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Individualized Prediction of Transition to Psychosis in 1,676 Individuals at Clinical High Risk: Development and Validation of a Multivariable Prediction Model Based on Individual Patient Data Meta-Analysis.

ABSTRACT: Background: The Clinical High Risk state for Psychosis (CHR-P) has become the cornerstone of modern preventive psychiatry. The next stage of clinical advancements rests on the ability to formulate a more accurate prognostic estimate at the individual subject level. Individual Participant Data Meta-Analyses (IPD-MA) are robust evidence synthesis methods that can also offer powerful approaches to the development and validation of personalized prognostic models. The aim of the study was to develop and validate an individualized, clinically based prognostic model for forecasting transition to psychosis from a CHR-P stage. Methods: A literature search was performed between January 30, 2016, and February 6, 2016, consulting PubMed, Psychinfo, Picarta, Embase, and ISI Web of Science, using search terms ("ultra high risk" OR "clinical high risk" OR "at risk mental state") AND [(conver* OR transition* OR onset OR emerg* OR develop*) AND psychosis] for both longitudinal and intervention CHR-P studies. Clinical knowledge was used to a priori select predictors: age, gender, CHR-P subgroup, the severity of attenuated positive psychotic symptoms, the severity of attenuated negative psychotic symptoms, and level of functioning at baseline. The model, thus, developed was validated with an extended form of internal validation. Results: Fifteen of the 43 studies identified agreed to share IPD, for a total sample size of 1,676. There was a high level of heterogeneity between the CHR-P studies with regard to inclusion criteria, type of assessment instruments, transition criteria, preventive treatment offered. The internally validated prognostic performance of the model was higher than chance but only moderate [Harrell's C-statistic 0.655, 95% confidence interval (CIs), 0.627-0.682]. Conclusion: This is the first IPD-MA conducted in the largest samples of CHR-P ever collected to date. An individualized prognostic model based on clinical predictors available in clinical routine was developed and internally validated, reaching only moderate prognostic performance. Although personalized risk prediction is of great value in the clinical practice, future developments are essential, including the refinement of the prognostic model and its external validation. However, because of the current high diagnostic, prognostic, and therapeutic heterogeneity of CHR-P studies, IPD-MAs in this population may have an limited intrinsic power to deliver robust prognostic models.

SUBMITTER: Malda A

PROVIDER: S-EPMC6537857 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Individualized Prediction of Transition to Psychosis in 1,676 Individuals at Clinical High Risk: Development and Validation of a Multivariable Prediction Model Based on Individual Patient Data Meta-Analysis.

Malda Aaltsje A Boonstra Nynke N Barf Hans H de Jong Steven S Aleman Andre A Addington Jean J Pruessner Marita M Nieman Dorien D de Haan Lieuwe L Morrison Anthony A Riecher-Rössler Anita A Studerus Erich E Ruhrmann Stephan S Schultze-Lutter Frauke F An Suk Kyoon SK Koike Shinsuke S Kasai Kiyoto K Nelson Barnaby B McGorry Patrick P Wood Stephen S Lin Ashleigh A Yung Alison Y AY Kotlicka-Antczak Magdalena M Armando Marco M Vicari Stefano S Katsura Masahiro M Matsumoto Kazunori K Durston Sarah S Ziermans Tim T Wunderink Lex L Ising Helga H van der Gaag Mark M Fusar-Poli Paolo P Pijnenborg Gerdina Hendrika Maria GHM

Frontiers in psychiatry 20190521

<b>Background:</b> The Clinical High Risk state for Psychosis (CHR-P) has become the cornerstone of modern preventive psychiatry. The next stage of clinical advancements rests on the ability to formulate a more accurate prognostic estimate at the individual subject level. Individual Participant Data Meta-Analyses (IPD-MA) are robust evidence synthesis methods that can also offer powerful approaches to the development and validation of personalized prognostic models. The aim of the study was to d ...[more]

