Cryptogenic Stroke: Clinical Consideration of a Heterogeneous Ischemic Subtype

Arterial Hypertension and Its Consequences Are the Main Predictors of Embolic Stroke of Undetermined Source

Methods

We performed a hospital-based prospective cohort study with 1,317 enrolled participants. We compared patients and healthy volunteers according to the main demographic, anthropometric parameters, stroke risk factors, comorbidities, and data of clinical and instrumental examination. In order to balance the study and the control groups for age and sex, the propensity score matching was performed. In order to generate the overall predictive model, a multivariate analysis was performed using the binary logistic regression method.

Results

The following predictors of ESUS were identified in current study: arterial hypertension (AH); increased heart rate and pulmonary arterial systolic pressure (PASP); the presence of conduction disturbance; the enlargement of left, right atrium, and left ventricle end-systolic length; increased intima-media thickness (IMT) in right and left common carotid artery (CCA); lowered Montreal Cognitive Assessment (MoСA) cognitive scale score; the presence of subcortical microbleeds; central brain atrophy; the larger size of third ventricle; and the higher medial temporal lobe atrophy (MTA) score. The following risk factors were included in the final predictive model: the presence of AH (p < 0.0005; OR = 12.98 (95% CI: 4.53-37.21)) and PASP (p=0.018; OR = 1.13 (95% CI: 1.02-1.25)) and male sex (p=0.046; OR = 2.771 (95% CI: 1.017-7.555)). The Nagelkerke's pseudo-R-squared value was 0.404 and the significance of the Hosmer-Lemeshow test was 0.733, which indicate the goodness of the final logistic regression model.

Conclusions

We propose that AH and its consequences are the main predictors of ESUS. The results of this study emphasize the importance of AH control for primary and secondary prevention of ESUS.

Targeted delivery of nanomedicines for promoting vascular regeneration in ischemic diseases

Ischemic diseases, the leading cause of disability and death, are caused by the restriction or blockage of blood flow in specific tissues, including ischemic cardiac, ischemic cerebrovascular and ischemic peripheral vascular diseases. The regeneration of functional vasculature network in ischemic tissues is essential for treatment of ischemic diseases. Direct delivery of pro-angiogenesis factors, such as VEGF, has demonstrated the effectiveness in ischemic disease therapy but suffering from several obstacles, such as low delivery efficacy in disease sites and uncontrolled modulation. In this review, we summarize the molecular mechanisms of inducing vascular regeneration, providing the guidance for designing the desired nanomedicines. We also introduce the delivery of various nanomedicines to ischemic tissues by passive or active targeting manner. To achieve the efficient delivery of nanomedicines in various ischemic diseases, we highlight targeted delivery of nanomedicines and controllable modulation of disease microenvironment using nanomedicines.

Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score

Background and objectives: Little is known about outcome improvements and disparities in cardiac arrest and active cancer. We performed the first known AI and propensity score (PS)-augmented clinical, cost-effectiveness, and computational ethical analysis of cardio-oncology cardiac arrests including left heart catheterization (LHC)-related mortality reduction and related disparities. Materials and methods: A nationally representative cohort analysis was performed for mortality and cost by active cancer using the largest United States all-payer inpatient dataset, the National Inpatient Sample, from 2016 to 2018, using deep learning and machine learning augmented propensity score-adjusted (ML-PS) multivariable regression which informed cost-effectiveness and ethical analyses. The Cardiac Arrest Cardio-Oncology Score (CACOS) was then created for the above population and validated. The results informed the computational ethical analysis to determine ethical and related policy recommendations. Results: Of the 101,521,656 hospitalizations, 6,656,883 (6.56%) suffered cardiac arrest of whom 61,300 (0.92%) had active cancer. Patients with versus without active cancer were significantly less likely to receive an inpatient LHC (7.42% versus 20.79%, p < 0.001). In ML-PS regression in active cancer, post-arrest LHC significantly reduced mortality (OR 0.18, 95%CI 0.14−0.24, p < 0.001) which PS matching confirmed by up to 42.87% (95%CI 35.56−50.18, p < 0.001). The CACOS model included the predictors of no inpatient LHC, PEA initial rhythm, metastatic malignancy, and high-risk malignancy (leukemia, pancreas, liver, biliary, and lung). Cost-benefit analysis indicated 292 racial minorities and $2.16 billion could be saved annually by reducing racial disparities in LHC. Ethical analysis indicated the convergent consensus across diverse belief systems that such disparities should be eliminated to optimize just and equitable outcomes. Conclusions: This AI-guided empirical and ethical analysis provides a novel demonstration of LHC mortality reductions in cardio-oncology cardiac arrest and related disparities, along with an innovative predictive model that can be integrated within the digital ecosystem of modern healthcare systems to improve equitable clinical and public health outcomes.

