A systematic review of the assessment model for palliative care needs in cancer patients

Clinical Snapshot

Key Findings

• P Value: Not applicable for systematic review

• Effect Size: AUC range 0.724 to 0.998 across 24 models

• Primary Outcome: Assessment model performance for identifying palliative care needs

• Nnt Or Sensitivity: Sensitivity and specificity values not reported in abstract

• Confidence Interval: Not reported

Clinical Application

Models use routinely collected clinical data but require further validation before implementation Relevant to Australian palliative care services, but would require local validation given identified bias risks. Could inform development of Australian-specific assessment tools aligned with national palliative care standards Cancer patients in various healthcare settings, though applicability limited by validation concerns

Abstract

BACKGROUND: Timely palliative care can reduce the disease burden and improve quality of life in patients with cancer. Although several studies have developed assessment models for palliative care needs in cancer patients, the quality and clinical applicability of these models remain unclear. OBJECTIVE: To systematically review existing assessment models for palliative care needs in patients with cancer, with a focus on their characteristics, predictors, risk of bias, and applicability. METHODS: A systematic search was conducted in PubMed, Cochrane Library, Embase, Web of Science, CINAHL, Scopus, China National Knowledge Infrastructure (CNKI) through September 12, 2025. Data extraction and evaluation were rigorously performed by two researchers based on the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: A total of 5714 articles were identified, and eight studies were included, which covered 24 models for assessing palliative care needs. The sample size of the included studies ranged from 179 to 54,628, with areas under the curve ranging from 0.724 to 0.998. The models in all the included studies encompassed four categories of predictive factors: general demographic data, symptom/functional assessments, laboratory indicators, and treatment status. Five studies were rated as having a high risk of bias, primarily due to high risks associated with participants and conclusions, with generally low applicability. CONCLUSION: Existing models demonstrate potential for identifying patients with cancer who have increased palliative care needs using routinely collected clinical data. Commonly included predictors were symptom burden, functional status, laboratory parameters, treatment-related factors, and demographic characteristics. However, the overall body of evidence is constrained by a substantial risk of bias, particularly arising from inappropriate data sources, limited sample sizes, suboptimal handling of continuous variables, insufficient reporting of missing data, and the lack of robust internal or external validation. In addition, many models adopted mortality-based surrogate outcomes rather than clinically meaningful indicators of palliative care needs. Therefore, the currently available models should be interpreted with caution, and further high-quality model development and external validation are required before they can support broader routine clinical implementation. Future research should prioritize clinically actionable outcomes and incorporate patient-, caregiver-, and family-level factors to improve the relevance of these models for referral decisions and care planning.

References

1. 1.Xu, C., Xie, Y., Li, Y., Guan, S., Cai, T., & Song, L. (2026). A systematic review of the assessment model for palliative care needs in cancer patients. Supportive Care in Cancer. https://doi.org/10.1080/15384047.2024.2344600