Decrease in Readmissions after Hospitalisation for COPD Exacerbation through a Home Care Model.

Objective: To decrease readmissions at 30 and 90 days post-discharge from a hospital admission for chronic obstructive pulmonary disease exacerbation (COPDE) through the home care model of the Ambulatory Chronic Respiratory Care Unit (ACRCU), increase patient survival at one year, and validate our readmission risk scale (RRS). Materials and methods: This was an observational study, with a prospective data collection and a retrospective data analysis. A total of 491 patients with a spirometry diagnosis of chronic obstructive pulmonary disease (COPD) requiring hospitalisation for an exacerbation were included in the study. Subjects recruited within the first year (204 cases) received conventional care (CC). In the following year a home care (HC) programme was implemented and of those recruited that year (287) 104 were included in the ACRCU, administered by a specialised nurse. Results: In the group of patients included in the home care model of the Ambulatory Chronic Respiratory Care Unit (ACRCU) a lower number of readmissions was observed at 30 and 90 days after discharge (30.5% vs. 50%, p = 0.012 and 47.7% vs. 65.2%, p = 0.031, respectively) and a greater one-year survival (85.3% vs. 59.1%, p < 0.001). The validation of our RRS revealed that the tool's capacity to predict readmissions at both 30 and 90 days was not high (AUC = 0.69 and AUC = 0.66, respectively). Conclusions: The inclusion of exacerbator or fragile COPD patients in the ACRCU could achieve a decrease in readmissions and an increase in survival. The number of episodes of exacerbation within the 12 months prior to the hospital admission is the variable that best predicts the risk of readmission.

Objetivo: Disminuir los reingresos a los 30 y 90 días tras el alta por un ingreso hospitalario por exacerbación de enfermedad pulmonar obstructiva crónica (EPOC) a través del modelo de atención domiciliaria de la Unidad de Cuidados Crónicos Respiratorios Ambulatorios (UCCRA), aumentar la supervivencia al año y validar nuestra escala de riesgo de reingreso (ERR). Material y métodos: Estudio observacional con recogida prospectiva de datos. Se incluyó en el estudio a un total de 491 pacientes con diagnóstico espirométrico de enfermedad pulmonar obstructiva crónica que requirieron hospitalización por una agudización. Los sujetos reclutados dentro del primer año (204 casos) recibieron atención convencional (AC). Al año siguiente se implementó un programa de atención domiciliaria (AD) y de los pacientes reclutados ese año (287), 104 fueron incluidos en la UCCRA con seguimiento de una enfermera especializada. Resultados: En el grupo de pacientes incluidos en el modelo de atención domiciliaria de la UCCRA se observó un menor número de reingresos a los 30 y 90 días tras el alta (30,5% vs 50%, p = 0,012 y 47,7% vs. 65,2%, p = 0,031, respectivamente) y una mayor supervivencia al año (85,3% vs. 59,1%, p < 0,001). La validación de nuestra ERR reveló que la capacidad de la misma para predecir reingresos tanto a los 30 como a los 90 días no era alta (AUC = 0,69 y AUC = 0,66, respectivamente). Conclusiones: La inclusión de pacientes con EPOC agudizadores o frágiles en la UCCRA podría conseguir una disminución de los reingresos y una aumento de la supervivencia. El número de agudizaciones en los 12 meses previos al ingreso hospitalario es la variable que mejor predice el riesgo de reingreso.

Patient Management Assisted by a Neural Network Reduces Mortality in an Intermediate Care Unit / El manejo del paciente asistido por una red neuronal disminuye la mortalidad en una unidad de cuidados intermedios

INTRODUCTION: Mortality risk prediction for Intermediate Respiratory Care Unit's (IRCU) patients can facilitate optimal treatment in high-risk patients. While Intensive Care Units (ICUs) have a long term experience in using algorithms for this purpose, due to the special features of the IRCUs, the same strategics are not applicable. The aim of this study is to develop an IRCU specific mortality predictor tool using machine learning methods. METHODS: Vital signs of patients were recorded from 1966 patients admitted from 2007 to 2017 in the Jiménez Díaz Foundation University Hospital's IRCU. A neural network was used to select the variables that better predict mortality status. Multivariate logistic regression provided us cut-off points that best discriminated the mortality status for each of the parameters. A new guideline for risk assessment was applied and mortality was recorded during one year. RESULTS: Our algorithm shows that thrombocytopenia, metabolic acidosis, anemia, tachypnea, age, sodium levels, hypoxemia, leukocytopenia and hyperkalemia are the most relevant parameters associated with mortality. First year with this decision scene showed a decrease in failure rate of a 50%. CONCLUSIONS: We have generated a neural network model capable of identifying and classifying mortality predictors in the IRCU of a general hospital. Combined with multivariate regression analysis, it has provided us with an useful tool for the real-time monitoring of patients to detect specific mortality risks. The overall algorithm can be scaled to any type of unit offering personalized results and will increase accuracy over time when more patients are included to the cohorts