Post-Cardiac Arrest PCI is Underutilized Among Cancer Patients: Machine Learning Augmented Nationally Representative Case-Control Study of 30 Million Hospitalizations

BACKGROUND

Cancer patients are less likely to undergo percutaneous coronary intervention (PCI) after cardiac arrest, although they demonstrate improved mortality benefit from the procedure. We produced the largest nationally representative analysis of mortality of cardiac arrest and PCI for patients with cancer versus non-cancer.

METHODS

Propensity score adjusted multivariable regression for mortality was performed in this case-control study of the United States' largest all-payer hospitalized dataset, the 2016 National Inpatient Sample. Regression models of mortality and PCI weighted by the complex survey design were fully adjusted for age, race, income, cancer metastases, NIS-calculated mortality risk by Diagnosis Related Group (DRG), acute coronary syndrome, and likelihood of undergoing PCI RESULTS: Of the 30,195,722 hospitalized adult patients, 15.43% had cancer, and 0.79% of the whole sample presented with cardiac arrest (of whom 20.57% underwent PCI). In fully adjusted regression analysis among patients with cardiac arrest, PCI significantly reduced mortality (OR 0.15, 95%CI 0.13-0.19; p<0.001) among patients with cancer greater than those without it (OR 0.21, 95%CI 0.20-0.23; p<0.001).

CONCLUSIONS

This nationally representative study suggests that post-cardiac arrest PCI is underutilized among patients with cancer despite its significant mortality reduction for such patients (independent of clinical acuity).

Fractional Flow Reserve Cardio-Oncology Effects on Inpatient Mortality, Length of Stay, and Cost Based on Malignancy Type: Machine Learning Supported Nationally Representative Case-Control Study of 30 Million Hospitalizations

Background and Objectives: There are no nationally representative studies of mortality and cost effectiveness for fractional flow reserve (FFR) guided percutaneous coronary interventions (PCI) in patients with cancer. Our study aims to show how this patient population may benefit from FFR-guided PCI. Materials and Methods: Propensity score matched analysis and backward propagation neural network machine learning supported multivariable regression was performed for inpatient mortality in this case-control study of the 2016 National Inpatient Sample (NIS). Regression results were adjusted for age, race, income, geographic region, metastases, mortality risk, and the likelihood of undergoing FFR versus non-FFR PCI. All analyses were adjusted for the complex survey design to produce nationally representative estimates. Results: Of the 30,195,722 hospitalized patients meeting criteria, 3.37% of the PCIs performed included FFR. In propensity score adjusted multivariable regression, FFR versus non-FFR PCI significantly reduced inpatient mortality (OR 0.47, 95%CI 0.35−0.63; p < 0.001) and length of stay (LOS) (in days; beta −0.23, 95%CI −0.37−−0.09; p = 0.001) while increasing cost (in USD; beta $5708.63, 95%CI, 3042.70−8374.57; p < 0.001), without significantly increasing complications overall. FFR versus non-FFR PCI did not specifically change cancer patients’ inpatient mortality, LOS, or cost. However, FFR versus non-FFR PCI significantly increased inpatient mortality for Hodgkin’s lymphoma (OR 52.48, 95%CI 7.16−384.53; p < 0.001) and rectal cancer (OR 24.38, 95%CI 2.24−265.73; p = 0.009). Conclusions: FFR-guided PCI may be safely utilized in patients with cancer as it does not significantly increase inpatient mortality, complications, and LOS. These findings support the need for an increased utilization of FFR-guided PCI and further studies to evaluate its long-term impact.

