Ricardo Luiz de Menezes Duarte

Marcadores moleculares no câncer de pulmão: papel prognóstico e sua relação com o tabagismo

Epidemiological studies have demonstrated a causal relationship between smoking and lung cancer. Although most lung cancer cases are linked to smoking, only a minority of heavy smokers develop lung cancer, leading to the notion that genetic factors affect individual susceptibility. The principal molecular changes in lung cancer are seen in tumor suppressor genes, proto-oncogenes, growth factors, telomerase activity, and methylation status of promoters. Well-known agents include angiogenesis-stimulating factors (such as vascular endothelial growth factor), as well as factors related to tumor cell proliferation and apoptosis (epidermal growth factor receptor, p53, K-ras, retinoblastoma and BCL-2). Several of these genetic factors have already been investigated, but no single parameter has yet presented sufficient selectivity regarding prognostic value or therapeutic efficacy. Treatment strategies to cure lung cancer should focus on these early genetic lesions in order to promote their repair or to eliminate these lung cancer cells.

The cigarette burden (measured by the number of pack-years smoked) negatively impacts the response rate to platinum-based chemotherapy in lung cancer patients

PURPOSE To evaluate the impact of the cigarette burden (CB) on the response rate to platinum-based chemotherapy (CT) in patients with lung cancer (LC). METHODS Retrospective study of patients with LC treated by CT from 2000 to 2005, in a tertiary referral center in Brazil. The CB was measured by the number of pack-years smoked (PY). To evaluate the response (by RECIST), it was necessary to accomplish two cycles of CT. The relevant variables were studied by univariate and multivariate statistical techniques. RESULTS Two hundred and eighty-five patients (203 men) were studied (mean age=60.6+/-10.1 years, mean PY=58.3+/-35.4). 62.8% were current smokers, 26.7% were former smokers, and 10.5% were non-smokers. 63.2% had non-small-cell lung cancer (NSCLC), and 36.8% had small-cell lung cancer (SCLC). The treatment intent was palliative in 63.9% and curative in 36.1%. All 285 patients received platinum-based CT (etoposide/cisplatin in 68.8% and etoposide/carboplatin in 31.2%). Of these, 155 patients (54.4%) received RT (median dose=50.0 Gy; range=45.0-80.0). The 94 patients (33.0%) who responded to treatment had a mean PY of 38.7+/-27.1, and the 191 patients (67.0%) who did not respond had a mean PY of 67.8+/-35.1, p<0.001. In the multivariate analysis, the main independent negative predictor was CB>or=40 PY (adjusted OR=10.42; 95% CI=5.13-21.28). The others independent negative predictors were: CT (no. of cycles=2-4) (adjusted OR=4.86; 95% CI=2.44-9.68), treatment regimen with CT alone (adjusted OR=3.38; 95% CI=1.67-6.84), and NSCLC histology (adjusted OR=2.75; 95% CI=1.12-6.76). CONCLUSION Patients with CB>or=40 PY have a worse response to platinum-based CT compared to those who have a CB<40 PY.

Factors predictive of obstructive sleep apnea in patients undergoing pre-operative evaluation for bariatric surgery and referred to a sleep laboratory for polysomnography.