INTRODUCCIÓN: La predicción del riesgo de mortalidad de los pacientes en la unidad de cuidados respiratorios intermedios (UCRI) puede facilitar un tratamiento óptimo en pacientes de alto riesgo. Si bien las unidades de cuidados intensivos (UCI) tienen una experiencia a largo plazo en el uso de algoritmos para este propósito, debido a las características especiales de las UCRI, no se pueden aplicar las mismas estrategias. El objetivo de este estudio es desarrollar una herramienta de predicción de mortalidad específica para la UCRI utilizando métodos de aprendizaje automático. MÉTODOS: Se registraron los signos vitales de 1.966 pacientes ingresados entre 2007 y 2017 en la UCRI del Hospital Universitario de la Fundación Jiménez Díaz. Se utilizó una red neuronal para seleccionar las variables que mejor predijeran el estado de mortalidad. La regresión logística multivariante nos proporcionó los puntos de corte que discriminaban mejor el estado de la mortalidad para cada uno de los parámetros. Se aplicó una nueva guía para la evaluación de riesgos, y se registró la mortalidad durante un año. RESULTADOS: Nuestro algoritmo muestra que la trombocitopenia, la acidosis metabólica, la anemia, la taquipnea, la edad, los niveles de sodio, la hipoxemia, la leucocitopenia y la hipercalemia son los parámetros más relevantes asociados con la mortalidad. En el primer año con este escenario de decisión se mostró una disminución en la tasa de fracaso de un 50%. CONCLUSIONES: Hemos generado un modelo de red neuronal capaz de identificar y clasificar predictores de mortalidad en la UCRI de un hospital general. Combinado con el análisis de regresión multivariante, nos ha proporcionado una herramienta útil para la monitorización en tiempo real de pacientes para detectar riesgos de mortalidad específicos. El algoritmo general se puede modificar a escala para cualquier tipo de unidad, lo que ofrecerá resultados personalizados, y su precisión aumentará con el tiempo, según se incluyan más pacientes en las cohortes

Mycobacterium malmoense, ¿ha llegado para quedarse? / Mycobacterium malmoense. Is It Here to Stay?

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Patient Management Assisted by a Neural Network Reduces Mortality in an Intermediate Care Unit.

INTRODUCTION: Mortality risk prediction for Intermediate Respiratory Care Unit's (IRCU) patients can facilitate optimal treatment in high-risk patients. While Intensive Care Units (ICUs) have a long term experience in using algorithms for this purpose, due to the special features of the IRCUs, the same strategics are not applicable. The aim of this study is to develop an IRCU specific mortality predictor tool using machine learning methods. METHODS: Vital signs of patients were recorded from 1966 patients admitted from 2007 to 2017 in the Jiménez Díaz Foundation University Hospital's IRCU. A neural network was used to select the variables that better predict mortality status. Multivariate logistic regression provided us cut-off points that best discriminated the mortality status for each of the parameters. A new guideline for risk assessment was applied and mortality was recorded during one year. RESULTS: Our algorithm shows that thrombocytopenia, metabolic acidosis, anemia, tachypnea, age, sodium levels, hypoxemia, leukocytopenia and hyperkalemia are the most relevant parameters associated with mortality. First year with this decision scene showed a decrease in failure rate of a 50%. CONCLUSIONS: We have generated a neural network model capable of identifying and classifying mortality predictors in the IRCU of a general hospital. Combined with multivariate regression analysis, it has provided us with an useful tool for the real-time monitoring of patients to detect specific mortality risks. The overall algorithm can be scaled to any type of unit offering personalized results and will increase accuracy over time when more patients are included to the cohorts.

Análisis de costes y mortalidad de una unidad de cuidados intermedios respiratorios. ¿Es realmente eficiente y segura? / Cost and Mortality Analysis of an Intermediate Respiratory Care Unit. Is It Really Efficient and Safe?

Introducción: Históricamente se ha asumido que las unidades de cuidados intermedios respiratorios (UCIR) eran estructuras eficientes por los costes evitados atribuibles a la reducción de los ingresos en las unidades de cuidados intensivos (UCI) y eficaces por la especialización neumológica. Métodos: Se evaluó el número de ingresos y mortalidad en la unidad, histórica y en el año 2016. Ese año además se describieron los grupos relacionados de diagnóstico (GRD) agrupados y el coste evitado por estancia en UCI en relación con todos los capítulos presupuestarios. Se realizó un análisis multivariante para asociar costes a pesos medios y complejidad y se realizó una regresión logística múltiple sobre la totalidad de enfermos ingresados de 2004 a 2017 para describir las variables asociadas a la mortalidad en nuestra unidad. Resultados: La UCIR evita un coste al hospital de 500.000 €/año al reducir días de estancia en las UCI. El análisis sobre la cohorte de 2016 describe que los costes se asocian al peso medio y mortalidad, y por tanto, a la complejidad. El análisis de regresión logística multivariante sobre la cohorte de 2004-2017 describe la frecuencia respiratoria, la leucopenia, la anemia, la hiperpotasemia y la acidosis como las variables que mejor se asocian con la mortalidad. El área bajo la curva para el modelo logístico fue de 0,75. Conclusión: La UCIR analizada ha demostrado ser eficiente en términos de «coste evitado» y ahorro ligado a la complejidad. Nuestros resultados sugieren que las UCIR son un entorno seguro para los pacientes al tener una mortalidad menor que otras unidades similares

