Lee Rebecca, Griffiths Sian Lowri, Gkoutos Georgios V, Wood Stephen J, Bravo-Merodio Laura, Lalousis Paris A, Everard Linda, Jones Peter B, Fowler David, Hodegkins Joanne, Amos Tim, Freemantle Nick, Singh Swaran P, Birchwood Max, Upthegrove Rachel
Institute for Mental Health, University of Birmingham, UK; Centre for Youth Mental Health, University of Melbourne, Australia.
Institute for Mental Health, University of Birmingham, UK.
Schizophr Res. 2024 Dec;274:66-77. doi: 10.1016/j.schres.2024.09.010. Epub 2024 Sep 10.
Treatment resistance (TR) in schizophrenia may be defined by the persistence of positive and/or negative symptoms despite adequate treatment. Whilst previous investigations have focused on positive symptoms, negative symptoms are highly prevalent, impactful, and difficult to treat. In the current study we aimed to develop easily employable prediction models to predict TR in positive and negative symptom domains from first episode psychosis (FEP).
Longitudinal cohort data from 1027 individuals with FEP was utilised. Using a robust definition of TR, n = 51 (4.97 %) participants were treatment resistant in the positive domain and n = 56 (5.46 %) treatment resistant in the negative domain 12 months after first presentation. 20 predictor variables, selected by existing evidence and availability in clinical practice, were entered into two LASSO regression models. We estimated the models using repeated nested cross-validation (NCV) and assessed performance using discrimination and calibration measures.
The prediction model for TR in the positive domain showed good discrimination (AUC = 0.72). Twelve predictor variables (male gender, cannabis use, age, positive symptom severity, depression and academic and social functioning) were retained by each outer fold of the NCV procedure, indicating importance in prediction of the outcome. However, our negative domain model failed to discriminate those with and without TR, with results only just over chance (AUC = 0.56).
Treatment resistance of positive symptoms can be accurately predicted from FEP using routinely collected baseline data, however prediction of negative domain-TR remains a challenge. Detailed negative symptom domains, clinical data, and biomarkers should be considered in future longitudinal studies.
精神分裂症的治疗抵抗（TR）可定义为尽管进行了充分治疗，但阳性和/或阴性症状仍持续存在。虽然先前的研究主要集中在阳性症状上，但阴性症状却非常普遍、影响重大且难以治疗。在本研究中，我们旨在开发易于应用的预测模型，以从首发精神病（FEP）预测阳性和阴性症状领域的TR。
利用了来自1027名FEP患者的纵向队列数据。使用TR的严格定义，在首次就诊12个月后，有51名（4.97%）参与者在阳性领域存在治疗抵抗，56名（5.46%）在阴性领域存在治疗抵抗。将根据现有证据和临床实践中的可得性选择的20个预测变量纳入两个套索回归模型。我们使用重复嵌套交叉验证（NCV）估计模型，并使用区分度和校准指标评估模型性能。
阳性领域TR预测模型显示出良好的区分度（AUC = 0.72）。NCV程序的每个外折都保留了12个预测变量（男性、使用大麻、年龄、阳性症状严重程度、抑郁以及学业和社会功能），表明它们对预测结果具有重要性。然而，我们的阴性领域模型未能区分有无TR的患者，结果仅略高于随机水平（AUC = 0.56）。
使用常规收集的基线数据可以从FEP准确预测阳性症状的治疗抵抗，然而阴性领域TR的预测仍然是一个挑战。未来的纵向研究应考虑详细的阴性症状领域、临床数据和生物标志物。
