Electronic health record-based predictive models for acute kidney injury screening in pediatric inpatients.

Abstract:

:BackgroundAcute kidney injury (AKI) is common in pediatric inpatients and is associated with increased morbidity, mortality, and length of stay. Its early identification can reduce severity.MethodsTo create and validate an electronic health record (EHR)-based AKI screening tool, we generated temporally distinct development and validation cohorts using retrospective data from our tertiary care children's hospital, including children aged 28 days through 21 years with sufficient serum creatinine measurements to determine AKI status. AKI was defined as 1.5-fold or 0.3 mg/dl increase in serum creatinine. Age, medication exposures, platelet count, red blood cell distribution width, serum phosphorus, serum transaminases, hypotension (ICU only), and pH (ICU only) were included in AKI risk prediction models.ResultsFor ICU patients, 791/1,332 (59%) of the development cohort and 470/866 (54%) of the validation cohort had AKI. In external validation, the ICU prediction model had a c-statistic=0.74 (95% confidence interval 0.71-0.77). For non-ICU patients, 722/2,337 (31%) of the development cohort and 469/1,474 (32%) of the validation cohort had AKI, and the prediction model had a c-statistic=0.69 (95% confidence interval 0.66-0.72).ConclusionsAKI screening can be performed using EHR data. The AKI screening tool can be incorporated into EHR systems to identify high-risk patients without serum creatinine data, enabling targeted laboratory testing, early AKI identification, and modification of care.

journal_name

Pediatr Res

journal_title

Pediatric research

authors

Wang L,McGregor TL,Jones DP,Bridges BC,Fleming GM,Shirey-Rice J,McLemore MF,Chen L,Weitkamp A,Byrne DW,Van Driest SL

doi

10.1038/pr.2017.116

subject

Has Abstract

pub_date

2017-09-01 00:00:00

pages

465-473

issue

3

eissn

0031-3998

issn

1530-0447

pii

pr2017116

journal_volume

82

pub_type

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