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Abstract
Background: Assessing decisional conflict in patients is a shared decision making competency.
Objectives: We sought to determine the psychometric properties of SURE, a four-item checklist designed to detect for clinically significant decisional conflict (CSDC) in clinical practice.
Methods: This study was a secondary analysis of a clustered randomized trial assessing the effect of DECISION+2, a 2-hour online tutorial followed by a 2-hour interactive workshop on shared decision making, on decisions to use antibiotics for acute respiratory infections. Patients completed SURE and also the Decisional Conflict Scale (DCS), as the gold standard, after consultation. We evaluated internal consistency of SURE using the Kuder-Richardson 20 coefficient (KR-20). We compared DCS and SURE scores using the Spearman correlation coefficient. We assessed sensitivity, specificity, predictive values and likelihood ratios of SURE scores (cut-off score ≤ 3 out of 4) by identifying patients with and without CSDC (DCS score > 37.5 on a scale of 0–100).
Results: Of the 712 patients recruited during the trial, 654 completed both tools. The prevalence of CSDC, as diagnosed by the DCS, was 5.2% (95% CI: 3.7–7.3). SURE showed adequate internal reliability (KR-20 coefficient of 0.7). There was a significant correlation between DCS and SURE scores (Spearman’s ρ = −0.45, p < 0.0001). The sensitivity and specificity of SURE were 94.1% (95% CI: 78.9–99.0) and 89.8% (95% CI: 87.1–92.0), respectively. The negative predictive value was 99.6% (95% CI: 98.6–99.8) and positive predictive value was 33.7% (95% CI: 24.5–44.2). The positive likelihood ratio was moderately high (9.26), whereas the negative likelihood ratio was clinically more significant (0.07).
Conclusions: SURE has the potential to be a useful tool for practitioners, responding to the growing need for detecting decisional conflict in patients. SURE should be tested in high decisional conflict prevalence populations of patients and in different clinical contexts.
Objectives: We sought to determine the psychometric properties of SURE, a four-item checklist designed to detect for clinically significant decisional conflict (CSDC) in clinical practice.
Methods: This study was a secondary analysis of a clustered randomized trial assessing the effect of DECISION+2, a 2-hour online tutorial followed by a 2-hour interactive workshop on shared decision making, on decisions to use antibiotics for acute respiratory infections. Patients completed SURE and also the Decisional Conflict Scale (DCS), as the gold standard, after consultation. We evaluated internal consistency of SURE using the Kuder-Richardson 20 coefficient (KR-20). We compared DCS and SURE scores using the Spearman correlation coefficient. We assessed sensitivity, specificity, predictive values and likelihood ratios of SURE scores (cut-off score ≤ 3 out of 4) by identifying patients with and without CSDC (DCS score > 37.5 on a scale of 0–100).
Results: Of the 712 patients recruited during the trial, 654 completed both tools. The prevalence of CSDC, as diagnosed by the DCS, was 5.2% (95% CI: 3.7–7.3). SURE showed adequate internal reliability (KR-20 coefficient of 0.7). There was a significant correlation between DCS and SURE scores (Spearman’s ρ = −0.45, p < 0.0001). The sensitivity and specificity of SURE were 94.1% (95% CI: 78.9–99.0) and 89.8% (95% CI: 87.1–92.0), respectively. The negative predictive value was 99.6% (95% CI: 98.6–99.8) and positive predictive value was 33.7% (95% CI: 24.5–44.2). The positive likelihood ratio was moderately high (9.26), whereas the negative likelihood ratio was clinically more significant (0.07).
Conclusions: SURE has the potential to be a useful tool for practitioners, responding to the growing need for detecting decisional conflict in patients. SURE should be tested in high decisional conflict prevalence populations of patients and in different clinical contexts.
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