title: Logic regression-derived algorithms for syndromic management of vaginal infections.
abstract: BACKGROUND
  Syndromic management of vaginal infections is known to have poor diagnostic accuracy. Logic regression is a machine-learning procedure which allows for the identification of combinations of variables to predict an outcome, such as the presence of a vaginal infection.
  
  
  METHODS
  We used logic regression to develop predictive models for syndromic management of vaginal infection among symptomatic, reproductive-age women in south India. We assessed the positive predictive values, negative predictive values, sensitivities and specificities of the logic regression procedure and a standard WHO algorithm against laboratory-confirmed diagnoses of two conditions: metronidazole-sensitive vaginitis [bacterial vaginosis or trichomoniasis (BV/TV)], and vulvovaginal candidiasis (VVC).
  
  
  RESULTS
  The logic regression procedure created algorithms which had a mean positive predictive value of 61 % and negative predictive value of 80 % for management of BV/TV, and a mean positive predictive value of 26 % and negative predictive value of 98 % for management of VVC. The results using the WHO algorithm were similarly mixed.
  
  
  CONCLUSIONS
  The logic regression procedure identified the most predictive measures for management of vaginal infections from the candidate clinical and laboratory measures. However, the procedure provided further evidence as to the limits of syndromic management for vaginal infections using currently available clinical measures.
authors.affiliation: Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK. sujit.rathod@lshtm.ac.uk.
authors.forename: Sujit D
authors.initials: SD
authors.lastname: Rathod
authors.affiliation: Department of Biostatistics, Florida International University, Miami, FL, USA.
authors.forename: Tan
authors.initials: T
authors.lastname: Li
authors.affiliation: Division of Infectious Diseases, University of California, Los Angeles, CA, USA.
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Klausner
authors.affiliation: Division of Epidemiology, University of California, Berkeley, CA, USA.
authors.forename: Alan
authors.initials: A
authors.lastname: Hubbard
authors.affiliation: Division of Epidemiology, University of California, Berkeley, CA, USA.
authors.forename: Arthur L
authors.initials: AL
authors.lastname: Reingold
authors.affiliation: Department of Biostatistics, Florida International University, Miami, FL, USA.|Public Health Research Institute of India, Mysore, India.
authors.forename: Purnima
authors.initials: P
authors.lastname: Madhivanan
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D007194:India; D000069550:Machine Learning; D008962:Models, Theoretical; D011379:Prognosis; D012680:Sensitivity and Specificity; D013577:Syndrome; D014627:Vaginitis / Q000628:therapy*