The life expectancy of individuals with schizophrenia spectrum disorders (SSDs) significantly trails behind that of the general population, exposing a grave mortality gap. This disparity is exacerbated by several factors, including a higher prevalence of cardiometabolic diseases, widespread tobacco use, suicides, and accidental deaths among those with SSDs. Addressing this mortality gap necessitates innovative approaches that go beyond traditional treatment modalities.

Personalising Care Through Predictive Analytics

The advent of risk stratification methods in managing first-episode psychosis (FEP) represents a paradigm shift towards individualised patient care. Similar to strategies employed in cardiology and oncology, these methods aim to customise treatment plans based on a comprehensive evaluation of an individual’s risk profile. The goal is to enhance treatment efficacy and patient outcomes by integrating predictive analytics into clinical decision-making processes.

Enhancing Treatment Strategies with Machine Learning

Central to this innovative approach is the application of machine learning models to predict mortality risks in patients with FEP. These models analyse a range of variables, from comorbid substance use disorders to the duration of initial hospitalisation due to psychosis, providing clinicians with a powerful tool to forecast patient outcomes. The utilisation of such predictive models has the potential to revolutionise the selection and administration of treatments, including Long-Acting Injectable (LAI) antipsychotics, ultimately aiming to improve the longevity and quality of life for those affected by schizophrenia spectrum disorders.

Source: JAMA Network Open