Red Cell Distribution Width is Associated with Future Incidence of Abdominal Aortic Aneurysm in a Population-Based Cohort Study

SOX6 expression and aneurysms of the thoracic and abdominal aorta

Abdominal and thoracic aortic aneurysms (AAAs, TAAs) remain a major cause of deaths worldwide, in part due to the lack of reliable prognostic markers or early warning signs. Sox6 has been found to regulate renin controlling blood pressure. We hypothesized that Sox6 may serve as an important regulator of the mechanisms contributing to hypertension-induced aortic aneurysms. Phenotype and laboratory-wide association scans in a clinical cohort found that SOX6 gene expression is associated with aortic aneurysm in subjects of European ancestry. Sox6 and tumor necrosis factor alpha (TNF-α) expression were upregulated in aortic tissues from patients affected by either AAA or TAA. In Sox6 knockout mice with angiotensin-II-induced AAA, we found that Sox6 plays critical role in the development and progression of AAA. Our data support a regulatory role of SOX6 in the development of hypertension-induced AAA, suggesting that Sox6 may be a therapeutic target for the treatment of aortic aneurysms.

Clinical value identification of RDW on in-hospital death in unruptured abdominal aortic aneurysm

This study aimed to identify highly valuable blood indicators for predicting the clinical outcomes of patients with aortic aneurysms (AA). Baseline data of 1180 patients and 16 blood indicators were obtained from the public Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. The association of blood indicators with 4 types of clinical outcomes was analyzed, and the prediction performance of core indicators on different outcomes was next evaluated. Then, we explored the detailed association between core indicators and key outcomes among subgroups. Finally, a machine learning model was established to improve the prediction performance. Generalized linear regression analysis indicated that only red cell volume distribution width (RDW) was commonly associated with 4 end-points including surgery requirement, ICU stay requirement, length of hospital stay, and in-hospital death (all P < .05). Further, RDW showed the best performance for predicting in-hospital death by receiver operating characteristic (ROC) analysis. The significant association between RDW and in-hospital death was then determined by 3 logistic regression models adjusting for different variables (all P < .05). Stratification analysis showed that their association was mainly observed in unruptured AA and abdominal AA (AAA, all P < .05). We subsequently established an RDW-based model for predicting the in-hospital death only in patients with unruptured AAA. The favorable prediction performance of the RDW-based model was verified in training, validation, and test sets. RDW was found to make the greatest contribution to in-hospital death within the model. RDW had favorable clinical value for predicting the in-hospital death of patients, especially in unruptured AAA.

Is Red Cell Distribution Width a Reliable Marker for Cardiovascular Diseases? A Narrative Review

Red cell distribution width (RDW) is an easy-to-access marker which is routinely measured in complete blood count (CBC) test. Besides the classic use of RDW as a marker for discriminating different types of anemia, recent studies had indicated the relationship between high RDW and cardiovascular diseases. High RDW is not only useful in the diagnosis and prognostication of various cardiovascular conditions but also could be used as a valuable tool for predicting the incidence of cardiovascular diseases. population-based studies have indicated that higher RDW could effectively predict the incidence of heart failure (HF), atherosclerotic diseases, and atrial fibrillation (AF). It has been also demonstrated that higher RDW is associated with worse outcomes in these diseases. Recent studies have shown that high RDW is also associated with other cardiovascular conditions including cardiomyopathies, and pulmonary hypertension. The predictive role of RDW in endovascular interventions has also been demonstrated by many recent studies. Here in this review, we attempt to compile the most recent findings with older reports regarding the relation between high RDW and HF, cardiomyopathies, pulmonary hypertension, AF, atherosclerotic disorders, primary hypertension, and the outcomes of endovascular interventions. we also discussed the role of RDW in the prognostication of different cardiovascular conditions when combined with classic classification criteria.

Systematic literature review and critical analysis of RDW in patients with aortic pathologies

Diseases of the aorta, such as aortic aneurysm, dissection, and rupture, account for a large proportion of acute clinical emergencies. The red blood cell distribution width (RDW), which directly reflects anisocytosis (i.e., the heterogeneity of erythrocyte volumes), has emerged as a promising biomarker for many cardiovascular pathologies. Thus, we aimed to explore the implication of RDW in aortic pathologies. We searched Scopus and PubMed using the keywords "RDW" OR "red blood cell distribution width" AND "aortic aneurysm" OR "aortic dilatation" OR "aortic dissection" for identifying studies in which RDW values were measured in patients with these aortic diseases. Ten observational studies were finally included. In all studies, RDW value was increased in patients with aortic diseases. In the four studies in which sufficient RDW data were available for pooling, the weighted mean difference (WMD) of RDW in patients with or without complicated aortic pathologies was 0.575 (95 %CI, 0.254-0.896). RDW may be a valuable diagnostic and prognostic biomarker in patients with aortic pathologies.

Development and validation of a machine-learning prediction model to improve abdominal aortic aneurysm screening

OBJECTIVE

Despite recommendations by the United States Preventive Services Task Force and the Society for Vascular Surgery, adoption of screening for abdominal aortic aneurysms (AAAs) remains low. One challenge is the low prevalence of AAAs in the unscreened population, and therefore a low detection rate for AAA screenings. We sought to use machine learning to identify factors associated with the presence of AAAs and create a model to identify individuals at highest risk for AAAs, with the aim of increasing the detection rate of AAA screenings.

