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. 2010 Aug;19(6):837-45.
doi: 10.1007/s11136-010-9636-2. Epub 2010 Mar 26.

Needs assessments can identify scores on HRQOL questionnaires that represent problems for patients: an illustration with the Supportive Care Needs Survey and the QLQ-C30

Claire F Snyder  1 , Amanda L Blackford, Julie R Brahmer, Michael A Carducci, Roberto Pili, Vered Stearns, Antonio C Wolff, Sydney M Dy, Albert W Wu
Affiliations

Needs assessments can identify scores on HRQOL questionnaires that represent problems for patients: an illustration with the Supportive Care Needs Survey and the QLQ-C30

Claire F Snyder et al. Qual Life Res. 2010 Aug.

Abstract

Purpose: A barrier to using HRQOL questionnaires for individual patient management is knowing what score represents a problem deserving attention. We explored using needs assessments to identify HRQOL scores associated with patient-reported unmet needs.

Methods: This cross-sectional study included 117 cancer patients (mean age 61 years; 51% men; 77% white) who completed the Supportive Care Needs Survey (SCNS) and EORTC QLQ-C30. SCNS scores were dichotomized as "no unmet need" versus "some unmet need" and served as an external criterion for identifying problem scores. We evaluated the discriminative ability of QLQ-C30 scores using receiver operating characteristic (ROC) analysis. Domains with an area under the ROC curve (AUC) >or= .70 were examined further to determine how well QLQ-C30 scores predicted presence/absence of unmet need.

Results: We found AUCs >or= .70 for 6 of 14 EORTC domains: physical, emotional, role, global QOL, pain, fatigue. All 6 domains had sensitivity >or= .85 and specificity >or= .50. EORTC domains that closely matched the content of SCNS item(s) were more likely to have AUCs >or= .70. The appropriate cut-off depends on the relative importance of false positives and false negatives.

Conclusions: Needs assessments can identify HRQOL scores requiring clinicians' attention. Future research should confirm these findings using other HRQOL questionnaires and needs assessments.

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Figures

Fig. 1
Fig. 1
Example boxplots for hypothesized strong, moderate, and weak associations between QLQ-C30 scores and presence/absence of an unmet need
Fig. 2
Fig. 2
ROC curves for select QLQ-C30 domains that discriminate SCNS classification with an AUC of .70 or higher
See this image and copyright information in PMC

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