Stroke and Liver Cirrhosis: A Brief Review of Current Evidence

Zheng, Kexin; Guo, Xiaozhong; Wang, Xinhong; Qi, Xingshun

doi:10.5772/intechopen.90420

Cited by 1 publication

(1 citation statement)

References 76 publications

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“…Reportedly, coagulopathies, anemia, and thrombocytopenia associated with cancers increases the risk of major bleeding complications in AMI [58]. Our finding is also in line with several studies reporting that liver disease increases the risk for overall, ischemic/haemorrhagic stroke and that most people with liver cirrhosis have coagulopathy, dyslipidaemia, and heart disease, which are associated with developing stroke [59,60].…”

Section: Plos Onesupporting

confidence: 89%

Comorbidity clusters and in-hospital outcomes in patients admitted with acute myocardial infarction in the USA: A national population-based study

Zghebi,

Rutter,

Sun

et al. 2023

PLoS ONE

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Background The prevalence of multimorbidity in patients with acute myocardial infarction (AMI) is increasing. It is unclear whether comorbidities cluster into distinct phenogroups and whether are associated with clinical trajectories. Methods Survey-weighted analysis of the United States Nationwide Inpatient Sample (NIS) for patients admitted with a primary diagnosis of AMI in 2018. In-hospital outcomes included mortality, stroke, bleeding, and coronary revascularisation. Latent class analysis of 21 chronic conditions was used to identify comorbidity classes. Multivariable logistic and linear regressions were fitted for associations between comorbidity classes and outcomes. Results Among 416,655 AMI admissions included in the analysis, mean (±SD) age was 67 (±13) years, 38% were females, and 76% White ethnicity. Overall, hypertension, coronary heart disease (CHD), dyslipidaemia, and diabetes were common comorbidities, but each of the identified five classes (C) included ≥1 predominant comorbidities defining distinct phenogroups: cancer/coagulopathy/liver disease class (C1); least burdened (C2); CHD/dyslipidaemia (largest/referent group, (C3)); pulmonary/valvular/peripheral vascular disease (C4); diabetes/kidney disease/heart failure class (C5). Odds ratio (95% confidence interval [CI]) for mortality ranged between 2.11 (1.89–2.37) in C2 to 5.57 (4.99–6.21) in C1. For major bleeding, OR for C1 was 4.48 (3.78; 5.31); for acute stroke, ORs ranged between 0.75 (0.60; 0.94) in C2 to 2.76 (2.27; 3.35) in C1; for coronary revascularization, ORs ranged between 0.34 (0.32; 0.36) in C1 to 1.41 (1.30; 1.53) in C4. Conclusions We identified distinct comorbidity phenogroups that predicted in-hospital outcomes in patients admitted with AMI. Some conditions overlapped across classes, driven by the high comorbidity burden. Our findings demonstrate the predictive value and potential clinical utility of identifying patients with AMI with specific comorbidity clustering.

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Section: Plos Onesupporting

confidence: 89%