Temporal Trends in Palliative Care Integration for High-Grade Glioma: A Seven-Year Retrospective Cohort Study

Abstract

Purpose

High-grade gliomas (HGG) have a poor prognosis and high symptom burden, yet palliative care (PC) integration remains limited. We evaluated temporal changes in PC referral and assessment patterns at a Brazilian tertiary center and explored prognostic factors for survival.

Methods

This is a non-concurrent retrospective cohort of 117 adults with HGG who underwent surgery between 2017 and 2023. Primary outcomes included rates of PC referral and effective assessment, time to referral, and functional status (Karnofsky Performance Status, KPS) at referral. Secondary outcomes included overall survival (OS) and identification of prognostic factors using Cox regression analysis. Temporal trends were analyzed using the Cochran-Armitage test.

Results

PC referral increased from 21.1% (2017) to 73.7% (2023) (P < 0.001), and effective assessment increased from 21.1% to 47.4% (P = 0.048). Median Karnofsky Performance Status at referral improved over time (P = 0.011). In multivariate analysis, baseline clinical factors (older age and lower preoperative KPS) predicted earlier mortality; PC referral and effective assessment were not independently associated with survival after adjustment.

Conclusion

Over seven years, a significant increase in PC referrals and an improvement in functional status at the time of PC access were observed. Preoperative functional status was the main determinant of survival.

Keywords

early palliative care glioblastoma high-grade glioma karnofsky performance status low- and middle-income countries neuro-oncology end-of-life care

Background

High-grade gliomas (HGG), including glioblastoma (WHO grade 4) and anaplastic astrocytoma (WHO grade 3), represent the most aggressive primary brain tumors in adults, with median survival ranging from 15 to 21 months despite maximal therapy.^1-3 In the United States, 86 030 deaths were attributed to malignant brain and other CNS tumors between 2016 and 2020, corresponding to an average of 17 206 deaths per year and an average annual mortality rate of 4.42 per 100 000 inhabitants.¹

These malignancies are characterized by rapid progression, substantial neurological deterioration, and a high symptom burden that profoundly impacts quality of life for both patients and caregivers.^4,5 The clinical trajectory of HGG is marked by progressive cognitive decline, motor deficits, seizures, and psychological distress, necessitating comprehensive supportive care throughout the disease course.^6,7

International guidelines from the American Society of Clinical Oncology (ASCO), the European Society for Medical Oncology (ESMO), and the National Comprehensive Cancer Network (NCCN) recommend early integration of palliative care (PC) for patients with advanced cancer, including those with primary brain tumors.^8,9 Early PC has been associated with improved quality of life, better symptom control, reduced caregiver burden, and, in some malignancies, prolonged survival.^10-12 Despite these recommendations, implementation of early PC in neuro-oncology remains inconsistent, with significant variability in referral patterns, timing, and access to specialized services across different healthcare systems.^13-15

Several barriers contribute to delayed or absent PC referral in HGG populations. These include prognostic uncertainty in the early stages, concerns about undermining hope, lack of awareness among referring physicians regarding the scope and benefits of PC, and limited availability of specialized PC services.^16,17 Additionally, the rapid clinical deterioration characteristic of HGG often results in late referrals when patients have already experienced significant functional decline, limiting the potential benefits of PC interventions.^18,19

In developing countries, PC services have expanded gradually over the past two decades, but access remains limited and geographically concentrated in major urban centers.²⁰ Both public and private sectors, faces challenges in PC integration related to workforce shortages, inadequate training, and insufficient infrastructure.

Understanding temporal trends in PC utilization and their relationship with clinical outcomes is essential for optimizing care delivery, identifying persistent barriers, and informing policy decisions regarding resource allocation and service development.

Methods

Study Design and Setting

This is a non-concurrent retrospective cohort study conducted at the Clinics Hospital of Botucatu Medical School, a tertiary reference center linked to the São Paulo State University (UNESP), located in the southeast region of Brazil. The institution acts as a regional reference center in neurosurgery and neuro-oncology for a population of approximately 2 million inhabitants. Eligible patients underwent initial surgery between January 1, 2017, and December 31, 2023. Outcomes were evaluated up to 730 days (two years) after surgery.