Percutaneous Coronary Intervention in Patients With Gynecological Cancer: Machine Learning-Augmented Propensity Score Mortality and Cost Analysis for 383,760 Patients

Background

Despite the growing number of patients with both coronary artery disease and gynecological cancer, there are no nationally representative studies of mortality and cost effectiveness for percutaneous coronary interventions (PCI) and this cancer type.

Methods

Backward propagation neural network machine learning supported and propensity score adjusted multivariable regression was conducted for the above outcomes in this case-control study of the 2016 National Inpatient Sample (NIS), the United States' largest all-payer hospitalized dataset. Regression models were fully adjusted for age, race, income, geographic region, cancer metastases, mortality risk, and the likelihood of undergoing PCI (and also with length of stay [LOS] for cost). Analyses were also adjusted for the complex survey design to produce nationally representative estimates. Centers for Disease Control and Prevention (CDC)-based cost effectiveness ratio (CER) analysis was performed.

Results

Of the 30,195,722 hospitalized patients meeting criteria, 1.27% had gynecological cancer of whom 0.02% underwent PCI including 0.04% with metastases. In propensity score adjusted regression among all patients, the interaction of PCI and gynecological cancer (vs. not having PCI) significantly reduced mortality (OR 0.53, 95%CI 0.36–0.77; p = 0.001) while increasing LOS (Beta 1.16 days, 95%CI 0.57–1.75; p &lt; 0.001) and total cost (Beta $31,035.46, 95%CI 26758.86–35312.06; p &lt; 0.001). Among gynecological cancer patients, mortality was significantly reduced by PCI (OR 0.58, 95%CI 0.39–0.85; p = 0.006) and being in East North Central, West North Central, South Atlantic, and Mountain regions (all p &lt; 0.03) compared to New England. PCI reduced mortality but not significantly for metastatic patients (OR 0.74, 95%CI 0.32–1.71; p = 0.481). Eighteen extra gynecological cancer patients' lives were saved with PCI for a net national cost of $3.18 billion and a CER of $176.50 million per averted death.

Conclusion

This large propensity score analysis suggests that PCI may cost inefficiently reduce mortality for gynecological cancer patients, amid income and geographic disparities in outcomes.

Racial and Socioeconomic Disparities in Out-Of-Hospital Cardiac Arrest Outcomes: Artificial Intelligence-Augmented Propensity Score and Geospatial Cohort Analysis of 3,952 Patients

Introduction

Social disparities in out-of-hospital cardiac arrest (OHCA) outcomes are preventable, costly, and unjust. We sought to perform the first large artificial intelligence- (AI-) guided statistical and geographic information system (GIS) analysis of a multiyear and multisite cohort for OHCA outcomes (incidence and poor neurological disposition).

Method

We conducted a retrospective cohort analysis of a prospectively collected multicenter dataset of adult patients who sequentially presented to Houston metro area hospitals from 01/01/07-01/01/16. Then AI-based machine learning (backward propagation neural network) augmented multivariable regression and GIS heat mapping were performed.

