Archives of Academic Emergency Medicine. 2021; 9(1): e52 OR I G I N A L RE S E A RC H Factors Affecting Pre-Hospital and In-Hospital Delays in Treatment of Ischemic Stroke; a Prospective Cohort Study Neda Ghadimi1, Nasrin Hanifi1, Mohammadreza Dinmohammadi1∗ 1. School of Nursing and Midwifery, Zanjan University of Medical Sciences, Zanjan, Iran. Received: May 2021; Accepted: June 2021; Published online: 24 July 2021 Abstract: Introduction: The outcomes of acute ischemic stroke (AIS) are highly affected by time-to-treatment. The present study aimed to determine the factors affecting in-hospital and pre-hospital delays in treatmentof AIS. Methods: This prospective study was carried out on 204 AIS patients referring to the stroke care unit in Zanjan (Iran) in 2019. The required data were collected by interviewing the patients and families and using patients’ records and observations. Results: The maximum delay was related to onset-to-arrival time (288.19 ± 339.02 minutes). The logistic regression analysis indicated a statistically significant decline in the treatment delay via consultation after the initiation of symptoms (p< 0.001), transferring the patient through emergency medical service to the hospital (p<0.001), and patients’ perception regarding AIS symptoms (P< 0.001). Conclusion: It is essential to inform people regarding AIS symptoms and referring to AIS treatment units to reduce the treatment time. Keywords: Ischemic stroke; Time-to-Treatment; Prospective studies; Iran Cite this article as: Ghadimi N, Hanifi N, Dinmohammadii M. Factors Affecting Pre-Hospital and In-Hospital Delays in Treatment of Ischemic Stroke; a Prospective Cohort Study. Arch Acad Emerg Med. 2021; 9(1): e52. https://doi.org/10.22037/aaem.v9i1.1267. 1. Introduction Stroke is one of the most prevalent neurological complica- tions (1). Acute ischemic stroke (AIS) is a medical emer- gency that requires intensive treatment and care in the early hours, because its fast diagnosis and proper interventions can lead to favorable results. Furthermore, delayed treat- ment can lead to considerable complications, higher mor- tality, and enormous costs for the person, families, and the healthcare system (2). The most effective approaches to treating AIS patients are re- canalization and reestablishing blood flow to the brain tis- sues using invasive and non-invasive therapies (3). In these processes, the blocked vessels are reopened using recom- binant tissue plasminogen activator (rTPA) and mechanical devices (angioplasty) (4, 5). In 1996, the Food and Drug Administration (FDA) recommended using rTPA in AIS pa- tients within the first 3 h of symptoms onset (6). American Stroke Association Standard (2018) recommends brain imag- ing within less than 20 min, the interval of less than 60 min ∗Corresponding Author: Nasrin Hanifi; Zanjan Nursing and Midwifery School, Zanjan University of Medical Sciences, Shahrak-e Karmandan street, Zanjan, Iran. P.O. Box:45154-13191, Email: nasrinhanifi@zums.ac.ir, Tel: 02433148336, https://orcid.org/0000-0002-4027-2399. between the hospital arrival, and thrombolytic therapy for over 50% of patients qualified for rTPA (7). Research has shown that rTPA is avoided as it wastes the golden time of medication use due to the delayed arrival at the hospital (8). The time-to-treatment delay in AIS patients may be caused by different factors such as pre-hospital and intra-hospital reasons. Delays in recognizing and transfer of patients are among the pre-hospital causes of their mortal- ity. Meanwhile, delays in neurologic visits, delays in decision- making regarding the treatment procedure, and delays in brain imaging are considered among the intra-hospital de- lay causes (9, 10). Treatment delay is a function of several factors, including the patient’s delay after the onset of early symptoms and delay by treatment staff (11, 12). Lack of ac- cess to medical centers (13) and lack of proper management of AIS patients in the hospital are among the factors influ- encing the time-to-treatment delay (14, 15). In this respect, Code 724 has been effective in reducing the delay in treating AIS patients (16). In Iran, the stroke code (Code 724) was announced by Iran’s Ministry of Health to the medical universities in 2016 to treat stroke patients more effectively. Thus, in addition to imple- menting this plan, it is essential to review the status of pre- hospital and hospital delays in Stroke Care Units (SCU) in Ira- nian cities. In every community, it is essential to investigate This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem N. Ghadimi et al. 2 the factors influencing pre-hospital and in-hospital delay, the quality of care delivered, and the individual factors affecting timely treatment. These influencing factors can vary from community