Establishment of the Palliative Care Team

A dedicated palliative care team (PCT) was established at the institution in 2017, offering consultancy services for inpatients and outpatients. From that year on, the PCT initiated a structured continuing education program. Workshops and educational activities on symptom management, primary and secondary PC were instituted. In 2018, participation of Neurosurgery and Neurology residents in rotations in the PC ward and outpatient clinic became mandatory. Furthermore, systematic clinical case discussions were held, focusing on the role of PC in brain tumors, symptom control, communication skills, and strengthening the bond between care teams. These activities were not part of a formal research intervention but represented the team’s standard approach during the service integration phase.

Participants

Consecutive adult patients (age 18 years or older) with histologically confirmed HGG (World Health Organization grades 3 or 4) who underwent surgical treatment (resection or biopsy) between January 2017 and December 2023 were included. Exclusion criteria were: (1) initial histopathological diagnosis of low-grade glioma with subsequent progression to high-grade glioma during follow-up; and (2) loss to follow-up immediately after surgery, defined as absence of any outpatient consultation after hospital discharge.

Data Collection

Clinical data was extracted from electronic medical records by trained investigators. Variables included: demographic characteristics (age, sex, race, occupation); clinical characteristics (symptoms, comorbidities); tumor characteristics (histology, location); treatment modalities; and functional status (Karnofsky Performance Status - KPS) at multiple time points. PC referral was defined as the presence of a request for formal evaluation by the institutional PCT in the medical record. Assessment was defined as the presence of a consultation record.

Statistical Analysis

Descriptive statistics were calculated for all variables. Continuous variables were reported as median and interquartile range (IQR) or mean and standard deviation (SD) as appropriate based on distribution. Categorical variables were reported as frequencies and percentages. Temporal trends in PC referral rates and effective assessment rates across the seven-year study period were evaluated using the Cochran-Armitage test for trend. Changes in time to PC referral and KPS at referral over time were assessed using the Kruskal-Wallis test.

Survival analysis was performed using Kaplan-Meier methods, with survival curves compared using the log-rank test. Univariate Cox proportional hazards regression was used to identify potential prognostic factors for OS. Variables with P < 0.10 in univariate analysis along with histological subtype were included in a multivariate Cox regression model to identify independent predictors of survival. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated. The proportional hazards assumption was assessed using Schoenfeld residuals.

Subgroup analyses were performed to evaluate survival differences by preoperative KPS (≥70 vs <70), age (≤60 vs >60 years), and PC referral timing (early referral <60 days, late referral ≥60 days, no referral). The 60-day threshold for early referral was prespecified prior to data analysis, based on the American Society of Clinical Oncology recommendation to integrate palliative care within the first eight weeks after diagnosis of advanced cancer.⁸ All statistical tests were two-sided, with P < 0.05 considered statistically significant. Analyses were performed using R version 4.5.2 (R Foundation for Statistical Computing, Vienna, Austria).

Ethical Statement

This study was approved by the Research Ethics Committee of Botucatu Medical School, São Paulo State University (CAAE: 69738223.0.0000.5411). The requirement for informed consent was waived due to the retrospective nature of the study and use of de-identified data. All procedures were conducted in accordance with the Declaration of Helsinki.

Results

Sample Characterization

183 patients were selected. After excluding 40 (21.8%) due to retrospective reclassification of low-grade gliomas that progressed to high-grade and 26 (14.2%) due to early loss to follow-up, the final cohort included 117 patients. During follow-up, 104 patients (88.9%) died.

Mean age was 61.5 ± 13.2 years, with a slight predominance of males (55.6%). The most frequent symptoms on the initial assessment were motor deficit (56%), headache (43%), and seizures (28%). Most prevalent comorbidities included systemic arterial hypertension (46%) and smoking (24%). Regarding histopathology, astrocytoma not otherwise specified accounted for 60% of cases, IDH-wildtype GBM for 26%, and IDH-mutant astrocytoma grade 4 for 4.3%. Histological subtype differed significantly by referral status (P = 0.002), with glioblastoma IDH-wildtype referred more often (77%, 24/31) than astrocytoma NOS (41%, 29/71). Tumor resection was performed in 92% of patients. Of the total cohort, 59 patients (50.4%) were referred to the PCT. Demographic and clinical characteristics stratified by referral status are summarized in Table 1.

Table 1.

