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Correlation of Perfusion Indices with Disease Severity in COVID-19 Patients

Year 2025, Volume: 1 Issue: 2, 71 - 76, 01.09.2025

Abstract

Background: In this study, we aimed to evaluate the correlation of perfusion indexes with disease severity in COVID-19 patients.
Methods: Our study was conducted as a prospective observational study after obtaining ethical committee approval. Patients over the age of 18, who presented with COVID-19 symptoms, were PCR (Polymerase Chain Reaction) positive, were included in the study. Demographic data, complaints, vital signs and laboratory values of the patients were recorded. The perfusion index (PI) and plethysmographic variability index (PVI) were measured
Results: A total of 154 patients were included in the study. 47.4% of the patients included in the study were male, and the median age of 57 (IQR 44-69.2). 33.1% of the patients were in the mild group, and 66.9% in the moderate-severe group. The median PI value was 4.2 (IQR 3.1-5.6) and the median PVI value was 14.2 (IQR 11.5-16.5) in the mild-moderate patient group, the median PI value was 1.54 (IQR 1.14-1.97), the median PVI value was 23.4 (IQR 11.5-16.5) in the moderate-severe patient group. IQR 19.8-26.1) (p<0.05 for all parameters). The area under the curve AUC for PVI was 0.928 (95% CI; 0.887-0.969 p<0.001). When the best cut-off value of the PVI was taken as 16.3 to distinguish patients from the moderate-severe group, the sensitivity and specificity were calculated as 95% and 71% for this value. The AUC for PI was 0.895 (%95 CI; 0.844-0.946, p<0.001). When the best cut-off value of the perfusion indices was taken as 1.76 to distinguish patients with mild to moderate severity, the sensitivity was calculated as 94% and the specificity as 70.9%.
Conclusion: We found that PVI levels were associated with mean arterial pressure, shock index, lactate and base deficit levels, especially saturation. We think that the cut-off value of high PVI and low PI levels in differentiating severe disease, with its high sensitivity and specificity, may be clinically useful in predicting patients with severe COVID-19 pneumonia.

Ethical Statement

This is a prospective, observational study approved by the Local Ethics Committee (2012-KAEK-15/2412).

Supporting Institution

None

Thanks

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References

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  • 3. Umakanthan S, Sahu P, Ranade AV, et al. Origin, transmission, diagnosis and management of coronavirus disease 2019 (COVID-19). Postgrad Med J. 2020;96(1142):753-758
  • 4. Dramé M, Tabue Teguo M, Proye E, et al. Should RT-PCR be considered a gold standard in the diagnosis of COVID-19? J Med Virol. 2020;92(11):2312-2313.
  • 5. Böger B, Fachi MM, Vilhena RO, Cobre AF, Tonin FS, Pontarolo R. Systematic review with meta-analysis of the accuracy of diagnostic tests for COVID-19. Am J Infect Control. 2021;49(1):21-9.
  • 6. Kaneda T, Suzuki T. [Evaluation of circulatory state using pulse oximeter: 2. PI (perfusion index) x PVI (pleth variability index)]. Masui. 2009;58(7):860-5.
  • 7. Öztekin Ö, Emektar E, Selvi H, Çevik Y. Perfusion indices can predict early volume depletion in a blood donor model. Eur J Trauma Emerg Surg. 2022 Feb;48(1):553-557.
  • 8. Broch O, Bein B, Gruenewald M, et al. Accuracy of the pleth variability index to predict fluid responsiveness depends on the perfusion index. Acta Anaesthesiol Scand. 2011;55(6):686-93.
  • 9. Li H, Liu L, Zhang D, Xu J, et al. SARS-CoV-2 and viral sepsis: observations and hypotheses. Lancet. 2020;395(10235):1517-1520
  • 10. Zhang C, Shi L, Wang FS. Liver injury in COVID-19: management and challenges. Lancet Gastroenterol Hepatol. 2020;5(5):428-430.
  • 11. Guan WJ, Ni ZY, Hu Y, et al. China Medical Treatment Expert Group for Covid-19. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;382(18):1708-1720.
  • 12. Merad M, Blish CA, Sallusto F, Iwasaki A. The immunology and immunopathology of COVID-19. Science. 2022;375(6585):1122-1127
  • 13. Keller G, Cassar E, Desebbe O, Lehot JJ, Cannesson M. Ability of pleth variability index to detect hemodynamic changes induced by passive leg raising in spontaneously breathing volunteers. Crit Care. 2008;12(2):R37.
  • 14. Manta C, Jain SS, Coravos A, Mendelsohn D, Izmailova ES. An Evaluation of Biometric Monitoring Technologies for Vital Signs in the Era of COVID-19. Clinical and Translational Science. 2020;13(6):1034-44.
  • 15. Wang Z, Yang B, Li Q, Wen L, Zhang R. Clinical Features of 69 Cases With Coronavirus Disease 2019 in Wuhan, China. Clinical Infectious Diseases. 2020;71(15):769-77.
  • 16. Mudatsir M, Fajar JK, Wulandari L, et al. Predictors of COVID-19 severity: a systematic review and meta-analysis. F1000Res. 2020;9:1107
  • 17. Ran J, Song Y, Zhuang Z, Han L, et al. Blood pressure control and adverse outcomes of COVID-19 infection in patients with concomitant hypertension in Wuhan, China. Hypertens Res. 2020;43(11):1267-76.
  • 18. Nechipurenko YD, Semyonov DA, Lavrinenko IA, et al. The Role of Acidosis in the Pathogenesis of Severe Forms of COVID-19. Biology. 2021;10(9):852.
  • 19. Sarıaydın T, Çorbacıoğlu ŞK, Çevik Y, Emektar E. Effect of initial lactate level on short-term survival in patients with out-of-hospital cardiac arrest. Turk J Emerg Med. 2017;17(4):123-127.
  • 20. De Felice C, Latini G, Vacca P, Kopotic RJ. The pulse oximeter perfusion index as a predictor for high illness severity in neonates. Eur J Pediatr. 2002 Oct;161(10):561-2
  • 21. Forget P, Lois F, de Kock M. Goal-directed fluid management based on the pulse oximeter-derived pleth variability index reduces lactate levels and improves fluid management. Anesth Analg. 2010;111(4):910-4.

