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Yoğun bakım ihtiyacı olan COVID-19 hastalarını ayırt etmede akciğer grafisinin performansının değerlendirilmesi

Year 2021, , 95 - 101, 31.03.2021
https://doi.org/10.18663/tjcl.842478

Abstract

Amaç: COVID-19 hastalarından yoğun bakım ünitesi (YBÜ) ihtiyacı olanları ayırt etmede akciğer grafisinin performansının değerlendirilmesi amaçlanmıştır.
Gereç ve Yöntemler: Nisan ve Ağustos 2020 tarihleri arasında, hastaneye yatırılan ve 24 saat içinde akciğer grafisi elde olunan ardışık 166 COVID-19 hastası çalışmaya dahil edildi. Tüm hastaların yaş, cinsiyet, eşlik eden hastalık, sigara içme durumu ve semptom süresi kaydedildi. Birinci gözlemci tüm hastaların akciğer grafilerinde radyolojik bulguları değerlendirdi. Radyografik bulguların dağılımı not edildi. Daha sonra iki gözlemci birbirinden bağımsız olarak tüm akciğer grafilerine akciğer ödemi radyografik değerlendirme (AÖRD) skoru verdi. Her iki gözlemci için COVID-19 hastalarından YBÜ ihtiyacı olanları belirlemede duyarlılık ve özgüllük değerleri hesaplandı. Intraclass Correlation Coefficient (ICC) testi gözlemciler arası uyumluluğu değerlendirmek için kullanıldı.
Bulgular: Hastaların 128’i (%77.1) sadece hastaneye yatırılırken, 38’i (%22.9) YBÜ’ne ihtiyaç duydu. AÖRD skoru için 7.5 eşik değeri olarak kullanıldığında YBÜ gereksinimi olan COVID-19 hastalarını ayırt etmede birinci gözlemci için %89.5 ve %93 duyarlılık ve özgüllük değerleri; ikinci gözlemci için %89.5 ve %91.4 duyarlılık ve özgüllük değerleri bulundu. Gözlemciler arası uyumluluk için ICC değeri 0.988 (%95 güven aralığı: 0.983 – 0.991) olarak bulundu.
Sonuç: Akciğer grafisi YBÜ ihtiyacı olan COVID-19 hastalarını belirlemede yardımcı olabilir ve 7.5’ten büyük AÖRD skoru YBÜ gereksinimini gösterir.

