Pub Date : 2021-11-01DOI: 10.1136/thorax-2021-btsabstracts.78
M. Riley, C. Doughty, R. Brugha
S72 Table 1Table of lung function results expressed as mean and 95% CI = Confidence IntervalDemographic N= Mean (95% CI) Age (years) N= 30 11.73 (10.70, 12.77) Sex, Female% N=14 (48%) Height (cm) N=30 152.24 (145.62, 158.86) FeNO (ppb) N=26 16.28 (9.01, 23.55) FEV1%pred (%) N=29 103.85 (98.04, 109.66) FEV1 z-score N= 29 0.32 (0.00, 1.00) FVC%pred (%) N= 29 103.3 (98.25, 108.35) FVC z-score N= 29 0.25 (-0.15, 0.66) FEV1:FVC Ratio%pred (%) N= 29 98.84 (96.69, 100.99) FEV1:FVC Ratio z-score N=29 -0.09 (-0.41, 0.23) TLCO%pred N=23 86.69 (80.00, 93.39) TLCO z-score N=23 -1.04 (-1.70, -0.37) KCO%pred N=23 97.22 (91.34, 103.01) KCO z-score N=23 -0.22 (-0.65, 0.21) VA%pred N=23 88.87 (84.09, 93.65) VA z-score N=23 -1.01 (-1.44, -0.58) FRCpleth%pred (%) N=15 88.00 (80.99, 95.01) FRCpleth z-score N=15 -0.77 (-1.26, -0.28) TLC%pred (%) N=15 98.2 (92.17, 104.23) TLC z-score N=15 -0.15 (-0.66, 0.36) RV%pred (%) N=15 83.47 (71.34, 95.60) RV z-score N=15 -0.43 (-0.88, 0.02) ConclusionSimilar to other systemic inflammatory syndromes (Staphylococcal toxic shock, Kawasaki disease), and unlike Covid-19 in adults, it appears that children’s lungs are at low risk of long term damage by PIMS-TS. This data is preliminary and we have not assessed exercise tolerance, or outcomes in those with presentations that did not require initial respiratory support. Assessments are ongoing in this cohort and in children presenting following infection with new variants of concern.ReferencePenner, et al. 6-month multidisciplinary follow-up and outcomes of patients with paediatric inflammatory multisystem syndrome (PIMS-TS) at a UK tertiary paediatric hospital: a retrospective cohort study. Lancet Child Adolesc Health 2021;5(7):473–482.
{"title":"S72 Lung function outcomes in children with paediatric inflammatory multisystem syndrome – temporally associated with SARS-CoV-2 (PIMS-TS)","authors":"M. Riley, C. Doughty, R. Brugha","doi":"10.1136/thorax-2021-btsabstracts.78","DOIUrl":"https://doi.org/10.1136/thorax-2021-btsabstracts.78","url":null,"abstract":"S72 Table 1Table of lung function results expressed as mean and 95% CI = Confidence IntervalDemographic N= Mean (95% CI) Age (years) N= 30 11.73 (10.70, 12.77) Sex, Female% N=14 (48%) Height (cm) N=30 152.24 (145.62, 158.86) FeNO (ppb) N=26 16.28 (9.01, 23.55) FEV1%pred (%) N=29 103.85 (98.04, 109.66) FEV1 z-score N= 29 0.32 (0.00, 1.00) FVC%pred (%) N= 29 103.3 (98.25, 108.35) FVC z-score N= 29 0.25 (-0.15, 0.66) FEV1:FVC Ratio%pred (%) N= 29 98.84 (96.69, 100.99) FEV1:FVC Ratio z-score N=29 -0.09 (-0.41, 0.23) TLCO%pred N=23 86.69 (80.00, 93.39) TLCO z-score N=23 -1.04 (-1.70, -0.37) KCO%pred N=23 97.22 (91.34, 103.01) KCO z-score N=23 -0.22 (-0.65, 0.21) VA%pred N=23 88.87 (84.09, 93.65) VA z-score N=23 -1.01 (-1.44, -0.58) FRCpleth%pred (%) N=15 88.00 (80.99, 95.01) FRCpleth z-score N=15 -0.77 (-1.26, -0.28) TLC%pred (%) N=15 98.2 (92.17, 104.23) TLC z-score N=15 -0.15 (-0.66, 0.36) RV%pred (%) N=15 83.47 (71.34, 95.60) RV z-score N=15 -0.43 (-0.88, 0.02) ConclusionSimilar to other systemic inflammatory syndromes (Staphylococcal toxic shock, Kawasaki disease), and unlike Covid-19 in adults, it appears that children’s lungs are at