A Multi-Center Evaluation of Early Acute Kidney Injury in Critically Ill Trauma Patients

Bagshaw, Sean M.; George, Carol; Gibney, R. T. Noel; Bellomo, Rinaldo

doi:10.1080/08860220802134649

Cited by 136 publications

(129 citation statements)

References 44 publications

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“…Similar results were obtained by other researchers such as: Kumar [38], Bagshaw [39] and Kim [36]. Sepsis was also introduced as considerable risk factors of AKI.…”

Section: Discussion and Concluding Remarkssupporting

confidence: 79%

Bayesian Analysis of Joint Modeling of Longitudinal and Time to Event Data Using Some Skew-Elliptical Distributions

Khoundabi¹,

Kazemnejad²,

Mansourian³

et al. 2017

BBIJ

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Joint modeling of longitudinal and time to event data have been widely used for analyzing medical data, where longitudinal measurements is gathered with a time to event or survival data. In most of these studies, distributional assumption for modeling longitudinal response is normal, which leads to vulnerable inference in the presence of outliers in longitudinal measurements and violation of this assumption. Violation of the normality assumption can also make the statistical inference vague. Powerful distributions for robust analyzing and relaxing normality assumption, are skew-elliptical distributions, which include univariate and multivariate versions of the student's t, the Laplace and normal distributions. In this paper, a linear mixed effects model with skew-elliptical distribution for both random effects and residuals and a Cox's model for time to event data are used for joint modeling. This strategy allows for the skewness and the heavy tails of random effect distributions and thus makes inferences robust to the violation. For estimation, a Bayesian parametric approach using Markov chain Monte Carlo is adopted. The method is illustrated in a real Intensive Care Unit (ICU) data set and the best model is selected using some Bayesian criteria for model selection.

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“…Similar results were obtained by other researchers such as: Kumar [38], Bagshaw [39] and Kim [36]. Sepsis was also introduced as considerable risk factors of AKI.…”

Section: Discussion and Concluding Remarkssupporting

confidence: 79%

Bayesian Analysis of Joint Modeling of Longitudinal and Time to Event Data Using Some Skew-Elliptical Distributions

Khoundabi¹,

Kazemnejad²,

Mansourian³

et al. 2017

BBIJ

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show abstract

“…AKI is induced by multiple factors and often coregulated by basal renal function and other nervehumoral and endocrine factors. 13 Patients with poor basal renal function had weak kidney reserve capacity and low compensatory ability. After AMI, they further suffered from heart and kidney hypoperfusion and strong stress response, therefore, leading to an increased renal damage.…”

Section: Discussionmentioning

confidence: 99%

Risk factors of acute kidney injury after acute myocardial infarction

et al. 2016

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Objectives: To study the risk factors for acute kidney injury (AKI) in-patients with acute myocardial infarction (AMI). Methods: A total of 1371 cases of adult in-patients with AMI in the First People's Hospital of Changzhou from January 2008 to December 2012 were retrospectively analyzed. Based on the occurrence of AKI diagnosed according to the 2012 KDIGO AKI criteria, they were divided into AKI group and non-AKI group and further into conservative treatment groups, coronary angiography (CAG) groups, and coronary artery bypass grafting (CABG) groups based on the timing of AKI occurrence, respectively. Related risk factors of AKI were analyzed by univariate and multivariate logistic regressions. Results: 410 (29.9%) developed AKI. Patients with AKI had significantly increased in-hospital mortality than patients without AKI. Multivariate logistic regression analysis showed that decreased baseline eGFR, increased fasting plasma glucose (FPG), use of diuretics and Killip grade IV were independent risk factors of AKI, while increased DBP on admission was a protective factor for patients in conservative treatment group. Decreased baseline eGFR, increased FPG, use of diuretics, intraoperative hypotension and acute infection were independent risk factors of AKI for patients in the CAG group. Decreased baseline eGFR, increased FPG, use of diuretics and low cardiac output syndrome after operation were independent risk factors of AKI for patients in the CABG group. Conclusions: AKI is a common complication and associated with increased mortality after AMI. Decreased baseline renal function, increased FPG and use of diuretics were common independent risk factors of AKI after AMI. ARTICLE HISTORY

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“…Bagshaw ve arkadaşlarının çalışmasında travma hastalarında AKI gelişme sıklığına bakılmış, AKI gelişen grubun ortalama yaşları 62,5, APACHE II skoru 17,5, mekanik ventilasyon ihtiyacı %48,3, AKI gelişme sıklığı %18,1 olarak bulunmuş ve altta yatan hastalıkların AKI gelişmesinde önemli olduğu vurgulanmıştır (10).…”

Section: Discussionunclassified

“…Çalışmamızda AKI gelişen grupta daha çok sistemik hastalık olduğu görülmektedir (Tablo V). Bagshaw ve arkadaşlarının çalışmasında AKI gelişimi ile yandaş hastalıklar arasında ilişki bulunmuştur (10).…”

Section: Discussionunclassified

Yoğun Bakımda Nörolojik Hasta Grubunda Akut Böbrek Hasarı Sınıflaması

Akıncı¹,

Çakar²

2012

Tybbd

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A Multi-Center Evaluation of Early Acute Kidney Injury in Critically Ill Trauma Patients

Cited by 136 publications

References 44 publications

Bayesian Analysis of Joint Modeling of Longitudinal and Time to Event Data Using Some Skew-Elliptical Distributions

Bayesian Analysis of Joint Modeling of Longitudinal and Time to Event Data Using Some Skew-Elliptical Distributions

Risk factors of acute kidney injury after acute myocardial infarction

Yoğun Bakımda Nörolojik Hasta Grubunda Akut Böbrek Hasarı Sınıflaması

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