Results

Of 3,952 OHCA patients across 38 hospitals, African Americans were the most likely to suffer OHCA despite representing a significantly lower percentage of the population (42.6 versus 22.8%; p < 0.001). Compared to Caucasians, they were significantly more likely to have poor neurological disposition (OR 2.21, 95%CI 1.25–3.92; p=0.006) and be discharged to a facility instead of home (OR 1.39, 95%CI 1.05–1.85; p=0.023). Compared to the safety net hospital system primarily serving poorer African Americans, the university hospital serving primarily higher income commercially and Medicare insured patients had the lowest odds of death (OR 0.45, p < 0.001). Each additional $10,000 above median household income was associated with a decrease in the total number of cardiac arrests per zip code by 2.86 (95%CI -4.26- -1.46; p < 0.001); zip codes with a median income above $54,600 versus the federal poverty level had 14.62 fewer arrests (p < 0.001). GIS maps showed convergence of the greater density of poor neurologic outcome cases and greater density of poorer African American residences.

Conclusion

This large, longitudinal AI-guided analysis statistically and geographically identifies racial and socioeconomic disparities in OHCA outcomes in a way that may allow targeted medical and public health coordinated efforts to improve clinical, cost, and social equity outcomes.

Carotid web: an occult mechanism of embolic stroke

The carotid web is a proposed stroke mechanism that may underlie cryptogenic stroke, particularly in younger patients without vascular risk factors. The web appears as a shelf-like projection into the lumen of the proximal cervical internal carotid artery without evidence of calcification. It is pathologically defined as intimal fibromuscular dysplasia. Altered haemodynamics distal to the web cause flow stagnation and remote embolisation of fibrin-based clots. It is best demonstrated and diagnosed on CT angiography (CTA) of the neck because of its ability to resolve calcium and create multiplanar reconstructions. Although they can be readily visualised on CTA, carotid webs may be missed or misinterpreted because they do not typically cause haemodynamically significant stenosis and can mimic arterial dissection, non-calcified atherosclerotic plaque and intraluminal thrombus. Options for management include antiplatelet therapy, carotid endarterectomy and carotid artery stenting. Modern management strategies for cryptogenic stroke include long-term cardiac monitoring, further investigation for structural cardiac disease and a diagnostic workup for arterial hypercoagulability, however, these strategies are not likely to capture the possibility of a carotid web. Carotid webs should be suspected in a young patient presenting with recurrent unihemispheric strokes particularly when conventional vascular risk factors are not present.

Embolic stroke of undetermined source (ESUS) cohort of Brazilian patients in a university hospital

OBJECTIVE

Embolic stroke of undetermined source (ESUS) is an important group of cryptogenic strokes that are in evidence due recent ongoing trials. We reviewed medical records at discharge from the stroke unit of all patients who met ESUS criteria and attended our institution between February 2016 and July 2017. Among 550 stroke patients, 51 had ESUS. We found that hypertension (60%), diabetes mellitus (34%), and smoking (36%) were the most prevalent risk factors. The mean National Institutes of Health Stroke Scale (NIHSS) scores were 7 at admission and 4 at discharge, while median scores on the modified Rankin scale were 0 and 2 at admission and discharge, respectively. Our sample had similar ages, risk factors prevalence and NIHSS scores at admission and discharge when compared with European and North American cohorts. Although a small cohort, our study suggests that the ESUS population is similar in countries with different health financing.

Ischemic stroke: experimental models and reality

The vast majority of cerebral stroke cases are caused by transient or permanent occlusion of a cerebral blood vessel (“ischemic stroke”) eventually leading to brain infarction. The final infarct size and the neurological outcome depend on a multitude of factors such as the duration and severity of ischemia, the existence of collateral systems and an adequate systemic blood pressure, etiology and localization of the infarct, but also on age, sex, comorbidities with the respective multimedication and genetic background. Thus, ischemic stroke is a highly complex and heterogeneous disorder. It is immediately obvious that experimental models of stroke can cover only individual specific aspects of this multifaceted disease. A basic understanding of the principal molecular pathways induced by ischemia-like conditions comes already from in vitro studies. One of the most frequently used in vivo models in stroke research is the endovascular suture or filament model in rodents with occlusion of the middle cerebral artery (MCA), which causes reproducible infarcts in the MCA territory. It does not require craniectomy and allows reperfusion by withdrawal of the occluding filament. Although promptly restored blood flow is far from the pathophysiology of spontaneous human stroke, it more closely mimics the therapeutic situation of mechanical thrombectomy which is expected to be increasingly applied to stroke patients. Direct transient or permanent occlusion of cerebral arteries represents an alternative approach but requires craniectomy. Application of endothelin-1, a potent vasoconstrictor, allows induction of transient focal ischemia in nearly any brain region and is frequently used to model lacunar stroke. Circumscribed and highly reproducible cortical lesions are characteristic of photothrombotic stroke where infarcts are induced by photoactivation of a systemically given dye through the intact skull. The major shortcoming of this model is near complete lack of a penumbra. The two models mimicking human stroke most closely are various embolic stroke models and spontaneous stroke models. Closeness to reality has its price and goes along with higher variability of infarct size and location as well as unpredictable stroke onset in spontaneous models versus unpredictable reperfusion in embolic clot models.

