Impact of the COVID-19 pandemic on acute Cardiology and Neurology services in a secondary peripheral hospital

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We compared the impact of COVID-19 on Cerebral Vascular Accident (CVA) and ST-elevation myocardial infarction (STEMI) management and outcome in an Israeli peripheral hospital. We included 1029 CVA and 497 STEMI patients. Those who arrived during (15/3/2020-15/4/2022) and before (1/1/2018-14/3/2020) the pandemic were demographically comparable. During the pandemic, median time for CVA patients from arrival to imaging was longer (23 vs. 19 minutes, p = 0.001); timing from arrival to tissue Plasminogen Activator administration was similar (49 vs. 45 min, p = 0.61); transfer to another hospital was more common (20.3% vs. 14.4% p = 0.01) and median length of stay (LOS) was shorter (3 vs. 4 days, p < 0.05). Among STEMI patients, median time from arrival to intervention intra- pandemic was shorter (45 vs. 50 minutes p = 0.02); Mean LOS shorter (3.86 vs. 4.48 p = 0.01), and unplanned re-admission less frequent (7.8% vs. 14.6% p = 0.01). Mortality didn’t significantly change. Our data shows no major negative impact of the COVID-19 pandemic on CVA outcomes, and possibly improved care for STEMI patients. Follow-up qualitative studies with neurology and cardiology staff will inform how quality of care was maintained during the crises. Health sciences/Diseases Health sciences/Health care/Health policy Health sciences/Health care/Health services COVID-19 ST Elevation Myocardial Infarction Stroke Treatment Outcome Length of Stay Introduction The COVID-19 pandemic caused hospitals to re-allocate resources to manage COVID-19 patients 1,2 , indirectly affecting the delivery of non-COVID healthcare services 3–5 . Staff re-allocation from non-COVID to COVID units decreased hospitals’ ability to treat patients at the usual standard 6 . Several studies recorded adverse health outcomes for non-COVID patients due to the pandemic in a range of settings, including excess mortality rates for cardiovascular disease, 7 , and stroke conditions 8 . The pandemic caused disruption to diagnostic and therapeutic health services, and emergency care at least until the 4th quarter of 2021 5 . ST-elevation myocardial infraction (STEMI) and cerebrovascular accidents (CVA) are acute emergencies that require rapid diagnosis and response 9,10 . Prompt management can improve clinical outcomes and residual disability 11,12 . They serve as useful indicators for health system disruption since changes in the delivery of these services can rapidly impact patients’ clinical outcomes. A Meta-analysis on the impact of COVID-19 on STEMI, focused on the early pandemic phase, highlighted delays in presenting at hospital, decrease in hospitalizations, no delays in door-to-PCI timing (Percutaneous coronary intervention), and higher in-hospital mortality 13,14 . With regards to CVA, a Meta-analysis describing stroke care during the pandemic showed decrease in admissions, higher NIHSS (NIH Stroke Score, a standardized tool for stroke severity) at admission, no difference in symptom onset-to-emergency department (ED) nor increased length of stay (LOS), and increased mortality 15,16 . The first case of COVID-19 in Israel was reported on 27/02/2020 17 . As part of mitigation measures, Israel declared three lockdowns in 2020-21 18 , and advised persons aged 65 + to stay at home 19 . A multi-center Israeli study conducted in March-April 2020 reported more STEMI patients arriving during the pandemic compared to before, more severe STEMI presentation, prolonged time from hospital admission to reperfusion, but no change in in-hospital mortality 20 . Another study conducted in tertiary hospitals in Israel in March-April 2020, found reduced CVA admissions during the pandemic without changes to NIHSS on admission. There were no delays of symptom onset until arrival at the hospital, nor hospital-to-imaging/ reperfusion 21 . These findings however, may not be generalizable to the entire pandemic period or to the Israeli periphery which is characterized by poorer access to healthcare and a diverse population that is geographically scattered and socio-economically deprived (33,34). Ziv Medical Center (ZMC), a 340-bed secondary-periphery hospital, in Safed, northern Israel is an example of a government hospital serving the periphery population. During the pandemic, ZMC opened several COVID in-patient and intensive care wards comprising up to 47 beds, thus reallocating staff and resources from usual care. As a result, the Neurology department was moved to a temporary location and its bed capacity was reduced from 22 to 7. In addition, one physician and 6 nurses were re-allocated from Neurology to the COVID department. Conversely, the Cardiology department was not relocated and did not experience reductions in staffing or beds. Comparing these two departments, one disrupted and one not, provides insight into the impact of organizational decisions on unintended outcomes. The aim of the current study was to describe and compare the impact of COVID-19 on STEMI and CVA care over 2-years of the pandemic period in the context of a secondary peripheral hospital in Israel. Results Between 1/1/2018-15/4/2022, 2,223 CVA and STEMI patients arrived at ZMC. Of the 1,552 CVA cases identified, 523 cases were excluded because these patients arrived due to another reason and developed CVA during their hospitalization or had a previous CVA diagnosis and attended outpatient services or didn’t undergo imaging in the ED or were eventually defined as TIA. Of the 1029 included patients, 537 were pre-pandemic, and 492 patients attended during the pandemic. Of the 671 STEMI cases, 174 were excluded due to either developing STEMI post-admission or arriving to the hospital with non-STEMI or unstable angina and later developing STEMI. Of the 497 included STEMI patients, 261 arrived pre-pandemic and 234 during the pandemic. There were no significant demographic differences between patients who arrived before or during the pandemic for either STEMI or CVA patients (Table 1). Neurology services: The hospital received an average of 20.2 CVA patients per month prior to the pandemic compared to 19.7 during the pandemic. Compared to the pre-pandemic period, more CVA patients arrived by EMS during the pandemic (57.3% vs. 51% p=0.049). There was no significant difference in the proportion of patients who arrived within 4.5 hours from symptom onset to the ED (38% vs. 34% p=0.12), and the severity of symptoms at presentation was similar before and during the pandemic. Median time from arrival to imaging was longer during the pandemic (23 vs. 19 minutes for patients who arrived within 4.5 hours, p=0.001. And 63 vs. 54.5 minutes in others, p=0.01). Time from arrival to the ED until tPA administration was similar in both time periods (49 vs. 45 minutes, p=0.6). In terms of outcomes for CVA patients, patients were more likely to be transferred for further treatment to another hospital during the pandemic (20.3% vs. 14.4% p=0.01). Median LOS for CVA patients was shorter during the pandemic (3 vs. 4 days, p<0.05) (Table 2). Cardiology services: The monthly average number of STEMI patients pre-pandemic was 9.8 compared to 9.4 during the pandemic. Median TIMI risk score was higher during the pandemic (4 vs. 3 p=0.02). Median timing from arrival to PCI was shorter than pre-pandemic (45 vs. 50 minutes p=0.02), with no statistically significant change in the length of the procedure (32 vs. 35 p=0.11). STEMI patients who had urgent PCI during the pandemic, had a better median EF during the pandemic at discharge (50% vs. 45% p=0.02). While mortality was higher during the pandemic (5.9% vs. 3.4%), the difference was not statistically significant (p=0.26). Mean LOS in days was lower during the pandemic (3.86 vs. 4.48 p=0.01), unplanned re-admission to the ED was lower in the pandemic (7.8% vs. 14.6% p=0.01) (Table 3). Discussion Our study describes the impact of the pandemic on the provision of clinical care for CVA and STEMI in a peripheral hospital in Northern Israel. Our intent was to use ZMC as a case study of the impact of the pandemic on non-COVID services in a peripheral hospital rather than an evaluation of this specific hospital. The setup of a peripheral, secondary care facility serving a diverse and socio-economically deprived population is similar to many facilities in the Israeli periphery as well as globally. Our data showed no change in the volume of patients presenting with acute STEMI or CVA, suggesting that despite lockdowns and fear of infection in hospitals, acute patients continued to arrive. The Cardiology department, largely undisrupted by COVID-19 re-organization, showed improved STEMI outcomes; in the Neurology department responsible for CVA patients, which underwent a major disruption, clinical outcomes didn’t change substantially compared to the pre-pandemic period. Evidence from the early phase of the pandemic on STEMI care suggested longer time from symptom onset to seeking medical care, significant