Pathophysiological Responses to Bloodstream Infection in Critically Ill Transplant Recipients Compared With Non-Transplant Recipients.

BACKGROUND: Identification of bloodstream infection (BSI) in transplant recipients may be difficult due to immunosuppression. Accordingly, we aimed to compare responses to BSI in critically ill transplant and non-transplant recipients and to modify systemic inflammatory response syndrome (SIRS) criteria for transplant recipients. METHODS: We analyzed univariate risks and developed multivariable models of BSI with 27 clinical variables from adult intensive care unit (ICU) patients at the University of Virginia (UVA) and at the University of Pittsburgh (Pitt). We used Bayesian inference to adjust SIRS criteria for transplant recipients. RESULTS: We analyzed 38.7 million hourly measurements from 41 725 patients at UVA, including 1897 transplant recipients with 193 episodes of BSI and 53 608 patients at Pitt, including 1614 transplant recipients with 768 episodes of BSI. The univariate responses to BSI were comparable in transplant and non-transplant recipients. The area under the receiver operating characteristic curve (AUC) was 0.82 (95% confidence interval [CI], .80-.83) for the model using all UVA patient data and 0.80 (95% CI, .76-.83) when using only transplant recipient data. The UVA all-patient model had an AUC of 0.77 (95% CI, .76-.79) in non-transplant recipients and 0.75 (95% CI, .71-.79) in transplant recipients at Pitt. The relative importance of the 27 predictors was similar in transplant and non-transplant models. An upper temperature of 37.5°C in SIRS criteria improved reclassification performance in transplant recipients. CONCLUSIONS: Critically ill transplant and non-transplant recipients had similar responses to BSI. An upper temperature of 37.5°C in SIRS criteria improved BSI screening in transplant recipients.

Age prediction from 12-lead electrocardiograms using deep learning: a comparison of four models on a contemporary, freely available dataset.

Objective.The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have been shown to have the identify features not immediately apparent to human interpreters including age and sex. Several models have been published but no direct comparisons exist.Approach.We implemented three previously published models and one unpublished model to predict age and sex from a 12-lead ECG and then compared their performance on an open-access data set.Main results.All models converged and were evaluated on the holdout set. The best preforming age prediction model had a hold-out set mean absolute error of 8.06 years. The best preforming sex prediction model had a hold-out set area under the receiver operating curve of 0.92.Significance.We compared performance of four models on an open-access dataset.