Document Type
Article
Publication Date
10-2020
Keywords
tuberculosis, clinical prediction rule, contact investigation
Digital Object Identifier (DOI)
https://doi.org/10.1093/cid/ciz1221
Abstract
Background: Efficient contact investigation strategies are needed for the early diagnosis of 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 adult pulmonary TB patients. We used information from a subset of this cohort to derive two clinical prediction tools that identify contacts of TB patients at elevated risk of progressing to active disease by training multivariable models that predict (1) co-prevalent TB among all household contacts and (2) one-year incident TB among adult contacts. We validated the models in a geographically distinct sub-cohort 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 co-prevalent TB and 145 (1.9%) adult contacts developed incident TB within one year of index patient diagnosis. We predicted co-prevalent disease using information that could be readily obtained at the time an index patient was diagnosed and predicted one-year incident TB by including additional contact-specific characteristics. The area under the receiver-operating-characteristic curves for co-prevalent TB and incident TB were 0.86 (95%CI 0.83 – 0.89) and 0.72 (0.67 – 0.77). These clinical tools give 5-10% higher relative utilities than existing methods.
Conclusions: We present two 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.
Rights Information
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Clinical Infectious Diseases, v. 71, issue 8, p. e338-e350
Scholar Commons Citation
Li, Ruoran; Nordio, Francesco; Huang, Chuan-Chin; Contreras, Carmen; Calderon, Roger; Yataco, Rosa; Galea, Jerome; Zhang, Zibiao; Becerra, Mercedes C.; Lecca, Leonid; and Murray, Megan B., "Two Clinical Prediction Tools to Improve Tuberculosis Contact Investigation" (2020). Social Work Faculty Publications. 159.
https://digitalcommons.usf.edu/sok_facpub/159
