Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.16/2838 |
Resumo: | Background: Point-of-care arterial blood gas (ABG) is a blood measurement test and a useful diagnostic tool that assists with treatment and therefore improves clinical outcomes. However, numerically reported test results make rapid interpretation difficult or open to interpretation. The arterial blood gas algorithm (ABG-a) is a new digital diagnostics solution that can provide clinicians with real-time interpretation of preliminary data on safety features, oxygenation, acid-base disturbances and renal profile. The main aim of this study was to clinically validate the algorithm against senior experienced clinicians, for acid-base interpretation, in a clinical context. Methods: We conducted a prospective international multicentre observational cross-sectional study. 346 sample sets and 64 inpatients eligible for ABG met strict sampling criteria. Agreement was evaluated using Cohen's kappa index, diagnostic accuracy was evaluated with sensitivity, specificity, efficiency or global accuracy and positive predictive values (PPV) and negative predictive values (NPV) for the prevalence in the study population. Results: The concordance rates between the interpretations of the clinicians and the ABG-a for acid-base disorders were an observed global agreement of 84,3% with a Cohen's kappa coefficient 0.81; 95% CI 0.77 to 0.86; p < 0.001. For detecting accuracy normal acid-base status the algorithm has a sensitivity of 90.0% (95% CI 79.9 to 95.3), a specificity 97.2% (95% CI 94.5 to 98.6) and a global accuracy of 95.9% (95% CI 93.3 to 97.6). For the four simple acid-base disorders, respiratory alkalosis: sensitivity of 91.2 (77.0 to 97.0), a specificity 100.0 (98.8 to 100.0) and global accuracy of 99.1 (97.5 to 99.7); respiratory acidosis: sensitivity of 61.1 (38.6 to 79.7), a specificity of 100.0 (98.8 to 100.0) and global accuracy of 98.0 (95.9 to 99.0); metabolic acidosis: sensitivity of 75.8 (59.0 to 87.2), a specificity of 99.7 (98.2 to 99.9) and a global accuracy of 97.4 (95.1 to 98.6); metabolic alkalosis sensitivity of 72.2 (56.0 to 84.2), a specificity of 95.5 (92.5 to 97.3) and a global accuracy of 93.0 (88.8 to 95.3); the four complex acid-base disorders, respiratory and metabolic alkalosis, respiratory and metabolic acidosis, respiratory alkalosis and metabolic acidosis, respiratory acidosis and metabolic alkalosis, the sensitivity, specificity and global accuracy was also high. For normal acid-base status the algorithm has PPV 87.1 (95% CI 76.6 to 93.3) %, and NPV 97.9 (95% CI 95.4 to 99.0) for a prevalence of 17.4 (95% CI 13.8 to 21.8). For the four-simple acid-base disorders and the four complex acid-base disorders the PPV and NPV were also statistically significant. Conclusions: The ABG-a showed very high agreement and diagnostic accuracy with experienced senior clinicians in the acid-base disorders in a clinical context. The method also provides refinement and deep complex analysis at the point-of-care that a clinician could have at the bedside on a day-to-day basis. The ABG-a method could also have the potential to reduce human errors by checking for imminent life-threatening situations, analysing the internal consistency of the results, the oxygenation and renal status of the patient. |
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Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validationBackground: Point-of-care arterial blood gas (ABG) is a blood measurement test and a useful diagnostic tool that assists with treatment and therefore improves clinical outcomes. However, numerically reported test results make rapid interpretation difficult or open to interpretation. The arterial blood gas algorithm (ABG-a) is a new digital diagnostics solution that can provide clinicians with real-time interpretation of preliminary data on safety features, oxygenation, acid-base disturbances and renal profile. The main aim of this study was to clinically validate the algorithm against senior experienced clinicians, for acid-base interpretation, in a clinical context. Methods: We conducted a prospective international multicentre observational cross-sectional study. 