Antigona Ulndreaj, Mingyue Wang, S. Misaghian, L. Paone, G. Sigal, M. Stengelin, C. Campbell, Logan R. Van Nynatten, A. Soosaipillai, Atefeh Ghorbani, A. Mathew, D. Fraser, E. Diamandis, I. Prassas
Abstract Objectives Infection by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative pathogen of coronavirus disease 2019 (COVID-19) presents occasionally with an aberrant autoinflammatory response, including the presence of elevated circulating autoantibodies in some individuals. Whether the development of autoantibodies against self-antigens affects COVID-19 outcomes remains unclear. To better understand the prognostic role of autoantibodies in COVID-19, we quantified autoantibodies against 23 markers that are used for diagnosis of autoimmune disease. To this end, we used serum samples from patients with severe [intensive care unit (ICU)] and moderate (ward) COVID-19, across two to six consecutive time points, and compared autoantibody levels to uninfected healthy and ICU controls. Methods Acute and post-acute serum (from 1 to 26 ICU days) was collected from 18 ICU COVID-19-positive patients at three to six time points; 18 ICU COVID-19-negative patients (sampled on ICU day 1 and 3); 21 ward COVID-19-positive patients (sampled on hospital day 1 and 3); and from 59 healthy uninfected controls deriving from two cohorts. Levels of IgG autoantibodies against 23 autoantigens, commonly used for autoimmune disease diagnosis, were measured in serum samples using MSD® U-PLEX electrochemiluminescence technology (MSD division Meso Scale Discovery®), and results were compared between groups. Results There were no significant elevations of autoantibodies for any of the markers tested in patients with severe COVID-19. Conclusions Sample collections at longer time points should be considered in future studies, for assessing the possible development of autoantibody responses following infection with SARS-CoV-2.
{"title":"Patients with severe COVID-19 do not have elevated autoantibodies against common diagnostic autoantigens","authors":"Antigona Ulndreaj, Mingyue Wang, S. Misaghian, L. Paone, G. Sigal, M. Stengelin, C. Campbell, Logan R. Van Nynatten, A. Soosaipillai, Atefeh Ghorbani, A. Mathew, D. Fraser, E. Diamandis, I. Prassas","doi":"10.1515/cclm-2022-0239","DOIUrl":"https://doi.org/10.1515/cclm-2022-0239","url":null,"abstract":"Abstract Objectives Infection by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative pathogen of coronavirus disease 2019 (COVID-19) presents occasionally with an aberrant autoinflammatory response, including the presence of elevated circulating autoantibodies in some individuals. Whether the development of autoantibodies against self-antigens affects COVID-19 outcomes remains unclear. To better understand the prognostic role of autoantibodies in COVID-19, we quantified autoantibodies against 23 markers that are used for diagnosis of autoimmune disease. To this end, we used serum samples from patients with severe [intensive care unit (ICU)] and moderate (ward) COVID-19, across two to six consecutive time points, and compared autoantibody levels to uninfected healthy and ICU controls. Methods Acute and post-acute serum (from 1 to 26 ICU days) was collected from 18 ICU COVID-19-positive patients at three to six time points; 18 ICU COVID-19-negative patients (sampled on ICU day 1 and 3); 21 ward COVID-19-positive patients (sampled on hospital day 1 and 3); and from 59 healthy uninfected controls deriving from two cohorts. Levels of IgG autoantibodies against 23 autoantigens, commonly used for autoimmune disease diagnosis, were measured in serum samples using MSD® U-PLEX electrochemiluminescence technology (MSD division Meso Scale Discovery®), and results were compared between groups. Results There were no significant elevations of autoantibodies for any of the markers tested in patients with severe COVID-19. Conclusions Sample collections at longer time points should be considered in future studies, for assessing the possible development of autoantibody responses following infection with SARS-CoV-2.","PeriodicalId":10388,"journal":{"name":"Clinical Chemistry and Laboratory Medicine (CCLM)","volume":"79 1","pages":"1116 - 1123"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81212319","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}
G. Koerbin, J. Potter, Marcela Pinto do Nascimento, L. Cullen, Samuel L. Scanlan, Catherine Woods, P. Hickman
Abstract Objectives Knowing the intra-individual variation (CVi), also termed within subject biological variation, of an analyte is essential to properly interpret apparent changes in concentration. While there have been many studies assessing the CVi of cardiac troponin (cTnI), they have been limited in looking at CVi in different settings, and there is no data available on whether CVi might change in different settings. Methods We used our large cTnI data bank to look at the CVi of cTnI in Emergency Department (ED) patients who had an acute myocardial infarction event excluded. We looked at the effects of gender, age, climatic season, and time between samples to assess whether CVi changed. To assess the effect of age, after exclusion, we collected two samples from each subject for each study which were used to calculate the CVi between