A new study of deep learning based on electronic health records promises to forecast acute kidney injury up to 48 hours before it can be diagnosed clinically. However, employing data science to predict acute kidney injury might be more challenging than it seems.
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References
Al-Jaghbeer, M. et al. Clinical decision support for in-hospital AKI. J. Am. Soc. Nephrol. 29, 654–660 (2018).
Selby, N. M. et al. An organizational-level program of intervention for AKI: a pragmatic stepped wedge cluster randomized trial. J. Am. Soc. Nephrol. 30, 505–515 (2019).
Tomasev, N. et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572, 116–119 (2019).
Shickel, B. et al. DeepSOFA: a continuous acuity score for critically ill patients using clinically interpretable deep learning. Sci. Rep. 9, 1879 (2019).
Kellum, J. A. & Prowle, J. R. Paradigms of acute kidney injury in the intensive care setting. Nat. Rev. Nephrol. 14, 217–230 (2018).
Kellum, J. A. et al. The effects of alternative resuscitation strategies on acute kidney injury in patients with septic shock. Am. J. Respir. Crit. Care Med. 193, 281–287 (2016).
Li, S., Wang, S., Priyanka, P. & Kellum, J. A. Acute kidney injury in critically ill patients after noncardiac major surgery: early versus late onset. Crit. Care Med. 47, e437–e444 (2019).
Wilson, F. P. et al. Automated, electronic alerts for acute kidney injury: a single-blind, parallel-group, randomised controlled trial. Lancet 385, 1966–1974 (2015).
Kellum, J. A. et al. Classifying AKI by urine output versus serum creatinine level. J. Am. Soc. Nephrol. 26, 2231–2238 (2015).
Kaddourah, A. et al. Epidemiology of acute kidney injury in critically ill children and young adults. N. Engl. J. Med. 376, 11–20 (2016).
Acknowledgements
A.B. is supported by R01 GM110240 from the National Institute of General Medical Sciences.
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J.A.K. received honoraria for consulting and grant support from Astute Medical, Biomerieux and Bioporto. A.B. and University of Florida have patents pending on the real-time use of clinical data for risk prediction of sepsis-associated and surgery-associated AKI using machine learning models.
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Kellum, J.A., Bihorac, A. Artificial intelligence to predict AKI: is it a breakthrough?. Nat Rev Nephrol 15, 663–664 (2019). https://doi.org/10.1038/s41581-019-0203-y
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DOI: https://doi.org/10.1038/s41581-019-0203-y
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