Predicting treatment resistance in people with a first-episode of psychosis using commonly recorded clinical information
Predicting treatment resistance in people with a first-episode of psychosis using commonly recorded clinical information
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DOI：
10.1192/j.eurpsy.2022.303
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发表时间：
2022-09-01
影响因子：
7.8
通讯作者：
Khandaker, G.
中科院分区：
文献类型：
--
作者：
Osimo, E. F.;Perry, B.;Mallikarjun, P.;Murray, G.;Howes, O.;Jones, P.;Upthegrove, R.;Khandaker, G.
23%的人经历了精神病的第一次发作（FEP）发展为难治性精神分裂症（TRS）。目前，还没有确定的方法来准确地确定谁将从基线发展TRS。在这项研究中，我们使用了来自三个英国早期干预服务（EIS）的患者数据，以调查在FEP基线时常规记录的社会人口统计学，生活方式和生物学数据对6年后TRS风险的预测潜力。我们开发了两种风险预测算法，使用基线时通常记录的信息预测FEP发作后2-8年的TRS风险。使用强制进入方法，我们建立了一个模型，包括年龄，性别，种族，甘油三酯，碱性磷酸酶水平和淋巴细胞计数。我们还制作了一个基于机器学习的模型，包括另外四个变量。该模型是使用来自两个和外部验证的数据在另一个英国精神病EIS。开发样本包括785名患者，验证样本包括1，110名患者。在内部验证中，模型很好地区分了TRS（强制进入：C 0.70，95%CI 0.63-0.76; LASSO：C 0.69，95%CI 0.63-0.77）。在外部验证时，区分性能减弱（强制进入：C 0.63，0.58-0.69; LASSO：C 0.64，0.58-0.69），但在重新校准和修订淋巴细胞预测因子后，强制进入模型的区分性能恢复（C：0.67，0.62-0.73）。使用通常记录的临床信息，包括在FEP发作时采集的生物标志物，可以帮助预测TRS。这些措施应考虑在未来的研究建模精神病学的结果。没有明显的关系。
23% of people experiencing a first episode of psychosis (FEP) develop treatment resistant schizophrenia (TRS). At present, there are no established methods to accurately identify who will develop TRS from baseline. In this study we used patient data from three UK early intervention services (EIS) to investigate the predictive potential of routinely recorded sociodemographic, lifestyle and biological data at FEP baseline for the risk of TRS up to six years later. We developed two risk prediction algorithms to predict the risk of TRS at 2-8 years from FEP onset using commonly recorded information at baseline. Using the forced-entry method, we created a model including age, sex, ethnicity, triglycerides, alkaline phosphatase levels and lymphocyte counts. We also produced a machine-learning-based model, including an additional four variables. The models were developed using data from two and externally validated in another UK psychosis EIS. The development samples included 785 patients, and 1,110 were included in the validation sample. The models discriminated TRS well at internal validation (forced-entry: C 0.70, 95%CI 0.63-0.76; LASSO: C 0.69, 95%CI 0.63-0.77). At external validation, discrimination performance attenuated (forced-entry: C 0.63, 0.58-0.69; LASSO: C 0.64, 0.58-0.69) but recovered for the forced entry model after recalibration and revision of the lymphocyte predictor (C: 0.67, 0.62-0.73). The use of commonly recorded clinical information including biomarkers taken at FEP onset could help to predict TRS. These measures should be considered in future studies modelling psychiatric outcomes. No significant relationships.