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Published on: January 17, 2013
Factors associated with mortality in posterior reversible encephalopathy syndrome: a nationwide analysis
Ali Al-Salahat1, Richard Cheung2, Danielle B Dilsaver3
1Neurology Department, Creighton University, Nebraska, USA. alsalahatmd@gmail.com.
Background:
To identify demographic and clinical factors associated with in-hospital mortality among hospitalizations of adults with posterior reversible encephalopathy syndrome (PRES) using the National Inpatient Sample (NIS) in the United States (US).
Methods:
Hospitalizations for PRES in patients ≥ 18 years were abstracted from the NIS (2016-2022). To identify clinical factors associated with in-hospital mortality, univariable logistic regression models were estimated and significant predictors were retained in a multivariable logistic regression model. Collinearity was assessed via variance inflation factors. Association was quantified using adjusted odds ratios (aOR).
Findings:
There were an estimated 75,830 PRES hospitalizations, of which, 3,665 in-hospital deaths occurred (4.8%). Decedents were older, had longer stays, and higher costs. In the adjusted model, the strongest factors associated with increased mortality were respiratory failure (aOR 5.43; 95% CI 4.40-6.71), sepsis (aOR 2.45; 95% CI 2.02-2.96), and ischemic stroke (aOR 2.25; 95% CI 1.86-2.72). Additional independent risk factors included cerebral edema, coma, intracerebral hemorrhage, severe liver disease, subarachnoid hemorrhage, kidney disorders, status epilepticus, encephalitis/encephalomyelitis, malignancy, complications of transplanted organs, and COVID-19. Documented hypertensive crisis (OR 0.63; 95% CI: 0.52 to 0.75) and history of epilepsy/seizures (OR 0.67; 95% CI: 0.55 to 0.81) were associated with lower odds of death.
Conclusion:
Respiratory failure, sepsis, and cerebrovascular complications drive in-hospital mortality in PRES. Early airway protection, aggressive supportive care, and prompt neurovascular evaluation for high-risk patients may improve outcomes. Prospective studies with granular clinical and imaging data are needed to refine prognostic models.
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