scholarly journals Validation Experiment of a New Brain Oxygen Saturation Monitoring Instrument

2021 ◽  
Author(s):  
Kai Chen ◽  
Zunxu liu ◽  
Meng-Yun Li ◽  
Lijuan Tang ◽  
Yufeng Zou ◽  
...  
Keyword(s):  
Oxygen Saturation ◽  
Radial Artery ◽  
Jugular Vein ◽  
Blood Gas Analysis ◽  
Gas Analysis ◽  
Blood Gas ◽  
Blood Samples ◽  
Brain Oxygen ◽  
Test Instrument ◽  
The Difference

Abstract Background The blood samples of jugular vein and radial artery were obtained from healthy adults by induced oxygen desaturation test under pulse oximetry conditions on each platform. The oxygen saturation of the two blood samples was analyzed and measured by a Co-oximeter. Thus, the oxygen saturation value of jugular vein (SjvO2) and radial artery (SaO2) were obtained. According to the clinical empirical formula Sa/vO2 = 0.7×S jvO2 + 0.3×SaO2, the oxygen saturation value of brain tissue for invasive blood gas analysis was calculated. To calculate the difference between brain oxygen saturation (rSO2) measured by brain oxygen saturation monitor (hereinafter referred to as brain oxygen analyzer) and brain oxygen saturation (Sa/vO2) measured by invasive blood gas analysis, analyze the consistency of brain oxygen saturation measured by brain oxygen saturation analyzer and blood gas analyzer, and calculate the accuracy of brain oxygen saturation monitoring. The blood samples of jugular vein and radial artery were obtained from healthy adults by induced oxygen desaturation test under pulse oximetry conditions on each platform. The oxygen saturation of the two blood samples was analyzed and measured by a Co-oximeter. Thus, the oxygen saturation value of jugular vein (SjvO2) and radial artery (SaO2) were obtained. According to the clinical empirical formula Sa/vO2 = 0.7×S jvO2 + 0.3×SaO2, the oxygen saturation value of brain tissue for invasive blood gas analysis was calculated. To calculate the difference between brain oxygen saturation (rSO2) measured by brain oxygen saturation monitor (hereinafter referred to as brain oxygen analyzer) and brain oxygen saturation (Sa/vO2) measured by invasive blood gas analysis, analyze the consistency of brain oxygen saturation measured by brain oxygen saturation analyzer and blood gas analyzer, and calculate the accuracy of brain oxygen saturation monitoring. MethodsIn healthy adult volunteers, the induced desaturation test, in which blood gas analysis measures the subjects' internal jugular vein and carotid artery blood samples at each pulse oximetry platform range. Clinical trials were conducted to verify the expected effectiveness and safety of the brain oxygen saturation monitor. Ten subjects were selected into the study according to strict inclusion criteria and exclusion criteria. Subjects should monitor their electrocardiogram, pulse, blood pressure, SPO2 and other vital signs, perform retrograde puncture catheterization of internal jugular vein and radial artery catheterization, ensure the safety of subjects during the period, and record the values of blood samples before and after collection. The oxygen was lowered according to the set platform(according to Figure2), and physiological parameters were monitored during the process. There were 9 platforms in total, and each platform lasted about 5 minutes. The oxygen saturation value of jugular vein (SJVO2) and the oxygen saturation value of carotid artery (SaO2) were obtained, and the tissue oxygen saturation value of sa1vO2 was calculated according to the clinical empirical formula SA1VO2 = 0.7xSJVO2 + 0.3xSaO2. During the blood collection process, the blood oxygen saturation (RSO2) of the subjects' brain was continuously monitored by tissue oximeter noninvastively. The consistency of non-invasive monitoring value RSO2 and invasive measurement value sa1vO2 was compared, and scientific statistical analysis was carried out to confirm whether the accuracy of tissue oxygen meter meets clinical requirements. ResultsAbsolute accuracy evaluation: Further linear regression analysis was performed on the non-invasive monitoring value of the test instrument and the blood gas analysis detection value. The fitting linear equation was rSO2=4.89+0.93×Sa/vO2, where the slope was 0.93, close to 1. The regression line was close to the 45° diagonal trend. The correlation coefficient between rSO2 and Sa/vO2 was 0.95, indicating that there was a good correlation between the non-invasive monitoring value and the invasive blood gas analysis value. Trend accuracy evaluation: It can be seen that the average difference between the trend change value of the test instrument monitoring value and the blood gas analysis value is very small (Bs=Means(△rSO2-△Sa/vO2)=-0.32%), indicating that the trend change of the test instrument monitoring value and the blood gas analysis value is basically consistent in statistical significance. The 95% consistency interval of the difference of trend change between the two devices is narrow ([BS-1.96SD, Bs+1.96SD]=[-6.13%, 5.5%]), indicating that the difference of trend change between the two devices has small variation. The above analysis shows that there is a good consistency between the non-invasive monitoring value of the test equipment and the invasive test results of the blood gas analysis equipment. The linear regression analysis was made on the changes of the test instrument monitoring value and blood gas analysis detection value. The fitting linear equation was △rSO2=-0.98+0.93△Sa/vO2, and the slope was 0.93, which was close to 1. The regression line was close to the 45° diagonal trend. The correlation coefficient of trend changes of the two equipment is 0.95, indicating that the change trend of the test equipment and blood gas analyzer has a good correlation. Analyze the trend changes value, due to the variation of every subjects is relative to the first platform first blood gas analysis values as the base to calculate, so the data points less than 10 absolute value analysis, the test equipment and the trend of blood gas analysis change the average deviation is 0.32%, the standard deviation is 2.97%, RMS very different trend is 2.97%, The clinical evaluation standard of trend Arms≤5% was met. ConclusionThere is good correlation and consistency between the test instrument monitoring value and the absolute value of blood gas analyzer.Trial Registration: The study has been retrospectively registered in Chinese Clinical Trial Registration with the registration number ChiCTR2100052321, date of registration 24/10/2021.

