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Comparison of acute kidney injury and clinical prognosis of vancomycin monotherapy and combination therapy with beta-lactams in the intensive care unit.

ABSTRACT: Antibiotics induced acute kidney injury (AKI) risk in critically ill patients is not well known. This study aimed to evaluate the AKI development and clinical outcomes in critically ill adult patients treated with vancomycin (VAN) or combined with piperacillin-tazobactam (TZP) or meropenem (MEM). This was a retrospective study on critically ill adult patients who were given VAN, TZP or MEM and maintained for at least 48 h. The risk of AKI development and clinical outcomes were compared using the simple analysis and multivariate logistic regression. Three hundred forty patients were eligible. The incidence of any AKI was significantly higher in patients treated with VAN + TZP than those with VAN + MEM or VAN alone (52.7% vs. 27.7% vs. 25.7%; p < .0001). The adjusted odds of AKI increased 2.43-fold in VAN + TZP versus VAN, but not different in VAN + MEM versus VAN. However, AKI duration and recovery rate were not statistically different. In addition, all-cause death within 30 days after AKI onset was not significantly associated with antibiotic regimens. AKI incidence is higher in critically ill patients administered with VAN + TZP than those with VAN + MEM or VAN. However, no obvious evidence was found to prove that antibiotic-induced AKI leads to poor clinical outcomes.

SUBMITTER: Kang S

PROVIDER: S-EPMC6550403 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Comparison of acute kidney injury and clinical prognosis of vancomycin monotherapy and combination therapy with beta-lactams in the intensive care unit.

Kang Soyoung S Park Jimin J Yu Yun Mi YM Park Min Soo MS Han Euna E Chang Min Jung MJ

PloS one 20190605 6

Antibiotics induced acute kidney injury (AKI) risk in critically ill patients is not well known. This study aimed to evaluate the AKI development and clinical outcomes in critically ill adult patients treated with vancomycin (VAN) or combined with piperacillin-tazobactam (TZP) or meropenem (MEM). This was a retrospective study on critically ill adult patients who were given VAN, TZP or MEM and maintained for at least 48 h. The risk of AKI development and clinical outcomes were compared using the ...[more]

PMID: 31166993

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Project description:BackgroundpRIFLE (Pediatric Risk, Injury, Failure, Loss, End Stage Renal Disease), KDIGO (Kidney Disease Improving Global Outcomes) and pROCK (Pediatric Reference Change Value Optimized for AKI) are diagnostic criteria used for acute kidney injury (AKI) incidence evaluation. The aim of this study was to explore the diagnostic consistency, incidence and mortality rate, clinical signs, and influencing factors of renal injury related to sepsis in children diagnosed by three different AKI diagnostic criteria, and then evaluate which one was more valuable.MethodsA retrospective analysis was performed on the clinical data of children with severe sepsis. The patients were diagnosed and staged according to the 2007 pRIFLE standard, the 2012 KDIGO standard, and the 2018 pROCK standard. The clinical characteristics and prognosis of children with different stages of sepsis were compared between the three diagnostic standards.ResultsA total of 62 patients with sepsis were included. Blood stream infection is common (11 cases, 17.74%). According to pRIFLE, KDIGO, and pROCK standards, the incidence of sepsis-associated AKI (SA-AKI) was 74.2%, 67.7%, and 56.5%, respectively. The pRIFLE had the highest diagnostic rate of early detection of SA-AKI. There was no statistical difference in SA-AKI incidence or staging consistency between the pRIFLE and KDIGO groups (κ=0.0671; κ>0.60); the consistency of SA-AKI diagnoses across the three standards was good (all P values <0.05), and pROCK demonstrated a higher specificity. A high Pediatric Risk of Mortality (PRISM) score and high procalcitonin level were independent risk factors. Shock and renal replacement therapy were independent risk factors for SA-AKI death. Death from admission to 28 days after admission was used as an endpoint to draw a survival graph, which revealed that the AKI group had a significantly higher risk of death than did the non-AKI group.ConclusionsThe consistency of diagnosing SA-AKI across the three classification criteria was similar, and mortality rate increased with increased SA-AKI staging. The pRIFLE criteria were more sensitive in the early detection of SA-AKI, while the pROCK had higher specificity. There was no significant difference between the pRIFLE and KDIGO in terms of incidence, diagnosis, or staging of SA-AKI.

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.

Dataset Information

Comparison of acute kidney injury and clinical prognosis of vancomycin monotherapy and combination therapy with beta-lactams in the intensive care unit.

Publications

Comparison of acute kidney injury and clinical prognosis of vancomycin monotherapy and combination therapy with beta-lactams in the intensive care unit.

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