et al. 2021). e
ndings from each study are presented in Table 2.
Risk of bias assessment
Figure 2 illustrates the risk of bias ratings. Nine studies were clas-
sied as having a low risk of bias (Ahn et al. 2021; Al-Ansari et al.
2022; Arai et al. 2014; Chang et al. 2021; Chou et al. 2015; Hung
et al. 2014; Kao et al. 2014; Lee et al. 2014; Palomar-Muñoz et al.
2018), 3 as moderate risk (Ahn et al. 2016; Gerber et al. 2021;
Miura et al. 2015), and 11 as high risk (Arkın and Aras 2021; Chen
et al. 2018; Cheng et al. 2012; Fernandes et al. 2021; Iizuka-Honma
et al. 2023; Inomata et al. 2014; Kiuchi et al. 2022; Shatri et al.
2021; Stone et al. 2008; Subramaniam et al. 2013; Trejo-Ayala et al.
2018).
In the study participation domain, most studies reported pop-
ulation characteristics well, although some did not specify the
recruitment period or exclusion criteria. Study attrition was low in
most studies, but some only analyzed a subset of participants from
larger cohorts, potentially limiting the generalizability of outcomes.
In certain studies, those lost to follow-up were excluded, lead-
ing to unclear attrition rates. For prognostic factor measurement,
the risk of bias was generally low for studies that recorded PPI
assessments during the rst consultation. However, retrospective
studies that calculated scores from available data may have been
aected by the quality of clinical documentation. Some studies
did not specify who completed the assessments. Outcome mea-
surements were generally well-reported, though a few studies did
not specify the duration of follow-up or how the date of death
was determined. e risk of bias for study confounding was high
or moderate when studies did not adjust for or specify relevant
covariates.
Synthesis results
Detailed GRADE ratings are provided in Table S9. Due to the lim-
ited number of studies (less than 10 per meta-analysis), subgroup
analyses based on study design, setting, risk of bias, and assessment
for publication bias could not be conducted.
PPI scores as categorical variables
PPI >6 vs PPI ≤4 risk groups
e pooled adjusted HR was 5.42 (95% CI 2.01–14.59, p=0.0009)
(Chou et al. 2015; Palomar-Muñoz et al. 2018), with considerable
heterogeneity (I2=84%, p=0.012) (n=539, high-quality evi-
dence). e pooled unadjusted HR (Cheng et al. 2012; Shatri et al.
2021) was 5.05 (95% CI 4.10–6.17, p<0.00001) with nonsignif-
icant heterogeneity (I2=0%, p=0.40) (n=783, high-quality
evidence) (Fig. 3A).
4<PPI ≤6 vs PPI ≤4 risk groups
Two studies were pooled (Chou et al. 2015; Palomar-Muñoz
et al. 2018), yielding as adjusted HR of 2.04 (95% CI 1.30–3.21,
p=0.002) with nonsignicant heterogeneity (I2=17.4%,
p=0.271) (n=539, high-quality evidence) (Fig. 3A).
PPI ≥6 vs PPI <6 risk groups
ree studies (Ahn et al. 2016; Chang et al. 2021; Inomata et al.
2014) were included in the meta-analysis, with a pooled adjusted
HR of 2.52 (95% CI 1.39–4.58, p=0.002), showing considerable
heterogeneity (I2=74.5%, p=0.01) (n =333, moderate quality
evidence) (Fig. 3A).
PPI scores as a continuous variable
Four studies analyzed PPI as continuous variables (Arai et al. 2014;
Lee et al. 2014; Stone et al. 2008; Subramaniam et al. 2013). e
pooled unadjusted HR was 1.30 (95% CI 1.22–1.38, p<0.00001)
with substantial heterogeneity (I2=60%, p=0.06) (n=815, low-
quality evidence) (Fig. 3B). is indicates that for each 1-point
increase in PPI score, there is a 30% higher risk of mortality.
Other comparisons
Only 3 studies used the PPI thresholds of 2 and 4 for survival anal-
yses, as dened in the original study (Morita et al. 1999) (Chen
et al. 2018; Iizuka-Honma et al. 2023; Kao et al. 2014) (Table 2).
