The combination of severe acidosis (base deficit ≥-10) with abdominal malperfusion was uniformly fatal. Further research is needed to determine whether the identification of extreme risk warrants consideration of alternate treatment options to address the cause of severe acidosis before ascending aortic procedures.

No abstract

Objective: Although many clinical metrics are associated with proximity to decompensation in heart failure (HF), none are individually accurate enough to risk-stratify HF patients on a patient-by-patient basis. The dire consequences of this inaccuracy in risk stratification have profoundly lowered the clinical threshold for application of high-risk surgical intervention, such as ventricular assist device placement. Machine learning can detect non-intuitive classifier patterns that allow for innovative combination of patient feature predictive capability. A machine learning-based clinical tool to identify proximity to catastrophic HF deterioration on a patient-specific basis would enable more efficient direction of high-risk surgical intervention to those patients who have the most to gain from it, while sparing others. Synthetic electronic health record (EHR) data are statistically indistinguishable from the original protected health information, and can be analyzed as if they were original data but without any privacy concerns. We demonstrate that synthetic EHR data can be easily accessed and analyzed and are amenable to machine learning analyses.Methods: We developed synthetic data from EHR data of 26,575 HF patients admitted to a single institution during the decade ending on 12/31/2018. Twenty-seven clinically-relevant features were synthesized and utilized in supervised deep learning and machine learning algorithms (i.e., deep neural networks [DNN], random forest [RF], and logistic regression [LR]) to explore their ability to predict 1-year mortality by five-fold cross validation methods. We conducted analyses leveraging features from prior to/at and after/at the time of HF diagnosis.Results: The area under the receiver operating curve (AUC) was used to evaluate the performance of the three models: the mean AUC was 0.80 for DNN, 0.72 for RF, and 0.74 for LR. Age, creatinine, body mass index, and blood pressure levels were especially important features in predicting death within 1-year among HF patients.Conclusions: Machine learning models have considerable potential to improve accuracy in mortality prediction, such that high-risk surgical intervention can be applied only in those patients who stand to benefit from it. Access to EHR-based synthetic data derivatives eliminates risk of exposure of EHR data, speeds time-to-insight, and facilitates data sharing. As more clinical, imaging, and contractile features with proven predictive capability are added to these models, the development of a clinical tool to assist in timing of intervention in surgical candidates may be possible.

Nitric oxide (NO) has a variety of actions in inflammation, haemostasis and thrombosis, including the potential to reduce the local toxicity of non-steroidal anti-inflammatory drugs (NSAIDs) in the gastrointestinal tract. NO-donating non-steroidal anti-inflammatory drugs (NO-NSAIDs) have been developed as one of several attempts to reduce the toxicity of their parent compounds. Such compounds should also dilate blood vessels and possibly increase local blood flow to aid absorption of ingested drug. A simple, reliable and economical method of study that did not use experimental animals was required. To this end, changes in isometric tension of isolated rings of the common digital artery of the fallow deer, killed for the venison market, in response to selected NO-NSAIDs and their parent NSAIDs (notably aspirin, ibuprofen, diclofenac and flurbiprofen) were measured. The nitrobutoxyl ester of aspirin (NO-aspirin) but not its butyl ester reduced contractions induced by 5-hydroxytryptamine (serotonin, 5HT) and phenylephrine (PHE). These effects of NO-aspirin were reduced by addition of methylene blue to sequester the released NO. Nitrobutoxyl esters of diclofenac, flurbiprofen, ibuprofen, and naproxen also caused relaxation of agonist-stimulated or electrically stimulated contractions. Relaxation was also caused by ibuprofen, flurbiprofen and diclofenac of agonist-stimulated or electrically stimulated arterial rings, with the R(-)-enantiomers of ibuprofen and flurbiprofen being more potent than their S(+)-enantiomers. The soluble guanylate cyclase (sGC) inhibitor, 1H-[1,2,4]oxadiazolo[4,3-a]quinoxalin-1-one (ODQ) reduced the effect, suggesting that these NSAIDs largely act through changes in sGC. These results show that NSAIDs and their NO derivatives may differ in their relaxant effects according to the type of NSAID. The observation that R(-)-ibuprofen is the more potent enantiomer in relaxing the vessel rings suggests that this component of racemic ibuprofen could be useful in enhancing the rate of absorption through the gut wall, with a consequent reduction in local toxicity.