Related factors and incidence risk of acute myocardial infarction among the people with disability: A national population-based study

The impact of obstructive sleep apnea and comorbidity status on cardiovascular outcomes in patients with acute coronary syndrome

BACKGROUND AND AIMS

The association of comorbidities on the prognosis of patients with acute coronary syndrome (ACS) was well documented. However, the impact of obstructive sleep apnea (OSA) on this association has been less studied.

METHODS AND RESULTS

Between June 2015 to Jan 2020, we included consecutively eligible patients with ACS who underwent cardiorespiratory polygraphy. The definition of OSA was apnea-hypopnea index (AHI) ≥15 events/hour. Charlson Comorbidity Index (CCI) was used to evaluate the comorbidities. The primary endpoint was major adverse cardiovascular and cerebrovascular events (MACCE), including cardiovascular death, myocardial infarction, stroke, ischemia-driven revascularization, and hospitalization for unstable angina or heart failure. In the 1927 ACS patients, 1014 (52.6%) had OSA. The prevalence of the mild (CCI = 0), moderate (CCI = 1-2), and severe (CCI≥3) comorbidity were 23.6%, 65.9%, and 10.5%, respectively. During a median follow-up of 2.9 (1.5, 3.6) years, compared with patients without OSA, the presence of OSA increased the risk of MACCE in the moderate comorbidity group (22.6% vs. 17.5%; adjusted HR: 1.327; 95% CI: 1.019-1.728, p = 0.036) and severe comorbidity group (36.2% vs. 18.6%; adjusted HR: 2.194; 95% CI: 1.170-4.117, p = 0.014). There was no significant difference between OSA and non-OSA patients in the mild comorbidity group.

CONCLUSION

Among ACS patients, OSA was associated with an increased risk of subsequent events in the moderate and severe comorbidity groups but not in the mild comorbidity group. ACS patients with comorbidities should not be overlooked for OSA screening.

Phonocardiogram signal analysis for classification of Coronary Artery Diseases using MFCC and 1D adaptive local ternary patterns

Coronary Artery Diseases (CADs) are a dominant cause of worldwide fatalities. The development of accurate and timely diagnosis routines is imperative to reduce these risks and mortalities. Coronary angiography, an invasive and expensive technique, is currently used as a diagnostic tool for the detection of CAD but it has some procedural hazards, i.e., it requires arterial puncture, and the subject gets exposed to iodinated radiation. Phonocardiography (PCG), a non-invasive and inexpensive technique, is a modality employing heart sounds to diagnose heart diseases but it requires only trained medical personnel to apprehend cardiac murmurs in clinical environments. Furthermore, there is a strong compulsion to characterize CAD into its types, such as Single vessel coronary artery disease (SVCAD), Double vessel coronary artery disease (DVCAD), and Triple vessel coronary artery disease (TVCAD) to assist the cardiologist in decision making about the treatment procedure followed. This paper presents a computer-aided diagnosis system for the categorization of CAD and its types based on Phonocardiogram (PCG) signal analysis. The raw PCG signals were denoised via empirical mode decomposition (EMD) to remove redundant information and noise. Next, we extract MFCC and proposed 1D-Adaptive Local Ternary Patterns (1D-ALTP) and fused them serially to get a strong feature representation of multiple PCG signal classes. Features were further reduced through Multidimensional Scaling (MDS) and subjected to several classification methods such as support vector machines (SVM), Decision Tree (DT), and K-nearest neighbors (KNN) in a comparative fashion. The best classification performances of 98.3% and 97.2% mean accuracies were obtained through SVM with the cubic kernel for binary and multiclass experiments, respectively. The performance of the proposed system is comprehensively tested through 10-fold cross-validation and hold-out train-test techniques to avoid model overfitting. Comparative analysis with existing approaches advocates the superiority of the proposed approach.

