Dataset Information

A Validation Study of Administrative Health Care Data to Detect Acute Kidney Injury in the Pediatric Intensive Care Unit.

ABSTRACT:

Background

Large studies evaluating pediatric acute kidney injury (AKI) epidemiology and outcomes are lacking, partially due to underuse of large administrative health care data.

Objective

To assess the diagnostic accuracy of administrative health care data-defined AKI in children admitted to the pediatric intensive care unit (PICU).

Design

Retrospective cohort study utilizing chart and administrative data.

Setting

Children admitted to the PICU at 2 centers in Montreal, QC.

Patients

Patients between 0 and 18 years old with a provincial health insurance number, without end-stage renal disease and admitted to the PICU between January 1, 2003, and March 31, 2005, were included.

Measurements

The AKI was defined from chart data using the Kidney Disease: Improving Global Outcomes (KDIGO) definition (Chart-AKI). The AKI defined using administrative health data (Admin-AKI) was based on International Classification of Disease, Ninth Revision (ICD-9) AKI codes.

Methods

Data available from retrospective chart review, including baseline and PICU patient characteristics, and serum creatinine (SCr) and urine output (UO) values during PICU admission, were merged with provincial administrative health care data containing diagnostic and procedure codes used for ascertaining Admin-AKI. Sensitivity, specificity, positive, and negative predictive value of Admin-AKI compared with Chart-AKI (reference standard) were calculated. Univariable associations between Admin-AKI and hospital mortality were evaluated.

Results

A total of 2051 patients (55% male, mean age at admission 6.1 ± 5.8 years, 355 cardiac surgery, 1696 noncardiac surgery) were included. The AKI defined by SCr or UO criteria occurred in 52% of cardiac surgery patients and 24% of noncardiac surgery patients. Overall, Admin-AKI detected Chart-AKI with low sensitivity, but high specificity in cardiac and noncardiac surgery patients. Sensitivity increased by 1.5 to 2 fold with each increase in AKI severity stage. Admin-AKI was associated with hospital mortality (13% in Admin-AKI vs 2% in non-AKI, P < .001).

Limitations

These data were generated in a PICU population; future research should study non-PICU populations.

Conclusions

Use of administrative health care data to define AKI in children leads to AKI incidence underestimation. However, for detecting more severe AKI, sensitivity is higher, while maintaining high specificity.

SUBMITTER: D'Arienzo D

PROVIDER: S-EPMC6376545 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

A Validation Study of Administrative Health Care Data to Detect Acute Kidney Injury in the Pediatric Intensive Care Unit.

D'Arienzo David D Hessey Erin E Ali Rami R Perreault Sylvie S Samuel Susan S Roy Louise L Lacroix Jacques J Jouvet Philippe P Morissette Genevieve G Dorais Marc M Lafrance Jean-Philippe JP Phan Veronique V Pizzi Michael M Chanchlani Rahul R Zappitelli Michael M

Canadian journal of kidney health and disease 20190210

<h4>Background</h4>Large studies evaluating pediatric acute kidney injury (AKI) epidemiology and outcomes are lacking, partially due to underuse of large administrative health care data.<h4>Objective</h4>To assess the diagnostic accuracy of administrative health care data-defined AKI in children admitted to the pediatric intensive care unit (PICU).<h4>Design</h4>Retrospective cohort study utilizing chart and administrative data.<h4>Setting</h4>Children admitted to the PICU at 2 centers in Montre ...[more]

PMID: 30792872

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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

A Validation Study of Administrative Health Care Data to Detect Acute Kidney Injury in the Pediatric Intensive Care Unit.

Background

Objective

Design

Setting

Patients

Measurements

Methods

Results

Limitations

Conclusions

Publications

A Validation Study of Administrative Health Care Data to Detect Acute Kidney Injury in the Pediatric Intensive Care Unit.

Similar Datasets

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