A case of hepatic cyst-induced internal jugular venous thrombosis

• Echocardiography can demonstrate hepatic cyst–induced right atrial compression. • Hepatic cyst–induced blood flow stasis can cause internal jugular venous thrombus. • Laparoscopic deroofing of hepatic cysts is a safe and effective treatment

Long-term outcomes of primary cardiac malignant tumors: Difference between African American and Caucasian population

BACKGROUND: The survival outcome for primary cardiac malignant tumors (PMCTs) based on race has yet to be fully elucidated in previously published literature. This study aimed to address the general long-term outcome and survival rate differences in PMCTs among African Americans and Caucasian populations. METHODS: The 18 cancer registries database from the Surveillance, Epidemiology, and End Results (SEER) Program from 1975 to 2016 were utilized. Ninety-four African American (AA) and 647 Caucasian (CAU) patients from the SEER registry were available for survival analysis. The log-rank test was used to compare the difference in mortality between two populations and presented by the Kaplan-Meier curves. A multivariate Cox proportional hazards regression was used to determine the independent predictors of all-cause mortality. RESULTS: The overall 30-day, 1-year, and 5-year survival rates were 74%, 44.3%, and 16.6%, respectively, with a median survival of 10 months. There was no significant difference in survival rate between the two races (p-value = 0.55). The 1-year survival rate improved significantly during the study timeline in the AA population (13.3% during 1975-1998, 40.9% during 1999-2004, 50% during 2005-2010, and 59.7% during 2011-2016, p-value = 0.0064). Age of diagnosis, type of tumor, disease stage, and chemotherapy administration are the main factors that predict survival outcomes of PMCT patients. Interactive nomogram was developed based on significant predictors. CONCLUSIONS: PMCTs have remained one of the most lethal diseases with poor survival outcome. Survival rate improved during the timeline in AA patients, but in general, racial differences in survival outcome were not observed

Shaping cardiac diagnostics: The role of myocardial tissue mapping in unraveling ring-like fibrosis

BACKGROUND: Patients with non-ischemic cardiomyopathy exhibit a range of myocardial fibrosis (MF) patterns on cardiovascular magnetic resonance (CMR) late gadolinium enhancement (LGE) imaging. Data suggests that ring-like MF is associated with worse prognosis. In the present study it was sought to analyze the prevalence of parametric mapping abnormalities in ring-like MF and their prognostic value for arrhythmic events. METHODS: Patients undergoing clinical CMR at 1.5T/3T were evaluated for ring-like MF defined as midwall/subepicardial fibrosis involving ≥ 3 contiguous left ventricular segments. CMR protocol included cine imaging, T1 and T2 mapping, and LGE. Mean native T1, ECV, and T2 values and a number of mid short axis segments with elevated values were calculated. LGE extent was assessed segmentally. Arrhythmic outcomes were defined as appropriate device shock, premature ventricular contractions ≥ 10%, non-sustained/sustained ventricular tachycardia, or ventricular fibrillation. RESULTS: In total 49 patients (53 ± 17 years, 26.5% female) were analyzed. Many patients had elevated global/segmental mapping values: 45%/76% in native T1, 57%/57% in T2, and 57%/78% in ECV. During median follow-up of 12 months, arrhythmic events occurred in 65% of patients. There was no association between native T1/T2 elevation or number of LGE segments and arrhythmic outcomes. There was a significant association between ECV and arrhythmic outcomes, both septal ECV (p = 0.036) and any segmental ECV elevation (p = 0.03). CONCLUSION: T1 and T2 myocardial tissue abnormalities are common in patients with ring-like MF. ECV elevation was associated with arrhythmic events in this cohort. Further studies are needed to establish the diagnostic and prognostic value of parametric mapping in patients with ring-like MF

T‐wave and its association with myocardial fibrosis on cardiovascular magnetic resonance examination

