A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression

Round 1

Reviewer 1 Report

The writers need to revise the conclusion. The conclusion should summarize all the important aspect of this research. Authors, you can do better. The figures ( 1, 2) are well illustrated. 

Author Response

We are grateful to the reviewer for their kind words and suggestion to better summarize all important aspects of this research in the conclusions section of the manuscript. We consider the revised version considerably improved. 

The conclusions section now reads as follows: 

The potential utility of patient stratification approaches for supporting clinical decision making have been demonstrated in areas of physical healthcare, and to a lesser extent in predicting response in mental health treatments, but research has not considered the value of stratification for predicting long-term outcomes. This novel study presents a patient stratification approach that identified subgroups of participants who were more and less likely to either relapse or follow a continued chronic course of depressive illness following treatment for depression. Seven profiles were identified using baseline severity, duration of illness, history of antidepressant use, employment status, and age. Profiles included young people presenting with their first episode, older people with more chronic illness, and individuals with severe symptoms and chronic illness pre-treatment. Substantial differences in the likelihood of relapse and following a continued chronic course between profiles were observed, with some profiles at particular risk of both later relapse and of chronic course of illness. Treatment type was not consistently associated with different outcomes in most profiles perhaps due to the numbers of participants in stratified profile-by-treatment-groups being low overall, making it difficult to confidently ascertain differences. However, members of profile five appeared to have better outcomes with psychological therapies compared to either antidepressants or treatment as usual, and there was some evidence that members of profiles four and six may potentially have had better outcomes from psychological therapy also. This study demonstrates the potential utility of patient identification approaches for ascertaining the likelihood of different long-term outcomes, and thereby supporting clinical decision making. Findings might inform subgroups of patients for whom the provision of addition treatments are likely to be needed due to poorer initial response, as well as interventions to reduce the risk of relapse for those who do initially respond.

 

For details of the changes made to this section (with track changes on) please see the attached 

Author Response File: Author Response.docx

Reviewer 2 Report

Dear Authors,

I read your work entitled "A patient stratification approach to identifying the likelihood of continued chronic depression and relapse following treatment for depression", and i founded it very interesting.

I have no further recommendations to do. 

 

Thank you!

Author Response

We are very grateful to the reviewer for their review and interest in this area of research. We have revised the conclusions section as per comments from another reviewer and consider this to have improved the manuscript. 

The conclusions now read: 

The potential utility of patient stratification approaches for supporting clinical decision making have been demonstrated in areas of physical healthcare, and to a lesser extent in predicting response in mental health treatments, but research has not considered the value of stratification for predicting long-term outcomes. This novel study presents a patient stratification approach that identified subgroups of participants who were more and less likely to either relapse or follow a continued chronic course of depressive illness following treatment for depression. Seven profiles were identified using baseline severity, duration of illness, history of antidepressant use, employment status, and age. Profiles included young people presenting with their first episode, older people with more chronic illness, and individuals with severe symptoms and chronic illness pre-treatment. Substantial differences in the likelihood of relapse and following a continued chronic course between profiles were observed, with some profiles at particular risk of both later relapse and of chronic course of illness. Treatment type was not consistently associated with different outcomes in most profiles perhaps due to the numbers of participants in stratified profile-by-treatment-groups being low overall, making it difficult to confidently ascertain differences. However, members of profile five appeared to have better outcomes with psychological therapies compared to either antidepressants or treatment as usual, and there was some evidence that members of profiles four and six may potentially have had better outcomes from psychological therapy also. This study demonstrates the potential utility of patient identification approaches for ascertaining the likelihood of different long-term outcomes, and thereby supporting clinical decision making. Findings might inform subgroups of patients for whom the provision of addition treatments are likely to be needed due to poorer initial response, as well as interventions to reduce the risk of relapse for those who do initially respond. 

Reviewer 3 Report

This is a great paper investigating seven clinical profiles in patients diagnosed with depression in primary care.

What it is really relevant is that they described profiles according to baseline characteristics, leading the clinicians to predict potential treatment strategies individualized for each patient profile.

I consider that the introduction is well written and it gives a good presentation of the topic. I would recommend to include some references about the differences about personalizing treatment according to clinical characteristics and biological features, for instance, the pharmacogenetics profiles of patients. Are both really different or linked ar baseline or before treatment?

Are there any gender differences in the clinical expression of depression capable of prediction treatment outcomes in depressive disorders. Are comorbid anxiety symptoms with or without somatic concerns predicting outcomes?

