Effect of comorbidities on ischemic stroke mortality: An analysis of the National Inpatient Sample (NIS) Database

Abstract

Background: Stroke risk has been attributed to many pathological and behavioral conditions. Various modifiable and non-modifiable risk factors have been recognized and found consistent throughout epidemiological studies. Herein, we investigate the effect of comorbidities seen with patient’s suffering from ischemic stroke and its effect on in-hospital mortality. Methods: We identified patients >18 year old in the National Inpatient Sample database with diseases of interest utilizing the tenth International Classification of Disease 10 diagnostic codes from the years 2016 to 2018. Interval data were analyzed using one-way ANOVA. Post hoc analysis was performed using Bonferroni correction methods. To determine independent predictors of in-hospital mortality, odds ratios were calculated using binary logistic regression for each comorbidity. Descriptive and numerical statistics, imputation, and logistic regression were calculated using SPSS software version 25. Results: Patients hospitalized with ischemic stroke were found to have the following comorbidities: atrial fibrillation (7.5%), carotid artery stenosis (1.1%), diabetes mellitus type 2 (11.4%), congestive heart failure (CHF) (7.5%), essential hypertension (21.2%), and ischemic heart disease (IHD) (2.3%). In-hospital mortality rates were higher in patients hospitalized with ischemic stroke and concomitant IHD (28.2%, P < 0.001). Hospital length of stay was longest in patients with concomitant CHF (5.96 days, P < 0.001). Similarly, patients with CHF accrued the greatest in-hospital costs (69,174 USD, P < 0.001). Conclusion: Patients hospitalized from ischemic stroke suffered from the coexistence of other comorbidities. Of the comorbidities studied, IHD was identified as having the most significant impact on in-hospital mortality.

Stroke–heart syndrome: current progress and future outlook

“…The hospitalization-related mortality rate of patients with AIS complicated by ischemic heart disease is approaching 30% [11]. Complex cardiovascular disease accounts for approximately 20% of post-stroke deaths, making it the second most frequent cause of post-stroke death after neurological death [7,12].…”

External Validation and Recalibration of a Mortality Prediction Model for Patients with Ischaemic Stroke

“…Other researchers have focused on predicting the severity and risk of mortality during admission for ischaemic stroke, in line with our own study goals. In one notable case [30], the US National Inpatient Sample (NIS) database was analysed using the main clinicaladministrative variables recorded in ICD-10 and for the same time period (2016)(2017)(2018). This paper concluded that in-hospital mortality due to ischaemic stroke was significantly associated with atrial fibrillation, carotid stenosis, diabetes, heart failure, and ischaemic heart disease.…”

Machine learning-based prognostication of mortality in stroke patients

“…Hospital-acquired infections, such as urinary tract infections and pneumonia, have been noted as significant factors in forecasting stroke prognosis, and mortality [ 12 , 27 ]. In addition to clinical findings and stroke severity upon presentation, patients' comorbid conditions, such as diabetes mellitus (DM), hypertension (HTN), dyslipidemia, prior stroke, coronary artery disease (CAD), history of atrial fibrillation (AF), or congestive heart failure (CHF), have demonstrated substantial predictive roles in shaping stroke patients' prognosis, including disabilities and mortality [ 21 , 22 , 25 , 28 ]. Further factors, such as decrease in the duration from stroke onset to hospital arrival, prompt intervention, especially thrombolysis for IS, increases the chances of a favorable prognosis [ [29] , [30] , [31] ].…”