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Predictors of Acute Kidney Injury and Mortality in Intensive Care Unit at a Teaching Tertiary Hospital_ID.

ABSTRACT: Background and aims:Despite the increased rates of acute kidney injury (AKI) in intensive care units (ICU) and associated mortality, information on the epidemiology of AKI is sparse in sub-Saharan Africa (SSA). We investigated the rates and predictors of AKI and associated mortality in a tertiary ICU. Materials and methods:This retrospective study analyzed 280 hospital records of patients admitted to the ICU at a tertiary teaching hospital who were aged ?15 years from January 2017 to May 31, 2018. The outcome parameters of the study were rates of AKI in the ICU, associated risk factors, and mortalities. Acute kidney injury and ICU mortality were established by the multivariate logistic analysis. Results:The median age was 36 years (IQR 28, 52). The rate of AKI was 52.9%, and the presence of human immunodeficiency virus (HIV) and oliguria was 2.3-fold (0.004) and 4-fold (0.016) positive predictors of ICU-AKI, respectively. Male gender (0.003), diabetes mellitus (DM) (0.010), respiratory disease (0.001), inotropes (0.004), and ventilator support (0.017) were predictors for ICU mortality after controlling for confounders. Conclusion:The rate of AKI is significantly higher in a referral tertiary hospital in Zambia compared to developed countries and the presence of HIV and noncommunicable diseases such as DM impacts severely on outcomes. How to cite this article:Banda J, Chenga N, Nambaya S, Bulaya T, Siziya S. Predictors of Acute Kidney Injury and Mortality in Intensive Care Unit at a Teaching Tertiary Hospital_ID. Indian J Crit Care Med 2020;24(2):116-121.

SUBMITTER: Banda J

PROVIDER: S-EPMC7075058 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Predictors of Acute Kidney Injury and Mortality in Intensive Care Unit at a Teaching Tertiary Hospital_ID.

Banda Justor J Chenga Natasha N Nambaya Suwilanji S Bulaya Tela T Siziya Seter S

Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine 20200201 2

<h4>Background and aims</h4>Despite the increased rates of acute kidney injury (AKI) in intensive care units (ICU) and associated mortality, information on the epidemiology of AKI is sparse in sub-Saharan Africa (SSA). We investigated the rates and predictors of AKI and associated mortality in a tertiary ICU.<h4>Materials and methods</h4>This retrospective study analyzed 280 hospital records of patients admitted to the ICU at a tertiary teaching hospital who were aged ≥15 years from January 2017 ...[more]

PMID: 32205943

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Project description:BackgroundAcute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes.ObjectiveThe primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes.MethodsWe developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older.ResultsThe developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm's predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy.ConclusionsNAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)-marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs.

Project description:BackgroundThe transmissibility and associated morbidity and mortality of severe acute respiratory syndrome-related coronavirus (SARS-Cov-2), have overwhelmed worldwide healthcare systems, resulting in an urgent need to understand this virus and its associated effects. The aim of our study was to identify patient symptoms, clinical characteristics, laboratory, and radiology findings that are associated with serious morbidity and mortality in COVID-19 patients.MethodsA cross sectional study was conducted in Jaber Al Ahmad Hospital, the designated COVID-19 center in Kuwait between August 1st, 2020 and January 31st, 2021. The main outcomes measured in this study were to identify variables associated with intensive care unit (ICU) admission, as proxy for serious morbidity, and in hospital mortality.ResultsTwo hundred and seventy-six patients were included in the study. Thirty-six (13%) patients were admitted to intensive care unit (ICU) and 33 (12%) patients expired. On multivariate analysis we found having elevated fibrinogen [OR 1.39, 95% CI 1.08-1.64, P = 0.04], low estimated glomerular filtration rate (eGFR) [OR 0.89, 95% CI 0.81-0.95, P = 0.02], and having bilateral patchy lung shadowing [OR 6.68, 95% CI 1.85-15.28, P < 0.01] to be significantly associated with increase odds of ICU admission. Elevated CRP [OR 1.25, 95% CI 1.10-1.98, P < 0.01], low eGFR [OR 0.95, 95% CI 0.90-0.99, P = 0.05] and having ischemic heart disease [OR 7.03, 95% CI 1.60-46.42, P = 0.04] were independently associated with increased odds of mortality.ConclusionCertain inflammatory and coagulopathy markers, and having certain lung radiological features, in addition to having medical comorbidities, specifically, ischemic heart disease and renal impairment are key predictors for serious morbidity and mortality in patients infected with COVID-19. These should be incorporated into medical institutes risk assessment tools used by physicians and policy makers to instigate, prioritize, and reprioritize care in patients with COVID-19 and instigate preventative strategy to reduce the impact of future outbreak.

Dataset Information

Predictors of Acute Kidney Injury and Mortality in Intensive Care Unit at a Teaching Tertiary Hospital_ID.

Publications

Predictors of Acute Kidney Injury and Mortality in Intensive Care Unit at a Teaching Tertiary Hospital_ID.

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