Decision Tree Algorithms Predict the Diagnosis and Outcome of Dengue Fever in the Early Phase of Illness
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{"title"=>"Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness", "type"=>"journal", "authors"=>[{"first_name"=>"Lukas", "last_name"=>"Tanner", "scopus_author_id"=>"57197265612"}, {"first_name"=>"Mark", "last_name"=>"Schreiber", "scopus_author_id"=>"14029100800"}, {"first_name"=>"Jenny G.H.", "last_name"=>"Low", "scopus_author_id"=>"8538590800"}, {"first_name"=>"Adrian", "last_name"=>"Ong", "scopus_author_id"=>"16834803700"}, {"first_name"=>"Thomas", "last_name"=>"Tolfvenstam", "scopus_author_id"=>"6603596680"}, {"first_name"=>"Yee Ling", "last_name"=>"Lai", "scopus_author_id"=>"15757514700"}, {"first_name"=>"Lee Ching", "last_name"=>"Ng", "scopus_author_id"=>"35574464200"}, {"first_name"=>"Yee Sin", "last_name"=>"Leo", "scopus_author_id"=>"7004240142"}, {"first_name"=>"Le Thi", "last_name"=>"Puong", "scopus_author_id"=>"24470722200"}, {"first_name"=>"Subhash G.", "last_name"=>"Vasudevan", "scopus_author_id"=>"35498684400"}, {"first_name"=>"Cameron P.", "last_name"=>"Simmons", "scopus_author_id"=>"7103173872"}, {"first_name"=>"Martin L.", "last_name"=>"Hibberd", "scopus_author_id"=>"7006197568"}, {"first_name"=>"Eng Eong", "last_name"=>"Ooi", "scopus_author_id"=>"7004519587"}], "year"=>2008, "source"=>"PLoS Neglected Tropical Diseases", "identifiers"=>{"issn"=>"1935-2735", "isbn"=>"1935-2735 (Electronic)\\r1935-2727 (Linking)", "pui"=>"352002946", "sgr"=>"48949118005", "doi"=>"10.1371/journal.pntd.0000196", "scopus"=>"2-s2.0-48949118005", "pmid"=>"18335069"}, "id"=>"973f9e34-673d-3331-95aa-ca0ad8ae3396", "abstract"=>"BACKGROUND Dengue is re-emerging throughout the tropical world, causing frequent recurrent epidemics. The initial clinical manifestation of dengue often is confused with other febrile states confounding both clinical management and disease surveillance. Evidence-based triage strategies that identify individuals likely to be in the early stages of dengue illness can direct patient stratification for clinical investigations, management, and virological surveillance. Here we report the identification of algorithms that differentiate dengue from other febrile illnesses in the primary care setting and predict severe disease in adults. METHODS AND FINDINGS A total of 1,200 patients presenting in the first 72 hours of acute febrile illness were recruited and followed up for up to a 4-week period prospectively; 1,012 of these were recruited from Singapore and 188 from Vietnam. Of these, 364 were dengue RT-PCR positive; 173 had dengue fever, 171 had dengue hemorrhagic fever, and 20 had dengue shock syndrome as final diagnosis. Using a C4.5 decision tree classifier for analysis of all clinical, haematological, and virological data, we obtained a diagnostic algorithm that differentiates dengue from non-dengue febrile illness with an accuracy of 84.7%. The algorithm can be used differently in different disease prevalence to yield clinically useful positive and negative predictive values. Furthermore, an algorithm using platelet count, crossover threshold value of a real-time RT-PCR for dengue viral RNA, and presence of pre-existing anti-dengue IgG antibodies in sequential order identified cases with sensitivity and specificity of 78.2% and 80.2%, respectively, that eventually developed thrombocytopenia of 50,000 platelet/mm(3) or less, a level previously shown to be associated with haemorrhage and shock in adults with dengue fever. CONCLUSION This study shows a proof-of-concept that decision algorithms using simple clinical and haematological parameters can predict diagnosis and prognosis of dengue disease, a finding that could prove useful in disease management and surveillance.", "link"=>"http://www.mendeley.com/research/decision-tree-algorithms-predict-diagnosis-outcome-dengue-fever-early-phase-illness", "reader_count"=>173, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>6, "Student > Doctoral Student"=>6, "Researcher"=>43, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>13, "Other"=>10, "Student > Master"=>29, "Student > Bachelor"=>23, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>6, "Student > Doctoral Student"=>6, "Researcher"=>43, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>13, "Other"=>10, "Student > Master"=>29, "Student > Bachelor"=>23, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>8, "Agricultural and Biological Sciences"=>48, "Veterinary Science and Veterinary Medicine"=>2, "Business, Management and Accounting"=>2, "Computer Science"=>23, "Earth and Planetary Sciences"=>1, "Engineering"=>6, "Environmental Science"=>1, "Nursing and Health Professions"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Mathematics"=>1, "Medicine and Dentistry"=>69, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Social Sciences"=>2, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>69}, "Social Sciences"=>{"Social Sciences"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>8}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Engineering"=>{"Engineering"=>6}, "Neuroscience"=>{"Neuroscience"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>48}, "Computer Science"=>{"Computer Science"=>23}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "Hong Kong"=>1, "United States"=>3, "United Kingdom"=>2, "Malaysia"=>2, "French Polynesia"=>1, "India"=>1, "Vietnam"=>1, "Bangladesh"=>1, "Korea (South)"=>1, "Brazil"=>3, "Mexico"=>1, "France"=>1, "Indonesia"=>1}, "group_count"=>14}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/936001"], "description"=>"†<p>indicates cases with DHF/SBP<90mmHg/serum transaminase>1000/transfusion.</p>*<p>p<0.05 and ** p<0.001 when compared to either probable severe dengue or likely severe dengue. # indicates p<0.05 when compared to the probable severe dengue only.</p>", "links"=>[], "tags"=>["cases", "days", "hospitalised", "clinically", "dengue", "eden", "grouped", "prognosis", "algorithm"], "article_id"=>606416, "categories"=>["Infectious Diseases"], "users"=>["Lukas Tanner", "Mark Schreiber", "Jenny G. H. Low", "Adrian Ong", "Thomas Tolfvenstam", "Yee Ling Lai", "Lee Ching Ng", "Yee Sin Leo", "Le Thi Puong", "Subhash G. Vasudevan", "Cameron P. Simmons", "Martin L. Hibberd", "Eng Eong Ooi"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000196.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_cases_hospitalised_mean_number_of_days_hospitalised_and_the_number_of_clinically_severe_dengue_cases_from_the_EDEN_cohort_grouped_according_to_the_dengue_case_prognosis_algorithm_shown_in_Figure_3A_/606416", "title"=>"Number of cases hospitalised; mean number of days hospitalised and the number of clinically severe dengue cases from the EDEN cohort, grouped according to the dengue case prognosis algorithm shown in Figure 3A.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-03-12 01:46:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/935926"], "description"=>"<p>A. Receiver operating characteristics (ROC) curve for the algorithm in predicting the development of severe disease among dengue cases. B. Summary of K-fold (k = 10) cross-validation for severity prediction algorithm with 2×2 analysis for the algorithm's sensitivity and specificity in predicting severe dengue disease.</p>", "links"=>[], "tags"=>["algorithm", "predicting", "dengue"], "article_id"=>606354, "categories"=>["Infectious Diseases"], "users"=>["Lukas Tanner", "Mark Schreiber", "Jenny G. H. Low", "Adrian Ong", "Thomas Tolfvenstam", "Yee Ling Lai", "Lee Ching Ng", "Yee Sin Leo", "Le Thi Puong", "Subhash G. Vasudevan", "Cameron P. Simmons", "Martin L. Hibberd", "Eng Eong Ooi"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000196.g004", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_decision_algorithm_for_predicting_severe_dengue_disease_/606354", "title"=>"Performance of the decision algorithm for predicting severe dengue disease.