those identified groups. There were 139 males and 98 females aged <65 years and 109 males and 98 females aged ≥65 years. For gender and season, there were 122 males and 94 females in the summer period and 126 males and 102 females in the winter period. To assess long term variation there were 195 males and 153 females who had further admissions after more than 12 months. Results For the four variables listed, there were no significant differences in within individual variation (CVi), but there was a significant difference in between individual variation (CVg) for men and women with regard to age. The Index of Individuality (II) was <0.20 for all conditions studied. We noted that >90% of subjects had an reference change value (RCV) <9 ng/L. Conclusions Because troponin concentration in patients without an identified cardiac condition change so little, delta changes are potentially of great value in assessing patients in the ED. Significant delta changes in troponin can occur without the 99th percentile being exceeded.
{"title":"The intra-individual variation of cardiac troponin I: the effects of sex, age, climatic season, and time between samples","authors":"G. Koerbin, J. Potter, Marcela Pinto do Nascimento, L. Cullen, Samuel L. Scanlan, Catherine Woods, P. Hickman","doi":"10.1515/cclm-2022-0125","DOIUrl":"https://doi.org/10.1515/cclm-2022-0125","url":null,"abstract":"Abstract Objectives Knowing the intra-individual variation (CVi), also termed within subject biological variation, of an analyte is essential to properly interpret apparent changes in concentration. While there have been many studies assessing the CVi of cardiac troponin (cTnI), they have been limited in looking at CVi in different settings, and there is no data available on whether CVi might change in different settings. Methods We used our large cTnI data bank to look at the CVi of cTnI in Emergency Department (ED) patients who had an acute myocardial infarction event excluded. We looked at the effects of gender, age, climatic season, and time between samples to assess whether CVi changed. To assess the effect of age, after exclusion, we collected two samples from each subject for each study which were used to calculate the CVi between those identified groups. There were 139 males and 98 females aged <65 years and 109 males and 98 females aged ≥65 years. For gender and season, there were 122 males and 94 females in the summer period and 126 males and 102 females in the winter period. To assess long term variation there were 195 males and 153 females who had further admissions after more than 12 months. Results For the four variables listed, there were no significant differences in within individual variation (CVi), but there was a significant difference in between individual variation (CVg) for men and women with regard to age. The Index of Individuality (II) was <0.20 for all conditions studied. We noted that >90% of subjects had an reference change value (RCV) <9 ng/L. Conclusions Because troponin concentration in patients without an identified cardiac condition change so little, delta changes are potentially of great value in assessing patients in the ED. Significant delta changes in troponin can occur without the 99th percentile being exceeded.","PeriodicalId":10388,"journal":{"name":"Clinical Chemistry and Laboratory Medicine (CCLM)","volume":"1 1","pages":"1101 - 1109"},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79030960","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}
A. Platteel, P. van der Pol, J. Murk, Ingrid Verbrugge-Bakker, Marian Hack-Steemers, Theo H.W.M. Roovers, M. Heron
Abstract Objectives Diagnosis of type I hypersensitivity is based on anamnesis, provocation as well as blood- and skin testing. Multiplex specific IgE (sIgE) testing enables determination of sIgE antibodies against multiple recombinant or purified natural allergen components. The aim of this study was to evaluate the performance of the novel ALEX2® (Allergy Explorer, ALEX2 test introduced on the market November 2019) multiplex platform and to compare it with the ImmunoCAP ISAC® test system. Methods Serum samples of 49 patients, routinely determined with ISAC, were selected based on positive results covering in total most of the 112 ISAC components. Cohen’s kappa, negative percent agreement (NPA), and positive percent agreement (PPA) of ALEX2 data compared to ISAC data (as a non-reference standard) were computed for those allergen components present on both platforms (n=103). Furthermore, in some samples sIgE results against allergen extracts and/or -components tested with either ImmunoCAP® (ThermoFisher) or IMMULITE® (Siemens) were available and compared to ALEX2 results. Results The overall agreement between ISAC and ALEX2 common allergen components was 94%. NPA and PPA were respectively 95 and 90%. Kappa values differed for specific allergen groups and varied between 0.60 and 0.92 showing moderate to almost perfect agreement. Of the qualitative discrepancies between ALEX2 and ISAC, 59% were related to weak positive results i.e. results under 1 kUA/L or 1 ISU, respectively. Conclusions The method comparison between ISAC and ALEX2 multiplex tests showed a high concordance for those allergen components present on both platforms.