Community-Based Aerobic Exercise Program for Primary Prevention of Cardiovascular Disease in Adults With Visual or Auditory Impairments: A Feasibility Study

OBJECTIVE

To investigate the feasibility of a public health center-based aerobic and resistance training program for primary prevention of cardiovascular disease in people with visual, auditory, or physical/brain impairments.

METHODS

The study included 25 adults aged >40 years who lived in Cheorwon-gun in South Korea, had a disability registered for visual, auditory, or physical/brain impairments under the Disability Welfare Act, and had either known cardiovascular disease or two or more risk factors for cardiovascular disease. The program comprised four education sessions and 12 weeks of customized aerobic and strengthening exercises performed twice a week at moderate intensity, with each exercise session lasting for 1 hour. The body mass index (BMI), percent body fat, 6-minute walk distance (6MWD), and 30-second sit-to-stand test results were measured at baseline and on program completion.

RESULTS

Seventeen subjects (68%) completed the program. There were significant decreases in BMI and percent body fat (both p<0.05), with a significant increase in 30-second sit-to-stand strength (p<0.05) but no changes in the 6MWD. In subjects with visual or auditory impairments, BMI and percent body fat were significantly decreased after the program; however, there was no significant change in the results of the 30-second sit-to-stand strength test or the 6MWD.

CONCLUSION

In people with disabilities, a 3-month community-based exercise program can decrease body mass index and percent body fat and increase sit-to-stand strength. The 30-second sit-to-stand test may be a useful measure of the strength and endurance of the lower extremities in people with disabilities.

A brief report of the prevalence of chronic and acute health conditions among blind American adults

BACKGROUND

Prior research demonstrates disparities in the prevalence of certain chronic and acute health conditions among persons who are blind (PWB) compared to non-blind persons, such as diabetes and infectious diseases. However, a comprehensive understanding of the prevalence of chronic and acute health conditions among PWB is currently lacking.

OBJECTIVE

The present study addressed this gap by examining the prevalence of chronic and acute conditions among blind persons, and examining differences by gender.

METHODS

The present study surveyed 410 PWB residing in the U.S. about their health conditions and activities. Lifetime prevalence for eight chronic and six acute health conditions were estimated separately for men and women. Engagement in physical activity, regular use of medication, and satisfaction with health were also estimated.

RESULTS

We found that men more often reported their health conditions interfered with daily activities compared to women, as well as higher prevalence of stroke and arthritis compared to women.

CONCLUSION

The current study contributes information that is vital to understanding the burden of specific health conditions on this population and necessary to understand the extent to which this burden disproportionately affects PWB.

Impact of Charlson Co-Morbidity Index Score on Management and Outcomes After Acute Coronary Syndrome

Patients presenting with acute coronary syndrome (ACS) are frequently co-morbid. However, there is limited data on how co-morbidity burden impacts their receipt of invasive management and subsequent outcomes. We analyzed all patients with a discharge diagnosis of ACS from the National Inpatient Sample (2004 to 2014), stratified by Charlson Co-morbidity Index (CCI) into 4 classes (CCI 0, 1, 2, and ≥3). Regression analyses were performed to examine associations between co-morbidity burden and receipt of invasive intervention and in-hospital clinical outcomes. Of all 6,613,623 ACS patients analyzed, the prevalence of patients with severe co-morbidity (CCI ≥3) increased from 10.8% (2004) to 18.1% (2014). CCI class negatively correlated with receipt of invasive management, with CCI ≥3 group being the least likely to receive coronary angiography and percutaneous coronary intervention (odds ratio (OR) 0.42 95% confidence interval [CI] 0.41 to 0.43 and OR 0.47, 95% CI 0.46 to 0.48, respectively). CCI class was independently associated with an increased risk of mortality and complications, especially CCI ≥3 that was associated with significantly increased odds of Major Acute Cardiovascular & Cerebrovascular Events (OR 1.70, 95% CI 1.66 to 1.75), mortality (OR 1.74, 95% CI 1.68 to 1.79), acute ischemic stroke (OR 2.35, 95% CI 2.23 to 2.46), and major bleeding (OR 1.64, 95% CI 1.59 to 1.69). Co-morbidity burden has significantly increased amongst those presenting with ACS over an 11-year period and correlates with reduced likelihood of receipt of invasive management and increased odds of mortality and adverse outcomes. In conclusion, objective assessment of co-morbidities using CCI score identifies high-risk ACS patients in whom targeted risk reduction strategies may reduce their inherent risk of mortality and complications.

