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Background: The use of instruments in clinical practice with measurement properties tested is highly recommended, in order to provide adequate assessment and measurement of outcomes. Objective: To calculate the minimum clinically important difference (MCID) and responsiveness of the Perme Intensive Care Unit Mobility Score (Perme Score). Methods: This retrospective, multicentric study investigated the clinimetric properties of MCID, estimated by constructing the Receiver Operating Characteristic (ROC). Maximizing sensitivity and specificity by Youden's, the ROC curve calibration was performed by the Hosmer and Lemeshow goodness-of-fit test. Additionally, we established the responsiveness, floor and ceiling effects, internal consistency, and predictive validity of the Perme Score. Results: A total of 1.200 adult patients records from four mixed general intensive care units (ICUs) were included. To analyze which difference clinically reflects a relevant evolution we calculated the area under the curve (AUC) of 0.96 (95% CI: 0.95-0.98), and the optimal cut-off value of 7.0 points was established. No substantial floor (8.8%) or ceiling effects (4.9%) were observed at ICU discharge. However, a moderate floor effect was observed at ICU admission (19.3%), in contrast to a very low incidence of ceiling effect (0.6%). The Perme Score at ICU admission was associated with hospital mortality, OR 0.86 (95% CI: 0.82-0.91), and the predictive validity for ICU stay presented a mean ratio of 0.97 (95% CI: 0.96-0.98). Conclusion: Our findings support the establishment of the minimum clinically important difference and responsiveness of the Perme Score as a measure of mobility status in the ICU.

Antecedentes: Se recomienda encarecidamente el uso de instrumentos en la práctica clínica con propiedades de medición probadas, con el fin de proporcionar una evaluación y medición adecuada de los resultados. Objetivo: Calcular la diferencia mínima clínicamente importante (MCID) y la capacidad de respuesta de la puntuación de movilidad de la unidad de cuidados intensivos de Perme (Perme Score). Métodos: Este estudio multicéntrico retrospectivo investigó las propiedades clinimétricas de MCID, estimadas mediante la construcción de la característica operativa del receptor (ROC). Maximizando la sensibilidad y especificidad mediante la prueba de Youden, la calibración de la curva ROC se realizó mediante la prueba de bondad de ajuste de Hosmer y Lemeshow. Además, establecimos la capacidad de respuesta, los efectos suelo y techo, la consistencia interna y la validez predictiva del Perme Score. Resultados: Se incluyeron un total de 1,200 registros de pacientes adultos de cuatro unidades de cuidados intensivos (UCI) generales mixtas. Para analizar qué diferencia refleja clínicamente una evolución relevante calculamos el área bajo la curva (AUC) de 0.96 (95% CI: 0.95-0.98); y se estableció el valor de corte óptimo de 7.0 puntos. No se observaron efectos suelo (8.8%) o techo (4.9%) sustanciales al alta de la UCI. Sin embargo, se observó un efecto suelo moderado al ingreso en la UCI (19.3%), en contraste con una incidencia muy baja del efecto techo (0.6%). El Perme Score al ingreso en UCI se asoció con la mortalidad hospitalaria, OR 0.86 (95% CI: 0.82-0.91), y la validez predictiva de estancia en UCI presentó una relación media de 0.97 (95% CI: 0.96-0.98). Conclusiones: Nuestros hallazgos respaldan el establecimiento de la diferencia mínima clínicamente importante y la capacidad de respuesta de el Perme Score como medida del estado de movilidad en la UCI.

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Background: The use of instruments in clinical practice with measurement properties tested is highly recommended, in order to provide adequate assessment and measurement of outcomes. Objective: To calculate the minimum clinically important difference (MCID) and responsiveness of the Perme Intensive Care Unit Mobility Score (Perme Score). Methods: This retrospective, multicentric study investigated the clinimetric properties of MCID, estimated by constructing the Receiver Operating Characteristic (ROC). Maximizing sensitivity and specificity by Youden's, the ROC curve calibration was performed by the Hosmer and Lemeshow goodness-of-fit test. Additionally, we established the responsiveness, floor and ceiling effects, internal consistency, and predictive validity of the Perme Score. Results: A total of 1.200 adult patients records from four mixed general intensive care units (ICUs) were included. To analyze which difference clinically reflects a relevant evolution we calculated the area under the curve (AUC) of 0.96 (95% CI: 0.95-0.98), and the optimal cut-off value of 7.0 points was established. No substantial floor (8.8%) or ceiling effects (4.9%) were observed at ICU discharge. However, a moderate floor effect was observed at ICU admission (19.3%), in contrast to a very low incidence of ceiling effect (0.6%). The Perme Score at ICU admission was associated with hospital mortality, OR 0.86 (95% CI: 0.82-0.91), and the predictive validity for ICU stay presented a mean ratio of 0.97 (95% CI: 0.96-0.98). Conclusion: Our findings support the establishment of the minimum clinically important difference and responsiveness of the Perme Score as a measure of mobility status in the ICU.

