|
|
The clinical significance of CAD in patients with severe and critically-severe Omicron infections |
ZHU Mingxin1, HUANG Jianliang1, XIA Mingkai1, PENG Bo1, LI Linjun3, ZHANG Jianhui1, WEN Fang4, HU Linlin1, LEI Mingsheng1,2 |
1. Zhangjiajie Hospital Affiliated to Hunan Normal University, Zhangjiajie 427000, China; 2. Jishou University Zhangjiajie College, Zhangjiajie 427000, China; 3. School of Public Health, Kunming Medical University, Kunming 650500, China; 4. Medical College of Jishou University, Jishou 416000, China |
|
|
Abstract Objective The purpose of this study is to investigate the clinical significance of computer-aided diagnosis (CAD) in patients with severe and critically-severe Omicron infections in Zhangjiajie. The study aims to provide additional references for the treatment of these patients. Method From December 2022 to January 2023, we collected various clinical data such as qSOFA score, laboratory test results, and lung CT scans from patients who were hospitalized with severe and critically-severe Omicron infections. Additionally, in this study, we utilized computer-aided diagnosis to segment and reconstruct the lung CT, enabling us to accurately calculate the proportion of inflammation. Our objective was to compare the clinical characteristics and inflammation proportion of severe and critically-severe, with the aim of identifying any potential differences and exploring the relationship between inflammation proportion and critical illness. Results A total of 116 severe and critically-severe patients were included in this study, The median age of the patients was 73.0(ranging from 65.0 to 84.0) years, and 60.3% (n=70) of patients were admitted to the ICU. Among the patients, 86.2% (n=100) patients had underlying diseases. proportion of inflammation, qSOFA score, C-reactive protein, and D-dimer were higher in critically-severe patients than in severe patients (P<0.05). The higher the proportion of inflammation, the easier it is to develop critical illness (r=0.24, P=0.009). The sensitivity, specificity and accuracy of inflammation proportion in the diagnosis of progress to critically-severe were 0.656, 0.745 and 0.638, the cutoff value was 31.46%. Conclusion Computer-aided diagnosis has a certain value in evaluating the condition of patients with severe COVID-19 pneumonia. The higher the proportion of inflammation, the greater the possibility of the disease progressing to critical illness, it is hypothesized that if the proportion of lung inflammation is equal to or greater than 31.46%, the patient is at a high risk of progressing to critical illness.
|
Received: 14 April 2023
|
|
|
|
|
[1] Sievers C, Zacher B, Ullrich A, et al.SARS-CoV-2 Omicron variants BA.1 and BA.2 both show similarly reduced disease severity of COVID-19 compared to Delta, Germany, 2021 to 2022[J]. Euro Surveill, 2022, 27(22): 2200396. [2] Hu XS, Hu CH, Yang Y, et al.Clinical characteristics and risk factors for severity of COVID-19 outside Wuhan: a double-center retrospective cohort study of 213 cases in Hunan, China[J]. Ther Adv Respir Dis, 2020, 14: 1496-1500. [3] Zhou LX, Li ZX, Zhou JX, et al.A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis[J]. IEEE Trans Med Imaging, 2020, 39(8): 2638-2652. [4] 新型冠状病毒肺炎诊疗方案 (试行第九版)[J]. 中国医药, 2022, 17(04): 481-487. [5] Song JU, Sin CK, Park HK, et al.Performance of the quick Sequential (sepsis-related) Organ Failure Assessment score as a prognostic tool in infected patients outside the intensive care unit: a systematic review and meta-analysis[J]. Crit Care, 2018, 22(1): 28. [6] Chen L, Song H, Wang C, et al.Liver tumor segmentation in CT volumes using an adversarial densely connected network[J]. BMC Bioinformatics, 2019, 20(Suppl 16): 587. [7] Yu H, Li JQ, Zhang LX, et al.Design of lung nodules segmentation and recognition algorithm based on deep learning[J]. BMC Bioinformatics, 2021, 22(Suppl 5): 314. [8] Wang S, Kang B, Ma JL, et al.A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)[J]. Eur Radiol, 2021, 31(8): 6096-6104. [9] Ramos-Rincon JM, Buonaiuto V, Ricci M, et al.Clinical Characteristics and Risk Factors for Mortality in Very Old Patients Hospitalized with COVID-19 in Spain[J]. J Gerontol A Biol Sci Med Sci, 2020, 76(3): e28-e37. [10] Citu C, Citu IM, Motoc A, et al.Predictive Value of SOFA and qSOFA for In-Hospital Mortality in COVID-19 Patients: A Single-Center Study in Romania[J]. J Pers Med, 2022, 12(6): 878. [11] Zhou F, Yu T, Du RH, et al.Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study[J]. Lancet, 2020, 395(10229): 1054-1062. [12] Francone M, Iafrate F, Masci GM, et al.Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis[J]. European radiology, 2020, 30(12): 6808-6817. [13] Huang CL, Wang YM, Li XW, et al.Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China[J]. Lancet, 2020, 395(10223): 497-506. [14] Shi CC, Wang LM, Ye J, et al.Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis[J]. BMC Infect Dis, 2021, 21(1): 663. [15] Chan JC, Tsui EL, Wong VC, et al.Prognostication in severe acute respiratory syndrome: a retrospective time-course analysis of 1312 laboratory-confirmed patients in Hong Kong[J]. Respirology (Carlton, Vic. ), 2007, 12(4): 531-542. [16] Hong KH, Choi JP, Hong SH, et al.Predictors of mortality in Middle East respiratory syndrome (MERS)[J]. Thorax, 2018, 73(3): 286-289. [17] Westblade LF, Simon MS, Satlin MJ.Bacterial coinfections in coronavirus disease 2019[J]. Trends Microbiol, 2021, 29(10): 930-941. [18] Ripa M, Galli L, Poli A, et al.Secondary infections in patients hospitalized with COVID-19: incidence and predictive factors[J]. Clin Microbiol Infect, 2021, 27(3): 451-457. |
|
|
|