The impact of urine flow on urine oxygen partial pressure monitoring during cardiac surgery

Lofgren, Lars R.; Silverton, Natalie A.; Kück, Kai; Hall, Isaac E.

doi:10.1007/s10877-022-00843-z

Cited by 2 publications

(1 citation statement)

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“…We completed a secondary analysis of previously collected clinical data to determine if the algorithm that included information related to intraoperative PuO 2 monitoring could predict the subsequent development of AKI better than a model that only included preoperative data. The data collection and processing methods have been previously reported [19][20][21]. In summary, after IRB approval, an observational study of 100 cardiac surgery patients who were at high risk for developing AKI based on the Cleveland Risk Score was conducted at the University of Utah Health Sciences (Salt Lake City, UT, USA).…”

Section: Methodsmentioning

confidence: 99%

Combining Machine Learning and Urine Oximetry: Towards an Intraoperative AKI Risk Prediction Algorithm

Lofgren,

Silverton,

Kuck

2023

JCM

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Acute kidney injury (AKI) affects up to 50% of cardiac surgery patients. The definition of AKI is based on changes in serum creatinine relative to a baseline measurement or a decrease in urine output. These monitoring methods lead to a delayed diagnosis. Monitoring the partial pressure of oxygen in urine (PuO2) may provide a method to assess the patient’s AKI risk status dynamically. This study aimed to assess the predictive capability of two machine learning algorithms for AKI in cardiac surgery patients. One algorithm incorporated a feature derived from PuO2 monitoring, while the other algorithm solely relied on preoperative risk factors. The hypothesis was that the model incorporating PuO2 information would exhibit a higher area under the receiver operator characteristic curve (AUROC). An automated forward variable selection method was used to identify the best preoperative features. The AUROC for individual features derived from the PuO2 monitor was used to pick the single best PuO2-based feature. The AUROC for the preoperative plus PuO2 model vs. the preoperative-only model was 0.78 vs. 0.66 (p-value < 0.01). In summary, a model that includes an intraoperative PuO2 feature better predicts AKI than one that only includes preoperative patient data.

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Section: Methodsmentioning

confidence: 99%