Elsevier

Journal of Affective Disorders

Volume 213, 15 April 2017, Pages 78-85
Journal of Affective Disorders

Research paper
A prognostic index (PI) as a moderator of outcomes in the treatment of depression: A proof of concept combining multiple variables to inform risk-stratified stepped care models

https://doi.org/10.1016/j.jad.2017.02.010Get rights and content

Highlights

  • Many patients with depression respond to treatments of different intensity.

  • Developed prognostic index to guide selection of treatments of different intensity.

  • For patient with a poorer prognosis, CBT was superior to brief therapy and TAU.

  • No differences for those with a good prognosis, who comprised most of the sample.

Abstract

Background

Prognostic indices (PIs) combining variables to predict future depression risk may help guide the selection of treatments that differ in intensity. We develop a PI and show its promise in guiding treatment decisions between treatment as usual (TAU), treatment starting with a low-intensity treatment (brief therapy (BT)), or treatment starting with a high-intensity treatment intervention (cognitive-behavioral therapy (CBT)).

Methods

We utilized data from depressed patients (N=622) who participated in a randomized comparison of TAU, BT, and CBT in which no statistically significant differences in the primary outcomes emerged between the three treatments. We developed a PI by predicting depression risk at follow-up using a LASSO-style bootstrap variable selection procedure. We then examined between-treatment differences in outcome as a function of the PI.

Results

Unemployment, depression severity, hostility, sleep problems, and lower positive emotionality at baseline predicted a lower likelihood of recovery across treatments. The PI incorporating these variables produced a fair classification accuracy (c=0.73). Among patients with a high PI (75% percent of the sample), recovery rates were high and did not differ between treatments (79–86%). Among the patients with the poorest prognosis, recovery rates were substantially higher in the CBT condition (60%) than in TAU (39%) or BT (44%).

Limitations

No information on additional treatment sought. Prospective tests needed.

Conclusion

Replicable PIs may aid treatment selection and help streamline stepped models of care. Differences between treatments for depression that differ in intensity may only emerge for patients with the poorest prognosis.

Introduction

There are a wide variety of treatments for depression. These treatments differ in how intense they are in terms of investment and resources required from patients and providers but the average superiority of higher-intensity treatments relative to lower-intensity ones appears to be small. For example, the combination of antidepressants and psychotherapy appears to be superior to either treatment as a monotherapy but that difference is small (Khan et al., 2012). Similarly, the superiority of antidepressants relative to placebos appears limited (Kirsch and Sapirstein, 1998) and the superiority of evidence-based psychotherapy relative to treatment as usual appears to be small as well (Flückiger et al., 2014, Wampold et al., 2011). Despite the availability and approximate equivalence of many treatments for depression, current models for the delivery of care are inadequate. At one end of the spectrum, many patients receive higher intensity interventions than they require to experience symptom relief (Lorenzo-Luaces et al., 2015, Lovell and Richards, 2000). Conversely, many do not receive the level of care that they might require to experience symptom relief (Kocsis et al., 2008, Lecrubier, 2007).

It is difficult to match patients to an appropriate level of care because depression is extremely heterogeneous in presentation and prognosis (Lorenzo-Luaces, 2015, Parker, 2005). We describe an approach, conceptually motivated by research on risk-stratified care in breast cancer (Akay et al., 2012, Chen et al., 2004; Chen et al., 2005; Huang et al., 2006), for combining variables to create a prognostic index (PI) that can be used when selecting between treatments that differ in intensity. PIs can be thought of as predictive of patients’ symptom status in a future time frame and they can also be used to determine the intensity level of care a patient should receive (see Delgadillo et al. (2016) and Garber et al. (2016)).

In the treatment of breast cancer after neoadjuvant chemotherapy, mastectomies are known to be a more aggressive treatment than breast-conservation therapy (BCT), which preserves a part of the breast tissue for cosmetic purposes. While these treatments clearly differ in intensity, on average there are only small (1–9%) differences in 5–10 year recurrence rates between the two treatments (Morris et al., 1997, van Dongen et al., 2000). Although this average difference is small, it is possible that BCT might be indicated for patients with less aggressive cancers whereas mastectomies, the more aggressive or higher intensity treatment, are better-suited for patients with more aggressive illnesses. This possibility was explored by Huang et al. (2006) who analyzed treatment differences according to a previously-developed PI (Chen et al., 2005). Their PI consisted of a score, ranging from 0 to 4, which indicated the number of risk factors for a cancer recurrence (see Chen et al., 2004). For patients with a low predicted risk of recurrence, recurrence rates were low and did not differ between the treatments (12% for BCT, 9% for mastectomy). For patients with a score of 3–4, however, recurrence rates were significantly higher for those treated with BCT (61%) than for those treated with mastectomy (19%). This pattern of results were subsequently replicated by Akay et al. (2012) who reported no differences between BCT and mastectomy for patients who were at a low risk of recurrence but a large difference in favor of mastectomy (6% vs. 32%) for patients with a high risk score. We propose that a similar phenomenon occurs in the treatment of depression and other disorders. Specifically, comparisons of treatments that appear to differ in intensity (e.g., combination treatment vs. psychotherapy alone or long term therapy vs. brief interventions) may produce small differences because many patients in treatment trials can be expected to benefit from minor interventions and it is only patients with an overall poorer prognosis that will evidence more benefit from the higher-intensity treatment.

