Fábio Silva Aguiar

Multidrug-Resistant Nontuberculous Mycobacteria Isolated from Cystic Fibrosis Patients

ABSTRACT Worldwide, nontuberculous mycobacteria (NTM) have become emergent pathogens of pulmonary infections in cystic fibrosis (CF) patients, with an estimated prevalence ranging from 5 to 20%. This work investigated the presence of NTM in sputum samples of 129 CF patients (2 to 18 years old) submitted to longitudinal clinical supervision at a regional reference center in Rio de Janeiro, Brazil. From June 2009 to March 2012, 36 NTM isolates recovered from 10 (7.75%) out of 129 children were obtained. Molecular identification of NTM was performed by using PCR restriction analysis targeting the hsp65 gene (PRA-hsp65) and sequencing of the rpoB gene, and susceptibility tests were performed that followed Clinical and Laboratory Standards Institute recommendations. For evaluating the genotypic diversity, pulsed-field gel electrophoresis (PFGE) and/or enterobacterial repetitive intergenic consensus sequence PCR (ERIC-PCR) was performed. The species identified were Mycobacterium abscessus subsp. bolletii (n = 24), M. abscessus subsp. abscessus (n = 6), Mycobacterium fortuitum (n = 3), Mycobacterium marseillense (n = 2), and Mycobacterium timonense (n = 1). Most of the isolates presented resistance to five or more of the antimicrobials tested. Typing profiles were mainly patient specific. The PFGE profiles indicated the presence of two clonal groups for M. abscessus subsp. abscessus and five clonal groups for M. abscesssus subsp. bolletii, with just one clone detected in two patients. Given the observed multidrug resistance patterns and the possibility of transmission between patients, we suggest the implementation of continuous and routine investigation of NTM infection or colonization in CF patients, including countries with a high burden of tuberculosis disease.

Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients

BackgroundTuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission.MethodsCross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART) model was generated and validated. The area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005.ResultsWe studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear) and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%.ConclusionsThe CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with clinical suspicion of TB in tertiary health facilities in countries with limited resources.

Liquid vs Solid Culture Medium to Evaluate Proportion and Time to Change in Management of Suspects of Tuberculosis-A Pragmatic Randomized Trial in Secondary and Tertiary Health Care Units in Brazil.

Background The use of liquid medium (MGIT960) for tuberculosis (TB) diagnosis was recommended by WHO in 2007. However, there has been no evaluation of its effectiveness on clinically important outcomes. Methods and Findings A pragmatic trial was carried out in a tertiary hospital and a secondary health care unit in Rio de Janeiro City, Brazil. Participants were 16 years or older, suspected of having TB. They were excluded if only cerebral spinal fluid or blood specimens were available for analysis. MGIT960 technique was compared with the Lowenstein-Jensen (LJ) method for laboratory diagnosis of active TB. Primary outcome was the proportion of patients who had their initial medical management changed within 2 months after randomisation. Secondary outcomes were: mean time for changing the procedure, patient satisfaction with the overall treatment and adverse events. Data were analysed by intention-to-treat. Between April 2008 and September 2011, 693 patients were enrolled (348 to MGIT, 345 to LJ). Smear and culture results were positive for 10% and 15.7% of participants, respectively. Patients in the MGIT arm had their initial medical management changed more frequently than those in the LJ group (10.1% MGIT vs 3.8% LJ, RR 2.67 95% CI 1.44–.96, p = 0.002, NNT 16, 95% CI 10–39). Mean time for changing the initial procedure was greater in LJ group at both sites: 20.0 and 29.6 days in MGIT group and 52.2 and 64.3 in LJ group (MD 33.5, 95% CI 30.6–36.4, p = 0.0001). No other important differences were observed. Conclusions This study suggests that opting for the MGIT960 system for TB diagnosis provides a promising case management model for improving the quality of care and control of TB. Trial Registration Controlled-Trials.com ISRCTN79888843

An intelligent system for managing the isolation of patients suspected of pulmonary tuberculosis

In hospital internment context, patients suspect to have pulmonary tuberculosis, especially those which have a higher transmission risk, should be allocated in isolation respiratory rooms in order to reduce infection risk. Due to high implementation costs, these rooms are only available in a restricted quantity and have to be shared with patients having other infectious diseases. Currently applied criteria have been resulting in a large number of unnecessary patient isolations. This work proposes a decision support system based on neural networks to guide patient respiratory isolation. The system identifies the probability of a patient to have pulmonary tuberculosis and was developed using medical records data from 290 Pulmonary TB suspect patients who were admitted to isolation rooms in an AIDS/TB reference hospital (IDT-HUCFF-UFRJ). Based on 26 clinical-radiological variables, the system achieved a sensitivity of 94% and specificity of 89%. This system should be validated in other settings in order to confirm this high performance and its usefulness by avoiding unnecessary patient isolation as providing an earlier diagnosis, which may reduce the contamination risk.