PMC:7160614 / 14269-25990 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7160614","sourcedb":"PMC","sourceid":"7160614","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7160614","text":"Results\n\nImaging and clinical manifestations between groups\nThe differences between patients with and without COVID-19 for all 67 features (41 imaging and 26 critical clinical features) are shown in Tables 1 and 2 and the Supplementary Materials (Tables E3 and E4). The differences between the primary cohort and validation cohort for the same features are shown in the Supplementary Materials (Tables E5 and E6). All characteristics except fatigue and white blood cell count in the CR model presented no significant difference between the primary and validation cohorts. A total of 1745 lesions were identified, with 1062 from the COVID-19 group and 683 from the non-COVID-19 group.\nTable 1 Radiological semantic features of patients in COVID-19 and non-COVID-19\nFeature Non-COVID-19 (n = 66) COVID-19 (n = 70) p value\nNumber of pure GGO\n  Total# 1.00 (0.00, 5.05) 3.50 (0.95, 8.05) 0.018b*\n  Peripheral area# 1.00 (0.00, 4.05) 2.00 (0.00, 6.05) 0.032b*\n  Central/both peripheral and central area# 0.00 (0.00, 0.00) 0.00 (0.00, 2.00) 0.001b*\nNumber of mixed GGO\n  Total# 1.00 (0.00, 3.05) 3.00 (1.00, 9.00) 0.001b*\n  Peripheral area# 0.00 (0.00, 2.00) 2.50 (1.00, 6.00) \u003c 0.001b*\n  Central/both peripheral and central area# 0.00 (0.00, 1.05) 0.00 (0.00, 2.00) 0.657b\nTotal number of consolidation\n  Consolidation# 1.00 (0.00, 3.00) 0.00 (0.00, 0.05) 0.001b*\n  Pure solid nodules# 0.00 (0.00, 0.00) 0.00 (0.00, 0.00) 0.309b\n  Solid nodules with GGO# 0.00 (0.00, 0.00) 0.00 (0.00, 1.00) 0.033b*\nTotal number of lesions\n  Peripheral area# 5.00 (2.00, 9.05) 7.00 (2.00, 13.00) 0.112b\n  Central area# 0.00 (0.00, 3.00) 0.00 (0.00, 1.05) 0.960b\n  Both peripheral and central area# 0.00 (0.00, 2.00) 0.00 (0.00, 2.05) 0.582b\nInterlobular septal thickening 0.009a*\n  Negative 44 (66.67%) 31 (44.29%)\n  Positive 22 (33.33%) 39 (55.71%)\nCrazy paving pattern \u003c 0.001a*\n  Negative 60 (90.91%) 32 (45.71%)\n  Positive 6 (9.09%) 38 (54.29%)\nTree-in-bud sign \u003c 0.001a*\n  Negative 37 (56.06%) 61 (87.14%)\n  Positive 29 (43.94%) 9 (12.86%)\nPleural thickening 0.030a*\n  Negative 46 (69.70%) 36 (51.43%)\n  Positive 20 (30.30%) 34 (48.57%)\nOffending vessel augmentation in lesions \u003c 0.001a*\n  Negative 55 (83.33%) 17 (24.29%)\n  Positive 11 (16.67%) 53 (75.71%)\nGGO ground-glass opacities\n#Results are median with interquartile range in parentheses, and the remainder results are measurements with corresponding ratio in parentheses\n*Data with statistical significance. pa: chi-square test, pb: Student’s t test\nTable 2 Clinical features of patients in COVID-19 and non-COVID-19\nFeature Non-COVID-19 (n = 66) COVID-19 (n = 70) p value\nSex\n  Male# 43 (65.15%) 41 (58.57%) 0.430a\n  Female# 23 (34.85%) 29 (41.43%)\n  Age (years) 46.73 ± 25.00 42.93 ± 13.32 0.275b\nVital signs\n  Systolic blood pressure (mmHg) 126.92 ± 23.07 127.07 ± 15.16 0.965b\n  Diastolic blood pressure (mmHg) 77.74 ± 15.72 80.39 ± 10.51 0.254b\n  Respiration rate (bpm) 25.20 ± 7.29 19.86 ± 1.90 \u003c 0.001b*\n  Heart rate (bpm) 101.59 ± 20.36 86.06 ± 13.34 \u003c 0.001b*\n  Temperature (°C) 37.61 ± 1.06 37.12 ± 0.83 0.003b*\nSigns\n  Dry cough# 56 (84.85%) 48 (68.57%) 0.025a*\n  Fatigue# 8 (12.12%) 22 (31.43%) 0.007a*\n  Sore throat# 6 (9.09%) 9 (12.86%) 0.483a\n  Stuffy# 4 (6.06%) 2 (2.86%) 0.623a\n  Runny nose# 3 (4.55%) 3 (4.29%) 0.731a\nWhite blood cell count (× 109/L) 11.48 ± 5.36 5.27 ± 2.33 \u003c 0.001b*\nWhite blood cell count category \u003c 0.001c*\n  Low# 0 (0.00%) 2 (2.86%)\n  Normal# 27 (40.91%) 63 (90.00%)\n  High# 39 (59.09%) 5 (7.14%)\nLymphocyte count (× 109/L) 1.57 ± 1.33 1.25 ± 0.68 0.086b\nLymphocyte count category \u003c 0.001c*\n  Low# 24 (36.36%) 32 (45.71%)\n  Normal# 35 (53.03%) 37 (52.86%)\n  High# 7 (10.61%) 1 (1.43%)\nNeutrophil count (× 109/L) 8.97 ± 4.90 3.53 ± 2.17 \u003c 0.001b*\nNeutrophil