Classification tree for the assessment of sedentary lifestyle among hypertensive / árbol de clasificación para la evaluación del estilo de vida sedentario entre las personas con hipertensión / árvore de classificação para a avaliação do estilo de vida sedentário entre pessoas com hipertensão

Objective.To develop a classification tree of clinical indicators for the correct prediction of the nursing diagnosis "Sedentary lifestyle" (SL) in people with high blood pressure (HTN). Methods. A cross-sectional study conducted in an outpatient care center specializing in high blood pressure and Mellitus diabetes located in northeastern Brazil. The sample consisted of 285 people between 19 and 59 years old diagnosed with high blood pressure and was applied an interview and physical examination, obtaining socio-demographic information, related factors and signs and symptoms that made the defining characteristics for the diagnosis under study. The tree was generated using the CHAID algorithm (Chi-square Automatic Interaction Detection). Results. The construction of the decision tree allowed establishing the interactions between clinical indicators that facilitate a probabilistic analysis of multiple situations allowing quantify the probability of an individual presenting a sedentary lifestyle. The tree included the clinical indicator Choose daily routine without exercise as the first node. People with this indicator showed a probability of 0.88 of presenting the SL. The second node was composed of the indicator Does not perform physical activity during leisure, with 0.99 probability of presenting the SL with these two indicators. The predictive capacity of the tree was established at 69.5%. Conclusion. Decision trees help nurses who care HTN people in decision-making in assessing the characteristics that increase the probability of SL nursing diagnosis, optimizing the time for diagnostic inference.

Objetivo.Desarrollar un árbol de clasificación de indicadores clínicos para la predicción correcta del diagnóstico de enfermería "Estilo de Vida Sedentario" (EVS) en personas con hipertensión arterial (HTA). Métodos. Estudio transversal, desarrollado en un centro ambulatorio especializado en hipertensión arterial y diabetes mellitus ubicado en el Noreste de Brasil. La muestra consistió en 285 personas entre 19 y 59 años con diagnóstico de hipertensión arterial a quienes se les practicó una entrevista y evaluación física, obteniéndose información sociodemográfica, factores relacionados y los signos y síntomas que conformaron las características definitorias para el diagnóstico en estudio. El árbol fue generado usando el algoritmo CHAID (Chi-square Automatic Interaction Detection). Resultados. La construcción del árbol de decisión permitió establecer las interacciones entre los indicadores clínicos que facilita un análisis probabilístico de múltiples situaciones, posibilitando cuantificar la probabilidad de un individuo de presentar un estilo de vida sedentario. El árbol incluyó el indicador clínico Elección de una rutina diaria de bajo contenido en actividad física en el primer nodo. Las personas con este indicador mostraron una probabilidad de 0.88 de presentar el EVS. El segundo nodo fue compuesto por el indicador No realiza actividades físicas en el tiempo libre, siendo la probabilidad de presentar el EVS por los individuos con estos dos indicadores de 0.99. La capacidad predictiva del árbol se estableció en un 69.5%. Conclusión. Los árboles de decisión ayudan a los enfermeros, que prestan cuidados a personas con HTA, en la toma de decisiones para realizar una evaluación de las características que aumentan la probabilidad de ocurrencia del diagnóstico de enfermería EVS, optimizando así el tempo para la inferencia diagnóstica.

