Abstract
BackgroundSelection of the optimal initial treatment in patients with major depressive disorder (MDD) in need of highly specialized care has the potential to benefit treatment outcomes and cost-effectiveness of treatment strategies. However, to date, there is a paucity of measures that could guide the selection of the initial treatment, in particular to indicate which patients with MDD are in need of highly specialized care. Recognizing this gap, this paper reports on the development and psychometric evaluation of the Decision Tool Unipolar Depression (DTUD), aimed to facilitate the early identification of patients with MDD in need of highly specialized care.MethodsThe DTUD was developed using a mixed-methods approach, consisting of a systematic review and a concept mapping study. To evaluate the psychometric features of the DTUD, a cross-sectional multicenter study was conducted. A total of 243 patients with MDD were evaluated with the DTUD. Feasibility was operationalized as the time required to complete the DTUD and the content clarity of the DTUD. Inter-rater reliability was evaluated using Krippendorf’s alpha. The Maudsley Staging Method (MSM) and the Dutch Measure for quantification of Treatment Resistance in Depression (DM-TRD) were administered to assess the convergent validity. A receiver operator characteristic curve was generated to evaluate the criterion validity and establish the optimal cut-off value.ResultsThe mean administration time was 4.49 min (SD = 2.71), and the content of the total DTUD was judged as clear in 94.7% of the evaluations. Inter-rater reliability values ranged from 0.69 to 0.91. Higher scores on the DTUD were associated with higher scores on the MSM (rs = 0.47) and DM-TRD (rs = 0.53). Based on the maximum Youden index (0.494), maximum discrimination was reached at a cut-off score of ≥5 (sensitivity 67%, specificity 83%).ConclusionThe DTUD demonstrated to be a tool with solid psychometric properties and, therefore, is a promising measure for the early identification of patients with MDD in need of highly specialized care. Use of the DTUD has the potential to facilitate the selection and initiation of the optimal initial treatment in patients with MDD, which in turn may improve the clinical effectiveness and cost-effectiveness of treatment strategies.
Highlights
Selection of the optimal initial treatment in patients with major depressive disorder (MDD) in need of highly specialized care has the potential to benefit treatment outcomes and cost-effectiveness of treatment strategies
To date, there is a paucity of measures that facilitate the selection of the optimal initial treatment, in particular to indicate which patients with MDD are in need of highly specialized care
Recognizing this gap, in this paper we report on the development and psychometric evaluation of the Decision support Tool for the assessment of highly specialized mental healthcare needs of patients with a Unipolar Depression, or the “Decision Tool Unipolar Depression” (DTUD) for short
Summary
Selection of the optimal initial treatment in patients with major depressive disorder (MDD) in need of highly specialized care has the potential to benefit treatment outcomes and cost-effectiveness of treatment strategies. The stepped care model of healthcare delivery is considered an appropriate approach in patients who recover with low intensity treatments [7, 8], the effectiveness and cost-effectiveness of the stepped care model is questionable in patients who, identifiably, are in need of high intensity treatment [6]. Subsequent referral of these patients to highly specialized mental healthcare (i.e. tertiary mental healthcare) is likely to prolong the treatment course and compromise clinical and functional outcomes and costeffectiveness of treatments. Selection of the optimal initial treatment in patients with MDD in need of highly specialized care is warranted, as it can improve the effectiveness and cost-effectiveness of treatment paths, but strongly relies on the availability of psychometrically sound instruments to aid clinicians in the early identification of these patients [4, 9]
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