Details

Autor(en) / Beteiligte

• Rodrigo, Teresa
• Caylà, Joan A
• Casals, Martí
• García-García, José M
• Caminero, José A
• Ruiz-Manzano, Juan
• Blanquer, Rafael
• Vidal, Rafael
• Altet, Neus
• Calpe, José L
• Penas, Antón

Titel

A predictive scoring instrument for tuberculosis lost to follow-up outcome

Ist Teil von

• Respiratory research, 2012-09, Vol.13 (1), p.75-75

Ort / Verlag

England: BioMed Central Ltd

Erscheinungsjahr

2012

Quelle

SpringerLink

Beschreibungen/Notizen

• 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. 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). 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]). 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.