Risk stratification and predictive value of serum sodium fluctuation for adverse prognosis in acute coronary syndrome patients.

BACKGROUND: Serum sodium fluctuation (SF) as an indicator of the extent of changes in serum sodium is associated with increased mortality in hospitalized patients. However, there is no consensus on diagnostic criteria for SF, and its impact on the outcome of patients with acute coronary syndrome (ACS) remains uncertain. We defined SF and assessed its association with adverse prognosis in hospitalized ACS patients. METHODS: Patients diagnosed with ACS were consecutively recruited. The serum SF rate (SFR) was defined as the ratio of the difference between the highest and lowest serum sodium levels during hospitalization to the initial serum sodium level on admission. The Cox proportional hazards model was performed to evaluate the association between SFR and mortality. The dose-response relationships of SFR with mortality was characterized by restricted cubic splines (RCS) model. The predictive performance of SF for mortality was assessed by the area under the receiver operating characteristic curves (AUCs). RESULTS: The study retrospectively enrolled 1856 ACS patients, of which 36 (1.94%) patients dead within 1 year. Multivariate Cox analysis showed that SFR was independently associated with higher risk of 1-year mortality (HR = 1.17, 95% CI: 1.111-1.244, P < 0.001). RCS analysis showed the optimal threshold for SFR was 5%, and the 1-year cumulative mortality was higher in the abnormal SF group (SFR ≥ 5%) compared with the normal SF group (SFR < 5%, P < 0.01). The AUCs of SF for predicting mortality within 1 month, 6 months, and 1 year were 0.842 (95% CI: 0.781-0.904), 0.830 (95% CI:0.736-0.926), 0.703 (95% CI:0.595--0.811), respectively. Even in patients with normal baseline serum sodium, abnormal SF group demonstrated a significantly higher 1-year mortality compared to normal SF group (HR = 4.955, 95% CI: 1.919-12.795). CONCLUSION: The SFR during hospitalization is an adequate predictor of adverse outcomes in ACS patients, independent of serum sodium level at admission. Additional research is warranted to ascertain whether interventions targeting SF confer measurable clinical benefits.

Very high HDL-C (high-density lipoprotein cholesterol) is associated with increased cardiovascular risk in patients with NSTEMI (non-ST-segment elevation myocardial infarction) undergoing PCI (percutaneous coronary intervention).

BACKGROUND: Studies in populations with or without cardiovascular disease have shown that very high HDL-C levels are associated with an increased risk of cardiovascular events. However, the exact relationship between HDL-C levels and long-term prognosis remains unknown in patients with myocardial infarction (MI) undergoing percutaneous coronary intervention (PCI). METHODS: This was a post hoc secondary analysis of long-term follow-up results in patients undergoing PCI open-label, observational cohort study. Patients with MI who had undergone PCI were enrolled. Restricted cubic spline (RCS) analysis and logistic regression analysis were performed to assess the relationship between HDL-C levels and the risk of cardiovascular events. RESULTS: A total of 1934 patients with MI undergoing PCI were enrolled in our analysis and our population was divided in 3 groups according to the HDL-C plasma levels: HDL-C < 40 mg/dL (low HDL-C); HDL-C between 40 and 80 mg/ dL (medium HDL-C); and HDL-C > 80 mg/dL (high HDL-C). RCS analysis showed a nonlinear U-shaped association between HDL-C levels and major adverse cardiac and cerebrovascular events (MACCE) in patients with NSTEMI with adjusted variables. After adjusting for potential confounders, the follow-up analysis indicated that high risk group had elevated occurrence of MACCE than low risk group (HDL-C 35 and 55 mg/dL) (OR:1.645, P = 0.006). CONCLUSIONS: Our analysis demonstrated that there is a U-shaped association between HDL-C and MACCE in patients with NSTEMI undergoing PCI.

TRIM44 aggravates cardiac fibrosis after myocardial infarction via TAK1 stabilization.

Myocardial infarction (MI) is one of the most dangerous cardiovascular events. Cardiac fibrosis is a common pathological feature of remodeling after injury that is related to adverse clinical results with no effective treatment. Previous studies have confirmed that TRIM44, an E3 ligase, can promote the proliferation and migration of various tumor cells. However, the role of TRIM44 in cardiac fibrosis remains unknown. Models of TGF-ß1 stimulation and MI-induced fibrosis were established to investigate the role and potential underlying mechanism of TRIM44 in cardiac fibrosis. The results showed that cardiac fibrosis was significantly inhibited after TRIM44 knockdown in a mouse model of MI, while it was enhanced when TRIM44 was overexpressed. Furthermore, in vitro studies showed that fibrosis markers were significantly reduced in cardiac fibroblasts (CFs) with TRIM44 knockdown, whereas TRIM44 overexpression promoted the expression of fibrosis markers. Mechanistically, TRIM44 maintains TAK1 stability by inhibiting the degradation of k48-linked polyubiquitination-mediated ubiquitination, thereby increasing phosphorylated TAK1 expression in the fibrotic environment and activating MAPKs to promote fibrosis. Pharmacological inhibition of TAK1 phosphorylation reversed the fibrogenic effects of TRIM44 overexpression. Combined, these results suggest that TRIM44 is a potential therapeutic target for cardiac fibrosis.

