Using Electronic Health Records to Facilitate Precision Psychiatry

Oliver, Dominic; Arribas, Maite; Perry, Benjamin I.; Whiting, Daniel; Blackman, Graham; Krakowski, Kamil; Seyedsalehi, Aida; Osimo, Emanuele F.; Griffiths, Siân Lowri; Stahl, Daniel; Cipriani, Andrea; Fazel, Seena; Fusar-Poli, Paolo; McGuire, Philip

doi:10.1016/j.biopsych.2024.02.1006

Cited by 3 publications

(2 citation statements)

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“…This is a key area of need in the field, where a large gap remains between models developed and those translated into clinically impactful tools. 32 A novel aspect developed here was harnessing the potential of electronic health records 33 to collect use-by-use structured feedback on utility, and data on the patterns of use, and combining this with qualitative interviews. Integrating these sources of data provided clear added value and facilitated a more detailed understanding than any of these approaches would have provided in isolation.…”

Section: Implications For Future Researchmentioning

confidence: 99%

Use of a violence risk prediction tool (OxMIV) in Early Intervention in Psychosis services: a mixed methods study of acceptability, feasibility and clinical role

Whiting,

Glogowska,

Mallett

et al. 2024

Preprint

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BACKGROUNDScalable assessment tools for precision psychiatry are of increasing clinical interest. One clinical risk assessment that might be improved by such approaches is assessment of violence perpetration risk. This is an important adverse outcome to reduce for some people presenting to services for first episode psychosis. A prediction tool (OxMIV) has been externally validated in these services, but clinical acceptability and role need to be examined and developed. AIMSThis study aimed to understand clinical use of OxMIV to support violence risk management in Early Intervention in Psychosis services in terms of acceptability to clinicians, patients and carers, practical feasibility, utility, impact and role. METHODMixed methods integrated quantitative data on utility and patterns of use of OxMIV over 12-months in two services with qualitative data from interviews of 20 clinicians and 12 patients and carers. RESULTSOxMIV was used 141 times, mostly in new assessments. Required information was available, with only family history items scored unknown to any notable degree. OxMIV was deemed helpful by clinicians in most cases, especially if previous risk concerns. It was acceptable practically, and broadly for the service, for which its concordance with clinical judgement was important. Patients and carers thought it may improve openness. There was limited impact on plans for clinical support.CONCLUSIONSThis study yielded a detailed understanding of practical issues around the adoption of OxMIV and how it meets an identified clinical need to support risk assessments. Future work should focus on implementation studies and developing linked intervention pathways.

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Section: Implications For Future Researchmentioning

confidence: 99%

Use of a violence risk prediction tool (OxMIV) in Early Intervention in Psychosis services: a mixed methods study of acceptability, feasibility and clinical role

Whiting,

Glogowska,

Mallett

et al. 2024

Preprint

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“…[1][2][3] Electronic health records (EHRs) can provide an opportunity to examine prodromal symptoms contemporaneously, reducing recall bias and enriching our insight into symptom presentation during the prodrome. 4 This knowledge can help enhance specialised preventive care for people at-risk of emerging SMD.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records

Arribas,

Barnby,

Patel

et al. 2024

Preprint

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BackgroundModelling the prodrome to severe mental disorders (SMD), including unipolar mood disorders (UMD), bipolar mood disorders (BMD) and psychotic disorders (PSY), should consider both the evolution and interactions of symptoms and substance use (prodromal features) over time. Temporal network analysis can address this by representing prodromal features as nodes, with their connections (edges) indicating the likelihood of one feature preceding the other. Node centrality could reveal insights into important prodromal features and potential intervention targets. We developed a SMD network and compared sub-networks specific to UMD, BMD and PSY.MethodsWe analysed 7,049 individuals with an SMD diagnosis (UMD:2,306; BMD:817; PSY:3,926) from the South London and Maudsley NHS Foundation Trust electronic health records. Using validated natural language processing algorithms, we extracted the occurrence of 61 prodromal features every three months from two years to six months prior to SMD onset. To construct temporal networks of prodromal features, we employed generalized vector autoregression panel analysis, adjusting for covariates. We computed edge weights (correlation coefficients,z) in autocorrelative, unidirectional and bidirectional relationships. Centrality was calculated as the sum of connections leaving (out-centrality,cout) or entering (in-centrality,cin) a node. We compared the three sub-networks (UMD, BMD, PSY) using permutation analysis.FindingsThe strongest autocorrelation in the SMD network was tearfulness (z=·10). Unidirectional positive relationships were observed for irritability-agitation (z12=·03), mood instability-tearfulness (z12=·03) and irritability-aggression (z12=·03). Aggression-hostility (z12=·04,z21=·03), delusions-hallucinations (z12=·04,z21=·03) and aggression-agitation (z12=·03,z21=·03) were the strongest bidirectional relationships. The most central features included aggression (cout=·082) and tearfulness (cin=·124). The PSY sub-network showed few significant differences compared to UMD (3·9%) and BMD (1·6%), and UMD-BMD showed even fewer (0·4%).InterpretationsThis study represents the most extensive temporal network analysis conducted on the longitudinal interplay of SMD prodromal features. These findings provide further evidence to support early detection services across SMD.Research in contextEvidence before this studyPreventive approaches for severe mental disorders (SMD) can improve outcomes, however, their effectiveness relies on accurate knowledge of the prodromal symptoms and substance use preceding their onset and how they evolve over time. We searched PubMed from database inception to 26thJanuary 2024 for studies investigating the dynamic prodromes for unipolar mood disorders (UMD), bipolar mood disorders (BMD) or psychotic disorders (PSY) published in English. The search terms were prodrom* AND (depression OR bipolar OR psychosis) AND (timecourse OR dynamic OR “network analysis” OR longitudinal). First, while many studies have investigated the prodromal phases of SMD, particularly for PSY, the majority of studies have taken a cross-sectional rather than longitudinal approach which are unable to detect causal dependence between and within prodromal symptoms and substance use. Second, there are no studies focusing on the evolution of features during the prodromal period. Finally, studies have focused on diagnosis-specific analyses, considering UMD, BMD or PSY alone, limiting the possibility for comparison between them.Added value of this studyWe have used a temporal network analysis approach, in combination with a large electronic health record database (n=7,049) and natural language processing, to examine the dynamic evolution of symptoms and substance use in the prodrome to an SMD diagnosis in secondary mental healthcare. This is the largest network analysis investigating prodromal features in SMD, the first assessing longitudinal changes and the first to directly compare the prodromes to UMD, BMD and PSY. Our results add to the growing evidence for a transdiagnostic prodrome to SMD, by showing small differences between UMD, BMD and PSY in how symptoms and substance use evolve over the course of the prodrome.Implications of all the available evidenceOur study explores the patterns of evolution of symptom and substance use events across and within SMD diagnostic groups. We highlight the importance of understanding the dynamic progression of these prodromal features to fully characterise the prodrome to SMD. These findings, together with a growing literature base, also support the potential for broader transdiagnostic early detection services that provide preventive psychiatric care to individuals at risk for SMD.

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