count category \u003c 0.001c*\n  Low# 3 (4.55%) 8 (11.43%)\n  Normal# 23 (34.85%) 59 (84.29%)\n  High# 40 (60.61%) 3 (4.29%)\nC-reactive protein (mg/L) 69.30 ± 65.88 26.37 ± 30.97 \u003c 0.001b*\nProcalcitonin (ng/mL) 3.36 ± 8.98 0.26 ± 0.84 0.007b*\n*Data with statistical significance. pa: chi-square test, pb: Student’s t test. pc: Kruskal-Wallis H test\n#Results are measurements with corresponding ratio in parentheses\nFor imaging manifestations, 7 patients in the COVID-19 group showed normal chest CT (10%). COVID-19 patients have a greater number of pure GGO and mixed GGO than non-COVID-19 patients (p = 0.018 and p = 0.001, respectively). For pure GGO lesions, the differences are significant both in peripheral (p = 0.032) and in central areas (p = 0.001). However, the number of mixed GGO is mainly distributed at the periphery in COVID-19 patients (p \u003c 0.001), with no statistical difference in the central area. The consolidation lesions without GGO occurred less in COVID-19 patients (p = 0.001). More lesions are between 1 and 3 cm (p = 0.027), and fewer lesions are larger than half of the lung segment (p = 0.017) in COVID-19 patients. Other significant differences between the two groups include the pleural traction sign (p = 0.019), bronchial wall thickening (p \u003c 0.001), interlobular septal thickening (p = 0.009), crazy paving (p \u003c 0.001), tree-in-bud (p \u003c 0.001), pleural effusions (p \u003c 0.001), pleural thickening (p = 0.030), and the offending vessel augmentation in lesions (p \u003c 0.001). The lung score presents no significant difference between the COVID-19 and non-COVID-19 groups.\nComparison of clinical features between the two groups of patients with and without COVID-19 is reported in Table 2. There is no significant difference in age and sex between the two groups. Significant differences are found in common symptoms between groups, including fever (p = 0.003), dry cough (p = 0.025), and fatigue (p = 0.007). The respiration rate and heart rate also show significant differences between the two groups (both p \u003c 0.001). Compared with non-COVID-19 pneumonia, the reduction of the WBC count is more pronounced in COVID-19 patients (p \u003c 0.001). The ratio of lymphocyte and ratio of neutrophil also show a significant difference between COVID-19 and non-COVID-19 groups. Although lymphopenia was observed in 32 COVID-19 patients (45.71%), it is not statistically different compared with that in the non-COVID-19 group. C-creative protein (CRP) level and procalcitonin level are also significantly different between the two groups (p \u003c 0.001 and p = 0.007, respectively). Most COVID-19 patients present normal procalcitonin level (82.86%).\n\nClinical and radiological feature selection\nOf the features, 18 radiological features and 17 clinical features were selected to form the predictors based on the result from Tables 1 and 2. Table 3 lists the features selected by univariate analysis and LASSO.\nTable 3 Selected features in C, R, and CR models\nModel and individual features Coefficients\nR, n = 8 (41)*\n  Intercept − 0.307\n  Total number of mixed GGO in peripheral area 0.359\n  Total number of consolidation − 1.262\n  Total number of solid nodules with ground-glass opacities 0.452\n  Interlobular septal thickening − 5.559\n  Crazy paving pattern 3.566\n  Tree-in-bud − 2.548\n  Pleural thickening 3.265\n  Offending vessel augmentation in lesions 5.504\nC, n = 7 (26)*\n  Intercept 29.273\n  Respiration − 0.359\n  Heart rate − 0.054\n  Temperature − 0.289\n  White blood cell count − 0.175\n  Cough − 1.866\n  Fatigue 2.855\n  Lymphocyte count category − 0.028\nCR, n = 10 (67)*\n  Intercept 45.117\n  Total number of mixed GGO in peripheral area 0.108\n  Tree-in-bud − 1.853\n  Offending vessel augmentation in lesions 6.000\n  Respiration − 0.583\n  Heart ratio − 0.084\n  Temperature − 0.536\n  White blood cell count − 0.471\n  Cough − 0.997\n  Fatigue − 0.228\n  Lymphocyte count category − 2.177\nC, R, and CR indicate the predicted model