Performance of the heart failure risk scores in predicting 1 year mortality and short-term readmission of patients.

AIMS: The aim of this study was to assess the performance of these main scores in predicting prognosis in patients with heart failure (HF). METHODS AND RESULTS: A total of 2008 patients who were admitted to the Fourth People's Hospital of Zigong, Sichuan, from December 2016 to June 2019 and diagnosed with HF were included in the study. We compared the prognostic predictive performance of Seattle Heart Failure Model (SHFM), Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, Get With the Guidelines-Heart Failure programme (GWTG-HF), Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure (ASCEND) risk scores, the Acute Decompensated Heart Failure National Registry (ADHERE) model, Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator, and Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure (GISSI-HF) for the endpoints. The primary endpoint was 1 year all-cause mortality and the secondary endpoint was the incidence of 28 day readmission post-discharge. At 1 year follow-up, 44 (2.21%) patients with HF died. Discrimination analyses showed that all risk scores performed reasonably well in predicting 1 year mortality, with areas under the receiver operating characteristic curve (AUCs) fluctuating between 0.757 and 0.822. GISSI-HF showed the best discrimination with the AUC of 0.822 (0.768-0.876), followed by MAGGIC-HF, BCN-Bio-HF, ASCEND, SHFM, GWTG-HF, and ADHERE with AUCs of 0.819 (0.756-0.883), 0.812 (0.758-0.865), 0.802 (0.742-0.862), 0.787 (0.725-0.849), 0.762 (0.684-0.840), and 0.757 (0.681-0.833), respectively. All risk scores were similarly predictive of 28 day emergency readmissions, with AUCs fluctuating between 0.609 and 0.680. Overestimation of mortality occurred in all scores except the ASCEND. The risk scores remained with good prognostic discrimination in patients with biventricular HF and in the subgroup of patients taking angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker. CONCLUSIONS: Currently assessed risk scores have limited clinical utility, with fair accuracy and calibration in assessing patients' 1 year risk of death and poor accuracy in assessing patients' risk of readmission. There is a need to incorporate more patient-level information, use more advanced technologies, and develop models for different subgroups of patients to achieve more practical, innovative, and accurate risk assessment tools.

Tripartite motif 38 attenuates cardiac fibrosis after myocardial infarction by suppressing TAK1 activation via TAB2/3 degradation.

The role of tripartite motif (TRIM) 38, a ubiquitin E3 ligase regulating various pathophysiological processes, in cardiac fibrosis remains unclear. Here, a model of angiotensin II and myocardial infarction (MI)-induced fibrosis was established to explore its role in cardiac fibrosis and its underlying mechanisms. Cardiac fibrosis in the mouse MI model was mitigated by TRIM38 overexpression, but aggravated by its depletion. Consistently, in vitro overexpression or knockdown of TRIM38 ameliorated or aggravated the proliferation and secretion of cardiac fibroblasts (CFs) exposed to fibrotic stimulation, respectively. Mechanistically, TRIM38 suppressed cardiac fibrosis progression by attenuating TAK1/MAPK signaling. Inhibiting TAK1/MAPK signaling with a pharmacological inhibitor greatly reversed the effects of TRIM38 knockdown on CF secretion. Specifically, TRIM38 interacted with and "targeted" TAB2 and TAB3 for degradation, subsequently inhibiting TAK1 phosphorylation and negatively regulating MAPK signaling. These findings can help develop therapeutic strategies to treat and prevent cardiac fibrosis.

The prognostic value of admission lymphocyte-to-monocyte ratio in critically ill patients with acute myocardial infarction.

BACKGROUND: Inflammation plays a critical role in acute myocardial infarction (AMI). Recent studies have shown the value of hematologic indicators in MI risk stratification and prognostic assessment. However, the association between lymphocyte-to-monocyte ratio (LMR) and the long-term mortality of critically ill MI patients remains unclear. METHODS: Clinical data were extracted from the Medical Information Mart for Intensive Care III database. Patients diagnosed with AMI on admission in the intensive care units were include. The optimal cutoff value of LMR was determined by X-tile software. The Cox proportional hazard model was applied for the identification of independent prognostic factors of 1-year mortality and survival curves were estimated using the Kaplan-Meier method. In order to reduce selection bias, a 1:1 propensity score matching (PSM) method was performed. RESULTS: A total of 1517 AMI patients were included in this study. The cutoff value for 1-year mortality of LMR determined by X-Tile software was 3.00. A total of 534 pairs of patients were matched after PSM. Multivariate analysis (HR = 1.369, 95%CI 1.110-1.687, P = 0.003) and PSM subgroups (HR = 1.299, 95%CI 1.032-1.634, P = 0.026) showed that 1-year mortality was significantly higher in patients with LMR < 3.00 than patients with LMR ≥ 3.00 in Cox proportional hazard models. The survival curves showed that patients with LMR < 3.00 had a significantly lower 1-year survival rate before (63.83 vs. 81.03%, Log rank P < 0.001) and after PSM (68.13 vs. 74.22%, Log rank P = 0.041). CONCLUSION: In this retrospective cohort analysis, we demonstrated that a low admission LMR (< 3.00) was associated with a higher risk of 1-year mortality in critically ill patients with AMI.

