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      We appreciate Dr. Jiang’s interest in our research and the thoughtful comments related to patient selection, model calibration, and performance evaluation metrics. We described the development and evaluation of machine-learning models to predict short-term risk for acute kidney injury in the emergency department (ED) setting.
      • Martinez D.A.
      • Levin S.R.
      • Klein E.Y.
      • et al.
      Early prediction of acute kidney injury in the emergency department with machine-learning methods applied to electronic health record data. Ann Emerg Med.
      This study serves as a proof of concept that electronic health record data, collected very early in the ED encounter, can be used to generate reliable estimations of short-term risk for kidney injury. Our ultimate objective is to incorporate model-based predictions into a clinical decision support (CDS) system that enables better management and outcomes for patients with or at risk for acute kidney injury. Within the context of a complete CDS system, predictive data will be used in combination with surveillance and detection algorithms to target patients with or at risk for acute kidney injury with renally protective CDS.
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      References

        • Martinez D.A.
        • Levin S.R.
        • Klein E.Y.
        • et al.
        Early prediction of acute kidney injury in the emergency department with machine-learning methods applied to electronic health record data. Ann Emerg Med.
        • Hodgson L.E.
        • Sarnowski A.
        • Roderick P.J.
        • et al.
        Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations.
        BMJ Open. 2017; 7e016591
        • Wang H.E.
        • Muntner P.
        • Chertow G.M.
        • et al.
        Acute kidney injury and mortality in hospitalized patients.
        Am J Nephrol. 2012; 35: 349-355
        • Diamond G.A.
        What price perfection? calibration and discrimination of clinical prediction models.
        J Clin Epidemiol. 1992; 45: 85-89

      Linked Article

      • Selection Bias in the Predictive Analytics With Machine-Learning Algorithm
        Annals of Emergency MedicineVol. 77Issue 2
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          I read with great interest the study by Martinez et al,1 who developed a machine-learning model for the early prediction of acute kidney injury. The model was created in the emergency department (ED) and can predict acute kidney injury development at 24, 48, and 72 hours after the index ED visit. The model showed good performance in terms of discrimination. However, model calibration was not performed, leaving unknown whether the model predicts equally well at various risk strata.
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      • Early Prediction of Acute Kidney Injury in the Emergency Department With Machine-Learning Methods Applied to Electronic Health Record Data
        Annals of Emergency MedicineVol. 76Issue 4
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          Acute kidney injury occurs commonly and is a leading cause of prolonged hospitalization, development and progression of chronic kidney disease, and death. Early acute kidney injury treatment can improve outcomes. However, current decision support is not able to detect patients at the highest risk of developing acute kidney injury. We analyzed routinely collected emergency department (ED) data and developed prediction models with capacity for early identification of ED patients at high risk for acute kidney injury.
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