Risk factors in patients with type 2 diabetes mellitus

El objetivo de este estudio fue verificar los factores de riesgo de las complicaciones de la diabetes mellitus tipo 2, por medio del levantamiento de datos sociodemográficos, hábitos de salud, perfil antropométrico y bioquímico, de pacientes diabéticos tipo 2 atendidos en una Unidad Básica de Salud en la ciudad de Maringá, Paraná. Fueron entrevistados y evaluados 66 pacientes con más de 50 años; 56 eran del sexo femenino. Se verificó una elevada presencia de factores de riesgo cardiovascular en los pacientes investigados: sobrepeso y obesidad, hipertensión, dislipidemia, sedentarismo y dieta no saludable. Los resultados indican la necesidad de la implantación de programas de intervención multidisciplinares en unidades básicas de la salud asociada a prácticas educativas, estimulando la adopción de una dieta saludable y la práctica de actividad física regular para estos pacientes.O objetivo deste estudo foi verificar os fatores de risco das complicações do diabetes mellitus tipo 2, por meio de levantamento de dados sociodemográficos, hábitos de saúde, perfil antropométrico e bioquímico de pacientes diabéticos tipo 2, atendidos em Unidade Básica de Saúde, na cidade de Maringá, Paraná. Foram entrevistados e avaliados 66 pacientes acima de 50 anos, sendo 56 do sexo feminino. Verificou-se elevada prevalência de fatores de risco cardiovascular nos pacientes investigados: sobrepeso e obesidade, hipertensão, dislipidemia, sedentarismo e dieta não saudável. Os resultados indicam a necessidade da implantação de programas de intervenção multidisciplinares em unidades básicas de saúde, associados a práticas educativas, estimulando a adoção de dieta saudável e a prática de atividade física regular para esses pacientes.This study was carried out to evaluate the risk factors of type 2 diabetic patients through sociodemographic data, habits of health, anthropometric and biochemist profiles, assisted at a basic public health care unit in Maringá, Paraná. Sixty-six patients, 56 women aged over than 50 years-old were interviewed. High prevalence factors for cardiovascular risk were observed, such as: overweight and obesity, hypertension, dyslipidemia, sedentariness and inadequate diet. Data suggested the need for multidisciplinary intervention programs in health care units associated to educative programs, adjusted diet intake and regular physical activity for these diabetic patients

Effect of aerobic physical exercise on the lipid profile in type 2 diabetic elderly women attended in a basic health unit in Maringá, Paraná State, Brazil

<p></p><p>ABSTRACT This study aims to evaluate the influence of a physical exercise program on the lipid profile in type 2 diabetic elderly women. The patients were selected from a Basic Unit of Health (Mandacaru - NIS II, Maringá, Paraná), that attends 200 type 2 diabetic subjects. Among these patients, 40 women aged 60 years or more were randomically selected. The sample was distributed in two groups of 20 patients each: the Trained Group (TG), that received nutritional instructions and participated in supervised sessions of aerobic physical exercise; Control Group (CG), that received only nutritional instructions. The exercises were applied three times a week, one hour each session. Results showed that the adopted protocol promoted a significant reduction in triglycerides levels (pre-test = 190 ± 76,67 and post-test = 125,33 ± 45,82 mg/dL, p<0,05) and also in the LDL-cholesterol (pre-test = 147,98 ± 29,98 and post-test = 122,24 ± 17,61 mg/dL, p<0,05) for the TG. Therefore, we concluded that the adopted exercise program improved the lipid profile in type 2 diabetic elderly women , showing the importance of practicing oriented physical activity in health primary units.</p><p></p

Risk factors in patients with type 2 diabetes mellitus Factores de riesgo en pacientes con diabetes mellitus tipo 2 Fatores de risco em pacientes com diabetes mellitus tipo 2