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-03-12 01:45:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/460844", "https://ndownloader.figshare.com/files/460882"], "description"=>"<div><h3>Background</h3><p>Dengue is re-emerging throughout the tropical world, causing frequent recurrent epidemics. The initial clinical manifestation of dengue often is confused with other febrile states confounding both clinical management and disease surveillance. Evidence-based triage strategies that identify individuals likely to be in the early stages of dengue illness can direct patient stratification for clinical investigations, management, and virological surveillance. Here we report the identification of algorithms that differentiate dengue from other febrile illnesses in the primary care setting and predict severe disease in adults.</p><h3>Methods and Findings</h3><p>A total of 1,200 patients presenting in the first 72 hours of acute febrile illness were recruited and followed up for up to a 4-week period prospectively; 1,012 of these were recruited from Singapore and 188 from Vietnam. Of these, 364 were dengue RT-PCR positive; 173 had dengue fever, 171 had dengue hemorrhagic fever, and 20 had dengue shock syndrome as final diagnosis. Using a C4.5 decision tree classifier for analysis of all clinical, haematological, and virological data, we obtained a diagnostic algorithm that differentiates dengue from non-dengue febrile illness with an accuracy of 84.7%. The algorithm can be used differently in different disease prevalence to yield clinically useful positive and negative predictive values. Furthermore, an algorithm using platelet count, crossover threshold value of a real-time RT-PCR for dengue viral RNA, and presence of pre-existing anti-dengue IgG antibodies in sequential order identified cases with sensitivity and specificity of 78.2% and 80.2%, respectively, that eventually developed thrombocytopenia of 50,000 platelet/mm<sup>3</sup> or less, a level previously shown to be associated with haemorrhage and shock in adults with dengue fever.</p><h3>Conclusion</h3><p>This study shows a proof-of-concept that decision algorithms using simple clinical and haematological parameters can predict diagnosis and prognosis of dengue disease, a finding that could prove useful in disease management and surveillance.</p></div>", "links"=>[], "tags"=>["algorithms", "dengue"], "article_id"=>150803, "categories"=>["Cancer"], "users"=>["Lukas Tanner", "Mark Schreiber", "Jenny G. H. Low", "Adrian Ong", "Thomas Tolfvenstam", "Yee Ling Lai", "Lee Ching Ng", "Yee Sin Leo", "Le Thi Puong", "Subhash G. Vasudevan", "Cameron P. Simmons", "Martin L. Hibberd", "Eng Eong Ooi"], "doi"=>["https://dx.doi.org/10.1371/journal.pntd.0000196.s001", "https://dx.doi.org/10.1371/journal.pntd.0000196.s002"], "stats"=>{"downloads"=>9, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Decision_Tree_Algorithms_Predict_the_Diagnosis_and_Outcome_of_Dengue_Fever_in_the_Early_Phase_of_Illness/150803", "title"=>"Decision Tree Algorithms Predict the Diagnosis and Outcome of Dengue Fever in the Early Phase of Illness", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2008-03-12 00:13:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/935814"], "description"=>"<p>A. Decision algorithm for severity prediction calculated on 169 patients with clinical data obtained at the first visit. PLT = <i>platelet count</i>; Ct  = <i>viral load whereby a high Ct-value indicates a low viral load;</i> DV IgG = <i>indicator for primary/secondary infection whereby a positive result indicates a secondary infection.