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Frontiers in Public Health 2024Ozone pollution is associated with cardiovascular disease mortality, and there is a high correlation between different pollutants. This study aimed to assess the...
BACKGROUND
Ozone pollution is associated with cardiovascular disease mortality, and there is a high correlation between different pollutants. This study aimed to assess the association between ozone and cardiovascular disease deaths and the resulting disease burden in Nanjing, China.
METHODS
A total of 151,609 deaths from cardiovascular disease were included in Nanjing, China from 2013 to 2021. Daily data on meteorological and air pollution were collected to apply a generalized additional model with multiple pollutants to perform exposure-response analyses, stratification analysis, and evaluation of excess deaths using various standards.
RESULTS
In the multi-pollutant model, an increase of 10 μg/m in O was significantly associated with a 0.81% (95%CI: 0.49, 1.12%) increase in cardiovascular disease deaths in lag05. The correlation weakened in both the single-pollutant model and two-pollutant models, but remained more pronounced in females, the older group, and during warm seasons. From 2013 to 2021, the number of excess deaths attributed to ozone exposure in cardiovascular disease continued to rise with an increase in ozone concentration in Nanjing. If the ozone concentration were to be reduced to the WHO standard and the minimum level, the number of deaths would decrease by 1,736 and 10,882, respectively.
CONCLUSION
The risk of death and excess deaths from cardiovascular disease due to ozone exposure increases with higher ozone concentration. Reducing ozone concentration to meet WHO standards or lower can provide greater cardiovascular disease health benefits.
Topics: Ozone; Humans; Cardiovascular Diseases; China; Female; Male; Air Pollutants; Environmental Exposure; Air Pollution; Middle Aged; Aged; Seasons; Adult; Rivers
PubMed: 38939565
DOI: 10.3389/fpubh.2024.1353384 -
Frontiers in Public Health 2024The concept of race is prevalent in medical, nursing, and public health literature. Clinicians often incorporate race into diagnostics, prognostic tools, and treatment...
The concept of race is prevalent in medical, nursing, and public health literature. Clinicians often incorporate race into diagnostics, prognostic tools, and treatment guidelines. An example is the recently heavily debated use of race and ethnicity in the Vaginal Birth After Cesarean (VBAC) calculator. In this case, the critics argued that the use of race in this calculator implied that race confers immutable characteristics that affect the ability of women to give birth vaginally after a c-section. This debate is co-occurring as research continues to highlight the racial disparities in health outcomes, such as high maternal mortality among Black women compared to other racial groups in the United States. As the healthcare system contemplates the necessity of utilizing race-a social and political construct, to monitor health outcomes, it has sparked more questions about incorporating race into clinical algorithms, including pulmonary tests, kidney function tests, pharmacotherapies, and genetic testing. This paper critically examines the argument against the race-based Vaginal Birth After Cesarean (VBAC) calculator, shedding light on its implications. Moreover, it delves into the detrimental effects of normalizing race as a biological variable, which hinders progress in improving health outcomes and equity.
Topics: Humans; Female; Pregnancy; Algorithms; United States; Maternal Health; Racial Groups; Cesarean Section
PubMed: 38939564
DOI: 10.3389/fpubh.2024.1417429 -
JACC. Advances Sep 2023Cardiovascular disease is a leading cause of morbidity and mortality, largely dominated by ischemic heart diseases (IHDs). Social determinants of health, including...
BACKGROUND
Cardiovascular disease is a leading cause of morbidity and mortality, largely dominated by ischemic heart diseases (IHDs). Social determinants of health, including geographic, psychosocial, and socioeconomic factors, influence the development of IHD.
OBJECTIVES
This study aimed to evaluate yearly trends and disparities in IHD mortality and to assess the impact of social vulnerability.
METHODS
We performed cross-sectional analyses using United States county-level mortality data and social vulnerability index (SVI) obtained from the Centers for Disease Control and Prevention databases. Age-adjusted mortality rates (AAMRs) per 100,000 population were compared between aggregated U.S. county groups, stratified by demographic information and SVI quartiles. Log-linear regression models were used to identify mortality trends from 1999 to 2020, with inflection points determined through the Monte-Carlo permutation test.
