NDLI: Predictors of in-hospital mortality among patients with symptoms of stroke, Mashhad, Iran: an application of auto-logistic regression model

Content Provider	Springer Nature : BioMed Central
Author	Hadianfar, Ali Sasannezhad, Payam Nazar, Eisa Yousefi, Razieh Shakeri, Mohammadtaghi Jafari, Zahra Hashtarkhani, Soheil
Abstract	Background Stroke is the second leading cause of death in adults worldwide. There are remarkable geographical variations in the accessibility to emergency medical services (EMS). Moreover, transport delays have been documented to affect stroke outcomes. This study aimed to examine the spatial variations in in-hospital mortality among patients with symptoms of stroke transferred by EMS, and determine its related factors using the auto-logistic regression model. Methods In this historical cohort study, we included patients with symptoms of stroke transferred to Ghaem Hospital of Mashhad, as the referral center for stroke patients, from April 2018 to March 2019. The auto-logistic regression model was applied to examine the possible geographical variations of in-hospital mortality and its related factors. All analysis was performed using the Statistical Package for the Social Sciences (SPSS, v. 16) and R 4.0.0 software at the significance level of 0.05. Results In this study, a total of 1,170 patients with stroke symptoms were included. The overall mortality rate in the hospital was 14.2% and there was an uneven geographical distribution. The results of auto-logistic regression model showed that in-hospital stroke mortality was associated with age (OR = 1.03, 95% CI: 1.01–1.04), accessibility rate of ambulance vehicle (OR = 0.97, 95% CI: 0.94–0.99), final stroke diagnosis (OR = 1.60, 95% CI: 1.07–2.39), triage level (OR = 2.11, 95% CI: 1.31–3.54), and length of stay (LOS) in hospital (OR = 1.02, 95% CI: 1.01–1.04). Conclusion Our results showed considerable geographical variations in the odds of in-hospital stroke mortality in Mashhad neighborhoods. Also, the age- and sex-adjusted results highlighted the direct association between such variables as accessibility rate of an ambulance, screening time, and LOS in hospital with in-hospital stroke mortality. Thus, the prognosis of in-hospital stroke mortality could be improved by reducing delay time and increasing the EMS access rate.
Related Links	https://archpublichealth.biomedcentral.com/counter/pdf/10.1186/s13690-023-01084-5.pdf
Ending Page	8
Page Count	8
Starting Page	1
File Format	HTM / HTML
ISSN	20493258
DOI	10.1186/s13690-023-01084-5
Journal	Archives of Public Health
Issue Number	1
Volume Number	81
Language	English
Publisher	BioMed Central
Publisher Date	2023-04-27
Access Restriction	Open
Subject Keyword	Public Health Medicine Health Policy Health Services Research Health Informatics Suspected stroke In-hospital mortality Emergency medical services Auto-logistic model Medicine/Public Health
Content Type	Text
Subject	Public Health, Environmental and Occupational Health
Journal Impact Factor	3.2/2023
5-Year Journal Impact Factor	3.3/2023

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