Biomarkers of Host Response Predict Primary End-Point Radiological Pneumonia in Tanzanian Children with Clinical Pneumonia: A Prospective Cohort Study
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{"title"=>"Biomarkers of host response predict primary end-point radiological pneumonia in Tanzanian children with clinical pneumonia: A prospective cohort study", "type"=>"journal", "authors"=>[{"first_name"=>"Laura K.", "last_name"=>"Erdman", "scopus_author_id"=>"6602079981"}, {"first_name"=>"Valérie", "last_name"=>"D'Acremont", "scopus_author_id"=>"6602807740"}, {"first_name"=>"Kyla", "last_name"=>"Hayford", "scopus_author_id"=>"57188952666"}, {"first_name"=>"Nimerta", "last_name"=>"Rajwans", "scopus_author_id"=>"26428933500"}, {"first_name"=>"Mary", "last_name"=>"Kilowoko", "scopus_author_id"=>"56079740300"}, {"first_name"=>"Esther", "last_name"=>"Kyungu", "scopus_author_id"=>"56080132400"}, {"first_name"=>"Philipina", "last_name"=>"Hongoa", "scopus_author_id"=>"56966646300"}, {"first_name"=>"Leonor", "last_name"=>"Alamo", "scopus_author_id"=>"6701598340"}, {"first_name"=>"David L.", "last_name"=>"Streiner", "scopus_author_id"=>"35243874100"}, {"first_name"=>"Blaise", "last_name"=>"Genton", "scopus_author_id"=>"35501901200"}, {"first_name"=>"Kevin C.", "last_name"=>"Kain", "scopus_author_id"=>"7102091953"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84947440607", "pmid"=>"26366571", "pui"=>"606945916", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-84947440607", "doi"=>"10.1371/journal.pone.0137592"}, "id"=>"f8b9d23e-9097-30dd-8861-09caa3b69c84", "abstract"=>"BACKGROUND: Diagnosing pediatric pneumonia is challenging in low-resource settings. The World Health Organization (WHO) has defined primary end-point radiological pneumonia for use in epidemiological and vaccine studies. However, radiography requires expertise and is often inaccessible. We hypothesized that plasma biomarkers of inflammation and endothelial activation may be useful surrogates for end-point pneumonia, and may provide insight into its biological significance. METHODS: We studied children with WHO-defined clinical pneumonia (n = 155) within a prospective cohort of 1,005 consecutive febrile children presenting to Tanzanian outpatient clinics. Based on x-ray findings, participants were categorized as primary end-point pneumonia (n = 30), other infiltrates (n = 31), or normal chest x-ray (n = 94). Plasma levels of 7 host response biomarkers at presentation were measured by ELISA. Associations between biomarker levels and radiological findings were assessed by Kruskal-Wallis test and multivariable logistic regression. Biomarker ability to predict radiological findings was evaluated using receiver operating characteristic curve analysis and Classification and Regression Tree analysis. RESULTS: Compared to children with normal x-ray, children with end-point pneumonia had significantly higher C-reactive protein, procalcitonin and Chitinase 3-like-1, while those with other infiltrates had elevated procalcitonin and von Willebrand Factor and decreased soluble Tie-2 and endoglin. Clinical variables were not predictive of radiological findings. Classification and Regression Tree analysis generated multi-marker models with improved performance over single markers for discriminating between groups. A model based on C-reactive protein and Chitinase 3-like-1 discriminated between end-point pneumonia and non-end-point pneumonia with 93.3% sensitivity (95% confidence interval 76.5-98.8), 80.8% specificity (72.6-87.1), positive likelihood ratio 4.9 (3.4-7.1), negative likelihood ratio 0.083 (0.022-0.32), and misclassification rate 0.20 (standard error 0.038). CONCLUSIONS: In Tanzanian children with WHO-defined clinical pneumonia, combinations of host biomarkers distinguished between end-point pneumonia, other infiltrates, and normal chest x-ray, whereas clinical variables did not. These findings generate pathophysiological hypotheses and may have potential research and clinical utility.", "link"=>"http://www.mendeley.com/research/biomarkers-host-response-predict-primary-endpoint-radiological-pneumonia-tanzanian-children-clinical", "reader_count"=>33, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>5, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>3, "Student > Bachelor"=>3, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>1, "Librarian"=>1, "Researcher"=>5, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>2, "Student > Master"=>7, "Other"=>3, "Student > Bachelor"=>3, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Biochemistry, Genetics and Molecular Biology"=>2, "Medicine and Dentistry"=>23, "Agricultural and Biological Sciences"=>1, "Psychology"=>1, "Social Sciences"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>23}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"Switzerland"=>1}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2261209"], "description"=>"<p>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.