Predictors of mortality in acute kidney injury patients admitted to medicine intensive care unit in a Rural Tertiary Care Hospital

Saxena, Amrish; Meshram, Shrikant V

doi:10.4103/ijccm.ijccm_462_17

Cited by 32 publications

(33 citation statements)

References 33 publications

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“…Notably, our study determined that the mortality rates of sepsis and acute renal failure were identical. A study that analysed this occurrence demonstrated that sepsis was the most common cause of acute renal failure (19). In our study, in the geriatric group aged over 85 years, the mortality rate was higher than 85% in patients with high fever and acute renal failure, probably secondary to sepsis.…”

Section: Discussionsupporting

confidence: 39%

Diagnosis and Mortality Outcomes of Three Different Geriatric Age Groups in the Intensive Care Unit: 10-Year Retrospective Analysis

Görgülü¹,

Koşar²

2020

tjgeri

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In addition to bringing many physiological changes with it, aging is a period in which morbidity and mortality rates increase due to the increase in the incidence of chronic diseases and malignant diseases. The aim of this study was to determine the prevalence of conditions requiring intensive care and the mortality incidence based on geriatric age groups followed-up in the intensive care unit. Materials and Method: Three different geriatric age groups were formed out of the 65 years old and older patients (n=5932) included in the study as young old (65-74 years), middle old (75-84 years) and oldest old (85 years and older). In each group, disease prevalence and mortality rate by diagnostic groups were analysed. Results: The resulting diagnostic groups were found to be at different rates in different age groups (p = 0.01 p <0.05). Malignancy was high in young elderly patients (p = 0.01 p <0.05), while cardiovascular diseases were higher in other groups (p = 0.01 p<0.05). Patients admitted to the intensive care unit with cardiac arrest had the highest mortality rate (p = 0.01 p<0.05). Conclusion: Geriatric patients constitute an important part of intensive care patients, therefore the mortality rate is high. In all three groups, 30-day mortality results were highest in patients with cardiac arrest followed by sepsis and cardiovascular diseases, respectively.

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Section: Discussionsupporting

confidence: 39%

Diagnosis and Mortality Outcomes of Three Different Geriatric Age Groups in the Intensive Care Unit: 10-Year Retrospective Analysis

Görgülü¹,

Koşar²

2020

tjgeri

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“…Acute kidney injury (AKI) is common in the intensive care unit (ICU), and there is evidence that even a small increase in serum creatinine may be associated with increased risk of mortality [1–3]. AKI can be defined by either an elevation in serum creatinine or a reduced urine output according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines [4].…”

Section: Introductionmentioning

confidence: 99%

Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care

2019

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Background and objectives Excess fluid balance in acute kidney injury (AKI) may be harmful, and conversely, some patients may respond to fluid challenges. This study aimed to develop a prediction model that can be used to differentiate between volume-responsive (VR) and volume-unresponsive (VU) AKI. Methods AKI patients with urine output < 0.5 ml/kg/h for the first 6 h after ICU admission and fluid intake > 5 l in the following 6 h in the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-III)) were considered. Patients who received diuretics and renal replacement on day 1 were excluded. Two predictive models, using either machine learning extreme gradient boosting (XGBoost) or logistic regression, were developed to predict urine output > 0.65 ml/kg/h during 18 h succeeding the initial 6 h for assessing oliguria. Established models were assessed by using out-of-sample validation. The whole sample was split into training and testing samples by the ratio of 3:1. Main results Of the 6682 patients included in the analysis, 2456 (36.8%) patients were volume responsive with an increase in urine output after receiving > 5 l fluid. Urinary creatinine, blood urea nitrogen (BUN), age, and albumin were the important predictors of VR. The machine learning XGBoost model outperformed the traditional logistic regression model in differentiating between the VR and VU groups (AU-ROC, 0.860; 95% CI, 0.842 to 0.878 vs. 0.728; 95% CI 0.703 to 0.753, respectively). Conclusions The XGBoost model was able to differentiate between patients who would and would not respond to fluid intake in urine output better than a traditional logistic regression model. This result suggests that machine learning techniques have the potential to improve the development and validation of predictive modeling in critical care research.

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“…Estudos recentes mostram que o APACHE II é considerado um adequado índice preditivo de complicações neurológicas em pacientes cardíacos comatosos após parada cardiorrespiratória 27 e uma excelente ferramenta prognóstica para mortalidade em pacientes com lesão renal aguda. 28 Entretanto, essas pesquisas, apesar de analisarem complicações neurológicas e renais ocorridas em UTI, não abordaram especificamente pacientes submetidos à CRM isolada.…”

Section: Discussionunclassified

Desempenho dos Índices de Gravidade na Predição de Complicações Pós-Operatórias de Revascularização Miocárdica

Franzotti¹,

Sloboda²,

Silva³

et al. 2020

Arquivos Brasileiros De Cardiologia

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Fundamento: Os pacientes em pós-operatório (PO) de cirurgia de revascularização miocárdica (CRM) internados em unidade de terapia intensiva (UTI) apresentam risco de complicações que aumentam o tempo de permanência e a morbimortalidade. Portanto, é fundamental o reconhecimento precoce desses riscos para otimizar estratégias de prevenção e desfecho clínico satisfatório. Objetivo: Analisar o desempenho de índices de gravidade na predição de complicações em pacientes no PO de CRM durante a permanência na UTI. Métodos: Estudo transversal, com análise retrospectiva de prontuários eletrônicos de pacientes com idade ≥ 18 anos submetidos à CRM isolada e admitidos na UTI de um hospital cardiológico, em São Paulo, Brasil. As áreas sob as curvas receiver operating characteristic (AUC) com intervalo de confiança de 95% foram analisadas para verificar a acurácia dos índices European System for Cardiac Operative Risk Evaluation (EuroScore), Acute Physiology and Chronic Health Evaluation (APACHE II), Simplified Acute Physiology Score (SAPS II) e Sequential Organ Failure Assessment (SOFA) na predição de complicações. Resultados: A casuística foi composta por 366 pacientes (64,58±9,42 anos; 75,96% sexo masculino). As complicações identificadas foram respiratórias (24,32%), cardiológicas (19,95%), neurológicas (10,38%), hematológicas (10,38%), infecciosas (6,56%) e renais (3,55%). O APACHE II apresentou satisfatório desempenho para a predição de complicações neurológicas (AUC 0,72) e renais (AUC 0,78). Conclusão: O APACHE II se destacou na previsão das complicações neurológicas e renais. Nenhum dos índices teve bom desempenho na predição das outras complicações analisadas. Portanto, os índices de gravidade não devem ser utilizados indiscriminadamente com o objetivo de predizer todas as complicações frequentemente apresentadas por pacientes após CRM.

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Predictors of mortality in acute kidney injury patients admitted to medicine intensive care unit in a Rural Tertiary Care Hospital

Cited by 32 publications

References 33 publications

Diagnosis and Mortality Outcomes of Three Different Geriatric Age Groups in the Intensive Care Unit: 10-Year Retrospective Analysis

Diagnosis and Mortality Outcomes of Three Different Geriatric Age Groups in the Intensive Care Unit: 10-Year Retrospective Analysis

Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care

Desempenho dos Índices de Gravidade na Predição de Complicações Pós-Operatórias de Revascularização Miocárdica

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