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Andreas Marneros - One of the best experts on this subject based on the ideXlab platform.

  • The concordance of ICD-10 acute and transient psychosis and DSM-IV Brief Psychotic Disorder
    Psychological Medicine, 2002
    Co-Authors: Frank Pillmann, Annette Haring, Sabine Balzuweit, Raffaela Blöink, Andreas Marneros
    Abstract:

    Background. ICD-10 acute and transient Psychotic Disorder (ATPD; F23) and DSM-IV Brief Psychotic Disorder (BPD; 298.8) are related diagnostic concepts, but little is known regarding the concordance of the two definitions. Method. During a 5-year period all in-patients with ATPD were identified; DSM-IV diagnoses were also determined. We systematically evaluated demographic and clinical features and carried out follow-up investigations at an average of 2·2 years after the index episode using standardized instruments. Results. Forty-two (4·1%) of 1036 patients treated for Psychotic Disorders or major affective episode fulfilled the ICD-10 criteria of ATPD. Of these, 61·9% also fulfilled the DSM-IV criteria of Brief Psychotic Disorder; 31·0%, of schizophreniform Disorder; 2·4%, of delusional Disorder; and 4·8%, of Psychotic Disorder not otherwise specified. BPD showed significant concordance with the polymorphic subtype of ATPD, and DSM-IV schizophreniform Disorder showed significant concordance with the schizophreniform subtype of ATPD. BPD patients had a significantly shorter duration of episode and more acute onset compared with those ATPD patients who did not meet the criteria of BPD (non-BPD). However, the BPD group and the non-BPD group of ATPD were remarkably similar in terms of sociodemography (especially female preponderance), course and outcome, which was rather favourable for both groups. Conclusions. DSM-IV BPD is a Psychotic Disorder with broad concordance with ATPD as defined by ICD-10. However, the DSM-IV time criteria for BPD may be too narrow. The group of acute Psychotic Disorders with good prognosis extends beyond the borders of BPD and includes a subgroup of DSM-IV schizophreniform Disorder.

Anderson-schmidt H. - One of the best experts on this subject based on the ideXlab platform.

  • An investigation of psychosis subgroups with prognostic validation and exploration of genetic underpinnings: The PsyCourse study.
    2020
    Co-Authors: Dwyer D.b., Kalman J.l., Budde M., Kambeitz J., Ruef A., Antonucci L.a., Kambeitz-ilankovic L., Hasan A., Anderson-schmidt H.
    Abstract:

    Importance: Identifying psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider a broader clinical spectrum, disentangle illness trajectories, and investigate genetic associations. Objective: To detect psychosis subgroups using data-driven methods and examine their illness courses over 1.5 years and polygenic scores for schizophrenia, bipolar Disorder, major depression Disorder, and educational achievement. Design, Setting, and Participants: This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began in January 2012 across 18 sites. Data from a referred sample of 1223 individuals (765 in the discovery sample and 458 in the validation sample) with DSM-IV diagnoses of schizophrenia, bipolar affective Disorder (I/II), schizoaffective Disorder, schizophreniform Disorder, and Brief Psychotic Disorder were collected from secondary and tertiary care sites. Discovery data were extracted in September 2016 and analyzed from November 2016 to January 2018, and prospective validation data were extracted in October 2018 and analyzed from January to May 2019. Main Outcomes and Measures: A clinical battery of 188 variables measuring demographic characteristics, clinical history, symptoms, functioning, and cognition was decomposed using nonnegative matrix factorization clustering. Subtype-specific illness courses were compared with mixed models and polygenic scores with analysis of covariance. Supervised learning was used to replicate results in validation data with the most reliably discriminative 45 variables. Results: Of the 765 individuals in the discovery sample, 341 (44.6%) were women, and the mean (SD) age was 42.7 (12.9) years. Five subgroups were found and labeled as affective psychosis (n = 252), suicidal psychosis (n = 44), depressive psychosis (n = 131), high-functioning psychosis (n = 252), and severe psychosis (n = 86). Illness courses with significant quadratic interaction terms were found for psychosis symptoms (R2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R2 = 0.28; 95% CI, 0.25-0.32), global functioning (R2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R2 = 0.20; 95% CI, 0.17-0.23). The depressive and severe psychosis subgroups exhibited the lowest functioning and quadratic illness courses with partial recovery followed by reoccurrence of severe illness. Differences were found for educational attainment polygenic scores (mean [SD] partial η2 = 0.014 [0.003]) but not for diagnostic polygenic risk. Results were largely replicated in the validation cohort. Conclusions and Relevance: Psychosis subgroups were detected with distinctive clinical signatures and illness courses and specificity for a nondiagnostic genetic marker. New data-driven clinical approaches are important for future psychosis taxonomies. The findings suggest a need to consider short-term to medium-term service provision to restore functioning in patients stratified into the depressive and severe psychosis subgroups.

