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The term "psychosis" refers to a spectrum of mental disorders characterized by abnormal thoughts, perceptions, and behaviors. It can manifest as mood disorders, dementia, delirium with psychotic features, substance-induced psychosis with psychotic features, brief psychotic disorder, delusional disorder, schizoaffective disorder, and schizophrenia. Among all these disorders, schizophrenia is the most common psychotic disorder, affecting 1% of the worldwide population. Psychotic...
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Adverse Short-Term Cardiometabolic Outcomes in Psychosis Early Intervention Services: Which Risk Prediction

James Hughes1, Ashwin Moothedath Asok2, Mao Fong Lim1,3

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Early Intervention in Psychiatry
|November 27, 2025
PubMed
Summary

The PsyMetRiC algorithm effectively predicts short-term weight and triglyceride changes in early psychosis patients, especially younger individuals. This tool shows promise for managing cardiometabolic risks associated with severe mental illness (SMI).

Keywords:
algorithmcardiometabolicpsychosis early interventionrisk predictionweight gain

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Area of Science:

  • Psychiatry and Mental Health
  • Cardiology
  • Metabolic Disorders

Background:

  • Adverse cardiometabolic changes, including weight gain, are common early in severe mental illness (SMI).
  • Preventative strategies are crucial, but the efficacy of existing risk prediction algorithms in early psychosis is unclear.
  • Younger populations with first-episode psychosis require specific attention for cardiometabolic risk assessment.

Purpose of the Study:

  • To evaluate the accuracy of population-based and SMI-specific risk prediction algorithms for early cardiometabolic changes in first-episode psychosis.
  • To assess algorithm performance, particularly in younger patients (16-35 years).

Main Methods:

  • Utilized data from 74 patients (aged 16-65) in an Early Intervention Service for Psychosis.
  • Compared population-based (QRISK3, QDIABETES) and SMI-specific (PsyMetRiC, PRIMROSE) algorithms for predicting changes in weight, total cholesterol, and triglycerides.
  • Assessed predictive accuracy using R-squared and RMSE, with sensitivity analysis in younger participants.

Main Results:

  • PsyMetRiC-full demonstrated the best prediction for weight gain (R²=0.54) and triglyceride increases (R²=0.36).
  • No algorithm accurately predicted total cholesterol changes; general population algorithms performed poorly.
  • Sensitivity analysis confirmed superior performance of PsyMetRiC algorithms in younger patients.

Conclusions:

  • The PsyMetRiC algorithm shows significant potential for predicting short-term cardiometabolic changes in early psychosis, particularly in younger individuals.
  • Further stakeholder engagement is needed to establish 'high-risk' thresholds and intervention strategies.
  • This approach can aid in mitigating the cardiometabolic burden of psychosis and its treatments.