Year 2025, Volume: 1 Issue: 2, 71 - 76, 01.09.2025

Abstract

References

  • 1. Cui X, Zhao Z, Tongqiang Z, et al. A systematic review and meta‐analysis of children with Coronavirus Disease 2019 (COVID‐19). J Med Virol. 2021 Feb;93(2):1057-1069.
  • 2. Alsharif W, Qurashi A. Effectiveness of COVID-19 diagnosis and management tools: A review. Radiography (Lond). 2021;27(2):682-687.
  • 3. Umakanthan S, Sahu P, Ranade AV, et al. Origin, transmission, diagnosis and management of coronavirus disease 2019 (COVID-19). Postgrad Med J. 2020;96(1142):753-758
  • 4. Dramé M, Tabue Teguo M, Proye E, et al. Should RT-PCR be considered a gold standard in the diagnosis of COVID-19? J Med Virol. 2020;92(11):2312-2313.
  • 5. Böger B, Fachi MM, Vilhena RO, Cobre AF, Tonin FS, Pontarolo R. Systematic review with meta-analysis of the accuracy of diagnostic tests for COVID-19. Am J Infect Control. 2021;49(1):21-9.
  • 6. Kaneda T, Suzuki T. [Evaluation of circulatory state using pulse oximeter: 2. PI (perfusion index) x PVI (pleth variability index)]. Masui. 2009;58(7):860-5.
  • 7. Öztekin Ö, Emektar E, Selvi H, Çevik Y. Perfusion indices can predict early volume depletion in a blood donor model. Eur J Trauma Emerg Surg. 2022 Feb;48(1):553-557.
  • 8. Broch O, Bein B, Gruenewald M, et al. Accuracy of the pleth variability index to predict fluid responsiveness depends on the perfusion index. Acta Anaesthesiol Scand. 2011;55(6):686-93.
  • 9. Li H, Liu L, Zhang D, Xu J, et al. SARS-CoV-2 and viral sepsis: observations and hypotheses. Lancet. 2020;395(10235):1517-1520
  • 10. Zhang C, Shi L, Wang FS. Liver injury in COVID-19: management and challenges. Lancet Gastroenterol Hepatol. 2020;5(5):428-430.
  • 11. Guan WJ, Ni ZY, Hu Y, et al. China Medical Treatment Expert Group for Covid-19. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;382(18):1708-1720.
  • 12. Merad M, Blish CA, Sallusto F, Iwasaki A. The immunology and immunopathology of COVID-19. Science. 2022;375(6585):1122-1127
  • 13. Keller G, Cassar E, Desebbe O, Lehot JJ, Cannesson M. Ability of pleth variability index to detect hemodynamic changes induced by passive leg raising in spontaneously breathing volunteers. Crit Care. 2008;12(2):R37.
  • 14. Manta C, Jain SS, Coravos A, Mendelsohn D, Izmailova ES. An Evaluation of Biometric Monitoring Technologies for Vital Signs in the Era of COVID-19. Clinical and Translational Science. 2020;13(6):1034-44.
  • 15. Wang Z, Yang B, Li Q, Wen L, Zhang R. Clinical Features of 69 Cases With Coronavirus Disease 2019 in Wuhan, China. Clinical Infectious Diseases. 2020;71(15):769-77.
  • 16. Mudatsir M, Fajar JK, Wulandari L, et al. Predictors of COVID-19 severity: a systematic review and meta-analysis. F1000Res. 2020;9:1107
  • 17. Ran J, Song Y, Zhuang Z, Han L, et al. Blood pressure control and adverse outcomes of COVID-19 infection in patients with concomitant hypertension in Wuhan, China. Hypertens Res. 2020;43(11):1267-76.
  • 18. Nechipurenko YD, Semyonov DA, Lavrinenko IA, et al. The Role of Acidosis in the Pathogenesis of Severe Forms of COVID-19. Biology. 2021;10(9):852.
  • 19. Sarıaydın T, Çorbacıoğlu ŞK, Çevik Y, Emektar E. Effect of initial lactate level on short-term survival in patients with out-of-hospital cardiac arrest. Turk J Emerg Med. 2017;17(4):123-127.
  • 20. De Felice C, Latini G, Vacca P, Kopotic RJ. The pulse oximeter perfusion index as a predictor for high illness severity in neonates. Eur J Pediatr. 2002 Oct;161(10):561-2
  • 21. Forget P, Lois F, de Kock M. Goal-directed fluid management based on the pulse oximeter-derived pleth variability index reduces lactate levels and improves fluid management. Anesth Analg. 2010;111(4):910-4.
There are 21 citations in total.

Details

Primary Language English
Subjects Emergency Medicine
Journal Section Original Article
Authors

Eren Yalçın This is me 0009-0002-5067-2705

Emine Emektar 0000-0002-6056-4401

Yunsur Çevik 0000-0003-1325-0909

Publication Date September 1, 2025
Submission Date March 10, 2025
Acceptance Date March 25, 2025
Published in Issue Year 2025 Volume: 1 Issue: 2

Cite

Vancouver Yalçın E, Emektar E, Çevik Y. Correlation of Perfusion Indices with Disease Severity in COVID-19 Patients. Sanatorium Med J. 2025;1(2):71-6.

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