Demographic, Clinical and Treatment Characteristics of the Study Population, Stratified by Palliative Care Referral Status

Characteristics^a	Total (N = 117)	Not referred to PC (N = 58)	Referred to PC (N = 59)	P-value^b
Age (years) – mean ± SD	61.5 ± 13.2	61.2 ± 13.7	61.8 ± 12.8	0.900
Sex – n (%)				0.032
Male	65 (56)	38 (66)	27 (46)
Female	52 (44)	20 (34)	32 (54)
Color/Race – n (%)				>0.900
White	108 (92)	54 (93)	54 (92)
Brown/Mixed	6 (5.1)	3 (5.2)	3 (5.1)
Asian	2 (1.7)	1 (1.7)	1 (1.7)
Black	1 (0.9)	0 (0)	1 (1.7)
Comorbidities – n (%)
Previous neoplasm	8 (6.8)	4 (6.9)	4 (6.8)	>0.900
Smoking	28 (24)	15 (26)	13 (22)	0.600
Alcoholism	13 (11)	6 (10)	7 (12)	0.800
Hypertension	54 (46)	28 (48)	26 (44)	0.600
Diabetes mellitus	22 (19)	9 (16)	13 (22)	0.400
Initial symptoms – n (%)
Headache	50 (43)	22 (38)	28 (47)	0.300
Seizure	33 (28)	14 (24)	19 (32)	0.300
Motor deficit	65 (56)	31 (53)	34 (58)	0.600
Sensory deficit	40 (34)	22 (38)	18 (31)	0.400
Pain^c	24 (21)	7 (12)	17 (29)	0.025
Altered consciousness	39 (33)	19 (33)	20 (34)	0.900
Histological type – n (%)				0.002
Astrocytoma NOS	71 (61)	42 (72)	29 (49)
Glioblastoma, IDH WT	31 (26)	7 (12)	24 (41)
Astrocytoma, IDH mutant	5 (4.3)	4 (6.9)	1 (1.7)
Others^d	10 (8.5)	5 (8.6)	5 (8.5)
First surgery – n (%)				0.200
Resection	108 (92)	56 (97)	52 (88)
Biopsy	9 (7.7)	2 (3.4)	7 (12)
Preoperative KPS – n (%)				0.300
<30	2 (1.7)	2 (3.4)	0 (0)
30-70	33 (28)	14 (24)	19 (32)
>70	82 (70)	42 (72)	40 (68)

^adescriptive statistics used (Median [p25; p75] for continuous variables; n (%) for categorical variables).

^bPearson’s Chi-square test and Fisher’s Exact test, respectively when applicable.

^cof any order, except headache.

^dtopographies grouped under “ Others” (insula, nucleus-capsular region or diencephalon). NOS: Not Otherwise Specified; WT: Wild-type; KPS: Karnofsky Performance Status; PC: Palliative Care.

Bold p-values indicate statistical significance (p < 0.05)

PC Referral Trends

Of the total 117 patients, 59 (50.4%) were referred to the PCT. The referral rate demonstrated a progressive increase over the seven years of observation, going from 21.1% in 2017 to 73.7% in 2023 (Cochran-Armitage χ² = 14.132; P < 0.001) (Figure 1).

Among the 59 referred patients, neurosurgery was the specialty that most frequently made referrals (62.5%), followed by oncology (23.2%).

The interval between surgery and PC referral decreased significantly. The global median time to referral was 103 days (IQR 24-263). In 2018, the median time to referral was 33.1 weeks (IQR 22.3-90.1), compared to 2.64 weeks (IQR 2.0-4.5) in 2023. The proportion of early referrals (<60 days) increased from 0% in 2017 to 78.6% in 2023. Table 2 details these indicators.

Table 2.

Temporal Evolution of Palliative Care Referral Indicators (2017-2023)

Year	Patients (N)	Referred (%)	Median time to referral in weeks (IQR)	Early referral (%)
2017	19	4 (21.1)	19.1 (19-26.1)	0 (0)
2018	15	8 (53.3)	33.1 (22.3-90.1)	2 (25.0)
2019	17	6 (35.3)	15.7 (8.93-29.1)	2 (33.3)
2020	15	5 (33.3)	18.7 (4.86-19.7)	2 (40.0)
2021	12	7 (58.3)	24.6 (12.2-64.8)	1 (14.3)
2022	20	15 (75.0)	6.57 (3.07-47.4)	8 (53.3)
2023	19	14 (73.7)	2.64 (2.0-4.5)	12 (85.7)

Data presented as n (%) or median (interquartile range), as appropriate. Early referral defined as ≤60 days. IQR: Interquartile Range.

