Chang Yue-Cune, Lane Hsien-Yuan, Yang Kung-Han, Huang Chieh-Liang
Institute of Life Science and Department of Mathematics, Tamkang University, Tamsui, Taipei, Taiwan.
J Clin Psychopharmacol. 2006 Dec;26(6):554-9. doi: 10.1097/01.jcp.0000246211.95905.8c.
Researchers, by studying first-generation antipsychotics, have established an early prediction model, which had a favorable specificity but a low sensitivity. This study aims to optimize early prediction of treatment response for schizophrenia using a novel statistic method that can be done even under the Microsoft Excel system of a personal computer.
One hundred twenty-three inpatients with acutely exacerbated schizophrenia were given optimal therapy of risperidone, a commonly used second-generation antipsychotic agent. Response was defined as a reduction of 20% or more in the Positive and Negative Syndrome Scale total score. We applied the generalized estimating equation method's logistic regression to establish an early prediction model based on the treatment results of the first and the second weeks.
The proposed method correctly predicted nonresponse at 4 and 6 weeks in 80.8% and 81.8% of the patients, respectively. The method also identified responder at 4 and 6 weeks in 80.0% and 82.8%, respectively. The predictive powers (or correct prediction rates) at 4 and 6 weeks were 80.3% and 82.4%, respectively. In addition, the results based on the responses in Positive and Negative Syndrome Scale scores were slightly better than those in Brief Psychiatric Rating Scale scores.
Using the first 2 weeks' treatment results to predict the fourth or sixth week's treatment response is acceptable in terms of specificity, sensitivity, and predictive power. Further studies are needed. Moreover, whether this model could be applied to establish a prediction system for other psychotropics, such as antidepressants, also deserves research.
研究人员通过对第一代抗精神病药物的研究，建立了一个早期预测模型，该模型具有良好的特异性，但敏感性较低。本研究旨在使用一种即使在个人计算机的Microsoft Excel系统下也能完成的新型统计方法，优化精神分裂症治疗反应的早期预测。
123例急性加重期精神分裂症住院患者接受了常用的第二代抗精神病药物利培酮的优化治疗。反应定义为阳性和阴性症状量表总分降低20%或更多。我们应用广义估计方程法的逻辑回归，根据第一周和第二周的治疗结果建立早期预测模型。
所提出的方法分别在4周和6周时正确预测无反应的患者比例为80.8%和81.8%。该方法在4周和6周时分别识别出有反应者的比例为80.0%和82.8%。4周和6周时的预测能力（或正确预测率）分别为80.3%和82.4%。此外，基于阳性和阴性症状量表评分反应的结果略优于简明精神病评定量表评分的结果。
就特异性、敏感性和预测能力而言，利用前两周的治疗结果预测第四周或第六周的治疗反应是可以接受的。需要进一步研究。此外，该模型是否可应用于建立其他精神药物（如抗抑郁药）的预测系统也值得研究。
