Background. The main purpose of this study was to explore the predictive value of the systemic immune inflammation index (SII), a novel clinical marker, in heart failure (HF) patients. Methods. Critically ill patients with HF were identified from the Medical Information Mart for Intensive Care III (MIMIC III) database. Patients were divided into three groups according to tertiles of SII (group 1, group 2, group 3). We used Kaplan-Meier curves and Cox proportional hazards regression models to evaluate the association between the SII and all-cause mortality in HF. Subgroup analysis was used to verify the predictive effect of the SII on mortality. Results. This study included 9107 patients with a diagnosis of HF from the MIMIC III database. After 30, 60, 180, and 365 days of follow-up, 25.60%, 32.10%, 41.30%, and 47.50% of the patients in group 3 had died. Using the Kaplan-Meier curve, we observed that patients with higher SII values had a shorter survival time (log rank p < 0.001 ). The Cox proportional hazards regression model adjusted for all possible confounders and indicated that the higher SII group had a higher mortality (30-day: HR = 1.304 , 95 % CI = 1.161 − 1.465 , 60-day: HR = 1.266 , 95% CI = 1.120 − 1.418 , 180-day: HR = 1.274 , 95 % CI = 1.163 − 1.395 , and 365-day: HR = 1.255 , 95 % CI = 1.155 − 1.364 ). Conclusions. SII values could be used as a predictor of prognosis in critically ill patients with HF.

Aims Abnormalities in potassium homeostasis are frequently seen in hospitalized patients. A poor outcome in heart failure (HF) has been linked to both hypokalaemia and hyperkalaemia. The studies on the connection between variations in potassium levels and all‐cause mortality remain scarce. We delineated trajectories of potassium levels and investigated the association of these trajectories with all‐cause mortality of critically ill patients with HF. Methods and results A retrospective analysis of blood potassium levels (9 times) in patients with HF after being admitted to the intensive care unit (ICU). Potassium levels were divided into three groups according to the first serum potassium level in ICU and thereafter categorized as follows: hypokalaemia group ( n = 336) (<3.5 mmol/L), normal blood potassium‐level group ( n = 3322) (3.5–5.0 mmol/L), and hyperkalaemia group ( n = 395) (>5.0 mmol/L). According to the group‐based trajectory modelling (GBTM), the hyperkalaemia group and the normal blood potassium‐level group can be divided into three trajectory groups: the low‐level stable group, the medium‐level stable group, and the high‐level decline group. The hypokalaemia group can be divided into two trajectory groups: the low‐level rise group and the high‐level rise group. A total of 4053 HF patients were included (mean age 71.81 ± 13.12 years, 54.90% males, 45.10% females). After adjusting for possible confounding variables, in the hyperkalaemia group, the low‐level stable group had lower 28 day [high‐level decline group vs. low‐level stable group hazard ratio (HR), 95% confidence interval (CI): 2.917, 1.555–5.473; P < 0.05] and 365 day (high‐level decline group vs. low‐level stable group HR, 95% CI: 2.854, 1.820–4.475; P < 0.05) all‐cause mortality. In the normal blood potassium‐level group, the medium‐level stable group had lower 28 day (medium‐level stable group vs. low‐level stable group HR, 95% CI: 0.776, 0.657–0.918; P < 0.05) and 365 day (medium‐level stable group vs. low‐level stable group HR, 95% CI: 0.827, 0.733–0.934; P < 0.05) all‐cause mortality. In the hypokalaemia group, the cumulative survival of the high‐level rise group and the low‐level rise group did not differ significantly. Conclusions Critically ill patients with HF have blood potassium trajectories. And the trajectories are associated with all‐cause mortality for hyperkalaemia and normal blood potassium‐level patients. GBTM is a granular method to describe the evolution of blood potassium, which may increase the current knowledge of blood potassium‐level adjustment.