Intracranial Thrombus Morphology and Composition Undergoes Time-Dependent Changes in Acute Ischemic Stroke: A CT Densitometry Study

The aim of our study was to assess whether cerebral artery clots undergo time-dependent morphological and compositional changes in acute ischemic stroke. We performed a retrospective chart review of patients admitted within 5 h from symptom onset to three European stroke centers and evaluated non-contrast-enhanced CT (NECT) for hyperdense artery signs (HAS) in 2565 scans. The occlusion site, density of HAS expressed in Hounsfield units (HU), area of HAS, and relative density (rHU) (HU clot/HU non-affected artery) were studied and related to time from symptom onset, clinical severity, stroke etiology, and laboratory parameters. A HAS was present in the middle cerebral artery (MCA) in 185 (7.2%) and further explored. The mean time from symptom onset to CT was 100 min (range 17–300). We found a time-dependent loss of density in the occluded M1 segment within the first 5 h (N = 118, 95% CI [−15, −2], p = 0.01). Further, the thrombus area in the M2 segment decreased with time (cubic trend N = 67, 95% CI [−63, −8], p = 0.02). Overall, and especially in the M2 segment, a lower clot area was associated with higher fibrinogen (−21.7%, 95% CI [−34.8, −5.8], p = 0.009). In conclusion, our results disclosed time-dependent changes of intracranial thrombi with regard to occlusion site, density and area.

Early Neurologic Deterioration after Stroke Depends on Vascular Territory and Stroke Etiology

BACKGROUND AND PURPOSE

Early neurologic deterioration (END) occurs in up to one-third of patients with ischemic stroke and is associated with poor outcomes. The purpose of the present study was to determine which stroke etiologies and vascular distributions pose a greater threat of END in stroke patients.

METHODS

Using a single-center registry of prospectively maintained clinical data, adult ischemic stroke patients admitted (July 2008 to June 2014) within 48 hours of symptom onset were evaluated according to stroke etiology and vascular distribution using diffusion-weighted MRI. Major stroke etiologies were divided into cardioembolic, large vessel, small vessel, other, unknown source, and multiple possible etiologies. END was defined as a worsening of 2 or more points on the National Institutes of Health Stroke Scale during a 24-hour period of hospitalization. Crude and backward stepwise regression models were generated to associate stroke etiology and vascular distribution with END.

RESULTS

Of the included 961 patients (median age 65 years, 47% female, 72% non-White), 323 (34%) experienced END. Strokes involving the internal carotid artery (ICA) were associated with a threefold higher odds of END in stepwise regression models (OR 3.0, 95% CI 1.4-6.6, P=0.006). Among stroke etiologies, those with unclear mechanisms had the lowest odds of END in the fully adjusted model (OR 0.6, 95% CI 0.4-1.0, P=0.029).

CONCLUSIONS

In our single-center cohort of patients, ICA infarctions were independently associated with END whereas strokes of unknown etiology were least often associated with END. Larger cohorts are necessary to determine which steps, if any, can be taken to prevent END in these vulnerable populations.