delays in door-to-angiography timing, and varied intra-hospital mortality 27 . Our data showed that although STEMI patients had more severe clinical presentations compared to pre-pandemic, their clinical outcomes improved, and LOS shortened. Lesiane and colleagues, conducted a regional study in France on the impact of organizational changes on STEMI and stroke care during the first wave of the pandemic. There was median increase of 8 minutes in time from symptom to arrival to the hospital for STEMI patients, hospital’s care management quality was maintained as median first medical contact to procedure time decreased by 5 minutes, however they didn’t measure LOS 28 . Short LOS is generally considered as an indicator for quality of care and efficiency, yet there many factors can contribute to the duration of LOS (Fluctuations in the day of week, availability of beds, etc.) 29 . The decrease in LOS, however, could raise the concern that medical teams released patients prematurely. The improved ejection fraction we found at discharge, along with lower re-admission rates than pre-pandemic, suggest this was not the case. Like other studies describing the impact of the pandemic on stroke care 30 , our results show a higher proportion of patients arriving by EMS compared to pre-pandemic, longer time from arrival to imaging, but no difference in time from arrival to tPA administration. This implies that the 4-minute median delay in imaging was offset by more rapid administration of the tPA provided by the staff during the pandemic. In our study, CVA patients had shorter LOS during the pandemic. Our data doesn’t show whether the shorter LOS represents more efficient care or premature discharge. Meta-analysis on the early phases of the pandemic revealed that there were no changes in LOS for stroke 15 . However, an Israeli national study reported a decrease in LOS for stroke patients during the pandemic 21 , suggesting that in Israel the pandemic may have encouraged early discharge of patients. The increase in the proportion of patients transferred to further treatment may indicate that in the disrupted department, the capacity to manage patients, especially complex patients, decreased. The implications of similar inter-hospital transfers of patients in care facilities shows that the healthcare staff in the accepting hospitals faced moral distress 31 . The setting of our study provides a unique opportunity of a natural experiment, as one department was heavily affected by organizational changes, while the other was not. The two-year study period provides a more comprehensive picture than most studies that focused on the early pandemic phase. While we found no change in clinical outcomes in the department heavily impacted by the pandemic, and improvements in the department that was less disrupted, our study design did not purport to determine how clinical teams maintained performance in times of disruption. Team performance is influenced by individual, departmental and the organizational level factors, that may negatively impact teamwork, as well as patient care 32,33 . When pressure is put on the healthcare delivery systems, maintaining performance with less resources suggests various adaptations mechanism with increased resilience from the organization 34 . Healthcare workers exposed to stressors such as pandemics, may exhibit more resilient coping strategies such as faster learning from experience or increased solidarity with co-workers 35 . Nevertheless, evidence suggests that non-COVID healthcare staff had high prevalence of burnout and depression 36,37 . Our methodology doesn’t enable us to determine how staff managed to work and adapt, nor to know whether there were long- or short-term consequences to maintaining high performance, such as burnout or enhanced sense of capability. A planned qualitative study will shed light on changes that occurred in the organization of health services at the ward level. Our study has several limitations. First, we did not have data on the NIHSS of CVA on discharge in the pre-pandemic period. This would have helped us determine with more confidence whether LOS decreased due to an increase in transfers to other hospitals, or because of higher quality care in the hospital. We could not access data on patients’ outcomes after transfer, which could have helped understand the complexity and severity of the transferred patients. Our finding of no significant change in mortality was not consistent with multiple studies globally that found increase in mortality in various locations for cardiovascular and cerebrovascular conditions 7,8 and could be due to the small number of deaths, and resulting statistical power, that occurred in the time period. Nevertheless, the comprehensive use of all cases over a long time period provides a more complete picture than previously described of the impact of COVID19 on acute clinical services in peripheral secondary care centers. The pandemic caused major disruptions to healthcare services. It forced major and sudden adaptations by management to balance the need of COVID patients while maintaining other services, causing possible unintended consequences to non-COVID patients. Our study analyzed the impact of organizational changes during the COVID-19 pandemic on the treatment and outcome of patients admitted with STEMI and CVA in the Northern periphery of Israel. In the Cardiology department, which did not see a major reallocation of staff and resources, care indicators suggested improved processes and outcomes for STEMI patients. The Neurology department, despite being severely affected by re-organizations made, performed similar to the pre-pandemic period. This suggests that secondary care institutions and their staff can demonstrate agility and resilience in the face of severe and unplanned disruption. Mixed-methods studies would help understand how clinical, staff adapted to maintain quality of services, at what personal cost, and where in the complex patient management processes did disruption occur and how it was mitigated. Methods Study design: This is a retrospective, descriptive time-series analysis. We included all patients aged 18 and above who presented at ZMC and were diagnosed with either CVA or STEMI, between 1/1/2018-15/4/2022. STEMI patients presented to ZMC through the ED or by Emergency Medical Services (EMS) directly to the catheterization room. CVA patients presented to the ED with symptoms of CVA and the discharged diagnosis was recorded as CVA. Israel implemented COVID related restrictions on 15/3/2020 when all places of entertainment, schools, kindergartens, and day-cares were shut down 18 . After more than two years, on 15/4/2022, ZMC’s COVID department closed, and the hospital’s organizational status returned to its pre-pandemic state. We therefore considered the COVID period as 15/3/2020-15/4/202 2 . The pre-pandemic period was considered as 1/1/2018-14/3/2020. We reviewed all cases of CVA and STEMI patients identified retrospectively based on the International Classification of Diseases (ICD) 9 codes for STEMI and CVA recorded in ZMC’s electronic medical records (EMR) at discharge. We included STEMI patients who presented to the ED with ST elevation in the ECG with ICD-9 codes 410.0-410.6 and 410.8. Patients that had STEMI in the hospital after admission to the ED or the in-patient ward and patients that had non-STEMI or unstable angina and later developed STEMI were also excluded. For CVA patients, we included all patients that presented to the ED with CVA symptoms and were assessed by a neurologist with imaging or were diagnosed with CVA by EMS and had an ICD-9 code at discharge/death compatible with CVA (Supplementary) 19 . Data collected: We extracted demographic and clinical data from patients’ EMR as well as from individual-level national health quality indicators on STEMI and CVA patients. Those indicators include timing from arrival to the hospital until PCI and the length of the PCI for STEMI patients; for CVA, indicators include the time from hospital arrival to performance of imaging (head CT/MRI) for patients with acute stroke presenting up to 3.5 hours from symptom onset and timing of receiving Intravenous thrombolytic treatment (IV-rtPA) 22 . Baseline demographic data included age, ethnicity, gender, and comorbidities (smoking -current or previous, hypertension and diabetes). For STEMI patients, we extracted transportation mode to the hospital (with/without EMS), and timing from symptom onset to ED arrival (above/below 2 hours). To estimate disease severity in those patients on admission, we extracted their Killip status 23 . We also calculated each patient’s TIMI risk score (a scoring tool to calculate the likelihood risk of all-cause mortality at 30 days) 24 and LOS. To assess post-PCI cardiac function, we reported the ejection fraction (EF) at discharge, measured by echocardiogram. Our dataset also included admission outcome (discharge home/death/transfer to another hospital/rehabilitation institute) and unplanned readmission within 30 days post-discharge. For CVA patients, we recorded transportation mode to the hospital and the proportion who arrived within 4.5 hours of symptom onset, reflecting the maximum time elapsed to receive Tissue plasminogen activator (tPA), the treatment for ischemic stroke. 