The clinical characterization of the adult patient with depression aimed at personalization of management

Abstract

Depression is widely acknowledged to be a heterogeneous entity, and the need to further characterize the individual patient who has received this diagnosis in order to personalize the management plan has been repeatedly emphasized. However, the research evidence that should guide this personalization is at present fragmentary, and the selection of treatment is usually based on the clinician's and/or the patient's preference and on safety issues, in a trial‐and‐error fashion, paying little attention to the particular features of the specific case. This may be one of the reasons why the majority of patients with a diagnosis of depression do not achieve remission with the first treatment they receive. The predominant pessimism about the actual feasibility of the personalization of treatment of depression in routine clinical practice has recently been tempered by some secondary analyses of databases from clinical trials, using approaches such as individual patient data meta‐analysis and machine learning, which indicate that some variables may indeed contribute to the identification of patients who are likely to respond differently to various antidepressant drugs or to antidepressant medication vs. specific psychotherapies. The need to develop decision support tools guiding the personalization of treatment of depression has been recently reaffirmed, and the point made that these tools should be developed through large observational studies using a comprehensive battery of self‐report and clinical measures. The present paper aims to describe systematically the salient domains that should be considered in this effort to personalize depression treatment. For each domain, the available research evidence is summarized, and the relevant assessment instruments are reviewed, with special attention to their suitability for use in routine clinical practice, also in view of their possible inclusion in the above‐mentioned comprehensive battery of measures. The main unmet needs that research should address in this area are emphasized. Where the available evidence allows providing the clinician with specific advice that can already be used today to make the management of depression more personalized, this advice is highlighted. Indeed, some sections of the paper, such as those on neurocognition and on physical comorbidities, indicate that the modern management of depression is becoming increasingly complex, with several components other than simply the choice of an antidepressant and/or a psychotherapy, some of which can already be reliably personalized.

The clinical characterization of the patient with primary psychosis aimed at personalization of management

“…One could argue that clinicians do not like to use standardized assessment instruments in their routine practice, and that they often do not even use formal diagnostic systems in that practice. However, as we already pointed out in the paper on the clinical characterization of the patient with a diagnosis of depression 14 , our experience with the above diagnostic systems is very telling in this respect. Although those systems are seldom formally used in routine practice, several elements of their description of major mental disorders have been incorporated by most clinicians in their personal prototypes of those disorders, so that the reliability of psychiatric diagnosis has become today, although certainly far from optimal, much better than it was in the 1970s.…”

“…The present paper, which has been produced in parallel with a similar one focusing on depression 14 , aims to address the situation we have just described. Its main objectives are: a) to reinforce the emerging awareness of the need to personalize the management of patients with primary psychosis, taking into account all the available treatment modalities whose efficacy is supported by research evidence; b) to help in the identification of the salient domains to be considered in the characterization of the individual patient with primary psychosis aimed at personalization of management (see Table 1); c) to help in the selection of simple assessment instruments that can already be considered for use in clinical practice today, and can be included in comprehensive batteries of measures to be tested in large observational studies in order to guide the development of standardized decision tools 15 ; and d) to encourage a clinical practice that is recovery‐oriented as well as evidence‐based.…”

Examining the association between exposome score for schizophrenia and functioning in schizophrenia, siblings, and healthy controls: Results from the EUGEI study

“…As a potential marker of functioning, ES-SCZ can be used for the severity stratification in large-scale clinical trials and observational studies [19]. Furthermore, ES-SCZ can be integrated into clinical characterization [55] and future transdiagnostic staging models [56]. However, prospective controlled studies, ideally conducted in first episode psychosis (FEP) cohorts, are required to assess the prognostic performance of ESC-SCZ for predicting outcome in psychotic disorders.…”

The promise of machine learning in predicting treatment outcomes in psychiatry

“…We also avoid a distinction between moderators versus mediators of treatment outcomes, or whether a model predicts outcomes specifically for a treatment versus others or predicts outcomes more generically for multiple treatments 8 . Finally, we do not aim to review the many sociodemographic and clinical variables that have been or can be used for prediction of treatment response in psychiatry, which generally have the most predictive power and are cheapest to collect 9,10 .…”