Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study.
Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study.
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DOI：
10.2196/38158
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发表时间：
2022-12-08
影响因子：
8.5
通讯作者：
Afshar, Majid
中科院分区：
文献类型：
--
作者：
Thompson, Hale M.;Sharma, Brihat;Smith, Dale L.;Bhalla, Sameer;Erondu, Ihuoma;Hazra, Aniruddha;Ilyas, Yousaf;Pachwicewicz, Paul;Sheth, Neeral K.;Chhabra, Neeraj;Karnik, Niranjan S.;Afshar, Majid
关键词：
COVID-19 大流行加剧了美国的健康不平等。不健康使用阿片类药物 (UOU) 的人可能会在采取 COVID-19 预防措施方面面临不成比例的挑战，而且疫情已经扰乱了阿片类药物和 UOU 治疗的获取。 UOU 损害免疫、心血管、肺、肾和神经系统，并可能增加 COVID-19 后果的严重程度。我们应用机器学习技术来探索 UOU 和 COVID-19 住院患者的临床表现，并测试 UOU 和 COVID-19 疾病严重程度之间的关联。这项回顾性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就诊的 4110 名电子健康记录患者的数据进行的。纳入标准是年龄≥18 岁的患者意外入院；如果 COVID-19 检测呈阳性或 2 个 COVID-19 国际疾病分类第十修订版代码呈阳性，则该接触被视为 COVID-19 阳性。我们使用具有最佳灵敏度和特异性的预定义截止值来识别 UOU，对 COVID-19 患者的数据运行机器学习 UOU 分类器，以估计 UOU 患者的子队列。使用主题模型来探索和比较 2 个亚组记录的临床表现：接触过 UOU 和 COVID-19 的亚组以及未接触过 UOU 和 COVID-19 的亚组。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 与 COVID-19 结果严重程度之间的关联。严重程度通过 3 个利用指标来衡量：低严重性计划外入院、中度严重性计划外入院并接受机械通气、以及高严重性计划外入院且院内死亡。所有模型均控制了年龄、性别、种族/民族、保险状况和体重指数。主题建模为每个亚组产生了 10 个主题，并强调了与 UOU 和 COVID-19（例如 HIV）以及无 UOU 和 COVID-19（例如糖尿病）相关的独特合并症。在回归分析中，分类器预测的 UOU 概率每增加一次，COVID-19 结果严重程度的几率就会增加 1.16（比值比 1.16，95% CI 1.04-1.29；P=.009）。在因 COVID-19 住院的患者中，UOU 是一个独立的危险因素，与更严重的结局（包括院内死亡）相关。健康的社会决定因素和阿片类药物相关过量是 UOU 患者亚组临床表现中独特的合并症。需要对 UOU 患者的 COVID-19 治疗和急性 COVID-19 肺炎住院治疗的作用进行更多研究。需要进一步研究来测试扩大的 UOU 循证减害策略与 UOU 和 COVID-19 患者的疫苗接种率、住院率以及用药过量和死亡风险之间的关联。机器学习技术可能为队列发现提供更详尽的手段，并为人口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.
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影响因子：
38.9
作者：
Kurtz P;Bastos LSL;Dantas LF;Zampieri FG;Soares M;Hamacher S;Salluh JIF;Bozza FA
通讯作者：
Bozza FA
DOI：
10.1093/rheumatology/keab250
发表时间：
2021-10-09
期刊：
Rheumatology (Oxford, England)
影响因子：
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通讯作者：
Prieto-Alhambra D
影响因子：
13.8
作者：
Joudrey PJ;Adams ZM;Bach P;Van Buren S;Chaiton JA;Ehrenfeld L;Guerra ME;Gleeson B;Kimmel SD;Medley A;Mekideche W;Paquet M;Sung M;Wang M;You Kheang ROO;Zhang J;Wang EA;Edelman EJ
通讯作者：
Edelman EJ
DOI：
10.1136/jamia.2009.001560
发表时间：
2010-09-01
影响因子：
6.4
作者：
Savova, Guergana K.;Masanz, James J.;Chute, Christopher G.
通讯作者：
Chute, Christopher G.
影响因子：
9.8
作者：
Du, Yanbin;Lv, Yuan;Hong, Xiuqin
通讯作者：
Hong, Xiuqin