10,25 . We also extracted NIHSS on admission, LOS, admission outcome (discharge/death/transfer to another hospital/ rehabilitation) and unplanned readmission within 30 days post-discharge. Data analysis: For each patient group we compared the pre-pandemic (1/1/2018-14/3/2020) to the pandemic period (15/3/2020-15/4/202 2 ), including patient characteristics, clinical presentation and intra-hospital metrics using means, medians, proportions with standard deviation and interquartile range. All binary and categorical variables were compared using Chi-square tests. For STEMI patients these included gender, co-morbidities, ethnicity, way of arrival at the hospital (with/without EMS), time from symptom onset to ED arrival (Above/below 2 hours), Killip status classification (1,2,3 and 4), clinical outcome and unplanned readmission within 30 days post-discharge to the ED. For CVA patients they included time from symptom onset to ED (above or below 4.5 hours), NIHSS on admission (0-4, 5-15, 16-20 and >21), unplanned readmission within 30 days post-discharge and discharge category: death, transfer to other hospital, home, or release to rehabilitation institute (or recommendation for such rehabilitation). Continuous variables were first assessed for normality using Shapiro-Wilk test 26 . For normally distributed variables we compared means using t-test. For others, we compared medians using the Wilcoxon–Mann–Whitney test. For STEMI patients, continuous variables were: age, TIMI risk score, time from arrival to the ED until PCI, length of PCI, LOS and EF at discharge. For CVA patients, continuous variables were: door-to-CT/MRI (Divided into those who arrived within 4.5 hours from symptom onset-to-arrivals and those who arrived above 4.5 hours from symptom onset-to-arrivals and thus weren’t eligible for TPA), door-to-IV-TPA and LOS. Patients who arrived with STEMI but were classified by physicians as “late STEMI” or died before PCI were excluded from analysis of timing from ED-to-PCI and EF after PCI but were included for all other analyses. 30 patients were identified as needed urgent bypass procedure during their catheterization, were excluded from “Time of PCI procedure”, but were included in other analyses. STEMI and CVA patients who died during the hospitalization were excluded from 30-days re-admission analysis. Missing observations were excluded from the analysis. 2 CVA patients were tourists; hence their ethnicity was missing. R statistical programming 4.2.1 was used for statistical analysis. A P-value of less than 0.05 was considered statistically significant. Declarations Acknowledgements and conflict of interest: Authers’ contributions: ME, AR and AA-O conceived and designed the study. JH contributed to the STEMI parameters identification and interpretation. RS contributed to the CVA parameters identification and interpretation. TB, RP and IK extracted the data. TB managed the data. TB, ME, and SS analysed the data. TB, ME, SS interpreted the data. TB drafted the first draft of the manuscript. All authors reviewed the manuscript. All authors agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work. All authors read and approved the manuscript. Availability of the data and materials: The datasets used and analysed during the current study are available from the corresponding author on reasonable request and pending further ethical and facility approval. Ethics approval and consent to participate: The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The study received approval and consent from the ZMC ethics committee, number ZIV-0014-22. Due to the retrospective nature of the study written informed consent was not required. Competing interest: The authors declare that they have no competing interests. Funding: The authors state that the study was performed at the hospital without external help or funding sources. Prior presentation: This manuscript has not been previously published and is not under consideration in the same or substantially similar form in any other peer-reviewed media. We presented preliminary results in a poster presented at our faculty’s annual research day in 2023. References Meschi, T. et al. Reorganization of a large academic hospital to face COVID-19 outbreak: The model of Parma, Emilia‐Romagna region, Italy. Eur J Clin Invest 50, (2020). Bar-On, E. et al. Establishing a COVID-19 treatment centre in Israel at the initial stage of the outbreak: challenges, responses and lessons learned. 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CVA patients STEMI patients Pre-pandemic (1/1/2018-14/3/2020) (n=537) Pandemic (15/3/2020-15/4/2022) (n=492) P-value Pre-pandemic (1/1/2018-14/3/2020) (n=261) Pandemic (15/3/2020-15/4/2022) (n=234) P-value Age, mean (SD)* 69.56 (12.34) 69.41 (13.1) 0.84 61.3 (13.1) 62.8 (12.3) 0.17 Gender (% male) 329 (61.3) 298 (60.6) 0.86 217 (83.1) 199 (85) 0.65 Ethnicity (% minorities) NA 152 (28.3) 1 159 (32.3) 1 0.18 69 (26.4) 74 (31.6) 0.24 Smoking (%) 117 (21.7) 125 (25.4) 0.19 104 (39.8) 100 (42.7) 0.52 Hypertension (%) 377 (70.2) 331 (67.2) 0.34 107 (40.9) 104 (44.4) 0.44 Diabetes (%) 239 (44.5) 243 (49.3) 0.13 79 (30.2) 63 (26.9) 0.50 *After Shapiro-Wilk test for normality, Student’s t-test was performed. SD- Standard Deviation. Table 2. CVA patient’s condition treatment and outcome. Pre-pandemic (1/1/2018-14/3/2020) (n=537) Pandemic (15/3/2020-15/4/2022) (n=492) P-value Way of arrival (% EMS) 274 (51) 282 (57.3) 0.049 Time from symptom to arrival (less than 4.5 hours, %) 183 (34) 191 (38.8) 0.12 NIHSS category (n, %) 0-4 5-15 16-20 21-42 NA 261 (49.3) 244 (46.1) 16 (3) 8 (1.5) 8 233 (48.9) 214 (44.9) 20 (4) 9 (1.8) 16 0.73 Median time in minutes from arrival to imaging (CT/MRI) in patients less than 4.5 hours, median [interquartile range] ** 19 [14,30] 23 [17,34] 0.001 Median time in minutes from arrival to imaging (CT/MRI) in patients above 4.5 hours, median [interquartile range] ** 55 [27,90] 63 [36,98] 0.01 Time from arrival to tPA (for eligible patients) ** 45 [37,60] 49 [35,64] 0.61 30 days re admission (%) 62 /530 (11.6%) 68/480 (14.1%) 0.28 Outcomes: Discharged home Transfer to another hospital Rehab institute/recommendation/ severe case for rehab Intra-hospital mortality Refuse to hospitalization 284 (53.1%) 77 (14.4%) 151 (28.2%) 7 (1.3%) 15 (2.8%) 235 (47.7%) 100 (20.3%) 126 (25.6%) 12 (2.4%) 19 (3.8%) 0.09 0.01 0.4 0.26 0.31 LOS in days, median [interquartile range] ** 4 [2,6] 3 [2,6] 0.049 *=After Shapiro-Wilk test for normality, Student’s t-test was performed. **=After Shapiro-Wilk test for normality, Mann-Whitney non-parametric test was performed. EMS- Emergency medical services. NIHSS- NIH stroke scale. tPA- tissue Plasminogen Activator. LOS- Length of stay. Table 3. STEMI patient’s condition, treatment, and outcome. Pre-pandemic (1/1/2018-14/3/2020) (n=261) Pandemic (15/3/2020-15/4/2022) (n=234) P-value Way of arrival (EMS %) 195 (74.7) 187 (79.9) 0.2 Late admission MI 11 (4.2) 5 (2.1%) 0.29 Killip (n, %) 1 2 3 4 240 (91.9) 10 (3.8) 3 (1.1) 8 (3) 209 (89) 17 (7.2) 0 (0) 8 (3.4) 0.13 TIMI risk score, median [interquartile range]** NA 3 [2,5] 26 4[2,5] 14 0.02 Time from symptom to arrival (% below 2 hours) 98 (39.6) 77 (34.6) 0.3 ED-to-needle in minutes, median [ interquartile range] ** 50 [33,77] 45 [26 , 70] 0.02 Time of PCI procedure in minutes, median [interquartile range] ** 35 [26, 46] 32 [25, 43] 0.11 EF after PCI, median [interquartile range] ** 45 [42.5, 50] 50 [45,52.5] 0.02 Mortality (Died, %) 9 (3.4) 14 (5.9) 0.26 LOS in days, median [interquartile range]** Mean * 4 [3,5] 4.48 4 [3,4] 3.84 <0.001 0.01 30 days re admission (arrived %) 37 (14.7) 16 (7.8) 0.01 *=After Shapiro-Wilk test for normality, Student’s t-test was performed. **=After Shapiro-Wilk test for normality, Mann-Whitney non-parametric test was performed. EMS- Emergency medical services. MI- Myocardial infraction. TIMI-Thrombolysis in Myocardial Infarction. ED- Emergency department. PCI- Percutaneous coronary intervention. EF- Ejection fraction. LOS- Length of stay. Additional Declarations No competing interests reported. Supplementary Files SupplamentryfoScientificReports.docx Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Aug, 2024 Reviews received at journal 06 Aug, 2024 Reviewers agreed at journal 05 Aug, 2024 Reviews received at journal 30 Jun, 2024 Reviewers agreed at journal 23 Jun, 2024 Reviewers invited by journal 22 May, 2024 Editor assigned by journal 22 May, 2024 Editor invited by journal 21 May, 2024 Submission checks completed at journal 20 May, 2024 First submitted to journal 14 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4420658","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":307093020,"identity":"7175f543-5005-4dfd-a616-c601dd6b3eaf","order_by":0,"name":"Tomer 