For advanced cancer inpatients, the established standard for gathering information about symptom burden involves a daily assessment by nursing staff using validated assessments. In contrast, a systematic assessment of patient-reported outcome measures (PROMs) is required, but it is not yet systematically implemented. We hypothesized that current practice results in underrating the severity of patients’ symptom burden. To explore this hypothesis, we have established systematic electronic PROMs (ePROMs) using validated instruments at a major German Comprehensive Cancer Center. In this retrospective, non-interventional study, lasting from September 2021 to February 2022, we analyzed collected data from 230 inpatients. Symptom burden obtained by nursing staff was compared to the data acquired by ePROMs. Differences were detected by performing descriptive analyses, Chi-Square tests, Fisher’s exact, Phi-correlation, Wilcoxon tests, and Cohen’s r. Our analyses pointed out that pain and anxiety especially were significantly underrated by nursing staff. Nursing staff ranked these symptoms as non-existent, whereas patients stated at least mild symptom burden (pain: mean_NRS/epaAC = 0 (no); mean_ePROM = 1 (mild); p < 0.05; r = 0.46; anxiety: mean_epaAC = 0 (no); mean_ePROM = 1 (mild); p < 0.05; r = 0.48). In conclusion, supplementing routine symptom assessment used daily by nursing staff with the systematic, e-health-enabled acquisition of PROMs may improve the quality of supportive and palliative care.

Purpose: Despite that early integration of palliative care is recommended in advanced cancer patients, referrals to outpatient specialised palliative care (SPC) frequently occur late. Well-defined referral criteria are still missing. We analysed indicators associated with early (ER) and late referral (LR) to SPC of an high volume outpatient unit of a comprehensive cancer center. Methods: Characteristics, laboratory parameters and symptom burden of 281 patients at first SPC referral were analysed. Timing of referral was categorized as early, intermediate and late (> 12, 3–12 and < 3 months before death). Ordinal logistic regression analysis was used to identify factors related to referral timing. Kruskal–Wallis test was used to determine symptom severity and laboratory parameter in each referral category. Results: LRs (50.7%) had worse scores of weakness, loss of appetite, drowsiness, assistance of daily living (all p < 0.001) and organisation of care (p < 0.01) in contrast to ERs. The mean symptom sum score was significantly higher in LRs than ERs (13.03 vs. 16.08; p < 0.01). Parameters indicative of poor prognosis, such as elevated LDH, CRP and neutrophil-to-lymphocyte ratio (NLR) (p < 0.01) as well as the presence of ascites (p < 0.05), were significantly higher (all p < 0.001) in LRs. In univariable analyses, psychological distress (p < 0.05) and female gender (p < 0.05) were independently associated with an ER. Conclusion: A symptom sum score and parameters of poor prognosis like NLR or LDH might be useful to integrate into palliative care screening tools.

Background/Objectives: The “Palliative Care and Rapid Emergency Screening Tool” (P-CaRES) is used to identify patients with palliative needs in the emergency department. This study aims to translate, adapt, and validate the P-CaRES tool for the German healthcare context. Methods: This is a monocentric, non-interventional, retrospective study conducted in the emergency department of the University Hospital Essen, Germany. After a structured translation process, the tool’s face and content validity were evaluated using questionnaires completed by healthcare workers. Construct validity was established by comparing the results with the German-validated Supportive and Palliative Care Indicators Tool (SPICT). A palliative care physician blinded to the tool, independently assessed the screened patients, and made recommendations on the appropriateness of palliative care referrals. Results: Two hundred eighty-nine emergency department visits were examined. In addition, a total of 26 healthcare professionals were surveyed. 258 screenings were conducted, with P-CaRES identifying 50 positive cases (19.4%). Agreement between SPICT and P-CaRES was 88.4% (kappa: 0.67, 95% confidence interval (CI): [0.56, 0.78]), showing 64.7% sensitivity and 96.8% specificity. Expert comparison yielded 85.5% agreement (kappa: 0.54, 95% CI: [0.41, 0.68]), with 64.0% sensitivity and 90.8% specificity. Face and content validity showed strong agreement regarding the tool’s design, including its comprehensibility, applicability, ease of use, and precision, as well as its usefulness in referring patients to a specialized palliative care team. Conclusions: The study successfully validated a cultural and linguistic equivalent German version of the P-CaRES tool. Further research is necessary to assess the tool’s effectiveness in clinical practice.