low risk of long term damage by PIMS-TS. This data is preliminary and we have not assessed exercise tolerance, or outcomes in those with presentations that did not require initial respiratory support. Assessments are ongoing in this cohort and in children presenting following infection with new variants of concern.ReferencePenner, et al. 6-month multidisciplinary follow-up and outcomes of patients with paediatric inflammatory multisystem syndrome (PIMS-TS) at a UK tertiary paediatric hospital: a retrospective cohort study. Lancet Child Adolesc Health 2021;5(7):473–482.","PeriodicalId":409571,"journal":{"name":"Gazing through the crystal ball: predicting outcomes from COVID-19","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115335569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1136/thorax-2021-btsabstracts.75
M. Long, H. Keir, Y. Giam, H. Abo Leyah, T. Pembridge, L. Delgado, R. Hull, A. Gilmour, C. Hughes, C. Hocking, B. New, D. Connell, H. Richardson, D. Cassidy, A. Shoemark, J. Chalmers
IntroductionCOVID-19 is reported to cause profound systemic inflammation. Anti-inflammatory treatments such as corticosteroids and anti-IL-6 receptor monoclonal antibodies reduce mortality. Identifying inflammatory biomarkers associated with increased morbidity and mortality may allow both prediction of outcomes and identification of further therapeutic targets.MethodsA prospective observational study of patients with PCR-confirmed SARS-CoV-2 admitted to a single centre in Dundee, UK. Patients were enrolled within 96 hours of hospital admission. 45 inflammatory biomarkers were measured in serum using the Olink Target48 proteomic-based biomarker panel. Additional markers were measured by ELISA/immunoassay and enzyme activity assays. Severe disease was defined as the requirement for non-invasive or mechanical ventilation or death within 28 days of admission. Discrimination between groups was evaluated using the area under the receiver operator characteristic curve (AUC).Results176 patients were included (mean age 64.9 years, SD 13.6), 101 were male (57.4%). 56 patients developed severe disease (31.8%), mortality was 16.5%. Using ROC analysis, the strongest predictors of severity (p<0.0001) were CCL7/MCP3 (AUC 0.78 95%CI 0.70–0.85), IL6 (0.73 95%CI 0.66–0.81), IL15 (0.73 95%CI 0.65–0.81), CXCL10/IP10 (0.73 95%CI 0.65–0.81). Further significant predictors of severity included CXCL11, IL10, CCL2/MCP1 and CSF2/GM-CSF. Predictors of mortality were CXCL10 (0.78 95%CI 0.69–0.86), IL6 (0.76 95%CI 0.67–0.85), IL15 (0.75 95%CI 0.66–0.84), IL10 (0.73 95%CI 0.64–0.82). Further significant predictors of mortality were CXCL9 and CCL7.ConclusionMultiple circulating biomarkers were identified which predicted disease severity and mortality in COVID19, indicating clinical value in measurement upon hospital admission to highlight high-risk patients. Associated biological processes for these proteins included anti-viral and interferon responses and immune cell chemotaxis. In particular, CCL7 and CXCL10, the strongest predictors of severity and mortality in this dataset, are key players in the cytokine storm and immune cell recruitment linked with COVID19. These chemokines are not currently therapeutic targets, highlighting key avenues for further clinical research.
