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ObjectiveTo construct and validate a predictive model for risk factors in children with severe adenoviral pneumonia based on chest low-dose CT imaging and clinical features.MethodsA total of 177 patients with adenoviral pneumonia who underwent low-dose CT examination were collected between January 2019 and August 2019. The assessment criteria for severe pneumonia were divided into mild group (N = 125) and severe group (N = 52). All cases divided into training cohort (N = 125) and validation cohort (N = 52). We constructed a prediction model by drawing a nomogram and verified the predictive efficacy of the model through the ROC curve, calibration curve and decision curve analysis.ResultsThe difference was statistically significant (P < 0.05) between the mild adenovirus pneumonia group and the severe adenovirus pneumonia group in gender, age, weight, body temperature, L/N ratio, LDH, ALT, AST, CK-MB, ADV DNA, bronchial inflation sign, emphysema, ground glass sign, bronchial wall thickening, bronchiectasis, pleural effusion, consolidation score, and lobular inflammation score. Multivariate logistic regression analysis showed that gender, LDH value, emphysema, consolidation score, and lobular inflammation score were severe independent risk factors for adenovirus pneumonia in children. Logistic regression was employed to construct clinical model, imaging semantic feature model, and combined model. The AUC values of the training sets of the three models were 0.85 (0.77–0.94), 0.83 (0.75–0.91), and 0.91 (0.85–0.97). The AUC of the validation set was 0.77 (0.64–0.91), 0.83 (0.71–0.94), and 0.85 (0.73–0.96), respectively. The calibration curve fit good of the three models. The clinical decision curve analysis demonstrates the clinical application value of the nomogram prediction model.ConclusionThe prediction model based on chest low-dose CT image characteristics and clinical characteristics has relatively clear predictive value in distinguishing mild adenovirus pneumonia from severe adenovirus pneumonia in children and might provide a new method for early clinical prediction of the outcome of adenovirus pneumonia in children.

Abstract Background Pneumonia is a serious public health issue and is concerned around the world. This study is to investigate the association between viral load in children with human adenovirus (HAdV) pneumonia and disease severity. Methods A total of 1313 cases of children hospitalized in Hunan Provincial People’s Hospital due to community acquired pneumonia (CAP) from April 2011 to May 2014 were enrolled in this study. Samples of nasopharyngeal aspirate were collected for the cohort. WHO criteria for CAP grading was emerged for pneumonia severity classification. Real-time fluorescence quantitative PCR (qRT-PCR) was used to detect 12 kinds of respiratory viruses. HAdV types were identified by nested PCR. The relationship between HAdV load and severity of disease was there by analyzed. Results Finally, 174 cases (174/1313, 13.3%) were positive for HAdV, and HAdV type 7 (HAdV-7) was the main serotype (76/174, 43.7%). Among the 174 cases, 70 cases were with HAdV infection alone and 104 cases were accompanied by other viruses. The patients were divided into mild pneumonia group (n = 108 cases) and severe pneumonia group (n = 66 cases). HAdV load of children in severe pneumonia group was higher than that in mild pneumonia group. Similar result was obtained in the 70 cases with HAdV infection alone after subgrouping. Relevant factors analysis results showed that severe pneumonia children presented lower onset age, more prone to fever, longer fever time, and longer hospital stay compared with that of mild pneumonia children. Children with HAdV-7 infection developed more frequently severe pneumonia. Multivariate regression analysis showed that HAdV load, age, and fever time were risk factors for pneumonia severity. Conclusion The severity of HAdV infection is significantly correlated with viral load and serotype.