PMID: 31178767

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Project description:AimsThe clinical outcomes of individuals at clinical high risk of psychosis (CHR-P) who do not transition to psychosis are heterogeneous and inconsistently reported. We aimed to comprehensively evaluate longitudinally a wide range of outcomes in CHR-P individuals not developing psychosis.Methods"Preferred Reporting Items for Systematic reviews and Meta-Analyses" and "Meta-analysis Of Observational Studies in Epidemiology"-compliant meta-analysis (PROSPERO: CRD42021229212) searching original CHR-P longitudinal studies in PubMed and Web of Science databases up to 01/11/2021. As primary analysis, we evaluated the following outcomes within CHR-P non-transitioning individuals: (a) change in the severity of attenuated psychotic symptoms (Hedge's g); (b) change in the severity of negative psychotic symptoms (Hedge's g); (c) change in the severity of depressive symptoms (Hedge's g); (d) change in the level of functioning (Hedge's g); (e) frequency of remission (at follow-up). As a secondary analysis, we compared these outcomes in those CHR-P individuals who did not transition vs. those who did transition to psychosis at follow-up. We conducted random-effects model meta-analyses, sensitivity analyses, heterogeneity analyses, meta-regressions and publication bias assessment. The risk of bias was assessed using a modified version of the Newcastle-Ottawa Scale (NOS).ResultsTwenty-eight studies were included (2756 CHR-P individuals, mean age = 20.4, 45.5% females). The mean duration of follow-up of the included studies was of 30.7 months. Primary analysis: attenuated psychotic symptoms [Hedges' g = 1.410, 95% confidence interval (CI) 1.002-1.818]; negative psychotic symptoms (Hedges' g = 0.683, 95% CI 0.371-0.995); depressive symptoms (Hedges' g = 0.844, 95% CI 0.371-1.317); and functioning (Hedges' g = 0.776, 95% CI 0.463-1.089) improved in CHR-P non-transitioning individuals; 48.7% remitted at follow-up (95% CI 39.3-58.2%). Secondary analysis: attenuated psychotic symptoms (Hedges' g = 0.706, 95% CI 0.091-1.322) and functioning (Hedges' g = 0.623, 95% CI 0.375-0.871) improved in CHR-P individuals not-transitioning compared to those transitioning to psychosis, but there were no differences in negative or depressive symptoms or frequency of remission (p > 0.05). Older age was associated with higher improvements of attenuated psychotic symptoms (β = 0.225, p = 0.012); publication years were associated with a higher improvement of functioning (β = -0.124, p = 0.0026); a lower proportion of Brief Limited Intermittent Psychotic Symptoms was associated with higher frequencies of remission (β = -0.054, p = 0.0085). There was no metaregression impact for study continent, the psychometric instrument used, the quality of the study or proportion of females. The NOS scores were 4.4 ± 0.9, ranging from 3 to 6, revealing the moderate quality of the included studies.ConclusionsClinical outcomes improve in CHR-P individuals not transitioning to psychosis but only less than half remit over time. Sustained clinical attention should be provided in the longer term to monitor these outcomes.

Project description:ImportanceEstimating the current likelihood of transitioning from a clinical high risk for psychosis (CHR-P) to psychosis holds paramount importance for preventive care and applied research.ObjectiveTo quantitatively examine the consistency and magnitude of transition risk to psychosis in individuals at CHR-P.Data sourcesPubMed and Web of Science databases until November 1, 2020. Manual search of references from previous articles.Study selectionLongitudinal studies reporting transition risks in individuals at CHR-P.Data extraction and synthesisMeta-analysis compliant with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guidelines; independent data extraction, manually and through digitalization of Kaplan-Meier curves.Main outcome and measuresPrimary effect size was cumulative risk of transition to psychosis at 0.5, 1, 1.5, 2, 2.5, 3, 4, and more than 4 years' follow-up, estimated using the numbers of individuals at CHR-P transitioning to psychosis at each time point. These analyses were complemented by meta-analytical Kaplan-Meier curves and speed of transition to psychosis (hazard rate). Random-effects meta-analysis, between-study heterogeneity analysis, study quality assessment, and meta-regressions were conducted.ResultsA total of 130 studies and 9222 individuals at CHR-P were included. The mean (SD) age was 20.3 (4.4) years, and 5100 individuals (55.3%) were male. The cumulative transition risk was 0.09 (95% CI, 0.07-0.10; k = 37; n = 6485) at 0.5 years, 0.15 (95% CI, 0.13-0.16; k = 53; n = 7907) at 1 year, 0.20 (95% CI, 0.17-0.22; k = 30; n = 5488) at 1.5 years, 0.19 (95% CI, 0.17-0.22; k = 44; n = 7351) at 2 years, 0.25 (95% CI, 0.21-0.29; k = 19; n = 3114) at 2.5 years, 0.25 (95% CI, 0.22-0.29; k = 29; n = 4029) at 3 years, 0.27 (95% CI, 0.23-0.30; k = 16; n = 2926) at 4 years, and 0.28 (95% CI, 0.20-0.37; k = 14; n = 2301) at more than 4 years. The cumulative Kaplan-Meier transition risk was 0.08 (95% CI, 0.08-0.09; n = 4860) at 0.5 years, 0.14 (95% CI, 0.13-0.15; n = 3408) at 1 year, 0.17 (95% CI, 0.16-0.19; n = 2892) at 1.5 years, 0.20 (95% CI, 0.19-0.21; n = 2357) at 2 years, 0.25 (95% CI, 0.23-0.26; n = 1444) at 2.5 years, 0.27 (95% CI, 0.25-0.28; n = 1029) at 3 years, 0.28 (95% CI, 0.26-0.29; n = 808) at 3.5 years, 0.29 (95% CI, 0.27-0.30; n = 737) at 4 years, and 0.35 (95% CI, 0.32-0.38; n = 114) at 10 years. The hazard rate only plateaued at 4 years' follow-up. Meta-regressions showed that a lower proportion of female individuals (β = -0.02; 95% CI, -0.04 to -0.01) and a higher proportion of brief limited intermittent psychotic symptoms (β = 0.02; 95% CI, 0.01-0.03) were associated with an increase in transition risk. Heterogeneity across the studies was high (I2 range, 77.91% to 95.73%).Conclusions and relevanceIn this meta-analysis, 25% of individuals at CHR-P developed psychosis within 3 years. Transition risk continued increasing in the long term. Extended clinical monitoring and preventive care may be beneficial in this patient population.

