Si Tianmei, Sun Ling, Zhang Yilong, Zhang Lili
Peking University Sixth Hospital (Institute of Mental Health), Beijing, China.
NHC Key Laboratory of Mental Health (Peking University) & National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China.
Front Psychiatry. 2021 Aug 23;12:723245. doi: 10.3389/fpsyt.2021.723245. eCollection 2021.
This study aimed to investigate the factors that influenced the clinicians to adjust the paliperidone dose in the acute phase of schizophrenia. This was a study of an 8-week, open-label, single-arm multicenter trial which evaluated the efficacy, safety, and tolerability of flexible doses of paliperidone ER (3-12 mg/day) in patients with acutely exacerbated schizophrenia. Patients were divided into groups according to the dose at week 8 (3, 6, and 9-12 mg). The responder was defined as the reduction percentage in the Positive and Negative Syndrome Scale (PANSS) total score of ≥30%. According to the chi-squared automatic interaction detection algorithm, decision tree models predicting an increase in the dose of paliperidone ER were established. A decision tree, based on 4-week Marder positive factor, Clinical Global Impression (CGI), and BMI, was established to guide the dose adjustments of paliperidone ER in the acute phase of schizophrenia. The multivariable logistic regression analysis showed that lower age at onset, higher baseline PANSS positive subscale score, and lower baseline Personal and Social Performance Scale (PSP) score were significant predictors of increased dose in responders. Patients with young-onset age, severe baseline symptoms, and poor function are more likely to benefit from high dosage.
本研究旨在调查在精神分裂症急性期影响临床医生调整帕利哌酮剂量的因素。这是一项为期8周的开放标签、单臂多中心试验,评估了灵活剂量的帕利哌酮缓释片(3 - 12毫克/天)对急性加重型精神分裂症患者的疗效、安全性和耐受性。患者根据第8周的剂量(3、6和9 - 12毫克)分组。缓解者定义为阳性和阴性症状量表(PANSS)总分降低百分比≥30%。根据卡方自动交互检测算法,建立了预测帕利哌酮缓释片剂量增加的决策树模型。基于4周的马德阳性因子、临床总体印象(CGI)和体重指数(BMI)建立了决策树,以指导精神分裂症急性期帕利哌酮缓释片的剂量调整。多变量逻辑回归分析显示,发病年龄较低、基线PANSS阳性分量表得分较高以及基线个人和社会功能量表(PSP)得分较低是缓解者剂量增加的显著预测因素。发病年龄小、基线症状严重和功能差的患者更有可能从高剂量中获益。