References

  • 1. 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: 1708-20.
  • 2. Chen N, Zhou M, Dong X et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020; 395: 507–13.
  • 3. Wang D, Hu B, Hu C, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China JAMA 2020; 323: 1061-9.
  • 4. Phua J, Weng L, Ling L et al. Asian Critical Care Clinical Trials Group. Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations. Lancet Respir Med 2020; 8: 506-17.
  • 5. Huang G, Gong T, Wang G et al. Timely Diagnosis and Treatment Shortens the Time to Resolution of Coronavirus Disease (COVID-19) Pneumonia and Lowers the Highest and Last CT Scores From Sequential Chest CT. AJR Am J Roentgenol 2020; 215: 367-73.
  • 6. Wong HYF, Lam HYS, Fong AH et al. Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19. Radiology 2020; 296: 72-8.
  • 7. Rubin GD, Ryerson CJ, Haramati LB et al. The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society. Radiology 2020; 296: 172-80. 
  • 8. Tekcan Sanli DE, Yildirim D, Sanli AN et al. Predictive value of CT imaging findings in COVID-19 pneumonia at the time of first-screen regarding the need for hospitalization or intensive care unit. Diagn Interv Radiol 2 December 2020 10.5152/dir.2020.20421 [Epub Ahead of Print]
  • 9. Colombi D, Bodini FC, Petrini M et al. Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia. Radiology 2020; 296: 86-96.
  • 10. Toussie D, Voutsinas N, Finkelstein M et al. Clinical and Chest Radiography Features Determine Patient Outcomes in Young and Middle-aged Adults with COVID-19. Radiology 2020; 297: 197-206.
  • 11. Balbi M, Caroli A, Corsi A et al. Chest X-ray for predicting mortality and the need for ventilatory support in COVID-19 patients presenting to the emergency department. Eur Radiol 2020; 8: 1–14. 
  • 12. Stephanie S, Shum T, Cleveland H et al. Determinants of Chest X-Ray Sensitivity for COVID- 19: A Multi-Institutional Study in the United States. Radiology: Cardiothoracic Imaging Sep 24 2020. doi:10.1148/ryct.2020200337
  • 13. Smith DL, Grenier JP, Batte C, Spieler B Characteristic Chest Radiographic Pattern in the Setting of COVID-19 Pandemic. Radiology: Cardiothoracic Imaging Sep 3 2020. doi:10.1148/ryct.2020200280.
  • 14. Warren MA, Zhao Z, Koyama T et al. Severity scoring of lung oedema on the chest radiograph is associated with clinical outcomes in ARDS. Thorax 2018; 73: 840-6.
  • 15. Cozzi D, Albanesi M, Cavigli E et al. Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome. Radiol Med 2020; 125: 730-7. 
  • 16. Homayounieh F, Zhang EW, Babaei R et al. Clinical and imaging features predict mortality in COVID-19 infection in Iran. PLoS One. 2020; 15: 239519
  • 17. Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 2016; 15: 155-63.
  • 18. CDC COVID-19 Response Team. Severe outcomes among patients with Coronavirus Disease 2019 (COVID-19) – United States, February 12- March 16, 2020. MMWR Morb Mortal Wkly Rep 2020; 69: 343-6.
  • 19. Zhou F, Yu T, Du R et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020; 395: 1054-62. 
  • 20. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA 2020; 323: 1239-42.
  • 21. Onder G, Rezza G, Brusaferro S. Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. JAMA 2020; 323: 1775-6.
  • 22. Zhao Q, Meng M, Kumar R et al. The impact of COPD and smoking history on the severity of COVID-19: A systemic review and meta-analysis. J Med Virol 2020; 92: 1915-21.
  • 23. Lippi G, Henry BM. Active smoking is not associated with severity of coronavirus disease 2019 (COVID-19). Eur J Intern Med 2020; 75: 107-8.
  • 24. Weinstock MB, Echenique A, Russell JW et al. Chest x-ray findings in 636 ambulatory patients with COVID-19 presenting to an urgent care center: a normal chest x-ray is no guarantee. J Urgent Care Med 2020; 14: 13-8
  • 25. Borghesi A, Maroldi R. COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression. Radiol Med 2020; 125: 509-13.

Evaluation of performance of chest x-ray in distinguishing intensıve care unit need among COVID-19 patients

Year 2021, , 95 - 101, 31.03.2021
https://doi.org/10.18663/tjcl.842478

Abstract

Aim: To investigate the performance of chest X-ray (CXR) in distinguishing the patients who necessitate intensive care unit (ICU) admission among COVID-19 patients.
Material and Methods: Between April to August 2020, 166 consecutive hospitalized COVID-19 patients who underwent acquisition of CXR within 24 hours of hospital admission were included in the study. Age, gender, number of comorbidities, smoking status and duration of symptoms for all patients were noted. Observer 1 interpreted the radiographic findings of CXRs of all patients. Distribution of radiographic findings were noted. Afterwards, Observer 1 and observer 2 assigned radiographic assessment of lung edema (RALE) score for each CXR independently. Sensitivity, specificity values in distinguishing COVID-19 patients who require ICU for each observer were calculated. Intraclass Correlation Coefficient (ICC) test was used to assess interobserver agreement levels.
Results: Of the included patients, 128 (77.1%) patients were hospitalized only whereas 38 (22.9%) patients had necessity for ICU admission. Using 7.5 for RALE score as a cut-off point in distinguishing COVID-19 patients who need ICU admission Observer 1 had 89.5% and 93% for sensitivity and specificity, respectively; and Observer 2 had 89.5% and 91.4% for sensitivity and specificity, respectively. The ICC value for the interobserver agreement in RALE scores was 0.988 (95% confidence interval: 0.983 – 0.991).
Conclusion: CXR can be helpful in distinguishing COVID-19 patients who necessitates ICU admission and a RALE score higher than 7.5 is indicative for ICU requirement.

