Predicting clinical outcome to specialist multimodal inpatient treatment in patients with treatment resistant depression

TY - JOUR

T1 - Predicting clinical outcome to specialist multimodal inpatient treatment in patients with treatment resistant depression

AU - Taylor, Rachael W

AU - Coleman, Jonathan R I

AU - Lawrence, Andrew J

AU - Strawbridge, Rebecca

AU - Zahn, Roland

AU - Cleare, Anthony J

N1 - Funding Information: This paper represents independent research part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The funder had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. Funding Information: In the last three years, AJC has received honoraria for speaking from Lundbeck and Janssen, honoraria for consulting from Allergan, Livanova, Janssen and NICE, sponsorship for attending an academic conference from Janssen and research grant support from the Medical Research Council (UK), Wellcome Trust (UK), the National Institute for Health Research (UK) and Protexin Probiotics International Ltd. RS has received honorarium for speaking from Lundbeck. RZ provides private psychiatric services at The London Depression Institute, is an honorary PI at D'OR Institute for Research & Education, Rio de Janeiro, a not-for-profit organisation, on the Advisory Board for Science – a US not-for-profit organisation, has consulted for Fortress Biotech, is a co-investigator on a Livanova-funded study, has received speaker honoraria for medical symposia and educational activities from Lundbeck and Janssen. Collaborates with Janssen. Collaborates with EMIS PLC and Alloc Modulo Ltd. No other authors have conflicts of interest to declare. Publisher Copyright: © 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/8/1

Y1 - 2021/8/1

N2 - Background: Treatment resistant depression (TRD) poses a significant clinical challenge, despite a range of efficacious specialist treatments. Accurately predicting response a priori may help to alleviate the burden of TRD. This study sought to determine whether outcome prediction can be achieved in a specialist inpatient setting. Methods: Patients at the Affective Disorders Unit of the Bethlam Royal Hospital, with current depression and established TRD were included (N = 174). Patients were treated with an individualised combination of pharmacotherapy and specialist psychological therapies. Predictors included clinical and sociodemographic characteristics, and polygenic risk scores for depression and related traits. Logistic regression models examined associations with outcome, and predictive potential was assessed using elastic net regularised logistic regressions with 10-fold nested cross-validation. Results: 47% of patients responded (50% reduction in HAMD-21 score at discharge). Age at onset and number of depressive episodes were positively associated with response, while degree of resistance was negatively associated. All elastic net models had poor performance (AUC<0.6). Illness history characteristics were commonly retained, and the addition of genetic risk scores did not improve performance. Limitations: The patient sample was heterogeneous and received a variety of treatments. Some variable associations may be non-linear and therefore not captured. Conclusions: This treatment may be most effective for recurrent patients and those with a later age of onset, while patients more severely treatment resistant at admission remain amongst the most difficult to treat. Individual level prediction remains elusive for this complex group. The assessment of homogenous subgroups should be one focus of future investigations.

AB - Background: Treatment resistant depression (TRD) poses a significant clinical challenge, despite a range of efficacious specialist treatments. Accurately predicting response a priori may help to alleviate the burden of TRD. This study sought to determine whether outcome prediction can be achieved in a specialist inpatient setting. Methods: Patients at the Affective Disorders Unit of the Bethlam Royal Hospital, with current depression and established TRD were included (N = 174). Patients were treated with an individualised combination of pharmacotherapy and specialist psychological therapies. Predictors included clinical and sociodemographic characteristics, and polygenic risk scores for depression and related traits. Logistic regression models examined associations with outcome, and predictive potential was assessed using elastic net regularised logistic regressions with 10-fold nested cross-validation. Results: 47% of patients responded (50% reduction in HAMD-21 score at discharge). Age at onset and number of depressive episodes were positively associated with response, while degree of resistance was negatively associated. All elastic net models had poor performance (AUC<0.6). Illness history characteristics were commonly retained, and the addition of genetic risk scores did not improve performance. Limitations: The patient sample was heterogeneous and received a variety of treatments. Some variable associations may be non-linear and therefore not captured. Conclusions: This treatment may be most effective for recurrent patients and those with a later age of onset, while patients more severely treatment resistant at admission remain amongst the most difficult to treat. Individual level prediction remains elusive for this complex group. The assessment of homogenous subgroups should be one focus of future investigations.

UR - http://www.scopus.com/inward/record.url?scp=85106875104&partnerID=8YFLogxK

U2 - 10.1016/j.jad.2021.04.074

DO - 10.1016/j.jad.2021.04.074

M3 - Article

C2 - 34044338

SN - 0165-0327

VL - 291

SP - 188

EP - 197

JO - Journal of Affective Disorders

JF - Journal of Affective Disorders

ER -