{"title":"A comprehensive comparison between ISAC and ALEX2 multiplex test systems","authors":"A. Platteel, P. van der Pol, J. Murk, Ingrid Verbrugge-Bakker, Marian Hack-Steemers, Theo H.W.M. Roovers, M. Heron","doi":"10.1515/cclm-2022-0191","DOIUrl":"https://doi.org/10.1515/cclm-2022-0191","url":null,"abstract":"Abstract Objectives Diagnosis of type I hypersensitivity is based on anamnesis, provocation as well as blood- and skin testing. Multiplex specific IgE (sIgE) testing enables determination of sIgE antibodies against multiple recombinant or purified natural allergen components. The aim of this study was to evaluate the performance of the novel ALEX2® (Allergy Explorer, ALEX2 test introduced on the market November 2019) multiplex platform and to compare it with the ImmunoCAP ISAC® test system. Methods Serum samples of 49 patients, routinely determined with ISAC, were selected based on positive results covering in total most of the 112 ISAC components. Cohen’s kappa, negative percent agreement (NPA), and positive percent agreement (PPA) of ALEX2 data compared to ISAC data (as a non-reference standard) were computed for those allergen components present on both platforms (n=103). Furthermore, in some samples sIgE results against allergen extracts and/or -components tested with either ImmunoCAP® (ThermoFisher) or IMMULITE® (Siemens) were available and compared to ALEX2 results. Results The overall agreement between ISAC and ALEX2 common allergen components was 94%. NPA and PPA were respectively 95 and 90%. Kappa values differed for specific allergen groups and varied between 0.60 and 0.92 showing moderate to almost perfect agreement. Of the qualitative discrepancies between ALEX2 and ISAC, 59% were related to weak positive results i.e. results under 1 kUA/L or 1 ISU, respectively. Conclusions The method comparison between ISAC and ALEX2 multiplex tests showed a high concordance for those allergen components present on both platforms.","PeriodicalId":10388,"journal":{"name":"Clinical Chemistry and Laboratory Medicine (CCLM)","volume":"65 1","pages":"1046 - 1052"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91089350","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}
F. C. Chume, P. A. C. Freitas, L. G. Schiavenin, A. L. Pimentel, J. L. Camargo
Abstract Objectives Guidelines recommend the diagnosis of diabetes should be based on either plasma glucose or glycated hemoglobin (HbA1C) findings. However, lately studies have advocated glycated albumin (GA) as a useful alternative to HbA1c. We conducted a systematic review and meta-analysis to determine the overall diagnostic accuracy of GA for the diagnosis of diabetes. Content We searched for articles of GA diabetes diagnostic accuracy that were published up to August 2021. Studies were selected if reported an oral glucose tolerance test as a reference test, measured GA levels by enzymatic methods, and had data necessary for 2 × 2 contingency tables. A bivariate model was used to calculate the pooled estimates. Summary This meta-analysis included nine studies, totaling 10,007 individuals. Of those, 3,106 had diabetes. The studies showed substantial heterogeneity caused by a non-threshold effect and reported different GA optimal cut-offs for diagnosing diabetes. The pooled diagnostic odds ratio (DOR) was 15.93 and the area under the curve (AUC) was 0.844, indicating a good level of overall accuracy for the diagnosis of diabetes. The effect of the GA threshold on diagnostic accuracy was reported at 15.0% and 17.1%. The optimal cut-off for diagnosing diabetes with GA was estimated as 17.1% with a pooled sensitivity of 55.1% (95% CI 36.7%–72.2%) and specificity of 94.4% (95% CI 85.3%–97.9%). Outlook GA has good diabetes diagnostic accuracy. A GA threshold of 17.1% may be considered optimal for diagnosing diabetes in previously undiagnosed individuals.
