Complement Components sC5b-9 and CH50 Predict Prognosis in Heart Failure Patients Combined With Hypertension

Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study

BACKGROUND

The mortality risk varies considerably among individual dialysis patients. This study aimed to develop a user-friendly predictive model for predicting all-cause mortality among dialysis patients.

METHODS

Retrospective data regarding dialysis patients were obtained from two hospitals. Patients in training cohort (N = 1421) were recruited from the Fifth Affiliated Hospital of Sun Yat-sen University, and patients in external validation cohort (N = 429) were recruited from the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. The follow-up endpoint event was all-cause death. Variables were selected by LASSO-Cox regression, and the model was constructed by Cox regression, which was presented in the form of nomogram and web-based tool. The discrimination and accuracy of the prediction model were assessed using C-indexes and calibration curves, while the clinical value was assessed by decision curve analysis (DCA).

RESULTS

The best predictors of 1-, 3-, and 5-year all-cause mortality contained nine independent factors, including age, body mass index (BMI), diabetes mellitus (DM), cardiovascular disease (CVD), cancer, urine volume, hemoglobin (HGB), albumin (ALB), and pleural effusion (PE). The 1-, 3-, and 5-year C-indexes in the training set (0.840, 0.866, and 0.846, respectively) and validation set (0.746, 0.783, and 0.741, respectively) were consistent with comparable performance. According to the calibration curve, the nomogram predicted survival accurately matched the actual survival rate. The DCA showed the nomogram got more clinical net benefit in both the training and validation sets.

CONCLUSIONS

The effective and convenient nomogram may help clinicians quantify the risk of mortality in maintenance dialysis patients.

Enhanced Levels of Adiposity, Stretch and Fibrosis Markers in Patients with Coexistent Heart Failure and Atrial Fibrillation

The coexistence of heart failure (HF) and atrial fibrillation (AF) worsens the prognosis of patients. We aimed to study the inflammation, metabolism, adiposity, and fibrosis markers on epicardial and subcutaneous fat and blood, and their relationship with HF and AF. Samples from 185 patients undergoing cardiac surgery were collected. Levels of multi-markers on fat biopsies and plasma were analyzed. Patients were grouped by HF or AF presence. Plasma adiposity markers were increased in AF patients, while increased stretch markers correlated with HF. Patients with both AF and HF had higher ANP and GDF-15 levels. After excluding AF patients, plasma FABP4 was identified as the main HF predictor. Fat biopsies from AF patients showed an enhanced inflammatory profile. Higher levels of adiposity markers are associated with AF or HF, and higher stretch and fibrosis markers with combined AF and HF, suggesting a role of adiposity-fibrosis pathway in HF and AF coexistence.

Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases

Background

Heart failure (HF) combined with hypertension is an extremely important cause of in-hospital mortality, especially for the intensive care unit (ICU) patients. However, under intense working pressure, the medical staff are easily overwhelmed by the large number of clinical signals generated in the ICU, which may lead to treatment delay, sub-optimal care, or even wrong clinical decisions. Individual risk stratification is an essential strategy for managing ICU patients with HF combined with hypertension. Artificial intelligence, especially machine learning (ML), can develop superior models to predict the prognosis of these patients. This study aimed to develop a machine learning method to predict the 28-day mortality for ICU patients with HF combined with hypertension.

Methods

We enrolled all critically ill patients with HF combined with hypertension in the Medical Information Mart for IntensiveCare Database-IV (MIMIC-IV, v.1.4) and the eICU Collaborative Research Database (eICU-CRD) from 2008 to 2019. Subsequently, MIMIC-IV was divided into training cohort and testing cohort in an 8:2 ratio, and eICU-CRD was designated as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression with internal tenfold cross-validation was used for data dimension reduction and identifying the most valuable predictive features for 28-day mortality. Based on its accuracy and area under the curve (AUC), the best model in the validation cohort was selected. In addition, we utilized the Shapley Additive Explanations (SHAP) method to highlight the importance of model features, analyze the impact of individual features on model output, and visualize an individual’s Shapley values.

Results

A total of 3,458 and 6582 patients with HF combined with hypertension in MIMIC-IV and eICU-CRD were included. The patients, including 1,756 males, had a median (Q1, Q3) age of 75 (65, 84) years. After selection, 22 out of a total of 58 clinical parameters were extracted to develop the machine-learning models. Among four constructed models, the Neural Networks (NN) model performed the best predictive performance with an AUC of 0.764 and 0.674 in the test cohort and external validation cohort, respectively. In addition, a simplified model including seven variables was built based on NN, which also had good predictive performance (AUC: 0.741). Feature importance analysis showed that age, mechanical ventilation (MECHVENT), chloride, bun, anion gap, paraplegia, rdw (RDW), hyperlipidemia, peripheral capillary oxygen saturation (SpO₂), respiratory rate, cerebrovascular disease, heart rate, white blood cell (WBC), international normalized ratio (INR), mean corpuscular hemoglobin concentration (MCHC), glucose, AIDS, mean corpuscular volume (MCV), N-terminal pro-brain natriuretic peptide (Npro. BNP), calcium, renal replacement therapy (RRT), and partial thromboplastin time (PTT) were the top 22 features of the NN model with the greatest impact. Finally, after hyperparameter optimization, SHAP plots were employed to make the NN-based model interpretable with an analytical description of how the constructed model visualizes the prediction of death.

Conclusion

We developed a predictive model to predict the 28-day mortality for ICU patients with HF combined with hypertension, which proved superior to the traditional logistic regression analysis. The SHAP method enables machine learning models to be more interpretable, thereby helping clinicians to better understand the reasoning behind the outcome and assess in-hospital outcomes for critically ill patients.

Immunomodulatory Treatment Strategies Targeting B Cells for Heart Failure

Cardio-oncology, a nascent specialty, has evolved as a concerted strategy to address the cardiovascular complications of cancer therapies. On the other hand, emerging evidence has shown that some anti-tumor drugs, such as CD20-targeted rotuximab, also have markedly cardioprotective effects in addition to treating cancers. Rituximab is a CD20-targeted monoclonal antibody and kill tumor B-cells through antibody-mediated and antibody-independent pathways, indicating that B cells participate and promote the progression of cardiovascular diseases. In this review, we mainly present the evidence that B cells contribute to the development of hypertrophy, inflammation, and maladaptive tissue remodeling, with the aim of proposing novel immunomodulatory therapeutic strategies targeting B cells and their products for the treatment of heart failure.

COVID-19-driven endothelial damage: complement, HIF-1, and ABL2 are potential pathways of damage and targets for cure

COVID-19 pandemia is a major health emergency causing hundreds of deaths worldwide. The high reported morbidity has been related to hypoxia and inflammation leading to endothelial dysfunction and aberrant coagulation in small and large vessels. This review addresses some of the pathways leading to endothelial derangement, such as complement, HIF-1α, and ABL tyrosine kinases. This review also highlights potential targets for prevention and therapy of COVID-19-related organ damage and discusses the role of marketed drugs, such as eculizumab and imatinib, as suitable candidates for clinical trials.