Dataset Information

Hyperchloremia and postoperative acute kidney injury: a retrospective analysis of data from the surgical intensive care unit.

ABSTRACT:

Background

Whether perioperative hyperchloremia can induce postoperative acute kidney injury (AKI) is controversial. We investigated the association between perioperative hyperchloremia and postoperative AKI in patients admitted to the intensive care unit (ICU) after surgery.

Methods

We performed a retrospective observational study of patients admitted to the surgical ICU at a single tertiary care hospital between January 2011 and June 2016. Our primary objective was to determine whether hyperchloremia or an increase in serum chloride levels was associated with postoperative AKI. Perioperative hyperchloremia was defined as serum chloride levels ??110 mmol·L^-?1 during postoperative days (PODs) 0-3. The increase in serum chloride levels was defined as the difference between preoperative and maximum postoperative serum chloride levels during the first 3 days after surgery.

Results

Of the 7991 patients included in the final analysis, 1876 (23.5%) developed hyperchloremia during PODs 0-3, and 1187 (14.9%) developed postoperative AKI. Exposure to hyperchloremia during the first 3 days after surgery was not associated with postoperative AKI (odds ratio, 1.09; 95% confidence interval, 0.80-1.49; P =?0.571). However, among patients with preoperative chronic kidney disease stage ??3 (estimated glomerular filtration rate?-?1·1.73·m^-?2), the incidence of postoperative AKI was higher in patients with an increase >?6 mmol·L^-?1 in serum chloride levels than in patients with an increase ??1 mmol·L^-?1 (odds ratio, 1.42; 95% confidence interval, 1.09-1.84; P =?0.009). In addition, the incidence of postoperative AKI stage ??2 was not associated with exposure to hyperchloremia or with the increase in serum chloride levels during PODs 0-3, regardless of preoperative kidney function.

Conclusions

Exposure to perioperative hyperchloremia is not associated with postoperative AKI in surgical ICU patients. However, in patients with moderate-to-severe chronic kidney disease (stage ??3), a substantial perioperative increase in serum chloride levels may reflect a higher risk of AKI.

SUBMITTER: Oh TK

PROVIDER: S-EPMC6206638 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

Hyperchloremia and postoperative acute kidney injury: a retrospective analysis of data from the surgical intensive care unit.

Oh Tak Kyu TK Song In-Ae IA Kim Se Joong SJ Lim Sung Yoon SY Do Sang-Hwan SH Hwang Jung-Won JW Kim Jinhee J Jeon Young-Tae YT

Critical care (London, England) 20181030 1

<h4>Background</h4>Whether perioperative hyperchloremia can induce postoperative acute kidney injury (AKI) is controversial. We investigated the association between perioperative hyperchloremia and postoperative AKI in patients admitted to the intensive care unit (ICU) after surgery.<h4>Methods</h4>We performed a retrospective observational study of patients admitted to the surgical ICU at a single tertiary care hospital between January 2011 and June 2016. Our primary objective was to determine ...[more]

PMID: 30373623

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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.

Dataset Information

Hyperchloremia and postoperative acute kidney injury: a retrospective analysis of data from the surgical intensive care unit.

Background

Methods

Results

Conclusions

Publications

Hyperchloremia and postoperative acute kidney injury: a retrospective analysis of data from the surgical intensive care unit.

Similar Datasets

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