scholarly journals Relating oxygen partial pressure, saturation and content: the haemoglobin–oxygen dissociation curve

Breathe ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 194-201 ◽  
Author(s):  
Julie-Ann Collins ◽  
Aram Rudenski ◽  
John Gibson ◽  
Luke Howard ◽  
Ronan O’Driscoll
Keyword(s):  
Oxygen Saturation ◽  
Partial Pressure ◽  
Pulse Oximetry ◽  
Blood Gas Analysis ◽  
Gas Analysis ◽  
Oxygen Dissociation Curve ◽  
Blood Gas ◽  
Dissociation Curve ◽  
Oxygen Dissociation ◽  
The Relationship

Key PointsIn clinical practice, the level of arterial oxygenation can be measured either directly by blood gas sampling to measure partial pressure (PaO2) and percentage saturation (SaO2) or indirectly by pulse oximetry (SpO2).This review addresses the strengths and weaknesses of each of these tests and gives advice on their clinical use.The haemoglobin–oxygen dissociation curve describing the relationship between oxygen partial pressure and saturation can be modelled mathematically and routinely obtained clinical data support the accuracy of a historical equation used to describe this relationship.Educational AimsTo understand how oxygen is delivered to the tissues.To understand the relationships between oxygen saturation, partial pressure, content and tissue delivery.The clinical relevance of the haemoglobin–oxygen dissociation curve will be reviewed and we will show how a mathematical model of the curve, derived in the 1960s from limited laboratory data, accurately describes the relationship between oxygen saturation and partial pressure in a large number of routinely obtained clinical samples.To understand the role of pulse oximetry in clinical practice.To understand the differences between arterial, capillary and venous blood gas samples and the role of their measurement in clinical practice.The delivery of oxygen by arterial blood to the tissues of the body has a number of critical determinants including blood oxygen concentration (content), saturation (SO2) and partial pressure, haemoglobin concentration and cardiac output, including its distribution. The haemoglobin–oxygen dissociation curve, a graphical representation of the relationship between oxygen satur­ation and oxygen partial pressure helps us to understand some of the principles underpinning this process. Historically this curve was derived from very limited data based on blood samples from small numbers of healthy subjects which were manipulated in vitro and ultimately determined by equations such as those described by Severinghaus in 1979. In a study of 3524 clinical specimens, we found that this equation estimated the SO2 in blood from patients with normal pH and SO2 >70% with remarkable accuracy and, to our knowledge, this is the first large-scale validation of this equation using clinical samples. Oxygen saturation by pulse oximetry (SpO2) is nowadays the standard clinical method for assessing arterial oxygen saturation, providing a convenient, pain-free means of continuously assessing oxygenation, provided the interpreting clinician is aware of important limitations. The use of pulse oximetry reduces the need for arterial blood gas analysis (SaO2) as many patients who are not at risk of hypercapnic respiratory failure or metabolic acidosis and have acceptable SpO2 do not necessarily require blood gas analysis. While arterial sampling remains the gold-standard method of assessing ventilation and oxygenation, in those patients in whom blood gas analysis is indicated, arterialised capillary samples also have a valuable role in patient care. The clinical role of venous blood gases however remains less well defined.

