External validation and refinement of the Psychosis Polyrisk Score to detect individuals at clinical high risk for psychosis.
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Stefanelli Riccardo, Oliver Dominic, Damiani Stefano, Orlandi Marika, Provenzani Umberto, Bortolin Elisa, Rovida Margherita, Addington Jean, Alameda Luis, Arango Celso, Breitborde Nicholas J, Broome Matthew, Cadenhead Kristin S, Calkins Monica E, Carrion Ricardo E, Castillo-Passi Rolando I, Chen Yu Hai Eric, Choi Jimmy, Conus Philippe, Cornblatt Barbara A, Díaz-Caneja Covadonga M, Ellman Lauren M, Gaspar Pablo A, Gerber Carla, Birkedal Glenthøj Louise, Horton Leslie E, Hui Christy Lm, Kambeitz Joseph, Kambeitz-Ilankovic Lana, Keshavan Matcheri S, Kim Minah, Kim Sung-Wan, Koutsouleris Nikolaos, Kwon Jun Soo, Langbein Kerstin, Mamah Daniel, Mathalon Daniel H, Mittal Vijay A, Nordentoft Meredete, Pearlson Godfrey D, Penzel Nora, Perez Jesus, Perkins Diana O, Powers Albert R, Rogers Jack R, Sabb Fred W, Schiffman Jason, Shah Jai, Silverstein Steven M, Smesny Stefan, Stone William S, Strauss Gregory P, Thompson Judy L, Upthegrove Rachel, Verma Swapna, Wang Jijun, Wolf Daniel H, Zhang TianHong, Bouix Sylvain, Corcoran Cheryl M, Kapur Tina, Pasternak Ofer, Bearden Carrie, Kahn Rene S, Kane John M, McGorry Patrick D, Nelson Barnaby, Shenton Martha E, Woods Scott W, Accelerating Medicines Partnership Schizophrenia (AMP SCZ), Fusar-Poli Paolo
Аннотация
OBJECTIVES: This study aimed to (1) perform an external validation of the PPS model in a large independent sample and (2) refine the model using expanded predictor data.
METHODS: Participants were recruited through the AMP SCZ program: n=1642 CHR-P, n=519 CC. We performed a formal external validation of the PPS original model. The model was then refined incorporating expanded predictor data (perceived loneliness, neighbourhood quality) and increasing the granularity of existing predictors (ethnicity, childhood trauma, parental severe mental illness). Next, we evaluated the refined model through repeated nested cross-validation and compared it to the original model retrained in the new dataset. Discrimination (Harrell's C-index) and calibration (intercept and slope) were used to assess model performance.
FINDINGS: The externally validated PPS original model showed good discrimination (C=0.777, 95% CI 0.756 to 0.797) between CHR-P and CC and good calibration (intercept=0; slope=1.003) after recalibration. The refined PPS model exhibited higher internally cross-validated discrimination than the retrained original model (C=0.876, 95% CI 0.860 to 0.891 vs C=0.796, 95% CI 0.775 to 0.816), as well as good calibration performance (intercept=-0.008; slope=1.031).
CONCLUSIONS: The PPS showed good generalisability to a large, independent sample, and performance improved after model refinement.
CLINICAL IMPLICATIONS: The PPS represents a potential scalable tool within stepped assessment frameworks for identifying individuals who warrant further CHR-P assessment.
TRIAL REGISTRATION NUMBER: NCT05905003.