The usefulness of selected biomarkers in patients with valve disease

Risk Factors of Postoperative Hospital-Acquired Pneumonia in Patients Undergoing Cardiac Surgery

Background and Objectives. Hospital-acquired pneumonia is one of the complications that may occur in the postoperative period in patients undergoing heart valve surgery, which may result in prolonged hospitalization, development of respiratory failure requiring mechanical ventilation or even death. This study investigated the preoperative risk factors of postoperative pneumonia after heart valve surgery. Materials and Methods: This was a prospective study in a group of consecutive patients with hemodynamically significant valvular heart disease undergoing valve surgery. The primary endpoint at the in-hospital follow-up was hospital-acquired pneumonia after heart valve surgery. Logistic regression analysis was used to assess which variables were predictive of the primary endpoint, and odds ratios (ORdis) were calculated with a 95% confidence interval (CI). Multivariate analysis was based on the results of single-factor logistic regression, i.e., in further steps all statistically significant variables were taken into consideration. Results: The present study included 505 patients. Postoperative pneumonia occurred in 23 patients. The mean time to diagnosis of pneumonia was approximately 3 days after heart valve surgery (±2 days). In multivariate analysis, preoperative level of high-sensitivity Troponin T (hs-TnT) (OR 2.086; 95% CI 1.211-3.593; p = 0.008) and right ventricular systolic pressure (RVSP) (OR 1.043; 95% CI 1.018-1.067; p 0.004) remained independent predictors of the postoperative pneumonia. Of the patients with postoperative pneumonia, 3 patients died due to the development of multiple organ dysfunction syndrome (MODS). Conclusions: Preoperative determination of serum hs-TnT concentration and echocardiographic measurement of the RVSP parameter may be useful in predicting postoperative pneumonia in patients undergoing heart valve surgery.

Differential Immune Response to Bioprosthetic Heart Valve Tissues in the α1,3Galactosyltransferase-Knockout Mouse Model

Structural valve deterioration (SVD) of bioprosthetic heart valves (BHVs) has great clinical and economic consequences. Notably, immunity against BHVs plays a major role in SVD, especially when implanted in young and middle-aged patients. However, the complex pathogenesis of SVD remains to be fully characterized, and analyses of commercial BHVs in standardized-preclinical settings are needed for further advancement. Here, we studied the immune response to commercial BHV tissue of bovine, porcine, and equine origin after subcutaneous implantation into adult α1,3-galactosyltransferase-knockout (Gal KO) mice. The levels of serum anti-galactose α1,3-galactose (Gal) and -non-Gal IgM and IgG antibodies were determined up to 2 months post-implantation. Based on histological analyses, all BHV tissues studied triggered distinct infiltrating cellular immune responses that related to tissue degeneration. Increased anti-Gal antibody levels were found in serum after ATS 3f and Freedom/Solo implantation but not for Crown or Hancock II grafts. Overall, there were no correlations between cellular-immunity scores and post-implantation antibodies, suggesting these are independent factors differentially affecting the outcome of distinct commercial BHVs. These findings provide further insights into the understanding of SVD immunopathogenesis and highlight the need to evaluate immune responses as a confounding factor.

The Prognostic Significance of Red Cell Distribution Width in Cardiac Surgery: A Systematic Review and Meta-Analysis

RED CELL DISTRIBUTION WIDTH (RDW) is a routinely available biomarker of likely erythropoietic dysfunction, which may be associated with adverse outcomes after cardiac surgery. This systematic review and meta-analysis aimed to clarify the prognostic value of RDW in patients undergoing cardiac surgery. The authors searched MEDLINE, Embase, and the Cochrane Library from inception to May 10, 2022 for studies investigating the association between elevated RDW (as defined by the authors of included studies) and adverse outcomes after cardiac surgery. Herein, the authors extracted maximally adjusted hazard ratios (HRs) and odds ratios (ORs) with associated CIs, and pooled them using random-effects inverse- variance modeling. The authors explored interstudy heterogeneity using metaregression. The authors included 26 studies involving 48,092 patients who had undergone cardiac surgery. Elevated preoperative RDW was associated with long-term mortality (pooled HR 1.63, 95% CI 1.05-2.52), short-term mortality (pooled OR 2.16, 95% CI 1.21-3.87), acute kidney injury (AKI; pooled OR 1.30, 95% CI 1.19-1.41) and postoperative atrial fibrillation (POAF; pooled OR 1.44, 95% CI 1.05-1.96). Some studies suggested a significant association between preoperative RDW elevation and neurologic complications; however, their number was insufficient for meta-analysis. The postoperative RDW levels were less consistently reported and could not be meta-analyzed. In conclusion, the authors found that elevated preoperative RDW was associated with increased short- and long-term mortality, POAF, and AKI after cardiac surgery. Further research is needed to investigate its role in the risk stratification of patients undergoing cardiac surgery.

Evaluation of Adipose Tissue Zinc-Alpha 2-Glycoprotein Gene Expression and Its Relationship with Metabolic Status and Bariatric Surgery Outcomes in Patients with Class III Obesity

Zinc-α2 glycoprotein (ZAG) is an adipokine involved in adipocyte metabolism with potential implications in the pathogenesis of metabolic disorders. Our aim was to evaluate the relationship between visceral (VAT) and subcutaneous adipose tissue (SAT) ZAG expression and metabolic parameters in patients with class III obesity, along with the impact of basal ZAG expression on short- and medium-term outcomes related to bariatric surgery. 41 patients with class III obesity who underwent bariatric surgery were included in this study. ZAG gene expression was quantified in SAT and VAT. Patients were classified into two groups according to SAT and VAT ZAG percentile. Anthropometric and biochemical variables were obtained before and 15 days, 45 days, and 1 year after surgery. The lower basal SAT ZAG expression percentile was associated with higher weight and waist circumference, while the lower basal VAT ZAG expression percentile was associated with higher weight, waist circumference, insulin, insulin resistance, and the presence of metabolic syndrome. Basal SAT ZAG expression was inversely related to weight loss at 45 days after surgery, whereas no associations were found between basal VAT ZAG expression and weight loss after surgery. Additionally, a negative association was observed between basal SAT and VAT ZAG expression and the decrease of gamma-glutamyl transferase after bariatric surgery. Therefore, lower SAT and VAT ZAG expression levels were associated with an adverse metabolic profile. However, this fact did not seem to confer worse bariatric surgery-related outcomes. Further research is needed to assess the clinical significance of the role of ZAG expression levels in the dynamics of hepatic enzymes after bariatric surgery.

Machine Learning-Based Risk Model for Predicting Early Mortality After Surgery for Infective Endocarditis

Background The early mortality after surgery for infective endocarditis is high. Although risk models help identify patients at high risk, most current scoring systems are inaccurate or inconvenient. The objective of this study was to construct an accurate and easy-to-use prediction model to identify patients at high risk of early mortality after surgery for infective endocarditis. Methods and Results A total of 476 consecutive patients with infective endocarditis who underwent surgery at 2 centers were included. The development cohort consisted of 276 patients. Eight variables were selected from 89 potential predictors as input of the XGBoost model to train the prediction model, including platelet count, serum albumin, current heart failure, urine occult blood ≥(++), diastolic dysfunction, multiple valve involvement, tricuspid valve involvement, and vegetation >10 mm. The completed prediction model was tested in 2 separate cohorts for internal and external validation. The internal test cohort consisted of 125 patients independent of the development cohort, and the external test cohort consisted of 75 patients from another center. In the internal test cohort, the area under the curve was 0.813 (95% CI, 0.670-0.933) and in the external test cohort the area under the curve was 0.812 (95% CI, 0.606-0.956). The area under the curve was significantly higher than that of other ensemble learning models, logistic regression model, and European System for Cardiac Operative Risk Evaluation II (all, P<0.01). This model was used to develop an online, open-access calculator (http://42.240.140.58:1808/). Conclusions We constructed and validated an accurate and robust machine learning-based risk model to predict early mortality after surgery for infective endocarditis, which may help clinical decision-making and improve outcomes.

