TY - JOUR
T1 - Automatic real-Time analysis and interpretation of arterial blood gas sample for Point-of-care testing
T2 - Clinical validation
AU - Rodríguez-Villar, Sancho
AU - Poza-Hernández, Paloma
AU - Freigang, Sascha
AU - Zubizarreta-Ormazabal, Idoia
AU - Paz-Martín, Daniel
AU - Holl, Etienne
AU - Pérez-Pardo, Osvaldo Ceferino
AU - Tovar-Doncel, María Sherezade
AU - Wissa, Sonja Maria
AU - Cimadevilla-Calvo, Bonifacio
AU - Tejón-Pérez, Guillermo
AU - Moreno-Fernández, Ismael
AU - Escario-Méndez, Alejandro
AU - Arévalo-Serrano, Juan
AU - Valentin, Antonio
AU - Do-Vale, Bruno Manuel
AU - Fletcher, Helen Marie
AU - Lorenzo-Fernández, Jesús Medardo
N1 - Publisher Copyright:
© 2021 Rodrı´guez-Villar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/3
Y1 - 2021/3
N2 - 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 acidbase 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 acidbase 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 acidbase 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.
AB - 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 acidbase 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 acidbase 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 acidbase 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.
UR - http://www.scopus.com/inward/record.url?scp=85102642375&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0248264
DO - 10.1371/journal.pone.0248264
M3 - Article
C2 - 33690724
AN - SCOPUS:85102642375
SN - 1932-6203
VL - 16
JO - PLoS ONE
JF - PLoS ONE
IS - 3 March
M1 - e0248264
ER -