Predictors of In-Hospital Mortality Among Stroke Patients in Mashhad, Iran : A Novel Circular Approach to Incorporate Spatial Component
Akbari Sharak, Nooshin ; Najibi, Morteza LU
and Shakeri, Mohammad Taghi
(2026)
In Journal of Research in Health Sciences
26(2).
- Abstract
Background: Stroke remains a global health challenge, with its burden disproportionately affecting developing nations, including Iran. Rapid access to medical care is crucial for improving outcomes. However, spatial and temporal factors often leads to delays, adversely impacting survival. This study investigated predictors of in-hospital mortality among stroke patients in Mashhad, Iran, with a novel focus on spatial directionality using circular statistical methods. Study Design: A retrospective cohort study. Methods: The data of 1,171 stroke patients transported to Ghaem Hospital (2018–2019) were analyzed in this study. Pre-hospital delays, demographics, and clinical factors were assessed alongside spatial directionality, represented... (More)
Background: Stroke remains a global health challenge, with its burden disproportionately affecting developing nations, including Iran. Rapid access to medical care is crucial for improving outcomes. However, spatial and temporal factors often leads to delays, adversely impacting survival. This study investigated predictors of in-hospital mortality among stroke patients in Mashhad, Iran, with a novel focus on spatial directionality using circular statistical methods. Study Design: A retrospective cohort study. Methods: The data of 1,171 stroke patients transported to Ghaem Hospital (2018–2019) were analyzed in this study. Pre-hospital delays, demographics, and clinical factors were assessed alongside spatial directionality, represented by the bearing angle between patients’ residences and the hospital. Circular logistic regression was used to model in-hospital mortality, incorporating both linear and circular predictors. Results: The in-hospital mortality rate was 14.3%. Independent predictors included age (OR: 1.03, 95% CI: 1.01–1.04), length of stay (OR: 1.02, 95% CI: 1.01–1.04), triage level (OR: 2.31, 95% CI: 1.20–4.45), ambulance accessibility (OR: 0.97, 95% CI: 0.96–0.99), and the sine of the bearing angle (OR: 1.37, 95% CI: 1.02–1.83). Mortality was higher along the north-south axis, potentially reflecting disparities in healthcare access and population characteristics. Gender and final diagnosis were not significant predictors. Conclusion: Overall, age, length of stay, triage level, ambulance accessibility, and spatial directionality were significant predictors of in-hospital stroke mortality. The circular statistical approach provided added value by detecting directional disparities not captured through conventional methods, underscoring the need for spatially informed interventions to reduce inequities in stroke outcomes.
(Less)
- author
- Akbari Sharak, Nooshin
; Najibi, Morteza
LU
and Shakeri, Mohammad Taghi
- organization
- publishing date
- 2026-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bearing angle, Circular data, Emergency medical services, In-hospital mortality, Stroke
- in
- Journal of Research in Health Sciences
- volume
- 26
- issue
- 2
- article number
- e00678
- publisher
- Hamadan University of Medical Sciences
- external identifiers
-
- scopus:105041597515
- ISSN
- 2228-7795
- DOI
- 10.34172/jrhs.11685
- language
- English
- LU publication?
- yes
- id
- cae1236c-ec08-46e7-84e8-23d214001b8a
- date added to LUP
- 2026-06-29 14:16:22
- date last changed
- 2026-06-30 03:16:12
@article{cae1236c-ec08-46e7-84e8-23d214001b8a,
abstract = {{<p>Background: Stroke remains a global health challenge, with its burden disproportionately affecting developing nations, including Iran. Rapid access to medical care is crucial for improving outcomes. However, spatial and temporal factors often leads to delays, adversely impacting survival. This study investigated predictors of in-hospital mortality among stroke patients in Mashhad, Iran, with a novel focus on spatial directionality using circular statistical methods. Study Design: A retrospective cohort study. Methods: The data of 1,171 stroke patients transported to Ghaem Hospital (2018–2019) were analyzed in this study. Pre-hospital delays, demographics, and clinical factors were assessed alongside spatial directionality, represented by the bearing angle between patients’ residences and the hospital. Circular logistic regression was used to model in-hospital mortality, incorporating both linear and circular predictors. Results: The in-hospital mortality rate was 14.3%. Independent predictors included age (OR: 1.03, 95% CI: 1.01–1.04), length of stay (OR: 1.02, 95% CI: 1.01–1.04), triage level (OR: 2.31, 95% CI: 1.20–4.45), ambulance accessibility (OR: 0.97, 95% CI: 0.96–0.99), and the sine of the bearing angle (OR: 1.37, 95% CI: 1.02–1.83). Mortality was higher along the north-south axis, potentially reflecting disparities in healthcare access and population characteristics. Gender and final diagnosis were not significant predictors. Conclusion: Overall, age, length of stay, triage level, ambulance accessibility, and spatial directionality were significant predictors of in-hospital stroke mortality. The circular statistical approach provided added value by detecting directional disparities not captured through conventional methods, underscoring the need for spatially informed interventions to reduce inequities in stroke outcomes.</p>}},
author = {{Akbari Sharak, Nooshin and Najibi, Morteza and Shakeri, Mohammad Taghi}},
issn = {{2228-7795}},
keywords = {{Bearing angle; Circular data; Emergency medical services; In-hospital mortality; Stroke}},
language = {{eng}},
number = {{2}},
publisher = {{Hamadan University of Medical Sciences}},
series = {{Journal of Research in Health Sciences}},
title = {{Predictors of In-Hospital Mortality Among Stroke Patients in Mashhad, Iran : A Novel Circular Approach to Incorporate Spatial Component}},
url = {{http://dx.doi.org/10.34172/jrhs.11685}},
doi = {{10.34172/jrhs.11685}},
volume = {{26}},
year = {{2026}},
}