  • An investigation of psychosis subgroups with prognostic validation and exploration of genetic underpinnings: The PsyCourse study.
    'American Medical Association (AMA)', 2020
    Co-Authors: Dwyer D.b., Kalman J.l., Budde M., Kambeitz J., Ruef A., Antonucci L.a., Kambeitz-ilankovic L., Hasan A., Anderson-schmidt H.
    Abstract:

    This cohort study aims to detect psychosis subgroups and examine their illness courses over 1.5 years and their polygenic scores for schizophrenia, bipolar Disorder, major depression Disorder, and educational achievement.Question Will data-driven clustering using high-dimensional clinical data reveal psychosis subgroups with relevance to prognoses and polygenic risk? Findings In this cohort study including 1223 individuals, in the discovery sample of 765 individuals with predominantly bipolar and schizophrenia diagnoses, 5 subgroups were detected with different clinical signatures, illness trajectories, and genetic scores for educational attainment. Results were validated in a sample of 458 individuals. Meaning New data-driven clustering paired with rigorous validation may offer a means to extend symptom-based psychosis taxonomies toward functional outcomes, genetic markers, and trajectory-based stratifications.Importance Identifying psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider a broader clinical spectrum, disentangle illness trajectories, and investigate genetic associations. Objective To detect psychosis subgroups using data-driven methods and examine their illness courses over 1.5 years and polygenic scores for schizophrenia, bipolar Disorder, major depression Disorder, and educational achievement. Design, Setting, and Participants This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began in January 2012 across 18 sites. Data from a referred sample of 1223 individuals (765 in the discovery sample and 458 in the validation sample) with DSM-IV diagnoses of schizophrenia, bipolar affective Disorder (I/II), schizoaffective Disorder, schizophreniform Disorder, and Brief Psychotic Disorder were collected from secondary and tertiary care sites. Discovery data were extracted in September 2016 and analyzed from November 2016 to January 2018, and prospective validation data were extracted in October 2018 and analyzed from January to May 2019. Main Outcomes and Measures A clinical battery of 188 variables measuring demographic characteristics, clinical history, symptoms, functioning, and cognition was decomposed using nonnegative matrix factorization clustering. Subtype-specific illness courses were compared with mixed models and polygenic scores with analysis of covariance. Supervised learning was used to replicate results in validation data with the most reliably discriminative 45 variables. Results Of the 765 individuals in the discovery sample, 341 (44.6%) were women, and the mean (SD) age was 42.7 (12.9) years. Five subgroups were found and labeled as affective psychosis (n = 252), suicidal psychosis (n = 44), depressive psychosis (n = 131), high-functioning psychosis (n = 252), and severe psychosis (n = 86). Illness courses with significant quadratic interaction terms were found for psychosis symptoms (R-2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R-2 = 0.28; 95% CI, 0.25-0.32), global functioning (R-2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R-2 = 0.20; 95% CI, 0.17-0.23). The depressive and severe psychosis subgroups exhibited the lowest functioning and quadratic illness courses with partial recovery followed by reoccurrence of severe illness. Differences were found for educational attainment polygenic scores (mean [SD] partial eta(2) = 0.014 [0.003]) but not for diagnostic polygenic risk. Results were largely replicated in the validation cohort. Conclusions and Relevance Psychosis subgroups were detected with distinctive clinical signatures and illness courses and specificity for a nondiagnostic genetic marker. New data-driven clinical approaches are important for future psychosis taxonomies. The findings suggest a need to consider short-term to medium-term service provision to restore functioning in patients stratified into the depressive and severe psychosis subgroups

Anastasia Theodoridou - One of the best experts on this subject based on the ideXlab platform.

