背景:关于抗抑郁药处方的共识指南建议,临床医生应保持警惕,将抗抑郁药与患者的病史相匹配,但没有提供针对特定病史的抗抑郁药的具体建议。
目的:对于接受心理治疗的重度抑郁症患者,这项研究为处方适合患者病史的抗抑郁药物提供了经验衍生的指南.
方法:本回顾性研究,观察,队列研究分析了一个包含3,678,082名患者的大型保险数据库。数据来自美国2001年1月1日至2018年12月31日之间的医疗保健提供商。这些患者有10,221,145次抗抑郁治疗。本研究报告了14种最常用的单一抗抑郁药(阿米替林,安非他酮,西酞普兰,去文拉法辛,多塞平,度洛西汀,艾司西酞普兰,氟西汀,米氮平,nortriptyline,帕罗西汀,舍曲林,曲唑酮,和文拉法辛)和一个名为“其他”(其他抗抑郁药/抗抑郁药组合)的类别。该研究使用稳健的LASSO回归来确定影响缓解率和临床医生选择抗抑郁药的因素。通过分层消除了观察数据中的选择偏差。我们将数据分为16,770个小组,至少100例,使用影响缓解和选择偏倚的最大因素的组合。本文报道了接受心理治疗的2,467例亚组患者。
结果:我们发现,并且具有统计学意义,亚组患者缓解率的差异。舍曲林的缓解率为4.5%至77.86%,氟西汀从2.86%降至77.78%,文拉法辛从5.07%到76.44%,安非他酮从0.5%到64.63%,对于从1.59%到75%的去文拉法辛,度洛西汀从3.77%到75%,帕罗西汀从6.48%上升到68.79%,艾司西酞普兰从1.85%到65%,西酞普兰从4.67%降至76.23%。显然,这些药物对于某些亚组的患者是理想的,但对于其他亚组则不是。如果患者与亚组匹配,临床医生可以开出该亚组中效果最好的药物.一些药物(阿米替林,多塞平,nortriptyline,曲唑酮)的缓解率始终低于11%,因此不适合作为任何亚组的单一抗抑郁治疗。
结论:这项研究为临床医生提供了一个机会,为他们的患者确定最佳的抗抑郁药,在他们进行抗抑郁药的反复试验之前。
结论:为了促进患者与最有效的抗抑郁药的匹配,这项研究提供了一个免费的,非商业,决策援助http://MeAgainMeds.com。
结论:政策制定者应评估如何通过医疗点分散的电子健康记录提供研究结果。或者,政策制定者可以建立一个人工智能系统,在网上向患者推荐抗抑郁药,在家里,并鼓励他们在下次访问时将建议带给临床医生。
结论:未来的研究可以调查(i)我们的建议在改变临床实践中的有效性,(ii)增加抑郁症状的缓解,和(iii)降低护理成本。这些研究需要前瞻性但务实。随机临床试验不太可能解决影响缓解的大量因素。
BACKGROUND: Consensus-guidelines for prescribing antidepressants recommend that clinicians should be vigilant to match antidepressants to patient\'s medical history but provide no specific advice on which antidepressant is best for a given medical history.
OBJECTIVE: For patients with major depression who are in psychotherapy, this study provides an empirically derived guideline for prescribing antidepressant medications that fit patients\' medical history.
METHODS: This retrospective, observational, cohort study analyzed a large insurance database of 3,678,082 patients. Data was obtained from healthcare providers in the U.S. between January 1, 2001, and December 31, 2018. These patients had 10,221,145 episodes of antidepressant treatments. This study reports the remission rates for the 14 most commonly prescribed single antidepressants (amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine,
nortriptyline, paroxetine, sertraline, trazodone, and venlafaxine) and a category named \"Other\" (other antidepressants/combination of antidepressants). The study used robust LASSO regressions to identify factors that affected remission rate and clinicians\' selection of antidepressants. The selection bias in observational data was removed through stratification. We organized the data into 16,770 subgroups, of at least 100 cases, using the combination of the largest factors that affected remission and selection bias. This paper reports on 2,467 subgroups of patients who had received psychotherapy.
RESULTS: We found large, and statistically significant, differences in remission rates within subgroups of patients. Remission rates for sertraline ranged from 4.5% to 77.86%, for fluoxetine from 2.86% to 77.78%, for venlafaxine from 5.07% to 76.44%, for bupropion from 0.5% to 64.63%, for desvenlafaxine from 1.59% to 75%, for duloxetine from 3.77% to 75%, for paroxetine from 6.48% to 68.79%, for escitalopram from 1.85% to 65%, and for citalopram from 4.67% to 76.23%. Clearly these medications are ideal for patients in some subgroups but not others. If patients are matched to the subgroups, clinicians can prescribe the medication that works best in the subgroup. Some medications (amitriptyline, doxepin,
nortriptyline, and trazodone) always had remission rates below 11% and therefore were not suitable as single antidepressant therapy for any of the subgroups.
CONCLUSIONS: This study provides an opportunity for clinicians to identify an optimal antidepressant for their patients, before they engage in repeated trials of antidepressants.
CONCLUSIONS: To facilitate the matching of patients to the most effective antidepressants, this study provides access to a free, non-commercial, decision aid at http://MeAgainMeds.com.
CONCLUSIONS: Policymakers should evaluate how study findings can be made available through fragmented electronic health records at point-of-care. Alternatively, policymakers can put in place an AI system that recommends antidepressants to patients online, at home, and encourages them to bring the recommendation to their clinicians at their next visit.
CONCLUSIONS: Future research could investigate (i) the effectiveness of our recommendations in changing clinical practice, (ii) increasing remission of depression symptoms, and (iii) reducing cost of care. These studies need to be prospective but pragmatic. It is unlikely random clinical trials can address the large number of factors that affect remission.