A young male with severe myocarditis and skeletal muscle myositis

A 34-year-old male presented with retrosternal chest pain, fatigue, shortness of breath, and a history of a previous episode of myocarditis four years prior. He had elevated troponin T, normal skeletal muscle enzymes, and negative inflammatory markers. Cardiac magnetic resonance imaging (MRI) confirmed active myocarditis with extensive myocardial fibrosis and normal left ventricular ejection fraction (LVEF). His myocarditis symptoms resolved with steroids and anti-inflammatory treatment, but on closer questioning, he reported a vague history of long-standing calf discomfort associated with episodes of stiffness, fatigue, and flu-like symptoms. MRI of the lower legs consequently demonstrated active myositis in the calf muscles. Immunomodulatory therapy was commenced with good effect. The patient is undergoing regular follow-up in both cardiology and rheumatology outpatient departments. Repeated MRI of the legs showed significant interval improvement in his skeletal muscle myositis, and repeat cardiac MRI demonstrated the resolution of myocarditis along with persistent stable extensive myocardial fibrosis and preserved LVEF. The patient has returned to full-time work.</p

Performing diagnostic radial access coronary angiography on uninterrupted direct oral anticoagulant therapy: a prospective analysis

Purpose We sought to assess the safety of performing diagnostic radial access coronary angiography with uninterrupted anticoagulation on patients receiving direct oral anticoagulant therapy. Background Direct oral anticoagulants have become a popular choice for the prevention of thromboembolism. Risk factors for thromboembolism are common among cardiovascular conditions and indications for direct oral anticoagulant therapy as well as coronary angiography often overlap in patients. It has been hypothesised that uninterrupted direct oral anticoagulant therapy would increase haemorrhagic and access site complications, however data in this area is limited. Methods This was a prospective observational analysis of 49 patients undergoing elective diagnostic coronary angiography while receiving uninterrupted anticoagulation with direct oral anticoagulants. This population was compared with a control group of 49 unselected patients presenting to the cardiology service for elective diagnostic coronary angiography. Continuous variables were analysed using the independent samples t-test and categorical variables using Pearson’s χ2 test. Results The mean duration of radial compression for the control group was 235.8±62.8min and for the uninterrupted direct oral anticoagulant group was 258.4±56.5min. There was no significant difference in mean duration of radial compression (p=0.07; 95% CI=-1.4 to 46.5). There was also no difference in the complication rate between the two groups (p=1). Conclusions We observed similar complication rates and radial artery compression time postangiography in both groups. This small prospective observational study suggests that uninterrupted continuation of direct oral anticoagulants during coronary angiography is safe. Larger randomised control studies in this area would be beneficial</p

Peak troponin T in STEMI: a predictor of all-cause mortality and left ventricular function

Background The clinical significance of peak troponin levels following ST-elevation myocardial infarction (STEMI) has not been definitively established. The purpose of this study was to examine the relationship between peak high-sensitivity cardiac troponin T (hs-cTnT) and all-cause mortality at 30 days and 1 year, and left ventricular ejection fraction (LVEF) in STEMI. Methods A single-centre retrospective observational study was conducted of all patients with STEMI between January 2015 and December 2017. Demographics and clinical data were obtained through electronic patient records. Standard Bayesian statistics were employed for analysis. Results During the study period, 568 patients presented with STEMI. The mean age was 63.6±12 years and 76.4% were men. Of these, 535 (94.2%) underwent primary percutaneous coronary intervention, 12 (2.1%) underwent urgent coronary artery bypass and 21 (3.7%) were treated medically. Mean peak hs-cTnT levels were significantly higher in those who died within 30 days compared with those who survived (12238ng/L vs 4657ng/L, respectively; p=0.004). Peak hs-cTnT levels were also significantly higher in those who died within 1year compared with those who survived (10319ng/L vs 4622ng/L, respectively; p=0.003). The left anterior descending artery was associated with the highest hs-cTnT and was the most common culprit in those who died at 1year. An inverse relationship was demonstrated between peak hs-cTnT and LVEF (Pearson’s R=0.379; p Conclusions In STEMI, those who died at 30 days and 1year had significantly higher peak troponin levels than those who survived. Peak troponin is also inversely proportional to LVEF with higher troponins associated with lower LVEF.</p

Performance of the Gradient-Boosted Decision Tree (GBDT) model trained with Input C.

(A) displays the normalized confusion matrix. The greatest difficulty this model faced was in distinguishing early arterial phase (E-AP) from late arterial phase (L-AP) and late arterial phase (L-AP) from portal venous phase (PVP). (B) displays the precision-recall curves (PRC) using a One vs. Rest (OvR) approach. The OvR approach measures the ability of the model to distinguish that phase from all the other phases. For each pair of graphs, the top row displays histograms of the probability calculated by the model that a given scan is the target label. If that probability is greater than 0.5, then the model will classify it as the target label. Scans with the target label with probabilities greater than 0.5 and scans not of the target label (“Rest”) with probabilities less than 0.5 are correctly classified. The bottom row of graphs in each pair displays the PRCs, which graph the model’s precision (positive predictive value) against recall (sensitivity or true positive rate). A no-skill classifier is displayed as a horizontal line of the number of scans of the target label divided by the total number of scans. As shown by the PRC, the model has the most difficulty classifying E-AP scans.</p

The average accuracy, AUPRC, and MCC of the supervised learning models for the main dataset.

The average accuracy, AUPRC, and MCC of the supervised learning models for the main dataset.</p

Flow chart of cross-validation dataset.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew’s correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.</div

This figure shows the precision-recall curves (PRCs) for each supervised learning model trained with Input B and tested on the external dataset.

(A), (B), (C), (D) and (E) display the PRCs for the logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and gradient-boosted decision tree (GBDT) models, respectively. For each model, the graphs evaluated using a One vs. Rest (OvR) approach are shown on the top and a One vs. One (OvO) approach are shown on the bottom (note that only the OvO PRCs for consecutive phases are shown). See S2 Fig for more details on their interpretation. (PDF)</p

This figure shows the precision-recall curves (PRCs) for each supervised learning model trained with Input B.

(A), (B), (C), (D) and (E) display the PRCs for the logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and gradient-boosted decision tree (GBDT) models, respectively. For each model, the graphs evaluated using a One vs. Rest (OvR) approach are shown on the top and a One vs. One (OvO) approach are shown on the bottom (note that only the OvO PRCs for consecutive phases are shown). See S2 Fig for more details on their interpretation. (PDF)</p

This figure shows the precision-recall curves (PRCs) for each supervised learning model trained with Input A and tested on the external dataset.

(A), (B), (C), (D) and (E) display the PRCs for the logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and gradient-boosted decision tree (GBDT) models, respectively. For each model, the graphs evaluated using a One vs. Rest (OvR) approach are shown on the top and a One vs. One (OvO) approach are shown on the bottom (note that only the OvO PRCs for consecutive phases are shown). See S2 Fig for more details on their interpretation. (PDF)</p

This figure shows the precision-recall curves (PRCs) for each supervised learning model trained with Input C.

(A), (B), (C), (D) and (E) display the PRCs for the logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and gradient-boosted decision tree (GBDT) models, respectively. For each model, the graphs evaluated using a One vs. Rest (OvR) approach are shown on the top and a One vs. One (OvO) approach are shown on the bottom (note that only the OvO PRCs for consecutive phases are shown). See S2 Fig for more details on their interpretation. (PDF)</p