Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

Abstract Background Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

The role of clouds in improving the regression model for hourly values of diffuse solar radiation

The study introduces a new regression model developed to estimate the hourly values of diffuse solar radiation at the surface. The model is based on the clearness index and diffuse fraction relationship, and includes the effects of cloud (cloudiness and cloud type), traditional meteorological variables (air temperature, relative humidity and atmospheric pressure observed at the surface) and air pollution (concentration of particulate matter observed at the surface). The new model is capable of predicting hourly values of diffuse solar radiation better than the previously developed ones (R-2 = 0.93 and RMSE = 0.085). A simple version with a large applicability is proposed that takes into consideration cloud effects only (cloudiness and cloud height) and shows a R-2 = 0.92. (C) 2011 Elsevier Ltd. All rights reserved.

The geometric Birnbaum-Saunders regression model with cure rate

In this paper, we propose a cure rate survival model by assuming the number of competing causes of the event of interest follows the Geometric distribution and the time to event follow a Birnbaum Saunders distribution. We consider a frequentist analysis for parameter estimation of a Geometric Birnbaum Saunders model with cure rate. Finally, to analyze a data set from the medical area. (C) 2011 Elsevier B.V. All rights reserved.

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

The Poisson-exponential regression model under different latent activation schemes

In this paper, a new family of survival distributions is presented. It is derived by considering that the latent number of failure causes follows a Poisson distribution and the time for these causes to be activated follows an exponential distribution. Three different activation schemes are also considered. Moreover, we propose the inclusion of covariates in the model formulation in order to study their effect on the expected value of the number of causes and on the failure rate function. Inferential procedure based on the maximum likelihood method is discussed and evaluated via simulation. The developed methodology is illustrated on a real data set on ovarian cancer.

A log-Birnbaum-Saunders regression model with asymmetric errors

This paper introduces a skewed log-Birnbaum-Saunders regression model based on the skewed sinh-normal distribution proposed by Leiva et al. [A skewed sinh-normal distribution and its properties and application to air pollution, Comm. Statist. Theory Methods 39 (2010), pp. 426-443]. Some influence methods, such as the local influence and generalized leverage, are presented. Additionally, we derived the normal curvatures of local influence under some perturbation schemes. An empirical application to a real data set is presented in order to illustrate the usefulness of the proposed model.

The log-exponentiated generalized gamma regression model for censored data

For the first time, we introduce a generalized form of the exponentiated generalized gamma distribution [Cordeiro et al. The exponentiated generalized gamma distribution with application to lifetime data, J. Statist. Comput. Simul. 81 (2011), pp. 827-842.] that is the baseline for the log-exponentiated generalized gamma regression model. The new distribution can accommodate increasing, decreasing, bathtub- and unimodal-shaped hazard functions. A second advantage is that it includes classical distributions reported in the lifetime literature as special cases. We obtain explicit expressions for the moments of the baseline distribution of the new regression model. The proposed model can be applied to censored data since it includes as sub-models several widely known regression models. It therefore can be used more effectively in the analysis of survival data. We obtain maximum likelihood estimates for the model parameters by considering censored data. We show that our extended regression model is very useful by means of two applications to real data.

The negative binomial-beta Weibull regression model to predict the cure of prostate cancer

In this article, for the first time, we propose the negative binomial-beta Weibull (BW) regression model for studying the recurrence of prostate cancer and to predict the cure fraction for patients with clinically localized prostate cancer treated by open radical prostatectomy. The cure model considers that a fraction of the survivors are cured of the disease. The survival function for the population of patients can be modeled by a cure parametric model using the BW distribution. We derive an explicit expansion for the moments of the recurrence time distribution for the uncured individuals. The proposed distribution can be used to model survival data when the hazard rate function is increasing, decreasing, unimodal and bathtub shaped. Another advantage is that the proposed model includes as special sub-models some of the well-known cure rate models discussed in the literature. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. We analyze a real data set for localized prostate cancer patients after open radical prostatectomy.

A log-linear regression model for the beta-Birnbaum-Saunders distribution with censored data

The beta-Birnbaum-Saunders (Cordeiro and Lemonte, 2011) and Birnbaum-Saunders (Birnbaum and Saunders, 1969a) distributions have been used quite effectively to model failure times for materials subject to fatigue and lifetime data. We define the log-beta-Birnbaum-Saunders distribution by the logarithm of the beta-Birnbaum-Saunders distribution. Explicit expressions for its generating function and moments are derived. We propose a new log-beta-Birnbaum-Saunders regression model that can be applied to censored data and be used more effectively in survival analysis. We obtain the maximum likelihood estimates of the model parameters for censored data and investigate influence diagnostics. The new location-scale regression model is modified for the possibility that long-term survivors may be presented in the data. Its usefulness is illustrated by means of two real data sets. (C) 2011 Elsevier B.V. All rights reserved.

