Background. Anion gap (AG) has been proved to be associated with prognosis of many cardiovascular diseases. This study is aimed at exploring the association of AG with inhospital all-cause mortality and adverse clinical outcomes in coronary care unit (CCU) patients. Method. All data of this study was extracted from Medical Information Mart for Intensive Care III (MIMIC-III, version 1.4) database. All patients were divided into four groups according to AG quartiles. Primary outcome was inhospital all-cause mortality. Lowess smoothing curve was drawn to describe the overall trend of inhospital mortality. Binary logistic regression analysis was performed to determine the independent effect of AG on inhospital mortality. Result. A total of 3593 patients were enrolled in this study. In unadjusted model, as AG quartiles increased, inhospital mortality increased significantly, OR increased stepwise from quartile 2 (OR, 95% CI: 1.01, 0.74-1.38, P=0.958) to quartile 4 (OR, 95% CI: 2.72, 2.08-3.55, P<0.001). After adjusting for possible confounding variables, this association was attenuated, but still remained statistically significant (quartile 1 vs. quartile 4: OR, 95% CI: 1.02, 0.72-1.45 vs. 1.49, 1.07-2.09, P=0.019). Moreover, CCU mortality (P<0.001) and rate of acute kidney injury (P<0.001) were proved to be higher in the highest AG quartiles. Length of CCU (P<0.001) and hospital stay (P<0.001) prolonged significantly in higher AG quartiles. Maximum sequential organ failure assessment score (SOFA) (P<0.001) and simplified acute physiology score II (SAPSII) (P<0.001) increased significantly as AG quartiles increased. Moderate predictive ability of AG on inhospital (AUC: 0.6291), CCU mortality (AUC: 0.6355), and acute kidney injury (AUC: 0.6096) was confirmed. The interactions were proved to be significant in hypercholesterolemia, congestive heart failure, chronic lung disease, respiratory failure, oral anticoagulants, Beta-blocks, angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor blocker (ARB), and vasopressin treatment subgroups. Conclusion. AG was an independent risk factor of inhospital all-cause mortality and was associated with adverse clinical outcomes in CCU patients.

Background. Neutrophil percentage-to-albumin ratio (NPAR) has been proved to be associated with clinical outcome of many diseases. This study was aimed at exploring the independent effect of NPAR on all-cause mortality of critically ill patients with coronary artery disease (CAD). Method. NPAR was calculated as neutrophil percentage numerator divided by serum albumin concentration. Clinical endpoints were 30-day, 90-day, and 365-day all-cause mortality. Multivariable Cox proportional hazard models were performed to confirm the association between NPAR and all-cause mortality. Result. 3106 patients with CAD were enrolled. All-cause mortality rates of 30 days (P<0.001), 90 days (P<0.001), and 365 days (P<0.001) increased as NPAR tertiles increased. And after adjusting for possible confounding variables, NPAR was still independently associated with 30-day (third tertile group versus first tertile group: HR, 95% CI: 1.924, 1.471-2.516; P for trend < 0.001), 90-day (third tertile group versus first tertile group: HR, 95% CI: 2.053, 1.646-2.560; P for trend < 0.001), and 365-day (third tertile group versus first tertile group: HR, 95% CI: 2.063, 1.717-2.480; P for trend < 0.001) all-cause mortality in patients with CAD. Subgroup analysis did not find obvious interaction in most subgroups. Conclusion. NPAR was independently correlated with 30-day, 60-day, and 365-day all-cause mortality in critically ill patients with CAD.

