scholarly journals Automatic real-time analysis and interpretation of arterial blood gas sample for Point-of-care testing: Clinical validation

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248264
Author(s):  
Sancho Rodríguez-Villar ◽  
Paloma Poza-Hernández ◽  
Sascha Freigang ◽  
Idoia Zubizarreta-Ormazabal ◽  
Daniel Paz-Martín ◽  
...  
Keyword(s):  
Metabolic Acidosis ◽  
Metabolic Alkalosis ◽  
Point Of Care ◽  
Respiratory Acidosis ◽  
Blood Gas ◽  
Acid Base ◽  
Predictive Values ◽  
Arterial Blood Gas ◽  
Arterial Blood ◽  
Global Accuracy

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.

scholarly journals Arterial Blood Gas Analysis of Critically Ill Corona Virus Disease 2019 Patients

2021 ◽  
pp. 51-63
Author(s):  
Jitendra Lakhani ◽  
Sajani Kapadia ◽  
Hetal Pandya ◽  
Roop Gill ◽  
Rohit Chordiya ◽  
...  
Keyword(s):  
Metabolic Acidosis ◽  
Critically Ill ◽  
Virus Disease ◽  
Respiratory Acidosis ◽  
Respiratory Alkalosis ◽  
Gas Analysis ◽  
Blood Gas ◽  
Arterial Blood Gas ◽  
Arterial Blood ◽  
Corona Virus

Background/Aims: The aim of present study was to find out profile and sequential pattern of Arterial Blood Gas (ABG) in critically ill Corona Virus Disease 2019 (COVID-19) patients. Study Design: Observational prospective study. Methodology: A total of 80 Reverse Transcription Polymerase Chain Reaction (RT PCR) positive cases; that needed ICU admission for their life-threatening conditions were included in this study done at teaching hospital of Gujarat, India. Non consenting patients and patients who could not be followed up as per protocol were excluded. Data of Arterial Blood Gas (ABG), performed on admission, day 5 and day 10 were taken for the analysis. Patients were followed up till they remained in ICU. Results: Of 80 patients, 3 patients had normal, 24 patients (30%) had primary disorder on ABG while 53 patients (66.25%) had mixed disorders. The most common ABG abnormality observed was respiratory alkalosis with metabolic acidosis in 16 patients (20%) while respiratory alkalosis with metabolic alkalosis in 15 patients (18.75%). There was difference in ABG pattern observed among survivors and non-survivors (P=.04); of which conspicuous was presence of “respiratory acidosis with metabolic acidosis” in 5 non-survivors (15.63%), which was not seen in survivors. Of 80 patients admitted in COVID ICU; 2 improved after day 1; 6 after day 5; 40 after day 10, making total of 48 patients surviving COVID critical condition. Of 32 non-survivors, 14 died within twenty-four hours of admission, 14 within first 5 days and 04 after 10 days of ICU stay. Conclusion: ABG done on admission and serially in severe COVID-19 patients gives useful information on underlying pathophysiology. Mixed ABG pattern was more common than single disorder which can be sign of multi-organ involvement.  Respiratory acidosis with metabolic acidosis was observed significantly higher in non-survivors. Respiratory alkalosis as a part of single or mixed pattern on ABG was the most common pattern found in critically ill COVID patients.

scholarly journals Re-Evaluation of Acid-Base Prediction Rules in Patients with Chronic Respiratory Acidosis

2003 ◽  
Vol 10 (6) ◽  
pp. 311-315 ◽  
Author(s):  
Tereza Martinu ◽  
Dick Menzies ◽  
Sandra Dial
Keyword(s):  
Linear Regression Analysis ◽  
Respiratory Acidosis ◽  
Canine Model ◽  
Regression Equations ◽  
Blood Gas ◽  
Acid Base ◽  
Prediction Rules ◽  
Arterial Blood Gas ◽  
Arterial Blood ◽  
Gas Measurements

RATIONALE: The prediction rules for the evaluation of the acid-base status in patients with chronic respiratory acidosis, derived primarily from an experimental canine model, suggest that complete compensation should not occur. This appears to contradict frequent observations of normal or near-normal pH levels in patients with chronic hypercapnia.METHODS: Linear regression analysis was used to estimate the relationships between arterial pH, bicarbonate and partial pressure of carbon dioxide (PCO2) from 18 separate arterial blood gas measurements in 18 clinically stable outpatients with chronic hypercapnic respiratory failure from chronic obstructive lung disease, and without clinical conditions or medications likely to cause a primary metabolic alkalosis.RESULTS: The PCO2ranged from 45 mmHg to 77 mmHg, and pH ranged from 7.37 to 7.44. In only three of the arterial blood gas measurements were the pH values lower than 7.38. From the regression equations derived from these measurements, the pH decreased by 0.014 for each 10 mmHg increase in the PCO2, and the bicarbonate level increased by 5.1 mmol/L. These values are quite different from a decrease in pH of 0.03 and an increase in bicarbonate of 3.5 mmol/L predicted using the rules derived from the canine model.CONCLUSIONS: In patients with chronic stable hypercapnia, acid-base compensatory mechanisms appear to be more effective than would be predicted using the classic rules.