Menopause is a period of time in Women's life when women can experience a worsening of depressive symptoms or the occurrence of newly diagnosed depressive symptoms.Should then menopausal status be considered a baseline characteristics to be defined in a specific profile?

I would like to encourage the authors to include these reflections on the introduction and discussion.

Which kind of differences may appear to exist between primary care and hospital based samples?

This should be discussed in the discussion  or limitations section.

Author Response

Response to reviewer.

 

This is a great paper investigating seven clinical profiles in patients diagnosed with depression in primary care. What it is really relevant is that they described profiles according to baseline characteristics, leading the clinicians to predict potential treatment strategies individualized for each patient profile.

 

Author response: Thank you for your review of our manuscript and kind words. We are grateful for the suggestions and have addressed them as detailed below.

 

I consider that the introduction is well written and it gives a good presentation of the topic. I would recommend to include some references about the differences about personalizing treatment according to clinical characteristics and biological features, for instance, the pharmacogenetics profiles of patients. Are both really different or linked ar baseline or before treatment?

 

Author response:

We are grateful for this suggestion and have added a comment about pharmacogenetics in the Introduction. It may be informative to note too that the methodological approach taken here applies equally to all forms of data (so long as they can reasonably be combined in a dataset).

 

Introduction, Page 4, Paragraph 2, Lines 5-7: “Although pharmacogenetics may hold promise for personalizing antidepressant treatments [15,16], it is becoming increasingly common that antidepressants are prescribed indefinitely for all patients with a history of previous relapses, to mitigate the risks of chronic illness and relapse [11,12,17,18].”

 

Are there any gender differences in the clinical expression of depression capable of prediction treatment outcomes in depressive disorders. Are comorbid anxiety symptoms with or without somatic concerns predicting outcomes?

 

Author response: We did not include gender in the current profiling model as through our own studies we have found that gender is not independently associated with treatment outcomes in primary care [1] but also that when we have included in profiling in alternative primary datasets it is not discriminating between profiles [2]. We have added to the Discussions section in combination with our response to the next comment, as detailed below.

              

1. Buckman, J.E.J.; Saunders, R.; Stott, J.; Arundell, L.-L.; O’Driscoll, C.; Davies, M.; Eley, T.C.; Hollon, S.D.; Kendrick, T.; Ambler, G.; et al. The role of Age, Gender, and Marital Status in prognosis for adults with depression: An Individual Patient Data Meta- analysis. Epidemiol. Psychiatr. Sci. 2021.
2. Saunders, R.; Cape, J.; Fearon, P.; Pilling, S. Predicting treatment outcome in psychological treatment services by identifying latent profiles of patients. J. Affect. Disord. 2016, 197, 107–115, doi:10.1016/j.jad.2016.03.011.

 

 

Menopause is a period of time in Women's life when women can experience a worsening of depressive symptoms or the occurrence of newly diagnosed depressive symptoms. Should then menopausal status be considered a baseline characteristics to be defined in a specific profile?

 

Author response: This is an interesting suggestion, unfortunately menopause status was not available in the dataset to be able to explore this. However, we have added discussion of this in the limitations section as follows:

 

Limitations, Page 18, Paragraph 4, Lines 5-11: “For example, childhood maltreatment, neuroticism, rumination and interpersonal stress have all been linked to increased risk of relapse [6]. As well as using these factors in future analyses, utility of profiling approaches to identify at risk groups may also be enhanced with subgroup specific factors not available here. For example, although gender is not independently prognostic for depression treatment outcomes or course [6,64], and does not typically discriminate between latent profiles of patients with depression [39,65], data on pregnancy or menopause may have helped further refine the latent profiles identified in this study [66–68].”

 

I would like to encourage the authors to include these reflections on the introduction and discussion.

Which kind of differences may appear to exist between primary care and hospital based samples?

This should be discussed in the discussion or limitations section.

 

Author response: Thank you for this important point. We have added to the limitations section as you suggest, with the following addition:

 

Limitations, Page 18, Paragraph 1, Final 4 Lines: “Further, primary care is one of the commonest routes into treatment for adults with depression [3,63] so results here maybe generalizable to large proportions of patients with depression. However, the observed profiles may not generalize as well to secondary or inpatient care settings, or to patients seen in different treatment contexts.”

Author Response File: Author Response.DOCX