  • checking the predictive accuracy of basic symptoms against ultra high risk criteria and testing of a multivariable prediction model evidence from a prospective three year observational study of persons at clinical high risk for psychosis
    European Psychiatry, 2017
    Co-Authors: Michael Pascal Hengartner, Karsten Heekeren, Diane Dvorsky, Susanne Walitza, Wulf Rossler, Anastasia Theodoridou
    Abstract:

    Abstract Background The aim of this study was to critically examine the prognostic validity of various clinical high-risk (CHR) criteria alone and in combination with additional clinical characteristics. Methods A total of 188 CHR positive persons from the region of Zurich, Switzerland (mean age 20.5 years; 60.2% male), meeting ultra high-risk (UHR) and/or basic symptoms (BS) criteria, were followed over three years. The test battery included the Structured Interview for Prodromal Syndromes (SIPS), verbal IQ and many other screening tools. Conversion to psychosis was defined according to ICD-10 criteria for schizophrenia (F20) or Brief Psychotic Disorder (F23). Results Altogether n =24 persons developed manifest psychosis within three years and according to Kaplan–Meier survival analysis, the projected conversion rate was 17.5%. The predictive accuracy of UHR was statistically significant but poor (area under the curve [AUC]=0.65, P P =.730). Sensitivity and specificity were 0.83 and 0.47 for UHR, and 0.96 and 0.09 for BS. UHR plus BS achieved an AUC=0.66, with sensitivity and specificity of 0.75 and 0.56. In comparison, baseline antiPsychotic medication yielded a predictive accuracy of AUC=0.62 (sensitivity=0.42; specificity=0.82). A multivariable prediction model comprising continuous measures of positive symptoms and verbal IQ achieved a substantially improved prognostic accuracy (AUC=0.85; sensitivity=0.86; specificity=0.85; positive predictive value=0.54; negative predictive value=0.97). Conclusions We showed that BS have no predictive accuracy beyond chance, while UHR criteria poorly predict conversion to psychosis. Combining BS with UHR criteria did not improve the predictive accuracy of UHR alone. In contrast, dimensional measures of both positive symptoms and verbal IQ showed excellent prognostic validity. A critical re-thinking of binary at-risk criteria is necessary in order to improve the prognosis of Psychotic Disorders.

Wulf Rossler - One of the best experts on this subject based on the ideXlab platform.

  • checking the predictive accuracy of basic symptoms against ultra high risk criteria and testing of a multivariable prediction model evidence from a prospective three year observational study of persons at clinical high risk for psychosis
    European Psychiatry, 2017
    Co-Authors: Michael Pascal Hengartner, Karsten Heekeren, Diane Dvorsky, Susanne Walitza, Wulf Rossler, Anastasia Theodoridou
    Abstract:

    Abstract Background The aim of this study was to critically examine the prognostic validity of various clinical high-risk (CHR) criteria alone and in combination with additional clinical characteristics. Methods A total of 188 CHR positive persons from the region of Zurich, Switzerland (mean age 20.5 years; 60.2% male), meeting ultra high-risk (UHR) and/or basic symptoms (BS) criteria, were followed over three years. The test battery included the Structured Interview for Prodromal Syndromes (SIPS), verbal IQ and many other screening tools. Conversion to psychosis was defined according to ICD-10 criteria for schizophrenia (F20) or Brief Psychotic Disorder (F23). Results Altogether n =24 persons developed manifest psychosis within three years and according to Kaplan–Meier survival analysis, the projected conversion rate was 17.5%. The predictive accuracy of UHR was statistically significant but poor (area under the curve [AUC]=0.65, P P =.730). Sensitivity and specificity were 0.83 and 0.47 for UHR, and 0.96 and 0.09 for BS. UHR plus BS achieved an AUC=0.66, with sensitivity and specificity of 0.75 and 0.56. In comparison, baseline antiPsychotic medication yielded a predictive accuracy of AUC=0.62 (sensitivity=0.42; specificity=0.82). A multivariable prediction model comprising continuous measures of positive symptoms and verbal IQ achieved a substantially improved prognostic accuracy (AUC=0.85; sensitivity=0.86; specificity=0.85; positive predictive value=0.54; negative predictive value=0.97). Conclusions We showed that BS have no predictive accuracy beyond chance, while UHR criteria poorly predict conversion to psychosis. Combining BS with UHR criteria did not improve the predictive accuracy of UHR alone. In contrast, dimensional measures of both positive symptoms and verbal IQ showed excellent prognostic validity. A critical re-thinking of binary at-risk criteria is necessary in order to improve the prognosis of Psychotic Disorders.

