The APACHE II measured on patients' discharge from the Intensive Care Unit in the prediction of mortality

Cardoso, Luciana Gonzaga dos Santos; Chiavone, Paulo Antônio

doi:10.1590/s0104-11692013000300022

Cited by 8 publications

(14 citation statements)

References 24 publications

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“…Esses dados corroboram com pesquisas anteriores, as quais também apresentaram uma maior população de sexo masculino, com uma porcentagem variando de 54% a 60%, e uma população com idade superior a 60 anos, com a porcentagem variando de 53% a 58% (5)(6)(7)(8) .…”

Section: Discussionunclassified

“…(22)3: e50609, 2017 Quanto à procedência dos pacientes, a maioria teve acesso à UTI a partir do setor de emergência 44% (n = 254), seguido do centro cirúrgico 42% (n = 241). Essa variável está de acordo com algumas pesquisas, aonde o setor de emergência foi o que apresentou a maior taxa de procedência dos pacientes para a UTI, com a porcentagem variando de 26% a 40% (6,(9)(10) mas diverge de outras pesquisas, em que a maioria dos pacientes teve acesso à UTI a partir do centro cirúrgico, com a porcentagem variando de 36% a 55% (3,(7)(8) .…”

Section: Discussionunclassified

“…A liberação do paciente da UTI ocorre quando a condição hemodinâmica é estabilizada e não há mais a necessidade de ventilação mecânica invasiva ou de monitorização contínua e cuidados intensivos (8) . Dos pacientes que foram transferidos para outras unidades (n = 425, 74% do total), após a assistência na UTI, 94% (n = 398) foram transferidos para as enfermarias e somente 6% (n = 27) foram para unidade semi-intensiva.…”

Section: Discussionunclassified

“…O óbito pode estar relacionado à evolução natural da doença, quando todas as possibilidades terapêuticas já foram esgotadas. Porém, pode também ser decorrente de fatores como a limitação de recursos humanos e de disponibilidade de equipamentos, principalmente em serviços que não possuem unidades de cuidados intermediários ou, ainda, pode indicar alta prematura da UTI (3,8) .…”

Section: Discussionunclassified

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Perfil Epidemiológico E Seguimento Após Alta De Pacientes Internados Em Unidade De Terapia Intensiva

Albuquerque

Silva

Souza³

2017

Cogitare Enferm.

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RESUMO: Descrever o perfil epidemiológico e o seguimento após alta de pacientes internados em uma unidade de terapia intensiva. Pesquisa descritiva, seccional e de abordagem quantitativa, por meio de análise documental dos prontuários eletrônicos de pacientes adultos, internados na unidade de terapia intensiva em um hospital de atenção terciária na cidade do Rio de Janeiro, no período de novembro de 2014 a novembro de 2015. Na amostra (n=573) 73% apresentaram idade igual ou acima de 60 anos, que foi associada a maior risco de morte. A média do tempo de internação na unidade de terapia intensiva foi de 10,7 dias, a taxa de óbito na unidade de terapia intensiva foi de 26% (n= 148), sendo que 56% (n=112) foram a óbito por choque séptico. Foram readmitidos na unidade de terapia intensiva 10% (n= 44) dos pacientes que apresentaram maior risco relativo para óbito (p=0,001). As variáveis sexo feminino, idade acima de 60 anos, internação na unidade de terapia intensiva acima de 30 dias e readmissão na unidade são fatores de risco para óbito. DESCRITORES: Unidades de terapia intensiva; Readmissão do paciente; Mortalidade hospitalar; Choque séptico. PERFIL EPIDEMIOLÓGICO E SEGUIMENTO APÓS ALTA DE PACIENTES INTERNADOS EM UNIDADE DE TERAPIA INTENSIVA EPIDEMIOLOGICAL PROFILE AND MONITORING AFTER DISCHARGE OF PATIENTS HOSPITALIZED AT AN INTENSIVE CARE UNITABSTRACT: To describe the epidemiological profile and monitoring after discharge of patients hospitalized at an intensive care unit. Descriptive and cross-sectional research with a quantitative approach, using documentary analysis of adult patients' electronic histories, who were hospitalized at a tertiary care service in the city of Rio de Janeiro between November 2014 and November 2015.In the sample (n=573), 73% were 60 years of age or older, which was associated with a higher risk of death. The mean length of hospitalization at the intensive care unit was 10.7 days, the death rate at the intensive care unit was 26% (n= 148) and 56% (n=112) died of septic shock. Ten percent (n= 44) of patients with relative death risk (p=0.001) were readmitted to the intensive care unit. The variables female sex, age over 60 years, hospitalized at the intensive care unit for more than 30 days and readmission to the unit are risk factors for death. Investigación descriptiva, seccional y de aproximación cuantitativa, mediante análisis documental de los archivos electrónicos de pacientes adultos, internados en la unidad de terapia intensiva en un hospital de atención terciaria en la ciudad de Rio de Janeiro, entre noviembre del 2014 y noviembre del 2015. En la muestra (n=573), 73% tuvo edad igual o superior a 60 años, que fue asociada a mayor riesgo de muerte. El promedio del tiempo de hospitalización en la unidad de terapia intensiva fue 10.7 días, la tasa de óbito en la unidad de cuidados intensivos fue del 26% (n= 148), siendo que 56% (n=112) fallecieron por choque séptico. Fue readmitido en la unidad de terapia intensiva 10% (n= 44) de los pacientes que presentaron mayor riesgo relativo p...