Other comparisons that could not be meta-analyzed are presented
in Table 2 (Ahn et al. 2021; Cheng et al. 2012; Kiuchi et al. 2022;
Miura et al. 2015).
PPI scores as dichotomous variables (RR or OR)
Six studies reported dichotomous outcomes (Al-Ansari et al. 2022;
Arkın and Aras 2021; Fernandes et al. 2021; Gerber et al. 2021;
Lee et al. 2022; Trejo-Ayala et al. 2018) (Table 2). Two studies
(Arkın and Aras 2021; Gerber et al. 2021) were included in the
meta-analysis, yielding a pooled unadjusted RR of 3.48 (95% CI
2.46–4.91, p<0.00001) for PPI ≤4 vs PPI >6 in predicting
inpatient death (n=274, high-quality evidence). Heterogeneity
was nonsignicant (I2=0%, p=0.64) (Fig. 3C). Findings from
other studies that could not be meta-analyzed are presented in
Table 2.
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Table 2.
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Palliative and Supportive Care
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Palliative and Supportive Care
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Figure 2.
Change in PPI
ree studies investigated changes in PPI scores as a predictor
of survival (Arai et al. 2014; Hung et al. 2014; Kao et al. 2014)
(Table 2). Hung et al. (2014) and Kao et al. (2014) examined the
same patient cohort, but Hung et al. (2014) only involved those
with PPI >6 (poor prognosis). Arai et al. (2014) calculated the
change in PPI per day. For Kao et al. (2014), the median survival
was the shortest for the group with <0 change in score, followed
by the 0 and >0 groups. In Hung et al. (2014), the group with
>20% change in score had the shortest median overall survival,
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Palliative and Supportive Care
Figure 3.
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et al.
followed by the 0–20%, 0, −20 to 0%, and <−20% change groups.
Although the studies used dierent methods to categorize and cal-
culate changes in PPI, most comparisons indicated that changes
in PPI score were a statistically signicant prognostic factor for
survival.
Discussion
Main findings
is review is the rst to conrm an independent association
between PPI scores and survival in advanced cancer patients. It
expands on the ndings of a previous systematic review, which con-
cluded that higher PPI scores signicantly predicted a shorter sur-
vival period based solely on unadjusted HR (Liu et al. 2018). Our
review includes more recent studies, larger sample sizes, and an
analysis of both adjusted and adjustable eect sizes. Additionally,
we assessed the risk of bias and the certainty of evidence using
the GRADE framework, an evaluation that was not conducted in
the previous review (Liu et al. 2018). Building on the ndings of
the original PPI development study (Morita et al. 1999), all meta-
analyses in this review found that the association between PPI and
survival remained signicant even aer adjusting for covariates,
with signicant dierences in survival among the risk groups.
Most included studies conducted an initial patient assessment
using the PPI upon admission. However, unexpected events at the
end-of-life are common, and reasons for hospitalizations can vary,
meaning that the rst assessment might not entirely reect the
patient’s overall prognosis. One study found that cancer patients
admitted to the palliative care unit with treatable acute conditions
(e.g. infections, hemorrhage) were more likely to survive than those
admitted due to cancer-related issues (e.g. refractory symptoms,
disease progression). Additionally, no signicant dierences in sur-
vival were observed among risk groups when using PPI at discharge
(Palomar-Muñoz et al. 2018). is highlights the need for caution
when interpreting PPI scores across dierent patient populations
and time points.
Changes in PPI scores could also serve as a signicant prognos-
tic factor in predicting survival, particularly in capturing sudden
shis in patients’ conditions during end-of-life care. A study found
that worsened symptom scores 1 week aer admission were asso-
ciated with shorter survival compared to patients with improved
symptoms. In contrast, those with stable and improved symptom
scores showed no signicant dierences in survival (Suh et al.