scholarly journals Be cautious during the interpretation of arterial blood gas analysis performed outside the intensive care unit

2020 ◽  
Author(s):  
Lukasz Krzych ◽  
Olga Wojnarowicz ◽  
Paweł Ignacy ◽  
Julia Dorniak
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Point Of Care ◽  
Blood Gas Analysis ◽  
Gas Analysis ◽  
Blood Gas ◽  
Acid Base Balance ◽  
Arterial Blood Gas ◽  
Arterial Blood ◽  
The Impact

Introduction. Reliable results of an arterial blood gas (ABG) analysis are crucial for the implementation of appropriate diagnostics and therapy. We aimed to investigate the differences (Δ) between ABG parameters obtained from point-of-care testing (POCT) and central laboratory (CL) measurements, taking into account the turnaround time (TAT). Materials and methods. A number of 208 paired samples were collected from 54 intensive care unit (ICU) patients. Analyses were performed using Siemens RAPIDPoint 500 Blood Gas System on the samples just after blood retrieval at the ICU and after delivery to the CL. Results. The median TAT was 56 minutes (IQR 39-74). Differences were found for all ABG parameters. Median Δs for acid-base balance ere: ΔpH=0.006 (IQR –0.0070–0.0195), ΔBEef=–0.9 (IQR –2.0–0.4) and HCO3–act=–1.05 (IQR –2.25–0.35). For ventilatory parameters they were: ΔpO2=–8.3 mmHg (IQR –20.9–0.8) and ΔpCO2=–2.2 mmHg (IQR –4.2––0.4). For electrolytes balance the differences were: ΔNa+=1.55 mM/L (IQR 0.10–2.85), ΔK+=–0.120 mM/L (IQR –0.295–0.135) and ΔCl–=1.0 mM/L (IQR –1.0–3.0). Although the Δs might have caused misdiagnosis in 51 samples, Bland-Altman analysis revealed that only for pO2 the difference was of clinical significance (mean: –10.1 mmHg, ±1.96SD –58.5; +38.3). There was an important correlation between TAT and ΔpH (R=0.45, p<0.01) with the safest time delay for proper assessment being less than 39 minutes. Conclusions. Differences between POCT and CL results in ABG analysis may be clinically important and cause misdiagnosis, especially for pO2. POCT should be advised for ABG analysis due to the impact of TAT, which seems to be the most important for the analysis of pH.

Blood gas analysis in the critically ill

2016 ◽  
Author(s):  
Gavin M. Joynt ◽  
Gordon Y. S. Choi
Keyword(s):  
Haemoglobin Concentration ◽  
Respiratory Acidosis ◽  
Blood Oxygenation ◽  
Arterial Oxygen ◽  
Buffer System ◽  
Blood Gas ◽  
Acid Base ◽  
Bicarbonate Buffer ◽  
Arterial Blood ◽  
Alveolar Hypoventilation

Arterial blood gases allow the assessment of patient oxygenation, ventilation, and acid-base status. Blood gas machines directly measure pH, and the partial pressures of carbon dioxide (PaCO2) and oxygen (PaO2) dissolved in arterial blood. Oxygenation is assessed by measuring PaO2 and arterial blood oxygen saturation (SaO2) in the context of the inspired oxygen and haemoglobin concentration, and the oxyhaemoglobin dissociation curve. Causes of arterial hypoxaemia may often be elucidated by determining the alveolar–arterial oxygen gradient. Ventilation is assessed by measuring the PaCO2 in the context of systemic acid-base balance. A rise in PaCO2 indicates alveolar hypoventilation, while a decrease indicates alveolar hyperventilation. Given the requirement to maintain a normal pH, functioning homeostatic mechanisms result in metabolic acidosis, triggering a compensatory hyperventilation, while metabolic alkalosis triggers a compensatory reduction in ventilation. Similarly, when primary alveolar hypoventilation generates a respiratory acidosis, it results in a compensatory increase in serum bicarbonate that is achieved in part by kidney bicarbonate retention. In the same way, respiratory alkalosis induces kidney bicarbonate loss. Acid-base assessment requires the integration of clinical findings and a systematic interpretation of arterial blood gas parameters. In clinical use, traditional acid-base interpretation rules based on the bicarbonate buffer system or standard base excess estimations and the interpretation of the anion gap, are substantially equivalent to the physicochemical method of Stewart, and are generally easier to use at the bedside. The Stewart method may have advantages in accurately explaining certain physiological and pathological acid base problems.