Introduction: Historically, it has been assumed that Intermediate Respiratory Care Units (IRCU) were efficient, because they saved costs by reducing the number of admissions to intensive care units (ICU), and effective, because they specialized in respiratory diseases. Methods: The number of IRCU admissions and mortality rate, historically and in 2016, were evaluated. For 2016, the grouped Related Diagnostic Groups (DRGs) were also described, and the savings achieved under all budgetary headings by avoiding UCI stays were calculated. A multivariate analysis was performed to associate costs with mean weights and complexity, and multiple logistic regression was performed on all patients admitted from 2004 to 2017 to describe the variables associated with mortality in our unit. Results: An IRCU generates savings of 500,000 €/year by reducing length of ICU stay. Analysis of the 2016 cohort shows that costs correlate with mean weight and mortality, and consequently complexity. The multivariate logistic regression analysis of the 2004-2017 cohort found respiratory frequency, leukopenia, anemia, hyperkalemia, and acidosis to be the variables best associated with mortality. The area under the curve for the logistic model was 0.75. Conclusion: The IRCU analyzed in our study was efficient in terms of "avoided costs" and savings associated with complexity. Our results suggest that IRCUs have a lower mortality rate than other similar units, and are therefore a safe environment for patients

Cost and Mortality Analysis of an Intermediate Respiratory Care Unit. Is It Really Efficient and Safe? / Análisis de costes y mortalidad de una unidad de cuidados intermedios respiratorios. ¿Es realmente eficiente y segura?

INTRODUCTION: Historically, it has been assumed that Intermediate Respiratory Care Units (IRCU) were efficient, because they saved costs by reducing the number of admissions to intensive care units (ICU), and effective, because they specialized in respiratory diseases. METHODS: The number of IRCU admissions and mortality rate, historically and in 2016, were evaluated. For 2016, the grouped Related Diagnostic Groups (DRGs) were also described, and the savings achieved under all budgetary headings by avoiding UCI stays were calculated. A multivariate analysis was performed to associate costs with mean weights and complexity, and multiple logistic regression was performed on all patients admitted from 2004 to 2017 to describe the variables associated with mortality in our unit. RESULTS: An IRCU generates savings of €500,000/year by reducing length of ICU stay. Analysis of the 2016 cohort shows that costs correlate with mean weight and mortality, and consequently complexity. The multivariate logistic regression analysis of the 2004-2017 cohort found respiratory frequency, leukopenia, anemia, hyperkalemia, and acidosis to be the variables best associated with mortality. The area under the curve for the logistic model was 0.75. CONCLUSION: The IRCU analyzed in our study was efficient in terms of 'avoided costs' and savings associated with complexity. Our results suggest that IRCUs have a lower mortality rate than other similar units, and are therefore a safe environment for patients.

Continuous monitoring of intrinsic PEEP based on expired CO2 kinetics: an experimental validation study.

BACKGROUND: Quantification of intrinsic PEEP (PEEPi) has important implications for patients subjected to invasive mechanical ventilation. A new non-invasive breath-by-breath method (etCO2D) for determination of PEEPi is evaluated. METHODS: In 12 mechanically ventilated pigs, dynamic hyperinflation was induced by interposing a resistance in the endotracheal tube. Airway pressure, flow, and exhaled CO2 were measured at the airway opening. Combining different I:E ratios, respiratory rates, and tidal volumes, 52 different levels of PEEPi (range 1.8-11.7 cmH2O; mean 8.45 ± 0.32 cmH2O) were studied. The etCO2D is based on the detection of the end-tidal dilution of the capnogram. This is measured at the airway opening by means of a CO2 sensor in which a 2-mm leak is added to the sensing chamber. This allows to detect a capnogram dilution with fresh air when the pressure coming from the ventilator exceeds the PEEPi. This method was compared with the occlusion method. RESULTS: The etCO2D method detected PEEPi step changes of 0.2 cmH2O. Reference and etCO2D PEEPi presented a good correlation (R2 0.80, P < 0.0001) and good agreement, bias - 0.26, and limits of agreement ± 1.96 SD (2.23, - 2.74) (P < 0.0001). CONCLUSIONS: The etCO2D method is a promising accurate simple way of continuously measure and monitor PEEPi. Its clinical validity needs, however, to be confirmed in clinical studies and in conditions with heterogeneous lung diseases.

Evolución y coexistencia de sarcoidosis con procesos linfoproliferativos / Development and co-existence of sarcoidosis with lymphoproliferative processes

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