Given the importance of patients' subjective experience and attitudes in the management of severe mental illness, the present study evaluated their potential role as predictors of future continuation of long-acting injectable antipsychotic maintenance treatment (LAI-AMT) in clinically stable outpatients with schizophrenia switching from an oral therapy. Retrospective data from 59 subjects receiving LAI-AMT for at least 6 months were collected. Patients who continued LAI treatment (n = 32) were compared to those who discontinued it (n = 27), assessing baseline socio-demographic and clinical characteristics, psychopathological features (Positive And Negative Syndrome Scale, Montgomery-Åsberg Depression Rating Scale and Young Mania Rating Scale) and patient-reported experience of treatment through Drug Attitude Inventory 10-item (DAI-10) and Subjective Well-being under Neuroleptics short form. Binary logistic and Cox regression analyses explored the predictive role of the mentioned variables on treatment discontinuation. The Kaplan-Meier estimator compared dropout from LAI treatment in subsamples with different characteristics. Unemployment and lower baseline DAI-10 scores predicted LAI-AMT discontinuation. No major differences were detected in other socio-demographic, clinical or psychometric indexes. When switching from oral to LAI-AMT, the preliminary assessment of attitude towards drug might be clinically relevant, allowing the identification of patients at risk for treatment discontinuation.

中文翻译：

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精神分裂症门诊患者中止长效注射抗精神病药物治疗的预测因素：药物态度清单 10 的相关性。

鉴于患者的主观经历和态度在严重精神疾病治疗中的重要性，本研究评估了它们作为未来继续对临床稳定的精神分裂症转换门诊患者进行长效注射抗精神病药物维持治疗（LAI-AMT）的预测因素的潜在作用来自口服疗法。收集了 59 名接受 LAI-AMT 至少 6 个月的受试者的回顾性数据。将继续 LAI 治疗的患者 (n = 32) 与停止治疗的患者 (n = 27) 进行比较，评估基线社会人口统计学和临床​​特征、精神病理特征（阳性和阴性综合征量表、蒙哥马利-阿斯伯格抑郁评定量表和年轻躁狂症量表）评定量表）以及患者通过药物态度量表 10 项 (DAI-10) 和精神安定药简表下的主观幸福感报告的治疗经历。二元 Logistic 和 Cox 回归分析探讨了上述变量对治疗中断的预测作用。Kaplan-Meier 估计器比较了具有不同特征的子样本中 LAI 治疗的退出情况。失业和较低的基线 DAI-10 分数预示着 LAI-AMT 的终止。其他社会人口统计学、临床或心理测量指标没有发现重大差异。当从口服转为 LAI-AMT 时，对药物态度的初步评估可能具有临床相关性，从而可以识别有停止治疗风险的患者。