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ayelet","middleName":"","lastName":"Armon-Omer","suffix":""},{"id":307093025,"identity":"cd123948-7023-4334-b85b-bae1bd709ddc","order_by":5,"name":"Isabelle Kains","email":"","orcid":"","institution":"Bar-Ilan University","correspondingAuthor":false,"prefix":"","firstName":"Isabelle","middleName":"","lastName":"Kains","suffix":""},{"id":307093026,"identity":"90410a21-0c74-41dc-a4a6-7eb07d36c20b","order_by":6,"name":"Jihad Hamudi","email":"","orcid":"","institution":"Rebecca Sieff Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jihad","middleName":"","lastName":"Hamudi","suffix":""},{"id":307093027,"identity":"007ca182-f799-4a78-9b6d-dfdab4181eaf","order_by":7,"name":"Radi Shahien","email":"","orcid":"","institution":"Rebecca Sieff Hospital","correspondingAuthor":false,"prefix":"","firstName":"Radi","middleName":"","lastName":"Shahien","suffix":""},{"id":307093028,"identity":"46c45934-3595-446e-a064-7913eee56305","order_by":8,"name":"Michael Edelstein","email":"","orcid":"","institution":"Bar-Ilan University","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Edelstein","suffix":""}],"badges":[],"createdAt":"2024-05-14 16:42:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4420658/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4420658/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-80872-7","type":"published","date":"2024-11-26T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70389875,"identity":"0c95ae4a-9d07-4422-a159-bd36c65d350a","added_by":"auto","created_at":"2024-12-02 17:29:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":436335,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4420658/v1/71d4f390-fe61-40d1-8c9d-89499f2f7483.pdf"},{"id":57399509,"identity":"3dce1c50-f120-4a9c-818d-730c54d729ad","added_by":"auto","created_at":"2024-05-30 07:49:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23581,"visible":true,"origin":"","legend":"","description":"","filename":"SupplamentryfoScientificReports.docx","url":"https://assets-eu.researchsquare.com/files/rs-4420658/v1/ae17339d583bb58d63894e29.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of the COVID-19 pandemic on acute Cardiology and Neurology services in a secondary peripheral hospital","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic caused hospitals to re-allocate resources to manage COVID-19 patients \u003csup\u003e1,2\u003c/sup\u003e, indirectly affecting the delivery of non-COVID healthcare services \u003csup\u003e3\u0026ndash;5\u003c/sup\u003e. Staff re-allocation from non-COVID to COVID units decreased hospitals\u0026rsquo; ability to treat patients at the usual standard \u003csup\u003e6\u003c/sup\u003e. Several studies recorded adverse health outcomes for non-COVID patients due to the pandemic in a range of settings, including excess mortality rates for cardiovascular disease, \u003csup\u003e7\u003c/sup\u003e, and stroke conditions \u003csup\u003e8\u003c/sup\u003e. The pandemic caused disruption to diagnostic and therapeutic health services, and emergency care at least until the 4th quarter of 2021 \u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eST-elevation myocardial infraction (STEMI) and cerebrovascular accidents (CVA) are acute emergencies that require rapid diagnosis and response \u003csup\u003e9,10\u003c/sup\u003e. Prompt management can improve clinical outcomes and residual disability \u003csup\u003e11,12\u003c/sup\u003e. They serve as useful indicators for health system disruption since changes in the delivery of these services can rapidly impact patients\u0026rsquo; clinical outcomes. A Meta-analysis on the impact of COVID-19 on STEMI, focused on the early pandemic phase, highlighted delays in presenting at hospital, decrease in hospitalizations, no delays in door-to-PCI timing (Percutaneous coronary intervention), and higher in-hospital mortality \u003csup\u003e13,14\u003c/sup\u003e. With regards to CVA, a Meta-analysis describing stroke care during the pandemic showed decrease in admissions, higher NIHSS (NIH Stroke Score, a standardized tool for stroke severity) at admission, no difference in symptom onset-to-emergency department (ED) nor increased length of stay (LOS), and increased mortality \u003csup\u003e15,16\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe first case of COVID-19 in Israel was reported on 27/02/2020 \u003csup\u003e17\u003c/sup\u003e. As part of mitigation measures, Israel declared three lockdowns in 2020-21 \u003csup\u003e18\u003c/sup\u003e, and advised persons aged 65\u0026thinsp;+\u0026thinsp;to stay at home \u003csup\u003e19\u003c/sup\u003e. A multi-center Israeli study conducted in March-April 2020 reported more STEMI patients arriving during the pandemic compared to before, more severe STEMI presentation, prolonged time from hospital admission to reperfusion, but no change in in-hospital mortality \u003csup\u003e20\u003c/sup\u003e. Another study conducted in tertiary hospitals in Israel in March-April 2020, found reduced CVA admissions during the pandemic without changes to NIHSS on admission. There were no delays of symptom onset until arrival at the hospital, nor hospital-to-imaging/ reperfusion \u003csup\u003e21\u003c/sup\u003e. These findings however, may not be generalizable to the entire pandemic period or to the Israeli periphery which is characterized by poorer access to healthcare and a diverse population that is geographically scattered and socio-economically deprived (33,34). Ziv Medical Center (ZMC), a 340-bed secondary-periphery hospital, in Safed, northern Israel is an example of a government hospital serving the periphery population. During the pandemic, ZMC opened several COVID in-patient and intensive care wards comprising up to 47 beds, thus reallocating staff and resources from usual care. As a result, the Neurology department was moved to a temporary location and its bed capacity was reduced from 22 to 7. In addition, one physician and 6 nurses were re-allocated from Neurology to the COVID department. Conversely, the Cardiology department was not relocated and did not experience reductions in staffing or beds. Comparing these two departments, one disrupted and one not, provides insight into the impact of organizational decisions on unintended outcomes. The aim of the current study was to describe and compare the impact of COVID-19 on STEMI and CVA care over 2-years of the pandemic period in the context of a secondary peripheral hospital in Israel.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBetween 1/1/2018-15/4/2022, 2,223 CVA and STEMI patients arrived at ZMC. Of the 1,552 CVA cases identified, 523 cases were excluded because these patients arrived due to another reason and developed CVA during their hospitalization or\u0026nbsp;had a\u0026nbsp;previous\u0026nbsp;CVA\u0026nbsp;diagnosis\u0026nbsp;and\u0026nbsp;attended outpatient services or didn\u0026rsquo;t undergo imaging in the ED\u0026nbsp;or were eventually defined as TIA.\u0026nbsp;Of the 1029 included patients, 537 were pre-pandemic, and 492 patients attended during the pandemic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOf the 671 STEMI cases, 174 were excluded due to either developing\u0026nbsp;STEMI\u0026nbsp;post-admission or arriving to the hospital with\u0026nbsp;non-STEMI\u0026nbsp;or\u0026nbsp;unstable angina\u0026nbsp;and later developing STEMI. Of the 497 included STEMI patients, 261 arrived pre-pandemic and 234 during the pandemic. There were no significant demographic differences between patients who arrived before or during the pandemic for either STEMI or CVA patients (Table 1).\u003c/p\u003e\n\u003cp\u003eNeurology services:\u003c/p\u003e\n\u003cp\u003eThe hospital received an average of 20.2 CVA patients per month prior to the pandemic compared to 19.7 during the pandemic. Compared to the pre-pandemic period, more CVA patients arrived by EMS during the pandemic (57.3% vs. 51% p=0.049). There was no significant difference in the proportion of patients who arrived within 4.5 hours from symptom onset to the ED (38% vs. 34% p=0.12), and the severity of symptoms at presentation was similar before and during the pandemic. Median time from arrival to imaging was longer during the pandemic (23 vs. 19 minutes for patients who arrived within 4.5 hours, p=0.001. And 63 vs. 54.5 minutes in others, p=0.01). Time from arrival to the ED until tPA administration was similar in both time periods (49 vs. 45 minutes, p=0.6). In terms of outcomes for CVA patients, patients were more likely to be transferred for further treatment to another hospital during the pandemic (20.3% vs. 14.4% p=0.01). Median LOS for CVA patients was shorter during the pandemic (3 vs. 4 days, p\u0026lt;0.05) (Table 2).