to community. The present study aimed to de- termine the factors influencing in-hospital and pre-hospital delays in treatment of AIS. 2. Methods 2.1. Study design and setting This cross-sectional descriptive study was performed in the SCU of Vali-Asr Hospital, Zanjan (northwest of Iran), from July to the end of October 2019. AIS cases or their relatives were interviewed about potentioal causes of delay in initia- tion of thrombolytic therapy using a predesigned question- naire (appendix 1). The Ethics Committee of Zanjan Univer- sity of Medical Sciences approved this study under the Ethics Code IR.ZUMS.REC.1398.095. The researcher described the study’s aims to the patients or their families, and written con- sent was obtained. The participants were assured about the confidentiality of all their information and the right to leave the study at any time. 2.2. Participants The samples were collected using convenience sampling. Therefore, the study participants included patients referring to the SCU during the sampling interval, who met the inclu- sion criteria. The physician confirmed the diagnosis of AIS based on clinical signs and brain CT-scan results. Willing- ness to participate in the study was considered the inclusion criterion. Patients diagnosed with a hemorrhagic stroke or transient ischemic attack (based on CT-scan results) were ex- cluded from the study. 2.3. Procedure SCU of Vali-Asr Hospital in Zanjan, was established in 2016 and is known as the stroke referral center in Zanjan Province. In Iran, Code 724 refers to stroke patients whose stroke symp- toms have initiated less than 4 hours and 30 minutes before. Based on this code, as soon as the patients call the Emer- gency Medical Services (EMS), they are asked about the Face- Arms-Speech-Time (FAST) symptoms. Then, after the ambu- lance is sent to the patient’s bedside, the emergency techni- cian examines the FAST symptoms, and need to SCU is re- ported followed by confirmation. Patients from the neigh- boring provinces are immediately transferred from all med- ical centers to the SCU in Zanjan. After transferring the patient to the hospital, a neurologist examines them at the triage unit and sends him/her to the brain computed tomo- gramphy (CT) scan if the diagnosis of a stroke is made based on the scan, the rTPA medication is administered there. 2.4. Data gathering The variables in this study included demographic charac- teristics, factors affecting the time of treatment initiation in both in-hospital and pre-hospital phases, and stroke risk fac- tors (hypertension, hyperlipidemia, smoking, and diabetes). A questionnaire was used to collect the data and identify the information on demographic features and factors affect- ing time-to-treatment and the average time between onset of symptoms to treatment (7, 11, 17-20). The questionnaire included three parts. Questions about the demographic fea- tures of the patients were included in the first part. The sec- ond part contained questions regarding the causes of pre- hospital delays. The third part included questions about the reasons for in-hospital delay (Appendix 1). These data were approved by the treating physician. To assess AIS severity, we considered the National Institutes of Health Stroke Scale (NIHSS) (21). This scale includes 11 items, for which a score of 0 denotes the individual’s normal performance in the stud- ied field, and a score of 4 represents maximum impairment in that field. The maximum and minimum scores on this scale are 42 and 0, respectively. In this regard, the score 0 denotes lack of stroke symptoms, 1 to 4 is mild stroke, 5-15 is moder- ate stroke, 16-20 is moderate to severe stroke, and 21-42 de- notes severe stroke. The content validity of the questionnaire was evaluated. The designed questionnaire was offered to 10 experts to make the essential modifications and alterations they believed to be necessary. Its reliability was assessed using inter-rater re- laibility. Two researchers completed the questionnaire for the same 10 patients, simultaneously. Then, Cohen’s kappa coefficient was assessed between the data of the researcher- completed questionnaires, and the evaluators’ reliability was confirmed by achieving K = 0.973. The reliability and validity of the NIHSS tool had been confirmed by Kasner et al. (21). The data were collected through observation and interviews with patients and their families, if necessary. The patients re- ferring to the SCU were chosen based on the inclusion crite- ria. The researcher completed the questionnaire after treat- ment and relative stabilization of the patient with the assis- tance of the patient or his/her caregivers. In this study, to reduce recall bias regarding the timing of the events by the patients and their families, and recording the times and fac- tors influencing pre-hospital delays as accurately as possible we highlighted the critical times like news time, Azan time, and events of the day when asking about the events . 