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Diagnostic Accuracy of Clinical Sign Algorithms to Identify Sepsis in Young Infants Aged 0 to 59 Days: A Systematic Review and Meta-analysis.
Fung, Alastair; Shafiq, Yasir; Driker, Sophie; Rees, Chris A; Mediratta, Rishi P; Rosenberg, Rebecca; Hussaini, Anum S; Adnan, Jana; Wade, Carrie G; Chou, Roger; Edmond, Karen M; North, Krysten; Lee, Anne Cc.
Afiliación
  • Fung A; Division of Paediatric Medicine, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.
  • Shafiq Y; Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health (CRIMEDIM), Università degli Studi del Piemonte Orientale "Amedeo Avogadro", Vercelli, Italy.
  • Driker S; Center of Excellence for Trauma and Emergencies and Community Health Sciences, The Aga Khan University, Karachi, Pakistan.
  • Rees CA; Global Advancement of Infants and Mothers (AIM), Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Mediratta RP; Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
  • Rosenberg R; Global Advancement of Infants and Mothers (AIM), Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Hussaini AS; Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia.
  • Adnan J; Division of Pediatric Hospital Medicine, Stanford University School of Medicine, Palo Alto, California.
  • Wade CG; Department of Pediatrics, School of Medicine, New York University, New York, New York.
  • Chou R; Harvard T. H. Chan School of Public Health, Boston, Massachusetts.
  • Edmond KM; American University of Beirut, Beirut, Lebanon.
  • North K; Countway Library, Harvard Medical School, Boston, Massachusetts.
  • Lee AC; Departments of Medicine and Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon.
Pediatrics ; 154(Suppl 1)2024 Aug 01.
Article en En | MEDLINE | ID: mdl-39087806
ABSTRACT
CONTEXT Accurate identification of possible sepsis in young infants is needed to effectively manage and reduce sepsis-related morbidity and mortality.

OBJECTIVE:

Synthesize evidence on the diagnostic accuracy of clinical sign algorithms to identify young infants (aged 0-59 days) with suspected sepsis. DATA SOURCES MEDLINE, Embase, CINAHL, Global Index Medicus, and Cochrane CENTRAL Registry of Trials. STUDY SELECTION Studies reporting diagnostic accuracy measures of algorithms including infant clinical signs to identify young infants with suspected sepsis. DATA EXTRACTION We used Cochrane methods for study screening, data extraction, risk of bias assessment, and determining certainty of evidence using Grading of Recommendations Assessment Development and Evaluation.

RESULTS:

We included 19 studies (12 Integrated Management of Childhood Illness [IMCI] and 7 non-IMCI studies). The current World Health Organization (WHO) 7-sign IMCI algorithm had a sensitivity of 79% (95% CI 77%-82%) and specificity of 77% (95% CI 76%-78%) for identifying sick infants aged 0-59 days requiring hospitalization/antibiotics (1 study, N = 8889). Any IMCI algorithm had a pooled sensitivity of 84% (95% CI 75%-90%) and specificity of 80% (95% CI 64%-90%) for identifying suspected sepsis (11 studies, N = 15523). When restricting the reference standard to laboratory-supported sepsis, any IMCI algorithm had a pooled sensitivity of 86% (95% CI 82%-90%) and lower specificity of 61% (95% CI 49%-72%) (6 studies, N = 14278).

LIMITATIONS:

Heterogeneity of algorithms and reference standards limited the evidence.

CONCLUSIONS:

IMCI algorithms had acceptable sensitivity for identifying young infants with suspected sepsis. Specificity was lower using a reference standard of laboratory-supported sepsis diagnosis.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Sepsis Límite: Humans / Infant / Newborn Idioma: En Revista: Pediatrics Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Sepsis Límite: Humans / Infant / Newborn Idioma: En Revista: Pediatrics Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos