Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
Journal of Medical Systems
Published in: Journal of Medical Systems 4/2011

01-08-2011 | Original Paper

Predicting Arterial Blood Gas Values from Venous Samples in Patients with Acute Exacerbation Chronic Obstructive Pulmonary Disease Using Artificial Neural Network

Authors: Mohammad Reza Raoufy, Parivash Eftekhari, Shahriar Gharibzadeh, Mohammad Reza Masjedi

Published in: Journal of Medical Systems | Issue 4/2011

Login to get access

Abstract

Arterial blood gas (ABG) has an important role in the clinical assessment of patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Because of ABG complications, an alternative method is beneficial. We have trained and tested five artificial neural networks (ANNs) with venous blood gas (VBG) values (pH, PCO2, HCO3, PO2, and O2 saturation) as inputs, to predict ABG values in patients with AECOPD. Venous and arterial blood samples were collected from 132 patients. Using the data of 106 patients, the ANNs were trained and validated by back-propagation algorithm. Subsequently, data from the remainder 26 patients was used for testing the networks. The ability of ANNs to predict ABG values and to detect significant hypercarbia was assessed and the results were compared with a linear regression model. Our results indicate that the ANNs provide an accurate method for predicting ABG values from VBG values and detecting hypercarbia in AECOPD.
Literature
1.
Barker, W. J., Arterial puncture and cannulation. In: Roberts, J. R., and Hedges, J. R. (Eds.), Clinical Procedures in Emergency Medicine, 3rd edition. Saunders, Philadelphia, pp. 308–322, 1998.
2.
Mortenson, J. D., Clinical sequelae from arterial needle puncture, cannulation and incision. Circulation. 35:1118–1125, 1967.
3.
McGillivray, D., Ducharme, F. M., Charron, Y., Mattimoe, C., and Treherne, S., Clinical decision making based on venous versus capillary blood gas values in the wellperfused child. Ann. Emerg. Med. 34:58–63, 1999.CrossRef
4.
Kelly, A. M., Kyle, E., and McAlpine, R., Venous PCO2 and pH can be used to screen for significant hypercarbia in emergency patients with acute respiratory disease. J. Emerg. Med. 22:15–19, 2002.CrossRef
5.
Kelly, A. M., Kerr, D., and Middleton, P., Validation of venous PCO2 to screen for arterial hypercarbia in patients with chronic obstructive airways disease. J. Emerg. Med. 28 (4)377–379, 2005.CrossRef
6.
Murphy, R., Thethy, S., Raby, S., Beckley, J., Terrace, J., Fiddler, C., et al., Capillary blood gases in acute exacerbations of COPD. Respir. Med. 100 (4)682–686, 2006.CrossRef
7.
Ibrahim, I., Ooi, S. B., Yiong Huak, C., Sethi, S., Point-of-care bedside gas analyzer: limited use of venous PCO2 in emergency patients. J Emerg Med. 2008 (in press).
8.
Ak, A., Ogun, C. O., Bayir, A., Kayis, S. A., and Koylu, R., Prediction of arterial blood gas values from venous blood gas values in patients with acute exacerbation of chronic obstructive pulmonary disease. Tohoku J Exp Med. 210 (4)285–290, 2006.CrossRef
9.
Chu, Y. C., Chen, C. Z., Lee, C. H., Chen, C. W., Chang, H. Y., and Hsiue, T. R., Prediction of arterial blood gas values from venous blood gas values in patients with acute respiratory failure receiving mechanical ventilation. J. Formos. Med. Assoc. 102 (8)539–543, 2003.
10.
Razi, E., and Moosavi, G. A., Comparison of arterial and venous blood gases analysis in patients with exacerbation of chronic obstructive pulmonary disease. Saudi Med. J. 28 (6)862–865, 2007.
11.
Gambino, S. R., Comparisons of pH in human arterial, venous and capillary blood. Am. J. Clin. Pathol. 32:298–300, 1959.
12.
Searcy, R. L., Gordon, G. F., and Simms, N. M., Choice of blood for acid-base studies (letter). Lancet. 41:12–32, 1963.
13.
Long, A. P., Jr, Venous or arterial blood gas measurement (letter). JAMA. 217:1706, 1971.CrossRef
