Longitudinal trajectory analysis of antipsychotic response in patients with schizophrenia: 6-week, randomised, open-label, multicentre clinical trial

Dai, Minhan; Wu, Ye; Tang, Yiguo; Yue, Weihua; Yan, Hao; Zhang, Yamin; Tan, Liwen; Deng, Wei; Chen, Qi; Yang, Guigang; Lu, Tianlan; Wang, Lifang; Yang, Fude; Zhang, Fuquan; Yang, Jianli; Li, Keqing; Lv, Luxian; Tan, Qingrong; Zhang, Hongyan; Ma, Xin; Li, Lingjiang; Wang, Chuanyue; Ma, Xiaohong; Zhang, Dai; Yu, Hao; Zhao, Liansheng; Ren, Hongyan; Wang, Yingcheng; Hu, Xun; Zhang, Guangya; Du, Xiangze; Wang, Qiang; Li, Tao

doi:10.1192/bjo.2020.105

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…However, in treating schizophrenia, some patients may have been in remission at the early stage but turned out to be non-responders later due to disease fluctuation, the placebo effect and other reasons. At present, these patients cannot be effectively distinguished, towards which our team managed to divide the early treatment response into a high-trajectory group of better responders and a low-trajectory group of worse responders through unsupervised machine learning K-means for longitudinal data [ 42 ]. Since the early relief of psychotic symptoms predict that long-term efficacy could provide a clinically empirical basis to help improve the early identification of poor outcomes, this field is worthy of further investigation [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Early Efficacy of Antipsychotic Medications at Week 2 Predicts Subsequent Responses at Week 6 in a Large-scale Randomized Controlled Trial

Tang

et al. 2023

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Background: Since the early clinical efficacy of antipsychotics has not yet been well perceived, this study sought to decide whether the efficacy of antipsychotics at week 2 can predict subsequent responses at week 6 and identify how such predictive capacities vary among different antipsychotics and psychotic symptoms. Methods: A total of 3010 patients with schizophrenia enrolled in a randomized controlled trial (RCT) and received a 6-week treatment with one antipsychotic drug randomly chosen from five atypical antipsychotics (risperidone 2-6 mg/d, olanzapine 5-20 mg/d, quetiapine 400-750 mg/d, aripiprazole 10-30 mg/d, and ziprasidone 80-160 mg/d) and two typical antipsychotics (perphenazine 20-60 mg/d and haloperidol 6-20 mg/d). Early efficacy was defined as the reduction rate using the Positive and Negative Syndrome Scale (PANSS) total score at week 2. With cut-offs at 50% reduction, logistic regression, receiver operating characteristic (ROC) and random forests were adopted. Results: The reduction rate of PANSS total score and improvement of psychotic symptoms at week 2 enabled subsequent responses to 7 antipsychotics to be predicted, in which improvements in delusions, lack of judgment and insight, unusual thought content, and suspiciousness/ persecution were endowed with the greatest weight. Conclusions: It is robust enough to clinically predict treatment responses to antipsychotics at week 6 using the reduction rate of PANSS total score and symptom relief at week 2. Psychiatric clinicians had better determine whether to switch the treatment plan by the first 2 weeks.

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Section: Discussionmentioning

confidence: 99%

Early Efficacy of Antipsychotic Medications at Week 2 Predicts Subsequent Responses at Week 6 in a Large-scale Randomized Controlled Trial

Tang

et al. 2023

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Machine and deep learning for longitudinal biomedical data: a review of methods and applications

Cascarano,

Mur-Petit,

Hernández-González

et al. 2023

Artif Intell Rev

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Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical field, as many diseases have a complex and multi-factorial time-course, and start to develop long before symptoms appear. With the increasing healthcare digitisation, the application of machine learning techniques for longitudinal biomedical data may enable the development of new tools for assisting clinicians in their day-to-day medical practice, such as for early diagnosis, risk prediction, treatment planning and prognosis estimation. However, due to the heterogeneity and complexity of time-varying data sets, the development of suitable machine learning models introduces major challenges for data scientists as well as for clinical researchers. This paper provides a comprehensive and critical review of recent developments and applications in machine learning for longitudinal biomedical data. Although the paper provides a discussion of clustering methods, its primary focus is on the prediction of static outcomes, defined as the value of the event of interest at a given instant in time, using longitudinal features, which has emerged as the most commonly employed approach in healthcare applications. First, the main approaches and algorithms for building longitudinal machine learning models are presented in detail, including their technical implementations, strengths and limitations. Subsequently, most recent biomedical and clinical applications are reviewed and discussed, showing promising results in a wide range of medical specialties. Lastly, we discuss current challenges and consider future directions in the field to enhance the development of machine learning tools from longitudinal biomedical data.

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