346 sample sets and 64 inpatients eligible for ABG met strict sampling criteria. Agreement was evaluated using Cohen's kappa index, diagnostic accuracy was evaluated with sensitivity, specificity, efficiency or global accuracy and positive predictive values (PPV) and negative predictive values (NPV) for the prevalence in the study population. Results: The concordance rates between the interpretations of the clinicians and the ABG-a for acid-base disorders were an observed global agreement of 84,3% with a Cohen's kappa coefficient 0.81; 95% CI 0.77 to 0.86; p < 0.001. For detecting accuracy normal acid-base status the algorithm has a sensitivity of 90.0% (95% CI 79.9 to 95.3), a specificity 97.2% (95% CI 94.5 to 98.6) and a global accuracy of 95.9% (95% CI 93.3 to 97.6). For the four simple acid-base disorders, respiratory alkalosis: sensitivity of 91.2 (77.0 to 97.0), a specificity 100.0 (98.8 to 100.0) and global accuracy of 99.1 (97.5 to 99.7); respiratory acidosis: sensitivity of 61.1 (38.6 to 79.7), a specificity of 100.0 (98.8 to 100.0) and global accuracy of 98.0 (95.9 to 99.0); metabolic acidosis: sensitivity of 75.8 (59.0 to 87.2), a specificity of 99.7 (98.2 to 99.9) and a global accuracy of 97.4 (95.1 to 98.6); metabolic alkalosis sensitivity of 72.2 (56.0 to 84.2), a specificity of 95.5 (92.5 to 97.3) and a global accuracy of 93.0 (88.8 to 95.3); the four complex acid-base disorders, respiratory and metabolic alkalosis, respiratory and metabolic acidosis, respiratory alkalosis and metabolic acidosis, respiratory acidosis and metabolic alkalosis, the sensitivity, specificity and global accuracy was also high. For normal acid-base status the algorithm has PPV 87.1 (95% CI 76.6 to 93.3) %, and NPV 97.9 (95% CI 95.4 to 99.0) for a prevalence of 17.4 (95% CI 13.8 to 21.8). For the four-simple acid-base disorders and the four complex acid-base disorders the PPV and NPV were also statistically significant. Conclusions: The ABG-a showed very high agreement and diagnostic accuracy with experienced senior clinicians in the acid-base disorders in a clinical context. The method also provides refinement and deep complex analysis at the point-of-care that a clinician could have at the bedside on a day-to-day basis. The ABG-a method could also have the potential to reduce human errors by checking for imminent life-threatening situations, analysing the internal consistency of the results, the oxygenation and renal status of the patient.Public Library of ScienceRepositório Científico do Centro Hospitalar Universitário de Santo AntónioRodríguez-Villar, SanchoPoza-Hernández, PalomaFreigang, SaschaZubizarreta-Ormazabal, IdoiaPaz-Martín, DanielHoll, EtiennePérez-Pardo, Osvaldo CeferinoTovar-Doncel, María SherezadeWissa, Sonja MariaCimadevilla-Calvo, BonifacioTejón-Pérez, GuillermoMoreno-Fernández, IsmaelEscario-Méndez, AlejandroArévalo-Serrano, JuanValentín, AntonioVale, BrunoFletcher, Helen MarieLorenzo- Fernández, Jesús Medardo2023-10-23T12:22:33Z2021-032021-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.16/2838engRodríguez-Villar S, Poza-Hernández P, Freigang S, et al. Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation. PLoS One. 2021;16(3):e0248264. doi:10.1371/journal.pone.02482641932-620310.1371/journal.pone.0248264info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-10-26T05:19:16Zoai:repositorio.chporto.pt:10400.16/2838Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:39:38.684594Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation |
title |
Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation |
spellingShingle |
Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation Rodríguez-Villar, Sancho |
title_short |
Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation |
title_full |
Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation |
title_fullStr |
Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation |
title_full_unstemmed |
Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation |
title_sort |
Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation |
author |
Rodríguez-Villar, Sancho |
author_facet |
Rodríguez-Villar, Sancho Poza-Hernández, Paloma Freigang, Sascha Zubizarreta-Ormazabal, Idoia Paz-Martín, Daniel Holl, Etienne Pérez-Pardo, Osvaldo Ceferino Tovar-Doncel, María Sherezade Wissa, Sonja Maria Cimadevilla-Calvo, Bonifacio Tejón-Pérez, Guillermo Moreno-Fernández, Ismael Escario-Méndez, Alejandro Arévalo-Serrano, Juan Valentín, Antonio Vale, Bruno Fletcher, Helen Marie Lorenzo- Fernández, Jesús Medardo |
author_role |
author |
author2 |
Poza-Hernández, Paloma Freigang, Sascha Zubizarreta-Ormazabal, Idoia Paz-Martín, Daniel Holl, Etienne Pérez-Pardo, Osvaldo Ceferino Tovar-Doncel, María Sherezade Wissa, Sonja Maria Cimadevilla-Calvo, Bonifacio Tejón-Pérez, Guillermo Moreno-Fernández, Ismael Escario-Méndez, Alejandro Arévalo-Serrano, Juan Valentín, Antonio Vale, Bruno Fletcher, Helen Marie Lorenzo- Fernández, Jesús Medardo |
