Elsevier

Journal of Affective Disorders

Volume 227, February 2018, Pages 854-860
Journal of Affective Disorders

Research paper
Development of a prognostic model for predicting depression severity in adult primary patients with depressive symptoms using the diamond longitudinal study

https://doi.org/10.1016/j.jad.2017.11.042Get rights and content

Highlights

  • Model developed to predict future depression severity in primary care patients.

  • Prognostic model is brief and easily administered in a busy primary care setting.

  • Model using psychosocial items is embedded in a clinical prediction tool (CPT).

  • CPT tailors type and intensity of treatment to predicted depression severity.

  • Is a systematic approach designed to support clinician treatment decision making.

Abstract

Background

Depression trajectories among primary care patients are highly variable, making it difficult to identify patients that require intensive treatments or those that are likely to spontaneously remit. Currently, there are no easily implementable tools clinicians can use to stratify patients with depressive symptoms into different treatments according to their likely depression trajectory. We aimed to develop a prognostic tool to predict future depression severity among primary care patients with current depressive symptoms at three months.

Methods

Patient-reported data from the diamond study, a prospective cohort of 593 primary care patients with depressive symptoms attending 30 Australian general practices. Participants responded affirmatively to at least one of the first two PHQ-9 items. Twenty predictors were pre-selected by expert consensus based on reliability, ease of administration, likely patient acceptability, and international applicability. Multivariable mixed effects linear regression was used to build the model.

Results

The prognostic model included eight baseline predictors: sex, depressive symptoms, anxiety, history of depression, self-rated health, chronic physical illness, living alone, and perceived ability to manage on available income. Discrimination (c-statistic =0.74; 95% CI: 0.70–0.78) and calibration (agreement between predicted and observed symptom scores) were acceptable and comparable to other prognostic models in primary care.

Limitations

More complex model was not feasible because of modest sample size. Validation studies needed to confirm model performance in new primary care attendees.

Conclusion

A brief, easily administered algorithm predicting the severity of depressive symptoms has potential to assist clinicians to tailor treatment for adult primary care patients with current depressive symptoms.

Introduction

Mental health disorders account for 7.4% of the total disease burden with depression the main contributor (Whiteford et al., 2013). Most people seeking help for depressive symptoms are treated in primary care, (Australian Bureau of Statistics, 2011, Australian Institute of Health and Welfare, 2015) and around one quarter of primary care attendees report current depressive symptoms (Gunn et al., 2008; Herrman et al., 2002). Ten percent of attendees with subthreshold symptoms and no history of depression develop major depression over six months (Davidson et al., 2015) and 21% over two years (Karsten et al., 2011). Nearly 60% of those with current major depression meet criteria for major depression at least once over the next three years (Stegenga et al., 2012). Of primary care attendees satisfying criteria for major depression, around 50% are estimated to also have current anxiety (Gunn et al., 2008).

In a busy primary care practice, it can be difficult for clinicians to identify which patients with current depressive symptoms are likely to recover and which are likely to worsen, and to provide treatment appropriate for each trajectory. Primary care clinicians are often criticised for either over-treating patients with subthreshold depression (Davidson et al., 2015) or for not providing minimally adequate treatment for patients with major depression (Wang et al., 2007). One systematic approach to informing clinician's treatment decisions is to use a clinical prediction tool.

A clinical prediction tool is built around a prognostic model that uses clinical and psychosocial information to predict future depression severity. The clinical prediction tool uses the information provided by the prognostic model to stratify patients into different depression severity groups. Type and intensity of treatment is tailored to each group to optimise clinical outcomes with the least intensive treatment (Rubenstein et al., 2007). To date, no such clinical prediction tool exists that can be used to stratify primary care patients with depressive symptoms into different treatment options based on their predicted depressive symptoms.

We also conducted a literature search to identify existing prognostic models that would be suitable for inclusion in a clinical prediction tool that predicts future depressive symptoms in primary care patients with depressive symptoms, ranging from sub-threshold to severe. The literature search identified nine different prognostic models for depression developed using data from five unique primary care studies. Only two of the models focussed on predicting future depression within samples experiencing current depressive symptoms (Dowrick et al., 2011; Rubenstein et al., 2007). Of the remaining studies, three developed or validated prognostic models to predict the onset of depression (primary prevention) (Bellon et al., 2011; King et al., 2013; King et al., 2008), two studies developed a prediction rule to screen for the presence of current mood disorders (Vohringer et al., 2013; Zuithoff et al., 2009) and two studies developed algorithms to predict treatment response to antidepressants(Chekroud et al.; Perlis, 2013).

