High time for a paradigm shift in psychiatry

Models Predicting Psychosis in Patients With High Clinical Risk: A Systematic Review

OBJECTIVE

The present study reviews predictive models used to improve prediction of psychosis onset in individuals at clinical high risk for psychosis (CHR), using clinical, biological, neurocognitive, environmental, and combinations of predictors.

METHODS

A systematic literature search on PubMed was carried out (from 1998 through 2019) to find all studies that developed or validated a model predicting the transition to psychosis in CHR subjects.

RESULTS

We found 1,406 records. Thirty-eight of them met the inclusion criteria; 11 studies using clinical predictive models, seven studies using biological models, five studies using neurocognitive models, five studies using environmental models, and 18 studies using combinations of predictive models across different domains. While the highest positive predictive value (PPV) in clinical, biological, neurocognitive, and combined predictive models were relatively high (all above 83), the highest PPV across environmental predictive models was modest (63%). Moreover, none of the combined models showed a superiority when compared with more parsimonious models (using only neurocognitive, clinical, biological, or environmental factors).

CONCLUSIONS

The use of predictive models may allow high prognostic accuracy for psychosis prediction in CHR individuals. However, only ten studies had performed an internal validation of their models. Among the models with the highest PPVs, only the biological and neurocognitive but not the combined models underwent validation. Further validation of predicted models is needed to ensure external validity.

Clinical and functional long-term outcome of patients at clinical high risk (CHR) for psychosis without transition to psychosis: A systematic review

BACKGROUND

Research on patients at clinical high risk (CHR) for psychosis has so far mainly focused on those with transition to frank psychosis (CHR-T patients). However, the majority of CHR patients do not transition (CHR-NT patients) and relatively little information is available on their clinical and functional outcome.

METHODS

We conducted a systematic review on clinical and functional long-term outcome of CHR-NT patients. Studies were included if they had an average follow-up period of at least 24 months and reported on long-term outcome of CHR-NT patients in one or more of the following domains: (non-)remission from CHR, prevalence of clinical symptoms and/or clinical diagnoses (axis I and II), and psychosocial functioning.

RESULTS

Ten publications from seven different single or multicenter studies with average follow-up durations of 2-7.5 years could be included. At the last follow-up assessment 28-71% of CHR-NT patients were not remitted from their CHR and 22-82% still had at least one clinical diagnosis. Approximately half of CHR-NT patients presented with poor psychosocial outcome at 2-year and 6-year follow-up.

CONCLUSIONS

The results suggest that, in the long-term, the majority of CHR-NT patients are not in full clinical remission and seem to suffer from one or more clinical disorders and psychosocial impairments. Since relatively few studies could be identified, further research is required to better understand the trajectories and clinical needs of CHR-NT patients.

Sex and gender differences in schizophrenic psychoses-a critical review

Many sex and gender differences in schizophrenic psychoses have been reported, but few have been soundly replicated. A stable finding is the later age of onset in women compared to men. Gender differences in symptomatology, comorbidity, and neurocognition seem to reflect findings in the general population. There is increasing evidence for estrogens being psychoprotective in women and for hypothalamic-pituitary-gonadal dysfunction in both sexes.More methodologically sound, longitudinal, multi-domain, interdisciplinary research investigating both sex (biological) and gender (psychosocial) factors is required to better understand the different pathogenesis and etiologies of schizophrenic psychoses in women and men, thereby leading to better tailored treatments and improved outcomes.

Prediction of conversion to psychosis in individuals with an at-risk mental state: a brief update on recent developments

PURPOSE OF REVIEW

So far, only little more than one-third of individuals classified as being at-risk for psychosis have been shown to actually convert to frank psychosis during follow-up. There have therefore been enormous efforts to improve the accuracy of predicting this transition. We reviewed the most recent studies in the field with the aim to clarify whether accuracy of prediction has been improved by the different research endeavors and what could be done to further improve it, and/or what alternative goals research should pursue.

RECENT FINDINGS

A total of 56 studies published between May 2015 and December 2016 were included, of which eight were meta-analyses. New meta-analytical evidence confirms that established instruments for checking clinical risk criteria have an excellent clinical utility in individuals referred to high-risk services. Within a such identified group of ultra-high-risk (UHR) individuals, especially Brief Limited Intermittent Psychotic Symptoms and Attenuated Psychotic Symptoms seem to predict transition. Further assessments should be performed within the UHR individuals, as risk of transition seems particularly high in those with an even higher severity of certain symptoms such as suspiciousness or anhedonia, in those with lower global or social functioning, poor neurocognitive performance or cannabis abuse. Also, electroencephalography, neuroimaging and blood biomarkers might contribute to improving individual prediction. The most promising approach certainly is a staged multidomain assessment. Risk calculators to integrate all data for an individualized prediction are being developed.

SUMMARY

Prediction of psychosis is already possible with an excellent prognostic performance based on clinical assessments. Recent studies show that this accuracy can be further improved by using multidomain approaches and modern statistics for individualized prediction. The challenge now is the translation into the clinic with a broad clinical implementation.