Antecedentes: Se recomienda encarecidamente el uso de instrumentos en la práctica clínica con propiedades de medición probadas, con el fin de proporcionar una evaluación y medición adecuada de los resultados. Objetivo: Calcular la diferencia mínima clínicamente importante (MCID) y la capacidad de respuesta de la puntuación de movilidad de la unidad de cuidados intensivos de Perme (Perme Score). Métodos: Este estudio multicéntrico retrospectivo investigó las propiedades clinimétricas de MCID, estimadas mediante la construcción de la característica operativa del receptor (ROC). Maximizando la sensibilidad y especificidad mediante la prueba de Youden, la calibración de la curva ROC se realizó mediante la prueba de bondad de ajuste de Hosmer y Lemeshow. Además, establecimos la capacidad de respuesta, los efectos suelo y techo, la consistencia interna y la validez predictiva del Perme Score. Resultados: Se incluyeron un total de 1,200 registros de pacientes adultos de cuatro unidades de cuidados intensivos (UCI) generales mixtas. Para analizar qué diferencia refleja clínicamente una evolución relevante calculamos el área bajo la curva (AUC) de 0.96 (95% CI: 0.95-0.98); y se estableció el valor de corte óptimo de 7.0 puntos. No se observaron efectos suelo (8.8%) o techo (4.9%) sustanciales al alta de la UCI. Sin embargo, se observó un efecto suelo moderado al ingreso en la UCI (19.3%), en contraste con una incidencia muy baja del efecto techo (0.6%). El Perme Score al ingreso en UCI se asoció con la mortalidad hospitalaria, OR 0.86 (95% CI: 0.82-0.91), y la validez predictiva de estancia en UCI presentó una relación media de 0.97 (95% CI: 0.96-0.98). Conclusiones: Nuestros hallazgos respaldan el establecimiento de la diferencia mínima clínicamente importante y la capacidad de respuesta de el Perme Score como medida del estado de movilidad en la UCI.

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OBJECTIVE: To verify the impact of the Lean Six Sigma methodology in reducing incorrect entries of non-appropriated revenues and expenses. METHODS: Process for the review and application of the Lean Six Sigma methodology between December 2015 and September 2016, in a high-complexity general hospital in the city of São Paulo (SP). RESULTS: A total of 3,756,814 (100%) entries were audited between December 2015 and September 2016. The Sigma level evolved over the course of the process and increased from 3.44 Sigma in December 2015 to 5.92 Sigma in September 2016. Entries classified as non-appropriated revenues and expenses were brought down to 0% at the end of the study. CONCLUSION: The use of the Lean Six Sigma methodology was efficient in reducing incorrect entries, calculating costs, ensuring compliance in rendering of accounts and accurately determining cost-outcome ratios.

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ABSTRACT Objective To verify the impact of the Lean Six Sigma methodology in reducing incorrect entries of non-appropriated revenues and expenses. Methods Process for the review and application of the Lean Six Sigma methodology between December 2015 and September 2016, in a high-complexity general hospital in the city of São Paulo (SP). Results A total of 3,756,814 (100%) entries were audited between December 2015 and September 2016. The Sigma level evolved over the course of the process and increased from 3.44 Sigma in December 2015 to 5.92 Sigma in September 2016. Entries classified as non-appropriated revenues and expenses were brought down to 0% at the end of the study. Conclusion The use of the Lean Six Sigma methodology was efficient in reducing incorrect entries, calculating costs, ensuring compliance in rendering of accounts and accurately determining cost-outcome ratios.

RESUMO Objetivo Verificar o impacto da metodologia Lean Seis Sigma na redução de lançamentos incorretos de receitas e despesas não apropriadas. Métodos Processo de revisão e aplicação de metodologia Lean Seis Sigma, no período de dezembro de 2015 e setembro de 2016, em um hospital geral de alta complexidade da cidade de São Paulo (SP). Resultados Foram auditados 3.756.814 (100%) lançamentos durante os meses de dezembro de 2015 e setembro de 2016. O nível Sigma foi demonstrado na evolução do processo e aumentou de 3,44 Sigma, em dezembro de 2015, para 5,92 Sigma, em setembro de 2016. Os lançamentos classificados como receitas e despesas não apropriadas chegaram a 0% ao término do estudo. Conclusão O uso da metodologia Lean Seis Sigma foi eficiente na redução de lançamentos incorretos, na apuração correta de custos, na garantia de compliance na prestação de contas e na acurácia no projeto de custos e desfechos.

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BACKGROUND: Diffuse alveolar damage (DAD), which is the histological surrogate for acute respiratory distress syndrome (ARDS), has a multifactorial aetiology. Therefore it is possible that the immunopathology differs among the various presentations of DAD. The aim of this study is to compare lung immunopathology of viral (influenza A(H1N1)pdm09) to non-viral, extrapulmonary aetiologies in autopsy cases with DAD. METHODS: The lung tissue of 44 patients, was divided in the H1N1 group (n = 15) characterized by severe pulmonary injury due to influenza A(H1N1)pdm09 infection; the ARDS group (n = 13), characterized by patients with DAD due to non-pulmonary causes; and the Control group (n = 16), consisting of patients with non-pulmonary causes of death. Immunohistochemistry and image analysis were used to quantify, in the parenchyma and small airways, several immune cell markers. RESULTS: Both DAD groups had higher expression of neutrophils and macrophages in parenchyma and small airways. However, there was a higher expression of CD4+ and CD8+ T lymphocytes, CD83+ dendritic cells, granzyme A+ and natural killer + cell density in the lung parenchyma of the H1N1 group (p < 0.05). In the small airways, there was a lower cell density of tryptase + mast cells and dendritic + cells and an increase of IL-17 in both DAD groups (p < 0.05). CONCLUSION: DAD due to viral A(H1N1)pdm09 is associated with a cytotoxic inflammatory phenotype, with partially divergent responses in the parenchyma relative to the small airways. In non-viral DAD, main immune cell alterations were found at the small airway level, reinforcing the role of the small airways in the pathogenesis of the exudative phase of DAD.

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