In this historical cohort study, all patients with suspected stroke that transferred to the Neurology ward at Ghaem Hospital in Mashhad by EMS were examined between March 2018 and March 2019. Mashhad is the second-most populous city in Iran with more than 3.00 million inhabitants, based on the 2016 national census of Iran (23). The neurology center of Ghaem educational Hospital of Mashhad University of Medical Sciences (MUMS) is a tertiary neurological referral center in the east of Iran, and a. All neurology emergency care is supplied at this hospital. The data in this study includes two parts: Baseline EMS information (prehospital) and Follow-up information (screening and hospital ward information), which were received from the EMS system database and HIS unit of Ghaem Hospital in Mashhad, respectively. Integrating two datasets was performed by using Emergency mission ID.
In-hospital mortality of suspected stroke patients was defined as the outcome variable. Independent variables were delay time (Sec), the response time (minute), transport time (minute), distance to the hospital (km), screening time (hour), length of hospital stay, and hypertension, as well as age, and gender were considered as confounders. Also, the accessibility rate of the ambulance (the number of ambulances per 1 million inhabitants) for each patient was obtained (24).
Descriptive statistics are used to summarize the basic features of the data. The continuous and qualitative variables are summarized as mean ± SD or median (IQR) and frequency (%). Comparisons mean of quantitative variables performed by independent T-Test, and the association between qualitative variables were assessed using Chi-square test. Local Moran's I Statistic was applied to measure the local spatial autocorrelation and clustering tendencies across neighborhoods. This statistic provides information related to the location of spatial clusters and outliers and the types of spatial correlation (25). We used the autologistic regression model to estimate odds ratios (OR) and 95% confidence intervals (CIs) of predictors of in-hospital mortality.
The autologistic regression model has been used widely for modeling spatially correlated binary data. Compared to the ordinary logistic model, the autologistic model introduces a spatial autocorrelation term, autocovariate, as shown in the following equations. Autocovariates are weighting coefficients calculated by Euclidean distance in the form of the dependent variable's total weight.
Where, i=1,.., 1222, is the index of the patient; Yi is outcome variable corresponding to the ith patient, 1 if dead, 0 if alive; pi is the probability of Yi being a die; X1i, . . ., Xmi are the covariates corresponding to the ith patient; β0, . . ., βm are the regression coefficients of covariates; γ is the regression coefficient of Autocovariatei that is the autocovariate of the ith patient to represent spatial correlations effects; ni is the number of neighbors of patient i; wij is the spatial weight between patient i and patient j, equal to the inverse of Euclidean distance (dij) between them.
Variable Selection and Model Development
Variable selection is made using backward logistic regression models and recorded the Akaike Information Criterion (AIC) of each fitted model (26). First, a full model was built containing all variables; to remove or select a variable, this model's AIC value with and without the variable is compared; if their difference is not greater than two, that variable is retained in the model.
Secondly, Moran's I statistic was used to check the spatial autocorrelation of residuals of the final ordinary logistic model. Moran's I is an index whose value is in the range from approximately -1 to 1. Positive signage indicates positive spatial autocorrelation, while negative signage indicates negative spatial autocorrelation—a value of zero representing no spatial autocorrelation (27).
Thirdly, to account for spatial autocorrelation, autocovariate was calculated using the autocov_dist function in the ''spdep'' R package (28). The autologistic model was built with autocovariate as an additional independent variable in the final ordinal logistic model. Finally, residuals of the autologistic model were assessed by Moran's I test to ensure that the inclusion of autocovariates led to residual's independence, and there was no spatial autocorrelation. All analyses were performed using SPSS version 16 and R statistical software version 4.0.0 at the significant level of 0.05.
The protocol was approved by the Ethics Committee of NIMAD (IR.NIMAD.REC.1397.078) and also MUMS (Code: IR.MUMS.REC.1399.459). Data collection was also accomplished by the official authorization from Mashhad University of Medical Sciences (MUMS), Iran (research project number: 981153). It was performed in accordance with the ethical standards of the Declaration of Helsinki. Given the retrospective design, the informed consent was waived by the Ethics Committee of NIMAD (National Institute for Medical Reseach Development).