Control charts for monitoring mood stability as a predictor of severe episodes in patients with bipolar disorder

Abstract

BackgroundRecurrent mood episodes and subsyndromal mood instability cause substantial disability in patients with bipolar disorder. Early identification of mood episodes enabling timely mood stabilization is an important clinical goal. This study investigates the ability of control chart methodology to predict manic and/or depressive episodes by applying Shewhart’s control rules to weekly self-reported scores from mania and depression questionnaires.MethodsShewhart’s control rules were applied to weekly self-reported scores from the Altman Self-Rating Mania Scale (ASRM) and the Quick Inventory of Depressive Symptomatology—Self-Report (QIDS) collected from 2001 to 2012 as part of the OXTEXT programme. Manic and depressive episodes were defined as an ASRM score ≥ 10 or a QIDS score ≥ 15, respectively. An episode-free run-in period of eight consecutive weeks without an episode of either type was used to calibrate control charts. Shewhart’s rules were then applied to follow-up data. Their sensitivity and positive predictive value for predicting manic or depressive episodes within the next 4 weeks were calculated focusing on the first episode. Secondary analyses varying control chart type, length of episode-free run-in period, time frames to evaluate diagnostic accuracy, thresholds defining either manic or depressive episodes, and missing data methods were performed.ResultsData from 146 participants (37% men) were included. The mean age was 43.4 (SD = 13.3) years. The median follow-up was 10 (IQR 5–40) weeks for mania and 10 (IQR 5–23) weeks for depression. A total of 53 (36%) participants had a manic episode and 67 (46%) had a depressive episode. For manic episodes, the sensitivity and positive predictive value of Shewhart’s control rules were 30% (95% CI 19–45%) and 7% (95% CI 5–9%), and for depressive episodes, 33% (95% CI 22–46%) and 9% (95% CI 6–12%), respectively. Results from secondary analyses were similar to these.ConclusionsTele-monitoring with control rules has the potential to predict about one-third of manic or depressive episodes before they occur, at the cost of a high false positive rate. Given the severe consequences of manic and depressive episodes, this trade-off may be desirable.Electronic supplementary materialThe online version of this article (10.1186/s40345-017-0116-2) contains supplementary material, which is available to authorized users.

A digital self‐report survey of mood for bipolar disorder

“…Over time, repeated measures can help in two ways: to understand symptom dynamics in BP and to forecast future moods. [8][9][10][11]54,55 Focusing on the latter, we examined whether personalized models could be built from an individual's past m and d scores in order to predict their future scores.…”

“…The most obvious are mobile apps to manage mood and deliver treatment for BP. [1][2][3][4][5][6] Other opportunities include the ability to build BP cohorts not limited by geography and to phenotype BP at finer time scales [7][8][9][10][11] with greater, inthe-moment context on an individual's life including their physiology. [12][13][14][15][16][17] To improve the successes of digital research, the community is looking for standardized reporting tools for BP symptoms in a digital setting.…”

Reporting guidelines on remotely collected electronic mood data in mood disorder (eMOOD)—recommendations

“…In the Cequel trial the time taken to respond to prompts was found to predict improvement in depressive symptoms in advance of any change in mood score 36 . Furthermore, remote self-monitoring methodology is a way to aid individuals to gain greater insight into the dynamic and temporal nature of mood and mood stability in mood disorder in daily life 11,37 .…”

Remote Monitoring for Understanding Mechanisms and Prediction in Psychiatry

“…The chronic relapsing and remitting nature of psychiatric disorders is such that many researchers have sought to use remote monitoring to predict future mood states with a view to enabling earlier initiation of preventative treatment. A variety of mathematical approaches have been used including relaxation oscillatory models [19], control charts [20] and rough paths theory [21] with varying results. It seems likely that for any model to gain the necessary accuracy upon which to base treatment decisions, predictions would need to be made at in individual rather than group level.…”