Matching research and practice: Prediction of individual patient progress and dropout risk for basic routine outcome monitoring

Mütze, Kaline; Witthöft, Michael; Lutz, Wolfgang; Bräscher, Anne-Kathrin

doi:10.1080/10503307.2021.1930244

Cited by 10 publications

(6 citation statements)

References 67 publications

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“…However, it should be noted that these meta-analyses identified very wide ranges in observed dropout between 0 and 50-74%. As such, the dropout rate in our study appears low but in line with results from earlier work in RCTs and other naturalistic samples [22,27,58]. It is unknown whether our setting and therapeutic approaches play a role in our dropout rate.…”

Section: Discussionsupporting

confidence: 87%

“…Comparison of this finding with meta-analyses that examined treatment dropout is difficult because baseline severity and episode duration were not assessed [6,9,36,60]. As previously described, the association between severity and dropout is not unambiguous (e.g., [13,23,27]). The finding that more severe patients were less likely to drop out is in line with several studies [8,22,23].…”

Section: Discussionmentioning

confidence: 83%

“…These inconsistencies also apply to the relationship between clinical features and the occurrence of dropout. The impact of higher baseline severity on the occurrence of dropout for example has not been consistently established, ranging from protective [5,8,[22][23][24] or no clear impact [13] to risk enhancing [19,21,[25][26][27]. Recurrent depression is found to be associated with lower dropout rates [28], whereas chronic depression may not affect the occurrence of dropout [21].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prevalence and prediction of dropout during depression treatment in routine outpatient care: an observational study

Dijk

Deen

Boogaard

et al. 2022

Eur Arch Psychiatry Clin Neurosci

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Efficacious treatments are available for major depressive disorder (MDD), but treatment dropout is common and decreases their effectiveness. However, knowledge about prevalence of treatment dropout and its risk factors in routine care is limited. The objective of this study was to determine the prevalence of and risk factors for dropout in a large outpatient sample. In this retrospective cohort analysis, routinely collected data from 2235 outpatients with MDD who had a diagnostic work-up between 2014 and 2016 were examined. Dropout was defined as treatment termination without achieving remission before the fourth session within six months after its start. Total and item scores on the Dutch Measure for Quantification of Treatment Resistance in Depression (DM-TRD) at baseline, and demographic variables were analyzed for their association with dropout using logistic regression and elastic net analyses. Data of 987 subjects who started routine outpatient depression treatment were included in the analyses of which 143 (14.5%) dropped out. Higher DM-TRD-scores were predictive for lower dropout odds [OR = 0.78, 95% CI = (0.70–0.86), p < 0.001]. The elastic net analysis revealed several clinical variables predictive for dropout. Higher SES, higher depression severity, comorbid personality pathology and a comorbid anxiety disorder were significantly associated with less dropout in the sample. In this observational study, treatment dropout was relatively low. The DM-TRD, an easy-to-use clinical instrument, revealed several variables associated with less dropout. When applied in daily practice and combined with demographical information, this instrument may help to reduce dropout and increase treatment effectiveness.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prevalence and prediction of dropout during depression treatment in routine outpatient care: an observational study

Dijk

Deen

Boogaard

et al. 2022

Eur Arch Psychiatry Clin Neurosci

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“…Several factors contribute to the hesitant reception of measurement in clinical practice. For instance, reluctancy on the use of technical equipment, financial support, time disposal, apprehension about the ecological validity of measures (e.g., thinking that empirical findings do not reflect their everyday practice; Boswell et al, 2015;Gilbody et al, 2002), perceiving measurement as a way of control, or concerns about data security ( de Jong & de Goede, 2015;Mütze et al, 2022). In this regard, appropriate training and institutional support (involving all stakeholders) are necessary for a correct implementation.…”

Section: Discussionmentioning

confidence: 99%

Data-Informed Case Formulation with the Trier Treatment Navigator

Moggia

Schaffrath

Bommer

et al. 2022

rdp

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The current article presents a way of practising case formulation using technology augmentation in the context of data-informed psychotherapy. First, we explain how case formulation guides the decision-making processes in psychotherapy and how decisions made under an intuitive clinical judgement can be biased. The use of actuarial methods is pointed out as a way of addressing these biases, mainly through clinical decision support systems based on statistical tools and machine learning algorithms. We present the Trier Treatment Navigator (TTN), a clinical decision support system developed in a University research-based clinical training programme. We show how the TTN can contribute to an initial case formulation and its dynamic adaptations during treatment with a clinical case. Finally, we discuss how case formulation and data-informed psychotherapy are aimed at the same goal: treatment personalisation. We argue that case formulation and data-informed psychotherapy enrich and feedback on each other.

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“…Especially for refugees and asylum seekers, who often have difficulties in finding a therapy and need to overcome various treatment barriers (Slobodin & de Jong, 2015), it is important that the benefit of treatment is always perceivable and outweighs the existing barriers. Mütze et al (2021) could show that the risk of treatment dropout could be predicted by routine outcome monitoring. If the therapist detects these changes at an early stage, adjustments can be made to the course of therapy before premature discontinuation can even occur (Swift et al, 2012).…”

Section: Preventing Dropoutmentioning

confidence: 99%

Predicting and preventing dropout in research, assessment and treatment with refugees

Semmlinger

Ehring

2021

Clin Psychology and Psychoth

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Refugees and asylum seekers are exposed to multiple burdensome experiences and suffer from ongoing post‐migration stressors that are known to affect the physical and mental health. In psychological treatment offered to refugees and asylum seekers, dropout is an important challenge. The current practice‐oriented review aims to provide for the first time knowledge on the prevalence, prediction and prevention of dropout in psychological treatment for refugees and asylum seekers. Due to the limited empirical evidence for this specific population, we synthesized refugee‐specific research but also reviewed the existing evidence on dropout from treatment in general and specifically discuss how the findings can be adapted to refugee populations. The review integrates literature from online databases, grey literature, hand search and expert contacts. Prevalence rates of dropout from psychological treatment in Western samples are reported at about 20%. For refugees and asylum seekers, evidence from single efficacy trials showed considerable variability in dropout rates (0%–64.7%). Further, for refugees and asylum seekers, specific sociodemographic variables, high initial impairment, deviating expectations and perceptions of mental health and psychological treatment, as well as external barriers seem to be important predictors for dropout. To prevent dropout, it is important to develop and promote cultural competencies, adapt the treatment to refugee‐specific needs and focus on role induction, preparation for treatment, fostering the therapeutic alliance and strengthening hope. Future specific research on dropout in treatment offered to refugees and asylum seekers is needed.

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Matching research and practice: Prediction of individual patient progress and dropout risk for basic routine outcome monitoring

Cited by 10 publications

References 67 publications

Prevalence and prediction of dropout during depression treatment in routine outpatient care: an observational study

Prevalence and prediction of dropout during depression treatment in routine outpatient care: an observational study

Data-Informed Case Formulation with the Trier Treatment Navigator

Predicting and preventing dropout in research, assessment and treatment with refugees

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