Alemi Farrokh, Min Hua, Yousefi Melanie, Becker Laura K, Hane Christopher A, Nori Vijay S, Wojtusiak Janusz
Department of Health Administration and Policy, George Mason University, Fairfax, VA.
OptumLabs Visiting Fellow.
EClinicalMedicine. 2021 Oct 25;41:101171. doi: 10.1016/j.eclinm.2021.101171. eCollection 2021 Nov.
This study summarizes the experiences of patients, who have multiple comorbidities, with 15 mono-treated antidepressants.
This is a retrospective, observational, matched case control study. The cohort was organized using claims data available through OptumLabs for depressed patients treated with antidepressants between January 1, 2001 and December 31, 2018. The cohort included patients from all states within United States of America. The analysis focused on 3,678,082 patients with major depression who had 10,221,145 antidepressant treatments. Using the robust, and large predictors of remission, and propensity to prescribe an antidepressant, the study created 16,770 subgroups of patients. The study reports the remission rate for the antidepressants within the subgroups. The overall impact of antidepressant on remission was calculated as the common odds ratio across the strata.
The study accurately modelled clinicians' prescription patterns (cross-validated Area under the Receiver Operating Curve, AROC, of 82.0%, varied from 77% to 90%) and patients' remission (cross-validated AROC of 72.0%, varied from 69.5% to 78%). In different strata, contrary to published randomized studies, remission rates differed significantly and antidepressants were not equally effective. For example, in age and gender subgroups, the best antidepressant had an average remission rate of 50.78%, 1.5 times higher than the average antidepressant (30.30% remission rate) and 20 times higher than the worst antidepressant. The Breslow-Day chi-square test for homogeneity showed that across strata a homogenous common odds-ratio did not exist (alpha<0.0001). Therefore, the choice of the optimal antidepressant depended on the strata defined by the patient's medical history.
Study findings may not be appropriate for specific patients. To help clinicians assess the transferability of study findings to specific patient, the web site http://hi.gmu.edu/ad assesses the patient's medical history, finds similar cases in our data, and recommends an antidepressant based on the experience of remission in our data. Patients can share this site's recommendations with their clinicians, who can then assess the appropriateness of the recommendations.
This project was funded by the Robert Wood Johnson foundation grant #76786. The development of related web site was supported by grant 247-02-20 from Virginia's Commonwealth Health Research Board.
本研究总结了患有多种合并症的患者使用15种单药治疗抗抑郁药的经验。
这是一项回顾性观察性匹配病例对照研究。该队列是利用OptumLabs提供的2001年1月1日至2018年12月31日期间接受抗抑郁药治疗的抑郁症患者的索赔数据组建而成。该队列包括来自美利坚合众国所有州的患者。分析集中于3678082名重度抑郁症患者,他们接受了10221145次抗抑郁治疗。利用缓解的稳健且大量的预测因素以及开具抗抑郁药的倾向,该研究创建了16770个患者亚组。该研究报告了各亚组内抗抑郁药的缓解率。抗抑郁药对缓解的总体影响以各层的共同优势比来计算。
该研究准确模拟了临床医生的处方模式(交叉验证的受试者工作特征曲线下面积,AROC,为82.0%,范围从77%至90%)以及患者的缓解情况(交叉验证的AROC为72.0%,范围从69.5%至78%)。在不同层中,与已发表的随机研究相反,缓解率差异显著,且抗抑郁药并非同样有效。例如,在年龄和性别亚组中,最佳抗抑郁药的平均缓解率为50.78%,比平均抗抑郁药(缓解率30.30%)高1.5倍,比最差抗抑郁药高20倍。用于检验同质性的Breslow-Day卡方检验表明,各层不存在同质的共同优势比(α<0.0001)。因此,最佳抗抑郁药的选择取决于由患者病史定义的层。
研究结果可能不适用于特定患者。为帮助临床医生评估研究结果对特定患者的可转移性,网站http://hi.gmu.edu/ad评估患者的病史,在我们的数据中查找相似病例,并根据我们数据中的缓解经验推荐一种抗抑郁药。患者可将该网站的推荐告知其临床医生,然后临床医生可评估这些推荐的适当性。
本项目由罗伯特·伍德·约翰逊基金会资助,资助编号为#76786。相关网站的开发得到弗吉尼亚联邦健康研究委员会247-02-20号资助的支持。