Pediatric in-Hospital Death from Infectious Disease in Uganda: Derivation of Clinical Prediction Models
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{"title"=>"Pediatric in-hospital death from infectious disease in Uganda: Derivation of clinical prediction models", "type"=>"journal", "authors"=>[{"first_name"=>"Nasim", "last_name"=>"Lowlaavar", "scopus_author_id"=>"57044860000"}, {"first_name"=>"Charles P.", "last_name"=>"Larson", "scopus_author_id"=>"26426376500"}, {"first_name"=>"Elias", "last_name"=>"Kumbakumba", "scopus_author_id"=>"55249701400"}, {"first_name"=>"Guohai", "last_name"=>"Zhou", "scopus_author_id"=>"56233697400"}, {"first_name"=>"J. Mark", "last_name"=>"Ansermino", "scopus_author_id"=>"9639115700"}, {"first_name"=>"Joel", "last_name"=>"Singer", "scopus_author_id"=>"7402619866"}, {"first_name"=>"Niranjan", "last_name"=>"Kissoon", "scopus_author_id"=>"7006364038"}, {"first_name"=>"Hubert", "last_name"=>"Wong", "scopus_author_id"=>"7402864998"}, {"first_name"=>"Andrew", "last_name"=>"Ndamira", "scopus_author_id"=>"55505862000"}, {"first_name"=>"Jerome", "last_name"=>"Kabakyenga", "scopus_author_id"=>"6508101992"}, {"first_name"=>"Julius", "last_name"=>"Kiwanuka", "scopus_author_id"=>"6602604666"}, {"first_name"=>"Matthew O.", "last_name"=>"Wiens", "scopus_author_id"=>"36880721900"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84962490898", "doi"=>"10.1371/journal.pone.0150683", "pui"=>"609059933", "pmid"=>"26963914", "scopus"=>"2-s2.0-84962490898", "issn"=>"19326203"}, "id"=>"8c9cbd6b-fec8-31a9-a80f-a3b5c2c275aa", "abstract"=>"BACKGROUND: Pediatric hospital mortality from infectious diseases in resource constrained countries remains unacceptably high. Improved methods of risk-stratification can assist in referral decision making and resource allocation. The purpose of this study was to create prediction models for in-hospital mortality among children admitted with suspected infectious diseases. METHODS: This two-site prospective observational study enrolled children between 6 months and 5 years admitted with a proven or suspected infection. Baseline clinical and laboratory variables were collected on enrolled children. The primary outcome was death during admission. Stepwise logistic regression minimizing Akaike's information criterion was used to identify the most promising multivariate models. The final model was chosen based on parsimony. RESULTS: 1307 children were enrolled consecutively, and 65 (5%) of whom died during their admission. Malaria, pneumonia and gastroenteritis were diagnosed in 50%, 31% and 8% of children, respectively. The primary model included an abnormal Blantyre coma scale, HIV and weight-for-age z-score. This model had an area under the curve (AUC) of 0.85 (95% CI, 0.80-0.89) with a sensitivity and specificity of 83% and 76%, respectively. The positive and negative predictive values were 15% and 99%, respectively. Two alternate models with similar performance characteristics were developed withholding HIV and weight-for-age z-score, for use when these variables are not available. CONCLUSIONS: Risk stratification of children admitted with infectious diseases can be calculated based on several easily measured variables. Risk stratification at admission can be used for allocation of scarce human and physical resources and to guide referral among children admitted to lower level health facilities.", "link"=>"http://www.mendeley.com/research/pediatric-inhospital-death-infectious-disease-uganda-derivation-clinical-prediction-models", "reader_count"=>17, "reader_count_by_academic_status"=>{"Student > Doctoral Student"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Master"=>6, "Student > Bachelor"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Student > Doctoral Student"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Master"=>6, "Student > Bachelor"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Medicine and Dentistry"=>9, "Psychology"=>1, "Social Sciences"=>3, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>9}, "Social Sciences"=>{"Social Sciences"=>3}, "Psychology"=>{"Psychology"=>1}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Canada"=>2}, "group_count"=>2}

Scopus | Further Information

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/4831063"], "description"=>"<p>Characteristics of Subjects (n = 1307).</p>", "links"=>[], "tags"=>["Risk stratification", "admission", "laboratory variables", "Infectious Disease", "Blantyre coma scale", "5 years", "referral decision", "available.ConclusionsRisk stratification", "guide referral", "multivariate models", "6 months", "performance characteristics", "Clinical Prediction Models BackgroundPediatric hospital mortality", "prediction models", "withholding HIV", "AUC", "parsimony.Results 1307 children", "CI", "resource allocation", "level health facilities"], "article_id"=>3108463, "categories"=>["Medicine", "Neuroscience", "Biotechnology", "Mathematical Sciences not elsewhere classified", "Cancer", "Infectious Diseases", "Virology", "Computational Biology"], "users"=>["Nasim Lowlaavar", "Charles P. Larson", "Elias Kumbakumba", "Guohai Zhou", "J. Mark Ansermino", "Joel Singer", "Niranjan Kissoon", "Hubert Wong", "Andrew Ndamira", "Jerome Kabakyenga", "Julius Kiwanuka", "Matthew O. Wiens"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0150683.t001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Characteristics_of_Subjects_n_1307_/3108463", "title"=>"Characteristics of Subjects (n = 1307).", "pos_in_sequence"=>3, "defined_type"=>3, "published_date"=>"2016-03-10 09:22:02"}

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Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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