History of Heart Failure in Patients Hospitalized Due to COVID-19: Relevant Factor of In-Hospital Complications and All-Cause Mortality up to Six Months

Racial Differences in Hospice Care Outcomes in Patients With Advanced Heart Failure: Systematic Review and Meta-analysis

There remains a paucity of investigational data about disparities in hospice services in people with non-cancer diagnoses, specifically in heart failure (HF). Black patients with advanced HF have been disproportionally affected by health care services inequities but their outcomes after hospice enrollment are not well studied. We aimed to describe race-specific outcomes in patients with advanced HF who were enrolled in hospice services. We obtained the data from PubMed, Scopus, and Embase for all investigations published until January 11, 2023. All studies that reported race-specific outcomes after hospice enrollment in patients with advanced HF were included. Of the 1,151 articles identified, 5 studies (n = 24,899) were considered for analysis involving a sample size ranging from 179 to 11,754 patients. Black patients had an increased risk of readmission (odds ratio 1.55, 95% confidence interval [CI] 1.34 to 1.79, I2 0%) and discharge (odds ratio 1.75, 95% CI 1.53 to 1.99, I2 0%) compared with White patients. Moreover, Black patients have a nonsignificant lower risk of mortality compared with White patients (relative risk 0.67, 95% CI 0.43 to 1.05, I2 90%). In conclusion, this study showed that Black patients with advanced HF receiving hospice care have a higher risk of readmission and discharge compared with White patients.

Antiplatelet therapy prior to COVID-19 infection impacts on patients mortality: a propensity score-matched cohort study

One of the major pathomechanisms of COVID-19 is the interplay of hyperinflammation and disruptions in coagulation processes, involving thrombocytes. Antiplatelet therapy (AP) by anti-inflammatory effect and inhibition of platelet aggregation may affect these pathways. The aim of this study was to investigate if AP has an impact on the in-hospital course and medium-term outcomes in hospitalized COVID-19 patients. The study population (2170 COVID-19 patients: mean ± SD age 60 ± 19 years old, 50% male) was divided into a group of 274 patients receiving any AP prior to COVID-19 infection (AP group), and after propensity score matching, a group of 274 patients without previous AP (non-AP group). Patients from the AP group were less frequently hospitalized in the intensive care unit: 9% vs. 15%, 0.55 (0.33-0.94), developed less often shock: 9% vs. 15%, 0.56 (0.33-0.96), and required less aggressive forms of therapy. The AP group had more coronary revascularizations: 5% vs. 1%, 3.48 (2.19-5.55) and strokes/TIA: 5% vs. 1%, 3.63 (1.18-11.2). The bleeding rate was comparable: 7% vs. 7%, 1.06 (0.54-2.06). The patients from the AP group had lower 3-month mortality: 31% vs. 39%, 0.69 (0.51-0.93) and didn't differ significantly in 6-month mortality: 34% vs. 41%, 0.79 (0.60-1.04). When analyzing the subgroup with a history of myocardial infarction and/or coronary revascularization and/or previous stroke/transient ischemic attack and/or peripheral artery disease, AP had a beneficial effect on both 3-month: 37% vs. 56%, 0.58 (0.40-0.86) and 6-month mortality: 42% vs. 57%, 0.63 (0.44-0.92). Moreover, the favourable effect was highly noticeable in this subgroup where acetylsalicylic acid was continued during hospitalization with reduction of in-hospital: 19% vs. 43%, 0.31 (0.15-0.67), 3-month: 30% vs. 54%, 044 (0.26-0.75) and 6-month mortality: 33% vs. 54%, 0.49 (0.29-0.82) when confronted with the subgroup who had acetylsalicylic acid suspension during hospitalization. The AP may have a beneficial impact on hospital course and mortality in COVID-19 and shouldn't be discontinued, especially in high-risk patients.

The Influence of SARS-CoV-2 Infection on Acute Myocardial Infarction Outcomes

BACKGROUND

This multicenter retrospective study with a control group was designed to assess the influence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the outcomes of patients with myocardial infarction (MI).

METHODS

A total of 129 patients with COVID-19 who were treated for MI were included in this study. The control group comprised 129 comparable patients without SARS-CoV-2 infection. The in-hospital, out-of-hospital, and overall mortality were analyzed.