The idea of using baseline variables to predict response to depression treatments is not new. For example, the efficacy of antidepressants relative to placebos seems to be limited to patients with more severe depressions (Barbui et al., 2011, Fournier et al., 2010, Khan et al., 2002, Kirsch et al., 2008). Similar findings have been reported for psychotherapy (Driessen et al., 2010). Likewise, the efficacy of combination treatment relative to antidepressants alone or psychotherapy alone seems to be limited to patients with more severe depression (Hollon et al., 2014, Thase et al., 1997). Some stepped models of care for depression make use of findings such as these. In stepped models of care, most patients are started on lower-intensity treatment options before entering more intense care. Illness severity, however, can be used as an indicator to bypass lower intensity treatments for more intensive ones. For example, the NICE guidelines in the United Kingdom (National Institute for Clinical Excellence, 2004) do not endorse the use of combined treatment as the initial treatment strategy in mild depression and instead suggest further assessment, a low-intensity intervention, or a monotherapy.

Research suggests that a multitude of variables, other than symptom severity, account for treatment response (see Kessler et al. (2016a)). For example, in a large dataset pooling comparisons of antidepressants vs. placebos, Nelson et al. (2013) reported that there were no statistically significant differences in outcomes for patients whose depression was non-chronic. However, a large difference (d=0.70) emerged for the subset of patients who had chronic and severe depression. Similarly, Thase et al. (1997) reported that, for individuals with mild depression, combining an antidepressant and cognitive-behavioral therapy (CBT) or interpersonal psychotherapy (IPT) was not superior to psychotherapy alone. However, for patients with severe and recurrent depression, combination treatment yielded superior recovery rates (60% vs. 19%; see also Hollon et al., 2014). Kessler et al. (2016a) conducted a review of patient self-reported variables that have been replicated at least once as predictors or moderators of treatment outcomes. According to these authors, predictors of depression can broadly be grouped into demographics (e.g., age, unemployment), features of depression (e.g., severity, prior episodes), co-morbidities (e.g., anxiety, sleep problems), stress history (e.g., childhood maltreatment), personality features (e.g., high negative affect), and other features (e.g., impairment). Broadly speaking, it appears as if the variables that predict overall depression treatment response are variables that predict the persistence and severity of depression. These are usually variables that relate to underlying vulnerabilities to depression and social-interpersonal functioning. However, an issue that obscures the interpretation of the existing literature is that most studies only explore single variables (DeRubeis et al., 2014a; Kessler et al., 2016b).

Kraemer (2013) asserted that “if there are multiple [moderators] related to the same underlying construct, these … should be combined in order both to increase the reliability of the measurement of that construct and to avoid problems associated with multicollinearity in combining them.” (p. 1969) DeRubeis et al. (2014b) argued for the existence of one such construct when they discussed patient response profiles. According to these authors, patients differ in the extent to which they can benefit from the active effects of treatments and processes. Patients who are likely to improve much irrespective of interventions, as is characteristic of samples of patients with depression, are unlikely to reveal specific intervention effects. Preliminary evidence supports the use of combined moderator variables like PIs in guiding treatment selection in mental health treatment (see Cloitre et al., 2016; Delgadillo et al., 2016). In the context of a clinical trial in which there were no differences in overall outcome between treatment as usual (TAU), stepped care starting with brief therapy (BT) or stepped care starting with CBT, we hypothesized that prognostic status would moderate the treatment differences. Our study is meant as a proof of concept in the treatment of depression. We hypothesize that among patients who, based on pre-treatment characteristics, are predicted to do well, few if any differences in outcome will emerge between the treatments we studied. However, among patients with a poorer prognosis, the more intensive CBT should outperform TAU and BT.

Section snippets

Methods

The aim of the trial from which these data were drawn was to compare TAU to each of two stepped care regimens. One of the regimens began with a low-intensity treatment (i.e., BT) and the other began with a high-intensity treatment (i.e., CBT; Van Straten et al., 2006).

The trial was designed so as to mimic conditions found in routine care settings. Patients were sampled from a representative subsample of 7 of the 47 regional mental health care centers (MHCs) that provide mental health care in

2.1 Outcomes and missing data

Patients were interviewed at baseline and then every 3 months, irrespective of the timing of treatment initiation and termination. The primary outcome for the current analyses was recovery, defined by the absence of MDD status, at the 18–24 month follow-up. This final follow-up interview occurred at least 18 months after enrollment in the study. The first 59 patients who entered the study were followed for 24 months; subsequent enrollees were followed for 21 months (n=105) or 18 months (n=256).

2.2 Analytic approach

Analyses were conducted using the R programming language (R Core Team, 2014). A total of 23 variables were available for analysis. These included demographics, clinical variables, personality traits as assessed by the NEO Five-Factor Inventory (Costa and MacCrae, 1992), and subscales of the Symptom Checklist 90 (Derogatis, 1983; Table 1). In choosing which variables to explore as predictors of treatment outcomes, we cross-referenced a recent review by Kessler et al. (2016a) on predictors of

Results

Descriptive statistics at baseline are presented in Table 1. There were no statistically significant differences in the rate of MDD recovery at the 18–24 month follow-up between TAU (68.4%) and either of the stepped care conditions (BT=75.4%, OR=1.33, 95% CI=0.84 –2.07, B =0.28, SE=0.23, χ2=1.47, p=0.23; CBT=74.5%, OR=1.38, 95% CI=0.91 –2.11, B =0.32, SE=0.22, χ2=2.25, p=0.13). Five of the 13 potential predictor variables submitted to the LASSO procedure were retained. Being unemployed, having

Discussion

We described a procedure that yields a prognostic index that can be used in determining which patients are most likely to benefit more from a high-intensity intervention, relative to a lower intensity one. We tested this procedure in the context of a randomized trial comparing TAU, BT, and CBT. Despite the trial being adequately powered to detect treatment differences and the fact that the treatments differed in intensity, on average, there were only small, nonsignificant differences in

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