Impact of in-flight use of FFP2 masks on oxygen saturation: an experimental crossover study

2021 ◽  
Author(s):  
Stefan Sammito ◽  
Geraldine P J Müller ◽  
Oliver Maria Erley ◽  
Andreas Werner
Keyword(s):  
Heart Rate ◽  
Oxygen Saturation ◽  
Crossover Study ◽  
Blood Gas Analysis ◽  
Face Mask ◽  
Gas Analysis ◽  
Crossover Design ◽  
Capillary Blood ◽  
Blood Gas

This study was able to show in a crossover design that neither at resting conditions nor during a simulated 80 min flight wearing the examined FFP2 face mask leads to changes in the SpO2, the heart rate or the parameters of the capillary blood gas analysis.

Design of Reflectance Pulse Oximeter and BPM using the Max30100 Sensor in Early Detection of Hypoxemia in Patients with Cardiovascular Disorders

2021 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Chiu-Hua Huang ◽  
Jia-Wei Guo
Keyword(s):  
Oxygen Saturation ◽  
Pulse Oximeter ◽  
Light Emitting Diode ◽  
Blood Gas Analysis ◽  
Large Error ◽  
Gas Analysis ◽  
Measuring Instruments ◽  
Blood Oxygen Saturation ◽  
Organic Light Emitting Diode ◽  
The Difference

Blood oxygen saturation meter is a tool used to monitor the state of oxygen saturation in the blood and also the patient's heart rate (BPM) and to assist in the physical assessment of the patient without going through blood gas analysis. Oxygen saturation measuring devices usually use the difference in the wavelengths of red and infrared led light that will be captured by the photodiode. The purpose of this research is to make a pulse oximeter equipped with a display of SPO2, BPM values ​​and an additional SP02 signal. The design of this measuring instrument uses the MAX30100 sensor, the minimum system circuit of Arduino ATmega328p and OLED (Organic Light-Emitting Diode). Data from the MAX30100 sensor enters the I2C pin on the minimum Arduino system, then the microcontroller is processed to produce the percentage of SPO2 value, BPM value, and SPO2 signal which is then displayed on the OLED. The test is done by comparing the module with standard measuring instruments which produces the largest % error of 0.81% for Spo2 and 0.87% for BPM. The error presentation is obtained from factor measurements, if there is finger movement it will cause a large error. From the results obtained, the tool is still feasible to use because in the "Guidelines for Testing and Calibrating Medical Devices" Ministry of Health RI 2001, the maximum limit in the pulse oximeter error tolerance is for Spo2 1% and BPM 5%.

scholarly journals Comparison of oxygen saturation measured by pulse oximetry and arterial blood gas analysis in neonates

2016 ◽  
Vol 43 (6) ◽  
pp. 211
Author(s):  
Srie Yanda ◽  
Munar Lubis ◽  
Yoyoh Yusroh
Keyword(s):  
Oxygen Saturation ◽  
Pulse Oximetry ◽  
Heart Defects ◽  
Blood Gas Analysis ◽  
Gas Analysis ◽  
Blood Oxygenation ◽  
Blood Gas ◽  
Cross Sectional ◽  
Arterial Blood Gas ◽  
Arterial Blood

