Details

Autor(en) / Beteiligte

• Li, Ruoran
• Nordio, Francesco
• Huang, Chuan-Chin
• Contreras, Carmen
• Calderon, Roger
• Yataco, Rosa
• Galea, Jerome T
• Zhang, Zibiao
• Becerra, Mercedes C
• Lecca, Leonid
• Murray, Megan B

Titel

Two Clinical Prediction Tools to Improve Tuberculosis Contact Investigation

Ist Teil von

• Clinical infectious diseases, 2020-11, Vol.71 (8), p.e338-e350

Ort / Verlag

US: Oxford University Press

Erscheinungsjahr

2020

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• Abstract Background Efficient contact investigation strategies are needed for the early diagnosis of tuberculosis (TB) disease and treatment of latent TB infections. Methods Between September 2009 and August 2012, we conducted a prospective cohort study in Lima, Peru, in which we enrolled and followed 14 044 household contacts of adults with pulmonary TB. We used information from a subset of this cohort to derive 2 clinical prediction tools that identify contacts of TB patients at elevated risk of progressing to active disease by training multivariable models that predict (1) coprevalent TB among all household contacts and (2) 1-year incident TB among adult contacts. We validated the models in a geographically distinct subcohort and compared the relative utilities of clinical decisions based on these tools to existing strategies. Results In our cohort, 296 (2.1%) household contacts had coprevalent TB and 145 (1.9%) adult contacts developed incident TB within 1 year of index patient diagnosis. We predicted coprevalent disease using information that could be readily obtained at the time an index patient was diagnosed and predicted 1-year incident TB by including additional contact-specific characteristics. The area under the receiver operating characteristic curves for coprevalent TB and incident TB were 0.86 (95% confidence interval [CI], .83–.89]) and 0.72 (95% CI, .67–.77), respectively. These clinical tools give 5%–10% higher relative utilities than existing methods. Conclusions We present 2 tools that identify household contacts at high risk for TB disease based on reportable information from patient and contacts alone. The performance of these tools is comparable to biomarkers that are both more costly and less feasible than this approach. We developed and validated 2 clinical prediction tools to identify contacts at high risk for tuberculosis disease using clinical and demographic information obtained from the patient and household contact. These tools perform similarly to more costly and less feasible biomarkers.