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Section: Discussionunclassified

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Perfil Epidemiológico E Seguimento Após Alta De Pacientes Internados Em Unidade De Terapia Intensiva

Albuquerque

Silva

Souza³

2017

Cogitare Enferm.

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“…An AUC resultabove 0.7 is considered meaningful by one generic value scale [22], but studies have characterized results between 0.6 and 0.7 as "moderate" or "good" [23][24][25][26][27]. ROC curves are frequently used to assess the value of predictive measures, and have been used to optimize the analysis of patients who had poor outcomes after hospitalization for inflammatory pelvic disease [28], and to create a prognostic index of patient mortality after intensive care [29].…”

Section: Roc Analysismentioning

confidence: 99%

Optimization of Decision Support Tool using Medication Regimens to Assess Rehospitalization Risks

et al. 2014

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SummaryBackground: Unnecessary hospital readmissions are costly for the U.S. health care system. An automated algorithm was developed to target this problem and proven to predict elderly patients at greater risk of rehospitalization based on their medication regimens. Objective: Improve the algorithm for predicting elderly patients' risks for readmission by optimizing the sensitivity of its medication criteria. Methods: Outcome and Assessment Information Set (OASIS) and medication data were reused from a study that defined and tested an algorithm for assessing rehospitalization risks of 911 patients from 15 Medicare-certified home health care agencies. Odds Ratio analyses, literature reviews and clinical judgments were used to adjust the scoring of patients' High Risk Medication Regimens (HRMRs). Receiver Operating Characteristic (ROC) analysis evaluated whether these adjustments improved the predictive strength of the algorithm's components. Results: HRMR scores are composed of polypharmacy (number of drugs), potentially inappropriate medications (PIM) (drugs risky to the elderly), and Medication Regimen Complexity Index (MRCI) (complex dose forms, dose frequency, instructions or administration). Strongest ROC results for the HRMR components were Areas Under the Curve (AUC) of .68 for polypharmacy when excluding supplements; and .60 for PIM and .69 for MRCI using the original HRMR criteria. The "cut point" identifying MRCI scores as indicative of medication-related readmission risk was increased from 20 to 33. Conclusion: The automated algorithm can predict elderly patients at risk of hospital readmissions and its underlying criteria is improved by a modification to its polypharmacy definition and MRCI cut point.

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Predictive Value of Combined LIPS and ANG-2 Level in Critically Ill Patients with ARDS Risk Factors

et al. 2018

Mediators of Inflammation

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To investigate the predictive value of the acute physiology and chronic health evaluation 2 (APACHE2) score and lung injury prediction score (LIPS) for acute respiratory distress syndrome (ARDS) when combined with biomarkers for this condition in patients with ARDS risk factors. In total, 158 Han Chinese patients with ARDS risk factors were recruited from the Respiratory and Emergency Intensive Care Units. The LIPS, APACHE2 score, primary diagnosis at admission, and ARDS risk factors were determined within 6 h of admission, and PaO2/FiO2 was determined on the day of admission. Blood was collected within 24 h of admission for the measurement of angiopoietin-2 (ANG-2), sE-selectin, interleukin-6 (IL-6), and interleukin-8 (IL-8) levels. ARDS was monitored for the next 7 days. Univariate and multivariate analyses and receiver operating characteristic (ROC) analyses were employed to construct a model for ARDS prediction. Forty-eight patients developed ARDS within 7 days of admission. Plasma ANG-2 level, sE-selectin level, LIPS, and APACHE2 score in ARDS patients were significantly higher than those in non-ARDS patients. ANG-2 level, LIPS, and APACHE2 score were correlated with ARDS (P < 0.001, P < 0.006, and P < 0.042, resp.). When the APACHE2 score was used in combination with the LIPS and ANG-2 level to predict ARDS, the area under the ROC curve (AUC) was not significantly increased. Compared to LIPS or ANG-2 alone, LIPS in combination with ANG-2 had significantly increased positive predictive value (PPV) and AUC for the prediction of ARDS. In conclusion, plasma ANG-2 level, LIPS, and APACHE2 score are correlated with ARDS. Combined LIPS and ANG-2 level displays favorable sensitivity, specificity, and AUC for the prediction of ARDS.

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The APACHE II measured on patients' discharge from the Intensive Care Unit in the prediction of mortality

Cited by 8 publications

References 24 publications

Perfil Epidemiológico E Seguimento Após Alta De Pacientes Internados Em Unidade De Terapia Intensiva

Perfil Epidemiológico E Seguimento Após Alta De Pacientes Internados Em Unidade De Terapia Intensiva

Optimization of Decision Support Tool using Medication Regimens to Assess Rehospitalization Risks

Predictive Value of Combined LIPS and ANG-2 Level in Critically Ill Patients with ARDS Risk Factors

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