2022). Similarly, 3 studies (Arai et al. 2014; Hung et al. 2014; Kao
et al. 2014) found signicant associations between change in PPI
scores and survival outcomes. In addition, Kao et al. (2014) found
a model combining the initial PPI score with the change in score
had the highest c-statistic, further supporting the importance of
monitoring PPI score changes over time. e Model combining the
initial PPI score with changes in the score proved to be a better pre-
dictor of 30-day survival than using the initial score, Week 1 PPI
score or score change individually. Another study found that a sec-
ond PPI assessment conducted on Days 3–5 in hospice residents
had better discriminative performance than the rst assessment
at admission (Subramaniam et al. 2019). e studies (Arai et al.
2014; Hung et al. 2014; Kao et al. 2014) included employed dierent
methods for calculating PPI. Future research should standardize
these calculation methods to provide more reliable conclusions
regarding the utility of PPI in prognostication.
PPI as a continuous variable may also have prognostic signi-
cance. is was observed in another study involving older adults
receiving home palliative care (7.5% had cancer), which reported
a 1.51-fold increased probability of death for each unit increase in
PPI (Moretti et al. 2019). An included study (Gerber et al. 2021)
reported an OR of 0.74, suggesting that a lower overall PPI score
signicantly predicted survival to discharge. However, this result
became nonsignicant in multivariate analysis. Hence, the utility
of PPI scores as a continuous variable warrants further research.
Finally, we found that the risk of inpatient death was signi-
cantly higher for patients with a PPI >6 compared to those with a
PPI ≤4. is nding aligns with a previous study, which reported
that the mean PPI scores of patients who died in the hospital were
signicantly higher than those of patients who survived to dis-
charge (8.2 ±3.8 vs 3.2 ±2.9, p<0.001) (Alshemmari et al.
2012).
What this study adds
PPI is not only a reasonably accurate prognostic tool for predict-
ing <3- and <6-week survival in cancer patients (Yoong et al.
2023), but the ndings of this review also suggest that a higher PPI
score is a strong and independent prognostic factor for poorer sur-
vival outcomes in advanced cancer patients. Furthermore, the PPI
could support current clinical practice guidelines, which recom-
mend the early integration of palliative care into standard oncology
treatment for patients with advanced cancer receiving concurrent
active treatment (Corsi et al. 2019; Ferrell et al. 2017; Lee et al.
2022). By assisting clinicians in identifying cancer patients suitable
for early palliative care, the PPI could enhance clinical decision-
making, helping clinicians determine whether additional curative
treatment may benet the patient or if palliative care should be ini-
tiated (Cohen and Miner 2019; Hasegawa et al. 2015; Pobar et al.
2021).
PPI could also be valuable when an objective estimate of sur-
vival is needed, e.g. determining participants’ eligibility for clinical
trials (Chu et al. 2020; Simms et al. 2013), conducting risk strati-
cation in stratied randomized trials, or avoiding bias in treatment
eect estimation by adjusting for PPI (Halabi and Owzar 2010).
It may also help identify patients with poorer outcomes, thereby
encouraging clinical trial participation for novel or experimental
treatments (Gospodarowicz et al. 2001). A study examining the
impact of palliative radiotherapy on gastric cancer patients’ symp-
toms found that, aer adjusting for baseline PPI (since patients
with limited life expectancy oen experience worsening symp-
toms), shortness of breath, pain, and distress signicantly improved
over 8 weeks. Additionally, higher PPI scores were associated with
higher symptom scores at all time points (Kawamoto et al. 2022).
Another study identied a baseline PPI of >2 as a reliable predictor
of death within 2 months in patients with advanced gastric cancer
patients, suggesting it may be suitable for guiding single-fraction
radiotherapy (Sekii et al. 2023).
Although various prognostic factors and prediction models
have been identied for cancer patients, many were specic to
certain cancer types or complications, limiting their clinical appli-
cability to the broader cancer population (Owusuaa et al. 2022). A
prediction model that is simple to use, applicable to heterogeneous
cancer populations, and accessible to medical specialists, general
practitioners, and nurses is highly desirable, as it could aid in treat-
ment planning and advance care decisions (Owusuaa et al. 2022).