</p>", "links"=>[], "tags"=>["von Willebrand factor", "World Health Organization", "Radiological Findings", "Prospective Cohort Study BackgroundDiagnosing", "7 host response biomarkers", "Host Response Predict", "Tanzanian outpatient clinics", "Regression tree analysis", "Regression Tree analysis.ResultsCompared", "elisa", "pneumonia", "misclassification rate 0.20"], "article_id"=>1541389, "categories"=>["Uncategorised"], "users"=>["Laura K. Erdman", "Valérie D’Acremont", "Kyla Hayford", "Nimerta Rajwans", "Mary Kilowoko", "Esther Kyungu", "Philipina Hongoa", "Leonor Alamo", "David L. Streiner", "Blaise Genton", "Kevin C. Kain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137592.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Biomarkers_of_host_response_in_febrile_children_with_WHO_defined_clinical_pneumonia_categorized_by_radiological_findings_/1541389", "title"=>"Biomarkers of host response in febrile children with WHO-defined clinical pneumonia, categorized by radiological findings.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-14 02:57:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2261208"], "description"=>"<p>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.</p>", "links"=>[], "tags"=>["von Willebrand factor", "World Health Organization", "Radiological Findings", "Prospective Cohort Study BackgroundDiagnosing", "7 host response biomarkers", "Host Response Predict", "Tanzanian outpatient clinics", "Regression tree analysis", "Regression Tree analysis.ResultsCompared", "elisa", "pneumonia", "misclassification rate 0.20"], "article_id"=>1541388, "categories"=>["Uncategorised"], "users"=>["Laura K. Erdman", "Valérie D’Acremont", "Kyla Hayford", "Nimerta Rajwans", "Mary Kilowoko", "Esther Kyungu", "Philipina Hongoa", "Leonor Alamo", "David L. Streiner", "Blaise Genton", "Kevin C. Kain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137592.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Description_of_study_population_/1541388", "title"=>"Description of study population.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-14 02:57:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2261217", "https://ndownloader.figshare.com/files/2261218", "https://ndownloader.figshare.com/files/2261219", "https://ndownloader.figshare.com/files/2261220", "https://ndownloader.figshare.com/files/2261221", "https://ndownloader.figshare.com/files/2261222", "https://ndownloader.figshare.com/files/2261223"], "description"=>"<div><p>Background</p><p>Diagnosing pediatric pneumonia is challenging in low-resource settings. The World Health Organization (WHO) has defined primary end-point radiological pneumonia for use in epidemiological and vaccine studies. However, radiography requires expertise and is often inaccessible. We hypothesized that plasma biomarkers of inflammation and endothelial activation may be useful surrogates for end-point pneumonia, and may provide insight into its biological significance.</p><p>Methods</p><p>We studied children with WHO-defined clinical pneumonia (n = 155) within a prospective cohort of 1,005 consecutive febrile children presenting to Tanzanian outpatient clinics. Based on x-ray findings, participants were categorized as primary end-point pneumonia (n = 30), other infiltrates (n = 31), or normal chest x-ray (n = 94). Plasma levels of 7 host response biomarkers at presentation were measured by ELISA. Associations between biomarker levels and radiological findings were assessed by Kruskal-Wallis test and multivariable logistic regression. Biomarker ability to predict radiological findings was evaluated using receiver operating characteristic curve analysis and Classification and Regression Tree analysis.