【摘要】目的指南推荐糖尿病的诊断应基于血浆葡萄糖或糖化血红蛋白(HbA1C)结果。然而，最近的研究提倡糖化白蛋白(GA)作为HbA1c的有用替代品。我们进行了一项系统综述和荟萃分析，以确定GA诊断糖尿病的总体诊断准确性。我们检索了截至2021年8月发表的关于GA糖尿病诊断准确性的文章。选择口服葡萄糖耐量试验作为参考试验，用酶法测量GA水平，并具有2 × 2列联表所需的数据的研究。采用双变量模型计算汇总估计。本荟萃分析包括9项研究，共计10,007人。其中，3106人患有糖尿病。研究显示了非阈值效应引起的大量异质性，并报告了不同的遗传算法诊断糖尿病的最佳截止值。合并诊断优势比(DOR)为15.93，曲线下面积(AUC)为0.844，表明诊断糖尿病的总体准确性较高。GA阈值对诊断准确率的影响分别为15.0%和17.1%。GA诊断糖尿病的最佳临界值估计为17.1%，合并敏感性为55.1% (95% CI 36.7%-72.2%)，特异性为94.4% (95% CI 85.3%-97.9%)。展望遗传算法对糖尿病的诊断具有良好的准确性。17.1%的GA阈值可能被认为是诊断以前未确诊个体糖尿病的最佳阈值。
{"title":"Glycated albumin in diabetes mellitus: a meta-analysis of diagnostic test accuracy","authors":"F. C. Chume, P. A. C. Freitas, L. G. Schiavenin, A. L. Pimentel, J. L. Camargo","doi":"10.1515/cclm-2022-0105","DOIUrl":"https://doi.org/10.1515/cclm-2022-0105","url":null,"abstract":"Abstract Objectives Guidelines recommend the diagnosis of diabetes should be based on either plasma glucose or glycated hemoglobin (HbA1C) findings. However, lately studies have advocated glycated albumin (GA) as a useful alternative to HbA1c. We conducted a systematic review and meta-analysis to determine the overall diagnostic accuracy of GA for the diagnosis of diabetes. Content We searched for articles of GA diabetes diagnostic accuracy that were published up to August 2021. Studies were selected if reported an oral glucose tolerance test as a reference test, measured GA levels by enzymatic methods, and had data necessary for 2 × 2 contingency tables. A bivariate model was used to calculate the pooled estimates. Summary This meta-analysis included nine studies, totaling 10,007 individuals. Of those, 3,106 had diabetes. The studies showed substantial heterogeneity caused by a non-threshold effect and reported different GA optimal cut-offs for diagnosing diabetes. The pooled diagnostic odds ratio (DOR) was 15.93 and the area under the curve (AUC) was 0.844, indicating a good level of overall accuracy for the diagnosis of diabetes. The effect of the GA threshold on diagnostic accuracy was reported at 15.0% and 17.1%. The optimal cut-off for diagnosing diabetes with GA was estimated as 17.1% with a pooled sensitivity of 55.1% (95% CI 36.7%–72.2%) and specificity of 94.4% (95% CI 85.3%–97.9%). Outlook GA has good diabetes diagnostic accuracy. A GA threshold of 17.1% may be considered optimal for diagnosing diabetes in previously undiagnosed individuals.","PeriodicalId":10388,"journal":{"name":"Clinical Chemistry and Laboratory Medicine (CCLM)","volume":"1 1","pages":"961 - 974"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73157860","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}
A. Padoan, C. Cosma, F. Della Rocca, F. Barbaro, C. Santarossa, L. Dall’Olmo, L. Galla, A. Cattelan, V. Cianci, D. Basso, M. Plebani
Abstract Objectives The waning of humoral immunity after COVID-19 vaccine booster (third dose) has not yet been fully evaluated. This study updates data on anti-SARS-CoV-2 spike protein receptor binding domain (S-RBD) binding antibodies (bAb) and neutralizing antibodies (NAb) levels in individuals with homologous vaccination 3–4 months after receiving the booster dose. Methods Fifty-five healthcare workers (HCW) from Padova University-Hospital were asked to collect serum samples for determining antibodies (Ab) at 12 (t12) and 28 (t28) days, at 6 months (t6m) after their first Comirnaty/BNT162b2 inoculation, and 3–4 months after receiving the 3rd homologous booster dose. HCW were monitored weekly for SARS-CoV-2 infection. Ab titers were measured by two chemiluminescent immunoassays, one targeting the S-RBD immunoglobulin G (IgG), and one surrogate viral neutralization test (sVNT), measuring NAb. Results Twenty of the HCW had natural COVID-19 infection (COVID+) at different times, before either the first or the second vaccination. Median S-RBD IgG and NAb levels and their interquartile ranges 3–4 months after the 3rd dose were 1,076 (529–3,409) kBAU/L and 15.8 (11.3–38.3) mg/L, respectively, for COVID−, and 1,373 (700–1,373) kBAU/L and 21 (12.8–53.9) mg/L, respectively, for COVID+. At multivariate regression analyses, with age and gender included as covariates, S-RBD IgG bAb and sVNT NAb levels were closely associated with the time interval between serological determination and the 3rd vaccine dose (log10 βcoeff=−0.013, p=0.012 and log10 βcoeff=−0.010, p=0.025) for COVID+, whereas no such association was found in COVID− individuals. Conclusions The third booster dose increases anti-SARS-CoV-2 Ab levels, elevated levels persisting for up to 3–4 months. Waning of Ab levels appears to be less pronounced for COVID+ individuals.
