Dataset Information

EResearch in acute kidney injury: a primer for electronic health record research.

ABSTRACT: Acute kidney injury (AKI) has a significant impact on patient morbidity and mortality as well as overall health care costs. eResearch, which integrates information technology and information management to optimize research strategies, provides a perfect platform for necessary ongoing AKI research. With the recent adoption of a widely accepted definition of AKI and near-universal use of electronic health records, eResearch is becoming an important tool in AKI research. Conducting eResearch in AKI should ideally be based on a relatively uniform methodology. This article is the first of its kind to describe a methodology for pursuing eResearch specific to AKI and includes an illustrative database example for critically ill patients. We discuss strategies for using serum creatinine and urine output in large databases to identify and stage AKI and ways to interpolate missing values and validate data. Issues specific to the pediatric population include variation in serum creatinine with growth, varied severity of illness scoring systems and medication dosage based on weight. Many of these same strategies used to optimize AKI eResearch can be applicable to real-time AKI alerts with potential integration of additional clinical variables.

SUBMITTER: Joyce EL

PROVIDER: S-EPMC6399481 | biostudies-other | 2019 Mar

REPOSITORIES: biostudies-other

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eResearch in acute kidney injury: a primer for electronic health record research.

Joyce Emily L EL DeAlmeida Dilhari R DR Fuhrman Dana Y DY Priyanka Priyanka P Kellum John A JA

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association 20190301 3

Acute kidney injury (AKI) has a significant impact on patient morbidity and mortality as well as overall health care costs. eResearch, which integrates information technology and information management to optimize research strategies, provides a perfect platform for necessary ongoing AKI research. With the recent adoption of a widely accepted definition of AKI and near-universal use of electronic health records, eResearch is becoming an important tool in AKI research. Conducting eResearch in AKI ...[more]

PMID: 29617846

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Project description:ObjectiveTo determine whether electronic health record alerts for acute kidney injury would improve patient outcomes of mortality, dialysis, and progression of acute kidney injury.DesignDouble blinded, multicenter, parallel, randomized controlled trial.SettingSix hospitals (four teaching and two non-teaching) in the Yale New Haven Health System in Connecticut and Rhode Island, US, ranging from small community hospitals to large tertiary care centers.Participants6030 adult inpatients with acute kidney injury, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria.InterventionsAn electronic health record based "pop-up" alert for acute kidney injury with an associated acute kidney injury order set upon provider opening of the patient's medical record.Main outcome measuresA composite of progression of acute kidney injury, receipt of dialysis, or death within 14 days of randomization. Prespecified secondary outcomes included outcomes at each hospital and frequency of various care practices for acute kidney injury.Results6030 patients were randomized over 22 months. The primary outcome occurred in 653 (21.3%) of 3059 patients with an alert and in 622 (20.9%) of 2971 patients receiving usual care (relative risk 1.02, 95% confidence interval 0.93 to 1.13, P=0.67). Analysis by each hospital showed worse outcomes in the two non-teaching hospitals (n=765, 13%), where alerts were associated with a higher risk of the primary outcome (relative risk 1.49, 95% confidence interval 1.12 to 1.98, P=0.006). More deaths occurred at these centers (15.6% in the alert group v 8.6% in the usual care group, P=0.003). Certain acute kidney injury care practices were increased in the alert group but did not appear to mediate these outcomes.ConclusionsAlerts did not reduce the risk of our primary outcome among patients in hospital with acute kidney injury. The heterogeneity of effect across clinical centers should lead to a re-evaluation of existing alerting systems for acute kidney injury.Trial registrationClinicalTrials.gov NCT02753751.

Project description:BackgroundA major problem in treating acute kidney injury (AKI) is that clinical criteria for recognition are markers of established kidney damage or impaired function; treatment before such damage manifests is desirable. Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient AKI and those at high risk of developing AKI could be identified.ObjectiveIn this study, we evaluate a machine learning algorithm for early detection and prediction of AKI.DesignWe used a machine learning technique, boosted ensembles of decision trees, to train an AKI prediction tool on retrospective data taken from more than 300 000 inpatient encounters.SettingData were collected from inpatient wards at Stanford Medical Center and intensive care unit patients at Beth Israel Deaconess Medical Center.PatientsPatients older than the age of 18 whose hospital stays lasted between 5 and 1000 hours and who had at least one documented measurement of heart rate, respiratory rate, temperature, serum creatinine (SCr), and Glasgow Coma Scale (GCS).MeasurementsWe tested the algorithm's ability to detect AKI at onset and to predict AKI 12, 24, 48, and 72 hours before onset.MethodsWe tested AKI detection and prediction using the National Health Service (NHS) England AKI Algorithm as a gold standard. We additionally tested the algorithm's ability to detect AKI as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. We compared the algorithm's 3-fold cross-validation performance to the Sequential Organ Failure Assessment (SOFA) score for AKI identification in terms of area under the receiver operating characteristic (AUROC).ResultsThe algorithm demonstrated high AUROC for detecting and predicting NHS-defined AKI at all tested time points. The algorithm achieves AUROC of 0.872 (95% confidence interval [CI], 0.867-0.878) for AKI detection at time of onset. For prediction 12 hours before onset, the algorithm achieves an AUROC of 0.800 (95% CI, 0.792-0.809). For 24-hour predictions, the algorithm achieves AUROC of 0.795 (95% CI, 0.785-0.804). For 48-hour and 72-hour predictions, the algorithm achieves AUROC values of 0.761 (95% CI, 0.753-0.768) and 0.728 (95% CI, 0.719-0.737), respectively.LimitationsBecause of the retrospective nature of this study, we cannot draw any conclusions about the impact the algorithm's predictions will have on patient outcomes in a clinical setting.ConclusionsThe results of these experiments suggest that a machine learning-based AKI prediction tool may offer important prognostic capabilities for determining which patients are likely to suffer AKI, potentially allowing clinicians to intervene before kidney damage manifests.

Dataset Information

EResearch in acute kidney injury: a primer for electronic health record research.

Publications

eResearch in acute kidney injury: a primer for electronic health record research.

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