Temporal Trends in Comorbidity Burden and Impact on Prognosis in Patients With Acute Coronary Syndrome Using the Elixhauser Comorbidity Index Score

Despite current evidence, little is known about the impact of comorbidity burden on invasive management strategies and clinical outcomes in the context of acute coronary syndrome (ACS). All ACS hospitalizations between 2004 and 2014 from the National Inpatient Sample were included, stratified by Elixhauser Comorbidity Score (ECS) and number of Elixhauser Comorbidities (NEC) to compare the receipt of invasive management and clinical outcomes between different ECS and NEC classes to the lowest class of either measure. A total of 6,613,623 records with ACS were included in the analysis. Overall comorbidity burden increased over the 11-year period, with higher comorbidity classes (ECS ≥ 14 and NEC ≥ 5) increasing from 2.1% to 4.6% and 4% to 16%, respectively. Higher ECS and NEC classes negatively correlated with the rates of utilization of coronary angiography (CA) and percutaneous coronary intervention (PCI) (ECS ≥14 vs <0: CA: 38.2% vs 69.3%, PCI: 18.6% vs 45.3%; NEC ≥5 vs 0: CA: 49.3% vs 73.4%, PCI: 24.4% vs 57.4%). Overall, higher ECS and NEC classes were independently associated with significantly increased odds of all complications, including major acute cardiovascular and cerebrovascular events, mortality, stroke and bleeding. In conclusion, among patients hospitalized for ACS, a higher comorbidity number or severity is associated with lower rates of receipt of CA and PCI, but not coronary artery bypass grafting, and worse clinical outcomes. Comorbidity burden assessment using ECS can help stratify patient groups at greatest risk of adverse outcomes in which invasive management is currently underutilized.

Applying Pulse Spectrum Analysis to Facilitate the Diagnosis of Coronary Artery Disease

Not all patients with angina pectoris have coronary artery stenosis. To facilitate the diagnosis of coronary artery disease (CAD), we sought to identify predictive factors of pulse spectrum analysis, which was developed by Wang and is one technique of modern pulse diagnosis. The patients suffered from chest pain and received cardiac catheterization to confirm the CAD diagnosis and Gensini score were recruited. Their pulse waves of radial artery were recorded. Then, by performing a fast Fourier transform, 10 amplitude values of frequency spectrum harmonics were obtained. Each harmonic amplitude was divided by the sum of all harmonic amplitude values, obtaining the relative percentages of 10 harmonics (C1-C10). Subsequently, multivariate logistic regression was conducted with two models and the areas under the receiver operating characteristic curves (ROC) of these 2 models were compared to see if combining the pulse diagnosis parameters with the risk factor of CAD can increase the prediction rate of CAD diagnosis. The predictive factors of CAD severity were analyzed by multivariate linear regression. A total of 83 participants were included; 63 were diagnosed CAD and 20 without CAD. In the CAD group, C1 was greater and C5 was lower than those of the non-CAD group. The CAD risk factors were put alone in Model 1 to perform the multivariate logistic regression analysis which had a prediction rate of 77.1%; while putting the C1 and C5 harmonics together with the risk factors into Model 2, the prediction rate increased to 80.7%. Finally, the area under ROC of Model 1 and Model 2 was 0.788 and 0.856, respectively. Furthermore, left C1, left C5, gender, and presence of hyperlipidemia were predictors of CAD severity. Therefore, pulse spectrum analysis may be a tool to facilitate CAD diagnosis before receiving cardiac catheterization. The harmonics C1 and C5 were favorable predictive indicators.

