Early Prediction of Disease Progression in 2019 Novel Coronavirus Pneumonia Patients Outside Wuhan with CT and Clinical Characteristics

Feng, Zhichao; Yu, Qizhi; Yao, Shanhu; Luo, Lei; Duan, Junhong; Zhang, Yan; Yang, Min; Tan, Hongpei; Ma, Mengtian; Li, Ting; Yi, Dali; Mi, Ze; Zhao, Hai Tao; Jiang, Yi; He, Zhenhu; Li, Huiling; Nie, Wei; Liu, Yin; Zhao, Jing; Luo, Muqing; Liu, Xuanhui; Rong, Pengfei; Wang, Wei

doi:10.1101/2020.02.19.20025296

Cited by 47 publications

(51 citation statements)

References 23 publications

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“…This indicates that the diversity of the initial lesions in severe patients was not solely due to GGO lesion caused by exudation, but also due to a large number of consolidations. This present study finding was consistent kjronline.org with that of a previous report, in which severe patients had a larger lung lesion involvement (i.e., total lesion CT score) than that in non-severe patients (25). The total lesion score on CT quantitatively represents the percentage of lung parenchyma, demonstrating evidence of abnormalities as a whole and assesses of the total extent of the disease.…”

Section: Discussionsupporting

confidence: 92%

“…In addition, the present study also revealed significant positive correlations between CT quantitative parameters and neutrophil percentage, hs-CRP levels, and procalcitonin levels. These results were consistent with those of another report (25). Neutrophil percentage and C-reactive protein levels may be correlated to the cytokine storm induced by viral infection (6) and increased procalcitonin level may be due to secondary bacterial infection (27).…”

Section: Discussionsupporting

confidence: 92%

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CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19

Sun

Guo

et al. 2020

Korean J Radiol

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Objective: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). Materials and Methods: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. Results: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cutoff was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8-100%), 91.3% (CI: 69.6-100%), and 91.8% (CI: 23.0-98.4%), respectively. Conclusion: CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 92%

CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19

Sun

Guo

et al. 2020

Korean J Radiol

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“…Furthermore, only seven easy-access features were included in our nomogram, including older age, higher LDH and CRP, DBIL, RDW, BUN, and lower ALB. Age, NLR and LDH has been reported to be risk factors for severe patients with SARS-CoV-2 infection [3,[5][6][7]. NLR, a widely used marker for the assessment of system inflammation, was not identified by LASSO as an important feature instead of LDH and CRP, which are associated with the systemic inflammatory response [8].…”

Section: Discussionmentioning

confidence: 99%

A Tool to Early Predict Severe Corona Virus Disease 2019 (COVID-19) : A Multicenter Study using the Risk Nomogram in Wuhan and Guangdong, China

Gong

Qiu

et al. 2020

Preprint

129

160

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Older age; higher LDH, CRP, RDW, DBIL, BUN; lower ALB on admission correlated with higher odds of severe COVID-19. An effective prognostic nomogram composed of 7 features could allow early identification of patients at risk of exacerbation to severe COVID-19. Abstract BackgroundDue to no reliable risk stratification tool for severe corona virus disease 2019 patients at admission, we aimed to construct an effective model for early identifying cases at high risk of progression to severe COVID-19. MethodsIn this retrospective three-centers study, 372 non-severe COVID-19 patients during hospitalization were followed for more than 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and patients who kept non-severe state were assigned to the severe and non-severe group, respectively. Based on baseline data of the two groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluate its performance. ResultsThe train cohort consisted of 189 patients, while the two independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.35%) patients developed severe COVID-19. We found that old age, and higher serum lactate dehydrogenase, C-reactive protein, the coefficient of variation of red blood cell distribution width, blood urea nitrogen, direct bilirubin, lower albumin, are associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the train cohort (AUC 0.912 [95% CI 0.846-0.978], sensitivity 85.71%, specificity 87.58%); in validation cohort (0.853 [0.790-0.916], 77.5%, 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analysis indicated that nomogram conferred high clinical net benefit. ConclusionOur nomogram could help clinicians to early identify patients who will exacerbate to severe COVID-19, which will enable better centralized management and early treatment of severe patients.

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“…30 studies were included in the qualitative synthesis and meta-analysis [ Figure 1]. [3,8,[17][18][19][20][21][22][23][24][25][26]9,[27][28][29][30][31][32][33][34][35][36][10][11][12][13][14][15][16] There were a total of 6452 patients from 30 studies.…”

Section: Study Selection and Characteristicsmentioning

confidence: 99%

Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia – A systematic review, meta-analysis, and meta-regression

Huang

Lim

Pranata

2020

Diabetes & Metabolic Syndrome: Clinical Research & Revi

822

747

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Background and AimsDiabetes Mellitus (DM) is chronic conditions with devastating multi-systemic complication and may be associated with severe form of Coronavirus Disease 2019 . We conducted a systematic review and meta-analysis in order to investigate the association between DM and poor outcome in patients with COVID-19 pneumonia. MethodsSystematic literature search was performed from several electronic databases on subjects that assess DM and outcome in COVID-19 pneumonia. The outcome of interest was composite poor outcome, including mortality, severe COVID-19, acute respiratory distress syndrome (ARDS), need for intensive care unit (ICU) care, and disease progression. ResultsThere were a total of 6452 patients from 30 studies. Meta-analysis showed that DM was associated with composite poor outcome (RR 2.38 [1.88,3.03], p<0.001; I 2 : 62%) and its subgroup which comprised of mortality (RR 2.12 [1.44, 3.11], p<0.001; I 2 : 72%), severe COVID-19 (RR 2.45 [1.79, 3.35], p<0.001; I 2 : 45%), ARDS (RR 4.64 [1.86,11.58], p=0.001; I 2 : 9%), and disease progression (RR 3.31 [1.08, 10.14], p=0.04; I 2 : 0%). Meta-regression showed that the association with composite poor outcome was influenced by age (p=0.003) and hypertension (p<0.001). Subgroup analysis showed that the association was weaker in studies with median age ≥55 years-old (RR 1.92) compared to <55 years-old (RR 3.48), and in prevalence of hypertension ≥25% (RR 1.93) compared to <25% (RR 3.06). Subgroup analysis on median age <55 years-old and prevalence of hypertension <25% showed strong association (RR 3.33) ConclusionDM was associated with mortality, severe COVID-19, ARDS, and disease progression in patients with COVID-19.

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Early Prediction of Disease Progression in 2019 Novel Coronavirus Pneumonia Patients Outside Wuhan with CT and Clinical Characteristics

Cited by 47 publications

References 23 publications

CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19

CT Quantitative Analysis and Its Relationship with Clinical Features for Assessing the Severity of Patients with COVID-19

A Tool to Early Predict Severe Corona Virus Disease 2019 (COVID-19) : A Multicenter Study using the Risk Nomogram in Wuhan and Guangdong, China

Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia – A systematic review, meta-analysis, and meta-regression

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