BackgroundIn previous studies, the TyG index (triglyceride-glucose index) has been proven to be closely associated with the prognosis of cardiovascular disease. However, the impact of TyG index on the prognosis of patients with ischemic HF (heart failure) undergoing PCI (percutaneous coronary intervention) is still unclear.MethodIn this study, 2055 patients with ischemic HF were retrospectively enrolled and classified into four groups based on quartiles of the TyG index. The primary endpoint was MACE (major adverse cardiovascular events) consisting of all-cause mortality, non-fatal MI (myocardial infarction), and any revascularization. The incidence of the endpoints among the four groups was assessed through Kaplan-Meier survival analysis. The independent correlation between TyG index and endpoints was analyzed with multivariate Cox regression models. Besides, the RCS (restricted cubic spline) analysis was performed to examine the nonlinear relationship between TyG index and MACE.ResultThe incidence of MACE was significantly higher in participants with a higher TyG index. The positive association between the TyG index and MACE was also confirmed in the Kaplan–Meier survival analyses. Multivariate cox proportional hazards analysis indicated that the TyG index was independently associated with the increased risk of MACE, regardless of whether TyG was a continuous [TyG, per 1−unit increase, HR (hazard ratio) 1.41, 95% CI (confidence interval) 1.22-1.62, P &lt; 0.001] or categorical variable [quartile of TyG, the HR (95% CI) values for quartile 4 was 1.92 (1.48-2.49), with quartile 1 as a reference]. In addition, the nonlinear association of TyG index with MACE was shown through RCS model and the risk of MACE increased as the TyG index increased in general (Nonlinear p=0.0215). Besides, no obvious interaction was found in the association of TyG with MACE between the DM (diabetes mellitus) group and the no-DM group.ConclusionAmong patients with ischemic HF undergoing PCI, the TyG index was correlated with MACE independently and positively.

Background: It has been demonstrated in previous studies that red blood cell distribution width (RDW) is correlated with the severity and prognosis of cardiovascular disease. The target of our study was to assess the relationship between RDW and the prognosis of ischemic cardiomyopathy (ICM) patients undergoing percutaneous coronary intervention (PCI). Methods: The study retrospectively enrolled 1986 ICM patients undergoing PCI. The patients were divided into three groups by RDW tertiles. The primary endpoint was major adverse cardiovascular events (MACE) and the secondary endpoints were each of the components of MACE (all-cause mortality, nonfatal myocardial infarction (MI) and any revascularization). Kaplan–Meier survival analyses were conducted to show the association between RDW and the incidence of adverse outcomes. The independent effect of RDW on adverse outcomes was determined by multivariate Cox proportional hazard regression analysis. In addition, the nonlinear relationship between RDW values and MACE was explored using restricted cubic spline (RCS) analysis. The relationship between RDW and MACE in different subgroups was determined using subgroup analysis. Results: As RDW tertiles increased, the incidences of MACE (Tertile 3 vs. Tertile 1: 42.6 vs. 23.7, p < 0.001), all-cause death (Tertile 3 vs. Tertile 1: 19.3 vs. 11.4, p < 0.001) and any revascularization (Tertile 3 vs. Tertile 1: 20.1 vs. 14.1, p < 0.001) increased significantly. The K–M curves showed that higher RDW tertiles were related to increased incidences of MACE (log-rank, p < 0.001), all-cause death (log-rank, p < 0.001) and any revascularization (log-rank, p < 0.001). After adjusting for confounding variables, RDW was proved to be independently associated with increased risks of MACE (Tertile 3 vs. Tertile 1: HR, 95% CI: 1.75, 1.43–2.15; p for trend < 0.001), all-cause mortality (Tertile 3 vs. Tertile 1: HR, 95% CI: 1.58, 1.17–2.13; p for trend < 0.001) and any revascularization (Tertile 3 vs. Tertile 1: HR, 95% CI: 2.10, 1.54–2.88; p for trend < 0.001). In addition, the RCS analysis suggested nonlinear association between RDW values and MACE. The subgroup analysis revealed that elderly patients or patients with angiotensin receptor blockers (ARBs) had a higher risk of MACE with higher RDW. Patients with hypercholesterolemia or without anemia also had a higher risk of MACE. Conclusions: RDW was significantly related to the increased risk of MACE among ICM patients undergoing PCI.