author2_role |
author author author author author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Centro Hospitalar Universitário de Santo António |
dc.contributor.author.fl_str_mv |
Rodríguez-Villar, Sancho Poza-Hernández, Paloma Freigang, Sascha Zubizarreta-Ormazabal, Idoia Paz-Martín, Daniel Holl, Etienne Pérez-Pardo, Osvaldo Ceferino Tovar-Doncel, María Sherezade Wissa, Sonja Maria Cimadevilla-Calvo, Bonifacio Tejón-Pérez, Guillermo Moreno-Fernández, Ismael Escario-Méndez, Alejandro Arévalo-Serrano, Juan Valentín, Antonio Vale, Bruno Fletcher, Helen Marie Lorenzo- Fernández, Jesús Medardo |
description |
Background: Point-of-care arterial blood gas (ABG) is a blood measurement test and a useful diagnostic tool that assists with treatment and therefore improves clinical outcomes. However, numerically reported test results make rapid interpretation difficult or open to interpretation. The arterial blood gas algorithm (ABG-a) is a new digital diagnostics solution that can provide clinicians with real-time interpretation of preliminary data on safety features, oxygenation, acid-base disturbances and renal profile. The main aim of this study was to clinically validate the algorithm against senior experienced clinicians, for acid-base interpretation, in a clinical context. Methods: We conducted a prospective international multicentre observational cross-sectional study. 346 sample sets and 64 inpatients eligible for ABG met strict sampling criteria. Agreement was evaluated using Cohen's kappa index, diagnostic accuracy was evaluated with sensitivity, specificity, efficiency or global accuracy and positive predictive values (PPV) and negative predictive values (NPV) for the prevalence in the study population. Results: The concordance rates between the interpretations of the clinicians and the ABG-a for acid-base disorders were an observed global agreement of 84,3% with a Cohen's kappa coefficient 0.81; 95% CI 0.77 to 0.86; p < 0.001. For detecting accuracy normal acid-base status the algorithm has a sensitivity of 90.0% (95% CI 79.9 to 95.3), a specificity 97.2% (95% CI 94.5 to 98.6) and a global accuracy of 95.9% (95% CI 93.3 to 97.6). For the four simple acid-base disorders, respiratory alkalosis: sensitivity of 91.2 (77.0 to 97.0), a specificity 100.0 (98.8 to 100.0) and global accuracy of 99.1 (97.5 to 99.7); respiratory acidosis: sensitivity of 61.1 (38.6 to 79.7), a specificity of 100.0 (98.8 to 100.0) and global accuracy of 98.0 (95.9 to 99.0); metabolic acidosis: sensitivity of 75.8 (59.0 to 87.2), a specificity of 99.7 (98.2 to 99.9) and a global accuracy of 97.4 (95.1 to 98.6); metabolic alkalosis sensitivity of 72.2 (56.0 to 84.2), a specificity of 95.5 (92.5 to 97.3) and a global accuracy of 93.0 (88.8 to 95.3); the four complex acid-base disorders, respiratory and metabolic alkalosis, respiratory and metabolic acidosis, respiratory alkalosis and metabolic acidosis, respiratory acidosis and metabolic alkalosis, the sensitivity, specificity and global accuracy was also high. For normal acid-base status the algorithm has PPV 87.1 (95% CI 76.6 to 93.3) %, and NPV 97.9 (95% CI 95.4 to 99.0) for a prevalence of 17.4 (95% CI 13.8 to 21.8). For the four-simple acid-base disorders and the four complex acid-base disorders the PPV and NPV were also statistically significant. Conclusions: The ABG-a showed very high agreement and diagnostic accuracy with experienced senior clinicians in the acid-base disorders in a clinical context. The method also provides refinement and deep complex analysis at the point-of-care that a clinician could have at the bedside on a day-to-day basis. The ABG-a method could also have the potential to reduce human errors by checking for imminent life-threatening situations, analysing the internal consistency of the results, the oxygenation and renal status of the patient. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-03 2021-03-01T00:00:00Z 2023-10-23T12:22:33Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.16/2838 |
url |
http://hdl.handle.net/10400.16/2838 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Rodríguez-Villar S, Poza-Hernández P, Freigang S, et al. Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation. PLoS One. 2021;16(3):e0248264. doi:10.1371/journal.pone.0248264 1932-6203 10.1371/journal.pone.0248264 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Public Library of Science |
publisher.none.fl_str_mv |
Public Library of Science |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799133657974177792 |