Objective of this study aimed at assessing glioma patients' supportive care needs in a neurosurgical outpatient setting and identifying factors that are associated with needs for support. In three neuro-oncological outpatient departments, glioma patients were assessed for their psychosocial needs using the Supportive Care Needs Survey short-form (SCNS-SF34-G). Associations between clinical, sociodemographic, treatment related factors as well as distress (measured with the distress thermometer) and supportive care needs were explored using multivariable general linear models. One-hundred and seventy three of 244 eligible glioma patients participated, most of them with primary diagnoses of a high-grade glioma (81%). Highest need for support was observed in 'psychological needs' (median 17.5, range 5-45) followed by 'physical and daily living needs' (median 12.5, range 0-25) and 'health system and information needs' (median 11.3, range 0-36). Needs in the psychological area were associated with distress (R = 0.36) but not with age, sex, Karnofsky performance status (KPS), extend of resection, currently undergoing chemotherapy and whether guidance during assessment was offered. Regarding 'health system and information needs', we observed associations with distress, age, currently undergoing chemotherapy and guidance (R = 0.31). In the domain 'physical and daily living needs' we found associations with KPS, residual tumor, as well as with distress (R = 0.37). Glioma patients in neuro-oncological departments report unmet supportive care needs, especially in the psychological domain. Distress is the factor most consistently associated with unmet needs requiring support and could serve as indicator for clinical neuro-oncologists to initiate support.

Neuro-oncological patients experience high symptom and psychosocial burden. The aim was to test feasibility and practicability of the Supportive Care Needs Survey Short Form (SCNS-SF34-G) and the SCNS-Screening Tool (SCNS-ST9) to assess supportive care needs of neuro-oncological patients in clinical routine. A total of 173 patients, most with a primary diagnosis of high-grade glioma (81%), were assessed first using SCNS-SF34-G in comparison to two well-established patient-reported outcome measures, the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQC30 + QLQ-BN20) and Distress Thermometer (DT). In a follow-up assessment, SCNS-ST9 was used in a subgroup (n = 90). Questionnaires were completed either with personal guidance offered (group A) or by patients alone (group B). Feasibility was compared between instruments and groups for possible associations with patient and treatment-related factors. Missing values occurred in similar frequencies in all instruments. Errors in completion occurred in SCNS-SF34-G in 20% and in SCNS-ST9 in 16%; difficulties in completion were observed more often in SCNS-SF34-G and SCNS-ST9 (39%) compared to DT and EORTC (13%, p < .001). Distress was found to be associated with difficulties in completion of SCNS (OR 1.4, [95% CI 1.1-1.9], p = .013). SCNS-SF34 and SCNS-ST9 are suitable tools for glioma patients as long as personal guidance is offered.

The association between health-related quality of life (HRQoL), psychosocial distress, and supportive care is in the focus of patient-centered neuro-oncology. We investigated the relationship between the aforementioned in glioma-patients to evaluate the association of these instruments and determine cut-off values for suitable HRQoL scales indicating a potential need for intervention. In an observational multi-center study, outpatients completed the Distress Thermometer (DT), EORTC Quality of Life Questionnaire (EORTC-QLQ-C30/BN20, HRQoL), and Supportive-Care-Needs-Survey-SF34-G (SCNS). Based on nine EORTC-function and selected -symptom scales items of the questionnaires were matched. Convergent validity of related single items and scores across the instruments was estimated. EORTC cut-off values were calculated. Data of 167 patients were analyzed. The strongest correlation of EORTC-QLQ-C30 and DT was found for cognitive function (cogf), global health status (GHS), emotional (emof), role function (rolef), future uncertainty (FU), fatigue, and between EORTC-QLQ-C30 and SCNS for FU, emof, rolef (r = |0.4-0.7|; p < 0.01). EORTC cut-off values of <54.2 (GHS/QoL) and <62.5 (emof) predicted a DT ≥ 6 (AUC 0.79, 0.85, p < 0.01). EORTC cut-off values of <70.8 (emof) and <52.8 (FU) predicted the need for supportive care (AUC 0.78, 0.85; p < 0.01). Worse EORTC-C30 scores correlate with higher DT and SCNS scores. With this exploratory assessment, cut-off values for EORTC-C30 subscores to predict distress and pathological SCNS-scores could be determined, which could influence patients' referral to further treatment. However, further prospective clinical trials are needed to confirm the clinical relevance of these cut-off values.

Background Patient-reported outcomes are of high importance in clinical neuro-oncology. However, assessment is still suboptimal. We aimed at exploring factors associated with the probability for a) drop out of study and b) death during follow-up. Methods Patients were assessed twice during follow-up visits scheduled within 3 to 5 months of each other by using 3 validated patient-reported outcome measures (t1: first assessment, t2: second assessment). As “death” was seen as a competing risk for drop out, univariate competing risk Cox regression models were applied to explore factors associated with dropping out (age, gender, WHO grade, living situation, recurrent surgery, Karnofsky Performance Status, time since diagnosis, and patient-reported outcomes assessed by Distress Thermometer, EORTC-QLQ-C30, EORTC-QLQ-BN20, and SCNS-SF-34G). Results Two hundred forty-six patients were eligible, 173 (70%) participated. Patients declining participation were diagnosed with glioblastomas more often than with other gliomas (56% vs 39%). At t2, 32 (18%) patients dropped out, n = 14 death-related, n = 18 for other reasons. Motor dysfunction (EORTC-QLQ-BN20) was associated with higher risk for non-death-related drop out (HR: 1.02; 95% CI, 1.00–1.03; P = .03). Death-related drop out was associated with age (HR: 1.09; 95% CI, 1.03–1.14; P = .002), Karnofsky Performance Status (HR: 0.92; 95% CI, 0.88–0.96; P < .001), lower physical functioning (EORTC-QLQ-C30; HR: 0.98; 95% CI, 0.96–1.00; P = .04) and lower motor functioning (EORTC-QLQ-BN20; HR: 1.020; 95% CI, 1.00–1.04; P = .02). Conclusion Patients with motor dysfunction and poorer clinical condition seem to be more likely to drop out of studies applying patient-reported outcome measures. This should be taken into account when planning studies assessing glioma patients and for interpretation of results of patient-reported outcome assessments in clinical routine.

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