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AIMS: To identify risk factors present in patients with dysphagia in a population of critically ill patients. METHODS: Case series of a cohort of patients recruited in the intensive care unit (ICU) until hospital discharge. Patients who gave consent and met the inclusion criteria were recruited. The Volume-Viscosity clinical examination method was used for the screening of dysphagia. An uni- and bivariate statistical analysis was performed using odds ratio (OR) to detect risk factors for dysphagia. OUTCOMES: 103 patients were recruited from 401 possible. The mean age was 59,33 ± 13,23, men represented 76,7%. The severity of the sample was: APACHE II (12,74 ± 6,17) and Charlson (2,98 ± 3,31). 45,6% of patients showed dysphagia, obtaining significant OR values (p < 0,050) for the development of dysphagia: older age, neurological antecedents, COVID19, long stay in ICU and hospitalization, and the presence of tracheotomy. COVID19 patients represented 46,6% of the sample, so an analysis of this subgroup was performed, showing similar results, with a Charlson risk (OR:4,65; 95% CI:1,31-16,47; p = 0,014) and a hospital stay (OR: 8,50; 95%CI: 2,20-32,83; p < 0,001) On discharge from the ICU, 37,9% of the population still had dysphagia; 12,6% maintained this problem at hospital discharge. CONCLUSIONS: Almost half of our patients developed dysphagia. Clinical severity and the presence of tracheotomy were risk factors. We observed in patients with dysphagia a longer stay in both ICU and hospitalization.
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Aims: To identify risk factors present in patients with dysphagia in a population of critically ill patients. Methods: Case series of a cohort of patients recruited in the intensive care unit (ICU) until hospital discharge. Patients who gave consent and met the inclusion criteria were recruited. The Volume-Viscosity clinical examination method was used for the screening of dysphagia. An uni- and bivariate statistical analysis was performed using odds ratio (OR) to detect risk factors for dysphagia. Outcomes: 103 patients were recruited from 401 possible. The mean age was 59,33±13,23, men represented 76,7%. The severity of the sample was: APACHE II (12,74±6,17) and Charlson (2,98±3,31). 45,6% of patients showed dysphagia, obtaining significant OR values (p<0,050) for the development of dysphagia: older age, neurological antecedents, COVID19, long stay in ICU and hospitalization, and the presence of tracheotomy. COVID19 patients represented 46,6% of the sample, so an analysis of this subgroup was performed, showing similar results, with a Charlson risk (OR:4,65;95% CI:1,31-16,47;p=0,014) and a hospital stay (OR: 8,50;95%CI: 2,20-32,83;p<0,001). On discharge from the ICU, 37,9% of the population still had dysphagia;12,6% maintained this problem at hospital discharge. Conclusions: Almost half of our patients developed dysphagia. Clinical severity and the presence of tracheotomy were risk factors. We observed in patients with dysphagia a longer stay in both ICU and hospitalization. © 2022 Sociedad Española de Enfermería Intensiva y Unidades Coronarias (SEEIUC)
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Resumen Objetivos Determinar los factores de riesgo presentes en los pacientes con disfagia en relación con una población de pacientes críticos. Método Serie de casos de una cohorte de pacientes reclutados en la unidad de cuidados intensivos (UCI) hasta el alta hospitalaria. Se reclutaron a aquellos pacientes que dieron su consentimiento y cumplían los criterios de inclusión. El método de exploración clínica Volumen-Viscosidad fue utilizado para la detección de la disfagia. Se realizó un análisis estadístico uni- y bivariante, a través del odds ratio (OR) para detectar los factores de riesgo en la disfagia. Resultados 103 pacientes fueron reclutados de 401 posibles. La media de edad fue de 59,33±13,23;los hombres representaban el 76,7%. La gravedad media fue: APACHE II (12,74±6,17) y Charlson (2,98±3,31). Un 45,6% de los pacientes desarrollaron disfagia, obteniendo valores significativos de OR (p<0,050) para el desarrollo de disfagia: la mayor edad, los antecedentes neurológicos, COVID19, la alta estancia en UCI y hospitalización y la presencia de traqueotomía. Los pacientes COVID19 representaban el 46,6%, por lo que se realizó un análisis de este subgrupo observando resultados similares, con un riesgo de Charlson (OR:4,65;IC95%: 1,31-16,47;p=0,014) y una estancia hospitalaria (OR: 8,50;IC95%: 2,20-32,83;p<0,001). Al alta de UCI, el 37,9% de la población presentaba todavía disfagia, y mantenía este problema al alta hospitalaria el 12,6%. Conclusiones Casi la mitad de nuestros pacientes presentaron disfagia. Fueron factores de riesgo la gravedad clínica y la presencia de traqueotomía. Se observó en estos pacientes una mayor estancia tanto en UCI como en hospitalización. Aims To identify risk factors present in patients with dysphagia in a population of critically ill patients. Methods Case series of a cohort of patients recruited in the intensive care unit (ICU) until hospital discharge. Patients who gave consent and met the inclusion criteria were recruited. The Volume-Viscosity clinical examination method was used for the screening of dysphagia. An uni- and bivariate statistical analysis was performed using odds ratio (OR) to detect risk factors for dysphagia. Outcomes 103 patients were recruited from 401 possible. The mean age was 59,33±13,23, men represented 76,7%. The severity of the sample was: APACHE II (12,74±6,17) and Charlson (2,98±3,31). 45,6% of patients showed dysphagia, obtaining significant OR values (p<0,050) for the development of dysphagia: older age, neurological antecedents, COVID19, long stay in ICU and hospitalization, and the presence of tracheotomy. COVID19 patients represented 46,6% of the sample, so an analysis of this subgroup was performed, showing similar results, with a Charlson risk (OR:4,65;95% CI:1,31-16,47;p=0,014) and a hospital stay (OR: 8,50;95%CI: 2,20-32,83;p<0,001). On discharge from the ICU, 37,9% of the population still had dysphagia;12,6% maintained this problem at hospital discharge. Conclusions Almost half of our patients developed dysphagia. Clinical severity and the presence of tracheotomy were risk factors. We observed in patients with dysphagia a longer stay in both ICU and hospitalization.
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Due to COVID-19 related complications, many of the diagnosed patients end up needing intensive care. Complications are often severe, to such an extent that mortality rates in these patients may be high. Among the wide variety of complications, we find necrotizing tracheobronchitis, which appears suddenly with the obstruction of the endotracheal tube. This complication can cause severe damage to the patient or even death. In order to help clinicians with the management of this situation, we propose a Machine Learning-based methodology for detecting and anticipating the obstruction phenomenon. Through the use of Bayesian classifiers, classifier combination, morphological filtering and a track-while-scan detection mode we are able to establish an indicator function that serves as a reference to clinicians. Our experiments show promising results and lay the foundations of an intelligent system for early detection of endotracheal obstruction. © 2021, Springer Nature Switzerland AG.