Asset Details

  • Description:
  • Comparison of performance of AKIpredictor and physicians for prediction of AKI-23 by SCr and UO. The black dot represents the classification threshold from the physicians. a At ICU admission (n = 120), AUROCs [95% CI] were 0.80 [0.69–0.92] and 0.75 [0.62–0.88] (P = 0.25), net benefit in ranges 0–26% and 0–74% for clinicians, and AKIpredictor respectively. Physicians’ classification threshold achieved 55% sensitivity, 82% specificity, 33% positive predictive value, and 94% negative predictive value. b On the first morning of ICU stay (n = 187), AUROCs [95% CI] were 0.94 [0.89–0.98] and 0.89 [0.82–0.97] (P = 0.27), net benefit in ranges (0–10% + 90–96%) and (0–48%) for clinicians and AKIpredictor respectively. Physicians’ classification threshold achieved 85% sensitivity, 86% specificity, 31% positive predictive value, and 99% negative predictive value. c After 24 h (n = 89), AUROCs [95% CI] were 0.95 [0.89–1.00] and 0.89 [0.79–0.99] (P = 0.09), with net benefit in ranges (0–36% + 40–48% + 50–67% + 80–100%) and (0–58%) for clinicians and AKIpredictor respectively. Clinicians’ classification threshold achieved 75% sensitivity, 90% specificity, 43% positive predictive value, and 97% negative predictive value. The wide confidence interval for high risk thresholds on the decision curve is amenable to the low number of patients. Therefore, findings should be interpreted with caution
  • License:
  • Rights Managed
  • Rights Holder:
  • Springer Nature
  • License Rights Holder:
  • © The Author(s). 2019
  • Asset Type:
  • Image
  • Asset Subtype:
  • Chart/Graph
  • Image Orientation:
  • Portrait
  • Image Dimensions:
  • 1418 x 1614
  • Image File Size:
  • 929 KB
  • Creator:
  • Marine Flechet, Stefano Falini, Claudia Bonetti, Fabian Güiza, Miet Schetz, Greet Van den Berghe, Geert Meyfroidt
  • Credit:
  • Flechet, M., Falini, S., Bonetti, C., Güiza, F., Schetz, M., Van den Berghe, G., & Meyfroidt, G. (2019). Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor. Critical Care, 23(1), 282. https://doi.org/10.1186/s13054-019-2563-x.
  • Collection:
  • Keywords:
  • Acute kidney injury, AKIpredictor, Predictive modeling, Machine learning
  • Restrictions:
  • Property Release:
  • No
  • Model Release:
  • No
  • Purchasable:
  • Yes
  • Sensitive Materials:
  • No
  • Article Authors:
  • Marine Flechet, Stefano Falini, Claudia Bonetti, Fabian Güiza, Miet Schetz, Greet Van den Berghe, Geert Meyfroidt
  • Article Copyright Year:
  • 2019
  • Publication Volume:
  • 23
  • Publication Issue:
  • 1
  • Publication Date:
  • 08/16/2019
  • DOI:
  • https://doi.org/10.1186/s13054-019-2563-x

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