The Management and Supervision Tool (MaST): an electronic crisis risk prediction tool to support safe and effective mental healthcare

Introduction

The increasing global burden of mental disorders has led to rising demand for mental healthcare services. Effective resource management is essential to ensure safe and timely access to care. Electronic health records (EHRs) provide a real-time source of data on clinical presentation and prognostic factors that could be harnessed to provide clinicians with actionable insights to prioritise mental healthcare delivery. We describe the development and evaluation of MaST, an EHR data visualisation tool that provides information to clinicians on risk of mental health crisis defined as an admission to a psychiatric hospital or acceptance into a community crisis service.

Objectives

(i) To develop an EHR-data driven risk prediction tool for risk of crisis. (ii) To evaluate predictive performance in a real-world clinical setting.

Methods

The risk of crisis algorithm was developed and evaluated with EHR data from six UK NHS mental health providers using Ordered Predictor List propensity scores grouped into 5 quintiles. The predictor variables were clinical and sociodemographic factors including previous mental health service contacts.

Results

Data from 2,620 patients contributed to algorithm development which was subsequently tested on data from 107,879 patients. The risk of crisis algorithm performed well with an overall accuracy for predicting the greatest risk of crisis (top quintile) ranging from 64% to 80%.

Conclusions

The MaST algorithm accurately predicted risk of mental health crisis in UK community mental health services. EHR data visualisation tools can provide actionable insights to clinicians to prioritise mental healthcare delivery in real-world clinical practice.

Disclosure

This study was funded in full by Holmusk.

Implementing a Digital Tool to Support Shared Care Planning in Community-Based Mental Health Services: Qualitative Evaluation

BACKGROUND

Mental health services aim to provide recovery-focused care and facilitate coproduced care planning. In practice, mental health providers can find supporting individualized coproduced care with service users difficult while balancing administrative and performance demands. To help meet this aim and using principles of coproduction, an innovative mobile digital care pathway tool (CPT) was developed to be used on a tablet computer and piloted in the West of England.

OBJECTIVE

The aim of this study was to examine mental health care providers' views of and experiences with the CPT during the pilot implementation phase and identify factors influencing its implementation.

METHODS

A total of 20 in-depth telephone interviews were conducted with providers participating in the pilot and managers in the host organization. Interviews were audio recorded, transcribed, anonymized, and thematically analyzed guided by the Consolidated Framework for Implementation Research.

RESULTS

The tool was thought to facilitate coproduced recovery-focused care planning, a policy and organizational as well as professional priority. Internet connectivity issues, system interoperability, and access to service users' health records affected use of the tool during mobile working. The organization's resources, such as information technology (IT) infrastructure and staff time and IT culture, influenced implementation. Participants' levels of use of the tool were dependent on knowledge of the tool and self-efficacy; perceived service-user needs and characteristics; and perceptions of impact on the therapeutic relationship. Training and preparation time influenced participants' confidence in using the tool.

CONCLUSIONS

Findings highlight the importance of congruence between staff, organization, and external policy priorities and digital technologies in aiding intervention engagement, and the need for ongoing training and support of those intended to use the technology during and after the end of implementation interventions.

A Surrogate Modelling Approach Based on Nonlinear Dimension Reduction for Uncertainty Quantification in Groundwater Flow Models

In this paper, we develop a surrogate modelling approach for capturing the output field (e.g. the pressure head) from groundwater flow models involving a stochastic input field (e.g. the hydraulic conductivity). We use a Karhunen–Loève expansion for a log-normally distributed input field and apply manifold learning (local tangent space alignment) to perform Gaussian process Bayesian inference using Hamiltonian Monte Carlo in an abstract feature space, yielding outputs for arbitrary unseen inputs. We also develop a framework for forward uncertainty quantification in such problems, including analytical approximations of the mean of the marginalized distribution (with respect to the inputs). To sample from the distribution, we present Monte Carlo approach. Two examples are presented to demonstrate the accuracy of our approach: a Darcy flow model with contaminant transport in 2-d and a Richards equation model in 3-d.

Models to predict relapse in psychosis: A systematic review

Background

There is little evidence on the accuracy of psychosis relapse prediction models. Our objective was to undertake a systematic review of relapse prediction models in psychosis.

Method

We conducted a literature search including studies that developed and/or validated psychosis relapse prediction models, with or without external model validation. Models had to target people with psychosis and predict relapse. The key databases searched were; Embase, Medline, Medline In-Process Citations & Daily Update, PsychINFO, BIOSIS Citation Index, CINAHL, and Science Citation Index, from inception to September 2016. Prediction modelling studies were assessed for risk of bias and applicability using the PROBAST tool.

Results

There were two eligible studies, which included 33,088 participants. One developed a model using prodromal symptoms and illness-related variables, which explained 14% of relapse variance but was at high risk of bias. The second developed a model using administrative data which was moderately discriminative (C = 0.631) and associated with relapse (OR 1.11 95% CI 1.10, 1.12) and achieved moderately discriminative capacity when validated (C = 0.630). The risk of bias was low.

Conclusions

Due to a lack of high quality evidence it is not possible to make any specific recommendations about the predictors that should be included in a prognostic model for relapse. For instance, it is unclear whether prodromal symptoms are useful for predicting relapse. The use of routine data to develop prediction models may be a more promising approach, although we could not empirically compare the two included studies.

Impaired insulin-dependent glucose metabolism in granulosa-lutein cells from anovulatory women with polycystic ovaries

BACKGROUND

Insulin resistance and hyperinsulinaemia are well-recognized characteristics of anovulatory women with polycystic ovary syndrome (PCOS) but, paradoxically, steroidogenesis by PCOS granulosa cells remains responsive to insulin. The hypothesis to be tested in this study is that insulin resistance in the ovary is confined to the metabolic effects of insulin (i.e. glucose uptake and metabolism), whereas the steroidogenic action of insulin remains intact.

METHODS

Granulosa-lutein cells were obtained during IVF cycles from seven women with normal ovaries, six ovulatory women with PCO (ovPCO) and seven anovulatory women with PCO (anovPCO). Mean body mass index was in the normal range in all three groups. Granulosa-lutein cells were cultured with insulin (1, 10, 100 and 1000 ng/ml) and LH (1, 2.5 and 5 ng/ml). Media were sampled at 24 and 48 h and analysed for glucose uptake, lactate production and (48 h only) progesterone production.

RESULTS

Insulin-stimulated glucose uptake by cells from anovPCO was attenuated at higher doses of insulin (100 and 1000 ng/ml) compared with that by cells from either ovPCO (P=0.02) or controls (P=0.02). Insulin and LH stimulated lactate production in a dose-dependent manner, but insulin-dependent lactate production was markedly impaired in granulosa-lutein cells from anovPCO compared with either normal (P=0.002) or ovPCO (P<0.0001). By contrast, there was no difference in insulin-stimulated progesterone production between granulosa-lutein cells from the three ovarian types.

CONCLUSIONS

Granulosa-lutein cells from women with anovPCOS are relatively resistant to the effects of insulin-stimulated glucose uptake and utilization compared with those from normal and ovPCO, whilst maintaining normal steroidogenic output in response to physiological doses of insulin. These studies support the probability of a post-receptor, signalling pathway-specific impairment of insulin action in PCOS.