Association of Mean Platelet Volume with Risk Factors and Functional Outcome in Acute Ischemic Stroke

Evaluation of mean platelet volume and platelet count in ischemic stroke and its subtypes: focus on degree of disability and thrombus formation

Background: Platelets are crucial players in thrombus formation during ischemic stroke. Platelet (PLT) count and Mean platelet volume (MPV) are important parameters that affect platelet functions. The current study has been carried out with an aim to evaluate the association of MPV and PLT count with ischemic stroke in a population from the Malwa region of Punjab.

Material and Methods: The study included one hundred and fifty ischemic stroke patients. The extent of disability occurs by stroke was measured by mRS. MPV and PLT was evaluated using cell counter. Further, PLT count was confirmed in 50% of patients using flow cytometer. Clot formation rate was evaluated using Sonoclot Coagulation and Platelet Function Analyzer. All the statistical analysis was carried out using SPSS.

Results: A significant association of increased MPV (p < 0.02) was found with the ischemic stroke. However, PLT count did not show a significant association with the disease (p < 0.07). Further, a stepwise multiple logistic regression (MLR) analysis controlling the other confounding risk factors evaluated the association of hypertension and MPV with the disease. Patients with higher mRS were found to have high MPV values confirming that higher MPV is correlated with disability occurs by ischemic stroke. MPV was also found to be significantly associated with large artery atherosclerosis (p < 0.001). Clot formation analysis revealed that ischemic stroke patients bear higher clot rate (CR) and Platelet function (PF) values.

Conclusions: Elevated MPV is an independent risk factor for Ischemic stroke along with hypertension. In addition, higher MPV associated significantly with stroke disability as well.

Phenotypic overlap between rare disease patients and variant carriers in a large population cohort informs biological mechanisms

The biological mechanisms giving rise to the extreme symptoms exhibited by rare disease patients are complex, heterogenous, and difficult to discern. Understanding these mechanisms is critical for developing treatments that address the underlying causes of diseases rather than merely the presenting symptoms. Moreover, the same dysfunctional biological mechanisms implicated in rare recessive diseases may also lead to milder and potentially preventable symptoms in carriers in the general population. Seizures are a common, extreme phenotype that can result from diverse and often elusive biological pathways in patients with ultrarare or undiagnosed disorders. In this pilot study, we present an approach to understand the biological pathways leading to seizures in patients from the Undiagnosed Diseases Network (UDN) by analyzing aggregated genotype and phenotype data from the UK Biobank (UKB). Specifically, we look for enriched phenotypes across UKB participants who harbor rare variants in the same gene known or suspected to be causally implicated in a UDN patient's recessively manifesting disorder. Analyzing these milder but related associated phenotypes in UKB participants can provide insight into the disease-causing molecular mechanisms at play in the rare disease UDN patient. We present six vignettes of undiagnosed patients experiencing seizures as part of their recessive genetic condition, and we discuss the potential mechanisms underlying the spectrum of symptoms associated with UKB participants to the severe presentations exhibited by UDN patients. We find that in our set of rare disease patients, seizures may result from diverse, multi-step pathways that involve multiple body systems. Analyses of large-scale population cohorts such as the UKB can be a critical tool to further our understanding of rare diseases in general.

Is dynamic change in mean platelet volume related with composite endpoint development after transcatheter aortic valve replacement?