Functional Profile and Assessment Gap

Median KPS at referral improved over the study period. In 2017, the median was 30 (IQR 10-52.5), rising to 50 (IQR 32.5-60) in 2023 (P = 0.011) (Figure 2). However, a disparity between referral and assessment was observed. In 2023, while 73.7% were referred, only 47.4% were assessed. The median time to assessment was 149 days, approximately 46 days longer than the time to referral. Eleven patients (18.6% of those referred) died before assessment.

Survival Analysis

Median survival of the cohort was 235 days (95% CI 156-318). Preoperative functional status was the main prognostic factor. Patients with initial KPS ≥70 had a median survival of 256 days, contrasting with 98 days in those with KPS <70 (P < 0.001).

Age also demonstrated a significant association with survival: patients aged 60 years or younger had a median of 440 days (14.5 months) vs 149 days (4.9 months) for those over 60 years (P < 0.001).

In stratification by time to referral, a significant difference was observed consistent with indication bias. Patients referred late (≥60 days) had a median survival of 399 days, whereas those referred early (<60 days) had a median survival of 114 days, and those not referred had 132 days (P = 0.002) (Figure 3).

The multivariate Cox regression analysis (Table 3) confirmed that baseline characteristics remained the strongest predictors of mortality. Advanced age (>60 years) was independently associated with a higher risk of death (HR 1.57; 95% CI 1.01-2.46; P = 0.047). In contrast, preserved functional status at diagnosis (KPS ≥70) remained a robust protective factor, reducing the risk of mortality by approximately 60% (HR 0.40; 95% CI 0.22-0.72; P = 0.002). Histological subtype was not independently associated with survival (P ≥ 0.200 for all categories versus glioblastoma IDH-wildtype).

Table 3.

Univariate and Multivariate Cox Analysis for Overall Survival (N = 117, events = 95)

Characteristics	Univariate analysis			Multivariate analysis
Characteristics	HR	95% CI	P-value	HR	95% CI	P-value
Referred to PC
No (n = 58)	Ref.	—		Ref.	—
Yes (n = 59)	0.98	0.66, 1.47	>0.900	0.91	0.41, 2.02	0.800
Assessed by PC
No (n = 69)	Ref.	—		Ref.	—
Yes (n = 48)	0.86	0.57, 1.30	0.500	0.90	0.43, 1.86	0.800
Sex
Female (n = 52)	Ref.	—		Ref.	—
Male (n = 65)	0.69	0.46, 1.03	0.068	0.77	0.51, 1.18	0.200
Age (years)
≤60 (n = 46)	Ref.	—		Ref.	—
>60 (n = 71)	1.92	1.25, 2.94	0.003	1.57	1.01, 2.46	0.047
Preoperative KPS
≥70 (n = 97)	Ref.	—		Ref.	—
<70 (n = 20)	3.00	1.79, 5.01	<0.001	2.51	1.38, 4.55	0.002
Histological type
Glioblastoma, IDH WT (n = 31)	Ref.	—		Ref.	—
Astrocytoma NOS (n = 71)	1.47	0.92, 2.36	0.110	1.43	0.86, 2.39	0.200
Astrocytoma, IDH mutant (Grade 4) (n = 5)	0.78	0.24, 2.61	0.700	0.84	0.24, 2.95	0.800
Other (n = 10)	0.45	0.17, 1.19	0.110	0.58	0.21, 1.55	0.300

HR = Hazard Ratio, CI = Confidence Interval; PC = Palliative Care; KPS = Karnofsky Performance Status. NOS = Not Otherwise Specified; IDH = Isocitrate Dehydrogenase; WT = Wild-type.

Bold p-values indicate statistical significance (p < 0.05)

Importantly, after adjusting for these clinical factors and for histological subtype, neither referral to the PCT nor effective assessment showed a statistically significant independent association with overall survival (HR 0.91; P = 0.8). Regarding the timing of the intervention, patients referred early (<60 days) exhibited shorter median survival compared to those referred later (114 vs 399 days; P = 0.002), a finding that likely reflects the prioritization of critical cases (indication bias) rather than a negative effect of the intervention itself.