Project description:IntroductionAcute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by applying multivariable risk prediction methods to individual patient data (IPD) of multiple randomised placebo-controlled trials.Methods and analysisThis is an update and re-analysis of a 2008 IPD meta-analysis on antibiotics for adults with clinically diagnosed ARS. First, the reference list of the 2018 Cochrane review on antibiotics for ARS will be reviewed for relevant studies published since 2008. Next, the systematic searches of CENTRAL, MEDLINE and Embase of the Cochrane review will be updated to 1 September 2020. Methodological quality of eligible studies will be assessed using the Cochrane Risk of Bias 2 tool. The primary outcome is cure at 8-15 days. Regression-based methods will be used to model the risk of being cured based on relevant predictors and treatment, while accounting for clustering. Such model allows for risk predictions as a function of treatment and individual patient characteristics and hence gives insight into individualised absolute benefit. Candidate predictors will be based on literature, clinical reasoning and availability. Calibration and discrimination will be evaluated to assess model performance. Resampling techniques will be used to assess internal validation. In addition, internal-external cross-validation procedures will be used to inform on between-study differences and estimate out-of-sample model performance. Secondarily, we will study possible heterogeneity of treatment effect as a function of outcome risk.Ethics and disseminationIn this study, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act (WMO) does not apply and official ethical approval is not required. Results will be submitted for publication in international peer-reviewed journals.Prospero registration numberCRD42020220108.

Project description:BackgroundPneumonia remains difficult to diagnose in primary care. Prediction models based on signs and symptoms (S&S) serve to minimize the diagnostic uncertainty. External validation of these models is essential before implementation into routine practice. In this study all published S&S models for prediction of pneumonia in primary care were externally validated in the individual patient data (IPD) of previously performed diagnostic studies.Methods and findingsS&S models for diagnosing pneumonia in adults presenting to primary care with lower respiratory tract infection and IPD for validation were identified through a systematical search. Six prediction models and IPD of eight diagnostic studies (N total = 5308, prevalence pneumonia 12%) were included. Models were assessed on discrimination and calibration. Discrimination was measured using the pooled Area Under the Curve (AUC) and delta AUC, representing the performance of an individual model relative to the average dataset performance. Prediction models by van Vugt et al. and Heckerling et al. demonstrated the highest pooled AUC of 0.79 (95% CI 0.74-0.85) and 0.72 (0.68-0.76), respectively. Other models by Diehr et al., Singal et al., Melbye et al., and Hopstaken et al. demonstrated pooled AUCs of 0.65 (0.61-0.68), 0.64 (0.61-0.67), 0.56 (0.49-0.63) and 0.53 (0.5-0.56), respectively. A similar ranking was present based on the delta AUCs of the models. Calibration demonstrated close agreement of observed and predicted probabilities in the models by van Vugt et al. and Singal et al., other models lacked such correspondence. The absence of predictors in the IPD on dataset level hampered a systematical comparison of model performance and could be a limitation to the study.ConclusionsThe model by van Vugt et al. demonstrated the highest discriminative accuracy coupled with reasonable to good calibration across the IPD of different study populations. This model is therefore the main candidate for primary care use.