based on clinical features, radiological features, and the combination of clinical features and clinical radiological features, respectively\n*n means corresponding selected features, and data in parentheses are total features. Coefficients: the estimate value of each feature in multivariate logistic regression model by “glm” package in R\n\nModel development and validation\nThe prediction models based on (i) clinical features (C model), (ii) radiological features (R model), and (iii) the combination of clinical features and radiological features (CR model) were developed. ROC analyses for the primary and validation cohort are shown in Table 4 and Fig. 3. The CR model yielded a maximum AUC of 0.986 (95% CI 0.966~1.000) in the primary cohort with the highest accuracy and specificity, which was 0.936 (95% CI 0.866~1.000) in the validation cohort. The AUC for the C model was 0.952 (95% CI 0.988~0.915) and 0.967 (95% CI 0.919~1.000) in the primary and validation cohorts, respectively. For the R model, the AUC of the two cohorts was 0.969 (95% CI 0.940~0.997) and 0.809 (95% CI 0.669~0.948), respectively.\nTable 4 Performance of the individualized prediction models\nPrimary cohort (n = 98) Validation cohort (n = 38)\nModels AUC 95% CI Accuracy Specificity Sensitivity AUC 95% CI Accuracy Specificity Sensitivity\nC model 0.952 0.915~0.988 0.888 0.894 0.882 0.967 0.919~1.000 0.868 0.859 0.842\nR model 0.969 0.940~0.997 0.929 0.851 1.000 0.809 0.669~0.948 0.684 0.368 1.000\nCR model 0.986 0.966~1.000 0.959 0.957 0.961 0.936 0.866~1.000 0.763 0.789 0.737\nC, R, and CR indicate the predicted model based on clinical features, radiological features, and the combination of clinical features and clinical radiological features, respectively. CI confidence interval\nFig. 3 ROC of the three models in primary and validation cohort curves. Comparison of receiver operating characteristic (ROC) curves among the radiological mode (R model), clinical model (C model), and the combination of clinical and radiological model (CR model) for the diagnosis of COVID-19 in the primary (a) and validation (b) cohorts\nTo determine the clinical usefulness of the diagnostic model, we developed the decision curve (Fig. 4), which showed better performances for the CR model compared with that for the C model and the R model. Across the majority of the range of reasonable threshold probabilities, the decision curve analysis showed that the CR model had a higher overall benefit than the C model and R model.\nFig. 4 Decision curve analysis for each model in the primary dataset. The y-axis measures the net benefit, which is calculated by summing the benefits (true-positive findings) and subtracting the harms (false-positive findings), weighting the latter by a factor related to the relative harm of undetected metastasis compared with the harm of unnecessary treatment. The decision curve shows that if the threshold probability is over 10%, the application of the combination of clinical and radiological model (CR model) to diagnose COVID-19 adds more benefit than the clinical model (C model) and radiological model (R model)\nThe nomogram (Fig. 5) was developed by the CR model in the primary cohort, with the factors of the total number of mixed GGO in peripheral area (TN_Mixed_GGO_IP), tree-in-bud, offending vessel augmentation in lesions (OVAIL), respiration, heart ratio, temperature, white blood cell count, cough, fatigue and lymphocyte count category incorporated. The total points were calculated by summing the points identified on the “points” scale for each factor. By comparing the “total points” scale and the “probability” scale, the individual probability of COVID-19 infection could be obtained.\nFig. 5 Nomogram of the CR model in the primary cohort. TN_Mixed_GGO_IP represented the total number of mixed GGO in peripheral area. AVAIL represented offending vessel segmentation in lesions. N was a negative result, and P was a positive result. Norm represented normal. 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