{"title":"A cohort analysis of SARS-CoV-2 anti-spike protein receptor binding domain (RBD) IgG levels and neutralizing antibodies in fully vaccinated healthcare workers","authors":"A. Padoan, C. Cosma, F. Della Rocca, F. Barbaro, C. Santarossa, L. Dall’Olmo, L. Galla, A. Cattelan, V. Cianci, D. Basso, M. Plebani","doi":"10.1515/cclm-2022-0322","DOIUrl":"https://doi.org/10.1515/cclm-2022-0322","url":null,"abstract":"Abstract Objectives The waning of humoral immunity after COVID-19 vaccine booster (third dose) has not yet been fully evaluated. This study updates data on anti-SARS-CoV-2 spike protein receptor binding domain (S-RBD) binding antibodies (bAb) and neutralizing antibodies (NAb) levels in individuals with homologous vaccination 3–4 months after receiving the booster dose. Methods Fifty-five healthcare workers (HCW) from Padova University-Hospital were asked to collect serum samples for determining antibodies (Ab) at 12 (t12) and 28 (t28) days, at 6 months (t6m) after their first Comirnaty/BNT162b2 inoculation, and 3–4 months after receiving the 3rd homologous booster dose. HCW were monitored weekly for SARS-CoV-2 infection. Ab titers were measured by two chemiluminescent immunoassays, one targeting the S-RBD immunoglobulin G (IgG), and one surrogate viral neutralization test (sVNT), measuring NAb. Results Twenty of the HCW had natural COVID-19 infection (COVID+) at different times, before either the first or the second vaccination. Median S-RBD IgG and NAb levels and their interquartile ranges 3–4 months after the 3rd dose were 1,076 (529–3,409) kBAU/L and 15.8 (11.3–38.3) mg/L, respectively, for COVID−, and 1,373 (700–1,373) kBAU/L and 21 (12.8–53.9) mg/L, respectively, for COVID+. At multivariate regression analyses, with age and gender included as covariates, S-RBD IgG bAb and sVNT NAb levels were closely associated with the time interval between serological determination and the 3rd vaccine dose (log10 βcoeff=−0.013, p=0.012 and log10 βcoeff=−0.010, p=0.025) for COVID+, whereas no such association was found in COVID− individuals. Conclusions The third booster dose increases anti-SARS-CoV-2 Ab levels, elevated levels persisting for up to 3–4 months. Waning of Ab levels appears to be less pronounced for COVID+ individuals.","PeriodicalId":10388,"journal":{"name":"Clinical Chemistry and Laboratory Medicine (CCLM)","volume":"83 1","pages":"1110 - 1115"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88106558","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}
E. Alegre, N. Varo, P. Fernández-Calle, Sofía Calleja, Álvaro González
Abstract Objectives Retrospective studies frequently assume analytes long-term stability at ultra-low temperatures. However, these storage conditions, common among biobanks and research, may increase the preanalytical variability, adding a potential uncertainty to the measurements. This study is aimed to evaluate long-term storage stability of different analytes at <−70 °C and to assess its impact on the reference change value formula. Methods Twenty-one analytes commonly measured in clinical laboratories were quantified in 60 serum samples. Samples were immediately aliquoted and frozen at <−70 °C, and reanalyzed after 11 ± 3.9 years of storage. A change in concentration after storage was considered relevant if the percent deviation from the baseline measurement was significant and higher than the analytical performance specifications. Results Preanalytical variability (CVP) due to storage, determined by the percentage deviation, showed a noticeable dispersion. Changes were relevant for alanine aminotransferase, creatinine, glucose, magnesium, potassium, sodium, total bilirubin and urate. No significant differences were found in aspartate aminotransferase, calcium, carcinoembryonic antigen, cholesterol, C-reactive protein, direct bilirubin, free thryroxine, gamma-glutamyltransferase, lactate dehydrogenase, prostate-specific antigen, triglycerides, thyrotropin, and urea. As nonnegligible, CVP must remain included in reference change value formula, which was modified to consider whether one or two samples were frozen. Conclusions After long-term storage at ultra-low temperatures, there was a significant variation in some analytes that should be considered. We propose that reference change value formula should include the CVP when analyzing samples stored in these conditions.