Metabolomics in Bariatric and Metabolic Surgery Research and the Potential of Deep Learning in Bridging the Gap

During the past several years, there has been a shift in terminology from bariatric surgery alone to bariatric and metabolic surgery (BMS). More than a change in name, this signifies a paradigm shift that incorporates the metabolic effects of operations performed for weight loss and the amelioration of related medical problems. Metabolomics is a relatively novel concept in the field of bariatrics, with some consistent changes in metabolite concentrations before and after weight loss. However, the abundance of metabolites is not easy to handle. This is where artificial intelligence, and more specifically deep learning, would aid in revealing hidden relationships and would help the clinician in the decision-making process of patient selection in an individualized way.

Combination of Transfer Learning Methods for Kidney Glomeruli Image Classification

The rising global incidence of chronic kidney disease necessitates the development of image categorization of renal glomeruli. COVID-19 has been shown to enter the glomerulus, a tissue structure in the kidney. This study observes the differences between focal-segmental, normal and sclerotic renal glomerular tissue diseases. The splitting and combining of allied and multivariate models was accomplished utilizing a combined technique using existing models. In this study, model combinations are created by using a high-accuracy accuracy-based model to improve other models. This research exhibits excellent accuracy and consistent classification results on the ResNet101V2 combination using a mix of transfer learning methods, with the combined model on ResNet101V2 showing an accuracy of up to 97 percent with an F1-score of 0.97, compared to other models. However, this study discovered that the anticipated time required was higher than the model employed in general, which was mitigated by the usage of high-performance computing in this study.

The red blood cell distribution width-albumin ratio: A promising predictor of mortality in heart failure patients - A cohort study

BACKGROUND

Epidemiological studies suggest that increases in red blood cell distribution width (RDW) and decreases in albumin level can independently predict adverse cardiovascular outcomes. The prognostic value of RDW-albumin ratio (RAR), an innovate biomarker of inflammation, in heart failure (HF) patients has not been assessed. This study aimed to explore the association between RAR and mortality of HF patients.

METHODS

Data on patients diagnosed with HF were extracted from MIMIC-III database version 1.4. Cox proportional hazards models were used to investigate the associations between RAR and mortality of HF patients. HF patients admitted to the Second Affiliated Hospital of Wenzhou Medical University were also enrolled to explore the relationship between RAR and existing indicators of HF.

RESULTS

For 90-day mortality, the HR (95% CI) for the second (4.33＜RAR＜5.44) and the third (RAR＞5.44) tertiles were 2.00 (1.58, 2.54) and 3.63 (2.91, 4.53), respectively, compared to the first tertile (RAR＜4.33). When adjusted for age, gender and ethnicity in Model 1, the adjusted HR (95% CI) value of third tertiles was 3.66 (2.93, 4.56). Further adjust the vital signs, blood biochemical indicators, SOFA score and other parameters in Model II, the adjusted HR value of third tertiles was still statistically significant (HR: 2.70, 95% CI: 2.07-3.51, P < 0.0001). A similar trend was observed for 30-day, one-year mortality. For HF patients, high RAR significantly increased the risk of sepsis and requirement for renal replacement therapy. Additionally, there is a positive correlation between RAR, CRP levels, and NT-proBNP respectively.

CONCLUSION

High level of RAR is associated with increased short- and long-term mortality of patients with heart failure. The RAR is a promising biomarker that is easy to obtain and readily predicts mortality in heart failure patients.