2.5. Data analysis According to a pilot study on 20 AIS patients, we considered a sample size of 181, an effect size of 0.05, a sampling error of 20 min, and a confidence level of 95%. In this study, 204 patients with AIS referring to the SCU were assessed. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 3 Archives of Academic Emergency Medicine. 2021; 9(1): e52 Statistical analyses were performed using SPSS V.16 software. The data were distributed based on the normalized cen- tral limit theorem. Data were gathered through interviews and observations. For detecting the predictors of delay to treatment, a logistic regression model was performed using the Forward-LR method. To determine the factors affect- ing time-to-treatment, variables including age (age less than 60 years and age over 60 years), gender, previous history of stroke, calling EMS, consultation after the onset of symp- toms, and patient’s perception of early symptoms were en- tered to the model as independent variables. In contrast, de- lay in treatment was used as the dependent variable. In this study, the significance level was considered less than 0.05. There was no missing data in the present study, because the researchers collected data through interviews, observations, and patient records. 3. Results 3.1. Baseline characteristics of participants This study was conducted on 230 patients with stroke refer- ring to the SCU from early July to late October 2019. The data of 16 patients with transient ischemic attack and 10 patients with hemorrhagic stroke were excluded from the study. Ulti- mately, the data of 204 patients with acute ischemic stroke who had referred to the SCU were assessed. The treating physician diagnosed the ischemic stroke in these patients. In total, 204 patients were included in this study, 55.9% of which were male, 19.6% had a high school diploma, and 72.5% were illiterate. The participants’ mean age was 68.99 ±13.91 (28 - 98) years. Fifty percent of the patients lived in Zanjan. According to patients’ statements, 87.7% had at least one risk factor. Hypertension (59.3%) was the most preva- lent risk factor for AIS, and ischemic heart disease was in the second rank (30.4%). Moreover, about 77.9% of the pa- tients were at home when the symptoms had initiated. The severity of the stroke was moderate in 52% of the patients. In this study, 140 (68.6%) patients were referred to the SCU with code 724. They arrived at the hospital within less than 4 hours (h) and 30 minutes (min) after the onset of symptoms. Moreover, rTPA was provided for 129 (63.2%) patients, but it was not used for 75 (36.8%) patients. 3.2. Analysis of delay to treatment The reason for not receiving rTPA in 64 (31.4%) patients was that more than 4 hours and 30 minutes had passed from the symptoms’ onset to referral to SCU. Table 1 shows the fre- quency of potentioal prehospital causes of delay in treatment of AIS cases. 70.6% of the patients considered their prime symptoms to be symptoms of other diseases and did not be- lieve they had a stroke. Furthermore, 17.2% had no consulta- tion with anyone after the onset of the symptoms and took no action. After the onset of symptoms, about 47.5% of the pa- tients referred to medical centers rather than SCU. It is note- worthy that they mostly (30.4%) referred to these centers be- cause of availability or proximity. 46.1% of them referred to SCU using personal vehicles. A neurologist performed the first visit for more than half of the patients (62.7%). The mean onset-to-arrival time and the mean onset-to-treatment time were 288.19 ± 339.02 minutes and 314.13 ± 341.04 minutes, respectively. Table 2 shows the time interval between onset of symptoms and treatment based on pre-hospital and in-hospital factors. Among the pre-hospital delay factors, the delay in deciding to contact the emergency service or making the effort to re- fer to medical centers (204.74 ± 321.38 minutes) was longer compared to the time of patient transfer to the hospital (83.52 ±72.38 minutes). In identifying the predictors of delay in treatment, among the predictor variables included in the model, calling EMS, patient’s perception of early symptoms, and consultation af- ter the onset of symptoms could effectively predict this delay. The odds of decreasing the delay in treatment for transporta- tion by EMS, patient’s perception of early symptoms, and consultation after the onset of symptoms were 0.12 (95%CI: 0.033-.435), 7.46 (95%CI: 2.04-27.3), and 0.008 (95%CI: 0.001- 0.05), respectively (table 3). 