Development and Validation of a Nonremission Risk Prediction Model in First-Episode Psychosis: An Analysis of 2 Longitudinal Studies

Predicting internalizing symptoms with machine learning: identifying individuals that need care

Objective The current project aims to identify individuals in urgent need of mental health care, using a machine learning algorithm (random forest). Comparison/contrast with conventional regression analyses is discussed. Participants: A total of 2,409 participants were recruited from an anonymous university, including undergraduate and graduate students, faculty, and staff. Methods: Answers to a COVID-19 impact survey, the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7) were collected. The total scores of PHQ-9 and GAD-7 were regressed on six composites that were created from the questionnaire items, based on their topics. A random forest was trained and validated. Results: Results indicate that the random forest model was able to make accurate, prospective predictions (R2 = .429 on average) and we review variables that were deemed predictively relevant. Conclusions: Overall, the study suggests that predictive models can be clinically useful in identifying individuals with internalizing symptoms based on daily life disruption experiences.

Effectiveness of the early intervention service for first-episode psychosis in Navarra (PEPsNa): Broadening the scope of outcome measures

BACKGROUND

This study compares the effectiveness of a new early intervention service for firstepisode psychosis (FEP) in patients under conventional treatment. Six primary and 10 secondary outcome measures are used to better characterize the comparative effectiveness between two FEP groups.

METHODS

This study plans to enroll 250 patients aged 15-55 years with FEP from all inpatient and outpatient mental health services and primary health care from January 2020 until December 2022. The control group will be composed of 130 FEP patients treated in mental health centers in the 2 years prior to the start of PEPsNa (Programa de Primeros Episodios de Psicosis de Navarra). The primary outcome measures are symptomatic remission, functional recovery, personal recovery, cognitive performance, functional capacity in real-world settings, and costs. The secondary outcome measures are duration of untreated psychosis, substance abuse rate, antipsychotic monotherapy, minimal effective dose of antipsychotic drugs, therapeutic alliance, drop-out rate, number of relapses, global mortality and suicidality, resource use, and general satisfaction in the program.

DISCUSSION

This study arises from the growing need to broaden the scope of outcome measures in FEP patients and to account for unmet needs of recovery for FEPs. It aims to contribute in the dissemination of the NAVIGATE model in Europe and to provide new evidence of the effectiveness of early intervention services for stakeholders of the National Health Service.

Five points to consider when reading a translational machine-learning paper

Machine-learning techniques are used in this BJPsych special issue on precision medicine in attempts to create statistical models that make clinically relevant predictions for individual patients. In this primer, we outline five key points that are helpful for a new reader to consider in order to engage with the field and evaluate the literature. These points include the consideration of why we are interested in new statistical approaches, how they may produce individualised predictions, what caveats need to be kept in-mind and why the interest and engagment of clinicians and clinical researchers is critical to successful model development and implementation. We hope that the following primer will provide shared understanding to encourage dialogue between clinical and methodological fields.

Prediction models in first-episode psychosis: systematic review and critical appraisal

BACKGROUND

People presenting with first-episode psychosis (FEP) have heterogenous outcomes. More than 40% fail to achieve symptomatic remission. Accurate prediction of individual outcome in FEP could facilitate early intervention to change the clinical trajectory and improve prognosis.

AIMS

We aim to systematically review evidence for prediction models developed for predicting poor outcome in FEP.

METHOD

A protocol for this study was published on the International Prospective Register of Systematic Reviews, registration number CRD42019156897. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance, we systematically searched six databases from inception to 28 January 2021. We used the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Prediction Model Risk of Bias Assessment Tool to extract and appraise the outcome prediction models. We considered study characteristics, methodology and model performance.

RESULTS

Thirteen studies reporting 31 prediction models across a range of clinical outcomes met criteria for inclusion. Eleven studies used logistic regression with clinical and sociodemographic predictor variables. Just two studies were found to be at low risk of bias. Methodological limitations identified included a lack of appropriate validation, small sample sizes, poor handling of missing data and inadequate reporting of calibration and discrimination measures. To date, no model has been applied to clinical practice.

CONCLUSIONS

Future prediction studies in psychosis should prioritise methodological rigour and external validation in larger samples. The potential for prediction modelling in FEP is yet to be realised.