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

Importance Modelling 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 detect causal dependence between and within prodromal features by representing prodromal features as nodes, with their connections (edges) indicating the likelihood of one feature preceding the other. In SMD, node centrality could reveal insights into important prodromal features and potential intervention targets. Community analysis can identify commonly occurring feature groups to define SMD at-risk states. Objective To develop a global transdiagnostic SMD network of the temporal relationships between prodromal features, and to examine within-group differences with sub-networks specific to UMD, BMD and PSY Design Retrospective (2-year), real-world, electronic health records (EHR) cohort study. Validated natural language processing algorithms 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. Setting South London and Maudsley NHS Foundation Trust EHRs. Participants 7,049 individuals with an SMD diagnosis (UMD:2,306; BMD:817; PSY:3,926). Main Outcomes 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. The three sub-networks (UMD, BMD, PSY) were compared using permutation analysis. Community analysis was performed using Spinglass. Results The SMD network was characterised by unidirectional positive relationships, with aggression ( cout =.082) and tearfulness ( cin =.124) as the most central features. 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%). Two positive psychotic (delusional thinking-hallucinations-paranoia, and aggression-agitation-hostility) and one depressive community (guilt-poor insight-tearfulness) were the most common. Conclusions and Relevance This study represents the most extensive temporal network analysis conducted on the longitudinal interplay of SMD prodromal features. These findings provide further evidence to support transdiagnostic early detection services across SMD, refine assessments to detect individuals at risk and identify central features as potential intervention targets. Question How does the dynamic evolution of the prodrome differ across severe mental disorder (SMD) diagnostic groups (unipolar mood disorders, bipolar mood disorders and psychotic disorders) in secondary mental healthcare? Findings This large temporal network analysis study (n=7,049) highlights a transdiagnostic overlap in the pattern of progression of prodromal symptoms of different SMD diagnostic groups in secondary mental healthcare. Meaning Transdiagnostic early detection services for SMD may be beneficial in extending the benefits of preventive psychiatry. We have identified prodromal symptoms that are central to SMD onset, which could be useful targets for preventive interventions to disrupt the progression of SMD. ### Competing Interest Statement MA has been employed by F. Hoffmann-La Roche AG outside of the current study. RP has received grant funding from Janssen, and consulting fees from Holmusk, Akrivia Health, Columbia Data Analytics, Boehringer Ingelheim and Otsuka. PFP has received research funds or personal fees from Lundbeck, Angelini, Menarini, Sunovion, Boehringer Ingelheim, Mindstrong, Proxymm Science, outside the current study. ### Funding Statement MA is supported by the UK Medical Research Council (MR/N013700/1) and Kings College London member of the MRC Doctoral Training Partnership in Biomedical Sciences. JMB has received funding from the Wellcome Trust (WT228268/Z/23/Z). RP has received funding from an NIHR Advanced Fellowship (NIHR301690) and a Medical Research Council (MRC) Health Data Research UK Fellowship (MR/S003118/1). PFP is supported by #NEXTGENERATIONEU (NGEU), funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Clinical Record Interactive Search Permissions (CRIS) received ethical approval as an anonymised dataset for secondary analyses from Oxfordshire REC C (Ref: 23/SC/0257). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data accessed by CRIS remain within an NHS firewall and governance is provided by a patient-led oversight committee. Subject to these conditions, data access is encouraged and those interested should contact Robert Stewart (robert.stewart{at}kcl.ac.uk), CRIS academic lead. There is no permission for data sharing. Covariance matrices to estimate networks and all analysis code are available on GitHub: .