Background: Inflammatory cells and remnant cholesterol (RC) play an important role in the development and progression of cardiovascular diseases. In order to understand their contribution to cardiovascular diseases, we proposed the RC to lymphocyte ratio (RCLR) that reflects the level of serum lipid and inflammation as a predictive indicator. In this study, we explored the correlation between RCLR and major adverse cardiovascular events (MACEs) in patients with unstable angina (UA) treated with percutaneous coronary intervention (PCI). Methods: RCLR was calculated by dividing RC by lymphocyte percentage. Patients were divided into four groups according to RCLR quartiles. The endpoint of the study was MACE, a composite endpoint including all-cause mortality, non-fatal myocardial infarction (MI), and ischemia-driven revascularization. The multivariable Cox proportional hazard model was used to determine the exclusive effect of RCLR on MACE. Results: The study was conducted on 1092 patients with UA. The rate of MACE increased as RCLR quartiles increased (quartile 4 vs quartile 1: 40.9% vs 9.2%, p < 0.001). An adjustment for confounding variables revealed that an increase in the rate of MACE was directly proportional to RCLR (quartile 4 vs quartile 1: HR -5.85 [95% CI, 3.77-9.08], p < 0.001, p for trend < 0.001). Conclusions: RCLR independently correlated with the incidence of MACE in patients with UA treated with PCI.

commonly used as an abbreviation for pulmonary embolism. Change this abbreviation throughout the text – I made a suggestion below. Limit 250 words Background: Pericardial effusion (PEf) can occur with acute heart failure (AHF). Objective: To evaluate the effect of PEf size on the prognosis of patients with AHF. Method: According to the maximum size of PEf, all patients were divided into five groups. The primary outcome was in-hospital mortality. The independent effect of PEf size was determined by binary logistic regression analysis. The curve in line with overall trend was drawn by local weighted regression (Lowess). Result: We included 192 patients with AHF complicated by PEf. As PEf size increased, in-hospital mortality increased significantly (Group 5 vs Group 1: 34.8 vs 8.9% p=0.042). After adjusting for confounders, there was no significant association between PEf groups and in-hospital mortality (Group 5 vs Group 1: odd ratio (OR), 95% confidence interval (CI) define all abbreviations: 2.72, 0.41-18.22, p=0.298). However, when PEf size was analysed as continuous variable, an independent association between increased risk of in-hospital mortality and PEf size was observed (OR, 95% CI: 1.08, 1.00-1.16, p=0.037). The Lowess curve showed a positive relationship between PEf size and in-hospital mortality. Furthermore, as PEf groups increased, the length of hospital stay (Group 5 vs Group 1 median and interquartile range: 16, 14-21 vs 13, 8-17 days, p<0.001) was significantly prolonged. An association between PEf size with acute kidney injury (AKI) was not observed. Conclusion: The PEf size was independently associated with the increased risk of in-hospital mortality in patients with AHF.

