COVID-19: clinical features and risk最新文献

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P93 Comparison of inflammatory profiles between COVID-19 and other acute lower respiratory tract infections: Results from the PREDICT-COVID19 study COVID-19与其他急性下呼吸道感染的炎症谱比较:来自predict - COVID-19研究的结果

COVID-19: clinical features and risk

Pub Date : 2021-11-01 DOI: 10.1136/thorax-2021-btsabstracts.203

H. Keir, M. Long, Y. Giam, H. Leyah, T. Pembridge, L. Delgado, R. Hull, C. Hughes, A. Gilmour, C. Hocking, B. New, D. Connell, H. Richardson, D. Cassidy, A. Shoemark, J. Chalmers

IntroductionCOVID-19 has been reported to induce a ‘cytokine storm’ distinct from other acute respiratory tract infections (LRTIs). Understanding the similarities and differences in inflammatory profiles between SARS-CoV-2 infection and other respiratory infections may aid diagnosis, as well as the potential to repurpose therapies such as steroids and anti-IL-6 receptor antagonists for other respiratory infections.MethodsA prospective observational study of patients in 3 groups 1) PCR confirmed SARS-CoV-2 infection, 2) community-acquired pneumonia (CAP) without SARS-CoV-2, and 3) controls hospitalized for reasons other than infection. Patients were enrolled from a single centre in Dundee, UK. Patients were enrolled within 96 hours of hospital admission. 45 inflammatory biomarkers were measured in blood using the Olink target proteomic based biomarker panel. Additional markers were measured by ELISA/immunoassay and enzyme activity assay as appropriate. Discrimination between groups was evaluated using the area under the receiver operator characteristic curve (AUC).Results294 patients were included (COVID-19 n=176, CAP n=76, controls n=42), mean age 64 (SD±15.2) and 150 subjects were male (51.0%). Using ROC analysis the most discriminating biomarkers for COVID-19 compared to CAP were CXCL-10 (AUC 0.84 95%CI 0.78–0.90 p<0.001), CCL-8 (0.87 95%CI 0.82–0.92, p<0.001), CCL-7 (0.84 95%CI 0.78–0.89, p<0.001), CXCL-11 (0.80 95%CI 0.73–0.88, p<0.001). Further biomarkers included IL-18, IL-7, IL-10 and IL-33. The most discriminating biomarkers for COVID-19 compared to controls were CXCL-10 (0.89 95%CI 0.85–0.93, p<0.001, CCL-7 (0.88 95%CI 0.83–0.92, p<0.001), CCL-8 (0.87 95%CI 0.82–0.92, p<0.001). Further biomarkers included IL-10, CXCL-11 and IL-18. IL-4 was significantly lower in COVID-19 patients compared to controls (0.27 95%CI 0.16–0.38, p<0.001). No significant difference in IL-6 was seen between COVID-19 and CAP (median 21.9pg/ml vs 19.8pg/ml,p=0.59).ConclusionDifferential markers of inflammation were identified between COVID-19, CAP and control samples, indicating distinct immunological pathways. The identification of a similar IL-6 signature between COVID-19 and CAP indicates that IL-6 targeting therapies currently being used to treat COIVD-19 may also be beneficial in the treatment of CAP.

据报道，covid -19可诱导不同于其他急性呼吸道感染(LRTIs)的“细胞因子风暴”。了解SARS-CoV-2感染与其他呼吸道感染之间炎症特征的异同可能有助于诊断，并有可能将类固醇和抗il -6受体拮抗剂等治疗方法重新用于其他呼吸道感染。方法对3组患者进行前瞻性观察研究:1)PCR确诊的SARS-CoV-2感染，2)无SARS-CoV-2的社区获得性肺炎(CAP)， 3)非感染原因住院的对照。患者从英国邓迪的一个中心入组。患者在入院后96小时内入组。使用基于Olink靶蛋白组学的生物标志物面板测量血液中的45种炎症生物标志物。酌情采用ELISA/免疫法和酶活性法测定其他标记物。采用受试者操作特征曲线下面积(AUC)评价各组间的区别。结果纳入患者294例(COVID-19 176例，CAP 76例，对照组42例)，平均年龄64 (SD±15.2)，男性150例(51.0%)。通过ROC分析，与CAP相比，COVID-19最具鉴别性的生物标志物是CXCL-10 (AUC 0.84 95%CI 0.78-0.90 p<0.001)、cccl -8 (0.87 95%CI 0.82-0.92, p<0.001)、cccl -7 (0.84 95%CI 0.78-0.89, p<0.001)、CXCL-11 (0.80 95%CI 0.73-0.88, p<0.001)。其他生物标志物包括IL-18、IL-7、IL-10和IL-33。与对照组相比，COVID-19最具鉴别性的生物标志物是CXCL-10 (0.89 95%CI 0.85-0.93, p<0.001)、cccl -7 (0.88 95%CI 0.83-0.92, p<0.001)、cccl -8 (0.87 95%CI 0.82-0.92, p<0.001)。进一步的生物标志物包括IL-10、CXCL-11和IL-18。与对照组相比，COVID-19患者IL-4明显降低(0.27 95%CI 0.16-0.38, p<0.001)。COVID-19和CAP患者IL-6水平无显著差异(中位数分别为21.9pg/ml和19.8pg/ml,p=0.59)。结论COVID-19、CAP和对照组之间存在差异的炎症标志物，表明不同的免疫途径。在COVID-19和CAP之间发现相似的IL-6特征表明，目前用于治疗COVID-19的IL-6靶向疗法也可能有益于CAP的治疗。

