Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K.
Patterns of multimorbidity and risk of severe SARS-CoV-2 infection: an observational study in the U.K.
复制标题
DOI：
10.1186/s12879-021-06600-y
复制
发表时间：
2021-09-04
影响因子：
3.7
通讯作者：
Khunti K
中科院分区：
文献类型：
--
作者：
Chudasama YV;Zaccardi F;Gillies CL;Razieh C;Yates T;Kloecker DE;Rowlands AV;Davies MJ;Islam N;Seidu S;Forouhi NG;Khunti K
先前存在的合并症与SARS-CoV-2感染有关，但关于多发病（2种或2种以上疾病）的重要性和模式以及住院或死亡所指示的感染严重程度的证据很少。我们的目的是使用专门为COVID-19开发的多发病指数来研究多发病与严重SARS-CoV-2感染风险之间的关系。我们使用了英国生物银行的数据，这些数据与2020年3月16日至7月26日期间英国公共卫生部的SARS-CoV-2感染和死亡率数据的实验室确认检测结果有关。通过回顾当前关于COVID-19的文献，我们推导出一个多发病指数，包括：（1）心绞痛;（2）哮喘;（3）房颤;（4）癌症;（5）慢性肾脏疾病;（6）慢性阻塞性肺疾病;（7）糖尿病;（8）心力衰竭;（9）高血压;（10）心肌梗死;（11）外周血管疾病;（12）糖尿病;（13）高血压;（14）高血压;（15）高血压;（16）高血压;（17）高血压;（18）高血压;（19）高血压;（19）高血压;（19）高血压;（10）高血压;（19）高血压;（19）高血压;（11）高血压;（19）高血压;（19）高血压;（10）高血压;（19）高血压;（19）高血压;（10）高血压;（19）高血压;（19）高血压;（19（12）中风。调整后的逻辑回归模型用于评估多重死亡与严重SARS-CoV-2感染风险（住院/死亡）之间的相关性。评估了相关性的潜在影响因素：年龄、性别、种族、剥夺、吸烟状况、体重指数、空气污染、25-羟基维生素D、心肺功能、高敏C反应蛋白。在360，283名参与者中，中位年龄为68岁[范围48 - 85]，大多数是白色（94.5%），1706人患有严重的SARS-CoV-2感染。严重SARS-CoV-2感染者的多发病率（25%）是未感染者（11%）的两倍多，严重SARS-CoV-2感染者中多发病的聚集性更常见。严重SARS-CoV-2感染最常见的集群是中风合并高血压（79%的中风患者患有高血压）;糖尿病和高血压（72%）;以及慢性肾脏疾病和高血压（68%）。多药耐药与严重SARS-CoV-2感染的风险增加独立相关（与无多药耐药相比，调整后的比值比为1.91 [95%置信区间为1.70，2.15]）。除了老年人的风险更高外，其他潜在的效应修饰因子的风险保持一致。严重感染的最高风险在CKD和糖尿病患者中得到了有力的证明（4.93 [95% CI 3.36，7.22]）。多发病指数可能有助于识别严重COVID-19后果风险较高的个体，并为定制有效治疗提供指导。
Pre-existing comorbidities have been linked to SARS-CoV-2 infection but evidence is sparse on the importance and pattern of multimorbidity (2 or more conditions) and severity of infection indicated by hospitalisation or mortality. We aimed to use a multimorbidity index developed specifically for COVID-19 to investigate the association between multimorbidity and risk of severe SARS-CoV-2 infection. We used data from the UK Biobank linked to laboratory confirmed test results for SARS-CoV-2 infection and mortality data from Public Health England between March 16 and July 26, 2020. By reviewing the current literature on COVID-19 we derived a multimorbidity index including: (1) angina; (2) asthma; (3) atrial fibrillation; (4) cancer; (5) chronic kidney disease; (6) chronic obstructive pulmonary disease; (7) diabetes mellitus; (8) heart failure; (9) hypertension; (10) myocardial infarction; (11) peripheral vascular disease; (12) stroke. Adjusted logistic regression models were used to assess the association between multimorbidity and risk of severe SARS-CoV-2 infection (hospitalisation/death). Potential effect modifiers of the association were assessed: age, sex, ethnicity, deprivation, smoking status, body mass index, air pollution, 25‐hydroxyvitamin D, cardiorespiratory fitness, high sensitivity C-reactive protein. Among 360,283 participants, the median age was 68 [range 48–85] years, most were White (94.5%), and 1706 had severe SARS-CoV-2 infection. The prevalence of multimorbidity was more than double in those with severe SARS-CoV-2 infection (25%) compared to those without (11%), and clusters of several multimorbidities were more common in those with severe SARS-CoV-2 infection. The most common clusters with severe SARS-CoV-2 infection were stroke with hypertension (79% of those with stroke had hypertension); diabetes and hypertension (72%); and chronic kidney disease and hypertension (68%). Multimorbidity was independently associated with a greater risk of severe SARS-CoV-2 infection (adjusted odds ratio 1.91 [95% confidence interval 1.70, 2.15] compared to no multimorbidity). The risk remained consistent across potential effect modifiers, except for greater risk among older age. The highest risk of severe infection was strongly evidenced in those with CKD and diabetes (4.93 [95% CI 3.36, 7.22]). The multimorbidity index may help identify individuals at higher risk for severe COVID-19 outcomes and provide guidance for tailoring effective treatment.
登录
查看更多内容
影响因子：
24.3
作者：
Guan, Wei-jie;Liang, Wen-hua;He, Jian-xing
通讯作者：
He, Jian-xing
影响因子：
37.8
作者：
Pearson, TA;Mensah, GA;Vinicor, F
通讯作者：
Vinicor, F
影响因子：
4
作者：
Woolford, S. J.;D'Angelo, S.;Harvey, N. C.
通讯作者：
Harvey, N. C.
影响因子：
105.7
作者：
Petrilli, Christopher M.;Jones, Simon A.;Horwitz, Leora I.
通讯作者：
Horwitz, Leora I.
影响因子：
33.9
作者：
Jorden, Michelle A.;Rudman, Sarah L.;Armstrong, Gregory L.
通讯作者：
Armstrong, Gregory L.