Evidence map›Paper›PMID 37106443›Full record

ArticleArchives of public health = Archives belges de sante publique2023

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

Ali Hadianfar, Payam Sasannezhad, Eisa Nazar, Razieh Yousefi, Mohammadtaghi Shakeri, Zahra Jafari, Soheil Hashtarkhani

Abstract readLetter
In one paragraph

Article in Archives of public health = Archives belges de sante publique, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Ali HadianfarStudent Research Committee, Mashhad University of Medical Sciences (MUMS), Mashhad, Iran.
Payam SasannezhadDepartment of Neurology, Mashhad University of Medical Sciences (MUMS), Mashhad, Iran.
Eisa NazarInternational UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences (MUMS), Mashhad, Iran.
Razieh YousefiStudent Research Committee, Mashhad University of Medical Sciences (MUMS), Mashhad, Iran.
Mohammadtaghi ShakeriDepartment of Biostatistics, School of Public Health, Social Determinants of Health Research Center, Mashhad University of Medical Sciences (MUMS), Mashhad, Iran. Shakerimt@mums.ac.ir.
Zahra JafariClinical Research Development Unit, Ghaem Hospital, Mashhad University of Medical Sciences (MUMS), Mashhad, Iran.
Soheil HashtarkhaniCenter for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStroke 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.

methodsIn 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.

resultsIn 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).

conclusionOur 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.

Indexed as

Auto-logistic modelEmergency medical servicesIn-hospital mortalitySuspected stroke

Identifiers

PMID37106443
PMCPMC10134659

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