Objective The predictors of in-hospital mortality for intensive care units (ICUs)-admitted heart failure (HF) patients remain poorly characterised. We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients. Design A retrospective cohort study. Setting and participants Data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Data on 1177 heart failure patients were analysed. Methods Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation (n=825, 70%) and a validation (n=352, 30%) group. Independent risk factors for in-hospital mortality were screened using the extreme gradient boosting (XGBoost) and the least absolute shrinkage and selection operator (LASSO) regression models in the derivation sample. Multivariate logistic regression analysis was used to build prediction models in derivation group, and then validated in validation cohort. Discrimination, calibration and clinical usefulness of the predicting model were assessed using the C-index, calibration plot and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients. Results Among the 1177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression and Get With the Guidelines-Heart Failure (GWTG-HF) risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (p<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram. Conclusions Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.

Background: Diabetes mellitus is one of the causes of poor ventricular remodelling and poor cardiac recovery after myocardial infarction (MI). We previously reported that tissue factor pathway inhibitor-2 (TFPI2) was downregulated in response to hyperglycaemia and that it played a pivotal role in extracellular matrix (ECM) degradation and cell migration. Nonetheless, the function and mechanism of TFPI2 in post-MI remodelling under diabetic conditions remain unclear. Therefore, in the present study, we investigated the role of TFPI2 in post-MI effects in a diabetic mouse model. Results: TFPI2 expression was markedly decreased in the infarcted myocardium of diabetic MI mice compared with that in non-diabetic mice. TFPI2 knockdown in the MI mouse model promoted fibroblast activation and migration as well as matrix metalloproteinase (MMP) expression, leading to disproportionate fibrosis remodelling and poor cardiac recovery. TFPI2 silencing promoted pro-inflammatory M1 macrophage polarization, which is consistent with the results of TFPI2 downregulation and M1 polarization under diabetic conditions. In contrast, TFPI2 overexpression in diabetic MI mice protected against adverse cardiac remodelling and functional deterioration. TFPI2 overexpression also inhibited MMP2 and MMP9 expression and attenuated fibroblast activation and migration, as well as excessive collagen production, in the infarcted myocardium of diabetic mice. TFPI2 promoted an earlier phenotype transition of pro-inflammatory M1 macrophages to reparative M2 macrophages via activation of peroxisome proliferator-activated receptor gamma. Conclusions: This study highlights TFPI2 as a promising therapeutic target for early resolution of post-MI inflammation and disproportionate ECM remodelling under diabetic conditions.

Diabetes mellitus significantly escalates the risk of accelerated atherosclerosis (AS), severely affecting cardiovascular health. Our research, leveraging Gene Expression Omnibus (GEO) database analysis (GSE118481), revealed diminished TFPI2 expression in diabetic patients' atherosclerotic plaques. Further validation in carotid artery plaques and an AS mouse model confirmed TFPI2's reduced expression in diabetes. Through TFPI2 knockdown in non-diabetic mice, we observed aggravated plaque burden and increased inflammatory M1 macrophage polarization. Conversely, TFPI2 overexpression in diabetic mice improved plaque stability and induced reparative M2 macrophage polarization, countering hyperglycemia's negative effects. Mechanistically, transcription factor activator protein 2α (AP-2α) is a repressor of PPPARg transcription, and the interaction of TFPI2 with the transcription factor AP-2α blocks AP-2α binding to the PPARγ gene promoter, which is essential for PPARγ-mediated transcription and the transition from M1 to M2 macrophages. Additionally, hyperglycemia-induced DNA methyltransferase 1 (DNMT1) upregulation heightens TFPI2 methylation, reducing its expression. Our findings spotlight the TFPI2/AP-2α/PPARγ axis as crucial in diabetic AS modulation, proposing its targeting as a new therapeutic strategy to halt diabetes-driven AS progression, highlighting TFPI2's therapeutic promise in addressing diabetes-related cardiovascular issues.

Few researchers have discussed the differences in gender between the age groups of patients who underwent transcatheter aortic valve implantation (TAVI). We searched the National Readmissions Database from 2012 to 2019 to identify adults who underwent TAVI. We studied hospital outcomes and short- to medium-term outcomes by age stratification (18 to 59, 60 to 69, 70 to 79, and 80 to 90 years) after TAVI and categorized by gender. We included 147,481 patients who underwent TAVI, and 54,802 pairs were matched using propensity score matching separately for each age group. Compared with men, women in all age groups had a similar rate of hospital death. Except the 18- to 59-year-old groups, female patients were less likely to undergo permanent pacemaker implantation and transfusion. Records of readmission at 30 days and 6 months were used as the follow-up outcome according to the presence or absence of readmission. Major adverse cardiovascular events (MACEs) were a composite of cardiovascular readmission, all-cause mortality during readmission, and stroke readmission. At the 30-day follow-up visit, there was no difference in the all-cause readmission and MACE between women and men in any group. At the 6-month follow-up visit, women in the 70- to 79-year-old and 80- to 90-year-old groups had a high risk of all-cause readmission. In conclusion, we reported that female patients have similar in-hospital death rates to male patients who underwent TAVI. During the 30-day follow-up visit, the all-cause readmission and MACE were not different in all age groups between men and women. At 6 months, women in the 70- to 79-year-old and 80- to 90-year-old groups had a higher risk of all-cause readmission.