scholarly journals Comparison of time taken to obtain an arterial blood gas result at the bedside using the ProximaTM point of care machine vs. a standard remote arterial blood gas analyser: A randomized controlled trial

2020 ◽  
pp. 175114372097384
Author(s):  
Kay Mitchell ◽  
Karen E Salmon ◽  
David Egbosimba ◽  
Gavin Troughton ◽  
Mike PW Grocott
Keyword(s):  
Point Of Care ◽  
Controlled Trial ◽  
Blood Gas ◽  
Sampling System ◽  
Arterial Blood Gas ◽  
Arterial Blood ◽  
The Mean ◽  
Time Away ◽  
Mean Time ◽  
Reduced Time

Introduction The ProximaTM point of care (POC) device enables arterial blood gas (ABG) samples to be analysed without the nurse leaving the patient. The benefits of this for work efficiency have not been evaluated. Methods We compared the time taken to obtain an ABG result using ProximaTM versus a standard ABG sampling system. Twenty patients were randomized to ABG sampling using ProximaTM, or a standard ABG system. Nurses were observed performing all ABG sampling episodes for a minimum of 24 hours and no more than 72 hours. Results The mean time taken to obtain a result using ProximaTM was 4:56 (SD = 1:40) minutes compared to 6:31 (SD = 1:53) minutes for the standard ABG technique (p < 0.001). Mean time away from the patient's bedside was 3.07 (SD = 1:17) minutes using the standard system and 0 minutes using ProximaTM (p < 0.001). Conclusions Reduced time for blood gas sampling and avoidance of time away from patients may have significant patient safety and resource management implications, but the clinical and financial significance were not evaluated.

THE STUDY ON ARTERIAL BLOOD GAS IN PATIENTS WITH ACUTE STROKE

2017 ◽  
pp. 37-45
Author(s):  
Xuan Tai Nguyen ◽  
Dinh Toan Nguyen
Keyword(s):  
Ischemic Stroke ◽  
Acute Stroke ◽  
Scale Score ◽  
Negative Correlation ◽  
Hemorrhagic Stroke ◽  
Respiratory Acidosis ◽  
Blood Gas ◽  
Arterial Blood Gas ◽  
Arterial Blood ◽  
Time Of Admission

Objectives: 1) To investigate the variation in arterial blood gas in patients with acute stroke according to ischemic stroke and hemorrhagic stroke. 2) To determine the correlation and relevance between arterial blood gas and Glasgow scale score, NIHSS, volume of brain damage on computed tomography imaging. Subjects and methods: A cross-sectional study was done in 70 patients with acute stroke. Results: Reduced rates of PCO2, PO2, SaO2 at the time of admission were 56.2%, 25%, 18.8% in ischemic stroke and 44.4%, 72.2%, 63% in hemorrhagic stroke. At the time of 24 hours, these rates were 75%, 56.2%, 50% in ischemic stroke and 50%, 79.6%, 70.4% in hemorrhagic stroke. At the time of 48 hours, these rates were 68.7%, 50%, 18.8% in ischemic stroke and 53.7%, 59.3%, 44.4% in hemorrhagic stroke. Respiratory acidosis was only present at hemorrhagic stroke. Respiratory alkalosis was in both stroke style and had the highest proportion. At the time of admission, SaO2 was negatively correlated with damage volume (r=- 0.264, p<0.05). HCO3- correlated with Glasgow (r=0.323; p<0.01) and NIHSS (r=-0.274; p<0.05). At the time of 24 hours, there was a negative correlation between PO2 (r=-0.375, p=0.001) and SaO2 (r =-0.39, p<0.01) with NIHSS. There was a negative correlation between PO2 (r=-0.435) and SaO2 (r=-0.457) with damage volume (p <0.0001). At the time of 48 hours, there was a negative correlation between PCO2, PO2 and SaO2 with NIHSS (r=-0.312, p<0.01, r=-0.35, p=0.01 and r=-0.0270, p<0.05). PCO2 was positively correlated with Glasgow (r = 0.260, p <0.05). There was a negative correlation between PO2 (r = - 0.391, p = 0.001) and SaO2 (r = - 0.421, p <0.001) with damage volume. Conclusions: In stroke patients, disturbances on ABG they are surfered from (acid-base disorders, hypoxemia) affect directly or indirectly on brain cells. Secondary brain damages could be well prevented if these disturbances is diagnosed and treated promptly. Key words: Stroke, arterial blood gas, Glasgow scale score, NIHSS

Sign in / Sign up

Export Citation Format

Share Document