PURPOSE: Palliative care is recommended for patients with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patients with cancer and may help distinguish who benefits the most from palliative care support. We aim to explore the importance of several objective and subjective self-reported variables. Subjective variables were collected through electronic psycho-oncologic and palliative care self-assessment screenings. We used these variables to predict 1-year mortality. MATERIALS AND METHODS: Between April 1, 2020, and March 31, 2021, a total of 265 patients with advanced cancer completed a patient-reported outcome tool. We documented objective and subjective variables collected from electronic health records, self-reported subjective variables, and all clinical variables combined. We used logistic regression (LR), 20-fold cross-validation, decision trees, and random forests to predict 1-year mortality. We analyzed the receiver operating characteristic (ROC) curve—AUC, the precision-recall curve—AUC (PR-AUC)—and the feature importance of the ML models. RESULTS: The performance of clinical nonpatient variables in predictions (LR reaches 0.81 [ROC-AUC] and 0.72 [F1 score]) are much more predictive than that of subjective patient-reported variables (LR reaches 0.55 [ROC-AUC] and 0.52 [F1 score]). CONCLUSION: The results show that objective variables used in this study are much more predictive than subjective patient-reported variables, which measure subjective burden. These findings indicate that subjective burden cannot be reliably used to predict survival. Further research is needed to clarify the role of self-reported patient burden and mortality prediction using ML.

Purpose: The Minimal Documentation System (MIDOS2) is recommended as a systematic screening tool for assessing symptom burden and patient needs in advanced cancer patients. Given the absence of an optimal weighting of individual symptoms and a corresponding cut-off value, this study aims to determine a threshold based on inpatient’s subjective need for palliative support. Additionally, we investigate the correlation between symptom burden and subjective need for palliative support collected through a patient-reported outcome measure (PROM) with survival duration of less or more than one year. Methods: Inpatients diagnosed with advanced solid cancer completed an electronic PROM, which included the MIDOS2 questionnaire among other tools. Differences in symptom burden were analysed between patients expressing subjective need for palliative support and those with survival of less or more than one year using ANOVA, Mann–Whitney-U Test, logistic regression, Pearson and Spearman correlation tests. Cut-off analyses were performed using a ROC curve. Youden-Index, sensitivity, and specificity measures were used as well. Results: Between April 2020 and March 2021, 265 inpatients were included in the study. Using a ROC curve, the MIDOS2 analysis resulted in an Area under the curve (AUC) of 0.732, a corresponding cut-off value of eight points, a sensitivity of 76.36% and a specificity of 62.98% in assessing the subjective need for palliative support. The MIDOS2, with double weighting of the significant symptoms, showed a cut-off value of 14 points, achieving a sensitivity of 78.18% and a specificity of 72.38%. A total of 55 patients (20.8%) expressed a need for support from the palliative care team. This need was independent of the oncological tumour entity and increased among patients with a survival of less than one year. These patients reported significantly poorer physical (p < 0.001) or mental (p < 0.001) condition. Additionally, they reported higher intensities of pain (p = 0.002), depressive symptoms (p < 0.001), weakness (p < 0.001), anxiety (p < 0.001), and tiredness (p < 0.001). Conclusion: Using the established MIDOS2 cut-off value with an adjusted double weighting in our study, a large proportion of inpatients may be accurately referred to SPC based on their subjective need for palliative support. Additionally, subjective reports of poor general, mental, and physical condition, as well as pain, depressive symptoms, weakness, anxiety, and tiredness, increase the subjective need for palliative support, particularly in patients with a survival prognosis of less than one year.