\u003c/p\u003e\n\u003cp\u003eCardiology services:\u003c/p\u003e\n\u003cp\u003eThe monthly average number of STEMI patients pre-pandemic was 9.8 compared to 9.4 during the pandemic. Median TIMI risk score was higher during the pandemic (4 vs. 3 p=0.02). Median timing from arrival to PCI was shorter than pre-pandemic (45 vs. 50 minutes p=0.02), with no statistically significant change in the length of the procedure (32 vs. 35 p=0.11). STEMI patients who had urgent PCI during the pandemic, had a better median EF during the pandemic at discharge (50% vs. 45% p=0.02). While mortality was higher during the pandemic (5.9% vs. 3.4%), the difference was not statistically significant (p=0.26). Mean LOS in days was lower during the pandemic (3.86 vs. 4.48 p=0.01), unplanned re-admission to the ED was lower in the pandemic (7.8% vs. 14.6% p=0.01) (Table 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study describes the impact of the pandemic on the provision of clinical care for CVA and STEMI in a peripheral\u0026nbsp;hospital in Northern Israel. Our intent was to use ZMC as a case study of the impact of the pandemic on non-COVID services in a peripheral hospital rather than an evaluation of this specific hospital. The setup of a peripheral, secondary care facility serving a diverse and socio-economically deprived population is similar to many facilities in the Israeli periphery as well as globally.\u003c/p\u003e\n\u003cp\u003eOur data showed no change in the volume of patients presenting with acute STEMI or CVA, suggesting that despite lockdowns and fear of infection in hospitals, acute patients continued to arrive. The Cardiology department, largely undisrupted by COVID-19 re-organization, showed improved STEMI outcomes; in the Neurology department responsible for CVA patients, which underwent a major disruption, clinical outcomes didn\u0026rsquo;t change substantially compared to the pre-pandemic period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvidence from the early phase of the pandemic on STEMI care suggested longer time from symptom onset to seeking medical care, significant delays in door-to-angiography timing, and varied intra-hospital mortality \u003csup\u003e27\u003c/sup\u003e. Our data showed that although STEMI patients had more severe clinical presentations compared to pre-pandemic, their clinical outcomes improved, and LOS shortened. Lesiane and colleagues, conducted a regional study in France on the impact of organizational changes on STEMI and stroke care during the first wave of the pandemic. There was median increase of 8 minutes in time from symptom to arrival to the hospital for STEMI patients, hospital\u0026rsquo;s care management quality was maintained as median first medical contact to procedure time decreased by 5 minutes, however they didn\u0026rsquo;t measure LOS \u003csup\u003e28\u003c/sup\u003e. Short LOS is generally considered as an indicator for quality of care and efficiency, yet there many factors can contribute to the duration of LOS (Fluctuations in the day of week, availability of beds, etc.) \u003csup\u003e29\u003c/sup\u003e. The decrease in LOS, however, could raise the concern that medical teams released patients prematurely. The improved ejection fraction we found at discharge, along with lower re-admission rates than pre-pandemic, suggest this was not the case. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLike other studies describing the impact of the pandemic on stroke care \u003csup\u003e30\u003c/sup\u003e, our results show a higher proportion of patients arriving by EMS compared to pre-pandemic, longer time from arrival to imaging, but no difference in time from arrival to tPA administration. This implies that the 4-minute median delay in imaging was offset by more rapid administration of the tPA provided by the staff during the pandemic. In our study, CVA patients had shorter LOS during the pandemic. Our data doesn\u0026rsquo;t show whether the shorter LOS represents more efficient care or premature discharge. Meta-analysis on the early phases of the pandemic revealed that there were no changes in LOS for stroke \u003csup\u003e15\u003c/sup\u003e. However, an Israeli national study reported a decrease in LOS for stroke patients during the pandemic \u003csup\u003e21\u003c/sup\u003e, suggesting that in Israel the pandemic may have encouraged early discharge of patients. The increase in the proportion of patients transferred to further treatment may indicate that in the disrupted department, the capacity to manage patients, especially complex patients, decreased. The implications of similar inter-hospital transfers of patients in care facilities shows that the healthcare staff in the accepting hospitals faced moral distress \u003csup\u003e31\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe setting of our study provides a unique opportunity of a natural experiment, as one department was heavily affected by organizational changes, while the other was not. The two-year study period provides a more comprehensive picture than most studies that focused on the early pandemic phase. While we found no change in clinical outcomes in the department heavily impacted by the pandemic, and improvements in the department that was less disrupted, our study design did not purport to determine how clinical teams maintained performance in times of disruption. Team performance is influenced by individual, departmental and the organizational level factors, that may negatively impact teamwork, as well as patient care \u003csup\u003e32,33\u003c/sup\u003e. When pressure is put on the healthcare delivery systems, maintaining performance with less resources suggests various adaptations mechanism with increased resilience from the organization \u003csup\u003e34\u003c/sup\u003e. Healthcare workers exposed to stressors such as pandemics, may exhibit more resilient coping strategies such as faster learning from experience or increased solidarity with co-workers \u003csup\u003e35\u003c/sup\u003e. Nevertheless, evidence suggests that non-COVID healthcare staff had high prevalence of burnout and depression \u003csup\u003e36,37\u003c/sup\u003e. Our methodology doesn\u0026rsquo;t enable us to determine how staff managed to work and adapt, nor to know whether there were long- or short-term consequences to maintaining high performance, such as burnout or enhanced sense of capability. A planned qualitative study will shed light on changes that occurred in the organization of health services at the ward level. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study has several limitations. First, we did not have data on the NIHSS of CVA on discharge in the pre-pandemic period. This would have helped us determine with more confidence whether LOS decreased due to an increase in transfers to other hospitals, or because of higher quality care in the hospital. We could not access data on patients\u0026rsquo; outcomes after transfer, which could have helped understand the complexity and severity of the transferred patients. Our finding of no significant change in mortality was not consistent with multiple studies globally that found increase in mortality in various locations for cardiovascular and cerebrovascular conditions \u003csup\u003e7,8\u003c/sup\u003e and could be due to the small number of deaths, and resulting statistical power, that occurred in the time period. Nevertheless, the comprehensive use of all cases over a long time period provides a more complete picture than previously described of the impact of COVID19 on acute clinical services in peripheral secondary care centers. \u003cu\u003e\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe pandemic caused major disruptions to healthcare services. It forced major and sudden adaptations by management to balance the need of COVID patients while maintaining other services, causing possible unintended consequences to non-COVID patients. Our study analyzed the impact of organizational changes during the COVID-19 pandemic on the treatment and outcome of patients admitted with STEMI and CVA in the Northern periphery of Israel. In the Cardiology department, which did not see a major reallocation of staff and resources, care indicators suggested improved processes and outcomes for STEMI patients. The Neurology department, despite being severely affected by re-organizations made, performed similar to the pre-pandemic period. This suggests that secondary care institutions and their staff can demonstrate agility and resilience in the face of severe and unplanned disruption. Mixed-methods studies would help understand how clinical, staff adapted to maintain quality of services, at what personal cost, and where in the complex patient management processes did disruption occur and how it was mitigated.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design:\u003c/p\u003e\n\u003cp\u003eThis is a retrospective,\u0026nbsp;descriptive\u0026nbsp;time-series analysis.\u0026nbsp;We included all patients aged 18 and above who presented at ZMC and were diagnosed with either CVA or STEMI, between 1/1/2018-15/4/2022. STEMI\u0026nbsp;patients presented to ZMC through the ED or by\u0026nbsp;Emergency Medical Services (EMS)\u0026nbsp;directly\u0026nbsp;to the catheterization\u0026nbsp;room. CVA\u0026nbsp;patients presented to the ED with\u0026nbsp;symptoms of\u0026nbsp;CVA\u0026nbsp;and the discharged diagnosis was recorded as CVA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIsrael implemented COVID related restrictions on 15/3/2020 when all places of entertainment, schools, kindergartens, and day-cares were shut down \u003csup\u003e18\u003c/sup\u003e. After more than two years, on 15/4/2022, ZMC\u0026rsquo;s COVID department closed, and the hospital\u0026rsquo;s organizational status returned to its pre-pandemic state. We therefore considered the COVID period as 15/3/2020-15/4/202\u003cspan dir=\"RTL\"\u003e2\u003c/span\u003e. The pre-pandemic period was considered as 1/1/2018-14/3/2020.