Cardiovascular Outcomes Associated With Exposure To Radiation Therapy In Thoracic Malignancies: An Insight Study Using the National Inpatient Database.

Background Thoracic irradiation is a widely used therapeutic and palliative treatment option for thoracic neoplasms. However, short- and long-term cardiovascular adverse effects of radiation exposure remain a major concern. The short-term adverse effects are observed within months of exposure such as pericardial diseases; meanwhile, the long-term complications are usually insidious and manifest over decades, such as congestive heart failure, coronary artery disease, cardiomyopathy, conduction disorders, constrictive pericarditis, and valvular heart disease. Hence, long-term cardiovascular adverse effects are challenging to predict, and the association with radiation exposure remains difficult to establish. Methodology This retrospective, observational study was conducted using data from the National Inpatient Sample (NIS) database from 2016 to 2019. Adult patients with primary thoracic malignancies who underwent radiation therapy (RT) were defined using principal and secondary International Classification of Diseases, Tenth Revision codes. Other malignancies that can be treated with RT and all secondary malignancies were excluded from the primary comparison group. Cardiac outcomes were defined as the prevalence of congestive heart failure, coronary artery disease, cardiomyopathy, conduction disorders, pericardial diseases, and valvular heart diseases in the primary group. The multivariate logistic and the linear regression analyses were used to adjust for confounders. Results When compared to the general population, adults with thoracic malignancies exposed to RT had higher odds of developing chronic pericarditis (adjusted odds ratio (aOR) = 2, 95% confidence interval (CI) = 1.9-2.2, p < 0.001), acute pericarditis (aOR = 2.3, 95% CI = 1.9-2.9, p < 0.001), constrictive pericarditis (aOR = 2.8, 95% CI = 2.1-3.7, p < 0.001), conduction disorders (aOR = 1.3, 95% CI = 1.2-1.35, p < 0.001), coronary artery disease (aOR = 1.24, 95% CI = 1.2-1.27, p < 0.001), heart failure (aOR = 1.44, 95% CI = 1.4-1.5, p < 0.001), and valvular heart disease (aOR = 1.37, 95% CI = 1.3-1.4, p < 0.001). There was no difference in the odds of developing cardiac arrest (aOR = 1, 95% CI = 0.9-1.10, p = 0.6) or acute myocardial infarction (aOR = 1.1, 95% CI = 1-1.15, p < 0.001). When compared to adults with thoracic malignancies not exposed to RT, adults with thoracic malignancies who were exposed to RT had higher odds of developing acute myocardial infarction (aOR = 1.14, 95% CI = 1.1-1.18, p < 0.001), chronic pericarditis (aOR = 1.3, 95% CI = 1.2-1.3, p < 0.001), acute pericarditis (aOR = 1.6, 95% CI = 1.2-2.1, p < 0.001), constrictive pericarditis (aOR = 2.2, 95% CI = 1.5-3.2, p < 0.001), conduction disorders (aOR = 1.1, 95% CI = 1.08-1.13, p < 0.001), coronary artery disease (aOR = 1.14, 95% CI = 1.12-1.16, p < 0.001), heart failure (aOR = 1.2, 95% CI = 1.17-1.23, p < 0.001), and valvular heart disease (aOR = 1.3, 95% CI = 1.2-1.35, p < 0.001). The odds were similar between the two groups for developing cardiac arrest (aOR = 0.86, 95% CI = 0.8-0.98, p = 0.05). Conclusions Adults with thoracic malignancies who were treated with RT have higher odds of developing chronic pericarditis, acute pericarditis, constrictive pericarditis, conduction disorders, coronary artery disease, heart failure, and valvular heart disease while similar odds of developing cardiac arrest or acute myocardial infarction compared to the general adult population.

Outcomes in patients with COVID-19 and new onset heart blocks: Insight from the National Inpatient Sample database.