14.
Forster, H. V., Dempsey, J. A., Thomson, J., Vidruk, E., and do Pico, G. A., Estimation of arterial PO2, PCO2, pH and lactate from arterialised venous blood. J. Appl. Physiol. 32:134–137, 1972.
15.
Brooks, D., and Wynn, V., Use of venous blood for pH and carbon dioxide studies especially in respiratory failure and during anaesthesia. Lancet. 1:227–230, 1959.CrossRef
16.
Paine, E. G., Boutwell, J. H., and Soloff, L. A., The reliability of arterialised venous blood for measuring arterial pH and PCO2. Am. J. Med. Sci. 242:431–434, 1961.CrossRef
17.
Gambino, S. R., Normal values for adult human venous plasma pH and CO2 content. Am. J. Clin. Pathol. 32:294–297, 1959.
18.
Kelly, A. M., McAlpine, R., and Kyle, E., Venous pH can safely replace arterial pH in the initial evaluation of patients in the emergency department. Emerg. Med. J. 18:340–342, 2001.CrossRef
19.
Ma, O. J., Rush, M. D., Godfrey, M. M., et al., Arterial blood gas results rarely influence emergency physician management of patients with suspected diabetic ketoacidosis. Acad. Emerg. Med. 10:836–841, 2003.CrossRef
20.
Gokel, Y., Paydas, S., Koseoglu, Z., et al., Comparison of blood gas and acid-base measurements in arterial and venous blood samples in patients with uraemic acidosis and diabetic ketoacidosis in the emergency room. Am. J. Nephrol. 20:319–323, 2000.CrossRef
21.
Cross, S. S., Harrison, R. F., and Kennedy, R. L., Introduction to neural networks. Lancet. 5:1075–1079, 1995.CrossRef
22.
Baxt, W. G., Application of artificial neural networks to clinical medicine. Lancet. 5:1135–1138, 1995.CrossRef
23.
Bos, M., Bos, A., and van der Linden, W. E., Data processing by neural networks in quantitative chemical analysis. Analyst. 5:323–328, 1993.CrossRef
24.
Ala-Korpela, M., Hiltunen, Y., and Bell, J. D., Quantification of biomedical NMR data using artificial neural network analysis: lipoprotein lipid profiles from 1H NMR data of human plasma. NMR Biomed. 5:235–244, 1995.CrossRef
25.
Burge, S., and Wedzicha, J. A., COPD exacerbations: definitions and classifications. Eur. Respir. J. Suppl. 41:46s–53s, 2003.CrossRef
26.
Dariani, S., Keshavarz, M., Parviz, M., Raoufy, M. R., and Gharibzadeh, S., Modeling force-velocity relation in skeletal muscle isotonic contraction using an artificial neural network. Biosystems. 90 (2)529–534, 2007.CrossRef
27.
Tu, J. V., Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49:1225–1231, 1996.CrossRef
28.
Lin, C. S., Li, Y. C., Mok, M. S., Wu, C. C., Chiu, H. W., and Lin, Y. H., Neural network modeling to predict the hypnotic effect of propofol bolus induction. Proc AMIA Symp. 2002:450–454, 2002.
Metadata
Title
Predicting Arterial Blood Gas Values from Venous Samples in Patients with Acute Exacerbation Chronic Obstructive Pulmonary Disease Using Artificial Neural Network
Authors
Mohammad Reza Raoufy
Parivash Eftekhari
Shahriar Gharibzadeh
Mohammad Reza Masjedi
Publication date
01-08-2011
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 4/2011
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-009-9384-4

Other articles of this Issue 4/2011

Journal of Medical Systems 4/2011 Go to the issue

Original Paper

Polarization Sensitive Subcutaneous and Muscular Imaging Based on Common Path Optical Coherence Tomography Using Near Infrared Source

Original Paper

A Literature Review on Distance Knowledge Exchange in Healthcare Groups: What Can We Learn From the ICT Literature?

Original Paper

Medical Image on the Go!

Original Paper

Using Health Smart Cards to Check Drug Allergy History: The Perspective from Taiwan’s Experiences

Original Paper

Vocabularies and Retrieval Tools in Biomedicine: Disentangling the Terminological Knot

Original Paper

Computer Based Synchronization Analysis on Sleep EEG in Insomnia