Objetivo.Desenvolver uma árvore de classificação de indicadores clínicos para a predição correta do diagnóstico de enfermagem "Estilo de vida sedentário" (EVS) em pessoas com hipertensão arterial (HTA). Métodos. Estudo transversal, desenvolvido em um Centro de atendimento ambulatorial especializado em hipertensão arterial e diabetes mellitus localizado no nordeste do Brasil. A amostra consistiu em 285 pessoas entre 19 e 59 anos com diagnóstico de hipertensão arterial às quais se aplicou uma entrevista e avaliação física, obtendo-se informação sócio-demográfica, fatores relacionados e os sinais e sintomas que compunham as características definidoras para o diagnóstico em estudo. A árvore foi gerada utilizando o algoritmo CHAID (Chi-square Automatic Interaction Detection). Resultados. A construção da árvore de decisão permitiu estabelecer as interações entre os indicadores clínicos que facilita uma análise probabilística de múltiplas situações possibilitando quantificar a probabilidade de um indivíduo apresentar um estilo de vida sedentário. A árvore incluiu o indicador clínico Escolhe rotina diária sem exercício físico como primeiro nó. As pessoas com este indicador mostraram uma probabilidade de 0.88 de apresentar o EVS. O segundo nó foi composto pelo indicador Não realiza atividades físicas no tempo de lazer, sendo a probabilidade de apresentar o EVS com estes dois indicadores de 0.99. A Capacidade preditiva da árvore foi estabelecida em 69.5%. Conclusão. As árvores de decisão ajudam os enfermeiros que prestam cuidados a pessoas com HTA na tomada de decisão na avaliação das características que aumentam a probabilidade de ocorrência do diagnóstico de enfermagem EVS, otimizando assim o tempo para a inferência diagnóstica.

Classification tree for the assessment of sedentary lifestyle among hypertensive.

OBJECTIVE: To develop a classification tree of clinical indicators for the correct prediction of the nursing diagnosis "Sedentary lifestyle" (SL) in people with high blood pressure (HTN). METHODS: A cross-sectional study conducted in an outpatient care center specializing in high blood pressure and Mellitus diabetes located in northeastern Brazil. The sample consisted of 285 people between 19 and 59 years old diagnosed with high blood pressure and was applied an interview and physical examination, obtaining socio-demographic information, related factors and signs and symptoms that made the defining characteristics for the diagnosis under study. The tree was generated using the CHAID algorithm (Chi-square Automatic Interaction Detection). RESULTS: The construction of the decision tree allowed establishing the interactions between clinical indicators that facilitate a probabilistic analysis of multiple situations allowing quantify the probability of an individual presenting a sedentary lifestyle. The tree included the clinical indicator Choose daily routine without exercise as the first node. People with this indicator showed a probability of 0.88 of presenting the SL. The second node was composed of the indicator Does not perform physical activity during leisure, with 0.99 probability of presenting the SL with these two indicators. The predictive capacity of the tree was established at 69.5%. CONCLUSION: Decision trees help nurses who care HTN people in decision-making in assessing the characteristics that increase the probability of SL nursing diagnosis, optimizing the time for diagnostic inference.

Profile of nursing diagnoses in patients with respiratory disorders.

OBJECTIVE: Identify the profile of nursing diagnoses in patients with respiratory disorders. METHODOLOGY: A descriptive and cross-sectional study involving 38 patients with respiratory problems, of referral hospitals, in a city in northeastern Brazil, in the period from August to October, 2012. Data collection was performed using a form and diagnostic inference was made according with the Taxonomy II of NANDA I. RESULTS: The average age of the patients was 46 years and males predominated (60.5%). The most frequent nursing diagnoses were: Risk for infection (97.3%), Acute pain (68.4%), Poor knowledge (68.4%), Sedentary lifestyle (65.7%), Ineffective airway clearance (65.7%), Risk-prone health behavior (63.1%), Activity intolerance (52.6%) and Disturbed sleep pattern (33.3%). Evaluated patients exhibited an average of 8.6 nursing diagnoses (SD = 2.8). With respect to the defining characteristics and related factors the average per person was 7.2 and 9.3, respectively. CONCLUSION: In this group of patients the most frequent diagnoses were the domain activity/rest. Knowledge of nursing diagnoses profile presented by people with respiratory disorders is important, because it is part of the Nursing Process and nurses who take care of such patients should exercise them in their care practice. Knowledge of the mains nursing diagnosis presented by patients with respiratory disorders are important for the practice of nurses who care for these patients, because it allows the choice of responses to problems of their clientele.