Discussion

The present study analyzed a cohort of 117 patients with high-grade gliomas (HGG) treated at a Brazilian tertiary center between 2017 and 2023. The observed demographic and clinical characteristics — mean age of 61.5 years, slight male predominance, and median overall survival of 235 days — are consistent with international epidemiological data.²¹ The predominance of astrocytoma not otherwise specified (60%) and IDH-wildtype glioblastoma (26%) reflects the expected histopathological profile, although the high proportion of cases without complete molecular classification highlights limited access to molecular testing, a known issue in the Brazilian public health system context.²² Histological subtype — a well-established prognostic factor in high-grade glioma²³ that also differed significantly by referral status (Table 1) — was therefore included as a covariate in the multivariate model, but was not independently associated with survival, reinforcing the robustness of the age- and KPS-driven findings. Notably, glioblastoma IDH-wildtype — the most aggressive subtype — did not show worse survival than the other categories (Table 3). This is likely attributable to the predominance of astrocytoma NOS in this cohort, a molecularly uncharacterized and prognostically heterogeneous category that may obscure genuine survival differences between subtypes.

In this cohort, 50.4% of patients were referred to the palliative care team (PCT). This is higher than rates reported in large population-based datasets, including 2022 U.S. SEER-Medicare data (15.24%),²⁴ and supports a comparatively stronger integration of PC in our setting. Although this rate exceeds most large international series, it is consistent with smaller real-world studies in which PC was already integrated into neuro-oncology pathways.^25,26

On the other hand, Hemminger and colleagues (2017), in a retrospective review of 117 deceased patients with GBM at a U.S. tertiary center—an identical sample size to ours—documented a PC consultation rate of 36.8%, showing significant variability between institutions even in high-income countries.²⁷ This difference may reflect variation in institutional referral culture, PC service availability, or local care-integration policies.

The marked increase in referrals over time (21.1% in 2017 to 73.7% in 2023; P < 0.001) suggests progressive institutional uptake and maturation of the local PC integration process.

Furthermore, the observed evolution in referral rates follows the global movement toward PC integration, driven by guidelines such as those from the European Association for Neuro-Oncology (EANO),²⁸ recent clinical trials such as the one conducted by Golla and colleagues (2025),²⁹ and the GLIOSUPPORT feasibility study.³⁰

Despite higher referral volume, access remained delayed: the median interval between referral and effective assessment was 46 days, and 18.6% of referred patients died before assessment. In a disease with median survival of 8-15 months, this delay is clinically meaningful,³¹ reinforcing the need for local implementation strategies such as prioritization pathways and teleconsultation models.³²

The interval between referral and effective evaluation has important implications for end-of-life outcomes and the need for more aggressive medical support.^33,34 Prior studies show that earlier palliative engagement is associated with more favorable end-of-life outcomes.^27,35

The association between referral and presence of non-headache pain (29% vs 12%) is consistent with the traditional PC-referral paradigm focused on physical symptoms. Sundararajan and colleagues (2014), in an Australian cohort of 678 patients with gliomas, demonstrated that symptomatic patients had more than five times greater odds of receiving PC (OR>5), suggesting that symptom burden is a robust predictor of referral regardless of geographic context or health system.³⁶ The presence of pain may function as a clinical marker that likely increases team sensitivity to the need for PC.³⁶

Greater survival among patients with late referral and worse survival among those referred early reflect indication bias already described in the literature: patients with rapid clinical deterioration tend to be referred earlier, whereas those with better trajectories survive long enough for later referral.³⁷

Specific populations, such as older adults, may present particular palliative needs. De Melo and colleagues (2020) reviewed the management of older patients with GBM, emphasizing the importance of individualized approaches that consider comorbidities, frailty, and care goals specific to this population.³⁸ Although age was not an independent predictor of referral in the present study, there was a positive association with lower survival in patients older than 60 years at diagnosis, which may suggest the need for comprehensive geriatric assessment focused on identifying early palliative needs in older patients with HGG.^23,39,40

Preoperative KPS remained an independent predictor of survival after adjustment, consistent with prior literature.^23,39 Together with advanced age, lower KPS can help identify patients who may benefit from earlier integrated PC.^23,28,39,40

The main goals of PC are quality of life and quality of death, rather than survival extension, although downstream survival effects may occur.³⁴ In our cohort, the increase in referrals and the improvement in functional status at the time of referral suggest that integration can be operationally advanced even in a middle-income public health setting. However, the observed delay between referral and first assessment indicates that gains in access must be accompanied by gains in timeliness to translate into stronger end-of-life outcomes.

Consistent with prior reports, early PC is associated with fewer futile interventions, better alignment with preferred place of death, and lower use of emergency and hospital resources near end of life.^41-45 Further prospective studies are needed in low- and middle-income countries to address ways to improve the patient-centered outcomes — eg, quality of life, quality of death, and goal-concordant care — particularly regarding referral triggers and access pathways.