References

  • 1. 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: 1708-20.
  • 2. Chen N, Zhou M, Dong X et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020; 395: 507–13.
  • 3. Wang D, Hu B, Hu C, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China JAMA 2020; 323: 1061-9.
  • 4. Phua J, Weng L, Ling L et al. Asian Critical Care Clinical Trials Group. Intensive care management of coronavirus disease 2019 (COVID-19): challenges and recommendations. Lancet Respir Med 2020; 8: 506-17.
  • 5. Huang G, Gong T, Wang G et al. Timely Diagnosis and Treatment Shortens the Time to Resolution of Coronavirus Disease (COVID-19) Pneumonia and Lowers the Highest and Last CT Scores From Sequential Chest CT. AJR Am J Roentgenol 2020; 215: 367-73.
  • 6. Wong HYF, Lam HYS, Fong AH et al. Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19. Radiology 2020; 296: 72-8.
  • 7. Rubin GD, Ryerson CJ, Haramati LB et al. The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society. Radiology 2020; 296: 172-80. 
  • 8. Tekcan Sanli DE, Yildirim D, Sanli AN et al. Predictive value of CT imaging findings in COVID-19 pneumonia at the time of first-screen regarding the need for hospitalization or intensive care unit. Diagn Interv Radiol 2 December 2020 10.5152/dir.2020.20421 [Epub Ahead of Print]
  • 9. Colombi D, Bodini FC, Petrini M et al. Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia. Radiology 2020; 296: 86-96.
  • 10. Toussie D, Voutsinas N, Finkelstein M et al. Clinical and Chest Radiography Features Determine Patient Outcomes in Young and Middle-aged Adults with COVID-19. Radiology 2020; 297: 197-206.
  • 11. Balbi M, Caroli A, Corsi A et al. Chest X-ray for predicting mortality and the need for ventilatory support in COVID-19 patients presenting to the emergency department. Eur Radiol 2020; 8: 1–14. 
  • 12. Stephanie S, Shum T, Cleveland H et al. Determinants of Chest X-Ray Sensitivity for COVID- 19: A Multi-Institutional Study in the United States. Radiology: Cardiothoracic Imaging Sep 24 2020. doi:10.1148/ryct.2020200337
  • 13. Smith DL, Grenier JP, Batte C, Spieler B Characteristic Chest Radiographic Pattern in the Setting of COVID-19 Pandemic. Radiology: Cardiothoracic Imaging Sep 3 2020. doi:10.1148/ryct.2020200280.
  • 14. Warren MA, Zhao Z, Koyama T et al. Severity scoring of lung oedema on the chest radiograph is associated with clinical outcomes in ARDS. Thorax 2018; 73: 840-6.
  • 15. Cozzi D, Albanesi M, Cavigli E et al. Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome. Radiol Med 2020; 125: 730-7. 
  • 16. Homayounieh F, Zhang EW, Babaei R et al. Clinical and imaging features predict mortality in COVID-19 infection in Iran. PLoS One. 2020; 15: 239519
  • 17. Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 2016; 15: 155-63.
  • 18. CDC COVID-19 Response Team. Severe outcomes among patients with Coronavirus Disease 2019 (COVID-19) – United States, February 12- March 16, 2020. MMWR Morb Mortal Wkly Rep 2020; 69: 343-6.
  • 19. Zhou F, Yu T, Du R et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020; 395: 1054-62. 
  • 20. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA 2020; 323: 1239-42.
  • 21. Onder G, Rezza G, Brusaferro S. Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. JAMA 2020; 323: 1775-6.
  • 22. Zhao Q, Meng M, Kumar R et al. The impact of COPD and smoking history on the severity of COVID-19: A systemic review and meta-analysis. J Med Virol 2020; 92: 1915-21.
  • 23. Lippi G, Henry BM. Active smoking is not associated with severity of coronavirus disease 2019 (COVID-19). Eur J Intern Med 2020; 75: 107-8.
  • 24. Weinstock MB, Echenique A, Russell JW et al. Chest x-ray findings in 636 ambulatory patients with COVID-19 presenting to an urgent care center: a normal chest x-ray is no guarantee. J Urgent Care Med 2020; 14: 13-8
  • 25. Borghesi A, Maroldi R. COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression. Radiol Med 2020; 125: 509-13.
There are 25 citations in total.