4. Discussion Our results indicated that pre-hospital delay was longer com- pared to the hospital delay. The delays in making the effort to refer to the medical center or the decision to call the emer- gency service were longer compared to the time of patient transfer to the hospital. In a study in Hamadan (Iran), Ghi- asian et al. reported that the time interval between symp- tom onset to arrival at the hospital was 282 min, while it was 192 min in the study of Griesser et al (11, 22). This re- sult is consistent with the findings of our study. Neverthe- less, in the study of Ayromlou et al. in Tabriz (Iran), this time was 916 min, which is not in line with our results (13). In the mentioned study, which was conducted in the metropoli- tan area of Tabriz, the delay in patients’ arrival could be caused by traffic problems in this city. In the smaller towns around the provinces equipped with SCUs, accurate diagno- sis of the stroke, the existence of neurologists, and admin- istering thrombolytic medication can dramatically decrease the onset-to-treatment time. Koksal et al., Ruiz et al., Faiz et al., Sobral et al., Springer et al., and Haiqiang et al. showed that access to transfer with EMS shortens the delay in hospital arrival (12, 17, 19, 23-25). In our study, also, less delay was experienced by the patients referring via EMS. Consistent with our study, the findings of studies conducted in America, Asia, and Europe indicated This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem N. Ghadimi et al. 4 Table 1: The frequency of potential pre-hospital causes of delay in initiation of treatment for patients with acute ischemic stroke Variables Number (%) Variables Patient’s perception of early symptoms Neurologic 60 (29.4) Non-neurologic 144 (70.6) Consultation after the onset of symptoms Spouse 49 (24.0) Children 94 (46.0) Colleague 4 (2.0) Relatives 18 (8.8) Nurse 4 (2) Not consulting anyone 35 (17.2) Center visited after symptom onset SCU in Zanjan 107 (52.5) Other medical centers 55 (27.0) Clinic 28 (13.6) Private office 13 (6.4) Private hospital 1 (0.5) Reasons for not referring toSCU Proximity or availability of another center 62 (30.4) Not being aware of stroke center at SCU 8 (3.9) Not considering the disease seriously by the pa- tient 27 (13.2) Referred to hospital by Personal vehicle 94 (46.1) Emergency medical Services (EMS) 64 (31.4) Ambulance from other medical centers 44 (21.5) Stroke inside the hospital 1 (0.5) Air emergency 1 (0.5) The first visitor of the patient General Practitioner 2 (1.0) Resident of Neurology 128 (62.7) Emergency medicine specialist 68 (33.3) Neurologist 5 (2.5) Non-neurology Resident 1 (0.5) SCU: stroke care unit. that absence of awareness of stroke symptoms, patients’ be- liefs and misconceptions about the prime symptoms, and failure to consult an individual after the onset of the symp- toms resulted in longer delays in hospital arrival and time- to-treatment for stroke patients (11, 12, 17, 19, 22-27). The results indicate that consulting with others after initiation of the symptoms may help prevent a delay in cases the symp- toms of the patients are not well-recognized or taken seri- ously. The results of our investigation on factors causing hospital delay in AIS patients revealed that there were no delay for AIS patients receiving Code 724. In this study, the time inter- val between hospital arrival to rTPA implementation (25.18 ±17.01 min) and between hospital arrival to brain CT scan (10.60 ± 6.79 min) was much shorter compared to the time proposed by the American Stroke Association guidelines (7). In the study by Dhaliwal et al. in the US, the mean initial CT Table 2: The time interval between onset of symptoms and treat- ment based on pre-hospital and in-hospital factors Variables Mean ± SD Pre-hospital time intervals (minutes) Onset –to- decision time 204.74 ± 321.4 The transfer time 83.52 ± 72.4 Onset –to- arrival time 288.19 ± 339 In-hospital time intervals (minutes) Door –to –examination time for with Code 724 3.07 ± 2.5 Door –to –examination time for without Code 724 15.08 ± 8.5 Door –to –SCU entry time for with Code 724 17.99 ± 13.1 Door –to –SCU entry time for without Code 724 216.98 ± 173.5 Door –to –imaging time for with Code 724 10.6 ± 6.9 Door –to –treatment decision making for with Code 724 21.87 ± 13.9 Door–to –order time for with Code 724 23.08 ± 16.5 Door –to –needle time for with Code 724 25.01 ± 17 Door –to – treatment time for without Code 724 29.07 ± 33.8 Onset –to –treatment time in stroke patients 314.13 ± 341 SD: standard deviation; SCU: stroke care unit. scan time was 13.66 min, the CT scan interpretation time was 25.20 min, and the time between the arrival of the patients and rTPA injection was 51.27 min (15). Hasankhani et al. in Tabriz (Iran) found that the mean time between hospital ar- rival and rTPA injection is 69 min (14). In the study of Mowla et al. in New York, the maximum imaging delay was longer than 25 min (28). According to the findings obtained in Iran and other countries, the time interval between hospital ar- rival and treatment in patients with Code 724 is much longer compared to our results. This indicates that the management of the stroke code team in Zanjan city have been able to sig- nificantly shorten time-to-treatment. 