{"title":"Impact of ultra-low temperature long-term storage on the preanalytical variability of twenty-one common biochemical analytes","authors":"E. Alegre, N. Varo, P. Fernández-Calle, Sofía Calleja, Álvaro González","doi":"10.1515/cclm-2022-0063","DOIUrl":"https://doi.org/10.1515/cclm-2022-0063","url":null,"abstract":"Abstract Objectives Retrospective studies frequently assume analytes long-term stability at ultra-low temperatures. However, these storage conditions, common among biobanks and research, may increase the preanalytical variability, adding a potential uncertainty to the measurements. This study is aimed to evaluate long-term storage stability of different analytes at <−70 °C and to assess its impact on the reference change value formula. Methods Twenty-one analytes commonly measured in clinical laboratories were quantified in 60 serum samples. Samples were immediately aliquoted and frozen at <−70 °C, and reanalyzed after 11 ± 3.9 years of storage. A change in concentration after storage was considered relevant if the percent deviation from the baseline measurement was significant and higher than the analytical performance specifications. Results Preanalytical variability (CVP) due to storage, determined by the percentage deviation, showed a noticeable dispersion. Changes were relevant for alanine aminotransferase, creatinine, glucose, magnesium, potassium, sodium, total bilirubin and urate. No significant differences were found in aspartate aminotransferase, calcium, carcinoembryonic antigen, cholesterol, C-reactive protein, direct bilirubin, free thryroxine, gamma-glutamyltransferase, lactate dehydrogenase, prostate-specific antigen, triglycerides, thyrotropin, and urea. As nonnegligible, CVP must remain included in reference change value formula, which was modified to consider whether one or two samples were frozen. Conclusions After long-term storage at ultra-low temperatures, there was a significant variation in some analytes that should be considered. We propose that reference change value formula should include the CVP when analyzing samples stored in these conditions.","PeriodicalId":10388,"journal":{"name":"Clinical Chemistry and Laboratory Medicine (CCLM)","volume":"18 1","pages":"1003 - 1010"},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83371280","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}
D. Negrini, E. Danese, B. Henry, G. Lippi, M. Montagnana
Abstract Objectives The development and use of artificial intelligence (AI) methodologies, especially machine learning (ML) and deep learning (DL), have been considerably fostered during the ongoing coronavirus disease 2019 (COVID-19) pandemic. Several models and algorithms have been developed and applied for both identifying COVID-19 cases and for assessing and predicting the risk of developing unfavourable outcomes. Our aim was to summarize how AI is being currently applied to COVID-19. Methods We conducted a PubMed search using as query MeSH major terms “Artificial Intelligence” AND “COVID-19”, searching for articles published until December 31, 2021, which explored the possible role of AI in COVID-19. The dataset origin (internal dataset or public datasets available online) and data used for training and testing the proposed ML/DL model(s) were retrieved. Results Our analysis finally identified 292 articles in PubMed. These studies displayed large heterogeneity in terms of imaging test, laboratory parameters and clinical-demographic data included. Most models were based on imaging data, in particular CT scans or chest X-rays images. C-Reactive protein, leukocyte count, creatinine, lactate dehydrogenase, lymphocytes and platelets counts were found to be the laboratory biomarkers most frequently included in COVID-19 related AI models. Conclusions The lion’s share of AI applied to COVID-19 seems to be played by diagnostic imaging. However, AI in laboratory medicine is also gaining momentum, especially with digital tools characterized by low cost and widespread applicability.
