Literature DB >> 18785863

Derivation and validation of a clinical prediction score for isolation of inpatients with suspected pulmonary tuberculosis.

Kara S Rakoczy1, Stuart H Cohen, Hien H Nguyen.   

Abstract

BACKGROUND: The use of a clinical prediction score to improve the practice of instituting airborne-transmission precautions in patients with suspected tuberculosis holds promise for increasing appropriate isolation and decreasing unnecessary isolation. The objective of this study was to derive and validate a clinical prediction score for patients with suspected tuberculosis.
METHODS: We used a case-control study design to evaluate differences between patients with a diagnosis of tuberculosis and those placed under airborne precautions who had negative culture results. We developed risk scores based on a multivariable analysis of independently significant factors associated with tuberculosis. Subsequently, we evaluated the sensitivity and specificity of the score in a separate (validation) cohort of patients.
RESULTS: Within our population, we found 4 clinical factors associated with tuberculosis: chronic symptoms (odds ratio [OR], 10.2 [95% confidence interval {CI}, 2.95-35.4]), upper lobe disease on chest radiograph (OR, 5.27 [95% CI, 1.6-17.23]), foreign-born status (OR, 7.01 [95% CI, 2.1-23.8]), and immunocompromised state other than human immunodeficiency virus infection (OR, 8.14 [95% CI, 2.08-31.8]). Shortness of breath (OR, 0.13 [95% CI, 0.04-0.45]) was found to be associated with non-tuberculosis diagnoses and considered a negative predictor in the model. Using a cut-off point to maximize sensitivity, we applied the prediction rule to the validation cohort, resulting in a sensitivity of 97% and a specificity of 42%.
CONCLUSION: The tuberculosis prediction rule derived from our patient population could improve utilization of airborne precautions. Clinical prediction rules continue to show their utility for improvement in isolation practices in different demographic areas.

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Year:  2008        PMID: 18785863     DOI: 10.1086/590667

Source DB:  PubMed          Journal:  Infect Control Hosp Epidemiol        ISSN: 0899-823X            Impact factor:   3.254


  7 in total

1.  Clinical prediction rule for stratifying risk of pulmonary multidrug-resistant tuberculosis.

Authors:  Dalila Martínez; Gustavo Heudebert; Carlos Seas; German Henostroza; Martin Rodriguez; Carlos Zamudio; Robert M Centor; Cesar Herrera; Eduardo Gotuzzo; Carlos Estrada
Journal:  PLoS One       Date:  2010-08-11       Impact factor: 3.240

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

Authors:  Teresa Rodrigo; Joan A Caylà; Martí Casals; José M García-García; José A Caminero; Juan Ruiz-Manzano; Rafael Blanquer; Rafael Vidal; Neus Altet; José L Calpe; Antón Penas
Journal:  Respir Res       Date:  2012-09-02

3.  The Bandim TBscore--reliability, further development, and evaluation of potential uses.

Authors:  Frauke Rudolf
Journal:  Glob Health Action       Date:  2014-05-22       Impact factor: 2.640

4.  Nomogram to predict multidrug-resistant tuberculosis.

Authors:  Saibin Wang; Junwei Tu
Journal:  Ann Clin Microbiol Antimicrob       Date:  2020-06-06       Impact factor: 3.944

5.  Development of a Predictive Model of Tuberculosis Transmission among Household Contacts.

Authors:  Saibin Wang
Journal:  Can J Infect Dis Med Microbiol       Date:  2019-07-30       Impact factor: 2.471

6.  Development of a simple reliable radiographic scoring system to aid the diagnosis of pulmonary tuberculosis.

Authors:  Lancelot M Pinto; Keertan Dheda; Grant Theron; Brian Allwood; Gregory Calligaro; Richard van Zyl-Smit; Jonathan Peter; Kevin Schwartzman; Dick Menzies; Eric Bateman; Madhukar Pai; Rodney Dawson
Journal:  PLoS One       Date:  2013-01-18       Impact factor: 3.240

7.  Evaluation of the performance of clinical predictors in estimating the probability of pulmonary tuberculosis among smear-negative cases in Northern Ethiopia: a cross-sectional study.

Authors:  Mala George; Geert-Jan Dinant; Efrem Kentiba; Teklu Teshome; Abinet Teshome; Behailu Tsegaye; Mark Spigt
Journal:  BMJ Open       Date:  2020-11-03       Impact factor: 2.692

  7 in total

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