RESULTS

A total of thirty-one (24%) patients died in the study group, and two (1.6%) patients died in the control group (OR = 20.09; CI: 4.69-85.97; p < 0.001). Similar results were observed in all analyzed patient subgroups. Multivariable Cox regression analysis confirmed the significant influence of SARS-CoV-2 infection on in-hospital outcomes (HR: 8.48459; CI: 1.982-36.320; p = 0.004). Subanalysis of the groups with COVID-19 plus ST-elevation MI (STEMI) or non-ST-elevation MI (NSTEMI) revealed comparable mortality rates: 14 (21.12%) patients in the NSTEMI group and 17 (26.98%) patients in the STEMI subgroup died (OR: 1.3; CI: 0.56-3.37; p = 0.45). During out-of-hospital observation, no differences in mortality were observed (OR: 0.77; CI: 0.11-4.07; p = 0.73).

CONCLUSIONS

SARS-CoV-2 infection affects the in-hospital outcomes of patients with both MI and COVID-19, regardless of MI type (STEMI vs. NSTEMI).

Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study

Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.

Cardiovascular Complications of Viral Respiratory Infections and COVID-19

Viral respiratory infections (VRI) are the most prevalent type of infectious diseases and constitute one of the most common causes of contact with medical care. Regarding the pathophysiology of the cardiovascular system, VRI can not only exacerbate already existing chronic cardiovascular disease (such as coronary artery disease or heart failure) but also trigger new adverse events or complications (e.g., venous thromboembolism), the latter particularly in subjects with multimorbidity or disease-related immobilization. In the current paper, we provide a narrative review of diverse cardiovascular complications of VRI as well as summarize available data on the pathology of the circulatory system in the course of coronavirus disease 2019 (COVID-19).

CT severity score in COVID-19 patients, assessment of performance in triage and outcome prediction: a comparative study of different methods

Background

Lung involvement in COVID-19 can be quantified by chest CT scan with some triage and prognostication value. Optimizing initial triage of patients could help decrease adverse health impacts of the disease through better clinical management. At least 6 CT severity score (CTSS) systems have been proposed. We aimed to evaluate triage and prognostication performance of seven different CTSSs, including one proposed by ourselves, in hospitalized COVID-19 patients diagnosed by positive polymerase chain reaction (PCR).

Results

After exclusion of 14 heart failure and significant preexisting pulmonary disease patients, 96 COVID-19, PCR-positive patients were included into our retrospective study, admitted from February 20, 2020, to July 22. Their mean age was 63.6 ± 17.4 years (range 21–88, median 67). Fifty-seven (59.4%) were men, and 39 (40.6%) were women. All CTSSs showed good interrater reliability as calculated intraclass correlation coefficients (ICCs) between two radiologists were 0.764–0.837. Those CTSSs with more numerous segmentations showed the best ICCs. As judged by area under curve (AUC) for each receiver operator characteristic (ROC) curve, only three CTSSs showed acceptable AUCs (AUC = 0.7) for triage of severe/critical patients. All CTSSs showed acceptable AUCs for prognostication (AUCs = 0.76–0.79). Calculated AUCs for different CTSSs were not significantly different for triage and for prediction of severe/critical disease, but some difference was shown for prediction of critical disease.

Conclusions

Men are probably affected more frequently than women by COVID-19. Quantification of lung disease in COVID-19 is a readily available and easy tool to be used in triage and prognostication, but we do not advocate its use in heart failure or chronic respiratory disease patients. The scoring systems with more numerous segmentations are recommended if any future imaging for comparison is contemplated. CTSS performance in triage was much lower than earlier reports, and only three CTSSs showed acceptable AUCs in this regard. CTSS performed better for prognostic purposes than for triage as all 7 CTSSs showed acceptable AUCs in both types of prognostic ROC curves. There is not much difference among performance of different CTSSs.

COVID-19 and the heart

An outbreak of coronavirus disease 2019 (COVID-19) occurred in December 2019 due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is a strain of SARS-CoV. Patients infected with the virus present a wide spectrum of manifestations ranging from mild flu-like symptoms, cough, fever and fatigue to severe lung injury, appearing as bilateral interstitial pneumonia or acute respiratory failure. Although SARS-CoV-2 infection predominantly offends the respiratory system, it has been associated with several cardiovascular complications as well. For example, patients with COVID-19 may either develop type 2 myocardial infarction due to myocardial oxygen demand and supply imbalance or acute coronary syndrome resulting from excessive inflammatory response to the primary infection. The incidence of COVID-19 related myocarditis is estimated to be accountable for an average of 7% of all COVID-19 related fatal cases, whereas heart failure (HF) may develop due to infiltration of the heart by inflammatory cells, destructive action of pro-inflammatory cytokines, micro-thrombosis and new onset or aggravated endothelial and respiratory failure. Lastly, SARS-CoV-2 can engender arrhythmias through direct myocardial damage causing acute myocarditis or through HF decompensation or secondary, through respiratory failure or severe respiratory distress syndrome. In this comprehensive review we summarize the COVID-19 related cardiovascular complications (acute coronary syndromes, myocarditis, HF, arrhythmias) and discuss the main underlying pathophysiological mechanisms.