The Red Blood Cell Distribution Width-Albumin Ratio: A Promising Predictor of Mortality in Stroke Patients

Objective

Within this study we attempt to express a correlation between the mortality of stroke and stroke related infection to a novel biomarker represented by the red blood cell width-albumin levels ratio within the patient. We hypothesize that this novel biomarker could be utilized as better predictive tool for stroke associated infections.

Methods

Patient data sets were obtained via the Medical Information Mart for Intensive Care Database iii V1.4 (MIMIC-iii). Data from 1480 patients were obtained to serve the testing for the RA biomarker tests. Clinical endpoints of 30-, 60-, and 365-day all-cause mortality in stroke patients were used as subgroups within the analyzed population. Estimation of hazard ratios (HR) were obtained from Cox regression models for stroke-associated infection and all-cause mortality in relation to RA values.

Results

A high-RA was associated with increased mortality in ICU patients suffering from a stroke. After adjusting for age and sex, compared to the reference group (the first quartile), the high-RA group had the highest 30-day (HR, 95% CI: 1.88 (1.36, 2.58)), 90-day (HR, 95% CI: 2.12 (1.59, 2.82)), and one-year (HR, 95% CI: 2.15 (1.65, 2.80)) all-cause mortality. The RA values were independently associated with an increased risk of stroke-associated infection when adjusting for confounders.

Conclusions

Our data suggest RA may be an easily accessible, reproducible, and low-cost biomarker for predicting stroke-associated infections and mortality in patients who have suffered from a stroke.

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.

MicroRNA profiling as a novel diagnostic tool for identification of patients with inflammatory and/or virally induced cardiomyopathies

AIMS

MicroRNAs (miRNAs) might be used as prospective biomarkers for the identification of unexplained heart failure caused by a viral and/or inflammatory process. The aim of this study was to identify and to evaluate prognostic miRNAs in serum of patients with inflammatory heart diseases diagnosed by endomyocardial biopsies.

METHODS AND RESULTS

After TaqMan® OpenArray® screening of 754 unique circulating miRNAs in serum of biopsy-proven patients [184 patients with inflammatory and/or virally induced myocardial diseases (DCMi), 25 patients with dilated cardiomyopathy (DCM), and 25 healthy donors], we identified seven miRNAs of interest (P < 0.05). These data have been verified by single qRT-PCR assays in other biopsy-proven patients (159 patients with viral and/or inflammatory myocardial diseases, 46 patients with DCM, and 60 healthy donors). The expression of let-7f, miR-197, miR-223, miR-93, and miR-379 allowed us to differentiate between patients with a virus and/or inflammation and healthy donors (P < 0.05) with the specificity over 93%. Based on the expression of miR-21 and miR-30a-5p, we could sort out patients with DCM from all other study groups (P < 0.05) with the specificity over 95%.

CONCLUSIONS

This miRNA profile provides for the first time a new non-invasive diagnostic perspective to identify patients with intramyocardial inflammation and/or viral persistence only from single serum sample, independently of prescribed therapy and time of symptoms onset. It allows the early finding of those patients relevant for myocardial biopsy for exact diagnosis and further proscription of causal aetiology-driven specific treatment.

High-sensitivity C-reactive protein as a prognostic marker in patients undergoing valve surgery

Introduction

An inflammatory reaction is a local or systemic response of the organs or tissues of the body to many damaging factors. One of the exponents of the inflammatory process is C-reactive protein (CRP).

Aim

To investigate the prognostic value of C-reactive protein (CRP) in patients undergoing valve surgery.

Material and methods

A prospective study was conducted on a group of consecutive patients with haemodynamically significant valve defects who underwent elective valve repair or replacement surgery. The primary end-point was in-hospital death from all causes. Patients were followed by direct observation during hospitalization. The risk of surgery using Euro-SCORE II was calculated for each patient. The plasma levels of C-reactive protein were measured by the Cardiac C-Reactive Protein (Latex) High Sensitive Test (Roche, Germany).

