On the possibility of estimating myocardial fiber architecture from cardiac strains.

The myocardium is composed of a complex network of contractile myofibers that are organized in such a way as to produce efficient contraction and relaxation of the heart. The myofiber architecture in the myocardium is a key determinant of cardiac motion and the global or organ-level function of the heart. Reports of architectural remodeling in cardiac diseases, such as pulmonary hypertension and myocardial infarction, potentially contributing to cardiac dysfunction call for the inclusion of an architectural marker for an improved assessment of cardiac function. However, the in-vivo quantification of three-dimensional myo-architecture has proven challenging. In this work, we examine the sensitivity of cardiac strains to varying myofiber orientation using a multiscale finite-element model of the LV. Additionally, we present an inverse modeling approach to predict the myocardium fiber structure from cardiac strains. Our results indicate a strong correlation between fiber orientation and LV kinematics, corroborating that the fiber structure is a principal determinant of LV contractile behavior. Our inverse model was capable of accurately predicting the myocardial fiber range and regional fiber angles from strain measures. A concrete understanding of the link between LV myofiber structure and motion, and the development of non-invasive and feasible means of characterizing the myocardium architecture is expected to lead to advanced LV functional metrics and improved prognostic assessment of structural heart disease.

Machine learning-based classification of cardiac relaxation impairment using sarcomere length and intracellular calcium transients.

Impaired relaxation of cardiomyocytes leads to diastolic dysfunction in the left ventricle. Relaxation velocity is regulated in part by intracellular calcium (Ca2+) cycling, and slower outflux of Ca2+ during diastole translates to reduced relaxation velocity of sarcomeres. Sarcomere length transient and intracellular calcium kinetics are integral parts of characterizing the relaxation behavior of the myocardium. However, a classifier tool that can separate normal cells from cells with impaired relaxation using sarcomere length transient and/or calcium kinetics remains to be developed. In this work, we employed nine different classifiers to classify normal and impaired cells, using ex-vivo measurements of sarcomere kinematics and intracellular calcium kinetics data. The cells were isolated from wild-type mice (referred to as normal) and transgenic mice expressing impaired left ventricular relaxation (referred to as impaired). We utilized sarcomere length transient data with a total of n = 126 cells (n = 60 normal cells and n = 66 impaired cells) and intracellular calcium cycling measurements with a total of n = 116 cells (n = 57 normal cells and n = 59 impaired cells) from normal and impaired cardiomyocytes as inputs to machine learning (ML) models for classification. We trained all ML classifiers with cross-validation method separately using both sets of input features, and compared their performance metrics. The performance of classifiers on test data showed that our soft voting classifier outperformed all other individual classifiers on both sets of input features, with 0.94 and 0.95 area under the receiver operating characteristic curves for sarcomere length transient and calcium transient, respectively, while multilayer perceptron achieved comparable scores of 0.93 and 0.95, respectively. However, the performance of decision tree, and extreme gradient boosting was found to be dependent on the set of input features used for training. Our findings highlight the importance of selecting appropriate input features and classifiers for the accurate classification of normal and impaired cells. Layer-wise relevance propagation (LRP) analysis demonstrated that the time to 50% contraction of the sarcomere had the highest relevance score for sarcomere length transient, whereas time to 50% decay of calcium had the highest relevance score for calcium transient input features. Despite the limited dataset, our study demonstrated satisfactory accuracy, suggesting that the algorithm can be used to classify relaxation behavior in cardiomyocytes when the potential relaxation impairment of the cells is unknown.

Infected mediastinal cysts following endobronchial ultrasound guided biopsy-a case series.

Mediastinal cysts are commonly an incidental finding simulating a benign or malignant diagnosis. Infection is a recognised complication of mediastinal cyst and therefore early surgical management is essential. Endobronchial ultrasound guided biopsy (EBUS) has been used to diagnose and manage mediastinal cyst. We present a case series of three patients who presented with sepsis following diagnostic EBUS of mediastinal cyst. We would recommend that EBUS guided biopsy be applied cautiously if there is a high suspicion of mediastinal cyst to avoid post procedural infection, which can thus complicate any future therapeutic options.

National survey of enhanced recovery after thoracic surgery practice in the United Kingdom and Ireland.

BACKGROUND: Evidence that Enhanced Recovery After Thoracic Surgery (ERAS) improves clinical outcomes is growing. Following the recent publications of the international ERAS guidelines in Thoracic surgery, the aim of this audit was to capture variation and perceived difficulties to ERAS implementation, thus helping its development at a national level. METHODS: We designed an anonymous online survey and distributed it via email to all 36 centres that perform lung lobectomy surgery in the UK and Ireland. It included 38 closed, open and multiple-choice questions on the core elements of ERAS and took an average of 10 min to complete. RESULTS: Eighty-two healthcare professionals from 34 out of 36 centres completed the survey; majority were completed by consultant thoracic surgeons (57%). Smoking cessation support varied and only 37% of individuals implemented the recommended period for fluid fasting; 59% screen patients for malnutrition and 60% do not give preoperative carbohydrate loading. The compliance with nerve sparing techniques when a thoracotomy is performed was poor (22%). 66% of respondents apply suction on intercostal drains and although 91% refer all lobectomies for physiotherapeutic assessment, the physiotherapy adjuncts varied across centres. Perceived barriers to implementation were staffing levels, lack of teamwork/consistency, limited resources over weekend and the reduced access to smoking cessation services. CONCLUSION: Centres across the UK are working to develop the ERAS pathway. This survey aids this process by providing insight into "real life" ERAS, increasing exposure of staff to the ESTS- ERAS recommendations and identifying barriers to implementation.

Predicting Postoperative Lung Function Following Lung Cancer Resection: A Systematic Review and Meta-analysis.

BACKGROUND: Lung resection remains the gold standard treatment for early stage lung cancer; prediction of postoperative lung function is a key selection criterion for surgery with the aim of determining risk of postoperative dyspnoea. We aimed to identify the different prediction techniques used, and compare their accuracy. METHODS: A systematic review and meta-analysis sought to synthesise studies conducted that assess prediction of postoperative lung function up to 18/02/2018 (n = 135). PROBAST was used to assess risk of bias in studies, 17 studies were judged to be at low risk of bias. FINDINGS: Meta-analysis revealed CT volume and density measurement to be the most accurate (mean difference 71 ml) and precise (standard deviation 207 ml) of the reported techniques used for predicting FEV1; evidence for predicting gas transfer was lacking. INTERPRETATION: The evidence suggests using CT volume and density is the preferred technique in the prediction of postoperative FEV1. Further studies are required to ensure that the methods and thresholds we propose are linked to patient reported outcomes. FUNDING: Salary support for NKO, RM, PN, BN, and AMT was provided by University Hospitals Birmingham NHS Foundation Trust.