Phd, Heike Anderson-schmidt - One of the best experts on this subject based on the ideXlab platform.

  • An investigation of psychosis subgroups with prognostic validation and exploration of genetic underpinnings.
    'American Medical Association (AMA)', 2020
    Co-Authors: Phd, Dominic B. Dwyer, Md, Janos L. Kalman, Dipl-psych, Monika Budde, Md, Joseph Kambeitz, Phd, Anne Ruef, Antonucci, Linda A., Phd, Lana Kambeitz-ilankovic, Md, Alkomiet Hasan, Phd, Ivan Kondofersky, Phd, Heike Anderson-schmidt
    Abstract:

    IMPORTANCE: Identifying psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider a broader clinical spectrum, disentangle illness trajectories, and investigate genetic associations. OBJECTIVE: To detect psychosis subgroups using data-driven methods and examine their illness courses over 1.5 years and polygenic scores for schizophrenia, bipolar Disorder, major depression Disorder, and educational achievement. DESIGN, SETTING, AND PARTICIPANTS: This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began in January 2012 across 18 sites. Data from a referred sample of 1223 individuals (765 in the discovery sample and 458 in the validation sample) with DSM-IV diagnoses of schizophrenia, bipolar affective Disorder (I/II), schizoaffective Disorder, schizophreniform Disorder, and Brief Psychotic Disorder were collected from secondary and tertiary care sites. Discovery data were extracted in September 2016 and analyzed from November 2016 to January 2018, and prospective validation data were extracted in October 2018 and analyzed from January to May 2019. MAIN OUTCOMES AND MEASURES: A clinical battery of 188 variables measuring demographic characteristics, clinical history, symptoms, functioning, and cognition was decomposed using nonnegative matrix factorization clustering. Subtype-specific illness courses were compared with mixed models and polygenic scores with analysis of covariance. Supervised learning was used to replicate results in validation data with the most reliably discriminative 45 variables. RESULTS: Of the 765 individuals in the discovery sample, 341 (44.6%) were women, and the mean (SD) age was 42.7 (12.9) years. Five subgroups were found and labeled as affective psychosis (n = 252), suicidal psychosis (n = 44), depressive psychosis (n = 131), high-functioning psychosis (n = 252), and severe psychosis (n = 86). Illness courses with significant quadratic interaction terms were found for psychosis symptoms (R2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R2 = 0.28; 95% CI, 0.25-0.32), global functioning (R2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R2 = 0.20; 95% CI, 0.17-0.23). The depressive and severe psychosis subgroups exhibited the lowest functioning and quadratic illness courses with partial recovery followed by reoccurrence of severe illness. Differences were found for educational attainment polygenic scores (mean [SD] partial η2 = 0.014 [0.003]) but not for diagnostic polygenic risk. Results were largely replicated in the validation cohort. CONCLUSIONS AND RELEVANCE: Psychosis subgroups were detected with distinctive clinical signatures and illness courses and specificity for a nondiagnostic genetic marker. New data-driven clinical approaches are important for future psychosis taxonomies. The findings suggest a need to consider short-term to medium-term service provision to restore functioning in patients stratified into the depressive and severe psychosis subgroups