背景:COVID-19大流行加剧了美国的健康不平等。使用不健康阿片类药物(UOU)的人可能会面临新冠肺炎预防措施的不成比例的挑战,大流行扰乱了阿片类药物和UOU治疗的获取。UOU损害了免疫学,心血管,肺,肾,和神经系统,并可能增加COVID-19结局的严重程度。
目的:我们应用机器学习技术探索UOU和COVID-19住院患者的临床表现,并检验UOU和COVID-19疾病严重程度之间的相关性。
方法:本回顾性研究,横断面队列研究是基于2020年1月1日至2020年12月31日期间在芝加哥学术健康中心遇到的4110例电子健康记录患者的数据进行的.纳入标准是年龄≥18岁的患者的计划外入院;如果COVID-19或COVID-19国际疾病分类2阳性,则被视为COVID-19阳性,第十修订代码。使用具有最佳灵敏度和特异性的预定义截止值来识别UOU,我们对COVID-19患者的数据运行了机器学习UOU分类器,以估计UOU患者的亚组.主题建模用于探索和比较2个亚组的临床表现:遇到UOU和COVID-19以及没有UOU和COVID-19的患者。混合效应逻辑回归解释了一些患者的多次遭遇,并测试了UOU和COVID-19结局严重程度之间的关联。严重程度用3个利用率指标衡量:低严重程度非计划入院,中度非计划入院和接受机械通气,以及严重程度高的计划外入院并住院死亡。所有型号都控制了年龄,性别,种族/民族,保险状况,BMI。
结果:主题建模每个亚组产生10个主题,并突出显示与UOU和COVID-19相关的独特合并症(例如,艾滋病毒),没有UOU和COVID-19(例如,糖尿病)。在回归分析中,分类器预测的UOU概率的每一次递增增加与COVID-19结局严重程度的1.16更高的几率相关(比值比1.16,95%CI1.04-1.29;P=.009).
结论:在COVID-19住院患者中,UOU是与更严重的结局相关的独立危险因素,包括住院死亡.健康和阿片类药物相关过量的社会决定因素是UOU患者亚组临床表现中的独特合并症。对于UOU患者的急性COVID-19肺炎的治疗和住院管理,还需要进行更多的研究。需要进一步的研究来测试扩大的基于证据的UOU伤害减少策略与疫苗接种率之间的关联。住院治疗,以及UOU和COVID-19患者用药过量和死亡的风险。机器学习技术可以为队列发现提供更详尽的手段,并为人群健康提供一种新颖的混合方法。
The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19.
We applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity.
This retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ≥18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI.
Topic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier\'s predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009).
Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.