RESULTS
We identified a total of 9,108,644 deaths related to IHD between 1999 and 2020. Overall AAMR decreased from 194.6 in 1999 to 91.8 in 2020. Males (AAMR: 161.51) and Black (AAMR: 141.49) populations exhibited higher AAMR compared to females (AAMR: 93.16) and White (AAMR: 123.34) populations, respectively. Disproportionate AAMRs were observed among nonmetropolitan (AAMR: 136.17) and Northeastern (AAMR: 132.96) regions. Counties with a higher SVI experienced a greater AAMR, with a cumulative excess of 20.91 deaths per 100,000 person-years associated with increased social vulnerability.
CONCLUSIONS
Despite a decline in IHD mortality from 1999 to 2020, disparities persisted among racial, gender, and geographic subgroups. A higher SVI was linked to increased IHD mortality. Policy interventions should prioritize integrating the SVI into health care delivery systems to effectively address these disparities.
PubMed: 38939497
DOI: 10.1016/j.jacadv.2023.100577 -
JACC. Advances Sep 2023
PubMed: 38939490
DOI: 10.1016/j.jacadv.2023.100561 -
JACC. Advances Sep 2023Most risk prediction models are confined to specific medical conditions, thus limiting their application to general medical populations.
BACKGROUND
Most risk prediction models are confined to specific medical conditions, thus limiting their application to general medical populations.
OBJECTIVES
The MARKER-HF (Machine learning Assessment of RisK and EaRly mortality in Heart Failure) risk model was developed in heart failure (HF) patients. We assessed the ability of MARKER-HF to predict 1-year mortality in a large community-based hospital registry database including patients with and without HF.
METHODS
This study included 41,749 consecutive patients who underwent echocardiography in a tertiary referral hospital (4,640 patients with and 37,109 without HF). Patients without HF were further subdivided into those with (n = 22,946) and without cardiovascular disease (n = 14,163) and also into cohorts based on recent acute coronary syndrome or history of atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, diabetes mellitus, hypertension, or malignancy.
RESULTS
The median age of the 41,749 patients was 65 years, and 56.2% were male. The receiver operated area under the curves for MARKER-HF prediction of 1-year mortality of patients with HF was 0.729 (95% CI: 0.706-0.752) and for patients without HF was 0.770 (95% CI: 0.760-0.780). MARKER-HF prediction of mortality was consistent across subgroups with and without cardiovascular disease and in patients diagnosed with acute coronary syndrome, atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, diabetes mellitus, or hypertension. Patients with malignancy demonstrated higher mortality at a given MARKER-HF score than did patients in the other groups.
CONCLUSIONS
MARKER-HF predicts mortality for patients with HF as well as for patients suffering from a variety of diseases.
PubMed: 38939487
DOI: 10.1016/j.jacadv.2023.100554 -
JACC. Advances Sep 2023Current guidelines recommend concomitant repair of certain non-severe cases of tricuspid regurgitation (TR) in patients undergoing cardiac surgery, but the prognostic...
BACKGROUND
Current guidelines recommend concomitant repair of certain non-severe cases of tricuspid regurgitation (TR) in patients undergoing cardiac surgery, but the prognostic relevance and postsurgical impact of the TR remain uncertain.
OBJECTIVES
The purpose of this study was to determine the prognostic impact of functional TR in patients undergoing diverse cardiac surgeries and to examine the effect-modifying role of patient characteristics in patients in whom TR confers a greater risk of adverse outcomes.
METHODS
Patients undergoing coronary artery bypass, aortic, and mitral valve surgery were included. Patients with severe TR, organic tricuspid valve pathology, undergoing tricuspid valve surgery or without a recent preoperative echocardiogram were excluded. Clinical variables were extracted from the Society of Thoracic Surgeons Adult Cardiac Surgery Database. An independent cohort was used for external validation.
RESULTS
Of 2,119 patients (mean age 67.4 years; 29% females), TR severity was moderate in 185 (9%), mild in 636 (30%), trivial in 1,126 (53%), and absent in 172 (8%). There were 238 deaths during the median follow-up period of 2.6 years. After adjusting for relevant factors, moderate TR was found to be independently associated with mid-term mortality (HR: 2.58; 95% CI: 1.22-5.47) and with in-hospital mortality or major morbidity (OR: 3.18; 95% CI: 1.37-7.42). The association between TR and mortality was apparent when preoperative pulmonary artery systolic pressure was <40 mm Hg but not ≥40 mm Hg ( for interaction = 0.036).
CONCLUSIONS
In this diverse cohort of contemporary cardiac surgery patients, moderate functional TR was associated with increased mortality and major morbidity, particularly in the absence of pulmonary hypertension.
