Cholesterol affects the relationship between albumin and major adverse cardiac events in patients with coronary artery disease: a secondary analysis

Development and validation of a prediction model for hypoproteinemia after traumatic spinal cord injury: A multicenter retrospective clinical study

A multicenter retrospective analysis of conventionally collected data. To identify the potential causes of hypoproteinemia after traumatic spinal cord injury (TSCI) and provide a diagnostic model for predicting an individual likelihood of developing hypoproteinemia. Hypoproteinemia is a complication of spinal cord injury (SCI), an independent risk factor for respiratory failure in elderly patients with SCI, and a predictor of outcomes in patients with cervical SCI. Few nomogram-based studies have used clinical indicators to predict the likelihood of hypoproteinemia following TSCI. This multicenter retrospective clinical analysis included patients with TSCI admitted to the First Affiliated Hospital of Guangxi Medical University, Wuzhou GongRen Hospital, and Dahua Yao Autonomous County People Hospital between 2016 and 2020. The data of patients from the First Affiliated Hospital of Guangxi Medical University were used as the training set, and those from the other 2 hospitals were used as the validation set. All patient histories, diagnostic procedures, and imaging findings were recorded. To predict whether patients with TSCI may develop hypoproteinemia, a least absolute shrinkage and selection operator regression analysis was conducted to create a nomogram. The model was validated by analyzing the consequences using decision curve analysis, calibration curves, the C-index, and receiver operating characteristic curves. After excluding patients with missing data, 534 patients were included in this study. Male/female sex, age ≥ 60 years, cervical SCI, pneumonia, pleural effusion, urinary tract infection (UTI), hyponatremia, fever, hypotension, and tracheostomy were identified as independent risk factors of hypoalbuminemia. A simple and easy-to-replicate clinical prediction nomogram was constructed using these factors. The area under the curve was 0.728 in the training set and 0.881 in the validation set. The predictive power of the nomogram was satisfactory. Hypoalbuminemia after TSCI may be predicted using the risk factors of male/female sex, age ≥ 60 years, cervical SCI, pneumonia, pleural effusion, UTI, hyponatremia, fever, hypotension, and tracheostomy.

Combined Serum Albumin and Left Ventricular Ejection Fraction Predict All-Cause Death in Patients with Stable Coronary Artery Disease

Objective

To explore the feasibility of serum albumin (Alb) and left ventricular ejection fraction (LVEF) in predicting all-cause death (ACD) in patients with stable coronary artery disease (SCAD).

Methods

Patients with SCAD were divided into 4 groups according to their Alb and LVEF levels: Group A: Alb ≤4 g/dL and LVEF > 50%; Group B: Alb ≤4 g/dL and LVEF ≤50%; Group C: Alb >4 g/dL and LVEF ≤50%; Group D: Alb >4 g/dL and LVEF >50%. The K-M curve and log-rank test were used to compare ACD among the four groups over three years. Receiver operating characteristic (ROC) curves were used to compare the efficacy of predicting ACD among the combination of Alb and LVEF and either Alb or LVEF alone. Cox regression analysis identified the influencing factors of ACD in patients with SCAD and detected the correlation between Alb and LVEF.

Results

ACD occurred in 18 (8.9%) of 203 patients with SCAD, with an average follow-up of 26.53 ± 14.34 months. In the Kaplan‒Meier analysis, the risk of ACD in the four groups ranged from high to low: Group B (17.6%) > Group A (26.7%) > Group D (0.9%) > Group C (0%, P < 0.001). The ROC curve showed that the combination of Alb and LVEF (AUC = 0.888) had better predictive value for ACD than either Alb (AUC = 0.879) or LVEF alone (AUC = 0.651), P < 0.001. Multivariate Cox regression analysis showed that Alb ≤4 g/dL predicted ACD events after adjusting for baseline (HR: 12.16, 95% CI: 1.57 to 94.41; P=0.017) and treatment (HR: 19.36, 95% CI: 2.53-147.78, P=0.004). Alb was positively correlated with LVEF (r = 0.22, P=0.002).

Conclusions

Alb combined with LVEF is more effective than a single index in predicting ACD in SCAD and could be used as a new model to judge the prognosis of SCAD.

The relationship of low-density lipoprotein cholesterol and all-cause or cardiovascular mortality in patients with type 2 diabetes: a retrospective study

Background

The optimal levels of low-density lipoprotein cholesterol (LDL-C) in patients with type 2 diabetes (T2D) are not currently clear. In this study, we determined the relationship between various mean LDL-C and all-cause or cardiovascular mortality risks in patients with T2D, stratifying by albumin level, age, sex, and antilipid medication use. We also evaluated the association of LDL-C standard deviation (LDL-C-SD) and all-cause and cardiovascular mortality by type of antilipid medication use.

Methods

A total of 46,675 T2D patients with a prescription for antidiabetic agents >6 months from outpatient visits (2003-2018) were linked to Taiwan's National Death Registry to identify all-cause and cardiovascular mortality. The Poisson assumption was used to estimate mortality rates, and the Cox proportional hazard regression model was used to assess the relative hazards of respective mortality in relation to mean LDL-C in patient cohorts by albumin level, age, sex, and antilipid use adjusting for medications, comorbidities, and laboratory results. We also determined the overall, and anti-lipid-specific mortality rates and relative hazards of all-cause and cardiovascular mortality associated with LDL-C-SD using the Poisson assumption and Cox proportional hazard regression model, respectively.

Results

All-cause and cardiovascular mortality rates were the lowest in T2D patients with a mean LDL-C > 90-103.59 mg/dL in the normal albumin group (≥ 3.5 g/dL). Compared to T2D patients with a mean LDL-C > 90-103.59 mg/dL, those with a mean LDL-C ≤ 77 mg/dL had an elevated risk of all-cause mortality in both the normal and lower albumin groups. T2D patients with a mean LDL-C ≤ 90 and > 103.59-119 mg/dL had relatively higher risk of cardiovascular mortality in the normal albumin group, but in the lower albumin group (<3.5 g/dL), any level of mean LDL-C ≤ 119 mg/dL was not significantly associated with cardiovascular mortality. Increased risks of all-cause and cardiovascular mortality were observed in patients with a mean LDL-C ≤ 77 mg/dL in both sexes and in all age groups except in those aged <50 years, a lower mean LDL-C was not associated with cardiovascular mortality. Similarly, patients with an LDL-C-SD <10^th and > 90^th percentiles were associated with significant risks of all-cause and cardiovascular mortality. In statin users, but not fibrate users, lower and higher levels of mean LDL-C and LDL-C-SD were both associated with elevated risks of all-cause and cardiovascular mortality.

Conclusions

The optimal level of LDL-C was found to be >90-103.59 mg/dL in T2D patients. Lower and higher levels of mean LDL-C and LDL-C-SD were associated with all-cause and cardiovascular mortality, revealing U-shaped associations. Further studies are necessary to validate the relationship between optimal LDL-C levels and all-cause and cardiovascular mortality in patients with diabetes.