Independent predictors and a prognostic model for surgical outcome in refractory frontal lobe epilepsy

Purpose: Refractory frontal lobe epilepsy (FLE) remains one of the most challenging surgically remediable epilepsy syndromes. Nevertheless, definition of independent predictors and predictive models of postsurgical seizure outcome remains poorly explored in FLE. Methods: We retrospectively analyzed data from 70 consecutive patients with refractory FLE submitted to surgical treatment at our center from July 1994 to December 2006. Univariate results were submitted to logistic regression models and Cox proportional hazards regression to identify isolated risk factors for poor surgical results and to construct predictive models for surgical outcome in FLE. Results: From 70 patients submitted to surgery, 45 patients (64%) had favorable outcome and 37 (47%) became seizure free. Isolated risk factors for poor surgical outcome are expressed in hazard ratio (H.R.) and were time of epilepsy (H.R.=4.2; 95% C.I.=.1.5-11.7; p=0.006), ictal EEG recruiting rhythm (H.R. = 2.9; 95% C.I. = 1.1-7.7; p=0.033); normal MRI (H.R. = 4.8; 95% C.I. = 1.4-16.6; p = 0.012), and MRI with lesion involving eloquent cortex (H.R. = 3.8; 95% C.I. = 1.2-12.0; p = 0.021). Based on these variables and using a logistic regression model we constructed a model that correctly predicted long-term surgical outcome in up to 80% of patients. Conclusion: Among independent risk factors for postsurgical seizure outcome, epilepsy duration is a potentially modifiable factor that could impact surgical outcome in FLE. Early diagnosis, presence of an MRI lesion not involving eloquent cortex, and ictal EEG without recruited rhythm independently predicted favorable outcome in this series. (C) 2011 Elsevier B.V. All rights reserved.

Why Are Women With Cervical Cancer Not Being Diagnosed in Preinvasive Phase? An Analysis of Risk Factors Using a Hierarchical Model

Objective: To assess the risk factors for delayed diagnosis of uterine cervical lesions. Materials and Methods: This is a case-control study that recruited 178 women at 2 Brazilian hospitals. The cases (n = 74) were composed of women with a late diagnosis of a lesion in the uterine cervix (invasive carcinoma in any stage). The controls (n = 104) were composed of women with cervical lesions diagnosed early on (low-or high-grade intraepithelial lesions). The analysis was performed by means of logistic regression model using a hierarchical model. The socioeconomic and demographic variables were included at level I (distal). Level II (intermediate) included the personal and family antecedents and knowledge about the Papanicolaou test and human papillomavirus. Level III (proximal) encompassed the variables relating to individuals' care for their own health, gynecologic symptoms, and variables relating to access to the health care system. Results: The risk factors for late diagnosis of uterine cervical lesions were age older than 40 years (odds ratio [OR] = 10.4; 95% confidence interval [CI], 2.3-48.4), not knowing the difference between the Papanicolaou test and gynecological pelvic examinations (OR, = 2.5; 95% CI, 1.3-4.9), not thinking that the Papanicolaou test was important (odds ratio [OR], 4.2; 95% CI, 1.3-13.4), and abnormal vaginal bleeding (OR, 15.0; 95% CI, 6.5-35.0). Previous treatment for sexually transmissible disease was a protective factor (OR, 0.3; 95% CI, 0.1-0.8) for delayed diagnosis. Conclusions: Deficiencies in cervical cancer prevention programs in developing countries are not simply a matter of better provision and coverage of Papanicolaou tests. The misconception about the Papanicolaou test is a serious educational problem, as demonstrated by the present study.

Prevalence of excessive daytime sleepiness and associated factors in women aged 35-49 years from the "Pindamonhangaba Health Project" (PROSAPIN)

OBJETIVO: Estimar a prevalência da sonolência diurna excessiva (SDE) e identificar os fatores associados em mulheres de 35 a 49 anos de idade do "Projeto de Saúde de Pindamonhangaba" (PROSAPIN). MÉTODOS: O estudo foi observacional transversal com 372 mulheres com idade entre 35 e 49 anos selecionadas aleatoriamente da Estratégia Saúde da Família (ESF) do município de Pindamonhangaba, São Paulo, onde é desenvolvido o "Projeto de Saúde de Pindamonhangaba" (PROSAPIN). A SDE foi avaliada por entrevista utilizando a Escala de Sonolência de Epworth e os fatores associados por meio de questões que investigaram as características sócio-demográficas, a história ginecológica, a presença de comorbidades, o estilo de vida, a rotina de sono e o uso de medicamentos capazes de alterar o estado de alerta, além de mensuradas as variáveis antropométricas. Estimou-se a prevalência da SDE com intervalo de confiança de 95% (IC 95%) e foram identificados os fatores associados por meio de um modelo de regressão logística múltipla realizado no Programa Stata, versão 10.0. RESULTADOS: A prevalência da SDE foi de 18,5% (IC 95%: 14,7- 22,9) e os fatores associados foram: profissão relacionada a serviços domésticos (OR = 2,2; IC 95%: 1,1-4,3), nível de atividade física acima da média da população estudada (OR = 1,9; IC 95%: 1,1-3,4); e a presença de características sugestivas de ansiedade (OR = 1,9; IC 95%: 1,1-3,4). CONCLUSÃO: A prevalência da SDE em mulheres de 35 a 49 anos do PROSAPIN foi elevada e associada à característica sociodemográfica, à presença de comorbidades e ao estilo de vida.

