Journal of clinical monitoring and computing
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J Clin Monit Comput · Jun 2022
Comparison of renal region, cerebral and peripheral oxygenation for predicting postoperative renal impairment after CABG.
Patients undergoing coronary artery bypass grafting (CABG) are at risk of developing postoperative renal impairment, amongst others caused by renal ischemia and hypoxia. Intra-operative monitoring of renal region tissue oxygenation (SrtO2) might be a useful tool to detect renal hypoxia and predict postoperative renal impairment. Therefore, the aim of this study was to assess the ability of intra-operative SrtO2 to predict postoperative renal impairment, defined as an increase of serum creatinine concentrations of > 10% from individual baseline, and compare this with the predictive abilities of peripheral and cerebral tissue oxygenation (SptO2 and SctO2, respectively) and renal specific tissue deoxygenation. ⋯ Tissue oxygenation of the renal region, although non-invasively and continuously available, cannot be used in adults to predict postoperative renal impairment after CABG. Instead, peripheral tissue deoxygenation was able to predict postoperative renal impairment, suggesting that SptO2 provides a better indication of 'general' tissue oxygenation status. Registered at ClinicalTrials.gov: NCT01347827, first submitted April 27, 2011.
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J Clin Monit Comput · Jun 2022
Bayesian hierarchical modeling of operating room times for surgeries with few or no historic data.
In this work it is proposed a modeling for operating room times based on a Bayesian Hierarchical structure. Specifically, it is employed a Bayesian generalized linear mixed model with an additional hierarchical level on the random effects. This configuration allows the estimation of operating room times (ORT) with few or no historical observations, without requiring a prior surgeon's estimate. ⋯ We find that lognormal models outperform the gamma models in estimating upper prediction bounds (UB). Especially, the best ORT predictions for cases with few or no historical data (i.e., between 0 and 3 historical cases) are obtained with the [Formula: see text], SBeta2 model. With a deviation of less than 1% with respect to the nominal coverage of the upper bound predictions UB80% and UB90% and an average absolute percentage error of 38.5% in the point estimate.