Details

Primary Language Turkish
Subjects Health Care Administration
Journal Section Orıgınal Artıcle
Authors

Halit Nahit Şendur

Mahi Cerit This is me 0000-0003-2878-6052

Emetullah Cindil This is me 0000-0002-9345-1577

Publication Date March 31, 2021
Published in Issue Year 2021

Cite

APA Şendur, H. N., Cerit, M., & Cindil, E. (2021). Yoğun bakım ihtiyacı olan COVID-19 hastalarını ayırt etmede akciğer grafisinin performansının değerlendirilmesi. Turkish Journal of Clinics and Laboratory, 12(1), 95-101. https://doi.org/10.18663/tjcl.842478
AMA Şendur HN, Cerit M, Cindil E. Yoğun bakım ihtiyacı olan COVID-19 hastalarını ayırt etmede akciğer grafisinin performansının değerlendirilmesi. TJCL. March 2021;12(1):95-101. doi:10.18663/tjcl.842478
Chicago Şendur, Halit Nahit, Mahi Cerit, and Emetullah Cindil. “Yoğun bakım Ihtiyacı Olan COVID-19 hastalarını ayırt Etmede akciğer Grafisinin performansının değerlendirilmesi”. Turkish Journal of Clinics and Laboratory 12, no. 1 (March 2021): 95-101. https://doi.org/10.18663/tjcl.842478.
EndNote Şendur HN, Cerit M, Cindil E (March 1, 2021) Yoğun bakım ihtiyacı olan COVID-19 hastalarını ayırt etmede akciğer grafisinin performansının değerlendirilmesi. Turkish Journal of Clinics and Laboratory 12 1 95–101.
IEEE H. N. Şendur, M. Cerit, and E. Cindil, “Yoğun bakım ihtiyacı olan COVID-19 hastalarını ayırt etmede akciğer grafisinin performansının değerlendirilmesi”, TJCL, vol. 12, no. 1, pp. 95–101, 2021, doi: 10.18663/tjcl.842478.
ISNAD Şendur, Halit Nahit et al. “Yoğun bakım Ihtiyacı Olan COVID-19 hastalarını ayırt Etmede akciğer Grafisinin performansının değerlendirilmesi”. Turkish Journal of Clinics and Laboratory 12/1 (March 2021), 95-101. https://doi.org/10.18663/tjcl.842478.
JAMA Şendur HN, Cerit M, Cindil E. Yoğun bakım ihtiyacı olan COVID-19 hastalarını ayırt etmede akciğer grafisinin performansının değerlendirilmesi. TJCL. 2021;12:95–101.
MLA Şendur, Halit Nahit et al. “Yoğun bakım Ihtiyacı Olan COVID-19 hastalarını ayırt Etmede akciğer Grafisinin performansının değerlendirilmesi”. Turkish Journal of Clinics and Laboratory, vol. 12, no. 1, 2021, pp. 95-101, doi:10.18663/tjcl.842478.
Vancouver Şendur HN, Cerit M, Cindil E. Yoğun bakım ihtiyacı olan COVID-19 hastalarını ayırt etmede akciğer grafisinin performansının değerlendirilmesi. TJCL. 2021;12(1):95-101.


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