Relationship between left main trifurcation angulation, calcium score, and the onset of plaque formation.

BACKGROUND: It has been suggested that a wider left main (LM) bifurcation angle is associated with the development of atherosclerosis. However, the relationship between LM trifurcation angulation and atherosclerosis has not been investigated. AIMS: We aimed to investigate the relationship between LM trifurcation angulation and the presence of calcifications in the left coronary artery (LCA) using coronary computed tomography angiography (CCTA). Furthermore, we assessed the relationship between LM trifurcation angulation and the age at which calcification originated. METHODS: The LM trifurcation angle and coronary artery calcium (CAC) score in the LCA were measured. Based on observational studies, we assumed that CAC progression is 25% per year on average. Then, we calculated the age at which LCA CAC scores were lower than 0.1 Agatston units. RESULTS: Of 266 patients, 52 patients (mean age of [standard deviation, SD] 61 [6] years; 28 men) with LM trifurcation were included in the study. Calcified plaques occurred in the LCA in 36 patients (69.2%). The mean LM trifurcation angle in patients with a diseased LCA was wider than that in patients with a normal LCA (108° [33°] vs. 91° [28°]; P = 0.04). Pearson correlation coefficient showed that the wider the LM trifurcation angle was, the earlier the calcification in the LCA may be expected (r = -0.34; P = 0.04 with outliers; r = -0.43; P = 0.009 without outliers). CONCLUSIONS: A wider LM trifurcation angle is associated with a higher LCA CAC score. Moreover, the LM trifurcation angle has a significant impact on the earlier onset of atherosclerosis.

Early detection of HVAD pump thrombosis based on technical analysis and power consumption measurements.

BACKGROUND: Continuous-flow left ventricular assist devices (LVADs) have been extensively used in a strategy of bridge to orthotopic heart transplant and destination therapy. The usage of LVAD, however, is not free from limitations such as device-related adverse events, including pump thrombosis (PT). We aimed to develop an algorithm of early PT detection based on the maintenance parameters monitored by the implanted device. METHODS: We analyzed log files of 101 patients implanted with HeartWare pump (HVAD) with 18 PT events among them. For signal processing, we used the open-high-low-close format transformation and typical price (TP) technical analysis indicator. Model parameters were tuned with 5-fold cross-validation, and the final performance was measured on a separate group of patients. RESULTS: Our algorithm achieved 100% sensitivity and 100% specificity of indications. In the final evaluation, alarms preceded the clinical acknowledgement of events by 2 days and 20 h on average. In the worst-case scenario, an alarm was raised 1 day and 8 h prior to the event. CONCLUSIONS: The proposed algorithm could be installed to work directly with the device controller and provide clinicians with automatic readings analysis, raising an alarm when there is a high probability of thromboembolism. Early event detection could enable better thrombosis management and improve prognosis in patients implanted with HVAD.

Acute respiratory failure and inflammatory response after out-of-hospital cardiac arrest: results of the Post-Cardiac Arrest Syndrome (PCAS) pilot study.

BACKGROUND: Although the lungs are potentially highly susceptible to post-cardiac arrest syndrome injury, the issue of acute respiratory failure after out-of-hospital cardiac arrest has not been investigated. The objectives of this analysis were to determine the prevalence of acute respiratory failure after out-of-hospital cardiac arrest, its association with post-cardiac arrest syndrome inflammatory response and to clarify its importance for early mortality. METHODS: The Post-Cardiac Arrest Syndrome (PCAS) pilot study was a prospective, observational, six-centre project (Poland 2, Denmark 1, Spain 1, Italy 1, UK 1), studying patients resuscitated after out-of-hospital cardiac arrest of cardiac origin. Primary outcomes were: (a) the profile of organ failure within the first 72 hours after out-of-hospital cardiac arrest; (b) in-hospital and short-term mortality, up to 30 days of follow-up. Respiratory failure was defined using a modified version of the Berlin acute respiratory distress syndrome definition. Inflammatory response was defined using leukocytes (white blood cells), platelet count and C-reactive protein concentration. All parameters were assessed every 24 hours, from admission until 72 hours of stay. RESULTS: Overall, 148 patients (age 62.9±15.27 years; 27.7% women) were included. Acute respiratory failure was noted in between 50 (33.8%) and 75 (50.7%) patients over the first 72 hours. In-hospital and short-term mortality was 68 (46.9%) and 72 (48.6%), respectively. Inflammation was significantly associated with the risk of acute respiratory failure, with the highest cumulative odds ratio of 748 at 72 hours (C-reactive protein 1.035 (1.001-1.070); 0.043, white blood cells 1.086 (1.039-1.136); 0.001, platelets 1.004 (1.001-1.007); <0.005). Early acute respiratory failure was related to in-hospital mortality (3.172, 95% confidence interval 1.496-6.725; 0.002) and to short-term mortality (3.335 (1.815-6.129); 0.0001). CONCLUSIONS: An inflammatory response is significantly associated with acute respiratory failure early after out-of-hospital cardiac arrest. Acute respiratory failure is associated with a worse early prognosis after out-of-hospital cardiac arrest.

Machine Learning-based Algorithm Enables the Exclusion of Obstructive Coronary Artery Disease in the Patients Who Underwent Coronary Artery Calcium Scoring.

RATIONALE AND OBJECTIVES: An application of artificial intelligence to screen for obstructive coronary artery disease (CAD) after coronary artery calcium scoring (CACS) test. MATERIALS AND METHODS: As an initial step we analyzed a group of 435 patients (23% male, mean age 61 ± 10) with low to moderate probability of CAD, who underwent clinically indicated CACS and coronary computed tomography angiography. Based on those data we elaborated a gradient boosting machine (GBM) model for prediction of obstructive CAD. Later the model was evaluated on a control group of 126 consecutive patients (31% male, mean age 59 ± 10). RESULTS: Stratified 10-fold cross-validation performed on the group of 435 patients demonstrated the GBM model's sensitivity at 100 ± 0% and specificity at 69.8 ± 3.6%, while the outcomes (confusion matrix) of a clinical application on the group of 126 patients were: 73 true negative, 0 false negative, 20 true positive, and 33 false positive. CONCLUSION: The GBM algorithm showcased a considerably high discriminatory power for excluding the presence of obstructive CAD, with negative predictive value and positive predictive value of 100% and 38%, respectively.