Cardiovascular disease and COVID-19, a deadly combination: A review about direct and indirect impact of a pandemic

Coronavirus disease 2019 (COVID-19) is known to present with respiratory symptoms, which can lead to severe pneumonia and respiratory failure. However, it can have multisystem complications such as cardiovascular manifestations. The cardiovascular manifestations reported comprise myocarditis, cardiogenic shock, arrhythmias, pulmonary embolism, deep vein embolism, acute heart failure, and myocardial infarction. There is also an indirect impact of the pandemic on the management of cardiovascular care that has been shown clearly in multiple publications. In this review, we summarize the deadly relation of COVID-19 with cardiovascular events and the wider impact on several cardiovascular care areas by the pandemic situation

Qiliqiangxin Modulates the Gut Microbiota and NLRP3 Inflammasome to Protect Against Ventricular Remodeling in Heart Failure

Aims: Pathological left ventricular (LV) remodeling induced by multiple causes often triggers fatal cardiac dysfunction, heart failure (HF), and even cardiac death. This study is aimed to investigate whether qiliqiangxin (QL) could improve LV remodeling and protect against HF via modulating gut microbiota and inhibiting nod-like receptor pyrin domain 3 (NLRP3) inflammasome activation.

Methods: Rats were respectively treated with QL (100 mg/kg/day) or valsartan (1.6 mg/kg/day) by oral gavage after transverse aortic constriction or sham surgery for 13 weeks. Cardiac functions and myocardial fibrosis were assessed. In addition, gut microbial composition was assessed by 16S rDNA sequencing. Furthermore, rats’ hearts were harvested for histopathological and molecular analyses including immunohistochemistry, immunofluorescence, terminal-deoxynucleotidyl transferase-mediated 2’-deoxyuridine 5’-triphosphated nick end labeling, and Western blot.

Key findings: QL treatment preserved cardiac functions including LV ejection fractions and fractional shortening and markedly improved the LV remodeling. Moreover, HF was related to the gut microbial community reorganization like a reduction in Lactobacillus, while QL reversed it. Additionally, the protein expression levels like IL-1β, TNF-α, NF-κB, and NLRP3 were decreased in the QL treatment group compared to the model one.

Conclusion: QL ameliorates ventricular remodeling to some extent in rats with HF by modulating the gut microbiota and NLRP3 inflammasome, which indicates the potential therapeutic effects of QL on those who suffer from HF.

Usefulness of C2HEST Score in Predicting Clinical Outcomes of COVID-19 in Heart Failure and Non-Heart-Failure Cohorts

Background: Patients with heart failure represent a vulnerable population for COVID-19 and are prone to having worse prognoses and higher fatality rates. Still, the clinical course of the infection is dynamic, and complication occurrence in particular in patients with heart failure is fairly unpredictable. Considering that individual components of the C2HEST (C2: Coronary Artery Diseases (CAD)/Chronic obstructive pulmonary disease (COPD); H: Hypertension; E: Elderly (Age ≥ 75); S: Systolic HF; T: Thyroid disease) are parallel to COVID-19 mortality risk factors, we evaluate the predictive value of C2HEST score in patients with heart failure (HF) Material and Methods: The retrospective medical data analysis of 2184 COVID-19 patients hospitalized in the University Hospital in Wroclaw between February 2020 and June 2021 was the basis of the study. The measured outcomes included: in-hospital mortality, 3-month and 6-month all-cause-mortality, non-fatal end of hospitalization, and adverse in-hospital clinical events. Results: The heart failure cohort consists of 255 patients, while 1929 patients were assigned to the non-HF cohort. The in-hospital, 3-month, and 6-month mortality rates were highest in the HF cohort high-risk C2HEST stratum, reaching 38.61%, 53.96%, and 65.36%, respectively. In the non-HF cohort, in-hospital, 3-month, and 6-month mortalities were also highest in the high-risk C2HEST stratum and came to 26.39%, 52.78%, and 65.0%, respectively. An additional point in the C2HEST score increased the total death intensity in 10% of HF subjects (HR 1.100, 95% CI 0.968−1.250 p = 0.143) while in the non-HF cohort, the same value increased by 62.3% (HR 1.623, 95% CI 1.518−1.734 p < 0.0001). Conclusions: The C2HEST score risk in the HF cohort failed to show discriminatory performance in terms of mortality and other clinical adverse outcomes during hospitalization. C2HEST score in the non-HF cohort showed significantly better performance in terms of predicting in-hospital and 6-month mortality and other non-fatal clinical outcomes such as cardiovascular events (myocardial injury, acute heart failure, myocardial infarction, cardiogenic shock), pneumonia, sepsis, and acute renal injury.

