An interesting method to emphasize student collaborative learning in the anatomy course has been developed by Dr. Emeka Anyanwu from the University of Nigeria.A board game entitled Anatomy Adventure encourages and motivates students to participate in the learning process, enhances learning outcomes and knowledge retention.In this picture students in Dr.
From 1973 to 1983 10 patients suffering from pulmonary embolism were seen at our clinic. All 10 underwent cardiopulmonary bypass and embolectomy of the pulmonary truncus and its branches including exploration of the right heart. 6 patients survived. The embolectomy of the pulmonary artery was supplemented by clipping the inferior vena cava. In chronic recurrent pulmonary embolism we only performed the clipping procedure of the inferior vena cava.
Abstract Background Big data has the potential to revolutionize echocardiography by enabling novel research and rigorous, scalable quality improvement. Text reports are a critical part of such analyses, and ontology is a key strategy for promoting interoperability of heterogeneous data through consistent tagging. Currently, echocardiogram reports include both structured and free text and vary across institutions, hampering attempts to mine text for useful insights. Natural language processing (NLP) can help and includes both non-deep learning and deep-learning (e.g., large language model, or LLM) based techniques. Challenges to date in using echo text with LLMs include small corpus size, domain-specific language, and high need for accuracy and clinical meaning in model results. Methods We tested whether we could map echocardiography text to a structured, three-level hierarchical ontology using NLP. We used two methods: statistical machine learning (EchoMap) and one-shot inference using the Generative Pre-trained Transformer (GPT) large language model. We tested against eight datasets from 24 different institutions and compared both methods against clinician-scored ground truth. Results Despite all adhering to clinical guidelines, there were notable differences by institution in what information was included in data dictionaries for structured reporting. EchoMap performed best in mapping test set sentences to the ontology, with validation accuracy of 98% for the first level of the ontology, 93% for the first and second level, and 79% for the first, second, and third levels. EchoMap retained good performance across external test datasets and displayed the ability to extrapolate to examples not initially included in training. EchoMap’s accuracy was comparable to one-shot GPT at the first level of the ontology and outperformed GPT at second and third levels. Conclusions We show that statistical machine learning can achieve good performance on text mapping tasks and may be especially useful for small, specialized text datasets. Furthermore, this work highlights the utility of a high-resolution, standardized cardiac ontology to harmonize reports across institutions.
Abstract Recent advancements in generative artificial intelligence have shown promise in producing realistic images from complex data distributions. We developed a denoising diffusion probabilistic model trained on the CheXchoNet dataset, encoding the joint distribution of demographic data and echocardiogram measurements. We generated a synthetic dataset skewed towards younger patients with a higher prevalence of structural left ventricle disease. A diagnostic deep learning model trained on the synthetic dataset performed comparably to one trained on real data producing an AUROC=0.75(95%CI 0.72-0.77), with similar performance on an internal dataset. Combining real data with positive samples from the synthetic data improved diagnostic accuracy producing an AUROC=0.80(95%CI 0.78-0.82). Subgroup analysis showed the largest performance improvement across younger patients. These results suggest diffusion models can increase diagnostic accuracy and fine-tune models for specific populations.
Introduction: Social determinants of health (SDOH) can critically impact healthcare access and outcomes. However, few studies have examined geospatial relationships between SDOH and healthcare util...
To determine the cardio-protective effect of heavy water on the ischemic myocardium, a thoracotomy was performed on 18 mongrel dogs. The animals were connected to the extracorporeal circulation in a standardized experimental procedure. Following total cardiopulmonary bypass, 2,000 ml of a standard cardioplegic solution (LK 352) was infused at the aortic root of 10 dogs, which served as controls (group I), and the same solution containing 20% of 99.8% deuterium oxide was given at the aortic root of the remaining animals (group II). At the end of 60 minutes of ischemia, 1,000 ml of the solutions was again administered at the aortic root of the corresponding animals. Myocardial biopsies were taken from the apex of the left ventricle of each dog before cardiopulmonary bypass, immediately after the infusion of the cardioplegic solutions, following 90 minutes of ischemia, and after 30 minutes of reperfusion, and studied ultrastructurally. Whereas the ultrastructure of the myocardium of group I was well preserved at the end of the ischemic period, deuterium-oxide-treated hearts showed extensive focal and global myofilamentolysis and lysis of whole myocytes. Structural damage to glycogen, nuclear chromatin dispersal, severe intracellular edema and complete rupture of the intercalated discs were characteristic findings. At the end of ischemia, all the hearts of group I could be resuscitated. During the ischemia, all the hearts of group II developed into stone hearts. Biochemical studies on a second series showed a higher ATP depletion and a significantly higher lactate accumulation in group II than in group I.(ABSTRACT TRUNCATED AT 250 WORDS)
