BackgroundThoracic aortic dissection (TAD) and aneurysm (TAA) are rare but catastrophic. Prompt recognition of TAD/TAA and differentiation from acute coronary syndrome (ACS) is difficult yet crucial. Earlier identification of TAA/TAD based upon routine emergency department screening is necessary.MethodsA retrospective analysis of patients that presented with acute thoracic complaints to the ED from January 2007 through June 2012 was performed. Cases of TAA/TAD were compared to an equal number of controls which consisted of patients with the diagnosis of ACS. Demographics, physical findings, EKG, and the results of laboratory and radiological imaging were compared. P-value of > 0.05 was considered statistically significant.ResultsIn total, 136 patients were identified with TAA/TAD, 0.36% of patients that presented with chest complaints. Compared to ACS patients, TAA/TAD group was older (68.9 vs. 63.2 years), less likely to be diabetic (13% vs 32%), less likely to complain of chest pain (47% vs 85%) and head and neck pain (4% vs 17%). The pain for the TAA/TAD group was less likely characterized as tight/heavy in nature (5% vs 37%). TAA/TAD patients were also less likely to experience shortness of breath (42% vs. 51%), palpitations (2% vs 9%) and dizziness (2% vs 13%) and had a greater incidence of focal lower extremity neurological deficits (6% vs 1%), bradycardia (15% vs. 5%) and tachypnea (53% vs. 22%). On multivariate analysis, increasing heart rate, chest pain, diabetes, head & neck pain, dizziness, and history of myocardial infarction were independent predictors of ACS.ConclusionsIncreasing heart rate, chest pain, diabetes, head & neck pain, dizziness, and history of myocardial infarction can be used to differentiate acute coronary syndromes from thoracic aortic dissections/aneurysms.

Breast conservation therapy (BCT) has a reported incidence of positive margins ranging widely in the literature from 20% to 70%. Efforts have been made to refine standards for partial mastectomy and to predict which patients are at highest risk for incomplete excision. Most have focused on histology and demographics. We sought to further define modifiable risk factors for positive margins and residual disease. A retrospective study was conducted of 567 consecutive partial mastectomies by 21 breast and general surgeons from 2009 to 2012. Four hundred fourteen cases of neoplasm were reviewed for localization, intraoperative assessment, excision technique, rates, and results of re-excision/mastectomy. Histologic margins were positive in 23% of patients, 25% had margins 0.1-0.9 mm, and 7% had tumor within 1-1.9 mm. Residual tumor was identified at-in 61 cases: 38% (disease at margin), 21% (0.1-0.9 mm), and 14% (1-1.9 mm). Ductal carcinoma in situ (DCIS) was present in 85% of residual disease on re-excision and correlated to higher rates of re-excision (p = <0.001), residual disease, and subsequent mastectomy. The use of multiple needles to localize neoplasms was associated with 2-3 times the likelihood for positive margins than when a single needle was required. The removal of additional margins at initial surgery correlated with improved rates of complete excision when DCIS was present. Patients must have careful analysis of specimen margins at the time of surgery and may benefit from additional tissue excision or routine shaving of the cavity of resection. Surgeons should conduct careful patient selection for BCT, in the context of multifocal, and multicentric disease. Patients for whom tumor localization requires bracketing may be at higher risk for positive margins and residual disease and should be counseled accordingly.

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Introduction: Increased B-type Natriuretic Peptide (NT-proBNP) levels have been associated with adverse outcomes in patients with heart failure with preserved ejection fraction (HFpEF). Global longitudinal strain (GLS) and wall stress (WS) are frequently reported to be abnormal in these patients as well, but studies examining how structural changes in those with HFpEF affect morbidity and mortality have been scarce. Hypothesis: NT-proBNP is a stronger predictor of death and heart failure readmissions in HFpEF patients when compared to GLS and WS. Methods: We conducted a retrospective study of 237 patients admitted with acute decompensated heart failure, all with EF > 50. Average age was 78 + 11, 69% of patients were female, and average BMI was 29 + 13. GLS was measured using speckled tracing echocardiography and WS was calculated from systolic blood pressure, end-systolic left ventricular (LV) dimension, and end-systolic posterior wall thickness. Results: During a follow-up period of 2 years, 55 patients died and 55 were readmitted with the diagnosis of acute decompensated heart failure. Mean NT-proBNP among all patients was 9982 + 16268, mean GLS -17.84 + 4.52, and mean WS was 52.27 + 25.23. 129 patients had an abnormal or borderline GLS of >-18, whereas nearly all patients (230) had an abnormal WS of <109. (See table for results.) Conclusion: GLS and WS are reduced in a significant proportion of HFpEF patients. However, our data suggests that compared to echocardiographic indices of LV systolic function, biomarkers have a stronger short-term prognostic value.

Introduction: Aortic Stenosis is the most common valvular disorder with a predominance in the elderly. Trans-Aortic Valve Replacement (TAVR) has been an effective procedure with marked improvement in quality of life for patients. The procedure carries a small, yet clinically significant risk of stroke. The use of Neutrophil-Lymphocyte Ratios (NLR) and Platelet-Lymphocyte Ratios (PLR) have been growing as novel markers of systemic inflammation. We investigated the ability of a machine learning algorithm (Light GBM) to predict and weigh these ratios along with other clinical parameters for prediction of stroke after TAVR. Objective: To demonstrate the efficacy of the Supervised Machine Learning algorithm, Light GBM, in identifying important variables to predict stroke after TAVR. Methods: We performed a retrospective analysis of 291 patients who underwent TAVR from 2015-2019 at Montefiore Medical Center. Age (80±8), 50.2% Female, BMI (28.7 ±6.3). Clinical data was collected through our Hospital EMR. NLR and PLR averages were obtained using the mean of baseline (prior to surgery), Immediate Post-operative, and Post-operative Day 1 values. A supervised machine learning algorithm, Light GBM, used decision tree algorithms with both level-wise growth and leaf-wise growth. The algorithm was trained on 80% of the data and internally validated on the other 20%. Results: We identified NLR and PLR as the second and third most important feature of importance (Table 1) Clinical and demographic features of importance included BMI, Age, and Sex. Our model when internally validated yield a Sensitivity of 75.0%, Specificity of 91.5%, Accuracy of 91.5%, and F1 of 0.75. The AUC for the model was 0.84. Conclusions: Using Novel hematological parameters in conjunction with machine learning algorithms have highlighted important variables in predicting stroke after TAVR. Extrapolated, average NLR and PLR can be an inexpensive tool in stratifying patients those patients most at risk.

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