Using the NYHA Classification as Forecasting Tool for Hospital Readmission and Mortality in Heart Failure Patients with COVID-19

During the COVID-19 pandemic, it was observed that patients with heart disease are more likely to be hospitalized and develop severe COVID-19. Cardiac disease takes the top position among patient comorbidities, heart failure (HF) prevalence reaching almost 5% in the general population older than 35 years in Romania. This retrospective study aimed to determine the potential use of the NYHA classification for HF in hospitalized patients with COVID-19 as prognostic tool for in-hospital mortality, length of hospitalization, and probability of rehospitalization for HF decompensation. We observed that patients with advanced HF had a history of significantly more comorbid conditions that are associated with worse disease outcomes than the rest of patients classified as NYHA I and II. However, regardless of existing diseases, NYHA III, and, especially, NYHA IV, patients were at greatest risk for mortality following SARS-CoV-2 infection. They required significantly longer durations of hospitalization, ICU admission for mechanical ventilation, and developed multiple severe complications. NYHA IV patients required a median duration of 20 days of hospitalization, and their in-hospital mortality was as high as 47.8%. Cardiac biomarkers were significantly altered in patients with SARS-CoV-2 and advanced HF. Although the study sample was small, all patients with NYHA IV who recovered from COVID-19 required a rehospitalization in the following month, and 65.2% of the patients at initial presentation died during the next six months. The most significant risk factor for mortality was the development of severe in-hospital complications (OR = 4.38), while ICU admission was the strongest predictor for rehospitalization (OR = 5.19). Our result highlights that HF patients continue to be vulnerable post SARS-CoV-2 infection. Physicians and policymakers should consider this population’s high likelihood of hospital readmissions when making discharge, hospital capacity planning, and post-discharge patient monitoring choices.

Risk Factors Associated with Mortality in COVID-19 Hospitalized Patients: Data from the Middle East

This study aimed to assess the risk factors for COVID-19 mortality among hospitalized patients in Jordan. All COVID-19 patients admitted to a tertiary hospital in Jordan from September 20, 2020, to August 8, 2021, were included in this study. Demographics, clinical characteristics, comorbidities, and laboratory results were extracted from the patients' electronic records. Multivariable logistic and machine learning (ML) methods were used to study variable importance. Out of 1,613 COVID-19 patients, 1,004 (62.2%) were discharged from the hospital (survived), while 609 (37.8%) died. Patients who were of elderly age (>65 years) (OR, 2.01; 95% CI, 1.28-3.16), current smokers (OR, 1.61; 95%CI, 1.17-2.23), and had severe or critical illness at admission ((OR, 1.56; 95%CI, 1.05-2.32) (OR, 2.94; 95%CI, 2.02-4.27); respectively), were at higher risk of mortality. Comorbidities including chronic kidney disease (OR, 2.90; 95% CI, 1.90-4.43), deep venous thrombosis (OR, 2.62; 95% CI, 1.08-6.35), malignancy (OR, 2.22; 95% CI, 1.46-3.38), diabetes (OR, 1.31; 95% CI, 1.04-1.65), and heart failure (OR, 1.51; 95% CI, 1.02-2.23) were significantly associated with increased risk of mortality. Laboratory abnormalities associated with mortality included hypernatremia (OR, 11.37; 95% CI, 4.33-29.81), elevated aspartate aminotransferase (OR, 1.81; 95% CI, 1.42-2.31), hypoalbuminemia (OR, 1.75; 95% CI, 1.37-2.25), and low platelets level (OR, 1.43; 95% CI, 1.05-1.95). Several demographic, clinical, and laboratory risk factors for COVID-19 mortality were identified. This study is the first to examine the risk factors associated with mortality using ML methods in the Middle East. This will contribute to a better understanding of the impact of the disease and improve the outcome of the pandemic worldwide.