</p><p>Results</p><p>Compared to children with normal x-ray, children with end-point pneumonia had significantly higher C-reactive protein, procalcitonin and Chitinase 3-like-1, while those with other infiltrates had elevated procalcitonin and von Willebrand Factor and decreased soluble Tie-2 and endoglin. Clinical variables were not predictive of radiological findings. Classification and Regression Tree analysis generated multi-marker models with improved performance over single markers for discriminating between groups. A model based on C-reactive protein and Chitinase 3-like-1 discriminated between end-point pneumonia and non-end-point pneumonia with 93.3% sensitivity (95% confidence interval 76.5–98.8), 80.8% specificity (72.6–87.1), positive likelihood ratio 4.9 (3.4–7.1), negative likelihood ratio 0.083 (0.022–0.32), and misclassification rate 0.20 (standard error 0.038).</p><p>Conclusions</p><p>In Tanzanian children with WHO-defined clinical pneumonia, combinations of host biomarkers distinguished between end-point pneumonia, other infiltrates, and normal chest x-ray, whereas clinical variables did not. These findings generate pathophysiological hypotheses and may have potential research and clinical utility.</p></div>", "links"=>[], "tags"=>["von Willebrand factor", "World Health Organization", "Radiological Findings", "Prospective Cohort Study BackgroundDiagnosing", "7 host response biomarkers", "Host Response Predict", "Tanzanian outpatient clinics", "Regression tree analysis", "Regression Tree analysis.ResultsCompared", "elisa", "pneumonia", "misclassification rate 0.20"], "article_id"=>1541397, "categories"=>["Uncategorised"], "users"=>["Laura K. Erdman", "Valérie D’Acremont", "Kyla Hayford", "Nimerta Rajwans", "Mary Kilowoko", "Esther Kyungu", "Philipina Hongoa", "Leonor Alamo", "David L. Streiner", "Blaise Genton", "Kevin C. Kain"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0137592.s001", "https://dx.doi.org/10.1371/journal.pone.0137592.s002", "https://dx.doi.org/10.1371/journal.pone.0137592.s003", "https://dx.doi.org/10.1371/journal.pone.0137592.s004", "https://dx.doi.org/10.1371/journal.pone.0137592.s005", "https://dx.doi.org/10.1371/journal.pone.0137592.s006", "https://dx.doi.org/10.1371/journal.pone.0137592.s007"], "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Biomarkers_of_Host_Response_Predict_Primary_End_Point_Radiological_Pneumonia_in_Tanzanian_Children_with_Clinical_Pneumonia_A_Prospective_Cohort_Study_/1541397", "title"=>"Biomarkers of Host Response Predict Primary End-Point Radiological Pneumonia in Tanzanian Children with Clinical Pneumonia: A Prospective Cohort Study", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-09-14 02:57:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2261213"], "description"=>"<p>Abbreviations: AUROCC, area under receiver operating characteristic curve; CHI3L1, Chitinase 3-like-1; CRP, C-reactive protein; CXR, chest x-ray; NLR, negative likelihood ratio; NPV, negative predictive value; PCT, procalcitonin; PLR, positive likelihood ratio; PPV, positive predictive value; ROC, receiver operating characteristic curve; sTie-2, soluble Tie-2; vWF, von Willebrand Factor.</p><p><sup>a</sup> Children with WHO-defined clinical pneumonia were categorized based on CXR findings: end-point pneumonia, other infiltrates, or normal CXR.</p><p><sup>b</sup> Cut-points based on Youden index: J = max(sensitivity + specificity – 1).</p><p>Receiver operating characteristic (ROC) curves and cut-points of biomarkers that significantly discriminate between radiological findings.<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137592#t002fn002\" target=\"_blank\"><sup>a</sup></a></p>", "links"=>[], "tags"=>["von Willebrand factor", "World Health Organization", "Radiological Findings", "Prospective Cohort Study BackgroundDiagnosing", "7 host response biomarkers", "Host Response Predict", "Tanzanian outpatient clinics", "Regression tree analysis", "Regression Tree analysis.ResultsCompared", "elisa", "pneumonia", "misclassification rate 0.20"], "article_id"=>1541393, "categories"=>["Uncategorised"], "users"=>["Laura K. Erdman", "Valérie D’Acremont", "Kyla Hayford", "Nimerta Rajwans", "Mary Kilowoko", "Esther Kyungu", "Philipina Hongoa", "Leonor Alamo", "David L. Streiner", "Blaise Genton", "Kevin C. Kain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137592.t002", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Receiver_operating_characteristic_ROC_curves_and_cut_points_of_biomarkers_that_significantly_discriminate_between_radiological_findings_a_/1541393", "title"=>"Receiver operating characteristic (ROC) curves and cut-points of biomarkers that significantly discriminate between radiological findings.