{"title":"S69 Inflammatory biomarkers predict clinical outcomes in patients with COVID-19 infection: results from the PREDICT-COVID19 study","authors":"M. Long, H. Keir, Y. Giam, H. Abo Leyah, T. Pembridge, L. Delgado, R. Hull, A. Gilmour, C. Hughes, C. Hocking, B. New, D. Connell, H. Richardson, D. Cassidy, A. Shoemark, J. Chalmers","doi":"10.1136/thorax-2021-btsabstracts.75","DOIUrl":"https://doi.org/10.1136/thorax-2021-btsabstracts.75","url":null,"abstract":"IntroductionCOVID-19 is reported to cause profound systemic inflammation. Anti-inflammatory treatments such as corticosteroids and anti-IL-6 receptor monoclonal antibodies reduce mortality. Identifying inflammatory biomarkers associated with increased morbidity and mortality may allow both prediction of outcomes and identification of further therapeutic targets.MethodsA prospective observational study of patients with PCR-confirmed SARS-CoV-2 admitted to a single centre in Dundee, UK. Patients were enrolled within 96 hours of hospital admission. 45 inflammatory biomarkers were measured in serum using the Olink Target48 proteomic-based biomarker panel. Additional markers were measured by ELISA/immunoassay and enzyme activity assays. Severe disease was defined as the requirement for non-invasive or mechanical ventilation or death within 28 days of admission. Discrimination between groups was evaluated using the area under the receiver operator characteristic curve (AUC).Results176 patients were included (mean age 64.9 years, SD 13.6), 101 were male (57.4%). 56 patients developed severe disease (31.8%), mortality was 16.5%. Using ROC analysis, the strongest predictors of severity (p<0.0001) were CCL7/MCP3 (AUC 0.78 95%CI 0.70–0.85), IL6 (0.73 95%CI 0.66–0.81), IL15 (0.73 95%CI 0.65–0.81), CXCL10/IP10 (0.73 95%CI 0.65–0.81). Further significant predictors of severity included CXCL11, IL10, CCL2/MCP1 and CSF2/GM-CSF. Predictors of mortality were CXCL10 (0.78 95%CI 0.69–0.86), IL6 (0.76 95%CI 0.67–0.85), IL15 (0.75 95%CI 0.66–0.84), IL10 (0.73 95%CI 0.64–0.82). Further significant predictors of mortality were CXCL9 and CCL7.ConclusionMultiple circulating biomarkers were identified which predicted disease severity and mortality in COVID19, indicating clinical value in measurement upon hospital admission to highlight high-risk patients. Associated biological processes for these proteins included anti-viral and interferon responses and immune cell chemotaxis. In particular, CCL7 and CXCL10, the strongest predictors of severity and mortality in this dataset, are key players in the cytokine storm and immune cell recruitment linked with COVID19. These chemokines are not currently therapeutic targets, highlighting key avenues for further clinical research.","PeriodicalId":409571,"journal":{"name":"Gazing through the crystal ball: predicting outcomes from COVID-19","volume":"9 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113990439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1136/thorax-2021-btsabstracts.77
D. Ritchie, S. Fairbairn
S71 Table 1 ROX<3.85 ROX3.85–4.87 ROI RR ROI RR CPAP 87.5% 7 (95%CI 1.1–44.6 P0.019) 66.6% 5.33 (95%CI 0.78- 36.3 P0.043) HFNO 87.5% 9.63 (95%CI 1.45- 63.92 P0.009) 33.3% 3.66 (95%CI 0.48- 29.48 P0.11) ConclusionOur study suggests ROX score is valid in predicting intubation in COVID patients requiring enhanced respiratory support. Given the small sample size, further research utilising data from multiple sites would be useful to corroborate findings
{"title":"S71 A retrospective analysis of ROX score for predicting treatment failure and progression to invasive ventilation in COVID patients requiring enhanced respiratory support","authors":"D. Ritchie, S. Fairbairn","doi":"10.1136/thorax-2021-btsabstracts.77","DOIUrl":"https://doi.org/10.1136/thorax-2021-btsabstracts.77","url":null,"abstract":"S71 Table 1 ROX<3.85 ROX3.85–4.87 ROI RR ROI RR CPAP 87.5% 7 (95%CI 1.1–44.6 P0.019) 66.6% 5.33 (95%CI 0.78- 36.3 P0.043) HFNO 87.5% 9.63 (95%CI 1.45- 63.92 P0.009) 33.3% 3.66 (95%CI 0.48- 29.48 P0.11) ConclusionOur study suggests ROX score is valid in predicting intubation in COVID patients requiring enhanced respiratory support. Given the small sample size, further research utilising data from multiple sites would be useful to corroborate findings","PeriodicalId":409571,"journal":{"name":"Gazing through the crystal ball: predicting outcomes from COVID-19","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124076515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1136/thorax-2021-btsabstracts.74
T. Knight, P. Parulekar, G. Rudge, F. Lesser, M. Dachsel, A. Aujayeb, D. Lasserson, N. Smallwood
{"title":"S68 National COVID point of care lung ultrasound evaluation (society for acute medicine with the intensive care society)","authors":"T. Knight, P. Parulekar, G. Rudge, F. Lesser, M. Dachsel, A. Aujayeb, D. Lasserson, N. Smallwood","doi":"10.1136/thorax-2021-btsabstracts.74","DOIUrl":"https://doi.org/10.1136/thorax-2021-btsabstracts.74","url":null,"abstract":"","PeriodicalId":409571,"journal":{"name":"Gazing through the crystal ball: predicting outcomes from COVID-19","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116246174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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