Predicting arterial blood gas values from venous samples in patients with acute exacerbation chronic obstructive pulmonary disease using artificial neural network

Journal of Medical Systems

Volumn 35, Issue 4, 2011, Pages 483-488

  Raoufy, Mohammad Reza ^a,c   Eftekhari, Parivash ^a,c   Gharibzadeh, Shahriar ^b   Masjedi, Mohammad Reza ^c  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

^c SHAHID BEHESHTI UNIVERSITY OF MEDICAL SCIENCES   (Iran)

Author keywords

Arterial blood gas; Artificial neural network; Venous blood gas

Indexed keywords

BICARBONATE; CARBON DIOXIDE; OXYGEN;

ACUTE EXACERBATION OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE; ADOLESCENT; ADULT; AGED; ARTERIAL GAS; ARTICLE; ARTIFICIAL NEURAL NETWORK; BICARBONATE BLOOD LEVEL; BLOOD CARBON DIOXIDE TENSION; BLOOD GAS ANALYSIS; BLOOD PH; CHRONIC OBSTRUCTIVE LUNG DISEASE; COMPARATIVE EFFECTIVENESS; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE EXACERBATION; FEMALE; HUMAN; HYPERCAPNIA; MAJOR CLINICAL STUDY; MALE; OXYGEN SATURATION; OXYGEN TENSION; PREDICTIVE VALUE; SENSITIVITY AND SPECIFICITY;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; ARTERIES; BLOOD GAS ANALYSIS; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PULMONARY DISEASE, CHRONIC OBSTRUCTIVE; SENSITIVITY AND SPECIFICITY; VEINS; YOUNG ADULT;

EID: 79961167301     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-009-9384-4     Document Type: Article

References (28)

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. J. D. Mortenson 1967 Clinical sequelae from arterial needle puncture, cannulation and incision Circulation 35 1118 1125
3. D. McGillivray F. M. Ducharme Y. Charron C. Mattimoe S. Treherne 1999 Clinical decision making based on venous versus capillary blood gas values in the wellperfused child Ann. Emerg. Med. 34 58 63 10.1016/S0196-0644(99)70272-6 (Pubitemid 29295752)
4. 2 and pH can be used to screen for significant hypercarbia in emergency patients with acute respiratory disease J. Emerg. Med. 22 15 19 10.1016/S0736-4679(01)00431-0 (Pubitemid 34070602)
5. 2 to screen for arterial hypercarbia in patients with chronic obstructive airways disease J. Emerg. Med. 28 4 377 379 10.1016/j.jemermed.2004. 10.017 (Pubitemid 40523683)
6. R. Murphy S. Thethy S. Raby J. Beckley J. Terrace C. Fiddler, et al. 2006 Capillary blood gases in acute exacerbations of COPD Respir. Med. 100 4 682 686 10.1016/j.rmed.2005.08.007 (Pubitemid 43285756)
7. 2 in emergency patients. J Emerg Med. 2008 (in press).
8. A. Ak C. O. Ogun A. Bayir S. A. Kayis R. Koylu 2006 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 10.1620/tjem.210.285 (Pubitemid 44883968)
9. Y. C. Chu C. Z. Chen C. H. Lee C. W. Chen H. Y. Chang T. R. Hsiue 2003 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
10. E. Razi G. A. Moosavi 2007 Comparison of arterial and venous blood gases analysis in patients with exacerbation of chronic obstructive pulmonary disease Saudi Med. J. 28 6 862 865
11. S. R. Gambino 1959 Comparisons of pH in human arterial, venous and capillary blood Am. J. Clin. Pathol. 32 298 300
12. R. L. Searcy G. F. Gordon N. M. Simms 1963 Choice of blood for acid-base studies (letter) Lancet 41 12 32
13. A. P.; Long Jr 1971 Venous or arterial blood gas measurement (letter) JAMA 217 1706 10.1001/jama.217.12.1706b
14. 2, pH and lactate from arterialised venous blood J. Appl. Physiol. 32 134 137
15. D. Brooks V. Wynn 1959 Use of venous blood for pH and carbon dioxide studies especially in respiratory failure and during anaesthesia Lancet 1 227 230 10.1016/S0140-6736(59)90053-4
16. 2 Am. J. Med. Sci. 242 431 434 10.1097/00000441-196110000-00005
17. 2 content Am. J. Clin. Pathol. 32 294 297
18. A. M. Kelly R. McAlpine E. Kyle 2001 Venous pH can safely replace arterial pH in the initial evaluation of patients in the emergency department Emerg. Med. J. 18 340 342 10.1136/emj.18.5.340 (Pubitemid 34070961)
19. O. J. Ma M. D. Rush M. M. Godfrey, et al. 2003 Arterial blood gas results rarely influence emergency physician management of patients with suspected diabetic ketoacidosis Acad. Emerg. Med. 10 836 841 10.1111/j.1553-2712.2003. tb00625.x (Pubitemid 37040526)
20. Y. Gokel S. Paydas Z. Koseoglu, et al. 2000 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 10.1159/000013607
21. S. S. Cross R. F. Harrison R. L. Kennedy 1995 Introduction to neural networks Lancet 5 1075 1079 10.1016/S0140-6736(95)91746-2
22. W. G. Baxt 1995 Application of artificial neural networks to clinical medicine Lancet 5 1135 1138 10.1016/S0140-6736(95)91804-3
23. M. Bos A. Bos W. E. van der Linden 1993 Data processing by neural networks in quantitative chemical analysis Analyst 5 323 328 10.1039/an9931800323
24. M. Ala-Korpela Y. Hiltunen J. D. Bell 1995 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 10.1002/nbm.1940080603
25. S. Burge J. A. Wedzicha 2003 COPD exacerbations: definitions and classifications Eur. Respir. J. Suppl. 41 46s 53s 10.1183/09031936.03.00078002 (Pubitemid 36648154)
26. S. Dariani M. Keshavarz M. Parviz M. R. Raoufy S. Gharibzadeh 2007 Modeling force-velocity relation in skeletal muscle isotonic contraction using an artificial neural network Biosystems 90 2 529 534 10.1016/j.biosystems.2006. 12.004 (Pubitemid 47364812)
27. J. V. Tu 1996 Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes J. Clin. Epidemiol. 49 1225 1231 10.1016/S0895-4356(96)00002-9 (Pubitemid 26386228)
28. C. S. Lin Y. C. Li M. S. Mok C. C. Wu H. W. Chiu Y. H. Lin 2002 Neural network modeling to predict the hypnotic effect of propofol bolus induction Proc AMIA Symp 2002 450 454