Of the two studies that developed prognostic models to predict future depression among people with current depressive symptoms, neither was suitable for inclusion in a clinical prediction tool (Dowrick et al., 2011; Rubenstein et al., 2007). In the first study, the prognostic model developed using trial data from 220 participants in the THREAD study was insufficiently robust to use in the clinical prediction tool because it had low prognostic accuracy (Dowrick et al., 2011). Furthermore, the development sample only included participants with mild to moderate depression, thus could not be generalised to new primary care patients who present with severe depression. The second study described the development of the Diagnostic Prognostic Index, which was derived using data from 1471 primary care attendees with current major depression participating in one of four randomised trials (Rubenstein et al., 2007). The Diagnostic Prognostic Index was also unsuitable because the development sample excluded patients with subthreshold depression. Given that in primary care subthreshold depression makes up the largest group of patients presenting with depressive symptoms, the prognostic model would not be generalisable to this population. Additionally, the Diagnostic Prognostic Index, consisting of over 60 items, would be too lengthy to administer in a primary care waiting room or during a consultation, limiting its usability and usefulness in routine clinical practice (Toll et al., 2008).

This study aimed to develop a prognostic model for future depression severity among adult primary care attendees with current depressive symptoms, ranging from sub-threshold to severe depression. To increase the utility and uptake of the clinical prediction tool we aimed to develop a model with relatively few items that were easy to collect in routine practice (Toll et al., 2008).

Section snippets

Source of data

We developed a prognostic model using data from the diamond (Diagnosis, Management and Outcomes of Depression) cohort study. Diamond is a 10-year prospective study of adult primary care patients with depressive symptoms (Gunn et al., 2008).

Cohort participants were recruited from 30 general practitioners (GPs) working at 30 different urban, regional and rural practices in Victoria, Australia between January and December 2005. Details of recruitment are published elsewhere (Gunn et al., 2008).

Participants

Distribution of participant characteristics in the development sample is shown in Table 1. The fraction of missing responses for each predictor variable for the development sample was small, ranging from zero for gender to 2.2% (13/593) for ever being afraid of a partner. Most participants (91%, 538/593) had complete data for the 20 candidate predictor variables, including the sensitive questions. Fourteen percent (82/593) had missing values for the outcome.

Model development

Table 2 shows the estimated

Discussion

We developed a brief, easily administered prognostic model to predict depression severity at three months in adult primary care patients with current depressive symptoms. The eight predictors were sex, depressive symptoms, current anxiety, history of depression, self-rated health, chronic physical illness, living alone, and perceived ability to manage available on income. The final model consists of 17 questions, nine of which are from the PHQ-9. Including potentially sensitive or distressing

Conclusion

We developed a brief, easily administered prognostic model for use in primary care across the depressive symptom range to predict depression severity at three months. A clinical prediction tool utilising this model has the potential to assist clinicians manage the large burden of mental health symptoms presenting to primary care. Widespread implementation of tools like this offers the best chance of ensuring that limited resources are allocated based on need.

Acknowledgements

We acknowledge the 30 dedicated general practitioners, their patients, and practice staff for making this research possible. We thank the cohort participants for their ongoing involvement in the study. We also thank the diamond project team and associate investigators involved in the study: A/Prof. Lena Sanci, A/Prof Cathy Mihalopoulos, Ms Maria Potiriadis, Ms Konstancja Densley, Ms Aves Middleton, and the casual research staff.

The authors submit this manuscript on behalf of the diamond study

Contributors

PC, SD and JG drafted the manuscript. PC conducted the analyses and produced the tables and figures. SD, GG, CD, FG, KH, HH, JG, and PC formed the multi-disciplinary expert group to identify and select candidate predictor variables. RW provided statistical expertise on the development of the prognostic model. All the authors contributed to development and drafting of the manuscript

Funding sources

The diamond study was initiated with pilot funding from the Victoria Centre of Excellence in Depression, Anxiety

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