Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

Abstract

Aims Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. Methods We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalized) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Results Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention versus control) or differential treatment outcomes (i.e., intervention A versus intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalized treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Conclusions Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Prediction of major depressive disorder onset in college students

“…First, response rates were moderate (61.0% at baseline; 57.5% at follow-up). It is likely that we will be able to improve on this performance in planned cross-national analyses using machine learning methods (Kessler et al, 2016(Kessler et al, , 2017. In addition, Finally, although we included a large set of known risk indicators for MDD onset, some important risk indicators were not assessed, such as subsyndromal depression, chronic somatic conditions, personality traits/disorders, psychotic experiences/disorders, poor self-perceived health, low emotion regulation skills, low self-esteem, low resilience, and neuroticism (Berking, Wirtz, Svaldi, & Hofmann, 2014;Cole & Dendukuri, 2003;Ebert, Hopfinger, & Berking, 2017;Korten, Comijs, Lamers, & Penninx, 2012;Pelkonen, Marttunen, Kaprio, Huurre, & Aro, 2008;Wild et al, 2016).…”

Developing a practical suicide risk prediction model for targeting high‐risk patients in the Veterans health Administration

“…the impact of treatment on a given patient) but also in relative treatment response (i.e. the specific treatment that is optimal for a given patient) and that a wide range of variables other than disorder severity predicts both types of differences (Kessler, van Loo et al, ).…”

The Generalizability of Randomized Controlled Trials of Self-Guided Internet-Based Cognitive Behavioral Therapy for Depressive Symptoms: Systematic Review and Meta-Regression Analysis (Preprint)

“…It is possible that self-guided iCBTs are less effective for patients who have more complex presentations, with symptom severity only being one index of case complexity [4]. For example, the presence of anxiety, and chronic depression duration have all been implicated in treatment outcomes in depression, and may relate to lower response to iCBT [36]. Although we did not find the use of psychosis as an exclusion criteria to be related to outcomes, psychotic depression is relatively rare and even studies allowing these patients into the trial may have had a low representation of psychotic depression.…”