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Electronic Health Record–Enabled Big‐Data Approaches to Nephrotoxin‐Associated Acute Kidney Injury Risk Prediction
Author(s) -
Sutherland Scott M.
Publication year - 2018
Publication title -
pharmacotherapy: the journal of human pharmacology and drug therapy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.227
H-Index - 109
eISSN - 1875-9114
pISSN - 0277-0008
DOI - 10.1002/phar.2150
Subject(s) - acute kidney injury , medicine , intensive care medicine , observational study , psychological intervention , nephrotoxicity , electronic health record , medline , health care , emergency medicine , kidney , psychiatry , political science , law , economics , economic growth
Nephrotoxin‐associated acute kidney injury (NTx‐AKI) has become one of the most common causes of AKI among hospitalized adults and children; across acute and intensive care populations, exposure to nephrotoxins accounts for 15–25% of AKI cases. Although some interventions have shown promise in observational studies, no treatments currently exist for NTx‐AKI once it occurs. Thus, nearly all effective strategies are aimed at prevention. The primary obstacle to prevention is risk prediction and the determination of which patients are more likely to develop NTx‐AKI when exposed to medications with nephrotoxic potential. Historically, traditional statistical modeling has been applied to previously recognized clinical risk factors to identify predictors of NTx‐AKI. However, increased electronic health record adoption and the evolution of “big‐data” approaches to predictive analytics may offer a unique opportunity to prevent NTx‐AKI events. This article describes prior and current approaches to NTx‐AKI prediction and offers three novel use cases for electronic health record–enabled NTx‐AKI forecasting and risk profiling.