A Validation Study of Administrative Health Care Data to Detect Acute Kidney Injury in the Pediatric Intensive Care Unit
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".