Dataset Information

Documentation of acute kidney injury at discharge from the neonatal intensive care unit and role of nephrology consultation.

ABSTRACT:

Objective

To investigate whether NICU discharge summaries documented neonatal AKI and estimate if nephrology consultation mediated this association.

Study design

Secondary analysis of AWAKEN multicenter retrospective cohort.

Exposures

AKI severity and diagnostic criteria.

Outcome

AKI documentation on NICU discharge summaries using multivariable logistic regression to estimate associations and test for causal mediation.

Results

Among 605 neonates with AKI, 13% had documented AKI. Those with documented AKI were more likely to have severe AKI (70.5% vs. 51%, p < 0.001) and SCr-only AKI (76.9% vs. 50.1%, p = 0.04). Nephrology consultation mediated 78.0% (95% CL 46.5-109.4%) of the total effect of AKI severity and 82.8% (95% CL 70.3-95.3%) of the total effect of AKI diagnostic criteria on documentation.

Conclusion

We report a low prevalence of AKI documentation at NICU discharge. AKI severity and SCr-only AKI increased odds of AKI documentation. Nephrology consultation mediated the associations of AKI severity and diagnostic criteria with documentation.

SUBMITTER: Chmielewski J

PROVIDER: S-EPMC9280854 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

Documentation of acute kidney injury at discharge from the neonatal intensive care unit and role of nephrology consultation.

Chmielewski Jennifer J Chaudhry Paulomi M PM Harer Matthew W MW Menon Shina S South Andrew M AM Chappell Ashley A Griffin Russell R Askenazi David D Jetton Jennifer J Starr Michelle C MC

Journal of perinatology : official journal of the California Perinatal Association 20220608 7

<h4>Objective</h4>To investigate whether NICU discharge summaries documented neonatal AKI and estimate if nephrology consultation mediated this association.<h4>Study design</h4>Secondary analysis of AWAKEN multicenter retrospective cohort.<h4>Exposures</h4>AKI severity and diagnostic criteria.<h4>Outcome</h4>AKI documentation on NICU discharge summaries using multivariable logistic regression to estimate associations and test for causal mediation.<h4>Results</h4>Among 605 neonates with AKI, 13% ...[more]

PMID: 35676535

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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

Documentation of acute kidney injury at discharge from the neonatal intensive care unit and role of nephrology consultation.

Objective

Study design

Exposures

Outcome

Results

Conclusion

Publications

Documentation of acute kidney injury at discharge from the neonatal intensive care unit and role of nephrology consultation.

Similar Datasets

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