Fig 1.
Description of study population.
This study was nested in a prospective fever etiology study conducted at the outpatient clinics of two district hospitals in Tanzania. The clinics function as primary care centres for local children. Children presenting with fever were enrolled consecutively, and clinical evaluation and investigations were performed according to pre-defined questionnaires and algorithms. Children with WHO-defined clinical pneumonia were included in the present study, with exclusions as illustrated, and categorized according to findings on chest x-ray (CXR): WHO-defined primary end-point pneumonia, other infiltrates, or normal CXR.
Table 1.
Demographic and clinical characteristics of study participants with WHO-defined clinical pneumonia, categorized by radiological findings.a
Fig 2.
Biomarkers of host response in febrile children with WHO-defined clinical pneumonia, categorized by radiological findings.
Plasma collected at presentation was assayed for biomarkers, and biomarker levels were compared between children with end-point pneumonia (End-point PNA; n = 30), other infiltrates (n = 31), and no abnormalities on chest x-ray (Normal CXR; n = 94). * p<0.05, ** p<0.01, and *** p<0.001 by Kruskal-Wallis test with Dunn’s post-tests. All other comparisons were not statistically significant. CHI3L1, Chitinase 3-like-1; CRP, C-reactive protein; CXR, chest x-ray; PCT, procalcitonin; PNA, pneumonia; sTie-2, soluble Tie-2; vWF, von Willebrand Factor; WBC, white blood cell.
Fig 3.
Adjusted associations for biomarkers of host response in children with abnormal versus normal chest x-ray (CXR).
Odds ratios were calculated comparing groups using multivariate logistic regression, adjusting for statistically significant demographic and clinical differences between groups. All markers except endoglin and WBC were log transformed since distributions were non-normal. Forest plots show odds ratio point estimates (black squares) and 95% confidence intervals (lines). (A) Odds ratios for end-point pneumonia versus normal CXR, adjusted for age and sex. (B) Odds ratios for other infiltrates versus normal CXR, adjusted for temperature. CHI3L1, Chitinase 3-like-1; CRP, C-reactive protein; CXR, chest x-ray; PCT, procalcitonin; sTie-2, soluble Tie-2; vWF, von Willebrand Factor; WBC, white blood cell.
Table 2.
Receiver operating characteristic (ROC) curves and cut-points of biomarkers that significantly discriminate between radiological findings.a
Table 3.
Classification and Regression Tree (CRT) models classify children with clinical pneumonia based on radiological findings.
Fig 4.
Classification and Regression Tree model uses biomarkers to discriminate between end-point pneumonia and non-end-point pneumonia.
Classification and Regression Tree (CRT) modelling was used to improve upon performance of single biomarkers for distinguishing between end-point pneumonia and non-end-point pneumonia (comprising “other infiltrates” and “normal chest x-ray (CXR)” groups combined). All 7 biomarkers were entered into the CRT analysis as independent variables. Minimum number of cases was designated as 10 for parent nodes (prior to split) and 5 for child nodes (following split). Children in terminal nodes of the tree were classified into the category indicated (i.e., “Predicted:…”). Shown here is a representation of Model 9 in Table 3. For this model, cut-points were pre-specified for CRP (40 μg/mL) and PCT (0.5 ng/mL) based on commercially available tests. Performance characteristics were as follows: sensitivity 93.3% (76.5–98.8), specificity 80.8% (72.6–87.1), positive likelihood ratio 4.9 (3.4–7.1), negative likelihood ratio 0.083 (0.022–0.32), positive predictive value 53.8% (39.6–67.5), negative predictive value 98.1% (92.5–99.7), misclassification risk 0.20 (standard error 0.038). CHI3L1, Chitinase 3-like-1; CRP, C-reactive protein; CRT, Classification and Regression Tree; CXR, chest x-ray; PCT, procalcitonin; PNA, pneumonia.