Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort,Biological Psychiatry

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Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort
Biological Psychiatry ( IF 10.6 ) Pub Date : 2021-07-06 , DOI: 10.1016/j.biopsych.2021.06.023
Nikolaos Koutsouleris ₁ , Michelle Worthington ₂ , Dominic B Dwyer ₃ , Lana Kambeitz-Ilankovic ₄ , Rachele Sanfelici ₃ , Paolo Fusar-Poli ₅ , Marlene Rosen ₆ , Stephan Ruhrmann ₆ , Alan Anticevic ₂ , Jean Addington ₇ , Diana O Perkins ₈ , Carrie E Bearden ₉ , Barbara A Cornblatt ₁₀ , Kristin S Cadenhead ₁₁ , Daniel H Mathalon ₁₂ , Thomas McGlashan ₁₃ , Larry Seidman ₁₄ , Ming Tsuang ₁₁ , Elaine F Walker ₁₅ , Scott W Woods ₁₃ , Peter Falkai ₃ , Rebekka Lencer ₁₆ , Alessandro Bertolino ₁₇ , Joseph Kambeitz ₆ , Frauke Schultze-Lutter ₁₈ , Eva Meisenzahl ₁₈ , Raimo K R Salokangas ₁₉ , Jarmo Hietala ₁₉ , Paolo Brambilla ₂₀ , Rachel Upthegrove ₂₁ , Stefan Borgwardt ₂₂ , Stephen Wood ₂₃ , Raquel E Gur ₂₄ , Philip McGuire ₂₅ , Tyrone D Cannon ₂₆

Affiliation

Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Max-Planck Institute of Psychiatry, Munich, Germany; Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom.
Department of Psychology, Yale University, New Haven, Connecticut.
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany; Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany.
Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom.
Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany.
Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada.
Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina.
Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California.
Zucker Hillside Hospital, Northwell Health, Queens, New York.
University of California San Diego, San Diego, California.
Department of Psychiatry, University of California San Francisco, San Francisco, California; San Francisco VA Medical Center, San Francisco, California.
Department of Psychiatry, Yale University, New Haven, Connecticut.
Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center, Boston, Massachusetts.
Department of Psychology and Psychiatry, Emory University, Atlanta, Georgia.
Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany.
Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.
Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Germany.
Department of Psychiatry, University of Turku, Turku, Finland.
Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
Institute of Mental Health, University of Birmingham, Birmingham, United Kingdom; School of Psychology, University of Birmingham, Birmingham, United Kingdom.
Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany; Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Basel, Switzerland.
Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Orygen, National Centre of Excellence for Youth Mental Health, Melbourne, Victoria, Australia.
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom.
Department of Psychology, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut.

Background

Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes.

Methods

We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation.

Results

After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA–CHR|ROD and validation in NAPLS-2–UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts.

Conclusions

Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.

中文翻译：

迈向用于精神病预测的通用和跨诊断工具：多站点 PRONIA 队列中 NAPLS-2 风险计算器的独立验证和改进

背景

过渡到精神病是临床高危 (CHR) 综合征最不利的结果之一，包括超高危 (UHR) 和基本症状状态。临床风险计算器可能有助于早期和个性化地拦截精神病，但它们的实际实施需要在不同风险人群中进行彻底验证，包括患有抑郁症的年轻患者。

方法

我们验证了先前描述的 NAPLS-2（北美前驱症纵向研究 2）计算器，用于 334 名患有 CHR 或新发抑郁症 (ROD) 的患者（26 名过渡到精神病），这些患者来自多站点欧洲 PRONIA（早期精神病的个性化预后工具）管理）学习。患者被分为三个风险丰富级别，从 UHR、CHR 到包括 CHR 或 ROD 患者的广泛风险人群 (CHR|ROD)。我们使用互惠的外部验证评估了风险丰富和不同的预测算法如何影响预后表现。

结果

校准后，NAPLS-2 模型预测精神病的平衡准确度 (BAC)（敏感性、特异性）在 PRONIA-UHR 队列中为 68%（73%、63%），在 PRONIA-UHR 队列中为 67%（74%、60%）。 CHR 队列，70% (73%, 66%) 在 CHR|ROD 患者中。PRONIA-CHR|ROD 的多模型推导和 NAPLS-2-UHR 患者的验证证实，更广泛的风险定义产生了更准确的风险计算器（基于 CHR|ROD 与基于 UHR 的性能：67% [68%, 66%]与 58% [61%, 56%])。支持向量机在 CHR|ROD (BAC = 71%) 中表现优异，而脊逻辑回归和支持向量机在 CHR (BAC = 67%) 和 UHR 队列 (BAC = 65%) 中表现相似。减轻的精神病症状预示着跨风险水平的精神病，而更年轻的年龄和降低的处理速度对于更广泛的风险人群变得越来越重要。