Some limitations of this study should be considered. This was a retrospective analysis conducted at a single tertiary center with a specialized neuro-oncology and PCT, which may limit generalizability to other care settings or health systems with lower resource availability. In addition, the sample had low ethnic-racial diversity, reflecting the demographic characteristics of the region, which prevents assessment of the effect of these factors on the outcomes studied.

The absence of systematic measures of quality of life and symptom burden limits assessment of patient-centered outcomes, which is particularly relevant in PC. Incomplete molecular characterization of tumors limits prognostic stratification by molecular subtype, although it reflects the reality of many services in developing countries context.²²

Because this was an observational study without a control group, associations with survival should be interpreted with caution given the indication bias inherent to the design. Patients referred to PC may systematically differ from non-referred patients in unmeasured characteristics. Likewise, it is not possible to directly attribute causality to the educational interventions implemented over the period; future prospective studies with a control group could clarify this relationship.

Conclusions

This study describes the structuring of a specialized PC service at a Brazilian tertiary center and the temporal association with changes in HGG patient care, with an increase in referral rate and reduction in time to referral. An elevation of median KPS at referral was observed, compatible with referrals in phases of more preserved functionality, while optimization of the flow between referral and effective assessment remained a logistical challenge. Regarding prognosis, age and initial functional status were independently associated with survival. Together, findings suggest operational feasibility of expanding and anticipating referrals in a middle-income context.

Footnotes

Acknowledgements

We thank the HCFMB team for their logistical support.

ORCID iDs

Eduardo Abrão Spínola Rezk

Mariana Soares Pinheiro

Pablo Afonso Melo

Laura Cardia Gomes Lopes

Pedro Tadao Hamamoto Filho

Ethical Considerations

This study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of the Botucatu Medical School (CAAE 69738223.0.0000.5411).

Consent to Participate

The requirement for informed consent was waived by the Research Ethics Committee due to the retrospective nature of the study and the use of de-identified data.

Author Contributions

EASR: study design, data collection, statistical analysis, drafting of the manuscript. PTHF, LCGL, and TAS: conception and design of the study, critical review of the manuscript. MSP and PAM: data collection and clinical analysis. All authors read and approved the final version.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: PTHF is supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, 313047/2023-5).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting the findings of this study are available upon reasonable request to the corresponding author, subject to appropriate ethical and privacy restrictions.*

Preprint Statement

This manuscript has not been posted on a preprint server.

References

Ostrom

Price

Neff

, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2016–2020. Neuro Oncol. 2023;25(Suppl 6):iv1-iv99. doi:10.1093/neuonc/noad149.

Stupp

Mason

van den Bent

, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987-996. doi:10.1056/NEJMoa043330.

Stupp

Taillibert

Kanner

, et al. Effect of tumor-treating fields plus maintenance temozolomide vs maintenance temozolomide alone on survival in patients with glioblastoma: a randomized clinical trial. JAMA. 2017;318(23):2306-2316. doi:10.1001/jama.2017.18718.

Sizoo

Braam

Postma

, et al. Symptoms and problems in the end-of-life phase of high-grade glioma patients. Neuro Oncol. 2010;12(11):1162-1166. doi:10.1093/neuonc/nop045.

Halkett

Lobb

Rogers

, et al. Predictors of distress and poorer quality of life in high grade glioma patients. Patient Educ Counsel. 2015;98(4):525-532. doi:10.1016/j.pec.2015.01.002.

Renovanz

Hechtner

Janko

, et al. Factors associated with supportive care needs in glioma patients in the neuro-oncological outpatient setting. J Neuro Oncol. 2017;133(3):653-662. doi:10.1007/s11060-017-2484-y.

Boele

Rooney

Grant

Klein

. Psychiatric symptoms in glioma patients: from diagnosis to management. Neuropsychiatric Dis Treat. 2015;11:1413-1420. doi:10.2147/NDT.S65874.

Ferrell

Temel

Temin

, et al. Integration of palliative care into standard oncology care: American society of clinical oncology clinical practice guideline update. J Clin Oncol. 2017;35(1):96-112. doi:10.1200/JCO.2016.70.1474.

Gaertner

Siemens

Meerpohl

, et al. Effect of specialist palliative care services on quality of life in adults with advanced incurable illness in hospital, hospice, or community settings: systematic review and meta-analysis. BMJ. 2017;357:j2925. doi:10.1136/bmj.j2925.

10.

