Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample

Rosen, Marlene; Betz, Linda T.; Schultze‐Lutter, Frauke; Chisholm, Katharine; Haidl, Theresa; Kambeitz-Ilankovic, Lana; Bertolino, Alessandro; Borgwardt, Stefan; Brambilla, Paolo; Lencer, Rebekka; Meisenzahl, Eva; Ruhrmann, Stephan; Salokangas, Raimo K. R.; Upthegrove, Rachel; Wood, Stephen; Koutsouleris, Nikolaos; Kambeitz, Joseph

doi:10.1016/j.neubiorev.2021.02.032

Cited by 36 publications

(44 citation statements)

References 63 publications

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“…9 Salazar de Pablo et al ‘s review was impressive in scope, but necessarily limited in detailed analysis of the specific models included. 6 Systematic reviews focusing on predicting the transition to psychosis 10,11 and relapse in psychosis have also been published. 12 In our present review, we focus on FEP with the aim to systematically review and critically appraise the prediction models for the prediction of poor outcomes.…”

Section: Prediction Modellingmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Prediction Modellingmentioning

confidence: 99%

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

et al. 2022

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“…Among the important limitations of our study, we note that missing assessments in several subjects for some timepoints hindered analysis of time-to-event data, which might otherwise have improved accuracy by disentangling further subjects’ risk 42 . Additionally, the patient population of our study is difficult to contact and typically present a high attrition rate 4 .…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, most studies have analyzed risk factors in isolation, without considering their potentially interconnected nature 40 . Progress in the field of predictive medicine using multivariable approaches has demonstrated that models enabling the simultaneous investigation of several risk factors and multiple data modalities can often outperform unimodal predictors for conversion to psychosis 41 , 42 , diagnostic approaches 43 and functional outcome 37 .…”

Section: Introductionmentioning

confidence: 99%

Pattern of predictive features of continued cannabis use in patients with recent-onset psychosis and clinical high-risk for psychosis

et al. 2022

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Continued cannabis use (CCu) is an important predictor for poor long-term outcomes in psychosis and clinically high-risk patients, but no generalizable model has hitherto been tested for its ability to predict CCu in these vulnerable patient groups. In the current study, we investigated how structured clinical and cognitive assessments and structural magnetic resonance imaging (sMRI) contributed to the prediction of CCu in a group of 109 patients with recent-onset psychosis (ROP). We tested the generalizability of our predictors in 73 patients at clinical high-risk for psychosis (CHR). Here, CCu was defined as any cannabis consumption between baseline and 9-month follow-up, as assessed in structured interviews. All patients reported lifetime cannabis use at baseline. Data from clinical assessment alone correctly classified 73% (p < 0.001) of ROP and 59 % of CHR patients. The classifications of CCu based on sMRI and cognition were non-significant (ps > 0.093), and their addition to the interview-based predictor via stacking did not improve prediction significantly, either in the ROP or CHR groups (ps > 0.065). Lower functioning, specific substance use patterns, urbanicity and a lack of other coping strategies contributed reliably to the prediction of CCu and might thus represent important factors for guiding preventative efforts. Our results suggest that it may be possible to identify by clinical measures those psychosis-spectrum patients at high risk for CCu, potentially allowing to improve clinical care through targeted interventions. However, our model needs further testing in larger samples including more diverse clinical populations before being transferred into clinical practice.

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“…Thus, the prediction of such functional outcomes should be targeting both CHRs and affective samples, to obtain more realistic, reliable and potentially transdiagnostic prediction models of future risk for further functional impairments and, ultimately, disability, in a more heterogeneous help-seeking population. 7,25,26…”

Section: Functioning Impairments and Risk For Psychosis And Depressionmentioning

confidence: 99%

Using combined environmental–clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression

et al. 2022

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Background Clinical high-risk states for psychosis (CHR) are associated with functional impairments and depressive disorders. A previous PRONIA study predicted social functioning in CHR and recent-onset depression (ROD) based on structural magnetic resonance imaging (sMRI) and clinical data. However, the combination of these domains did not lead to accurate role functioning prediction, calling for the investigation of additional risk dimensions. Role functioning may be more strongly associated with environmental adverse events than social functioning. Aims We aimed to predict role functioning in CHR, ROD and transdiagnostically, by adding environmental adverse events-related variables to clinical and sMRI data domains within the PRONIA sample. Method Baseline clinical, environmental and sMRI data collected in 92 CHR and 95 ROD samples were trained to predict lower versus higher follow-up role functioning, using support vector classification and mixed k-fold/leave-site-out cross-validation. We built separate predictions for each domain, created multimodal predictions and validated them in independent cohorts (74 CHR, 66 ROD). Results Models combining clinical and environmental data predicted role outcome in discovery and replication samples of CHR (balanced accuracies: 65.4% and 67.7%, respectively), ROD (balanced accuracies: 58.9% and 62.5%, respectively), and transdiagnostically (balanced accuracies: 62.4% and 68.2%, respectively). The most reliable environmental features for role outcome prediction were adult environmental adjustment, childhood trauma in CHR and childhood environmental adjustment in ROD. Conclusions Findings support the hypothesis that environmental variables inform role outcome prediction, highlight the existence of both transdiagnostic and syndrome-specific predictive environmental adverse events, and emphasise the importance of implementing real-world models by measuring multiple risk dimensions.

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Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample

Cited by 36 publications

References 63 publications

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

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

Pattern of predictive features of continued cannabis use in patients with recent-onset psychosis and clinical high-risk for psychosis

Using combined environmental–clinical classification models to predict role functioning outcome in clinical high-risk states for psychosis and recent-onset depression

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