A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort
Tekijät: Koutsouleris Nikolaos, Worthington Michelle, Dwyer Dominic B., Kambeitz-Ilankovic Lana, Sanfelici Rachele, Fusar-Poli Paolo, Rosen Marlene, Ruhrmann Stephan, Anticevic Alan, Addington Jean, Perkins Diana O., Bearden Carrie E., Cornblatt Barbara A., Cadenhead Kristin S., Mathalon Daniel H., McGlashan Thomas, Seidman Larry, Tsuang Ming, Walker Elaine F., Woods Scott W., Falkai Peter, Lencer Rebekka, Bertolino Alessandro, Kambeitz Joseph, Schultze-Lutter Frauke, Meisenzahl Eva, Salokangas Raimo K.R., Hietala Jarmo, Brambilla Paolo, Upthegrove Rachel, Borgwardt Stefan, Wood Stephen, Gur Raquel E., McGuire Philip, Cannon Tyrone D.
Kustantaja: Elsevier Inc.
Julkaisuvuosi: 2021
Journal: Biological Psychiatry
Tietokannassa oleva lehden nimi: Biological Psychiatry
Vuosikerta: 90
Numero: 9
Aloitussivu: 632
Lopetussivu: 642
eISSN: 1873-2402
DOI: https://doi.org/10.1016/j.biopsych.2021.06.023
Verkko-osoite: https://www.sciencedirect.com/science/article/pii/S0006322321014335?via%3Dihub
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/Publication/67270858
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