Temel

Greer

Muzikansky

, et al. Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med. 2010;363(8):733-742. doi:10.1056/NEJMoa1000678.

11.

Zimmermann

Swami

Krzyzanowska

, et al. Early palliative care for patients with advanced cancer: a cluster-randomised controlled trial. Lancet. 2014;383(9930):1721-1730. doi:10.1016/S0140-6736(13)62416-2.

12.

Kavalieratos

Corbelli

Zhang

, et al. Association between palliative care and patient and caregiver outcomes: a systematic review and meta-analysis. JAMA. 2016;316(20):2104-2114. doi:10.1001/jama.2016.16840.

13.

Walbert

Puduvalli

Taphoorn

Taylor

Jalali

Iwamoto

. International patterns of palliative care in neuro-oncology: a survey of physician members of the Asian society for neuro-oncology, the European association of neuro-oncology, and the society for neuro-oncology. Neuro-Oncol Pract. 2016;3(2):62-73. doi:10.1093/nop/npv049.

14.

Pace

Villani

Di Pasquale

, et al. Home care for brain tumor patients. J Exp Clin Cancer Res. 2012;31(1):1-7. doi:10.1186/1756-9966-31-1.

15.

Koekkoek

Dirven

Reijneveld

, et al. End of life care in high-grade glioma patients: shifts in care utilization and patients’ place of death. Support Care Cancer. 2014;22(5):1217-1224. doi:10.1007/s00520-013-2077-6.

16.

Schenker

Crowley-Matoka

Dohan

, et al. Oncologist factors that influence referrals to subspecialty palliative care clinics. J Oncol Pract. 2014;10(2):e37-e44. doi:10.1200/JOP.2013.001130.

17.

Hui

Mori

Watanabe

, et al. Referral criteria for outpatient specialty palliative cancer care: an international consensus. Lancet Oncol. 2016;17(12):e552-e559. doi:10.1016/S1470-2045(16)30577-0.

18.

Sizoo

Pasman

Dirven

, et al. The end-of-life phase of high-grade glioma patients: a systematic review. Support Care Cancer. 2014;22(3):847-857. doi:10.1007/s00520-013-2088-3.

19.

Walbert

Khan

. End-of-life symptoms and care in patients with primary malignant brain tumors: a systematic literature review. J Neuro Oncol. 2014;117(2):217-224. doi:10.1007/s11060-013-1330-x.

20.

Rodrigues

Zago

MMF

. Palliative care in brazil: challenges and perspectives. Rev Bras Enferm. 2019;72(6):1758-1766. doi:10.1590/0034-7167-2018-0718.

21.

Ostrom

Patil

Cioffi

Waite

Kruchko

Barnholtz-Sloan

. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2013–2017. Neuro Oncol. 2020;22(Supplement_1):iv1-96. doi:10.1093/neuonc/noaa200.

22.

Marta

Moraes

Feher

, et al. Social determinants of health and survival on Brazilian patients with glioblastoma: a retrospective analysis of a large populational database. Lancet Reg Health, Am. 2021;4:100066. doi:10.1016/j.lana.2021.100066.

23.

Curran

Scott

Horton

, et al. Recursive partitioning analysis of prognostic factors in three radiation therapy oncology group malignant glioma trials. J Natl Cancer Inst. 1993;85(9):704-710. doi:10.1093/jnci/85.9.704.

24.

Ugiliweneza

Wang

Hsin

Boakye

Skirboll

. Trends and outcomes of early and late palliative care consultation for adult patients with glioblastoma: a SEER-medicare retrospective study. Neuro-Oncol Pract. 2022;9(4):299-309. doi:10.1093/nop/npac026.

25.

Drummond De Camargo

De Souza

De Carvalho Ramalho De Oliveira

Barbosa

. A retrospective chart review to identify the involvement of palliative care with glioblastoma multiforme patients. Can Oncol Nurs J. 2023;33(4):436-443. doi:10.5737/23688076334436.

26.

Ellenbogen

Taslimi

Shellenberger

Brogly

Zadeh

Alkins

. End-of-life care in glioblastoma: a population-based study. Neuro Oncol. 2025;27(6):1640-1653. doi:10.1093/neuonc/noaf043.

27.

Hemminger

Pittman

Korones

, et al. Palliative and end-of-life care in glioblastoma: defining and measuring opportunities to improve care. Neuro-Oncol Pract. 2017;4(3):182-188. doi:10.1093/nop/npw022.

