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.

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: Identifying 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.Method: The 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.Result: A 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 < 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 < 0.001) and CICU stay (High risk vs. No risk: 6.4, 3.8–11.9 vs. 3.2, 2.3–5.1, p < 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 < 0.001, p for trend <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.Conclusion: Among 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.

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: The atherogenic index of plasma (AIP) is calculated as the logarithm of the triglyceride (TG) to high-density lipoprotein cholesterol (HDL-C) ratio. While previous studies suggested that TG and HDL-C levels were linked to the prognosis in various cardiovascular conditions, including ischemic heart failure (IHF), there is limited research specifically examining AIP in the context of IHF. Therefore, our study sought to explore the association between AIP and the prognosis of IHF and to compare the predictive value of AIP, HDL-C, and TG levels for identifying patients with poor outcomes. Methods: This retrospective cohort study was conducted at a single institution involving 2036 IHF patients with post-percutaneous coronary intervention (PCI) who were followed for 36 months. Patients were divided into four groups categorized according to AIP quartiles. The primary outcome of interest was major adverse cardiovascular events (MACEs), while secondary outcomes included all-cause mortality, non-fatal myocardial infarction (MI), and any revascularization. Kaplan–Meier survival curves were used to evaluate the occurrence of endpoints across the four groups. Multivariate Cox regression analysis reinforced that AIP independently predicted primary and secondary outcomes. Restricted cubic spline (RCS) method was employed to examine the non-linear association between AIP and endpoints. Receiver operating characteristic (ROC) curves, combined with the Delong test, were used to assess and compare the predictive accuracy of AIP, TG, and HDL-C. Result: The incidence of MACEs (Q4:Q1 = 50.6:23.0, p < 0.001), all-cause death (Q4:Q1 = 25.0:11.6, p < 0.001), and any revascularization (Q4:Q1 = 21.6:9.6, p < 0.001) were significantly higher in patients with elevated AIP. The Kaplan– Meier curve analysis further supported a positive association between AIP and MACEs (plog-rank < 0.001). Multivariate Cox analysis showed that AIP was independently associated with the increased risk of MACEs (Q4:Q1 (HR (95% CI)): 2.84 (2.25–3.59), ptrend < 0.001), all-cause death (Q4:Q1 (HR (95% CI)): 2.76 (1.98–3.84), ptrend < 0.001), non-fatal MI (Q4:Q1 (HR (95% CI)): 3.01 (1.32–6.90), ptrend < 0.001), and any revascularization (Q4:Q1 (HR (95% CI)): 2.92 (2.04–4.19), ptrend < 0.001). In RCS, higher AIP was non-linearly relevant to an increased risk of MACEs (pnon-linear = 0.0112). In subgroup analysis, the predictive value of AIP for MACEs was more pronounced in the younger patient subgroup (pinteraction = 0.003). The ROC curves showed the predictive value of AIP (area under curve [AUC] = 0.641), HDL-C (AUC = 0.600), and TG (AUC = 0.629), and AIP had the best predictive value among TG (AIP:TG: difference in AUC (95% CI), 0.012 (0.001–0.024), p for Delong test = 0.028) and HDL-C (AIP:HDL-C: difference in AUC (95% CI), 0.041 (0.018–0.064), p for Delong test <0.001). Conclusion: In IHF patients after PCI, AIP was strongly relevant to an increased risk of MACEs and had the best predictive validity compared with TG and HDL-C.

Background: To compare the surgical effects of coronary intervention through the transradial intervention (TRI) versus distal transradial intervention (dTRI) approach. Methods: From September 2020 to April 2021, 302 patients undergoing coronary artery angiography (CAG) or percutaneous coronary intervention in our hospital were retrospectively included. Patients were divided into the TRI group and dTRI group with 151 cases in each group. The technique features, lesion features, and cannulation process were compared between the two groups. Results: The number of patients who underwent CAG in the dTRI group (35.1%) was significantly greater compared with the TRI group (12.6%) (P < 0.01). The rates of triple vessel lesions, calcification lesions and chronic total occlusion lesions were increased in the TRI group compared with the dTRI group (P < 0.05). The average radial artery diameter (RAD) in the TRI group (2.550 ± 0.417 mm) was greater than that in the dTRI group (2.070 ± 0.360 mm) (P < 0.05). The hemostasis time of the dTRI group (173.272 ± 41.807 min) was lower than that of the TRI group (273.417 ± 42.098 min) (P < 0.05). The radial artery occlusion (RAO) rates in the dTRI group (2.6%) were lower than those in the TRI group (8.6%) (P < 0.05). The dTRI group had a higher satisfaction score than the TRI group (P > 0.05). RAD at the puncture site was a predictor of the overall cannulation success rate with an AUC of 0.747 (95% CI 0.663–0.860; P < 0.05). Conclusions: Despite a steep learning curve, the dTRI approach had a shorter hemostasis time, reduced RAO rates, and notable preliminary safety results compared with the TRI approach. The dTRI approach can be used as a supplemental method to the TRI approach.