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Objective: To assess the accuracy measurements for predisposing and precipitating Risk Factors for delirium in an adult Intensive Care Unit. Method: Cohort, prospective study with patients over 18 who had been hospitalized for over 24 hours and were able to communicate. The patients were assessed once a day until the onset of delirium or permanence in the Intensive Care Unit. Instruments were employed to track delirium, characterize the sample, and identify the risk factors. Descriptive statistics was employed for sample characterization and accuracy tests for risk factors. Results: The included patients amounted to 102, 31 of which presented delirium. The predisposing predictive risk factors were hypoalbuminemia, American Society of Anesthesiology over three, severity, altered tissue perfusion, dehydration, and being a male, whereas precipitating predictive factors were physical restraint, infection, pharmacological agent, polypharmacy, anemia, altered renal function, dehydration, invasive devices, altered tissue perfusion and altered quality and quantity of sleep. Conclusion: An accurate identification of predisposing and precipitating risk factors may contribute to planning preventive measures against delirium.
Objective: To generate a Classification Tree for the correct inference of the Nursing Diagnosis Fluid Volume Excess (00026) in chronic renal patients on hemodialysis. Method: Methodological, cross-sectional study with patients undergoing renal treatment. The data were collected through interviews and physical evaluation, using an instrument with socio-demographic variables, related factors, associated conditions and defining characteristics of the studied diagnosis. The classification trees were generated by the Chi-Square Automation Interaction Detection method, which was based on the Chi-square test. Results: A total of 127 patients participated, of which 79.5% (101) presented the diagnosis studied. The trees included the elements “Excessive sodium intake” and “Input exceeds output”, which were significant for the occurrence of the event, as the probability of occurrence of the diagnosis in the presence of these was 0.87 and 0.94, respectively. The prediction accuracy of the trees was 63% and 74%, respectively. Conclusion: The construction of the trees allowed to quantify the probability of the occurrence of Fluid Volume Excess (00026) in the studied population and the elements “Excessive sodium intake” and “Input exceeds output” were considered predictors of this diagnosis in the sample.
OBJECTIVE To clinically validate the nursing diagnosis, disturbed sleep pattern (00198), in patients with acute coronary syndrome. DATA COLLECTION A clinical validation study using the patient history, Visual Analog Sleep scale, and diagnostic inference. Accuracy techniques were performed. SYNTHESIS OF DATA There were 75 patients: 76.00% were men, and 82.66% had a myocardial infarction. The defining characteristic, changes in sleep pattern, presented a sensitivity of 0.900 and specificity of 0.9714. CONCLUSION Changes in sleep pattern was a predictor of the nursing diagnosis. IMPLICATIONS FOR PRACTICE Results will contribute to the identification of sleep disturbances, by nurses, emphasizing the importance of improving sleep during hospitalization. OBJETIVO Validar clinicamente o Diagnóstico de Enfermagem Padrão de Sono Prejudicado (00198) em pacientes com Síndrome Coronariana Aguda. MÉTODOS Estudo de validação clínica, utilizando Histórico de Enfermagem, Escalas Visuais Análogas do Sono e inferência diagnóstica. Realizada medidas de acurácia. RESULTADOS 75 pacientes, 76,00% homens e 82,66% com Infarto do Miocárdio. A característica definidora Alteração do Padrão de Sono teve 0,900 de Sensibilidade e 0,9714 de Especificidade. CONCLUSÕES Alteração do padrão de sono foi preditora do diagnóstico de enfermagem. IMPLICAÇÕES PARA A PRÁTICA Contribuirá na identificação de distúrbios do sono, pelos enfermeiros, ressaltando a importância da melhoria do sono durante a internação.
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