28.

Pace

Dirven

Koekkoek

JAF

, et al. European association for neuro-oncology (EANO) guidelines for palliative care in adults with glioma. Lancet Oncol. 2017;18(6):e330-e340. doi:10.1016/S1470-2045(17)30345-5.

29.

Golla

Nettekoven

Hellmich

, et al. Early palliative care for patients with glioblastoma: a randomized phase III clinical trial (EPCOG). Neuro Oncol. 2025;28:noaf230-noaf240. doi:10.1093/neuonc/noaf230.

30.

Fabbro

Thomaso

Darlix

, et al. Integrated early palliative care for patients with newly diagnosed glioblastoma: the GLIOSUPPORT feasibility study. Neuro-Oncology Advances. 2025;7(1):vdaf064. doi:10.1093/noajnl/vdaf064.

31.

Diamond

Russell

Kryza-Lacombe

, et al. Rates and risks for late referral to hospice in patients with primary malignant brain tumors. Neuro Oncol. 2016;18(1):78-86. doi:10.1093/neuonc/nov156.

32.

Calton

Rabow

Page

, et al. Embedding palliative care in a neuro-oncology clinic at an academic medical center: our structure, experience, and lessons learned. Neuro-Oncology Advances. 2022;4(1):vdac114. doi:10.1093/noajnl/vdac114.

33.

Harrison

Naqvi

SMAA

, et al. Aggressiveness of care at end of life in patients with high‐grade glioma. Cancer Med. 2021;10(23):8387-8394. doi:10.1002/cam4.4344.

34.

Lind

Fischl

Goettl

, et al. Palliative care interventions and outcome in patients with glioblastoma: a retrospective, single-center study. BMC Palliat Care. 2026;25:29. doi:10.1186/s12904-026-01987-4.

35.

Shieh

Guo

. Comparison of healthcare utilization and life-sustaining interventions between patients with glioblastoma receiving palliative care or not: a population-based study. Palliat Med. 2023;37(6):824-833. doi:10.1177/02692163231152526.

36.

Sundararajan

Bohensky

Moore

, et al. Mapping the patterns of care, the receipt of palliative care and the site of death for patients with malignant glioma. J Neuro Oncol. 2014;116(1):119-126. doi:10.1007/s11060-013-1263-7.

37.

Hui

Kim

Roquemore

Dev

Chisholm

Bruera

. Impact of timing and setting of palliative care referral on quality of end‐of‐life care in cancer patients. Cancer. 2014;120(11):1743-1749. doi:10.1002/cncr.28628.

38.

De Melo

Marta

Yan

Cruz

Moraes

Riera

. Management of elderly patients with glioblastoma: current status with a focus on the postoperative radiation therapy. Ann Palliat Med. 2020;9(5):3553-3561. doi:10.21037/apm-20-768.

39.

Lacroix

Abi-Said

Fourney

, et al. A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg. 2001;95(2):190-198. doi:10.3171/jns.2001.95.2.0190.

40.

Pham

Sisti

Cote

, et al. Trends in glioblastoma treatment, survival, and disparities in access to care in the United States from 2004 to 2019: a national cancer database analysis. J Neurosurg. 2025;143(3):780-792. doi:10.3171/2025.1.JNS242671.

41.

Fritz

Zaloumis

Zwinkels

, et al. End-of-life care in patients with glioblastoma after early advance care planning. Neuro-Oncol Pract. 2025;12(5):821-829. doi:10.1093/nop/npaf050.

42.

Crooms

Johnson

Leeper

Mehta

McWhirter

Sharma

. Easing the journey—an updated review of palliative care for the patient with high-grade glioma. Curr Oncol Rep. 2022;24(4):501-515. doi:10.1007/s11912-022-01210-6.

43.

Fortunato

Van Harn

Haider

Phillips

Walbert

. Caregiver perceptions of end-of-life care in patients with high-grade glioma. Neuro-Oncol Pract. 2021;8(2):171-178. doi:10.1093/nop/npaa077.

44.

Hertler

. Palliative care implementation in neuro-oncology—where do we come from, where do we go? Neuro-Oncol Pract. 2022;9(4):257-258. doi:10.1093/nop/npac039.

45.

Nåhls

Anttonen

Kitti

, et al. Early palliative care decision in patients with primary brain tumor reduces emergency department visits and hospitalization at the end of life. J Neuro Oncol. 2026;176(2):137. doi:10.1007/s11060-025-05377-3.