Background Arterial blood gas is usually beneficial to discern thenature of gas exchange disturbances, the effectiveness of com-pensation, and is required for adequate management. AlthoughPaO 2 is the standard measurement of blood oxygenation, oxygensaturation measured by pulse oximetry (SapO 2 ) is now a custom-ary noninvasive assessment of blood oxygenation in newborn in-fants.Objective To compare oxygen saturation measured by pulse oxi-metry (SapO 2 ) and arterial blood gas (SaO 2 ), its correlation withother variables, and to predict arterial partial pressure of oxygen(PaO 2 ) based on SapO 2 values.Methods A cross sectional study was conducted on all neonatesadmitted to Pediatric Intensive Care Unit (PICU) during February2001 to May 2002. Neonates were excluded if they had impairedperipheral perfusion and/or congenital heart defects. Paired t-testwas used to compare SapO 2 with SaO 2 . Correlation between twoquantitative data was performed using Pearson’s correlation. Re-gression analysis was used to predict PaO 2 based on SapO 2 val-ues.Results Thirty neonates were included in this study. The differ-ence between SaO 2 and SapO 2 was significant . There were sig-nificant positive correlations between heart rate /pulse rate andTCO 2 , HCO 3 ; respiratory rate and TCO 2 , HCO 3 , base excess (BE);core temperature and HCO 3 , BE; surface temperature and pH,TCO 2, HCO 3, BE; SapO 2 and pH, PaO 2 ; and significant negativecorrelation between SapO 2 and PaCO 2 ; the correlations were weak.The linear regression equation to predict PaO 2 based on SapO 2values was PaO 2 = -79.828 + 1.912 SapO 2 .Conclusion Pulse oximetry could not be used in place of arterialblood gas analysis available for clinical purpose

Ear lobe blood samples for blood gas analysis at rest and during exercise

1971 ◽  
Vol 65 ◽  
pp. 58-64 ◽  
Author(s):  
S. Godfrey ◽  
E.R. Wozniak ◽  
R.J. Courtenay Evans ◽  
C.S. Samuels
Keyword(s):  
Blood Gas Analysis ◽  
Gas Analysis ◽  
Blood Gas ◽  
Blood Samples

scholarly journals Sleep-disordered breathing in patients with COPD and mild hypoxemia: prevalence and predictive variables

2017 ◽  
Vol 43 (3) ◽  
pp. 176-182 ◽  
Author(s):  
José Laerte Rodrigues Silva Júnior ◽  
Marcus Barreto Conde ◽  
Krislainy de Sousa Corrêa ◽  
Helena Rabahi ◽  
Arthur Alves Rocha ◽  
...  
Keyword(s):  
Oxygen Saturation ◽  
Sleep Disordered Breathing ◽  
Blood Gas Analysis ◽  
Gas Analysis ◽  
Blood Gas ◽  
Arterial Blood Gas ◽  
Arterial Blood ◽  
Nocturnal Hypoxemia ◽  
Arterial Blood Gas Analysis ◽  
Disordered Breathing

ABSTRACT Objective: To infer the prevalence and variables predictive of isolated nocturnal hypoxemia and obstructive sleep apnea (OSA) in patients with COPD and mild hypoxemia. Methods: This was a cross-sectional study involving clinically stable COPD outpatients with mild hypoxemia (oxygen saturation = 90-94%) at a clinical center specializing in respiratory diseases, located in the city of Goiânia, Brazil. The patients underwent clinical evaluation, spirometry, polysomnography, echocardiography, arterial blood gas analysis, six-minute walk test assessment, and chest X-ray. Results: The sample included 64 patients with COPD and mild hypoxemia; 39 (61%) were diagnosed with sleep-disordered breathing (OSA, in 14; and isolated nocturnal hypoxemia, in 25). Correlation analysis showed that PaO2 correlated moderately with mean sleep oxygen saturation (r = 0.45; p = 0.0002), mean rapid eye movement (REM) sleep oxygen saturation (r = 0.43; p = 0.001), and mean non-REM sleep oxygen saturation (r = 0.42; p = 0.001). A cut-off point of PaO2 ≤ 70 mmHg in the arterial blood gas analysis was significantly associated with sleep-disordered breathing (OR = 4.59; 95% CI: 1.54-13.67; p = 0.01). The model showed that, for identifying sleep-disordered breathing, the cut-off point had a specificity of 73.9% (95% CI: 51.6-89.8%), a sensitivity of 63.4% (95% CI: 46.9-77.9%), a positive predictive value of 81.3% (95% CI: 67.7-90.0%), and a negative predictive value of 53.1% (95% CI: 41.4-64.4%), with an area under the ROC curve of 0.69 (95% CI: 0.57-0.80), correctly classifying the observations in 67.2% of the cases. Conclusions: In our sample of patients with COPD and mild hypoxemia, the prevalence of sleep-disordered breathing was high (61%), suggesting that such patients would benefit from sleep studies.

Sign in / Sign up

Export Citation Format

Share Document