Comorbidity assessment for mortality risk stratification in elderly patients with acute coronary syndrome

BACKGROUND

The Charlson's is the most used comorbidity index. It comprises 19 comorbidities, some of which are infrequent in elderly patients with acute coronary syndrome (ACS), while some others are manifestations of cardiac disease rather than comorbidities. Our goal was to simplify comorbidity assessment in elderly non-ST-segment elevation ACS patients.

METHODS

The study group consisted of 1 training (n = 920, 76 ± 7 years) and 1 testing (n = 532; 84 ± 4 years) cohorts. The end-point was all-cause mortality at 1-year follow-up. Comorbidities were assessed selecting those medical disorders other than cardiac disease that were independently associated with mortality by multivariable analysis.

RESULTS

A total of 130 (14%) patients died in the training cohort. Six comorbidities were predictive: renal failure, anemia, diabetes, peripheral artery disease, cerebrovascular disease and chronic lung disease. The increase in the number of comorbidities yielded a gradient of risk on top of well-known clinical predictors: ≥3 comorbidities (27% mortality, HR = 1.90, 95% CI 1.20-3.03, p = .006); 2 comorbidities (16% mortality, HR = 1.29, 95% CI 0.81-2.04, p = .30); and 0-1 comorbidities (7.6% mortality, reference category). The discrimination accuracy (C-statistic = 0.80) and calibration (Hosmer-Lemeshow test, p = .20) of the predictive model using the 6 comorbidities was comparable to the predictive model using the Charlson index (C-statistic = 0.80; Hosmer-Lemeshow test, p = .70). Similar results were reproduced in the testing cohort (≥3 comorbidities: 24% mortality, HR = 2.37, 95% CI 1.25-4.49, p = .008; 2 comorbidities: 14% mortality, HR = 1.59, 95% CI 0.82-3.07, p = .20; 0-1 comorbidities: 7.5% reference category).

CONCLUSION

A simplified comorbidity assessment comprising 6 comorbidities provides useful risk stratification in elderly patients with ACS.

Impact of co-morbid burden on mortality in patients with coronary heart disease, heart failure, and cerebrovascular accident: a systematic review and meta-analysis

Aims

We sought to investigate the prognostic impact of co-morbid burden as defined by the Charlson Co-morbidity Index (CCI) in patients with a range of prevalent cardiovascular diseases.

Methods and results

We searched MEDLINE and EMBASE to identify studies that evaluated the impact of CCI on mortality in patients with cardiovascular disease. A random-effects meta-analysis was undertaken to evaluate the impact of CCI on mortality in patients with coronary heart disease (CHD), heart failure (HF), and cerebrovascular accident (CVA). A total of 11 studies of acute coronary syndrome (ACS), 2 stable coronary disease, 5 percutaneous coronary intervention (PCI), 13 HF, and 4 CVA met the inclusion criteria. An increase in CCI score per point was significantly associated with a greater risk of mortality in patients with ACS [pooled relative risk ratio (RR) 1.33; 95% CI 1.15-1.54], PCI (RR 1.21; 95% CI 1.12-1.31), stable coronary artery disease (RR 1.38; 95% CI 1.29-1.48), and HF (RR 1.21; 95% CI 1.13-1.29), but not CVA. A CCI score of >2 significantly increased the risk of mortality in ACS (RR 2.52; 95% CI 1.58-4.04), PCI (RR 3.36; 95% CI 2.14-5.29), HF (RR 1.76; 95% CI 1.65-1.87), and CVA (RR 3.80; 95% CI 1.20-12.01).

Conclusion

Increasing co-morbid burden as defined by CCI is associated with a significant increase in risk of mortality in patients with underlying CHD, HF, and CVA. CCI provides a simple way of predicting adverse outcomes in patients with cardiovascular disease and should be incorporated into decision-making processes when counselling patients.