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Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with advanced lung cancer inflammation index, computed tomography and chest radiograph

  • Authors:
    • Akitoshi Inoue
    • Hiroaki Takahashi
    • Tatsuya Ibe
    • Hisashi Ishii
    • Yuhei Kurata
    • Yoshikazu Ishizuka
    • Bolorkhand Batsaikhan
    • Yoichiro Hamamoto
  • View Affiliations / Copyright

    Affiliations: Department of Radiology, Shiga University of Medical Science Seta, Otsu, Shiga 520‑2192, Japan, Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA, Department of Plumonary Medicine, National Hospital Organization Nishisaitama‑Chuo National Hospital, Tokorozawa, Saitama 359‑1151, Japan, Department of Radiology, National Hospital Organization Nishisaitama‑Chuo National Hospital, Tokorozawa, Saitama 359‑1151, Japan, Department of Radiological Science, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo 116‑8551, Japan
    Copyright: © Inoue et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY 4.0].
  • Article Number: 388
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    Published online on: April 12, 2022
       https://doi.org/10.3892/etm.2022.11315
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Abstract

The purpose of the present study was to evaluate the feasibility of applying the advanced lung cancer inflammation index (ALI) in patients with coronavirus disease 2019 (COVID‑19) and to establish a combined ALI and radiologic risk prediction model for disease exacerbation. The present study included patients diagnosed with COVID‑19 infection in our single institution from March to October 2020. Patients without clinical information and/or chest computed tomography (CT) upon admission were excluded. A radiologist assessed the CT severity score and abnormality on chest radiograph. The combined ALI and radiologic risk prediction model was developed via random forest classification. Among 79 patients (age, 43±19 years; male/female, 45:34), 72 experienced improvement and seven patients experienced exacerbation after admission. Significant differences were observed between the improved and exacerbated groups in the ALI (median, 47.6 vs. 13.2; P=0.011), frequency of chest radiograph abnormality (24.7 vs. 83.3%; P<0.001), and chest CT score (CCTS; median, 1 vs. 9; P<0.001). For the accuracy of predicting exacerbation, the receiver‑operating characteristic curve analysis demonstrated an area under the curve of 0.79 and 0.92 for the ALI and CCTS, respectively. The combined ALI and radiologic risk prediction model had a sensitivity of 1.00 and a specificity of 0.81. Overall, ALI alone and CCTS alone modestly predicted the exacerbation of COVID‑19, and the combined ALI and radiologic risk prediction model exhibited decent sensitivity and specificity.
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Copy and paste a formatted citation
Spandidos Publications style
Inoue A, Takahashi H, Ibe T, Ishii H, Kurata Y, Ishizuka Y, Batsaikhan B and Hamamoto Y: Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with advanced lung cancer inflammation index, computed tomography and chest radiograph. Exp Ther Med 23: 388, 2022.
APA
Inoue, A., Takahashi, H., Ibe, T., Ishii, H., Kurata, Y., Ishizuka, Y. ... Hamamoto, Y. (2022). Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with advanced lung cancer inflammation index, computed tomography and chest radiograph. Experimental and Therapeutic Medicine, 23, 388. https://doi.org/10.3892/etm.2022.11315
MLA
Inoue, A., Takahashi, H., Ibe, T., Ishii, H., Kurata, Y., Ishizuka, Y., Batsaikhan, B., Hamamoto, Y."Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with advanced lung cancer inflammation index, computed tomography and chest radiograph". Experimental and Therapeutic Medicine 23.6 (2022): 388.
Chicago
Inoue, A., Takahashi, H., Ibe, T., Ishii, H., Kurata, Y., Ishizuka, Y., Batsaikhan, B., Hamamoto, Y."Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with advanced lung cancer inflammation index, computed tomography and chest radiograph". Experimental and Therapeutic Medicine 23, no. 6 (2022): 388. https://doi.org/10.3892/etm.2022.11315
Copy and paste a formatted citation
x
Spandidos Publications style
Inoue A, Takahashi H, Ibe T, Ishii H, Kurata Y, Ishizuka Y, Batsaikhan B and Hamamoto Y: Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with advanced lung cancer inflammation index, computed tomography and chest radiograph. Exp Ther Med 23: 388, 2022.
APA
Inoue, A., Takahashi, H., Ibe, T., Ishii, H., Kurata, Y., Ishizuka, Y. ... Hamamoto, Y. (2022). Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with advanced lung cancer inflammation index, computed tomography and chest radiograph. Experimental and Therapeutic Medicine, 23, 388. https://doi.org/10.3892/etm.2022.11315
MLA
Inoue, A., Takahashi, H., Ibe, T., Ishii, H., Kurata, Y., Ishizuka, Y., Batsaikhan, B., Hamamoto, Y."Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with advanced lung cancer inflammation index, computed tomography and chest radiograph". Experimental and Therapeutic Medicine 23.6 (2022): 388.
Chicago
Inoue, A., Takahashi, H., Ibe, T., Ishii, H., Kurata, Y., Ishizuka, Y., Batsaikhan, B., Hamamoto, Y."Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with advanced lung cancer inflammation index, computed tomography and chest radiograph". Experimental and Therapeutic Medicine 23, no. 6 (2022): 388. https://doi.org/10.3892/etm.2022.11315
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