This study was carried out to evaluate the risk factors of type 2 diabetic patients through sociodemographic data, habits of health, anthropometric and biochemist profiles, assisted at a basic public health care unit in Maringá, Paraná. Sixty-six patients, 56 women aged over than 50 years-old were interviewed. High prevalence factors for cardiovascular risk were observed, such as: overweight and obesity, hypertension, dyslipidemia, sedentariness and inadequate diet. Data suggested the need for multidisciplinary intervention programs in health care units associated to educative programs, adjusted diet intake and regular physical activity for these diabetic patients.<br>El objetivo de este estudio fue verificar los factores de riesgo de las complicaciones de la diabetes mellitus tipo 2, por medio del levantamiento de datos sociodemográficos, hábitos de salud, perfil antropométrico y bioquímico, de pacientes diabéticos tipo 2 atendidos en una Unidad Básica de Salud en la ciudad de Maringá, Paraná. Fueron entrevistados y evaluados 66 pacientes con más de 50 años; 56 eran del sexo femenino. Se verificó una elevada presencia de factores de riesgo cardiovascular en los pacientes investigados: sobrepeso y obesidad, hipertensión, dislipidemia, sedentarismo y dieta no saludable. Los resultados indican la necesidad de la implantación de programas de intervención multidisciplinares en unidades básicas de la salud asociada a prácticas educativas, estimulando la adopción de una dieta saludable y la práctica de actividad física regular para estos pacientes.<br>O objetivo deste estudo foi verificar os fatores de risco das complicações do diabetes mellitus tipo 2, por meio de levantamento de dados sociodemográficos, hábitos de saúde, perfil antropométrico e bioquímico de pacientes diabéticos tipo 2, atendidos em Unidade Básica de Saúde, na cidade de Maringá, Paraná. Foram entrevistados e avaliados 66 pacientes acima de 50 anos, sendo 56 do sexo feminino. Verificou-se elevada prevalência de fatores de risco cardiovascular nos pacientes investigados: sobrepeso e obesidade, hipertensão, dislipidemia, sedentarismo e dieta não saudável. Os resultados indicam a necessidade da implantação de programas de intervenção multidisciplinares em unidades básicas de saúde, associados a práticas educativas, estimulando a adoção de dieta saudável e a prática de atividade física regular para esses pacientes

Variables selected for this study.

Smoking cessation is an important public health policy worldwide. However, as far as we know, there is a lack of screening of variables related to the success of therapeutic intervention (STI) in Brazilian smokers by machine learning (ML) algorithms. To address this gap in the literature, we evaluated the ability of eight ML algorithms to correctly predict the STI in Brazilian smokers who were treated at a smoking cessation program in Brazil between 2006 and 2017. The dataset was composed of 12 variables and the efficacies of the algorithms were measured by accuracy, sensitivity, specificity, positive predictive value (PPV) and area under the receiver operating characteristic curve. We plotted a decision tree flowchart and also measured the odds ratio (OR) between each independent variable and the outcome, and the importance of the variable for the best model based on PPV. The mean global values for the metrics described above were, respectively, 0.675±0.028, 0.803±0.078, 0.485±0.146, 0.705±0.035 and 0.680±0.033. Supporting vector machines performed the best algorithm with a PPV of 0.726±0.031. Smoking cessation drug use was the roof of decision tree with OR of 4.42 and importance of variable of 100.00. Increase in the number of relapses also promoted a positive outcome, while higher consumption of cigarettes resulted in the opposite. In summary, the best model predicted 72.6% of positive outcomes correctly. Smoking cessation drug use and higher number of relapses contributed to quit smoking, while higher consumption of cigarettes showed the opposite effect. There are important strategies to reduce the number of smokers and increase STI by increasing services and drug treatment for smokers.</div

Decision tree flowchart.

Smoking cessation is an important public health policy worldwide. However, as far as we know, there is a lack of screening of variables related to the success of therapeutic intervention (STI) in Brazilian smokers by machine learning (ML) algorithms. To address this gap in the literature, we evaluated the ability of eight ML algorithms to correctly predict the STI in Brazilian smokers who were treated at a smoking cessation program in Brazil between 2006 and 2017. The dataset was composed of 12 variables and the efficacies of the algorithms were measured by accuracy, sensitivity, specificity, positive predictive value (PPV) and area under the receiver operating characteristic curve. We plotted a decision tree flowchart and also measured the odds ratio (OR) between each independent variable and the outcome, and the importance of the variable for the best model based on PPV. The mean global values for the metrics described above were, respectively, 0.675±0.028, 0.803±0.078, 0.485±0.146, 0.705±0.035 and 0.680±0.033. Supporting vector machines performed the best algorithm with a PPV of 0.726±0.031. Smoking cessation drug use was the roof of decision tree with OR of 4.42 and importance of variable of 100.00. Increase in the number of relapses also promoted a positive outcome, while higher consumption of cigarettes resulted in the opposite. In summary, the best model predicted 72.6% of positive outcomes correctly. Smoking cessation drug use and higher number of relapses contributed to quit smoking, while higher consumption of cigarettes showed the opposite effect. There are important strategies to reduce the number of smokers and increase STI by increasing services and drug treatment for smokers.</div

Flowchart of medical records selected for descriptive and predictive analysis.

Flowchart of medical records selected for descriptive and predictive analysis.</p

Mean and standard deviation of the predictive values of machine learning models evaluated.

Mean and standard deviation of the predictive values of machine learning models evaluated.</p

Forest plot of odds ratio and confidence interval of variables according to smoking cessation.

Forest plot of odds ratio and confidence interval of variables according to smoking cessation.</p

Flowchart of supervised and classificatory machine learning algorithms performance evaluation and best model selection.

Flowchart of supervised and classificatory machine learning algorithms performance evaluation and best model selection.</p

Image of therapeutic intervention success probability equator prototype.

Image of therapeutic intervention success probability equator prototype.</p