<sup>a</sup>", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-09-14 02:57:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2261211"], "description"=>"<p>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 <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137592#pone.0137592.t003\" target=\"_blank\">Table 3</a>. 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.</p>", "links"=>[], "tags"=>["von Willebrand factor", "World Health Organization", "Radiological Findings", "Prospective Cohort Study BackgroundDiagnosing", "7 host response biomarkers", "Host Response Predict", "Tanzanian outpatient clinics", "Regression tree analysis", "Regression Tree analysis.ResultsCompared", "elisa", "pneumonia", "misclassification rate 0.20"], "article_id"=>1541391, "categories"=>["Uncategorised"], "users"=>["Laura K. Erdman", "Valérie D’Acremont", "Kyla Hayford", "Nimerta Rajwans", "Mary Kilowoko", "Esther Kyungu", "Philipina Hongoa", "Leonor Alamo", "David L. Streiner", "Blaise Genton", "Kevin C. Kain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137592.g004", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_and_Regression_Tree_model_uses_biomarkers_to_discriminate_between_end_point_pneumonia_and_non_end_point_pneumonia_/1541391", "title"=>"Classification and Regression Tree model uses biomarkers to discriminate between end-point pneumonia and non-end-point pneumonia.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-14 02:57:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2261212"], "description"=>"<p>Abbreviations: CXR, chest x-ray.</p><p><sup>a</sup> Kruskal-Wallis test with Dunn’s post-tests used to compare continuous variables; Chi-square test with Bonferroni correction used to compare categorical variables.</p><p>*, p<0.05 for other infiltrates versus end-point pneumonia.</p><p><sup>#</sup>, p<0.05 for normal CXR versus end-point pneumonia. Other comparisons were not significantly different.</p><p><sup>b</sup> All continuous variables had non-normal distributions and are represented as: Median [Interquartile range].</p><p><sup>c</sup> Study sites were located in Dar es Salaam and Ifakara.</p><p><sup>d</sup> Severe disease defined according to WHO criteria for the district hospital level.</p><p><sup>e</sup> Participants were subdivided according to age, as normal values for respiratory rate are age-dependent [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137592#pone.0137592.ref007\" target=\"_blank\">7</a>].</p><p><sup>f</sup> One child in the normal CXR group did not have a <i>S</i>. <i>pneumoniae</i> swab performed.</p><p>Demographic and clinical characteristics of study participants with WHO-defined clinical pneumonia, categorized by radiological findings.<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137592#t001fn002\" target=\"_blank\"><sup>a</sup></a></p>", "links"=>[], "tags"=>["von Willebrand factor", "World Health Organization", "Radiological Findings", "Prospective Cohort Study BackgroundDiagnosing", "7 host response biomarkers", "Host Response Predict", "Tanzanian outpatient clinics", "Regression tree analysis", "Regression Tree analysis.ResultsCompared", "elisa", "pneumonia", "misclassification rate 0.20"], "article_id"=>1541392, "categories"=>["Uncategorised"], "users"=>["Laura K. Erdman", "Valérie D’Acremont", "Kyla Hayford", "Nimerta Rajwans", "Mary Kilowoko", "Esther Kyungu", "Philipina Hongoa", "Leonor Alamo", "David L. Streiner", "Blaise Genton", "Kevin C. Kain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137592.t001", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Demographic_and_clinical_characteristics_of_study_participants_with_WHO_defined_clinical_pneumonia_categorized_by_radiological_findings_a_/1541392", "title"=>"Demographic and clinical characteristics of study participants with WHO-defined clinical pneumonia, categorized by radiological findings.<sup>a</sup>", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-09-14 02:57:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2261210"], "description"=>"<p>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.