METHODS

A machine-learning model was trained using longitudinal medical records containing lab results, medications, and other data from our institutional database. A retrospective cohort study was performed identifying current or past smoking in patients aged 65 to 75 years and stratifying the patients by sex and smoking status as well as determining which patients had a confirmed diagnosis of AAA. The model was then adjusted to maximize fairness between sexes without significantly reducing precision and validated using six-fold cross validation.

RESULTS

Validation of the algorithm on the single-center institutional data utilized 18,660 selected patients over 2 years and identified 314 AAAs. There were 41 factors identified in the medical record included in the machine-learning algorithm, with several factors never having been previously identified to be associated with AAAs. With an estimated 100 screening ultrasounds completed monthly, detection of AAAs is increased with a lift of 200% using the algorithm as compared with screening based on guidelines. The increased detection of AAAs in the model-selected individuals is statistically significant across all cutoff points.

CONCLUSIONS

By utilizing a machine-learning model, we created a novel algorithm to detect patients who are at high risk for AAAs. By selecting individuals at greatest risk for targeted screening, this algorithm resulted in a 200% lift in the detection of AAAs when compared with standard screening guidelines. Using machine learning, we also identified several new factors associated with the presence of AAAs. This automated process has been integrated into our current workflows to improve screening rates and yield of high-risk individuals for AAAs.

Red cell distribution width and its polygenic score in relation to mortality and cardiometabolic outcomes

Introduction

Elevated red cell distribution width (RDW) has been associated with a range of health outcomes. This study aims to examine prognostic and etiological roles of RDW levels, both phenotypic and genetic predisposition, in predicting cardiovascular outcomes, diabetes, chronic kidney disease (CKD) and mortality.

Methods

We studied 27,141 middle-aged adults from the Malmö Diet and Cancer study (MDCS) with a mean follow up of 21 years. RDW was measured with a hematology analyzer on whole blood samples. Polygenic scores for RDW (PGS-RDW) were constructed for each participant using genetic data in MDCS and published summary statistics from genome-wide association study of RDW (n = 408,112). Cox proportional hazards regression was used to assess associations between RDW, PGS-RDW and cardiovascular outcomes, diabetes, CKD and mortality, respectively.

Results

PGS-RDW was significantly associated with RDW (Pearson's correlation coefficient = 0.133, p < 0.001). RDW was significantly associated with incidence of stroke (hazard ratio (HR) per 1 standard deviation = 1.06, 95% confidence interval (CI): 1.02-1.10, p = 0.003), atrial fibrillation (HR = 1.09, 95% CI: 1.06-1.12, p < 0.001), heart failure (HR = 1.13, 95% CI: 1.08-1.19, p < 0.001), venous thromboembolism (HR = 1.21, 95% CI: 1.15-1.28, p < 0.001), diabetes (HR = 0.87, 95% CI: 0.84-0.90, p < 0.001), CKD (HR = 1.08, 95% CI: 1.03-1.13, p = 0.004) and all-cause mortality (HR = 1.18, 95% CI: 1.16-1.20, p < 0.001). However, PGS-RDW was significantly associated with incidence of diabetes (HR = 0.96, 95% CI: 0.94-0.99, p = 0.01), but not with any other tested outcomes.

Discussion

RDW is associated with mortality and incidence of cardiovascular diseases, but a significant association between genetically determined RDW and incident cardiovascular diseases were not observed. However, both RDW and PGS-RDW were inversely associated with incidence of diabetes, suggesting a putative causal relationship. The relationship with incidence of diabetes needs to be further studied.

Usefulness of red cell distribution width to predict mortality in patients undergoing endovascular repair of abdominal aortic aneurysms

BACKGROUND

Red cell distribution width (RDW) reflecting impaired erythropoyesis, has been associated with poor prognosis and mortality in several conditions. The aim of this study was to determine the relationship between RDW and the 5-year survival after the endovascular repair of abdominal aortic aneurysms (EVAR) and its ability to improve the discriminative power of a survival predictive score.

METHODS

Retrospective analysis of 284 patients undergoing EVAR at a single centre. The pattern of relationship between RDW and survival was assessed with penalized smoothing splines. Categorized RDW values were added to a predictive score based in standard preoperative variables, whose improvement in discriminative power was calculated on the basis of changes in the C-statistics and the continuous Net Reclassification Index (c-NRI).

RESULTS

The survival rate at 5 years was 66.2% and was independently associated with hemoglobin (HR=0.85,p<0.004), statin intake (HR=0.54,p<0.004), heart failure (HR=2.53,p<0.018), atrial fibrillation (HR=2.53,p<0.000) and the non-revascularized coronary artery disease (HR=2.15, p<0.005). The relationship between RDW values and 5-year survival was linear. RDW-CV and RDW-SD were categorized to cut-off values of ≥15% (n=83,29.2%) and ≥50 fL (n=82, 28.9%) that were independently associated with poorer 5-year survival rates (HR=2.03,CI95%=1.29-3.19,p=0.002 and HR=1.89, CI95%=1.21-2.95,p=0.005, respectively). The addition of the RDW CV or the RDW-SD to the baseline predictive score significantly improved the c-NRI (0.437,p<0.001 and 0.442,p<0.001, respectively).

CONCLUSIONS

High preoperative RDW levels were linear and adversely related to 5-year survival after EVAR, improved the discriminative power of a predictive score based in standard preoperative variables and may help in decision-making at the time of surgical planning.