Trial reportPloS one2010
Clinical prediction rule for stratifying risk of pulmonary multidrug-resistant tuberculosis.
Trial report in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia.PloS one · 2021Article
- Derivation and Validation of a Clostridium difficile Infection Recurrence Prediction Rule in a National Cohort of Veterans.Pharmacotherapy · 2018Article
- Outcomes from patients with presumed drug resistant tuberculosis in five reference centers in Brazil.BMC infectious diseases · 2017Observational
- Serious fungal infections in Peru.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2017Article
- Xpert(®) MTB/RIF detection of rifampin resistance and time to treatment initiation in Harare, Zimbabwe.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2016Article
- Article
- A predictive scoring instrument for tuberculosis lost to follow-up outcome.Respiratory research · 2012Article
- GeneXpert--a game-changer for tuberculosis control?PLoS medicine · 2011Article
- Article
- Drug-resistant tuberculosis in subjects included in the Second National Survey on Antituberculosis Drug Resistance in Porto Alegre, Brazil.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e TisilogiaArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundMultidrug-resistant tuberculosis (MDR-TB), resistance to at least isoniazid and rifampin, is a worldwide problem.
objectiveTo develop a clinical prediction rule to stratify risk for MDR-TB among patients with pulmonary tuberculosis.
methodsDerivation and internal validation of the rule among adult patients prospectively recruited from 37 health centers (Perú), either a) presenting with a positive acid-fast bacillus smear, or b) had failed therapy or had a relapse within the first 12 months.
resultsAmong 964 patients, 82 had MDR-TB (prevalence, 8.5%). Variables included were MDR-TB contact within the family, previous tuberculosis, cavitary radiologic pattern, and abnormal lung exam. The area under the receiver-operating curve (AUROC) was 0.76. Selecting a cut-off score of one or greater resulted in a sensitivity of 72.6%, specificity of 62.8%, likelihood ratio (LR) positive of 1.95, and LR negative of 0.44. Similarly, selecting a cut-off score of two or greater resulted in a sensitivity of 60.8%, specificity of 87.5%, LR positive of 4.85, and LR negative of 0.45. Finally, selecting a cut-off score of three or greater resulted in a sensitivity of 45.1%, specificity of 95.3%, LR positive of 9.56, and LR negative of 0.58.
conclusionA simple clinical prediction rule at presentation can stratify risk for MDR-TB. If further validated, the rule could be used for management decisions in resource-limited areas.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.