title: The medical management of central nervous system infections in Uganda and the potential impact of an algorithm-based approach to improve outcomes.
abstract: BACKGROUND
  In sub-Saharan Africa, HIV has increased the spectrum of central nervous system (CNS) infections. The etiological diagnosis is often difficult. Mortality from CNS infections is higher in sub-Saharan Africa compared to Western countries. This study examines the medical management of CNS infections in Uganda. We also propose a clinical algorithm to manage CNS infections in an effective, systematic, and resource-efficient manner.
  
  
  METHODS
  We prospectively followed 100 consecutive adult patients who were admitted to Mulago Hospital with a suspected diagnosis of a CNS infection without any active participation in their management. From the clinical and outcome data, we created an algorithm to manage CNS infections, which was appropriate for this resource-limited, high HIV prevalence setting.
  
  
  RESULTS
  Only 32 patients had a laboratory confirmed diagnosis and 23 of these were diagnosed with cryptococcal meningitis. Overall mortality was 39%, and mortality trended upward when the diagnosis was delayed past 3 days. The initial diagnoses were made clinically without significant laboratory data in 92 of the 100 patients. Because HIV positive patients have a unique spectrum of CNS infections, we created an algorithm that identified HIV-positive patients and diagnosed those with cryptococcal meningitis. After cryptococcal infection was ruled out, previously published algorithms were used to assist in the early diagnosis and treatment of bacterial meningitis, tuberculous meningitis, and other common central nervous system infections. In retrospective comparison with current management, the CNS algorithm reduced overall time to diagnosis and initiate treatment of cryptococcal meningitis from 3.5 days to less than 1 day.
  
  
  CONCLUSIONS
  CNS infections are complex and difficult to diagnose and treat in Uganda, and are associated with high in-hospital mortality. A clinical algorithm may significantly decrease the time to diagnose and treat CNS infections in a resource-limited setting.
authors.affiliation: Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84124, USA. jtrachtenberg99@hotmail.com
authors.forename: Joel D
authors.initials: JD
authors.lastname: Trachtenberg
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Kambugu
authors.forename: Mehri
authors.initials: M
authors.lastname: McKellar
authors.forename: Fred
authors.initials: F
authors.lastname: Semitala
authors.forename: Harriet
authors.initials: H
authors.lastname: Mayanja-Kizza
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Samore
authors.forename: Allan
authors.initials: A
authors.lastname: Ronald
authors.forename: Merle A
authors.initials: MA
authors.lastname: Sande
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D000890:Anti-Infective Agents / Q000627:therapeutic use*; D002494:Central Nervous System Infections / Q000175:diagnosis* / Q000188:drug therapy* / Q000401:mortality; D003937:Diagnosis, Differential; D005260:Female; D015658:HIV Infections; D006801:Humans; D008297:Male; D008875:Middle Aged; D017410:Practice Guidelines as Topic; D012307:Risk Factors; D016896:Treatment Outcome; D014454:Uganda
chemical_list: D000890:Anti-Infective Agents