Discovery and Validation of Prediction Algorithms for Psychosis in Youths at Clinical High Risk

Worthington, Michelle; Cao, Hengyi; Cannon, Tyrone D.

doi:10.1016/j.bpsc.2019.10.006

Cited by 16 publications

(17 citation statements)

References 61 publications

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“…17 A recent review of psychosis risk prediction models found that clinical variables such as paranoia and unusual thought content consistently appear as significant predictors of psychosis. 41 NLP techniques can extract symptom data at a fraction of the cost of individual patient recruitment. Prognostic performance of our NLP-based refinement of the transdiagnostic risk calculator also exceeds that achieved using harder-to-obtain neuroanatomical predictors (eg, grey matter volume), with accuracies ranging from 0.50 to 0.63.…”

Section: External Model Validationmentioning

confidence: 99%

Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk

Irving

Patel

Oliver

et al. 2020

Schizophrenia Bulletin

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Background Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis. Method The study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and Maudsley (SLaM) NHS Foundation Trust between January 1, 2008, and July 28, 2018. Least Absolute Shrinkage and Selection Operator (LASSO)-regularized Cox regression was used to refine and externally validate a refined version of a five-item individualized, transdiagnostic, clinically based risk calculator previously developed (Harrell’s C = 0.79) and piloted for implementation. The refined version included 14 additional NLP-predictors: tearfulness, poor appetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia. Results A total of 92 151 patients with a first index diagnosis of nonorganic and nonpsychotic mental disorder within the SLaM Trust were included in the derivation (n = 28 297) or external validation (n = 54 716) data sets. Mean age was 33.6 years, 50.7% were women, and 67.0% were of white race/ethnicity. Mean follow-up was 1590 days. The overall 6-year risk of psychosis in secondary mental health care was 3.4 (95% CI, 3.3–3.6). External validation indicated strong performance on unseen data (Harrell’s C 0.85, 95% CI 0.84–0.86), an increase of 0.06 from the original model. Conclusions Using NLP on EHRs can considerably enhance the prognostic accuracy of psychosis risk calculators. This can help identify patients at risk of psychosis who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes.

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Section: External Model Validationmentioning

confidence: 99%

Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk

Irving

Patel

Oliver

et al. 2020

Schizophrenia Bulletin

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“…Finally, overfitting of the model, due to small sample sizes, may explain some of the difficulties in validating external datasets and may also explain why accuracies appear to decrease with increasing sample size. It has been suggested that limiting the number of predictors compared to the number of converters may assist in solving this problem ( 119 ). One example of a large multi-site consortium trying to overcome these issues is the PSYSCAN Consortium ( 140 ).…”

Section: Discussionmentioning

confidence: 99%

“…Four hundred sixteen subjects were included, and the accuracy of individual prediction was 64.6% with reported sensitivity and specificity of 68.6 and 60.6%, respectively ( 118 ). For an excellent table summarizing studies of clinical predictors of conversion to psychosis, see Worthington et al, Biol Psych, 2020, ref ( 119 ).…”

Section: Machine Learning and Prediction Algorithmsmentioning

confidence: 99%

Selective Review of Neuroimaging Findings in Youth at Clinical High Risk for Psychosis: On the Path to Biomarkers for Conversion

2020

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First episode psychosis (FEP), and subsequent diagnosis of schizophrenia or schizoaffective disorder, predominantly occurs during late adolescence, is accompanied by a significant decline in function and represents a traumatic experience for patients and families alike. Prior to first episode psychosis, most patients experience a prodromal period of 1–2 years, during which symptoms first appear and then progress. During that time period, subjects are referred to as being at Clinical High Risk (CHR), as a prodromal period can only be designated in hindsight in those who convert. The clinical high-risk period represents a critical window during which interventions may be targeted to slow or prevent conversion to psychosis. However, only one third of subjects at clinical high risk will convert to psychosis and receive a formal diagnosis of a primary psychotic disorder. Therefore, in order for targeted interventions to be developed and applied, predicting who among this population will convert is of critical importance. To date, a variety of neuroimaging modalities have identified numerous differences between CHR subjects and healthy controls. However, complicating attempts at predicting conversion are increasingly recognized co-morbidities, such as major depressive disorder, in a significant number of CHR subjects. The result of this is that phenotypes discovered between CHR subjects and healthy controls are likely non-specific to psychosis and generalized for major mental illness. In this paper, we selectively review evidence for neuroimaging phenotypes in CHR subjects who later converted to psychosis. We then evaluate the recent landscape of machine learning as it relates to neuroimaging phenotypes in predicting conversion to psychosis.

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“…To date, various imaging modalities have been employed to predict both conversion to psychosis and functional outcomes in youth at CHR [ 27 ]. Structural MRI studies report pronounced cortical thinning [ 28 , 29 ] and longitudinal reduction in gray matter volume [ 30 ] in the ACC of CHR patients.…”

Section: Introductionmentioning

confidence: 99%

Glutathione as a Molecular Marker of Functional Impairment in Patients with At-Risk Mental State: 7-Tesla 1H-MRS Study

et al. 2021

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A substantial number of individuals with clinical high-risk (CHR) mental state do not transition to psychosis. However, regardless of future diagnostic trajectories, many of these individuals develop poor social and occupational functional outcomes. The levels of glutathione, a crucial cortical antioxidant, may track variations in functional outcomes in early psychosis and prodromal states. Thirteen clinical high-risk and 30 healthy control volunteers were recruited for a 7-Tesla magnetic resonance spectroscopy scan with a voxel positioned within the dorsal anterior cingulate cortex (ACC). Clinical assessment scores were collected to determine if any association was observable with glutathione levels. The Bayesian Spearman’s test revealed a positive association between the Social and Occupational Functioning Assessment Scale (SOFAS) and the glutathione concentration in the clinical high-risk group but not in the healthy control group. After accounting for variations in the SOFAS scores, the CHR group had higher GSH levels than the healthy subjects. This study is the first to use 7-Tesla magnetic resonance spectroscopy to test whether ACC glutathione levels relate to social and occupational functioning in a clinically high-risk group and offers preliminary support for glutathione levels as a clinically actionable marker of prognosis in emerging adults presenting with risk features for various severe mental illnesses.

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Discovery and Validation of Prediction Algorithms for Psychosis in Youths at Clinical High Risk

Cited by 16 publications

References 61 publications

Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk

Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk

Selective Review of Neuroimaging Findings in Youth at Clinical High Risk for Psychosis: On the Path to Biomarkers for Conversion

Glutathione as a Molecular Marker of Functional Impairment in Patients with At-Risk Mental State: 7-Tesla 1H-MRS Study

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