PubMed: 38939486
DOI: 10.1016/j.jacadv.2023.100551 -
JACC. Advances Sep 2023
PubMed: 38939482
DOI: 10.1016/j.jacadv.2023.100576 -
JACC. Advances Aug 2023Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or...
BACKGROUND
Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or indeterminate.
OBJECTIVES
The authors applied artificial intelligence (AI) to analyze a single apical 4-chamber transthoracic echocardiogram video clip to detect HFpEF.
METHODS
A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber video clips to classify patients with HFpEF (diagnosis of heart failure, ejection fraction ≥50%, and echocardiographic evidence of increased filling pressure; cases) vs without HFpEF (ejection fraction ≥50%, no diagnosis of heart failure, normal filling pressure; controls). Model outputs were classified as HFpEF, no HFpEF, or nondiagnostic (high uncertainty). Performance was assessed in an independent multisite data set and compared to previously validated clinical scores.
RESULTS
Training and validation included 2,971 cases and 3,785 controls (validation holdout, 16.8% patients), and demonstrated excellent discrimination (area under receiver-operating characteristic curve: 0.97 [95% CI: 0.96-0.97] and 0.95 [95% CI: 0.93-0.96] in training and validation, respectively). In independent testing (646 cases, 638 controls), 94 (7.3%) were nondiagnostic; sensitivity (87.8%; 95% CI: 84.5%-90.9%) and specificity (81.9%; 95% CI: 78.2%-85.6%) were maintained in clinically relevant subgroups, with high repeatability and reproducibility. Of 701 and 776 indeterminate outputs from the Heart Failure Association-Pretest Assessment, Echocardiographic and Natriuretic Peptide Score, Functional Testing (HFA-PEFF), and Final Etiology and Heavy, Hypertensive, Atrial Fibrillation, Pulmonary Hypertension, Elder, and Filling Pressure (H2FPEF) scores, the AI HFpEF model correctly reclassified 73.5% and 73.6%, respectively. During follow-up (median: 2.3 [IQR: 0.5-5.6] years), 444 (34.6%) patients died; mortality was higher in patients classified as HFpEF by AI (HR: 1.9 [95% CI: 1.5-2.4]).
CONCLUSIONS
An AI HFpEF model based on a single, routinely acquired echocardiographic video demonstrated excellent discrimination of patients with vs without HFpEF, more often than clinical scores, and identified patients with higher mortality.
PubMed: 38939447
DOI: 10.1016/j.jacadv.2023.100452 -
JACC. Advances Aug 2023COVID-19 is known to be associated with acute myocardial infarction (MI).
BACKGROUND
COVID-19 is known to be associated with acute myocardial infarction (MI).
OBJECTIVES
The purpose of this study was to evaluate the outcomes of 30-day readmissions for MI among survivors of COVID-19 hospitalization.
METHODS AND RESULTS
We used the U.S. Nationwide Readmission Database to identify COVID-19 admissions from April 1, 2020, to November 30, 2020, using International Classification of Diseases-10th Revision-Clinical Modification (ICD-10-CM) claims. The primary outcome was 30-day readmission incidence for MI. A total of 521,251 cases of COVID-19 were included, of which 11.6% were readmitted within 30 days of discharge. The 30-day readmission incidence for MI was 0.6%. The 30-day all-cause readmission mortality incidence was 1.3%. Patients readmitted for MI were more frequently males (61.6% vs 38.4%) and had a higher Charlson comorbidity burden score (7 vs 4). The most common diagnosis among 30-day MI readmission was type 2 MI (51.1%), followed by a diagnosis of a type 1 non-ST-segment elevation MI (41.7%). ST-segment elevation MI cases constituted 7.6% of all MI-readmission whereas 0.6% of patients had unstable angina. 30-day MI readmissions with a recurrent diagnosis of COVID-19 had higher readmission mortality and incidence of complications. Conversely, the odds of performing revascularization procedures were lower for MI with recurrent COVID-19. Furthermore, MI readmissions with recurrent COVID-19 had a higher length of stay (7 vs 5 days) and cost of hospitalization ($18,398 vs $16,191) when compared with non-COVID-19 MI readmissions.
CONCLUSIONS
Among survivors of COVID-19 hospitalization, 5.2% of all-cause 30-day readmissions and 12% of all-cause readmission mortality were attributed to MI. MI-related readmissions were a significant source of mortality, morbidity, and resource utilization.
PubMed: 38939438
DOI: 10.1016/j.jacadv.2023.100453 -
JACC. Advances Feb 2024
PubMed: 38939395
DOI: 10.1016/j.jacadv.2023.100771