\u003c/p\u003e\n\u003cp\u003eWe reviewed all cases of CVA and STEMI patients identified retrospectively based on the International Classification of Diseases \u0026nbsp;(ICD) 9 codes for STEMI and CVA recorded in ZMC\u0026rsquo;s electronic medical records (EMR) at discharge. We included STEMI patients who presented to the ED with ST elevation in the ECG with ICD-9 codes 410.0-410.6 and 410.8. Patients that had STEMI in the hospital after admission to the ED or the in-patient ward and patients that had non-STEMI or unstable angina and later developed STEMI were also excluded.\u003c/p\u003e\n\u003cp\u003eFor CVA patients, we included all patients that presented to the ED with CVA symptoms and were assessed by a neurologist with imaging or were diagnosed with CVA by EMS and had an ICD-9 code at discharge/death compatible with CVA (Supplementary) \u003csup\u003e19\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData collected:\u003c/p\u003e\n\u003cp\u003eWe\u0026nbsp;extracted demographic and clinical data from\u0026nbsp;patients\u0026rsquo;\u0026nbsp;EMR as well as from individual-level national\u0026nbsp;health quality indicators\u0026nbsp;on STEMI and CVA patients. Those indicators include timing from arrival to the hospital until PCI and the length of the PCI for STEMI patients; for CVA, indicators include the time\u0026nbsp;from\u0026nbsp;hospital arrival to\u0026nbsp;performance of imaging (head CT/MRI)\u0026nbsp;for patients with acute stroke\u0026nbsp;presenting up to 3.5 hours from symptom onset and timing of receiving\u0026nbsp;Intravenous thrombolytic treatment (IV-rtPA) \u003csup\u003e22\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBaseline demographic data included age,\u0026nbsp;ethnicity,\u0026nbsp;gender, and\u0026nbsp;comorbidities (smoking\u0026nbsp;-current or previous,\u0026nbsp;hypertension\u0026nbsp;and\u0026nbsp;diabetes).\u0026nbsp;For STEMI patients, we extracted transportation mode to the hospital (with/without EMS), and timing\u0026nbsp;from symptom onset to ED arrival (above/below 2 hours).\u0026nbsp;To estimate disease severity in those patients on admission, we extracted their\u0026nbsp;Killip status\u003csup\u003e23\u003c/sup\u003e.\u0026nbsp;We also calculated each patient\u0026rsquo;s\u0026nbsp;TIMI risk score\u0026nbsp;(a scoring tool to calculate the likelihood risk of all-cause mortality at 30 days) \u003csup\u003e24\u003c/sup\u003e and LOS.\u0026nbsp;To assess post-PCI cardiac function, we reported the ejection fraction (EF) at discharge, measured by echocardiogram.\u0026nbsp;Our dataset also included admission\u0026nbsp;outcome\u0026nbsp;(discharge home/death/transfer to another hospital/rehabilitation institute) and unplanned\u0026nbsp;readmission within 30 days post-discharge.\u003c/p\u003e\n\u003cp\u003eFor CVA patients, we recorded transportation mode to the hospital and the proportion who arrived within 4.5 hours of symptom onset, reflecting the maximum time elapsed to receive\u0026nbsp;Tissue plasminogen activator (tPA), the treatment\u0026nbsp;for\u0026nbsp;ischemic stroke.\u0026nbsp;\u003csup\u003e10,25\u003c/sup\u003e. We also extracted NIHSS on admission, LOS, admission outcome (discharge/death/transfer to another hospital/ rehabilitation) and unplanned readmission within 30 days post-discharge.\u003c/p\u003e\u003cp\u003eData analysis:\u003c/p\u003e\n\u003cp\u003eFor each patient group we compared the pre-pandemic (1/1/2018-14/3/2020) to the pandemic period (15/3/2020-15/4/202\u003cspan dir=\"RTL\"\u003e2\u003c/span\u003e), including patient characteristics, clinical presentation and intra-hospital metrics\u0026nbsp;using means, medians,\u0026nbsp;proportions\u0026nbsp;with standard deviation and interquartile range.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll binary and categorical variables were compared using Chi-square tests. For STEMI patients these included gender, co-morbidities, ethnicity, way of arrival at the hospital (with/without EMS), time\u0026nbsp;from symptom onset to ED arrival (Above/below 2 hours),\u0026nbsp;Killip status\u0026nbsp;classification (1,2,3 and 4), clinical outcome and\u0026nbsp;unplanned\u0026nbsp;readmission within 30 days post-discharge\u0026nbsp;to the\u0026nbsp;ED.\u0026nbsp;For CVA patients they included time from\u0026nbsp;symptom onset to ED (above or below 4.5 hours), NIHSS\u0026nbsp;on\u0026nbsp;admission (0-4,\u0026nbsp;5-15, 16-20 and \u0026gt;21),\u0026nbsp;unplanned\u0026nbsp;readmission within 30 days post-discharge\u0026nbsp;and\u0026nbsp;discharge category:\u0026nbsp;death,\u0026nbsp;transfer to\u0026nbsp;other hospital, home, or release to rehabilitation institute\u0026nbsp;(or recommendation for such rehabilitation). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContinuous variables\u0026nbsp;were first assessed for normality using Shapiro-Wilk test \u003csup\u003e26\u003c/sup\u003e. For normally distributed variables we compared means using t-test. For others, we compared medians using the\u0026nbsp;Wilcoxon\u0026ndash;Mann\u0026ndash;Whitney test. For STEMI patients, continuous variables were: age,\u0026nbsp;TIMI\u0026nbsp;risk score, time\u0026nbsp;from arrival to the ED until PCI,\u0026nbsp;length of PCI,\u0026nbsp;LOS\u0026nbsp;and EF at discharge.\u0026nbsp;For CVA patients, continuous variables were: door-to-CT/MRI (Divided into those who arrived within 4.5 hours from symptom onset-to-arrivals and those who arrived above 4.5 hours from symptom onset-to-arrivals and thus weren\u0026rsquo;t eligible for TPA),\u0026nbsp;door-to-IV-TPA\u0026nbsp;and\u0026nbsp;LOS.\u003c/p\u003e\n\u003cp\u003ePatients who arrived with STEMI but were classified by physicians as \u0026ldquo;late STEMI\u0026rdquo; or died before PCI were excluded from analysis of timing from ED-to-PCI and EF after PCI but were included for all other analyses. 30 patients were identified as needed urgent bypass procedure during their catheterization, were excluded from \u0026ldquo;Time of PCI procedure\u0026rdquo;, but were included in other analyses.\u0026nbsp; STEMI and CVA patients who died during the hospitalization were excluded from 30-days re-admission analysis. Missing observations were excluded from the analysis. 2 CVA patients were tourists; hence their ethnicity was missing.\u003c/p\u003e\n\u003cp\u003eR statistical programming 4.2.1 was used for statistical analysis. A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements and conflict of interest:\u003c/p\u003e\n\u003cp\u003eAuthers\u0026rsquo; contributions:\u0026nbsp;ME, AR and AA-O\u0026nbsp;conceived and designed the study.\u0026nbsp;JH contributed to the STEMI parameters identification and interpretation. RS contributed to the CVA parameters identification and interpretation.\u0026nbsp;TB, RP and IK\u0026nbsp;extracted the data. TB managed the data. TB, ME, and\u0026nbsp;SS\u0026nbsp;analysed the data. TB, ME,\u0026nbsp;SS\u0026nbsp;interpreted the data. TB drafted the first draft of the manuscript. All authors reviewed the manuscript. All authors agreed both to be personally accountable for the author\u0026rsquo;s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003eAvailability of the data and materials:\u0026nbsp;The datasets used and analysed during the current study are available from the corresponding author on reasonable request\u0026nbsp;and pending further ethical and facility approval.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate: The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The study received approval\u0026nbsp;and consent\u0026nbsp;from the ZMC ethics committee, number\u0026nbsp;ZIV-0014-22.\u0026nbsp;Due to the retrospective nature of the study written informed consent was not required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interest:\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding:\u0026nbsp;The authors state that the study was performed at the hospital without external help or funding sources.\u003c/p\u003e\n\u003cp\u003ePrior presentation: This manuscript has not been previously published and is not under consideration in the same or substantially similar form in any other peer-reviewed media. We presented preliminary results in a poster presented at our faculty\u0026rsquo;s annual research day in 2023.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMeschi, T. \u003cem\u003eet al.\u003c/em\u003e Reorganization of a large academic hospital to face COVID-19 outbreak: The model of Parma, Emilia‐Romagna region, Italy. Eur J Clin Invest 50, (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBar-On, E. \u003cem\u003eet al.