5. Limitations The low accuracy of recalling the times, particularly in el- derly patients, was among the limitations of this study. The researchers tried to record the times and factors influencing pre-hospital delays as accurately as possible by highlighting the critical times like news time, Azan time, and events of the day. Considering the geographical and cultural position of Zanjan, the present results cannot be generalized to other communities. 6. Conclusion In the present study, a longer pre-hospital delay was found compared to hospital delay in stroke events. Among the pre- hospital delay factors, the delay in visiting a medical center or This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 5 Archives of Academic Emergency Medicine. 2021; 9(1): e52 Table 3: Indipendent predictors of delay in treatment of acute ischemic stroke cases Variables Logistic regression analysis B S.E Wald df P EXP(B) Consultation after the onset of symptoms 4.536 917 24.468 1 0.001 0.008 Transportation by EMS 2.369 0.646 13.433 1 0.001 0.12 Patient’s perception of early symptoms -1.565 0.536 8.532 1 0.003 7.46 Constant -2.796 0.627 19.886 1 0.001 8.92 B=Beta, S.E= Standard Error, df=degrees of freedom, EXP (B)= Ecpected Beta; *P-value< 0.05. deciding to call the EMS was longer than the time of patient transfer to the hospital. In other words, a more significant portion of the delays in the pre-hospital phase is caused by the delay in patients’ decision to refer to the hospital. It ap- pears that giving information to at-risk people, particularly those over 60 years, about the stroke risk factors, the impor- tance of rapidly initiating treatment to enhance the disease outcomes, and the early stroke symptoms will help patients comprehend their symptoms properly. Hence, they will be transferred to the hospital faster by calling the emergency system. 7. Declarations 7.1. Acknowledgments This study is based on a research project with the code A-11- 148-19. Here, the researchers thank the research participants for their cooperation and Zanjan University of Medical Sci- ences Vice-Chancellor for financial support. 7.2. Funding and Support This article results from a Master of Nursing thesis funded by the research department of Zanjan University of Medical Sciences. 7.3. Author contribution NH designed the study, carried out statistical analyses of the data, was involved in interpreting the data, and wrote the manuscript. NG, who also collected the data, was involved in the interpretation of the data. MR D was involved in the interpretation of the data. All authors read and approved the final manuscript. 7.4. Competing interests The authors declare that they have no competing interests. 7.5. Pre print this article Part of this article on the site Research Sequare is online as pre print. Available in: https://www.researchsquare.com/article/rs- 135115/v1 DOI: 10.21203/rs.3.rs-135115/v1 References 1. Ropper AH. Adams and Victor’s principles of neurology: McGraw-Hill Medical Pub. Division New York; 2005. 2. Bernaitis N, Anoopkumar-Dukie S, Bills S, Crilly J. Evaluation of adult stroke presentations at an Emergency Department in Queensland Australia. International emer- gency nursing. 2019;44:25-9. 3. Kuhrij LS, Marang-van de Mheen PJ, van den Berg-Vos RM, de Leeuw F-E, Nederkoorn PJ. 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Downloaded from: http://journals.sbmu.ac.ir/aaem 7 Archives of Academic Emergency Medicine. 2021; 9(1): e52 Appendix 1: TIA: transient ischemic attack; OCP: oral contraceptive; MI: myocardial infarction; CHF: Chronic Heart Failure; IHD: Ischemic Heart Disease. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem N. Ghadimi et al. 8 Appendix 2: EMS: Emergency Medical Services; SCU: Stroke Care Unit. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 9 Archives of Academic Emergency Medicine. 2021; 9(1): e52 Appendix 3: BS: blood sugar; NIHSS: NIH stroke scale; PT: prothrombin time; PTT: partial thromboplastin time; INR: international normal- ized ratio; SCU: Stroke Care Unit; CT: computed tomography; ICH: intracranial hemorrhage; TIA: transient ischemic attack; SAH: Subarachnoid hemorrhage; CVT: Cerebral venous thrombosis; TPA: tissue plasminogen activator. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem Introduction Methods Results Discussion Limitations Conclusion Declarations References