External validation and refinement of the Psychosis Polyrisk Score to detect individuals at clinical high risk for psychosis.
The Psychosis Polyrisk Score (PPS) was developed to characterise exposure to environmental and developmental risk factors for psychosis. In previous studies, the PPS showed promise particularly in discriminating between individuals at clinical high risk for psychosis (CHR-P) and community controls (CC).
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.
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.
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).
The PPS showed good generalisability to a large, independent sample, and performance improved after model refinement.
The PPS represents a potential scalable tool within stepped assessment frameworks for identifying individuals who warrant further CHR-P assessment.
NCT05905003.
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.
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.
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).
The PPS showed good generalisability to a large, independent sample, and performance improved after model refinement.
The PPS represents a potential scalable tool within stepped assessment frameworks for identifying individuals who warrant further CHR-P assessment.
NCT05905003.
Authors
Stefanelli Stefanelli, Oliver Oliver, Damiani Damiani, Orlandi Orlandi, Provenzani Provenzani, Bortolin Bortolin, Rovida Rovida, Addington Addington, Alameda Alameda, Arango Arango, Breitborde Breitborde, Broome Broome, Cadenhead Cadenhead, Calkins Calkins, Carrion Carrion, Castillo-Passi Castillo-Passi, Chen Chen, Choi Choi, Conus Conus, Cornblatt Cornblatt, Díaz-Caneja Díaz-Caneja, Ellman Ellman, Gaspar Gaspar, Gerber Gerber, Birkedal Glenthøj Birkedal Glenthøj, Horton Horton, Hui Hui, Kambeitz Kambeitz, Kambeitz-Ilankovic Kambeitz-Ilankovic, Keshavan Keshavan, Kim Kim, Kim Kim, Koutsouleris Koutsouleris, Kwon Kwon, Langbein Langbein, Mamah Mamah, Mathalon Mathalon, Mittal Mittal, Nordentoft Nordentoft, Pearlson Pearlson, Penzel Penzel, Perez Perez, Perkins Perkins, Powers Powers, Rogers Rogers, Sabb Sabb, Schiffman Schiffman, Shah Shah, Silverstein Silverstein, Smesny Smesny, Stone Stone, Strauss Strauss, Thompson Thompson, Upthegrove Upthegrove, Verma Verma, Wang Wang, Wolf Wolf, Zhang Zhang, Bouix Bouix, Corcoran Corcoran, Kapur Kapur, Pasternak Pasternak, Bearden Bearden, Kahn Kahn, Kane Kane, McGorry McGorry, Nelson Nelson, Shenton Shenton, Woods Woods, , Fusar-Poli Fusar-Poli
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