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

BACKGROUND: Tuberculosis (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. METHODS: Cross 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. RESULTS: We 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%. CONCLUSIONS: The 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.

Autologous bone marrow-derived mononuclear cell therapy in three patients with severe asthma.

BACKGROUND: Despite recent advances in understanding its pathophysiology and development of novel therapies, asthma remains a serious public health issue worldwide. Combination therapy with inhaled corticosteroids and long-acting ß2-adrenoceptor agonists results in disease control for many patients, but those who exhibit severe asthma are often unresponsive to conventional treatment, experiencing worse quality of life, frequent exacerbations, and increasing healthcare costs. Bone marrow-derived mononuclear cell (BMMC) transplantation has been shown to reduce airway inflammation and remodeling and improve lung function in experimental models of allergic asthma. METHODS: This is a case series of three patients who presented severe asthma, unresponsive to conventional therapy and omalizumab. They received a single intravenous dose of autologous BMMCs (2 × 107) and were periodically evaluated for 1 year after the procedure. Endpoint assessments included physical examination, quality of life questionnaires, imaging (computed tomography, single-photon emission computed tomography, and ventilation/perfusion scan), lung function tests, and a 6-min walk test. RESULTS: All patients completed the follow-up protocol. No serious adverse events attributable to BMMC transplantation were observed during or after the procedure. Lung function remained stable throughout. A slight increase in ventilation of the right lung was observed on day 120 after BMMC transplantation in one patient. All three patients reported improvement in quality of life in the early post-procedure course. CONCLUSIONS: This paper described for the first time the effects of BMMC therapy in patients with severe asthma, providing a basis for subsequent trials to assess the efficacy of this therapy.

Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil.

Pulmonary tuberculosis (PTB) remains a worldwide public health problem. Diagnostic algorithms to identify the best combination of diagnostic tests for PTB in each setting are needed for resource optimization. We developed one artificial neural network model for classification (multilayer perceptron-MLP) and another risk group assignment (self-organizing map-SOM) for PTB in hospitalized patients in a high complexity hospital in Rio de Janeiro City, using clinical and radiologic data collected from 315 presumed PTB cases admitted to isolation rooms from March 2003 to December 2004 (TB prevalence = 21.5 %). The MLP model included 7 variables-radiologic classification, age, gender, cough, night sweats, weight loss and anorexia. The sensitivity of the MLP model was 96.0 % (95 % CI ±2.0), the specificity was 89.0 % (95 % CI ±2.0), the positive predictive value was 72.5 % (95 % CI ±3.5) and the negative predictive value was 98.5 % (95 % CI ±0.5). The variable with the highest discriminative power was the radiologic classification. The high negative predictive value found in the MLP model suggests that the use of this model at the moment of hospital admission is safe. SOM model was able to correctly assign high-, medium- and low-risk groups to patients. If prospective validation in other series is confirmed, these models can become a tool for decision-making in tertiary health facilities in countries with limited resources.