Schizophrenia is a severe mental illness that usually begins in late adolescence or early adulthood. Current pharmacological treatments, while acceptably effective for many patients, are rarely ...clinically tailored or individualized. The lack of sufficient etiopathological knowledge of the disease, together with overall comparable effect sizes for efficacy between available antipsychotics and the absence of clinically actionable biomarkers, has hindered the advance of individualized medicine in the treatment of schizophrenia. Nevertheless, some degree of stratification based on clinical markers could guide treatment choices and help clinicians move toward individualized psychiatry. To this end, a panel of experts met to formally discuss the current approach to individualized treatment in schizophrenia and to define how treatment individualization could help improve clinical outcomes.
A task force of seven experts iteratively developed, evaluated, and refined questionnaire items, which were then evaluated using the Delphi method. Descriptive statistics were used to summarize and rank expert responses. Expert discussion, informed by the results of a scoping review on personalizing the pharmacologic treatment of adults and adolescents with schizophrenia, ultimately generated recommendations to guide individualized pharmacologic treatment in this population.
There was substantial agreement among the expert group members, resulting in the following recommendations: 1) individualization of treatment requires consideration of the patient's diagnosis, clinical presentation, comorbidities, previous treatment response, drug tolerability, adherence patterns, and social factors; 2) patient preferences should be considered in a shared decision-making approach; 3) identified barriers to personalized care that need to be overcome include the lack of actionable biomarkers and mechanistic similarities between available treatments, but digital tools should be increasingly used to enhance individualized treatment.
Individualized care can help provide effective, tailored treatments based on an individual's clinical characteristics, disease trajectory, family and social environment, and goals and preferences.
We develop a two-stage diagnostic classification system for psychotic disorders using an extremely randomized trees machine learning algorithm. Item bank was developed from clinician-rated items ...drawn from an inpatient and outpatient sample. In stage 1, we differentiate schizophrenia and schizoaffective disorder from depression and bipolar disorder (with psychosis). In stage 2 we differentiate schizophrenia from schizoaffective disorder. Out of sample classification accuracy, determined by area under the receiver operator characteristic (ROC) curve, was outstanding for stage 1 (Area under the ROC curve (AUC) = 0.93, 95% confidence interval (CI) = 0.89, 0.94), and excellent for stage 2 (AUC = 0.86, 95% CI = 0.83, 0.88). This is achieved based on an average of 5 items for stage 1 and an average of 6 items for stage 2, out of a bank of 73 previously validated items.
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