Some studies have pointed out the challenges of using certain prog-
nostic tools due to the unavailability of blood test results (Baba
et al. 2015; Kishino et al. 2022). In addition, many existing predic-
tion models lack external validation, and model calibration is rarely
https://doi.org/10.1017/S1478951525000021 Published online by Cambridge University Press
Palliative and Supportive Care
assessed, underscoring the need for well-performing, validated
models that are applicable to most cancer patients (Kreuzberger
et al. 2020; Owusuaa et al. 2022). e PPI tool could help address
this gap, as it has been widely validated and accepted across diverse
settings and cancer populations.
Strengths and limitations of the study
is is the rst meta-analysis to report an independent association
between PPI scores and survival, and it represents the most com-
prehensive systematic review on the prognostic utility of PPI to
date. e nding may oer valuable insights that can benet both
clinicians and researchers.
is review has several limitations. First, only articles in English
were included, which may have resulted in the exclusion of rele-
vant studies published in other languages. ere were also limited
studies in each meta-analysis, so the results should be interpreted
with caution. As a result, subgroup analysis and tests for publica-
tion bias could not be conducted. We also did not estimate HR from
the published Kaplan–Meier curves, as most studies did not report
numbers at risk, which hindered this estimation. Moreover, stud-
ies that did not provide eect sizes (e.g. only reporting a signicant
log-rank test) were excluded, meaning this review does not rep-
resent all available literature on the association between PPI and
survival. Despite these limitations, this review aimed to evaluate
whether PPI is a prognostic factor for survival; thus, making the
synthesis of time-to-event outcome measures the most appropriate
approach.
Implications for research and practice
e PPI was initially developed using a heterogeneous sample
of patients with dierent types of cancers (Morita et al. 1999).
Subsequently, its utility has been investigated and validated in spe-
cic cancer types, including lung cancer (Arkın and Aras 2021;
Inomata et al. 2014), hematological malignancies (Chang et al.
2021; Chou et al. 2015; Iizuka-Honma et al. 2023; Trejo-Ayala et al.
2018), and ovarian cancer (Kiuchi et al. 2022). One study also
found that PPI was associated with survival in patients with non-
Hodgkin’s lymphoma but not in those with acute myeloid leukemia
in the palliative care setting (Yamane et al. 2023). Future research
should continue to explore whether the prognostic utility of PPI
diers across cancer types, patient care settings (such as acute
wards, home palliative care, hospices, etc.) and stages of the cancer
treatment journey (e.g. during active treatment or palliative care),
similar to how the Glasgow Prognostic Score has been comprehen-
sively evaluated for various cancers (He et al. 2018; Tong et al. 2020;
Wu et al. 2021).
Most of the included studies had a moderate to high risk of bias,
highlighting the need for improving reporting in future research.
To strengthen credibility and ensure the ndings are more reliable
for practical application, future studies should adhere to estab-
lished reporting guidelines (Altman et al. 2012; Hayden et al. 2013).
It is also crucial to report adjusted prognostic eect measures, as
these are important for quantifying the extent of the increased
mortality risk across PPI risk groups. We observed that the catego-
rization of PPI risk groups was inconsistent, with only 3 out of 23
studies using the risk groups dened in the original development
study, and a maximum of 3 studies testing the same comparison.
As a result, our meta-analyses were limited by the small number
of studies. Further research should validate our ndings by further
examining the predictive value of PPI score categories (PPI ≤2,
2<PPI ≤4, and PPI >4) as dened in the original development
study.
Conclusion
Higher PPI scores were strongly associated with poorer survival
outcomes in advanced cancer patients. While the limited number
of studies in each risk group comparison constrained our meta-
analyses, the ndings were consistent in both direction and signi-
cance. Future studies should adhere to the risk categories dened in
the original development study and report adjusted eect estimates
with 95% CI to strengthen the evidence base.
Supplementary material. e supplementary material for this article can
be found at https://doi.org/.10.1017/S1478951525000021.
Funding. is research did not receive any funding from agencies in the
public, commercial, or not-for-prot sectors.
Competing interests. None.
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