It is well known that cardiac dysfunction in sepsis is associated with significantly increased mortality. The pathophysiology of sepsis-induced cardiac dysfunction can be summarized as involving impaired myocardial circulation, direct myocardial depression, and mitochondrial dysfunction. Impaired blood flow to the myocardium is associated with microvascular dysfunction, impaired endothelium, and ventriculo-arterial uncoupling. The mechanisms behind direct myocardial depression consist of downregulation of β-adrenoceptors and several myocardial suppressants (such as cytokine and nitric oxide). Recent research has highlighted that mitochondrial dysfunction, which results in energy depletion, is a major factor in sepsis-induced cardiac dysfunction. Therefore, the authors summarize the pathophysiological process of cardiac dysfunction in sepsis based on the results of recent studies.

Background: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. Objective: To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. Methods: We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. Results: In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. Conclusions: Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.

Kim GS, et al. J Korean Med Sci. 2017 Apr;32(4):593-598. https://doi.org/10.3346/jkms.2017.32.4.593

This systematic review and meta-analysis aimed to investigate the ultrasonographic variation of the diameter of the inferior vena cava (IVC), internal jugular vein (IJV), subclavian vein (SCV), and femoral vein (FV) to predict fluid responsiveness in critically ill patients. Relevant articles were obtained by searching PubMed, EMBASE, and Cochrane databases (articles up to 21 October 2021). The number of true positives, false positives, false negatives, and true negatives for the index test to predict fluid responsiveness was collected. We used a hierarchical summary receiver operating characteristics model and bivariate model for meta-analysis. Finally, 30 studies comprising 1719 patients were included in this review. The ultrasonographic variation of the IVC showed a pooled sensitivity and specificity of 0.75 and 0.83, respectively. The area under the receiver operating characteristics curve was 0.86. In the subgroup analysis, there was no difference between patients on mechanical ventilation and those breathing spontaneously. In terms of the IJV, SCV, and FV, meta-analysis was not conducted due to the limited number of studies. The ultrasonographic measurement of the variation in diameter of the IVC has a favorable diagnostic accuracy for predicting fluid responsiveness in critically ill patients. However, there was insufficient evidence in terms of the IJV, SCV, and FV.

Background: Within the trauma system, the emergency department (ED) is the hospital’s first contact and is vital for allocating medical resources. However, there is generally limited information about patients that die in the ED. Objective: The aim of this study was to develop an artificial intelligence (AI) model to predict trauma mortality and analyze pertinent mortality factors for all patients visiting the ED. Methods: We used the Korean National Emergency Department Information System (NEDIS) data set (N=6,536,306), incorporating over 400 hospitals between 2016 and 2019. We included the International Classification of Disease 10th Revision (ICD-10) codes and chose the following input features to predict ED patient mortality: age, sex, intentionality, injury, emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and vital signs. We compared three different feature set performances for AI input: all features (n=921), ICD-10 features (n=878), and features excluding ICD-10 codes (n=43). We devised various machine learning models with an ensemble approach via 5-fold cross-validation and compared the performance of each model with that of traditional prediction models. Lastly, we investigated explainable AI feature effects and deployed our final AI model on a public website, providing access to our mortality prediction results among patients visiting the ED. Results: Our proposed AI model with the all-feature set achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.9974 (adaptive boosting [AdaBoost], AdaBoost + light gradient boosting machine [LightGBM]: Ensemble), outperforming other state-of-the-art machine learning and traditional prediction models, including extreme gradient boosting (AUROC=0.9972), LightGBM (AUROC=0.9973), ICD-based injury severity scores (AUC=0.9328 for the inclusive model and AUROC=0.9567 for the exclusive model), and KTAS (AUROC=0.9405). In addition, our proposed AI model outperformed a cutting-edge AI model designed for in-hospital mortality prediction (AUROC=0.7675) for all ED visitors. From the AI model, we also discovered that age and unresponsiveness (coma) were the top two mortality predictors among patients visiting the ED, followed by oxygen saturation, multiple rib fractures (ICD-10 code S224), painful response (stupor, semicoma), and lumbar vertebra fracture (ICD-10 code S320). Conclusions: Our proposed AI model exhibits remarkable accuracy in predicting ED mortality. Including the necessity for external validation, a large nationwide data set would provide a more accurate model and minimize overfitting. We anticipate that our AI-based risk calculator tool will substantially aid health care providers, particularly regarding triage and early diagnosis for trauma patients.

Background: The poor performance of conventional techniques used in cardiovascular disease patients requiring hemodialysis or arterial bypass grafting has prompted tissue engineers to search for clinically appropriate off-the-shelf vascular grafts. Most patients with cardiovascular disease lack suitable autologous tissue because of age or previous surgery. Commercially available vascular grafts with diameters of < 5 mm often fail because of thrombosis and intimal hyperplasia. Result: Here, we tested tubular biodegradable poly-e-caprolactone/polydioxanone (PCL/PDO) electrospun vascular grafts in a rat model of aortic interposition for up to 12 weeks. The grafts demonstrated excellent patency (100%) confirmed by Doppler Ultrasound, resisted aneurysmal dilation and intimal hyperplasia, and yielded neoarteries largely free of foreign materials. At 12 weeks, the grafts resembled native arteries with confluent endothelium, synchronous pulsation, a contractile smooth muscle layer, and co-expression of various extracellular matrix components (elastin, collagen, and glycosaminoglycan). Conclusions: The structural and functional properties comparable to native vessels observed in the neoartery indicate their potential application as an alternative for the replacement of damaged small-diameter grafts. This synthetic off-the-shelf device may be suitable for patients without autologous vessels. However, for clinical application of these grafts, long-term studies (> 1.5 years) in large animals with a vasculature similar to humans are needed.