Royal College of Surgeons in Ireland
Machine learning based prediction and the influence of complement - coagulation....pdf (1.11 MB)

Machine learning based prediction and the influence of complement – coagulation pathway proteins on clinical outcome: results from the NEURAPRO trial

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journal contribution
posted on 2022-05-23, 15:49 authored by Subash Raj Susai, David MonganDavid Mongan, Colm Healy, Mary CannonMary Cannon, Gerard Cagney, Kieran Wynne, Jonah Byrne, Connie Markulev, Miriam R Schäfer, Maximus Berger, Nilufar Mossaheb, Monika Schlögelhofer, Stefan Smesny, Ian B Hickie, Gregor E Berger, Eric YH Chen, Lieuwe de Haan, Dorien H Nieman, Merete Nordentoft, Anita Riecher-Rössler, Swapna Verma, Rebekah Street, Andrew Thompson, Alison Ruth Yung, Barnaby Nelson, Patrick D McGorry, Melanie FockingMelanie Focking, G Paul Amminger, David CotterDavid Cotter

Background: Functional outcomes are important measures in the overall clinical course of psychosis and individuals at clinical high-risk (CHR), however, prediction of functional outcome remains difficult based on clinical information alone. In the first part of this study, we evaluated whether a combination of biological and clinical variables could predict future functional outcome in CHR individuals. The complement and coagulation pathways have previously been identified as being of relevance to the pathophysiology of psychosis and have been found to contribute to the prediction of clinical outcome in CHR participants. Hence, in the second part we extended the analysis to evaluate specifically the relationship of complement and coagulation proteins with psychotic symptoms and functional outcome in CHR.

Materials and methods: We carried out plasma proteomics and measured plasma cytokine levels, and erythrocyte membrane fatty acid levels in a sub-sample (n = 158) from the NEURAPRO clinical trial at baseline and 6 months follow up. Functional outcome was measured using Social and Occupational Functional assessment Score (SOFAS) scale. Firstly, we used support vector machine learning techniques to develop predictive models for functional outcome at 12 months. Secondly, we developed linear regression models to understand the association between 6-month follow-up levels of complement and coagulation proteins with 6-month follow-up measures of positive symptoms summary (PSS) scores and functional outcome.

Results and conclusion: A prediction model based on clinical and biological data including the plasma proteome, erythrocyte fatty acids and cytokines, poorly predicted functional outcome at 12 months follow-up in CHR participants. In linear regression models, four complement and coagulation proteins (coagulation protein X, Complement C1r subcomponent like protein, Complement C4A & Complement C5) indicated a significant association with functional outcome; and two proteins (coagulation factor IX and complement C5) positively associated with the PSS score. Our study does not provide support for the utility of cytokines, proteomic or fatty acid data for prediction of functional outcomes in individuals at high-risk for psychosis. However, the association of complement protein levels with clinical outcome suggests a role for the complement system and the activity of its related pathway in the functional impairment and positive symptom severity of CHR patients.


Irish Health Research Board research grant (HRB ILP POR 2019–005)

Health Research Board (HRB) under grant number HRB/HRA/PHR/2015–1293

Science Foundation Ireland (SFI) under Grant Number 413 16/RC/3948

European Regional Development Fund

FutureNeuro industry partners

Orygen, Australia

Wellcome Trust and the Health Research Board (Grant Number 203930/B/16/Z)

Health Service Executive National Doctors Training and Planning and the Health and Social Care, Research and Development Division, Northern Ireland

Wellcome Flagship Innovations Award (220438Z/20/Z)

NHMRC Senior Research Fellowship (1137687)

University of Melbourne Dame Kate Campbell Fellowship

MRFF RART Fellowship

The Comprehensive Molecular Analytical Platform (CMAP) under The SFI Research Infrastructure Programme, reference 18/RI/5702



The original article is available at

Published Citation

Susai SR. et al. Machine learning based prediction and the influence of complement - coagulation pathway proteins on clinical outcome: results from the NEURAPRO trial. Brain Behav Immun. 2022;103:50-60

Publication Date

24 March 2022

PubMed ID



  • Beaumont Hospital
  • Psychiatry

Research Area

  • Neurological and Psychiatric Disorders
  • Population Health and Health Services
  • Immunity, Infection and Inflammation


Elsevier BV


  • Published Version (Version of Record)