Association of polymorphisms of endothelial nitric oxide synthase (eNOS) gene with the risk of primary open angle glaucoma in a Brazilian population

The present study aimed to investigate the association of endothelial nitric oxide synthase (eNOS) gene polymorphisms with primary open angle glaucoma (POAG). We conducted a case-control study that included 90 patients with POAG and 127 healthy controls whose blood samples were genotyped for the functional polymorphisms T-786C and Glu298Asp of the eNOS gene by Taqman fluorescent allelic discrimination assay. The T-786C polymorphism was significantly associated as a risk factor for POAG among women (OR: 228; 95% CI: 1.11 to 4.70, p = 0.024) and marginally associated to the risk of POAG in the patients >= 52 years of age at diagnosis (OR: 2.11; 95% CI: 0.98 to 4.55, p = 0,055). However, these results was not confirmed after adjustments for gender, age, self-declared skin color, tobacco smoking and eNOS genotypes by multivariate logistic regression model (OR: 2.08; 95% CI: 0.87 to 5.01, p = 0.101 and OR: 2.20; 95% CI: 0.95 to 5.12, p = 0.067, respectively). The haplotype CG of T-786C and Glu298Asp showed a borderline association with risk of POAG in the overall analysis (OR: 1.76; 95% CI: 0.98 to 3.14, p = 0.055) and among women (OR: 2.02; 95% CI: 0.98 to 4.16, p = 0.052). Furthermore, the CG haplotype was significantly associated with the development of POAG for the age at diagnosis group >= 52 years (OR: 3.48; 95% CI: 1.54 to 7.84, p = 0.002). We suggested that haplotypes of the polymorphisms T-786C and Glu298Asp of eNOS may interact with gender and age in modulating the risk of POAG. (C) 2012 Elsevier B.V. All rights reserved.

Factors associated with preterm birth in Campina Grande, Paraiba State, Brazil: a case-control study

A case-control study (2008-2009) analyzed risk factors for preterm birth in the city of Campina Grande, Paraiba State, Brazil. A total of 341 preterm births and 424 controls were included. A multiple logistic regression model was used. Risk factors for preterm birth were: previous history of preterm birth (OR = 2.32; 95% CI: 1.25-4.29), maternal age (OR = 2.00; 95% CI: 1.00-4.03), inadequate prenatal care (OR = 2.15; 95% CI: 1.40-3.27), inadequate maternal weight gain (OR = 2.33; 95% CI: 1.45-3.75), maternal physical injury (OR = 2.10; 95% CI: 1.22-3.60), hypertension with eclampsia (OR = 17.08; 95% CI: 3.67-79.43) and without eclampsia (OR = 6.42; 95% CI: 3.50-11.76), hospitalization (OR = 5.64; 95% CI: 3.47-9.15), altered amniotic fluid volume (OR = 2.28; 95% CI: 1.32-3.95), vaginal bleeding (OR = 1.54; 95% CI: 1.01-2.34), and multiple gestation (OR = 22.65; 95% CI: 6.22-82.46). High and homogeneous prevalence of poverty and low maternal schooling among both cases and controls may have contributed to the fact that socioeconomic variables did not remain significantly associated with preterm birth.

Maternal obesity in high-risk pregnancies and postpartum infectious complications

Objective: To analyze the association between maternal obesity and postnatal infectious complications in high-risk pregnancies. Methods: Prospective study from August 2009 through August 2010 with the following inclusion criteria: women up to the 5th postpartum day; age L 18 years; high-risk pregnancy; singleton pregnancy with live fetus at labor onset; delivery at the institution; maternal weight measured on day of delivery. The nutritional status in late pregnancy was assessed by the body mass index (BMI), with the application of the Atalah et al. curve. Patients were graded as underweight, adequate weight, overweight, or obese. Postpartum complications investigated during the hospital stay and 30 days post-discharge were: surgical wound infection and/or secretion, urinary infection, postpartum infection, fever, hospitalization, antibiotic use, and composite morbidity (at least one of the complications mentioned). Results: 374 puerperal women were included, graded according to the final BMI as: underweight (n = 54, 14.4%); adequate weight (n = 126, 33.7%); overweight (n = 105, 28.1%); and obese (n = 89, 23.8%). Maternal obesity was shown to have a significant association with the following postpartum complications: surgical wound infection (16.7%, p = 0.042), urinary infection (9.0%, p = 0.004), antibiotic use (12.3%, p < 0.001), and composite morbidity (25.6%, p = 0.016). By applying the logistic regression model, obesity in late pregnancy was found to be an independent variable regardless of the composite morbidity predicted (OR: 2.09; 95% CI: 1.15-3.80, p = 0.015). Conclusion: Maternal obesity during late pregnancy in high-risk patients is independently associated with postpartum infectious complications, which demonstrates the need for a closer follow-up of maternal weight gain in these pregnancies.