\u003c/em\u003e Establishing a COVID-19 treatment centre in Israel at the initial stage of the outbreak: challenges, responses and lessons learned. 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Characteristics of CVA and STEMI patients.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.652173913043477%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.47826086956522%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eCVA patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.869565217391305%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSTEMI patients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.686411149825783%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37630662020906%\" valign=\"top\"\u003e\n \u003cp\u003ePre-pandemic (1/1/2018-14/3/2020) (n=537)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.80836236933798%\" valign=\"top\"\u003e\n \u003cp\u003ePandemic (15/3/2020-15/4/2022) (n=492)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\" valign=\"top\"\u003e\n \u003cp\u003ePre-pandemic (1/1/2018-14/3/2020) (n=261)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.414634146341463%\" valign=\"top\"\u003e\n \u003cp\u003ePandemic (15/3/2020-15/4/2022) (n=234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.498257839721255%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.686411149825783%\" valign=\"top\"\u003e\n \u003cp\u003eAge, mean (SD)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37630662020906%\" valign=\"top\"\u003e\n \u003cp\u003e69.56\u0026nbsp;(12.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.80836236933798%\" valign=\"top\"\u003e\n \u003cp\u003e69.41 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\" valign=\"top\"\u003e\n \u003cp\u003e61.3 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.414634146341463%\" valign=\"top\"\u003e\n \u003cp\u003e62.8 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.498257839721255%\" valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.686411149825783%\" valign=\"top\"\u003e\n \u003cp\u003eGender (% male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37630662020906%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;329 (61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.80836236933798%\" valign=\"top\"\u003e\n \u003cp\u003e298\u0026nbsp;(60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\" valign=\"top\"\u003e\n \u003cp\u003e217 (83.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.414634146341463%\" valign=\"top\"\u003e\n \u003cp\u003e199 (85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.498257839721255%\" valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.686411149825783%\" valign=\"top\"\u003e\n \u003cp\u003eEthnicity (% minorities)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37630662020906%\" valign=\"top\"\u003e\n \u003cp\u003e152\u0026nbsp;(28.3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.80836236933798%\" valign=\"top\"\u003e\n \u003cp\u003e159\u0026nbsp;(32.3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\" valign=\"top\"\u003e\n \u003cp\u003e69 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.414634146341463%\" valign=\"top\"\u003e\n \u003cp\u003e74 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.498257839721255%\" valign=\"top\"\u003e\n \u003cpre\u003e0.24\u003c/pre\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.686411149825783%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37630662020906%\" valign=\"top\"\u003e\n \u003cp\u003e117\u0026nbsp;(21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.80836236933798%\" valign=\"top\"\u003e\n \u003cp\u003e125\u0026nbsp;(25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\" valign=\"top\"\u003e\n \u003cp\u003e104 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.414634146341463%\" valign=\"top\"\u003e\n \u003cp\u003e100 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.498257839721255%\" valign=\"top\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.686411149825783%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37630662020906%\" valign=\"top\"\u003e\n \u003cp\u003e377\u0026nbsp;(70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.80836236933798%\" valign=\"top\"\u003e\n \u003cp\u003e331\u0026nbsp;(67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\" valign=\"top\"\u003e\n \u003cp\u003e107 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.414634146341463%\" valign=\"top\"\u003e\n \u003cp\u003e104 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.498257839721255%\" valign=\"top\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.686411149825783%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.37630662020906%\" valign=\"top\"\u003e\n \u003cp\u003e239 (44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.80836236933798%\" valign=\"top\"\u003e\n \u003cp\u003e243 (49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.18815331010453%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\" valign=\"top\"\u003e\n \u003cp\u003e79 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.414634146341463%\" valign=\"top\"\u003e\n \u003cp\u003e63 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.498257839721255%\" valign=\"top\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*After Shapiro-Wilk test for normality, Student\u0026rsquo;s t-test was performed. \u0026nbsp; SD- Standard Deviation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. CVA patient\u0026rsquo;s condition treatment and outcome.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.48909657320872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.79127725856698%\" valign=\"top\"\u003e\n \u003cp\u003ePre-pandemic (1/1/2018-14/3/2020) (n=537)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003ePandemic (15/3/2020-15/4/2022) (n=492)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.48909657320872%\" valign=\"top\"\u003e\n \u003cp\u003eWay of arrival (% EMS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.79127725856698%\" valign=\"top\"\u003e\n \u003cp\u003e274 (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e282 (57.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.48909657320872%\" valign=\"top\"\u003e\n \u003cp\u003eTime from symptom to arrival (less than 4.5 hours, %)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.79127725856698%\" valign=\"top\"\u003e\n \u003cp\u003e183 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e191 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.48909657320872%\" valign=\"top\"\u003e\n \u003cp\u003eNIHSS category (n, %)\u003c/p\u003e\n \u003cp\u003e0-4\u003c/p\u003e\n \u003cp\u003e5-15\u003c/p\u003e\n \u003cp\u003e16-20\u003c/p\u003e\n \u003cp\u003e21-42\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.79127725856698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e261 (49.3)\u003c/p\u003e\n \u003cp\u003e244 (46.1)\u003c/p\u003e\n \u003cp\u003e16 (3)\u003c/p\u003e\n \u003cp\u003e8 (1.5)\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e233 (48.9)\u003c/p\u003e\n \u003cp\u003e214 (44.9)\u003c/p\u003e\n \u003cp\u003e20 (4)\u003c/p\u003e\n \u003cp\u003e9 (1.8)\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.48909657320872%\" valign=\"top\"\u003e\n \u003cp\u003eMedian time in minutes from arrival to imaging (CT/MRI) in patients less than 4.5 hours, median [interquartile range] **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.79127725856698%\" valign=\"top\"\u003e\n \u003cp\u003e19 [14,30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e23 [17,34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.48909657320872%\" valign=\"top\"\u003e\n \u003cp\u003eMedian time in minutes from arrival to imaging (CT/MRI) in patients above 4.5 hours, median [interquartile range] **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.79127725856698%\" valign=\"top\"\u003e\n \u003cp\u003e55 [27,90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e63 [36,98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.48909657320872%\" valign=\"top\"\u003e\n \u003cp\u003eTime from arrival to tPA (for eligible patients) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.79127725856698%\" valign=\"top\"\u003e\n \u003cp\u003e45 [37,60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e49 [35,64]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cpre\u003e0.61\u003c/pre\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.48909657320872%\" valign=\"top\"\u003e\n \u003cp\u003e30 days re admission (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.79127725856698%\" valign=\"top\"\u003e\n \u003cpre\u003e\u003cspan dir=\"RTL\"\u003e62\u003c/span\u003e/530 (11.6%)\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e68/480 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.48909657320872%\" valign=\"top\"\u003e\n \u003cp\u003eOutcomes:\u003c/p\u003e\n \u003cp\u003eDischarged home\u003c/p\u003e\n \u003cp\u003eTransfer to another hospital\u003c/p\u003e\n \u003cp\u003eRehab institute/recommendation/ severe case for rehab\u003c/p\u003e\n \u003cp\u003eIntra-hospital mortality\u003c/p\u003e\n \u003cp\u003eRefuse to hospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.79127725856698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e284 (53.1%)\u003c/p\u003e\n \u003cp\u003e77 (14.4%)\u003c/p\u003e\n \u003cp\u003e151 (28.