Background: Neutrophil percentage to albumin ratio (NPAR) has been shown to be correlated with the prognosis of various diseases. This study aimed to explore the effect of NPAR on the prognosis of patients in coronary care units (CCU). Method: All data in this study were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III, version1.4) database. All patients were divided into four groups according to their NPAR quartiles. The primary outcome was in-hospital mortality. Secondary outcomes were 30-day mortality, 365-day mortality, length of CCU stay, length of hospital stay, acute kidney injury (AKI), and continuous renal replacement therapy (CRRT). A multivariate binary logistic regression analysis was performed to confirm the independent effects of NPAR. Cox regression analysis was performed to analyze the association between NPAR and 365-day mortality. The curve in line with overall trend was drawn by local weighted regression (Lowess). Subgroup analysis was used to determine the effect of NPAR on in-hospital mortality in different subgroups. Receiver operating characteristic (ROC) curves were used to evaluate the ability of NPAR to predict in-hospital mortality. Kaplan-Meier curves were constructed to compare the cumulative survival rates among different groups. Result: A total of 2364 patients in CCU were enrolled in this study. The in-hospital mortality rate increased significantly as the NPAR quartiles increased (p < 0.001). In multivariate logistic regression analysis, NPAR was independently associated with in-hospital mortality (quartile 4 versus quartile 1: odds ratio [OR], 95% confidence interval [CI]: 1.83, 1.20-2.79, p = 0.005, p for trend <0.001). In Cox regression analysis, NPAR was independently associated with 365-day mortality (quartile 4 versus quartile 1: OR, 95% CI: 1.62, 1.16-2.28, p = 0.005, p for trend <0.001). The Lowess curves showed a positive relationship between NPAR and in-hospital mortality. The moderate ability of NPAR to predict in-hospital mortality was demonstrated through ROC curves. The area under the curves (AUC) of NPAR was 0.653 (p < 0.001), which is better than that of the platelet to lymphocyte ratio (PLR) (p < 0.001) and neutrophil count (p < 0.001) but lower than the Sequential Organ Failure Assessment (p = 0.046) and Simplified Acute Physiology Score II (p < 0.001). Subgroup analysis did not reveal any obvious interactions in most subgroups. However, Kaplan-Meier curves showed that as NPAR quartiles increased, the 30-day (log-rank, p < 0.001) and 365-day (log-rank, p < 0.001) cumulative survival rates decreased significantly. NPAR was also independently associated with AKI (quartile 4 versus quartile 1: OR, 95% CI: 1.57, 1.19-2.07, p = 0.002, p for trend = 0.001). The CCU and hospital stay length was significantly prolonged in the higher NPAR quartiles. Conclusions: NPAR is an independent risk factor for in-hospital mortality in patients in CCU and has a moderate ability to predict in-hospital mortality.

BackgroundIdentifying risk factors associated with cardiac intensive care unit (CICU) patients’ prognosis can help clinicians intervene earlier and thus improve their prognosis. The correlation between the geriatric nutrition risk index (GNRI), which reflects nutritional status, and in-hospital mortality among CICU patients has yet to be established.MethodThe present study retrospectively enrolled 4,698 CICU patients. Based on the nutritional status, the participants were categorized into four groups. The primary endpoint was in-hospital mortality. The length of hospital stay and length of CICU stay were the secondary endpoints. To explore the correlation between nutritional status and in-hospital mortality, a logistic regression analysis was conducted. The nonlinear associations of GNRI with in-hospital mortality were evaluated using restricted cubic spline (RCS). Furthermore, subgroup analyses were conducted to evaluate the effect of the GNRI on in-hospital mortality across different subgroups, with calculation of the p for interaction.ResultA higher risk of malnutrition was significantly linked to an increased incidence of in-hospital mortality (High risk vs. No risk: 26.2% vs. 4.6%, p &lt; 0.001), as well as a longer length of hospital stay (High risk vs. No risk: 15.7, 9.1–25.1 vs. 8.9, 6.9–12.9, p &lt; 0.001) and CICU stay (High risk vs. No risk: 6.4, 3.8–11.9 vs. 3.2, 2.3–5.1, p &lt; 0.001). An elevated GNRI was significantly associated with an increased risk of in-hospital mortality even after controlling for pertinent confounding factors (High risk vs. No risk: OR, 95% CI: 2.37, 1.67–3.37, p &lt; 0.001, p for trend &lt;0.001). Additionally, the RCS model showed a linear relationship between GNRI and in-hospital mortality, with the risk of in-hospital mortality significantly decreasing as GNRI increased (non-linear p = 0.596). Furthermore, in the subgroups of hypertension, ventricular arrhythmias, cardiac arrest, shock, and chronic kidney disease, there was a significant interaction between nutritional status and in-hospital mortality.ConclusionAmong CICU patients, a low GNRI was a significant predictor of in-hospital mortality. Furthermore, patients with a higher risk of malnutrition, as indicated by low GNRI values, experienced significantly longer hospital and CICU stays.