Background COVID-19 has significantly altered health care delivery, requiring clinicians and hospitals to adapt to rapidly changing hospital policies and social distancing guidelines. At our large academic medical center, clinicians reported that existing information on distribution channels, including emails and hospital intranet posts, was inadequate to keep everyone abreast with these changes. To address these challenges, we adapted a mobile app developed in-house to communicate critical changes in hospital policies and enable direct telephonic communication between clinical team members and hospitalized patients, to support social distancing guidelines and remote rounding. Objective This study aimed to describe the unique benefits and challenges of adapting an app developed in-house to facilitate communication and remote rounding during COVID-19. Methods We adapted moblMD, a mobile app available on the iOS and Android platforms. In conjunction with our Hospital Incident Command System, resident advisory council, and health system innovation center, we identified critical, time-sensitive policies for app usage. A shared collaborative document was used to align app-based communication with more traditional communication channels. To minimize synchronization efforts, we particularly focused on high-yield policies, and the time of last review and the corresponding reviewer were noted for each protocol. To facilitate social distancing and remote patient rounding, the app was also populated with a searchable directory of numbers to patient bedside phones and hospital locations. We monitored anonymized user activity from February 1 to July 31, 2020. Results On its first release, 1104 clinicians downloaded moblMD during the observation period, of which 46% (n=508) of downloads occurred within 72 hours of initial release. COVID-19 policies in the app were reviewed most commonly during the first week (801 views). Users made sustained use of hospital phone dialing features, including weekly peaks of 2242 phone number dials, 1874 directory searches, and 277 patient room phone number searches through the last 2 weeks of the observation period. Furthermore, clinicians submitted 56 content- and phone number–related suggestions through moblMD. Conclusions We rapidly developed and deployed a communication-focused mobile app early during COVID-19, which has demonstrated initial and sustained value among clinicians in communicating with in-patients and each other during social distancing. Our internal innovation benefited from our team’s familiarity with institutional structures, short feedback loops, limited security and privacy implications, and a path toward sustainability provided by our innovation center. Challenges in content management were overcome through synchronization efforts and timestamping review. As COVID-19 continues to alter health care delivery, user activity metrics suggest that our solution will remain important in our efforts to continue providing safe and up-to-date clinical care.
Introduction: Contemporary cardiovascular fellowship training is based in the principles of competency-based medical education (CBME). Inclusive of CBME, core cardiovascular training statements suggest a minimum number of procedures necessary to achieve levels of training. However, current platforms for procedure logging create an onerous burden on trainees and program leadership which can result in incomplete data capture. There is a paucity of experience with automated case logging tools across training programs. Herein, we present the experience of fully automating trainee case logging within the echo lab at a large cardiovascular fellowship program. Methods: Structured trainee procedure attribution data (patient age, gender, and procedure type) were captured during routine clinical activity within our EHR structured reporting module (Epic Cupid). Attribution was extracted from the EHR reporting warehouse (Epic Clarity) and summarized for trainees weekly via email. Following a weeklong buffer period to allow for corrections, the data were uploaded to the institutional educational reporting platform (MedHub) via a web application programming interface. All stages of this workflow were fully automated. Results: During the post-intervention period (June 2022 to May 2023), 32 cardiovascular fellows participated. Attribution was initially captured for transthoracic and transesophageal echocardiograms and later expanded to include exercise and dobutamine stress echocardiograms. A monthly average of 584 procedures were captured for trainees. There were no attribution errors reported. The intervention was uniformly perceived as helpful by both trainees and leadership with requests for expansion to other cardiovascular procedure types. Conclusion: Fully automated educational experience reporting is feasible in a large cardiovascular training program and can be implemented without change to clinical workflows or administrative burden.
Background: According to the 2020 ACC/AHA guidelines, patients with severe aortic stenosis (AS) should either undergo rapid AVR or be followed by an echocardiogram (echo) every 6-12 months to assess disease progression. Discrepancies have been reported in the care of severe AS. Goal: This study aims to evaluate adherence to guidelines, and identify factors associated with guidelines recommended care in patients with severe AS. Methods: All patients with a first echo report of severe AS diagnosis from January 2019 to December 2022 in a tertiary care academic health center were included. Adherence to guidelines was defined as undergoing AVR in 6 months or having a follow-up echo within 6-12 months of the index echo. Univariable and multivariable Cox proportional hazards models were performed to identify factors associated with guidelines recommended care. Patients who did not meet the endpoint and died before one year were censored at the time of death. Results: Of the 1655 patients included in the study (mean ± SD age: 77±12 years, 56% male, 80% white, 10% African-American, and 10% other race), 727 (43%) had AVR in 6 months, 184 (11%) had no AVR in 6 months but a follow-up echo within 6-12 months, and 744 (45%) did not achieve the guidelines recommended care. Two hundred and thirty-eight (14%) patients died within one year (178 patients did not meet guidelines). The multivariable Cox proportional hazards model (Table) revealed that younger age, lower LVEF, higher aortic valve mean gradient, cardiovascular history or risk factors, and having a cardiac specialty provider increased the rate of adherence to guidelines, while African-Americans and patients with renal failure were less likely to receive the guidelines recommended care. Conclusions: A substantial proportion of patients with severe AS does not receive the guidelines recommended care and follow-up. Factors including age, race, comorbidities such as renal failure, cardiovascular history and risk factors, provider specialty, and characteristics of the AS are associated with these discrepancies. These results call for urgent action to guide patient and provider behavior towards equitably implementing guideline-recommended valvular heart disease therapies.
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