</i> Low = <i>platelet nadir of 50,000/mm<sup>3</sup> or less</i>; high = <i>platelet nadir greater than 50,000/mm<sup>3</sup></i>. The prediction of the algorithm is shown in colours: Red indicates probably severe dengue; brown indicates likely severe dengue; green indicates likely non-severe dengue and blue indicates probable non-severe dengue B. Statistical (chi square) analysis of splitting criteria performed on each subgroup at the decision nodes. OR = <i>odds ratio</i>; CI = 95% <i>confidence interval.</i>. PLT = <i>platelet count</i>; Ct = <i>crossover threshold value of real-time RT-PCR and indicative of level of viremia;</i> DV IgG = <i>indicator for primary/secondary infection whereby a positive result indicates a secondary infection</i>; OR = <i>odds ratio</i>; CI = <i>confidence interval</i>.</p>", "links"=>[], "tags"=>["algorithm", "predicting", "dengue"], "article_id"=>606245, "categories"=>["Infectious Diseases"], "users"=>["Lukas Tanner", "Mark Schreiber", "Jenny G. H. Low", "Adrian Ong", "Thomas Tolfvenstam", "Yee Ling Lai", "Lee Ching Ng", "Yee Sin Leo", "Le Thi Puong", "Subhash G. Vasudevan", "Cameron P. Simmons", "Martin L. Hibberd", "Eng Eong Ooi"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000196.g003", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Decision_algorithm_for_predicting_severe_dengue_disease_/606245", "title"=>"Decision algorithm for predicting severe dengue disease.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-03-12 01:44:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/935545"], "description"=>"<p>A. Decision algorithm for predicting dengue diagnosis calculated on 1200 patients with data obtained in the first 72 hours of illness. PLT = <i>platelet count</i>; WBC = <i>white blood cell count</i>; T = <i>body temperature</i>; HCT = <i>hematocrit</i>; Lymphocyte = <i>absolute number of lymphocytes</i>; Neutrophil = <i>absolute number of neutrophils</i>. The prediction of the algorithm is shown in colours: Red indicates probable dengue; brown indicate likely dengue; green indicates likely non-dengue and blue indicates probably non-dengue. B. Statistical (chi-square) analysis of splitting criteria performed on each subgroup at the decision nodes. OR = <i>odds ratio</i>; CI = 95% <i>confidence interval.</i></p>", "links"=>[], "tags"=>["algorithm", "dengue"], "article_id"=>605977, "categories"=>["Infectious Diseases"], "users"=>["Lukas Tanner", "Mark Schreiber", "Jenny G. H. Low", "Adrian Ong", "Thomas Tolfvenstam", "Yee Ling Lai", "Lee Ching Ng", "Yee Sin Leo", "Le Thi Puong", "Subhash G. Vasudevan", "Cameron P. Simmons", "Martin L. Hibberd", "Eng Eong Ooi"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000196.g001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Decision_algorithm_for_dengue_diagnosis_/605977", "title"=>"Decision algorithm for dengue diagnosis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-03-12 01:39:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/935679"], "description"=>"<p>A. Receiver operating characteristics (ROC) curve for the diagnostic algorithm in predicting dengue positive and dengue negative cases. B. Summary of K-fold (k = 10) cross-validation analysis for the dengue diagnostic algorithm with 2×2 analysis for the algorithm's sensitivity and specificity in dengue diagnosis.</p>", "links"=>[], "tags"=>["algorithm", "dengue"], "article_id"=>606109, "categories"=>["Infectious Diseases"], "users"=>["Lukas Tanner", "Mark Schreiber", "Jenny G. H. Low", "Adrian Ong", "Thomas Tolfvenstam", "Yee Ling Lai", "Lee Ching Ng", "Yee Sin Leo", "Le Thi Puong", "Subhash G. Vasudevan", "Cameron P. Simmons", "Martin L. Hibberd", "Eng Eong Ooi"], "doi"=>"https://dx.doi.org/10.1371/journal.pntd.0000196.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_decision_algorithm_for_dengue_diagnosis_/606109", "title"=>"Performance of the decision algorithm for dengue diagnosis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-03-12 01:41:49"}

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Relative Metric

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