{"title":"Artificial intelligence at the time of COVID-19: who does the lion’s share?","authors":"D. Negrini, E. Danese, B. Henry, G. Lippi, M. Montagnana","doi":"10.1515/cclm-2022-0306","DOIUrl":"https://doi.org/10.1515/cclm-2022-0306","url":null,"abstract":"Abstract Objectives The development and use of artificial intelligence (AI) methodologies, especially machine learning (ML) and deep learning (DL), have been considerably fostered during the ongoing coronavirus disease 2019 (COVID-19) pandemic. Several models and algorithms have been developed and applied for both identifying COVID-19 cases and for assessing and predicting the risk of developing unfavourable outcomes. Our aim was to summarize how AI is being currently applied to COVID-19. Methods We conducted a PubMed search using as query MeSH major terms “Artificial Intelligence” AND “COVID-19”, searching for articles published until December 31, 2021, which explored the possible role of AI in COVID-19. The dataset origin (internal dataset or public datasets available online) and data used for training and testing the proposed ML/DL model(s) were retrieved. Results Our analysis finally identified 292 articles in PubMed. These studies displayed large heterogeneity in terms of imaging test, laboratory parameters and clinical-demographic data included. Most models were based on imaging data, in particular CT scans or chest X-rays images. C-Reactive protein, leukocyte count, creatinine, lactate dehydrogenase, lymphocytes and platelets counts were found to be the laboratory biomarkers most frequently included in COVID-19 related AI models. Conclusions The lion’s share of AI applied to COVID-19 seems to be played by diagnostic imaging. However, AI in laboratory medicine is also gaining momentum, especially with digital tools characterized by low cost and widespread applicability.","PeriodicalId":10388,"journal":{"name":"Clinical Chemistry and Laboratory Medicine (CCLM)","volume":"25 1","pages":"1881 - 1886"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85262565","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}
{"title":"Assessment of the humoral response in Omicron breakthrough cases in healthcare workers who received the BNT162b2 booster","authors":"J. Favresse, J. Dogné, J. Douxfils","doi":"10.1515/cclm-2022-0323","DOIUrl":"https://doi.org/10.1515/cclm-2022-0323","url":null,"abstract":"","PeriodicalId":10388,"journal":{"name":"Clinical Chemistry and Laboratory Medicine (CCLM)","volume":"506 1","pages":"e153 - e156"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77064643","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}
S. Dierks, R. Andag, Friederike Gauß, Kathrin Budde, Paul Francke, M. Peschka, A. Fischer, J. Schanz, A. Petersmann
Abstract Objectives Thyroid-stimulating hormone (TSH) is the routine primary screening test to assess thyroid function and rapid measurement of TSH levels is highly desirable especially in emergency situations. In the present study, we compared the analytical performance of a commercially available point-of-care test (AFIAS-1) and five laboratory-based systems. Methods Left over material of 60 patient plasma samples was collected from patient care and used in the respective assay. For statistical analysis of the produced data Bland-Altman and Passing-Bablok regression analysis were applied. Results Good correlation (r=0.982 or higher) was found between all devices. Slopes from regression analysis ranged from 0.972 (95% CI: 0.927–1.013) to 1.276 (95% CI: 1.210–1.315). Among the compared devices, imprecision was high in terms of coefficient of variation (CV=10.3%) for low TSH concentrations and lower (CV=7.3%) for high TSH concentrations. Independent of the method used, we demonstrated a poor standardization of TSH assays, which might impact clinical diagnosis e.g. of hyperthyreosis. Conclusions This study shows that the point-of-care (POC) test AFIAS-1 can serve as an alternative to laboratory-based assays. In addition the data imply that better standardization of TSH measurements is needed.