Objective: To identify the main predictive factors for obtaining a diagnosis of obstructive sleep apnea (OSA) in patients awaiting bariatric surgery. Methods: Retrospective study of consecutive patients undergoing pre-operative evaluation for bariatric surgery and referred for in-laboratory polysomnography. Eight variables were evaluated: sex, age, neck circumference (NC), BMI, Epworth Sleepiness Scale (ESS) score, snoring, observed apnea, and hypertension. We employed ROC curve analysis to determine the best cut-off value for each variable and multiple linear regression to identify independent predictors of OSA severity. Results: We evaluated 1,089 patients, of whom 781 (71.7%) were female. The overall prevalence of OSA-defined as an apnea/hypopnea index (AHI) ≥ 5.0 events/h-was 74.8%. The best cut-off values for NC, BMI, age, and ESS score were 42 cm, 42 kg/m2, 37 years, and 10 points, respectively. All eight variables were found to be independent predictors of a diagnosis of OSA in general, and all but one were found to be independent predictors of a diagnosis of moderate/severe OSA (AHI ≥ 15.0 events/h), the exception being hypertension. We devised a 6-item model, designated the NO-OSAS model (NC, Obesity, Observed apnea, Snoring, Age, and Sex), with a cut-off value of ≥ 3 for identifying high-risk patients. For a diagnosis of moderate/severe OSA, the model showed 70.8% accuracy, 82.8% sensitivity, and 57.9% specificity. Conclusions: In our sample of patients awaiting bariatric surgery, there was a high prevalence of OSA. At a cut-off value of ≥ 3, the proposed 6-item model showed good accuracy for a diagnosis of moderate/severe OSA.

Simplifying the Screening of Obstructive Sleep Apnea With a 2-Item Model, No-Apnea: A Cross-Sectional Study

STUDY OBJECTIVES To develop and validate a practical model for obstructive sleep apnea (OSA) screening in adults based on objectively assessed criteria, and then compare it with two widely used tools, namely STOP-BANG and NoSAS. METHODS This is a retrospective study of an existing database of consecutive outpatients who were referred for polysomnography for suspected sleep-disordered breathing by their primary care physicians. Area under the curve (AUC) and 2 × 2 contingency tables were employed to obtain the performance of the new instrument. RESULTS A total of 4,072 subjects were randomly allocated into two independent cohorts: one for derivation (n = 2,037) and one for validation (n = 2,035). A mnemonic model, named No-Apnea, with two variables (neck circumference and age) was developed (total score: 0-9 points). We used the cutoff ≥ 3 to classify patients at high risk of having OSA. OSA severity was categorized by apnea-hypopnea index (AHI): any OSA (AHI 5 ≥ events/h; OSA-5), moderate/ severe OSA (AHI 15 ≥ events/h; OSA-15); and severe OSA (AHI 30 ≥ events/h; OSA-30). In the derivation cohort, the AUCs for screening of OSA-5, OSA-15, and OSA-30 were: 0.784, 0.758, and 0.754; respectively. The rate of subjects correctly screened was 78.1%, 68.8%, and 54.4%, respectively for OSA-5, OSA-15, and OSA-30. Subsequently, the model was validated confirming its reproducibility. In both cohorts, No-Apnea discrimination was similar to STOP-BANG or NoSAS. CONCLUSIONS The No-Apnea, a 2-item model, appears to be a useful and practical tool for OSA screening, mainly when limited resources constrain referral evaluation. Despite its simplicity when compared to previously validated tools (STOP-BANG and NoSAS), the instrument exhibits similar performance characteristics.

Screening for Sleep Apnea: When and How?

Purpose of ReviewSeveral models aimed at screening for obstructive sleep apnea (OSA) have been published, and most of these are based on questions containing clinical, demographic, and anthropometric features previously identified as OSA risk factors. Here, our main objective was to review the usefulness of some of these screening tools and delineate their performance when attempting to identify subjects at risk for OSA.Recent FindingsWe evaluated some of the most cited screening tools including Sleep Apnea Clinical Score, Berlin and STOP-Bang questionnaires, Four-Variable Screening Tool, NoSAS score, and the Epworth Sleepiness Scale. Analysis of the predictive performance of the different tools is influenced by the sleep test used, the type of population studied, and the threshold of the apnea/hypopnea index used for OSA diagnosis.SummaryNowadays, it would appear that the most employed screening instrument is the STOP-Bang questionnaire. It is a mnemonic method with eight questions dichotomized into yes-or-no responses and exhibits high sensitivity at all levels of OSA severity while also having been widely validated in several different populations.