Backgrounds Increased risk of in-hospital mortality is critical to guide medical decisions and it played a central role in intensive care unit (ICU) with high risk of in-hospital mortality after primary percutaneous coronary intervention (PCI). At present，most predicting tools for in-hospital mortality after PCI were based on the results of coronary angiography, echocardiography, and laboratory results which are difficult to obtain at admission. The difficulty of using these tools limit their clinical application. This study aimed to develop a clinical prognostic nomogram to predict the in-hospital mortality of patients in ICU after PCI. Methods We extracted data from a public database named the Medical Information Mart for Intensive Care (MIMIC III). Adult patients with coronary artery stent insertion were included. They were divided into two groups according to the primary outcome (death in hospital or survive). All patients were randomly divided into training set and validation set randomly at a ratio of 6:4. Least absolute shrinkage and selection operator (LASSO) regression was performed in the training set to select optimal variables to predict the in-hospital mortality of patients in ICU after PCI. The multivariate logistical analysis was performed to develop a nomogram. Finally, the predictive efficiency of the nomogram was assessed by area under the receiver operating characteristic curve (AUROC)，integrated discrimination improvement (IDI), and net reclassification improvement (NRI), and clinical net benefit was assessed by Decision curve analysis (DCA). Results A total of 2160 patients were recruited in this study. By using LASSO, 17 variables were finally included. We used multivariate logistic regression to construct a prediction model which was presented in the form of a nomogram. The calibration plot of the nomogram revealed good fit in the training set and validation set. Compared with the sequential organ failure assessment (SOFA) and scale for the assessment of positive symptoms II (SAPS II) scores, the nomogram exhibited better AUROC of 0.907 (95% confidence interval [CI] was 0.880-0.933, p <  0.001) and 0.901 (95% CI was 0.865-0.936, P <  0.001) in the training set and validation set, respectively. In addition, DCA of the nomogram showed that it could achieve good net benefit in the clinic. Conclusions A new nomogram was constructed, and it presented excellent performance in predicting in-hospital mortality of patients in ICU after PCI.

AimTo examine the direction, strength and causality of the associations of resting heart rate (RHR) with cardiac morphology and function in 20,062 UK Biobank participants.Methods and resultsParticipants underwent cardiac magnetic resonance (CMR) and we extracted CMR biventricular structural and functional metrics using automated pipelines. Multivariate linear regression adjusted for the main cardiovascular risk factors and Two-sample Mendelian Randomization analyses were performed to assess the potential relationship, grouped by heart rate and stratified by sex. Each 10 beats per minute increase in RHR was linked with smaller ventricular structure (lower biventricular end-diastolic volume and end-systolic volume), poorer left ventricular (LV) function (lower LV ejection fraction, global longitude strain and global function index) and unhealthy pattern of LV remodeling (higher values of myocardial contraction fraction), but there is no statistical difference in LV wall thickness. These trends are more pronounced among males and consistent with the causal effect direction of genetic variants interpretation. These observations reflect that RHR has an independent and broad impact on LV remodeling, however, genetically-predicted RHR is not statistically related to heart failure.ConclusionWe demonstrate higher RHR cause smaller ventricular chamber volume, poorer systolic function and unhealthy cardiac remodeling pattern. Our findings provide effective evidence for the potential mechanism of cardiac remodeling and help to explore the potential scope or benefit of intervention.

PurposeIn recent years, the complete blood count with differential (CBC w/diff) test has drawn strong interest because of its prognostic value in cardiovascular diseases. We aimed to develop a CBC w/diff-based prediction model for in-hospital mortality among patients with severe acute myocardial infarction (AMI) in the coronary care unit (CCU).Materials and methodsThis single-center retrospective study used data from a public database. The neural network method was applied. The performance of the model was assessed by discrimination and calibration. The discrimination performance of our model was compared to that of seven other classical machine learning models and five well-studied CBC w/diff clinical indicators. Finally, a permutation test was applied to evaluate the importance rank of the predictor variables.ResultsA total of 2,231 patient medical records were included. With a mean area under the curve (AUC) of 0.788 [95% confidence interval (CI), 0.736–0.838], our model outperformed all other models and indices. Furthermore, it performed well in calibration. Finally, the top three predictors were white blood cell count (WBC), red blood cell distribution width-coefficient of variation (RDW-CV), and neutrophil percentage. Surprisingly, after dropping seven variables with poor prediction values, the AUC of our model increased to 0.812 (95% CI, 0.762–0.859) (P &lt; 0.05).ConclusionWe used a neural network method to develop a risk prediction model for in-hospital mortality among patients with AMI in the CCU based on the CBC w/diff test, which performed well and would aid in early clinical decision-making. The top three important predictors were WBC, RDW-CV and neutrophil percentage.