Aortic valve stenosis (AS) is the most common valvular disease, and surgical or transcatheter aortic valve replacement (TAVR) are the treatment options. Diminish in platelet production or dysfunction may occur due to shear stress, advanced age, and other coexisting diseases in AS patients. Bleeding is one of the complications of TAVR and associated with increased mortality. MPV (mean platelet volume) indicates platelet's thrombogenic activity. Overproduction or consumption of platelets in various cardiac conditions may affect MPV values. We aimed to investigate the pre and postprocedure MPV percentage change (MPV-PC) and its association with post-TAVR short-term complications. A total of 204 patients who underwent TAVR with a diagnosis of severe symptomatic AS were included. The mean age was 78.66 ± 6.45 years, and 49.5% of patients were women. Two groups generated according to composite end point (CEP) development: CEP(+) and CEP(-).110 patients(53.9%) formed CEP(+) group. Although baseline MPV and platelet levels were similar between groups, MPV was increased ( P  < 0.001) and platelet was decreased ( P  < 0.001) significantly following the procedure when compared to baseline. MPV-PC was significantly higher in the VARC type 2-4 bleeding ( P   =  0.036) and major vascular, access-related, or cardiac structural complication groups ( P   =  0.048) when CEP subgroups were analyzed individually. Regression analysis revealed that diabetes mellitus [ P   =  0.044, β: 1.806 odds ratio (95% confidence interval): 1.016-3.21] and MPV-PC [ P   =  0.007,β: 1.044 odds ratio (95% confidence interval): 1.012-1.077] as independent predictors of CEP development at 1 month after TAVR. The MPV increase following TAVR may be an indicator of adverse outcomes following TAVR procedure within 1-month.

Predicting new-onset post-stroke depression from real-world data using machine learning algorithm

Introduction

Post-stroke depression (PSD) is a serious mental disorder after ischemic stroke. Early detection is important for clinical practice. This research aims to develop machine learning models to predict new-onset PSD using real-world data.

Methods

We collected data for ischemic stroke patients from multiple medical institutions in Taiwan between 2001 and 2019. We developed models from 61,460 patients and used 15,366 independent patients to test the models' performance by evaluating their specificities and sensitivities. The predicted targets were whether PSD occurred at 30, 90, 180, and 365 days post-stroke. We ranked the important clinical features in these models.

Results

In the study's database sample, 1.3% of patients were diagnosed with PSD. The average specificity and sensitivity of these four models were 0.83-0.91 and 0.30-0.48, respectively. Ten features were listed as important features related to PSD at different time points, namely old age, high height, low weight post-stroke, higher diastolic blood pressure after stroke, no pre-stroke hypertension but post-stroke hypertension (new-onset hypertension), post-stroke sleep-wake disorders, post-stroke anxiety disorders, post-stroke hemiplegia, and lower blood urea nitrogen during stroke.

Discussion

Machine learning models can provide as potential predictive tools for PSD and important factors are identified to alert clinicians for early detection of depression in high-risk stroke patients.

A new marker of short-term mortality and poor outcome in patients with acute ischemic stroke: Mean platelet volume-to-lymphocyte ratio

BACKGROUND

The mean platelet volume-to-lymphocyte ratio (MPVLR), as a novel marker of thrombosis and inflammation, has been demonstrated to be closely linked to poor cardiovascular disease prognosis. However, the correlation between MPVLR and acute ischemic stroke (AIS) remains unclear. This study, therefore, aimed to clarify the relationship between MPVLR and the short-term prognosis of AIS.

METHODS

A total of 315 patients with first-time AIS diagnoses were recruited and divided into 3 groups based on the tri-sectional quantiles for MPVLR on admission: group 1 (N = 105) with a MPVLR ≤ 4.93, group 2 (N = 105) with a MPVLR of 4.94 to 7.21, and group 3 (N = 105) with a MPVLR ≥ 7.22. All patients were followed-up for 3 months, and death within 3 months was defined as the endpoint. Baseline characteristics, stroke severity, and functional outcomes were evaluated.

RESULTS

The Spearman's correlation coefficient test showed that MPVLR was significantly positively correlated with the National Institutes of Health Stroke Scale score (R = 0.517, P < .001). Multivariate analysis revealed that MPVLR was an independent predictor of both short-term mortality (adjusted odds ratio [OR] 1.435, P < .001) and poor outcome (adjusted OR 1.589, P < .001). The receiver operating characteristic (ROC) curve analysis showed that the best cutoff value of MPVLR for short-term mortality and poor outcome were 6.69 (sensitivity: 86.4%, specificity: 68.6%) and 6.38 (sensitivity: 78.8%, specificity: 72.3%), respectively.

CONCLUSIONS

MPVLR on admission was positively associated with stroke severity. An elevated MPVLR is an independent predictor of short-term mortality and poor outcome after AIS.