{"title":"Evaluation of the AFIAS-1 thyroid-stimulating hormone point of care test and comparison with laboratory-based devices","authors":"S. Dierks, R. Andag, Friederike Gauß, Kathrin Budde, Paul Francke, M. Peschka, A. Fischer, J. Schanz, A. Petersmann","doi":"10.1515/cclm-2022-0054","DOIUrl":"https://doi.org/10.1515/cclm-2022-0054","url":null,"abstract":"Abstract Objectives Thyroid-stimulating hormone (TSH) is the routine primary screening test to assess thyroid function and rapid measurement of TSH levels is highly desirable especially in emergency situations. In the present study, we compared the analytical performance of a commercially available point-of-care test (AFIAS-1) and five laboratory-based systems. Methods Left over material of 60 patient plasma samples was collected from patient care and used in the respective assay. For statistical analysis of the produced data Bland-Altman and Passing-Bablok regression analysis were applied. Results Good correlation (r=0.982 or higher) was found between all devices. Slopes from regression analysis ranged from 0.972 (95% CI: 0.927–1.013) to 1.276 (95% CI: 1.210–1.315). Among the compared devices, imprecision was high in terms of coefficient of variation (CV=10.3%) for low TSH concentrations and lower (CV=7.3%) for high TSH concentrations. Independent of the method used, we demonstrated a poor standardization of TSH assays, which might impact clinical diagnosis e.g. of hyperthyreosis. Conclusions This study shows that the point-of-care (POC) test AFIAS-1 can serve as an alternative to laboratory-based assays. In addition the data imply that better standardization of TSH measurements is needed.","PeriodicalId":10388,"journal":{"name":"Clinical Chemistry and Laboratory Medicine (CCLM)","volume":"22 1","pages":"1031 - 1038"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81291636","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}
G. Lippi, J. Favresse, M. M. Gromiha, J. SoRelle, M. Plebani, B. Henry
Abstract This document, endorsed by the IFCC Working Group on SARS-CoV-2 Variants, aims to update previous indications for diagnosing acute SARS-CoV-2 infection, taking into consideration the evidence that has emerged after the origin and spread of new lineages and sub-lineages of the virus characterized by mutated genetics and altered biochemical, biological and clinical characteristics. These indications encompass the use of different diagnostic strategies in specific clinical settings, such as high risk of SARS-CoV-2 infection (symptomatic patients), low risk of SARS-CoV-2 infection (asymptomatic subjects) at hospital admission/contact tracing, testing in asymptomatic subjects, in epidemiologic surveys and/or population screening, along with tentative indications for identification of new lineages and/or sub-lineages of SARS-CoV-2.
{"title":"Ad interim recommendations for diagnosing SARS-CoV-2 infection by the IFCC SARS-CoV-2 variants working group","authors":"G. Lippi, J. Favresse, M. M. Gromiha, J. SoRelle, M. Plebani, B. Henry","doi":"10.1515/cclm-2022-0345","DOIUrl":"https://doi.org/10.1515/cclm-2022-0345","url":null,"abstract":"Abstract This document, endorsed by the IFCC Working Group on SARS-CoV-2 Variants, aims to update previous indications for diagnosing acute SARS-CoV-2 infection, taking into consideration the evidence that has emerged after the origin and spread of new lineages and sub-lineages of the virus characterized by mutated genetics and altered biochemical, biological and clinical characteristics. These indications encompass the use of different diagnostic strategies in specific clinical settings, such as high risk of SARS-CoV-2 infection (symptomatic patients), low risk of SARS-CoV-2 infection (asymptomatic subjects) at hospital admission/contact tracing, testing in asymptomatic subjects, in epidemiologic surveys and/or population screening, along with tentative indications for identification of new lineages and/or sub-lineages of SARS-CoV-2.","PeriodicalId":10388,"journal":{"name":"Clinical Chemistry and Laboratory Medicine (CCLM)","volume":"66 1","pages":"975 - 981"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83903066","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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