title: A predictive scoring instrument for tuberculosis lost to follow-up outcome.
abstract: BACKGROUND
  Adherence to tuberculosis (TB) treatment is troublesome, due to long therapy duration, quick therapeutic response which allows the patient to disregard about the rest of their treatment and the lack of motivation on behalf of the patient for improved. The objective of this study was to develop and validate a scoring system to predict the probability of lost to follow-up outcome in TB patients as a way to identify patients suitable for directly observed treatments (DOT) and other interventions to improve adherence.
  
  
  METHODS
  Two prospective cohorts, were used to develop and validate a logistic regression model. A scoring system was constructed, based on the coefficients of factors associated with a lost to follow-up outcome. The probability of lost to follow-up outcome associated with each score was calculated. Predictions in both cohorts were tested using receiver operating characteristic curves (ROC).
  
  
  RESULTS
  The best model to predict lost to follow-up outcome included the following characteristics: immigration (1 point value), living alone (1 point) or in an institution (2 points), previous anti-TB treatment (2 points), poor patient understanding (2 points), intravenous drugs use (IDU) (4 points) or unknown IDU status (1 point). Scores of 0, 1, 2, 3, 4 and 5 points were associated with a lost to follow-up probability of 2,2% 5,4% 9,9%, 16,4%, 15%, and 28%, respectively. The ROC curve for the validation group demonstrated a good fit (AUC: 0,67 [95% CI; 0,65-0,70]).
  
  
  CONCLUSIONS
  This model has a good capacity to predict a lost to follow-up outcome. Its use could help TB Programs to determine which patients are good candidates for DOT and other strategies to improve TB treatment adherence.
authors.affiliation: Programa Integrado de Investigación en Tuberculosis (PII TB) de la Sociedad Española de Neumología y Cirugía Torácica (SEPAR), Barcelona, Spain. pii_tb_teresa_rodrigo@separ.es
authors.forename: Teresa
authors.initials: T
authors.lastname: Rodrigo
authors.forename: Joan A
authors.initials: JA
authors.lastname: Caylà
authors.forename: Martí
authors.initials: M
authors.lastname: Casals
authors.forename: José M
authors.initials: JM
authors.lastname: García-García
authors.forename: José A
authors.initials: JA
authors.lastname: Caminero
authors.forename: Juan
authors.initials: J
authors.lastname: Ruiz-Manzano
authors.forename: Rafael
authors.initials: R
authors.lastname: Blanquer
authors.forename: Rafael
authors.initials: R
authors.lastname: Vidal
authors.forename: Neus
authors.initials: N
authors.lastname: Altet
authors.forename: José L
authors.initials: JL
authors.lastname: Calpe
authors.forename: Antón
authors.initials: A
authors.lastname: Penas
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D004641:Emigration and Immigration / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D059012:Lost to Follow-Up; D008297:Male; D017533:Marital Status / Q000706:statistics & numerical data*; D008875:Middle Aged; D010349:Patient Compliance / Q000706:statistics & numerical data*; D018579:Patient Selection; D013030:Spain / Q000453:epidemiology; D014376:Tuberculosis / Q000188:drug therapy* / Q000453:epidemiology*; D055815:Young Adult