Background: Risk stratification in non-ischemic myocardial disease poses a challenge. While cardiovascular magnetic resonance (CMR) is a comprehensive tool, the electrocardiogram (ECG) provides quick impactful clinical information. Studying the relationships between CMR and ECG can provide much-needed risk stratification. We evaluated the electrocardiographic signature of myocardial fibrosis defined as presence of late gadolinium enhancement (LGE) or extracellular volume fraction (ECV) ≥29%. Methods: We evaluated 240 consecutive patients (51% female, 47.1 ± 16.6 years) referred for a clinical CMR who underwent 12-lead ECGs within 90 days. ECG parameters studied to determine association with myocardial fibrosis included heart rate, QRS amplitude/duration, T-wave amplitude, corrected QT and QT peak, and Tpeak-Tend. Abnormal T-wave was defined as low T-wave amplitude ≤200 µV or a negative T wave, both in leads II and V5. Results: Of the 147 (61.3%) patients with myocardial fibrosis, 67 (28.2%) had ECV ≥ 29%, and 132 (54.6%) had non-ischemic LGE. An abnormal T-wave was more prevalent in patients with versus without myocardial fibrosis (66% versus 42%, p < .001). Multivariable analysis demonstrated that abnormal T-wave (OR 1.95, 95% CI 1.09-3.49, p = .03) was associated with myocardial fibrosis (ECV ≥ 29% or LGE) after adjustment for clinical covariates (age, gender, history of hypertension, and heart failure). Dynamic nomogram for predicting myocardial fibrosis using clinical parameters and the T-wave was developed: https://normogram.shinyapps.io/CMR_Fibrosis/. Conclusion: Low T-wave amplitude ≤ 200 µV or negative T-waves are independently associated with myocardial fibrosis. Prospective evaluation of T-wave amplitude may identify patients with a high probability of myocardial fibrosis and guide further indication for CMR

Indexed left ventricular mass to QRS voltage ratio is associated with heart failure hospitalizations in patients with cardiac amyloidosis

In cardiac amyloidosis (CA), amyloid infiltration results in increased left ventricular (LV) mass disproportionate to electrocardiographic (EKG) voltage. We assessed the relationship between LV mass-voltage ratio with subsequent heart failure hospitalization (HHF) and mortality in CA. Patients with confirmed CA and comprehensive cardiovascular magnetic resonance (CMR) and EKG exams were included. CMR-derived LV mass was indexed to body surface area. EKG voltage was assessed using Sokolow, Cornell, and Limb-voltage criteria. The optimal LV mass-voltage ratio for predicting outcomes was determined using receiver operating characteristic curve analysis. The relationship between LV mass-voltage ratio and HHF was assessed using Cox proportional hazards analysis adjusting for significant covariates. A total of 85 patients (mean 69 ± 11 years, 22% female) were included, 42 with transthyretin and 43 with light chain CA. At a median of 3.4-year follow-up, 49% of patients experienced HHF and 60% had died. In unadjusted analysis, Cornell LV mass-voltage ratio was significantly associated with HHF (HR, 1.05; 95% CI 1.02-1.09, p = 0.001) and mortality (HR, 1.05; 95% CI 1.02-1.07, p = 0.001). Using ROC curve analysis, the optimal cutoff value for Cornell LV mass-voltage ratio to predict HHF was 6.7 gm/m2/mV. After adjusting for age, NYHA class, BNP, ECV, and LVEF, a Cornell LV mass-voltage ratio > 6.7 gm/m2/mV was significantly associated with HHF (HR 2.25, 95% CI 1.09-4.61; p = 0.03) but not mortality. Indexed LV mass-voltage ratio is associated with subsequent HHF and may be a useful prognostic marker in cardiac amyloidosis

Cardiac magnetic resonance tissue tracking in right ventricle::Feasibility and normal values

Purpose: To investigate right ventricular (RV) strain in patients without identified cardiac pathology using cardiac magnetic resonance tissue tracking (CMR TT).Methods: A total of 50 consecutive patients with no identified cardiac pathology were analyzed. RV longitudinal and circumferential strain was assessed by CMR TT. The age range was 4–81 years with a median of 32 years (interquartile range, 15 to 56 years).Results: Analysis time per patient was &lt; 5 min. The peak longitudinal strain (Ell) was − 22.11 ± 3.51%. The peak circumferential strains (Ecc) for global, basal, mid-cavity and apical segments were as follows: − 11.69 ± 2.25%, − 11.00 ± 2.45%, − 11.17 ± 3.36%, − 12.90 ± 3.34%. There were significant gender differences in peak Ecc at the base (P = 0.04) and the mid-cavity (P = 0.03) with greater deformation in females than in males. On Bland-Altman analysis, peak Ell (mean bias, 0.22 ± 1.67; 95% CI − 3.05 to 3.49) and mid-cavity Ecc (mean bias, 0.036 ± 1.75; 95% CI, − 3.39 to 3.47) had the best intra-observer agreement and inter-observer agreement, respectively.Conclusions: RV longitudinal and circumferential strains can be quickly assessed with good intra-observer and inter-observer variability using TT

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic