Diagnostic utility of total adenosine deaminase as a biomarker in patients with transudates from pleural effusion syndrome
Main Article Content
Abstract
Introduction: Accurately identifying pleural fluid as either transudate or exudate is critical. P-ADA, or the total pleural adenosine deaminase enzyme, is a reliable biomarker for distinguishing between transudates and exudates. Objective: This study aimed to identify P-ADA diagnostic parameters for pleural transudate diagnosis by establishing a cutoff value using the receiver operating characteristic curve. Methods: The P-ADA assay was performed using a kinetic technique. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC-ROC) and diagnostic parameters. The ideal cutoff value for P-ADA in pleural transudates was determined using the Youden index in the ROC curve. Results: A total of 157 patients with exudative pleural effusion (n=124, 79%) and transudative pleural effusion (n=33, 21%) were included in this observational retrospective cohort study. The optimal cutoff value of P-ADA was ≤8.21 U/L. The diagnostic parameters as sensitivity, specificity, positive and negative predictive values, positive and negative likelihood values, odds ratio, and accuracy were 66.0 (95% CI, 0.48-0.82); 81.0 (95% CI, 0.73-0.87); 48.0 and 90.0 (95% CI, 0.33-0.64; 0.83-0.94); 4.9 and 0.56 (95%CI, 2.21-11.2; 0.39-0.82); 8.78 (95%CI, 0.78-18.34), and 78.0 (95%CI, 0.80-0.91), respectively (chi-square=29.51, p=0.00001). The AUC was 0.8203 (95% CI, 0.7270-0.8838); SE, 0.0393; Z-value to test, 8.157; p<0.0001. An AUC >0.75 was clinically useful. The Hosmer-Lemeshow test showed excellent discrimination. Conclusion: P-ADA levels can be used to obtain reliable diagnostic and predictive parameters for discriminating between transudative and exudative pleural effusions.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY) that allows others to share and adapt the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.References
1. Hooper C, Lee YCG, Maskell N; BTS Pleural Guideline Group. Investigation of a unilateral pleural effusion in adults: British Thoracic Society Pleural Disease Guideline 2010. Thorax. 2010;65(Suppl 2):ii4-17. https://doi.org/10.1136/thx.2010.136978
2. Light RW, Macgregor MI, Luchsinger PC, Ball Jr WC. Pleural effusions: the diagnostic separation of transudates and exudates. Ann Intern Med. 1972;77:507-13. https://doi.org/10.7326/0003-4819-77-4-507
3. Maranhão BHF, Silva Junior CT, Chibante AMS, Cardoso GP. Determination of total proteins and lactate dehydrogenase for the diagnosis of pleural transudates and exudates: redefining the classical criterion with a new statistical approach. J Bras Pneumol. 2010;36(4):468-74. https://doi.org/10.1590/s1806-37132010000400012
4. Zhulai G, Oleinik E, Shibaev M, Ignatev K. Adenosine-metabolizing enzymes, adenosine kinase and adenosine deaminase, in cancer. Biomolecules. 2022;12(13):418. https://doi.org/10.3390/biom12030418
5. Jadhav AA, Bardapurkar JS. Diagnostic value of adenosine deaminase to differentiate exudates and transudates. Indian J Physiol Pharmacol. 2007;51(2):170-4.
6. Mehta M, Marwah S, Shah H, Trivedi A. A study of the utility of lactate dehydrogenase, total proteins, and adenosine deaminase in the diagnosis of pleural exudates: A new statistical approach. Int J Med Sci Public Health. 2015;4(2):286-91. https://doi.org/10.5455/ijmsph.2015.2110201453
7. Atalay F, Ernam D, Hasanoglu HC, Karalezli A, Kaplan O. Pleural adenosine deaminase in the separation of transudative and exudative pleural effusions. Clin Biochem. 2005;38:1066-70. https://doi.org/10.1016/j.clinbiochem.2005.07.009
8. Zocchi L. Physiology and pathophysiology of pleural fluid turnover. Eur Respir J. 2002;20(6):1545-58. https://doi.org/10.1183/09031936.97.10010219
9. Apostolidou E, Tsilioni I, Hatzoglou C, Paschalis-Adam M, Konstantinos GI. Pleural transport physiology: insights from biological marker measurements in transudates. Open Respir Med J. 2011;5:70-2. https://doi.org/10.2174/1874306401105010070
10. Bossuyt PM, Reitsma JB, Bruns DE, Constantine AG, Glasziou PP, Irwig L, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015;351:h5527. https://doi.org/10.1136/bmj.h5527
11. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP, STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61(4):344-9. https://doi.org/10.1016/j.jclinepi.2007.11.008
12. Michael CW, Davidson B. Pre-analytical issues in effusion cytology. Pleura Peritoneum. 2016;1(1):45-56. https://doi.org/10.1515/pp-2016-0001
13. Ronca G. Competitive inhibition of adenosine deaminase by urea, guanidine, biuret, and guanylurea. Biochim Biophys Acta. 1967;132(1):214-16. https://doi.org/10.1016/0005-2744(67)90216-1
14. Ajloo D, Saboury AA, Haghi-Asli N, Ataei-Jafarai G, Moosavi-Movahedi AA, Ahmadi M, et al. Kinetic, thermodynamic, and statistical studies on the inhibition of adenosine deaminase by aspirin and diclofenac. J Enzyme Inhib Med Chem. 2007;22(4):395-406. https://doi.org/10.1080/14756360701229085
15. Ataie G, Bagheri S, Divsalar A, Saboury AA, Safarian S. A kinetic comparison on the inhibition of adenosine deaminase by purine drugs. Iran J Pharm Res. 2007;6(1):43-50. https://doi.org/10.22037/ijpr.2010.697
16. Delacour H, Sauvanet C, Ceppa F, Burnat P. Analytical performances of the Diazyme ADA assay on the Cobas® 6000 system. Clin Biochem. 2010;43(18):1468-71. https://doi.org/10.1016/j.clinbiochem.2010.09.005
17. Negida A, Fahim NK, Negida Y. Sample size calculation guide - Part 4: How to calculate the sample size for a diagnostic test accuracy study based on sensitivity, specificity, and the area under the ROC curve. Adv J Emerg Med. 2019;3(3):e33. https://doi.org/10.22114/ajem.v0i0.158
18. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837-45.
19. Hajian-Tilaki K. The choice of methods in determining the optimal cut-off value for quantitative diagnostic test evaluation. Stat Methods Med Res. 2018;27(8):2374-83. https://doi.org/10.1177/0962280216680383
20. Dwivedi AK. How to write a statistical analysis section in medical research. J Investig Med. 2022;70(8):1759-70. https://doi.org/10.1136/jim-2022-002479
21. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159-74.
22. Hosmer Jr, DW, Lemeshow S. Applied logistic regression. 2nd Edition. New York: John Wiley & Sons, 2004; pp. 50-5.
23. Metz CE. Basic principles of ROC analysis. Semin Nucl Med. 1978;8(4):283-98. https://doi.org/10.1016/s0001-2998(78)80014-2
24. Nahm FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol. 2022;75(1):25-36. https://doi.org/10.4097/kja.21209
25. Åsberg A, Mikkelsen G, Odsæter IH. A new index of clinical utility for diagnostic tests. Scand J Clin Lab Invest. 2019;79(8):560-5. https://doi.org/10.1080/00365513.2019.1677938
26. Fan J, Upadhye S, Worster A. Understanding receiver operating characteristic (ROC) curves. CJEM. 2006;8(1):19-20. https://doi.org/10.1017/s1481803500013336
27. Evans PT, Zhang RS, Cao Y, Breslin S, Panebianco N, Baston CM, Dibardino DM. The use of thoracic ultrasound to predict transudative and exudative pleural effusion. POCUS J. 2021;6(2):97–102. https://doi.org/10.24908/pocus.v6i2.15193
28. Tsikriktsis N. A review of techniques for treating missing data in OM survey research. J Oper Manag. 2005;24(1):53-62. https://doi.org/10.1016/j.jom.2005.03.001
29. Habibzadeh F, Habibzadeh P, Yadollahie M. On determining the most appropriate test cut-off value: the case of tests with continuous results. Biochem Med (Zagreb). 2016;26(3):297-307. https://doi.org/10.11613/bm.2016.034
30. Caraguel CGB, Stryhn H, Gagné N, Dohoo IR, Hammell KL. Selection of a cutoff value for real-time polymerase chain reaction results to fit a diagnostic purpose: analytical and epidemiologic approaches. J Vet Diagn Invest. 2011;23(1):2-15. https://doi.org/10.1177/104063871102300102
31. Le CT. A solution for the most basic optimization problem associated with an ROC curve. Stat Methods Med Res. 2006;15(6):571-84. https://doi.org/10.1177/0962280206070637
32. Chang C-H. Cohen's kappa for capturing discrimination. Int Health. 2014;6(2):125-9. https://doi.org/10.1093/inthealth/ihu010
33. Behrsin RF, Silva Junior CT, Cardoso GP, Barillo JL, Souza JBS, Araujo EG. Combined evaluation of adenosine deaminase level and histopathological findings from pleural biopsy with Cope’s needle for the diagnosis of tuberculous pleurisy. Int J Clin Exp Pathol. 2015;8(6):7239-46.
34. Turner R, Samaranayaka A, Cameron C. Parametric vs nonparametric statistical methods: which is better, and why? N Z Med Stud J. 2020;30:61-2.
35. Nahm FS. Nonparametric statistical tests for the continuous data: the basic concept and the practical use. Korean J Anesthesiol. 2016;69(1):8-14. https://doi.org/10.4097/kjae.2016.69.1.8
36. Schober P, Vetter TR. Nonparametric statistical methods in medical research. Anesth Analg. 2020;131(6):1862-3. https://doi.org/10.1213/ANE.0000000000005101