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P97 Disease severity and patient recovery in COVID-19: an observational study comparing first and second wave admissions in London P97 COVID-19的疾病严重程度和患者康复:一项比较伦敦第一波和第二波住院的观察性研究

COVID-19: clinical features and risk

Pub Date : 2021-11-01 DOI: 10.1136/thorax-2021-btsabstracts.207

A. Saigal, CN Niklewicz, SB Naidu, HM Bintalib, AJ Shah, G. Seligmann, A. Hunter, D. Miller, I. Abubakar, E. Wey, C. Smith, N. Jain, J. Barnett, S. Brill, J. Goldring, H. Jarvis, J. Hurst, M. Lipman, S. Mandal

P97 Table 1Demographics and clinical characteristics of participants at hospital admission and follow up for wave 1 and 2 admissions Wave 1 Wave 2 p-value N = 400 N = 400 Demographics and Lifestyle Age (years) (Median, IQR) 61 (50 -74) 61 (51 - 74) 0.59 Male gender (N,%) 247 (61.8%) 237 (59.3%) 0.47 Ethnicity (White) (N,%) 200 (50.0%) 195 (48.8%) 0.001* Smoking status – Never smoker (N,%) 215 (53.8%) 219 (54.8%) 0.58 BMI (kg/m2) (Median, IQR) 26.8 (24.1 - 29.4) 27.7 (24.3 - 31.6) 0.015 Underlying clinical status Clinical Frailty Score (Median, IQR) 2 (2, 4) N = 332 3 (2, 3) N = 384 0.001 Shielding Status (N,%) Extremely vulnerable HCP issued letter 32 (10.1%) 23 (7.2%) 39 (11.2%) 5 (1.4%) 0.001 Covid Admission Severity Parameters Total number of symptoms (Median, IQR) 4 (3 - 6) 3 (2 - 3) <0.0001 NEWS2 score (Median, IQR) 5 (2 - 7) N = 372 4 (3 - 6) N = 379 0.60 TEP status – For full escalation (N,%) 284/365 (77.8%) 361/400 (90.3%) <0.0001 Maximum respiratory support (N,%) CPAP NIV N= 377 10 (2.7%) 2 (0.5%) N = 400 32 (8.0%) 5 (1.3%) <0.0001 Received anti-viral or immunosuppressive drugs (N,%) 23/374 (6.2%) 127/400 (31.8%) <0.0001 ITU admission (N,%) 62/377 (16.5%) 43/400 (10.8%) 0.02 Intubation (N,%) 49/364 (13.5%) 19/400 (4.8%) <0.0001 Pulmonary Embolus (N,%) 22/360 (6.1%) 24/395 (6.1%) 0.98 Follow-up Outcomes N = 322 N = 365 Mental Health Outcomes PHQ2 score ≥ 3 (N,%) 47 (15.4%) 34 (9.9%) 0.04 TSQ score ≥ 5 (N,%) 44 (14.9%) 12 (3.3%) <0.0001 Physical Recovery and Symptoms Not returned to work (N,%) 76 (24.8%) 114 (33.6%) 0.03 Improved Sleep quality (N,%) 168 (61.5%) 265 (78.4%) <0.0001 Improved Fatigue (N,%) 241 (87.6%) 307 (88.7%) 0.91 Improved Cough (N,%) 194 (69.5%) 291 (84.8%) <0.0001 Improved Breathlessness (N,%) 213 (76.1%) 311 (89.6%) <0.0001 Total Number of Symptoms (Median, IQR) 1 (0 - 2) N=314 0 (0 – 1) N=364 Radiology outcomes (N,%) Normalised Significantly Improved Not significantly improved Worsened N=309 211 (68.3%) 55 (17.8%) 2 (0.7%) 30 (9.7%) N=279 187 (67.0%) 65 (23.3%) 13 (4.7%) 14 (5.0%) <0.0001 *p value likely attributable to differences in unknown ethnicityConclusionThese data suggest second wave pa ients, although frailer, presented with fewer symptoms and experienced improved hospital admission trajectory. They demonstrated improved self-reported mental health and physical recovery outcomes despite earlier follow-up, possibly attributed to improved in-hospital treatment. Supporting recovery remains a clinical priority given many patients had not returned to work.ReferenceSaito S, et al. First and second COVID-19 waves in Japan: comparison of disease severity and characteristics. J Infect. 2021;82(4):84-123.

