Value of monitoring negative emotional bias in primary care in England for personalised antidepressant treatment: a modelling study.
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2019
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Abstract
Depressed patients often focus on negative life events. Effective antidepressant therapy reverses this negative emotional bias (NEB) within 1 week. Clinical therapeutic effect usually requires 4-6 weeks. The value of implementing NEB monitoring for the personalisation of antidepressant therapy is unknown.To estimate the likely outcome and cost consequences of adopting the P1vital Oxford Emotional Test Battery (ETB) for this purpose in routine primary care in England.A hybrid decision analytic model (decision tree plus Markov model) was developed to estimate the cost-effectiveness of ETB monitoring versus no ETB over 52 weeks using quality-adjusted life years (QALYs). Differences in depression severity, episode type and analytical perspectives were considered. Input data were derived from relevant guidelines, literature, national databases, expert opinion and the developers for the year 2013. Multiple sensitivity analyses addressed uncertainty.The mean number of ETB tests is 2.162 per newly diagnosed patient and 2.166 per patient with recurrent depression. The incremental cost-effectiveness of ETB versus 'no ETB' is £4355/QALY from the healthcare perspective. From the broader societal perspective, ETB is more effective and cost saving.Monitoring negative emotional bias in primary care in England for personalised antidepressant treatment using ETB seems as an effective and cost-effective option under all considered scenarios (including worst case). Its main economic value seems to lie in reduced productivity loss as opposed to healthcare savings.The test supports accelerated application of evidence-based depression care. Further optimisation and implementation in the ongoing European PReDicT trial is ongoing.
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simon2019valueevidencebased
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| Authors | Simon, Judit;Harmer, Catherine J;Kingslake, Jonathan;Dawson, Gerard R;Dourish, Colin T;Goodwin, Guy M; |
| Journal | evidence-based mental health |
| Year | 2019 |
| DOI |
ebmental-2019-300109
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