</p>", "links"=>[], "tags"=>["von Willebrand factor", "World Health Organization", "Radiological Findings", "Prospective Cohort Study BackgroundDiagnosing", "7 host response biomarkers", "Host Response Predict", "Tanzanian outpatient clinics", "Regression tree analysis", "Regression Tree analysis.ResultsCompared", "elisa", "pneumonia", "misclassification rate 0.20"], "article_id"=>1541390, "categories"=>["Uncategorised"], "users"=>["Laura K. Erdman", "Valérie D’Acremont", "Kyla Hayford", "Nimerta Rajwans", "Mary Kilowoko", "Esther Kyungu", "Philipina Hongoa", "Leonor Alamo", "David L. Streiner", "Blaise Genton", "Kevin C. Kain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137592.g003", "stats"=>{"downloads"=>0, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Adjusted_associations_for_biomarkers_of_host_response_in_children_with_abnormal_versus_normal_chest_x_ray_CXR_/1541390", "title"=>"Adjusted associations for biomarkers of host response in children with abnormal versus normal chest x-ray (CXR).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-14 02:57:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2261214"], "description"=>"<p>Abbreviations: CHI3L1, Chitinase 3-like-1; CRP, C-reactive protein; CRT, Classification and Regression Tree; CXR, chest x-ray; MC, misclassification cost; NLR, negative likelihood ratio; NPV, negative predictive value; PCT, procalcitonin; PLR, positive likelihood ratio; PPV, positive predictive value; sTie-2, soluble Tie-2; vWF, von Willebrand Factor.</p><p><sup>a</sup> Minimum number of cases was 10 per parent node (prior to split) and 5 per child node (following split). Misclassification cost is how many fold worse it is to misclassify the group of interest versus the other group. Pruning reduces overfitting by trimming trees down to simpler structures.</p><p><sup>b</sup> Generated using cross-validation with 10 sample folds.</p><p><sup>c</sup> x2 = model contains 2 different cut-points for a single biomarker.</p><p><sup>d</sup> Other infiltrates and normal CXR groups were combined into “non-end-point pneumonia.”</p><p><sup>e</sup> CRP and PCT were entered as categorical variables based on cut-points used in available point-of-care tests: CRP 40 μg/mL and PCT 0.5 ng/mL.</p><p>Classification and Regression Tree (CRT) models classify children with clinical pneumonia based on radiological findings.</p>", "links"=>[], "tags"=>["von Willebrand factor", "World Health Organization", "Radiological Findings", "Prospective Cohort Study BackgroundDiagnosing", "7 host response biomarkers", "Host Response Predict", "Tanzanian outpatient clinics", "Regression tree analysis", "Regression Tree analysis.ResultsCompared", "elisa", "pneumonia", "misclassification rate 0.20"], "article_id"=>1541394, "categories"=>["Uncategorised"], "users"=>["Laura K. Erdman", "Valérie D’Acremont", "Kyla Hayford", "Nimerta Rajwans", "Mary Kilowoko", "Esther Kyungu", "Philipina Hongoa", "Leonor Alamo", "David L. Streiner", "Blaise Genton", "Kevin C. Kain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0137592.t003", "stats"=>{"downloads"=>2, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_and_Regression_Tree_CRT_models_classify_children_with_clinical_pneumonia_based_on_radiological_findings_/1541394", "title"=>"Classification and Regression Tree (CRT) models classify children with clinical pneumonia based on radiological findings.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-09-14 02:57:42"}

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  • {"unique-ip"=>"14", "full-text"=>"14", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
  • {"unique-ip"=>"12", "full-text"=>"13", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
  • {"unique-ip"=>"21", "full-text"=>"20", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"26", "full-text"=>"24", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"15", "full-text"=>"16", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"12", "full-text"=>"10", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"13", "full-text"=>"14", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"9", "full-text"=>"11", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"13", "full-text"=>"22", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"12", "full-text"=>"12", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
  • {"unique-ip"=>"18", "full-text"=>"15", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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