BACKGROUND: Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a worldwide health crisis since it first appeared. Numerous studies demonstrated the virus's predilection to cardiomyocytes; however, the effects that COVID-19 has on the cardiac conduction system still need to be fully understood. AIM: To analyze the impact that COVID-19 has on the odds of major cardiovascular complications in patients with new onset heart blocks or bundle branch blocks (BBB). METHODS: The 2020 National Inpatient Sample (NIS) database was used to identify patients admitted for COVID-19 pneumonia with and without high-degree atrioventricular blocks (HDAVB) and right or left BBB utilizing ICD-10 codes. The patients with pre-existing pacemakers, suggestive of a prior diagnosis of HDAVB or BBB, were excluded from the study. The primary outcome was inpatient mortality. Secondary outcomes included total hospital charges (THC), the length of hospital stay (LOS), and other major cardiac outcomes detailed in the Results section. Univariate and multivariate regression analyses were used to adjust for confounders with Stata version 17. RESULTS: A total of 1058815 COVID-19 hospitalizations were identified within the 2020 NIS database, of which 3210 (0.4%) and 17365 (1.6%) patients were newly diagnosed with HDAVB and BBB, respectively. We observed a significantly higher odds of in-hospital mortality, cardiac arrest, cardiogenic shock, sepsis, arrythmias, and acute kidney injury in the COVID-19 and HDAVB group. There was no statistically significant difference in the odds of cerebral infarction or pulmonary embolism. Encounters with COVID-19 pneumonia and newly diagnosed BBB had a higher odds of arrythmias, acute kidney injury, sepsis, need for mechanical ventilation, and cardiogenic shock than those without BBB. However, unlike HDAVB, COVID-19 pneumonia and BBB had no significant impact on mortality compared to patients without BBB. CONCLUSION: In conclusion, there is a significantly higher odds of inpatient mortality, cardiac arrest, cardiogenic shock, sepsis, acute kidney injury, supraventricular tachycardia, ventricular tachycardia, THC, and LOS in patients with COVID-19 pneumonia and HDAVB as compared to patients without HDAVB. Likewise, patients with COVID-19 pneumonia in the BBB group similarly have a higher odds of supraventricular tachycardia, atrial fibrillation, atrial flutter, ventricular tachycardia, acute kidney injury, sepsis, need for mechanical ventilation, and cardiogenic shock as compared to those without BBB. Therefore, it is essential for healthcare providers to be aware of the possible worse predicted outcomes that patients with new-onset HDAVB or BBB may experience following SARS-CoV-2 infection.

Impact of Hospital Readmissions Reduction Program Policy on 30-Day and 90-Day Readmissions in Patients With Acute Myocardial Infarction: A 10-Year Trend From the National Readmissions Database.

Hospital readmissions following acute myocardial infarction (AMI) pose a significant economic burden on health care utilization. The hospital readmission reduction program (HRRP) enacted in 2012 focused on reducing readmissions by penalizing Centers for Medicare & Medicaid Services (CMS) Medicare hospitals. We aim to assess the trend of readmissions after AMI hospitalization between 2010 and 2019 and assess the impact of HRRP. The National Readmission Database was queried to identify AMI hospitalizations between 2010 and 2019. In the primary analysis, trends of 30-day and 90-day all-cause and AMI specific readmissions were assessed from 2010 to 2019. In the secondary analysis, trend of readmission means length of stay and mean adjusted total cost were calculated. There were a total of 592,015 30-day readmissions and 787,008 90-day readmissions after an index hospitalization for AMI between 2010 and 2019. The rates of 30-day and 90-day all-cause readmissions decreased significantly from 12.8% to 11.6%, (P = 0.0001) and 20.6 to 18.8, (P = 0.0001) respectively in the decade under study. With regards to HRRP policy intervals, the pre-HRRP period from 2010 to 2012 showed a downward trend in all-cause readmission (12.8% to 11.6%) and similarly a downward trend was also seen in the post HRRP period (2013-2015:11.0%-8.2%, 2016-2019-12.3-11.7%). Secondary analysis showed a trend towards increase in mean length of stay (4.54-4.96 days, P = 0.0001) and adjusted total cost ($13,449-$16,938) in 30-day all-cause readmission for AMI in the decade under review. In our National Readmission Database-based analysis of patients readmitted to hospitals within 30-days and 90-days after AMI, the rate of all-cause readmissions down trended from 2010 to 2019.

A general skull stripping of multiparametric brain MRIs using 3D convolutional neural network.

Accurate skull stripping facilitates following neuro-image analysis. For computer-aided methods, the presence of brain skull in structural magnetic resonance imaging (MRI) impacts brain tissue identification, which could result in serious misjudgments, specifically for patients with brain tumors. Though there are several existing works on skull stripping in literature, most of them either focus on healthy brain MRIs or only apply for a single image modality. These methods may be not optimal for multiparametric MRI scans. In the paper, we propose an ensemble neural network (EnNet), a 3D convolutional neural network (3DCNN) based method, for brain extraction on multiparametric MRI scans (mpMRIs). We comprehensively investigate the skull stripping performance by using the proposed method on a total of 15 image modality combinations. The comparison shows that utilizing all modalities provides the best performance on skull stripping. We have collected a retrospective dataset of 815 cases with/without glioblastoma multiforme (GBM) at the University of Pittsburgh Medical Center (UPMC) and The Cancer Imaging Archive (TCIA). The ground truths of the skull stripping are verified by at least one qualified radiologist. The quantitative evaluation gives an average dice score coefficient and Hausdorff distance at the 95th percentile, respectively. We also compare the performance to the state-of-the-art methods/tools. The proposed method offers the best performance.The contributions of the work have five folds: first, the proposed method is a fully automatic end-to-end for skull stripping using a 3D deep learning method. Second, it is applicable for mpMRIs and is also easy to customize for any MRI modality combination. Third, the proposed method not only works for healthy brain mpMRIs but also pre-/post-operative brain mpMRIs with GBM. Fourth, the proposed method handles multicenter data. Finally, to the best of our knowledge, we are the first group to quantitatively compare the skull stripping performance using different modalities. All code and pre-trained model are available at: https://github.com/plmoer/skull_stripping_code_SR .