A time-incorporated SOFA score-based machine learning model for predicting mortality in critically ill patients: A multicenter, real-world study.

BACKGROUND: Organ dysfunction (OD) assessment is essential in intensive care units (ICUs). However, current OD assessment scores merely describe the number and the severity of each OD, without evaluating the duration of organ injury. The objective of this study is to develop and validate a machine learning model based on the Sequential Organ Failure Assessment (SOFA) score for the prediction of mortality in critically ill patients. MATERIAL AND METHODS: Data from the eICU Collaborative Research Database and Medical Information Mart for Intensive Care (MIMIC) -III were mixed for model development. The MIMIC-IV and Nanjing Jinling Hospital Surgical ICU database were used as external test set A and set B, respectively. The outcome of interest was in-ICU mortality. A modified SOFA model incorporating time-dimension (T-SOFA) was stepwise developed to predict ICU mortality using extreme gradient boosting (XGBoost), support vector machine, random forest and logistic regression algorithms. Time-dimensional features were calculated based on six consecutive SOFA scores collected every 12 h within the first three days of admission. The predictive performance was assessed with the area under the receiver operating characteristic curves (AUROC) and calibration plot. RESULTS: A total of 82,132 patients from the real-world datasets were included in this study, and 7,494 patients (9.12%) died during their ICU stay. The T-SOFA M3 that incorporated the time-dimension features and age, using the XGBoost algorithm, significantly outperformed the original SOFA score in the validation set (AUROC 0.800 95% CI [0.787-0.813] vs. 0.693 95% CI [0.678-0.709], p < 0.01). Good discrimination and calibration were maintained in the test set A and B, with AUROC of 0.803, 95% CI [0.791-0.815] and 0.830, 95% CI [0.789-0.870], respectively. CONCLUSIONS: The time-incorporated T-SOFA model could significantly improve the prediction performance of the original SOFA score and is of potential for identifying high-risk patients in future clinical application.

Development and Validation of a Nomogram of In-hospital Major Adverse Cardiovascular and Cerebrovascular Events in Patients With Acute Coronary Syndrome.

Background and Objective: This study aims to develop and validate a nomogram for the occurrence of in-hospital major adverse cardiovascular and cerebrovascular events (MACCE) in acute coronary syndrome (ACS) patients. Methods: A total of 1,360 ACS patients admitted between November 2014 and October 2019 from Zhongda Hospital and Yancheng Third People's Hospital were included. Patients admitted in Zhongda Hospital before 2018 were split into the training cohort (n = 793). Those admitted after 2018 in Zhongda Hospital and patients from Yancheng Third People's Hospital were split into the validation cohort (n = 567). Twenty eight clinical features routinely assessed including baseline characteristics, past medical history and auxiliary examinations were used to inform the models to predict in-hospital MACCE (all-cause mortality, reinfarction, stroke, and heart failure) in ACS patients. The best-performing model was tested in the validation cohort. The accuracy and clinical applicability were tested by the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analyses (DCA). Results: The in-hospital MACCE occurred in 93 (6.83%) patients. The final prediction model consists of four variables: age, Killip grading, fasting blood-glucose (FBG) and whether percutaneous coronary intervention (PCI) was performed at early stage. A nomogram was used to present the final result. Individualized nomogram exhibited comparable discrimination to the Global Registry of Acute Coronary Events (GRACE) score [AUC: 0.807 (95% CI 0.736-0.878) vs. 0.761 (95% CI 0.69-0.878)], P = 0.10) and a better discrimination than the Evaluation of the Methods and Management of Acute Coronary Events (EMMACE) score [AUC: 0.807 (95% CI 0.736-0.878) vs. 0.723(95% CI 0.648-0.798), P = 0.01] in predicting the risk of in-hospital MACCE in ACS patients. A good prediction performance was maintained in the validation cohort (AUC =0.813, 95% CI 0.738-0.889). The prediction model also exhibited decent calibration (P = 0.972) and clinical usefulness. Conclusion: The nomogram may be a simple and effective tool in predicting the occurrence of in-hospital MACCE in ACS patients. Further longitudinal studies are warranted to validate its value in guiding clinical decision-making and optimizing the treatment of high-risk patients.