Results

The study group included 562 patients. The mean age in the studied population was 63 (standard deviation (SD) ±12). The mean plasma preoperative CRP level was 0.39 ±0.3 mg/dl. The primary endpoint occurred in 25 patients. At multivariate analysis age (p = 0.01), CRP (p = 0.02) and NT-proBNP (p = 0.03) remained independent predictors of the primary endpoint. A significant correlation was found between the level of CRP and haemoglobin (r = -0.3; p < 0.0001), red cell distribution width (r = 0.22; p < 0.0001), ejection fraction (r = -0.24, p = 0.007), troponin T (r = 0.3; p < 0.0001), creatinine (r = 0.26; p = 0.001) and body mass index (r = -0.29; p = 0.005). The average total time of hospitalization after the operation in patients with occurrence of the primary endpoint was 25 ±13 days.

Conclusions

Elevated preoperative CRP was associated with a poorer outcome following valve surgery.

Usefulness of FRAIL Scale in Heart Valve Diseases

Background

The frailty syndrome is a serious health problem for an aging population. The occurrence of frailty in the group of symptomatic patients undergoing heart valve surgery may have additional clinical implications. The predictive ability of the FRAIL scale in patients undergoing heart valve surgery during a 30-day follow-up has not yet been described.

Patients and Methods

A prospective study was conducted on a group of consecutive patients with hemodynamically significant valve disease (aortic stenosis, aortic regurgitation, mitral stenosis and mitral regurgitation) that underwent elective valve surgery in 2014–2019. The primary endpoint was 30-day mortality. Univariate analysis, followed by multivariate regression analysis, was performed.

Results

The study group included 672 consecutive patients (aortic valve stenosis, aortic regurgitation, mitral stenosis and mitral regurgitation) who underwent replacement or repair of the valve. Twenty-five patients died during the 30-day follow-up. At multivariate analysis, FRAIL scale result (OR 2.802; 95% CI 1.275–6.157; p=0.01) and red cell distribution width (RDW) (OR 1.810; 95% CI 1.181–2.775; p=0.006) remained independent predictors of the primary endpoint.

Conclusion

The presented study showed the predictive ability of the FRAIL scale result in patients undergoing heart valve surgery for 30-day mortality.

The usefulness of perioperative lactate blood levels in patients undergoing heart valve surgery

Aim

The aim of the study was to assess the usefulness of lactate blood levels in the perioperative period in patients undergoing heart valve surgery.

Material and methods

A prospective study was conducted on a group of consecutive patients with significant valvular heart disease who underwent elective valve surgery. The primary endpoint was total mortality in a 30-day follow-up. Univariate analysis, followed by multivariate regression analysis, was performed.

Results

The study included 801 patients. The primary end point occurred in 36 patients. At multivariate analysis lactate blood level measured one day after surgery and pH measured one day after surgery remained independent predictors of the primary end-point.

Conclusions

Elevated postoperative lactate blood level was associated with a higher risk of postoperative death.

N-terminal of the prohormone brain natriuretic peptide is a predictor of hemodynamic instability in valve disease

Aim: The usefulness of N-terminal of the prohormone brain natriuretic peptide (NT-proBNP) as a predictor of perioperative hemodynamic instability is not fully elucidated. Methods: This prospective study was conducted on a group of 455 patients undergoing elective heart valve surgery. The primary end point at the 30-day follow-up was postoperative hemodynamic instability. The secondary end point was death from all causes in patients with postoperative hemodynamic instability. Results: The postoperative hemodynamic instability occurred in 85 patients. At multivariate analysis NT-proBNP, New York Heart Association classes and glomerular filtration rate remained independent predictors of the primary end point. Age and NT-proBNP were associated with an increased risk of death. Conclusion: The preoperative NT-proBNP can be used to predict a postoperative hemodynamic instability as well as death in the group of patients with postoperative hemodynamic instability.