表1第1和第2波入院时参与者的人口学和临床特征以及第1和第2波入院时随访的人口学和临床特征p值N = 400 N = 400人口统计学和生活方式年龄(年龄)(中位数，IQR) 61(50 -74) 61(51 -74) 0.59男性性别(N，%) 247(61.8%) 237(59.3%) 0.47种族(白人)(N，%) 200(50.0%) 195(48.8%) 0.001*吸烟状况-从不吸烟(N，%) 215 (53.8%) 219 (54.8%) 0.58 BMI (kg/m2)(中位数，IQR) 26.8(24.1 - 29.4) 27.7(24.3 - 31.6) 0.015潜在临床状态临床虚弱评分(中位数，IQR) 2 (2,4) N = 332 3 (2,3) N = 384 0.001防护状态(N，%)极度脆弱的HCP签发信32(10.1%)23(7.2%)39(11.2%)5(1.4%)0.001新冠入院严重程度参数症状总数(中位数，IQR) 4 (3 - 6) 3 (2 - 3) <0.0001 NEWS2评分(中位数，IQR)，差)5 (2 - 7)N = 372 4 (3 - 6) N = 379 0.60 TEP中的地位,全面升级(N, %) 284/365(77.8%)的361/400(90.3%)< 0.0001最大呼吸支持(N, %) CPAP和合N = 377 10 (2.7%) 2 (0.5%) N = 400 32例(8.0%)5(1.3%)< 0.0001收到抗病毒或免疫抑制药物(N, %) 23/374(6.2%)的127/400(31.8%)< 0.0001电联入学(N, %) 62/377(16.5%)的43/400(10.8%)0.02插管(N, %) 49/364(13.5%)的19/400(4.8%)< 0.0001肺动脉栓子(N, %) 22/360(6.1%)的24/395(6.1%)0.98的后续结果N = 322 N = 365心理健康结果PHQ2分数≥3 (N, %) 47 (15.4%) 34 (9.9%) 0.04 TSQ评分≥5 (N, %) 44(14.9%) 12(3.3%) < 0.0001体能恢复和症状不回到工作岗位(N, %) 76(24.8%) 114(33.6%) 0.03改善睡眠质量(N, %) 168(61.5%) 265(78.4%) < 0.0001改善疲劳(N, %) 241(87.6%) 307(88.7%) 0.91改善咳嗽(N, %) 194(69.5%) 291(84.8%) < 0.0001改善呼吸困难(N, %) 213(76.1%) 311(89.6%) < 0.0001的症状总数(中位数,IQR) 1 (0 - 2) N=314 0 (0 - 1) N=364放射学结果(N，%)正常化显著改善未显著改善恶化N=309 211 (68.3%) 55 (17.8%) 2 (0.7%) 30 (9.7%) N=279 187 (67.0%) 65 (23.3%) 13 (4.7%) 14 (5.0%) <0.0001 *p值可能归因于未知种族差异结论这些数据提示第二波患者虽然较弱，但症状较少，住院轨迹改善。尽管随访时间较早，但他们表现出自我报告的心理健康和身体恢复结果有所改善，这可能归因于住院治疗的改善。鉴于许多患者尚未重返工作岗位，支持康复仍然是临床优先事项。参考文献:saito S等。日本第一波和第二波COVID-19:疾病严重程度和特征的比较中华检验医学杂志，2013;32(4):391 - 391。

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