Establishment of a predictive nomogram and its validation for severe adenovirus pneumonia in children

Abstract

Background: Severe adenovirus pneumonia (SAP) of children is prone to multi-system complications, has the high mortality rate and high incidence of sequelae. Severity prediction can facilitate an adequate individualized treatment plan. Our study try to develop and evaluate a predictive nomogram for children with SAP.Methods: An observational study was designed and performed retrospectively. The data were categorized as training and validation datasets using the method of credible random split-sample (split ratio =0.7:0.3).The predictors were selected using Lasso (least absolute shrinkage and selection operator) logistic regression and the nomogram was developed. Nomogram discrimination was assessed using the receiver operating characteristic (ROC) curve, and the prediction accuracy was evaluated using a calibration curve. The nomogram was also evaluated for clinical effectiveness by the decision curve analysis (DCA). A P value of <0.05 was deemed statistically significant.Results: The identified predictors were fever duration, and interleukin-6 and CD4+ T cells and were assembled into the nomogram. The nomogram exhibited good discrimination with area under ROC curve in training dataset (0.79, 95% CI: 0.60-0.92) and test dataset (0.76, 95% CI: 0.63-0.87). The nomogram seems to be useful clinically as per DCA.Conclusions: A nomogram with a potentially effective application was developed to facilitate individualized prediction for SAP in children.

Roles of Interleukin-6-mediated immunometabolic reprogramming in COVID-19 and other viral infection-associated diseases

“…While markedly elevated levels of IL-6 are identified as a warning indicator for poor prognosis of AdV pneumonia, the high level of IL-6 (>100 ng/L) serves as an independent risk factor for death in children with critical AdV pneumonia [131] . Moreover, the level of IL-6 in children with AdV pneumonia and kawasaki disease is significantly higher than that in those with other type of viral pneumonia [132] . Overall, IL-6 could be an independent predictive factor for childhood AdV pneumonia.…”

Clinical model of pulmonary metastasis in patients with osteosarcoma: A new multiple machine learning-based risk prediction

“…The black line indicates the net benefit assuming that no patients had pulmonary metastasis. The net benefit was estimated by subtracting the proportion of patients who tested false positive from the true positive proportion, weighted by the relative harm of not getting treated in comparison to the adverse outcomes of an unrequired treatment 21,22 . The decision curve indicates that when the threshold probability is >20% and <60%, there is greater advantage using the nomogram to predict pulmonary metastasis of osteosarcoma.

Figure 8.Clinical Impact Curve of nomogram.

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“…In conclusion, our study developed and validated a novel nomogram for predicting pulmonary metastasis in patients with osteosarcoma, involving N Stage, ALP, TSH and FT3. Decision curve analysis (DCA) and clinical impact curve 21,22 . The decision curve indicates that when the threshold probability is >20% and <60%, there is greater advantage using the nomogram to predict pulmonary metastasis of osteosarcoma.…”

The Clinical Risk Factors of Adenovirus Pneumonia in Children Based on the Logistic Regression Model: Correlation with Lactate Dehydrogenase

“…AUC value of the model was 0.981, and the model had high accuracy. Yang et al screened predictors for adenovirus pneumonia applying LASSO regression and developed nomogram, followed by the evaluation of nomogram discrimination via ROC curves, whose AUC value is 0.79 [ 11 ]. Whereas, the prediction model constructed in our study had higher accuracy, and we subsequently explored the correlation between LDH level and adenovirus pneumonia in children.…”