A machine learning approach identifies 5-ASA and ulcerative colitis as being linked with higher COVID-19 mortality in patients with IBD.
A machine learning approach identifies 5-ASA and ulcerative colitis as being linked with higher COVID-19 mortality in patients with IBD.
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DOI：
10.1038/s41598-021-95919-2
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发表时间：
2021-08-13
影响因子：
4.6
通讯作者：
Furey TS
中科院分区：
文献类型：
--
作者：
Roy S;Sheikh SZ;Furey TS
炎症性肠病（IBD），即克罗恩病（CD）和溃疡性结肠炎（UC）是胃肠道内的慢性炎症。IBD患者的病情和治疗，如使用免疫抑制剂，可能导致病毒和细菌感染的风险更高，感染的后果更严重。临床和人口统计学因素对IBD患者中COVID-19预后的影响仍然是一个重要的研究领域。缺乏大量COVID-19感染IBD患者的可用数据阻碍了进展。为了避免缺乏大量患者数据，我们提出了一种随机抽样方法来生成临床COVID-19结果（门诊管理、住院和康复以及住院和死亡），对20，000例IBD患者进行建模，基于从研究排除的冠状病毒监测流行病学中获得的报告汇总统计量（SECURE-IBD），这是一个国际数据库，用于监测和报告IBD患者发生的COVID-19的结果。我们应用机器学习方法对主要和次要协变量进行全面分析，以预测IBD患者的COVID-19结局。我们的分析显示，年龄、药物使用和合并症的数量是主要协变量，而IBD严重程度、吸烟史、性别和IBD亚型（CD或UC）是关键的次要特征。特别是，患有溃疡性结肠炎、几种既存疾病和吸烟的老年男性患者构成了高度脆弱的IBD人群。此外，5-ASA（柳氮磺胺吡啶/美沙拉嗪）治疗显示与COVID-19/IBD死亡率高度相关。考虑年龄、合并症数量和药物使用的监督机器学习可以预测COVID-19/IBD的结果，准确率约为70%。我们探讨了从现有COVID-19/IBD数据中得出人口统计推断的挑战。总体而言，来自美国各州的IBD病例报告较少，健康排名较差阻碍了这些分析。基于已知的汇总统计数据生成患者特征，可以提高检测导致可变COVID-19结果的IBD因素的能力。健康排名较差的美国各州的IBD患者中存在COVID-19报告不足，这支持了使用数据库获取人口统计信息的风险。
Inflammatory bowel diseases (IBD), namely Crohn’s disease (CD) and ulcerative colitis (UC) are chronic inflammation within the gastrointestinal tract. IBD patient conditions and treatments, such as with immunosuppressants, may result in a higher risk of viral and bacterial infection and more severe outcomes of infections. The effect of the clinical and demographic factors on the prognosis of COVID-19 among IBD patients is still a significant area of investigation. The lack of available data on a large set of COVID-19 infected IBD patients has hindered progress. To circumvent this lack of large patient data, we present a random sampling approach to generate clinical COVID-19 outcomes (outpatient management, hospitalized and recovered, and hospitalized and deceased) on 20,000 IBD patients modeled on reported summary statistics obtained from the Surveillance Epidemiology of Coronavirus Under Research Exclusion (SECURE-IBD), an international database to monitor and report on outcomes of COVID-19 occurring in IBD patients. We apply machine learning approaches to perform a comprehensive analysis of the primary and secondary covariates to predict COVID-19 outcome in IBD patients. Our analysis reveals that age, medication usage and the number of comorbidities are the primary covariates, while IBD severity, smoking history, gender and IBD subtype (CD or UC) are key secondary features. In particular, elderly male patients with ulcerative colitis, several preexisting conditions, and who smoke comprise a highly vulnerable IBD population. Moreover, treatment with 5-ASAs (sulfasalazine/mesalamine) shows a high association with COVID-19/IBD mortality. Supervised machine learning that considers age, number of comorbidities and medication usage can predict COVID-19/IBD outcomes with approximately 70% accuracy. We explore the challenge of drawing demographic inferences from existing COVID-19/IBD data. Overall, there are fewer IBD case reports from US states with poor health ranking hindering these analyses. Generation of patient characteristics based on known summary statistics allows for increased power to detect IBD factors leading to variable COVID-19 outcomes. There is under-reporting of COVID-19 in IBD patients from US states with poor health ranking, underpinning the perils of using the repository to derive demographic information.
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影响因子：
2.2
作者：
Jain, AK;Mao, JC;Mohiuddin, KM
通讯作者：
Mohiuddin, KM
影响因子：
3.1
作者：
Gutin, Liat S.;Lam, Angela Y.;Santos, Stephanie A.
通讯作者：
Santos, Stephanie A.
DOI：
10.1016/j.cgh.2020.08.003
发表时间：
2020-11
期刊：
Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
影响因子：
--
作者：
D'Amico F;Danese S;Peyrin-Biroulet L
通讯作者：
Peyrin-Biroulet L
影响因子：
24.5
作者：
Bezzio, Cristina;Saibeni, Simone;Fiorino, Gionata
通讯作者：
Fiorino, Gionata
影响因子：
8
作者：
Attauabi, Mohamed;Poulsen, Anja;Burisch, Johan
通讯作者：
Burisch, Johan