Suppr超能文献

利用倾向得分匹配法从日本医疗报销数据推断新冠住院患者的药物疗效。

Drug effectiveness for COVID-19 inpatients inferred from Japanese medical claim data using propensity score matching.

作者信息

Mitsushima Shingo, Horiguchi Hiromasa, Taniguchi Kiyosu

机构信息

Center for Field Epidemic Intelligence, Research and Professional Development, National Institute of Infectious Diseases, Shinjuku-ku, Tokyo, 1620052, Japan.

Department of Clinical Data Management and Research, Clinical Research Center, National Hospital Organization, Meguro-ku, Tokyo, 1528621, Japan.

出版信息

F1000Res. 2024 Jan 22;12:398. doi: 10.12688/f1000research.131102.1. eCollection 2023.

Abstract

BACKGROUND

Earlier studies and clinical trials of Coronavirus 2019 (COVID-19) showed that drugs such as antiviral drugs, antibody cocktails, and steroids and anti-inflammatory drugs can prevent severe outcomes and death.

METHODS

Observational data in Japan assess drug effectiveness against COVID-19. We applied the average treatment effect model, particularly propensity scoring, which can treat the choice of administered drug as if administration were randomly assigned to inpatients. Data of the Medical Information Analysis Databank, operated by National Hospital Organization in Japan, were used. The outcome was defined as mortality. Subjects were all inpatients, inpatients with oxygen administration, and inpatients using respiratory ventilation, classified by three age classes: all ages, 65 years old or older, and younger than 65 years old. Information about demographic characteristics, underlying disease, administered drug, the proportions of Alpha, Beta and Omicron variant strains, and vaccine coverage were used as explanatory variables for logistic regression.

RESULTS

Estimated results indicated that only one antibody cocktail (sotrovimab, casirivimab and imdevimab) was associated with raising the probability of survival consistently and significantly. By contrast, other drugs, an antiviral drug (remdesivir), a steroid (dexamethasone), and an anti-inflammatory drug (baricitinib and tocilizumab) were related to reduce the probability of survival. However, propensity score matching method might engender biased results because of a lack of data such as detailed information related to intervention and potential confounders. Therefore, the effectiveness of some drugs might not be evaluated properly in this study.

CONCLUSIONS

Results indicate high likelihood that antibody cocktails were consistently associated with high probability of survival, although low likelihood was found for other drugs for older patients with mild to severe severity and all age patients with moderate severity. Further study is necessary in light of the lack of available data.

摘要

背景

早期关于2019冠状病毒病(COVID-19)的研究和临床试验表明,抗病毒药物、抗体鸡尾酒以及类固醇和抗炎药物等可以预防严重后果和死亡。

方法

日本的观察性数据评估了药物对COVID-19的有效性。我们应用了平均治疗效果模型,特别是倾向评分法,该方法可以将给药药物的选择视为如同随机分配给住院患者一样。使用了由日本国立医院组织运营的医疗信息分析数据库的数据。结局定义为死亡率。受试者为所有住院患者、接受吸氧治疗的住院患者以及使用呼吸通气的住院患者,按三个年龄组分类:所有年龄段、65岁及以上以及65岁以下。有关人口统计学特征、基础疾病、给药药物、阿尔法、贝塔和奥密克戎变异株的比例以及疫苗接种覆盖率的信息被用作逻辑回归的解释变量。

结果

估计结果表明,只有一种抗体鸡尾酒(索托维单抗、卡西瑞维单抗和英德维单抗)与持续且显著提高生存概率相关。相比之下,其他药物,一种抗病毒药物(瑞德西韦)、一种类固醇(地塞米松)以及一种抗炎药物(巴瑞替尼和托珠单抗)与降低生存概率有关。然而,倾向评分匹配法可能会产生有偏差的结果,因为缺乏诸如与干预和潜在混杂因素相关的详细信息等数据。因此,本研究中某些药物的有效性可能未得到恰当评估。

结论

结果表明,抗体鸡尾酒与高生存概率持续相关的可能性很高,尽管对于病情从轻到重的老年患者以及病情为中度的所有年龄患者,其他药物与之相关的可能性较低。鉴于可用数据的缺乏,有必要进一步研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae20/11305680/108aaa887fed/f1000research-12-161283-g0000.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验