Project description:BackgroundIndividuals at clinical high risk of psychosis (CHR-P) recruited in randomized clinical trials (RCTs) and observational cohorts may display a different enrichment and hence risk of transition to psychosis. No meta-analysis has ever addressed this issue.Methods"Preferred Reporting Items for Systematic reviews and Meta-Analyses" (PRISMA) and "Meta-analysis Of Observational Studies in Epidemiology" (MOOSE)-compliant meta-analysis. PubMed and Web of Science were searched until November 2020 (PROSPERO:CRD42021229223). We included nonoverlapping longitudinal studies (RCTs-control condition and observational cohorts) reporting the transition to psychosis in CHR-P individuals. The primary effect size measure was the cumulative risk of transition at 0.5, 1, and 2 years follow-up in RCTs compared to observational cohorts. Random effects meta-analyses, heterogeneity assessment, quality assessment, and meta-regressions were conducted.ResultsNinety-four independent studies (24 RCTs, 70 observational cohorts) and 9,243 individuals (mean age = 20.1 ± 3.0 years; 43.7% females) were included. The meta-analytical risk of transitioning to psychosis from a CHR-P stage was 0.091 (95% confidence intervals [CI] = 0.068-0.121) at 0.5 years, 0.140 (95% CI = 0.101-0.191) at 1 year and 0.165 (95% CI = 0.097-0.267) at 2 years follow-up in RCTs, and 0.081 (95% CI = 0.067-0.099) at 0.5 years, 0.138 (95% CI = 0.114-0.167) at 1 year, and 0.174 (95% CI = 0.156-0.193) at 2 years follow-up in observational cohorts. There were no between-group differences in transition risks (p > 0.05). The proportion of CHR-P individuals with substance use disorders (excluding alcohol and cannabis) was higher in observational cohorts (16.8, 95% CI = 13.3-21.0%) than in RCTs (3.4, 95% CI = 0.8-12.7%; p = 0.018).ConclusionsThere is no meta-analytic evidence supporting sampling biases in RCTs of CHR-P individuals. Further RCTs are needed to detect effective interventions to prevent psychosis in this at-risk group.

Project description:BackgroundBefore considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the model (referred to as external validation). We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models.MethodsWe conducted a systematic review of articles describing some form of external validation of one or more multivariable prediction models indexed in PubMed core clinical journals published in 2010. Study data were extracted in duplicate on design, sample size, handling of missing data, reference to the original study developing the prediction models and predictive performance measures.Results11,826 articles were identified and 78 were included for full review, which described the evaluation of 120 prediction models. in participant data that were not used to develop the model. Thirty-three articles described both the development of a prediction model and an evaluation of its performance on a separate dataset, and 45 articles described only the evaluation of an existing published prediction model on another dataset. Fifty-seven percent of the prediction models were presented and evaluated as simplified scoring systems. Sixteen percent of articles failed to report the number of outcome events in the validation datasets. Fifty-four percent of studies made no explicit mention of missing data. Sixty-seven percent did not report evaluating model calibration whilst most studies evaluated model discrimination. It was often unclear whether the reported performance measures were for the full regression model or for the simplified models.ConclusionsThe vast majority of studies describing some form of external validation of a multivariable prediction model were poorly reported with key details frequently not presented. The validation studies were characterised by poor design, inappropriate handling and acknowledgement of missing data and one of the most key performance measures of prediction models i.e. calibration often omitted from the publication. It may therefore not be surprising that an overwhelming majority of developed prediction models are not used in practice, when there is a dearth of well-conducted and clearly reported (external validation) studies describing their performance on independent participant data.

Dataset Information

Individualized Prediction of Transition to Psychosis in 1,676 Individuals at Clinical High Risk: Development and Validation of a Multivariable Prediction Model Based on Individual Patient Data Meta-Analysis.

Publications

Individualized Prediction of Transition to Psychosis in 1,676 Individuals at Clinical High Risk: Development and Validation of a Multivariable Prediction Model Based on Individual Patient Data Meta-Analysis.

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