Med Decis Making - Development of inpatient risk stratification models of acute kidney injury for use in electronic health records.

Tópicos

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Resumo

JECTIVE: Patients with hospital-acquired acute kidney injury (AKI) are at risk for increased mortality and further medical complications. Evaluating these patients with a prediction tool easily implemented within an electronic health record (EHR) would identify high-risk patients prior to the development of AKI and could prevent iatrogenically induced episodes of AKI and improve clinical management.METHODS: The authors used structured clinical data acquired from an EHR to identify patients with normal kidney function for admissions from 1 August 1999 to 31 July 2003. Using administrative, computerized provider order entry and laboratory test data, they developed a 3-level risk stratification model to predict each of 2 severity levels of in-hospital AKI as defined by RIFLE criteria. The severity levels were defined as 150% or 200% of baseline serum creatinine. Model discrimination and calibration were evaluated using 10-fold cross-validation.RESULTS: Cross-validation of the models resulted in area under the receiver operating characteristic (AUC) curves of 0.75 (150% elevation) and 0.78 (200% elevation). Both models were adequately calibrated as measured by the Hosmer-Lemeshow goodness-of-fit test chi-squared values of 9.7 (P = 0.29) and 12.7 (P = 0.12), respectively.CONCLUSIONS: The authors generated risk prediction models for hospital-acquired AKI using only commonly available electronic data. The models identify patients at high risk for AKI who might benefit from early intervention or increased monitoring.

Resumo Limpo

jectiv patient hospitalacquir acut kidney injuri aki risk increas mortal medic complic evalu patient predict tool easili implement within electron health record ehr identifi highrisk patient prior develop aki prevent iatrogen induc episod aki improv clinic managementmethod author use structur clinic data acquir ehr identifi patient normal kidney function admiss august juli use administr computer provid order entri laboratori test data develop level risk stratif model predict sever level inhospit aki defin rifl criteria sever level defin baselin serum creatinin model discrimin calibr evalu use fold crossvalidationresult crossvalid model result area receiv oper characterist auc curv elev elev model adequ calibr measur hosmerlemeshow goodnessoffit test chisquar valu p p respectivelyconclus author generat risk predict model hospitalacquir aki use common avail electron data model identifi patient high risk aki might benefit earli intervent increas monitor

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