Abstract
AbstractPatients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86–0.89) classify an individual patient’s baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.
Publisher
Springer Science and Business Media LLC
Subject
Health Information Management,Health Informatics,Computer Science Applications,Medicine (miscellaneous)
Reference52 articles.
1. Deyo, D., Khaliq, A., Mitchell, D. & Hughes, D. R. Electronic sharing of diagnostic information and patient outcomes. Am. J. Manag Care 24, 32–37 (2018).
2. Rudin, R. S., Motala, A., Goldzweig, C. L. & Shekelle, P. G. Usage and effect of health information exchange: a systematic review. Ann. Intern. Med. 161, 803–811 (2014).
3. World Health Organization. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity (2011).
4. Tyler, P. D. et al. Assessment of intensive care unit laboratory values that differ from reference ranges and association with patient mortality and length of stay. JAMA Netw. Open 1, e184521–e184521 (2018).
5. Hébert, P. C. et al. A multicenter, randomized, controlled clinical trial of transfusion requirements in critical care. Transfusion Requirements in Critical Care Investigators, Canadian Critical Care Trials Group. N. Engl. J. Med. 340, 409–417 (1999).
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