Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Berlin, Germany; Universität Regensburg, Department of Psychiatry and Psychotherapy, Regensburg, Germany; East London NHS Foundation Trust, City and Hackney Centre for Mental Health, Donald Winnicott Centre, London, United Kingdom; kbo-Lech-Mangfall-Klinik Garmisch-Partenkirchen, Department of Psychiatry and Psychotherapy, Garmisch-Partenkirchen, Germany; Ludwig-Maximilians-Universität, Department of Psychiatry and Psychotherapy, München, Germany; kbo-Isar-Amper-Klinikum, Department of Psychiatry and Psychotherapy, München, Germany; Heinrich-Heine-Universität Düsseldorf, Department of Psychiatry and Psychotherapy, Düsseldorf, Germany; Institut für Psychologische Medizin, Haag, Germany; St. Joseph-Krankenhaus, Department of Psychiatry and Psychotherapy, Berlin, Germany; LWL-Klinikum Gütersloh, Department of Psychiatry and Psychotherapy, Gütersloh, Germany; Universitätsklinikum Carl Gustav Carus, Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany; Jena University Hospital, Department of Medical Statistics, Informatics and Documentation, Friedrich-Schiller-Universität Jena, Jena, Germany; Fliedner Klinik Berlin, Center for Psychiatry, Psychotherapy and Psychosomatic Medicine.
Int J Neuropsychopharmacol. 2017 Sep 1;20(9):721-730. doi: 10.1093/ijnp/pyx043.
Treatment algorithms are considered as key to improve outcomes by enhancing the quality of care. This is the first randomized controlled study to evaluate the clinical effect of algorithm-guided treatment in inpatients with major depressive disorder.
Inpatients, aged 18 to 70 years with major depressive disorder from 10 German psychiatric departments were randomized to 5 different treatment arms (from 2000 to 2005), 3 of which were standardized stepwise drug treatment algorithms (ALGO). The fourth arm proposed medications and provided less specific recommendations based on a computerized documentation and expert system (CDES), the fifth arm received treatment as usual (TAU). ALGO included 3 different second-step strategies: lithium augmentation (ALGO LA), antidepressant dose-escalation (ALGO DE), and switch to a different antidepressant (ALGO SW). Time to remission (21-item Hamilton Depression Rating Scale ≤9) was the primary outcome.
Time to remission was significantly shorter for ALGO DE (n=91) compared with both TAU (n=84) (HR=1.67; P=.014) and CDES (n=79) (HR=1.59; P=.031) and ALGO SW (n=89) compared with both TAU (HR=1.64; P=.018) and CDES (HR=1.56; P=.038). For both ALGO LA (n=86) and ALGO DE, fewer antidepressant medications were needed to achieve remission than for CDES or TAU (P<.001). Remission rates at discharge differed across groups; ALGO DE had the highest (89.2%) and TAU the lowest rates (66.2%).
A highly structured algorithm-guided treatment is associated with shorter times and fewer medication changes to achieve remission with depressed inpatients than treatment as usual or computerized medication choice guidance.
治疗算法被认为是通过提高护理质量来改善结果的关键。这是第一项评估算法指导治疗对住院抑郁症患者的临床效果的随机对照研究。
来自 10 家德国精神病学部门的年龄在 18 至 70 岁的住院抑郁症患者被随机分配到 5 种不同的治疗组（2000 年至 2005 年），其中 3 种是标准化逐步药物治疗算法（ALGO）。第四组根据计算机化文档和专家系统（CDES）提出药物治疗方案并提供不太具体的建议，第五组接受常规治疗（TAU）。ALGO 包括 3 种不同的第二步策略：锂增效（ALGO LA）、抗抑郁药剂量递增（ALGO DE）和转换为不同的抗抑郁药（ALGO SW）。缓解时间（21 项汉密尔顿抑郁评定量表≤9）是主要结局。
与 TAU（n=84）相比，ALGO DE（n=91）的缓解时间明显更短（HR=1.67；P=.014），与 CDES（n=79）相比（HR=1.59；P=.031），与 ALGO SW（n=89）相比，与 TAU（HR=1.64；P=.018）和 CDES（HR=1.56；P=.038）相比，缓解时间更短。对于 ALGO LA（n=86）和 ALGO DE，与 CDES 或 TAU 相比，达到缓解所需的抗抑郁药更少（P<.001）。出院时的缓解率因组而异；ALGO DE 的缓解率最高（89.2%），TAU 的缓解率最低（66.2%）。
与常规治疗或计算机药物选择指导相比，高度结构化的算法指导治疗与住院抑郁症患者达到缓解的时间更短，所需药物变化更少。
