Schizophrenia bulletin · 2026
Real-Time Precision Psychiatry for Schizophrenia: A Microfluidic Genotyping and Super-Learner Platform for Treatment Response Prediction.
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Li Ji-Qing, Tang Tian, Yu Tian-Gui, Wang Qing, Feng Yan-Hong, Qu Jing, Hu Xi-Feng, Zhao Qing-Bo, Cao Hong-Qian, Xue Fu-Zhong
Аннотация
BACKGROUND: Antipsychotic treatment response in schizophrenia shows substantial interindividual variability. While pharmacogenomics (PGx) holds potential for personalized prescribing, current models fall short in integrating multimodal factors and meeting real-time clinical decision-making needs.
HYPOTHESIS: The precision medicine platform integrating multi-dimensional factors can provide guidance for selecting antipsychotic medications for patients with schizophrenia.
STUDY DESIGN: This study employed a retrospective cohort design for model development (2019-2021, N = 735) and external validation (2021-2022, N = 90). We integrated PGx profiles, clinical characteristics, and medication data, selected features via random forest recursive feature elimination (RF-RFE), and built a Super Learner ensemble model (incorporating 5 machine learning algorithms) to predict Positive and Negative Syndrome Scale (PANSS) reduction. For 13 prioritized single-nucleotide polymorphisms (SNPs), we developed a kompetitive allele-specific PCR-based microfluidic chip and validated its accuracy against Sanger sequencing in 24 clinical samples. A clinical decision support (CDS) tool integrating genotyping and predictive analytics was deployed.
STUDY RESULTS: The Super Learner model achieved a cross-validated RMSE of 7.08 (R2 = 0.89) and an external validation RMSE of 9.02 (R2 = 0.76). The microfluidic chip showed 100% concordance with Sanger sequencing across all 13 SNPs. The integrated CDS system demonstrated sample-to-report feasibility within 3 h.
CONCLUSIONS: Microfluidic genotyping and Super Learner platform enables rapid, real-time prediction of antipsychotic treatment response in schizophrenia, providing a scalable tool for early personalized treatment selection during hospitalization.
HYPOTHESIS: The precision medicine platform integrating multi-dimensional factors can provide guidance for selecting antipsychotic medications for patients with schizophrenia.
STUDY DESIGN: This study employed a retrospective cohort design for model development (2019-2021, N = 735) and external validation (2021-2022, N = 90). We integrated PGx profiles, clinical characteristics, and medication data, selected features via random forest recursive feature elimination (RF-RFE), and built a Super Learner ensemble model (incorporating 5 machine learning algorithms) to predict Positive and Negative Syndrome Scale (PANSS) reduction. For 13 prioritized single-nucleotide polymorphisms (SNPs), we developed a kompetitive allele-specific PCR-based microfluidic chip and validated its accuracy against Sanger sequencing in 24 clinical samples. A clinical decision support (CDS) tool integrating genotyping and predictive analytics was deployed.
STUDY RESULTS: The Super Learner model achieved a cross-validated RMSE of 7.08 (R2 = 0.89) and an external validation RMSE of 9.02 (R2 = 0.76). The microfluidic chip showed 100% concordance with Sanger sequencing across all 13 SNPs. The integrated CDS system demonstrated sample-to-report feasibility within 3 h.
CONCLUSIONS: Microfluidic genotyping and Super Learner platform enables rapid, real-time prediction of antipsychotic treatment response in schizophrenia, providing a scalable tool for early personalized treatment selection during hospitalization.