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Development of a clinical prediction rule for the diagnosis of pleural tuberculosis in Peru

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Authors
Solari, Lely
Soto, Alonso
Van der Stuyft, Patrick
Issue Date
2018-04
Keywords
Aadenosine deaminase
Clinical prediction rules
Pleural effusion
Tuberculosis

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Citation
Development of a clinical prediction rule for the diagnosis of pleural tuberculosis in Peru 2018, 69:103 International Journal of Infectious Diseases
Publisher
Elsevier B.V.
Journal
International Journal of Infectious Diseases
URI
http://hdl.handle.net/10757/623065
DOI
10.1016/j.ijid.2018.01.026
Additional Links
http://linkinghub.elsevier.com/retrieve/pii/S1201971218300274
Abstract
Objectives: To develop a clinical prediction rule (CPR) for the diagnosis of pleural tuberculosis (PT) in patients with pleural exudates in Peru. Methods: Clinical and laboratory information was collected from patients with exudative pleural effusion attending two reference hospitals in Lima, Peru. Predictive findings associated with PT in a multiple logistic regression model were used to develop the CPR. A definite diagnosis of PT was based on a composite reference standard including bacteriological and/or histological analysis of pleural fluid and pleural biopsy specimens. Results: A total of 238 patients were included in the analysis, of whom 176 had PT. Age, sex, previous contact with a TB patient, presence of lymphadenopathy, and pleural adenosine deaminase (ADA) levels were found to be independently associated with PT. These predictive findings were used to construct a CPR, for which the area under the receiver operating characteristics curve (AUC) was 0.92. The single best cut-off point was a score of ≥60 points, which had a sensitivity of 88%, specificity of 92%, a positive likelihood ratio of 10.9, and a negative likelihood ratio of 0.13. Conclusions: The CPR is accurate for the diagnosis of PT and could be useful for treatment initiation while avoiding pleural biopsy. A prospective evaluation is needed before its implementation in different settings.
Type
info:eu-repo/semantics/article
Rights
info:eu-repo/semantics/openAccess
Language
eng
ISSN
12019712
ae974a485f413a2113503eed53cd6c53
10.1016/j.ijid.2018.01.026
Scopus Count
Collections
Medicina

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