Literature DB >> 22057296

Predictive models for tuberculous pleural effusions in a high tuberculosis prevalence region.

Ersin Demirer1, Andrew C Miller, Erdogan Kunter, Zafer Kartaloglu, Scott D Barnett, Elamin M Elamin.   

Abstract

BACKGROUND: Patients with pleural effusions who reside in geographic areas with a high prevalence of tuberculosis frequently have similar clinical manifestations of other diseases. The aim of our study was to develop a simple but accurate clinical score for differential diagnosis of tuberculosis pleural effusion (TPE) from non-TB pleural effusion (NTPE).
METHODS: This was an unblinded, prospective study of Turkish patients 18 years of age or older with pleural effusion of indeterminate etiology conducted from June 2003 to June 2005. Unconditional logistic regression models were used to discriminate TPE cases from NTPE cases. Standard errors for the area under the curve (AUC) were calculated using the Mann-Whitney method. Data were statistically significance if two-tailed P < 0.05.
RESULTS: A total of 63.3% (157/248) of the patients had TPE while 36.7% (91/248) of the patients had other etiologies for pleural effusions. We were able to provide a predictive model of TPE that included age <47 years and either pleural fluid adenosine deaminase enzyme (PADA) >35 U/l or pleural serum protein ratio >0.710. However, only the combination of age <47 and PADA >35 U/l was significant (odds ratio [OR]: 7.46; 95% confidence interval [CI]: 3.99-13.96). The generated summary score (range = 0-6) was significantly predictive of TPE (OR: 2.91; 95% CI: 2.18-3.89) and with high AUC (0.79).
CONCLUSION: We propose an affordable model that includes age <47 years and PADA >35 U/l for timely diagnosis of TPE in geographical regions with a high prevalence of TB.

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Year:  2011        PMID: 22057296     DOI: 10.1007/s00408-011-9342-z

Source DB:  PubMed          Journal:  Lung        ISSN: 0341-2040            Impact factor:   2.584


  20 in total

1.  Mesothelial cells in tuberculous pleural effusions of HIV-infected patients.

Authors:  D Jones; T Lieb; M Narita; E S Hollender; A E Pitchenik; D Ashkin
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2.  The optimal number of pleural biopsy specimens for a diagnosis of tuberculous pleurisy.

Authors:  C M Kirsch; D M Kroe; R L Azzi; W A Jensen; F T Kagawa; J H Wehner
Journal:  Chest       Date:  1997-09       Impact factor: 9.410

3.  Diagnosing tuberculous pleural effusion using clinical data and pleural fluid analysis A study of patients less than 40 years-old in an area with a high incidence of tuberculosis.

Authors:  Luis Valdés; Maria Esther San José; Antonio Pose; Francisco Gude; Francisco J González-Barcala; José M Alvarez-Dobaño; Steven A Sahn
Journal:  Respir Med       Date:  2010-03-26       Impact factor: 3.415

4.  Screening of tuberculous pleural effusion by discriminant analysis.

Authors:  F Carrion-Valero; M Perpiñá-Tordera
Journal:  Int J Tuberc Lung Dis       Date:  2001-07       Impact factor: 2.373

5.  Simple method for rapid diagnosis of tuberculosis pleuritis: a statistical approach.

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Journal:  Asian Cardiovasc Thorac Ann       Date:  2004-03

6.  Utility of quantitative T-cell responses versus unstimulated interferon-{gamma} for the diagnosis of pleural tuberculosis.

Authors:  K Dheda; R N van Zyl-Smit; L A Sechi; M Badri; R Meldau; S Meldau; G Symons; P L Semple; A Maredza; R Dawson; H Wainwright; A Whitelaw; Y Vallie; P Raubenheimer; E D Bateman; A Zumla
Journal:  Eur Respir J       Date:  2009-04-22       Impact factor: 16.671

7.  A decision tree for differentiating tuberculous from malignant pleural effusions.

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8.  Differentiating tuberculous from malignant pleural effusions: a scoring model.

Authors:  José Manuel Porcel; Manuel Vives
Journal:  Med Sci Monit       Date:  2003-05

9.  Tuberculous pleurisy: a study of 254 patients.

Authors:  L Valdés; D Alvarez; E San José; P Penela; J M Valle; J M García-Pazos; J Suárez; A Pose
Journal:  Arch Intern Med       Date:  1998-10-12

10.  Predictive model for the diagnosis of tuberculous pleural effusion.

Authors:  Denise Duprat Neves; Ricardo Marques Dias; Antônio José Ledo A da Cunha
Journal:  Braz J Infect Dis       Date:  2007-02       Impact factor: 1.949

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  1 in total

1.  Developing Predictive Models to Determine Patients in End-of-Life Care in Administrative Datasets.

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Journal:  Drug Saf       Date:  2020-05       Impact factor: 5.606

  1 in total

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