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7 (1.3%)\u003c/p\u003e\n \u003cp\u003e15 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e235 (47.7%)\u003c/p\u003e\n \u003cp\u003e100 (20.3%)\u003c/p\u003e\n \u003cp\u003e126 (25.6%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12 (2.4%)\u003c/p\u003e\n \u003cp\u003e19 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.48909657320872%\" valign=\"top\"\u003e\n \u003cp\u003eLOS in days, median [interquartile range] **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.79127725856698%\" valign=\"top\"\u003e\n \u003cpre\u003e4 [2,6]\u003c/pre\u003e\n \u003cpre\u003e \u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cpre\u003e3 [2,6]\u003c/pre\u003e\n \u003cpre\u003e \u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cpre\u003e0.049\u003c/pre\u003e\n \u003cpre\u003e \u003c/pre\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*=After Shapiro-Wilk test for normality, Student\u0026rsquo;s t-test was performed. **=After Shapiro-Wilk test for normality, Mann-Whitney non-parametric test was performed. EMS- Emergency medical services. NIHSS- NIH stroke scale. tPA- tissue Plasminogen Activator. LOS- Length of stay.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. STEMI patient\u0026rsquo;s condition, treatment, and outcome.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-pandemic (1/1/2018-14/3/2020)\u003c/p\u003e\n \u003cp\u003e(n=261)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePandemic (15/3/2020-15/4/2022)\u003c/p\u003e\n \u003cp\u003e(n=234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWay of arrival (EMS %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e195 (74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e187 (79.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLate admission MI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKillip (n, %) 1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e240 (91.9)\u003c/p\u003e\n \u003cp\u003e10 (3.8)\u003c/p\u003e\n \u003cp\u003e3 (1.1)\u003c/p\u003e\n \u003cp\u003e8 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e209 (89)\u003c/p\u003e\n \u003cp\u003e17 (7.2)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003cp\u003e8 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e0.13\u003c/pre\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTIMI risk score, median [interquartile range]**\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e3 [2,5]\u003c/pre\u003e\n \u003cpre\u003e \u003c/pre\u003e\n \u003cpre\u003e26\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e4[2,5]\u003c/pre\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e0.02\u003c/pre\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTime from symptom to arrival (% below 2 hours)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e98 (39.6)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e0.3\u003c/pre\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eED-to-needle in minutes, median [\u003c/p\u003e\n \u003cp\u003einterquartile range] **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e50 [33,77] \u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45 [26\u003cspan dir=\"RTL\"\u003e,\u003c/span\u003e 70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTime of PCI procedure in minutes, median [interquartile range] **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 [26, 46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 [25, 43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEF after PCI, median [interquartile range] **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e45 [42.5, 50]\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e50 [45,52.5]\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e0.02\u003c/pre\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMortality (Died, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e9 (3.4)\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e14 (5.9)\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLOS in days, median [interquartile range]**\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e4 [3,5]\u003c/pre\u003e\n \u003cpre\u003e \u003c/pre\u003e\n \u003cpre\u003e4.48\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e4 [3,4]\u003c/pre\u003e\n \u003cpre\u003e \u003c/pre\u003e\n \u003cpre\u003e3.84\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e\u0026lt;0.001\u003c/pre\u003e\n \u003cpre\u003e \u003c/pre\u003e\n \u003cpre\u003e0.01\u003c/pre\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30 days re admission (arrived %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e37 (14.7)\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cpre\u003e16 (7.8)\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*=After Shapiro-Wilk test for normality, Student\u0026rsquo;s t-test was performed. **=After Shapiro-Wilk test for normality, Mann-Whitney non-parametric test was performed. \u0026nbsp;EMS- Emergency medical services. MI- Myocardial infraction. TIMI-Thrombolysis in Myocardial Infarction. ED- Emergency department. PCI- Percutaneous coronary intervention. EF- Ejection fraction. LOS- Length of stay.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, ST Elevation Myocardial Infarction, Stroke, Treatment Outcome, Length of Stay","lastPublishedDoi":"10.21203/rs.3.rs-4420658/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4420658/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe indirect impact of the COVID-19 pandemic on clinical services in peripheral hospitals is not fully described. We compared the impact of COVID-19 on Cerebral Vascular Accident (CVA) and ST-elevation myocardial infarction (STEMI) management and outcome in an Israeli peripheral hospital.\u003c/p\u003e \u003cp\u003eWe included 1029 CVA and 497 STEMI patients. Those who arrived during (15/3/2020-15/4/2022) and before (1/1/2018-14/3/2020) the pandemic were demographically comparable. During the pandemic, median time for CVA patients from arrival to imaging was longer (23 vs. 19 minutes, p\u0026thinsp;=\u0026thinsp;0.001); timing from arrival to tissue Plasminogen Activator administration was similar (49 vs. 45 min, p\u0026thinsp;=\u0026thinsp;0.61); transfer to another hospital was more common (20.3% vs. 14.4% p\u0026thinsp;=\u0026thinsp;0.01) and median length of stay (LOS) was shorter (3 vs. 4 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among STEMI patients, median time from arrival to intervention intra- pandemic was shorter (45 vs. 50 minutes p\u0026thinsp;=\u0026thinsp;0.02); Mean LOS shorter (3.86 vs. 4.48 p\u0026thinsp;=\u0026thinsp;0.01), and unplanned re-admission less frequent (7.8% vs. 14.6% p\u0026thinsp;=\u0026thinsp;0.01). Mortality didn\u0026rsquo;t significantly change.\u003c/p\u003e \u003cp\u003eOur data shows no major negative impact of the COVID-19 pandemic on CVA outcomes, and possibly improved care for STEMI patients. Follow-up qualitative studies with neurology and cardiology staff will inform how quality of care was maintained during the crises.\u003c/p\u003e","manuscriptTitle":"Impact of the COVID-19 pandemic on acute Cardiology and Neurology services in a secondary peripheral hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-30 07:49:17","doi":"10.21203/rs.3.rs-4420658/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-08T04:54:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-06T23:17:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67050577135911918552958627011703185998","date":"2024-08-05T14:31:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-30T07:58:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291647302805337309834445456187701914177","date":"2024-06-23T12:13:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-22T06:42:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-22T06:41:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-21T17:26:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-20T05:09:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-14T16:41:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90775a8d-7b07-4ee0-9f07-4485453d5921","owner":[],"postedDate":"May 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32433750,"name":"Health sciences/Diseases"},{"id":32433751,"name":"Health sciences/Health care/Health policy"},{"id":32433752,"name":"Health sciences/Health care/Health services"}],"tags":[],"updatedAt":"2024-12-02T17:25:29+00:00","versionOfRecord":{"articleIdentity":"rs-4420658","link":"https://doi.org/10.1038/s41598-024-80872-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-26 15:57:54","publishedOnDateReadable":"November 26th, 2024"},"versionCreatedAt":"2024-05-30 07:49:17","video":"","vorDoi":"10.1038/s41598-024-80872-7","vorDoiUrl":"https://doi.org/10.1038/s41598-024-80872-7","workflowStages":[]},"version":"v1","identity":"rs-4420658","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4420658","identity":"rs-4420658","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}