Literature DB >> 32632635

Effect of lockdown on the management of ischemic stroke: an Italian experience from a COVID hospital.

Giovanni Frisullo1, Valerio Brunetti2, Riccardo Di Iorio1, Aldobrando Broccolini1,3, Pietro Caliandro1, Mauro Monforte1, Roberta Morosetti1, Carla Piano1, Fabio Pilato1, Paolo Calabresi1,3, Giacomo Della Marca1,3.   

Abstract

OBJECTIVE: To evaluate the impact of the lockdown measures, consequent to the outbreak of COVID-19 pandemic, on the quality of pre-hospital and in-hospital care of patients with acute ischemic stroke.
METHODS: This is an observational cohort study. Data sources were the clinical reports of patients admitted during the first month of lockdown and discharged with a confirmed diagnosis of stroke or TIA. Data were collected in the interval ranging from March 11th to April 11th 2020. As controls, we evaluated the clinical reports of patients with stroke or TIA admitted in the same period of 2019.
RESULTS: The clinical reports of patients eligible for the study were 52 in 2020 (71.6 ± 12.2 years) and 41 in 2019 (73.7 ± 13.1 years). During the lockdown, we observed a significant increase in onset-to-door time (median = 387 vs 161 min, p = 0.001), a significant reduction of the total number of thrombolysis (7 vs 13, p = 0.033), a non-significant increase of thrombectomy (15 vs 9, p = 0.451), and a significant increase in door-to-groin time (median = 120 vs 93 min, p = 0.048). No relevant difference was observed between 2019 and 2020 in the total number of patients admitted.
CONCLUSIONS: Due to the COVID-19 pandemic and lockdown measures, the stroke care pathway changed, involving both pre-hospital and in-hospital performances.

Entities:  

Keywords:  Covid; Hub-and-spoke; Lockdown; SARS-CoV-2; Stroke

Mesh:

Year:  2020        PMID: 32632635      PMCID: PMC7338130          DOI: 10.1007/s10072-020-04545-9

Source DB:  PubMed          Journal:  Neurol Sci        ISSN: 1590-1874            Impact factor:   3.307


Introduction

Since the outbreak of the Coronavirus disease 2019 (COVID-19) pandemic, Italian healthcare system has suffered a heavy backlash. On March 11th, 2020, the extension of restrictive measures to the entire Italian territory with closure of all non-essential businesses and industries and limitation to the movement of people (“lockdown”) required some adaptations of the integrated care pathway (ICP) focused on time-dependent diseases such as stroke [1]. Intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT) in ischemic stroke (IS) are extremely time-sensitive. The “hub-and-spoke model” in the pre-hospital phase and the definition of ICPs with standardized processes within the in-hospital phase are essential to reduce delay and increase the number of treatable patients [2-4]. The quality of pre-hospital care is reflected by the onset-to-door time (ODT) that is the time from stroke onset, or from the last moment the patient was known without symptoms, to emergency department (ED) arrival [5]. The indicators of the in-hospital care pathway are the time elapsing from the moment the patient enters the ED to the moment he/she receives revascularization procedures such as IVT, the door-to-needle time (DNT), and/or EVT, the door-to-groin time (DGT) [6]. The primary objective of this study is to evaluate how the first month of lockdown has influenced the quality of pre-hospital and in-hospital care of patients with acute stroke, by analyzing the performance indicators of our hospital ICP dedicated to IS.

Methods

In this observational cohort study, we evaluated the performance indicators of our hospital ICP for acute IS. The sources of the data were the clinical reports of all consecutive patients admitted in the ED of Policlinico A. Gemelli Hospital in Rome for IS, in the time interval between March 11th (according to the Decree of the President of the Council of Ministers, the date of the extension of the quarantine to all of Italy) and April 11th 2020. During this period, before admission to ED, all patients after a pre-triage were classified as suspected (s-COVID) or unsuspected COVID-19 (n-COVID) according to WHO recommendations [7] and divided into separate dedicated pathways. The clinical report was included in the study only if the diagnosis of stroke (ICD-X codes 433,434,436) or TIA (ICD-X codes 435) was confirmed at discharge. The following variables were considered: total amount of patients admitted, age, gender, diagnosis (stroke or TIA), type of access (directly to ED or from a spoke center), ODT, door-to-CT time, time spent in Emergency Department, NIHSS score at admission, NIHSS score at discharge, intra-venous thrombolysis, DNT, endovascular treatment, DGT, fever, pneumonia, length of stay in hospital, death, and stroke team evaluation. As controls, we used the clinical reports of all consecutive patients admitted in the same period of 2019 and successively discharged with a diagnosis of IS or TIA. Statistical analysis was performed in two steps. In the first step, patients admitted for stroke during the lockdown period were compared to those admitted in March–April 2019. Finally, stroke patients admitted during the lockdown, based on the COVID-19 triage, were classified as s-COVID or n-COVID. S-COVID and n-COVID were compared for the same clinical variables. Continuous and categorical data were summarized using median and range or counts and percentages, respectively. Before the comparison, the normality of distribution of numerical variables of the samples has been tested by means of the Shapiro-Wilk test, with a significance level of p 0 < .05; the distribution was not normal, and therefore, a non-parametric test (Mann-Whitney U test) was applied for numerical variables. Pearson’s Chi-square (χ2) was used for categorical variables. The threshold for significance was p < 0.05. All statistics were performed by means of a dedicated software (Statistical Package for Social Science, SPSS® version 20). The study was conducted according to the declaration of Helsinki and was approved by the local Ethics Committee (Prot. 13729/20 ID:3065).

Results

In years 2020 and 2019, the clinical reports of patients eligible for the study were 52 (31 men and 21 women, mean age: 71.6 ± 12.2 years) and 41 (19 men and 22 women, mean age: 73.7 ± 13.1 years), respectively. No significant differences were observed between groups concerning age and gender. Regarding pre-hospital measures, a significant increase in ODT was observed in 2020 (median = 387 vs 161 min, p = 0.001). Regarding in-hospital performance indicators, we observed a significant reduction of the total number of thrombolysis (7 vs 13, p = 0.033) and a non-significant increase of EVT (15 vs 9, p = 0.451) performed during the first month of lockdown. Moreover, we observed a significant increase in DGT in the 2020 period (median = 120 vs 93 min, p = 0.048), while no significant difference was observed in DNT. Finally, a significant reduction of length of hospitalization was observed in 2020 (median = 4 vs 6 days; p = 0.007). Detailed results are shown in Table 1.
Table 1

Demographic, clinical features, and performance indicators: 2020 vs 2019

2019 (n = 41)2020 (n = 52)p value
Age, years, mean (SD)73.7 (13.1)71.6 (12.2)NS
Gender, male, No. (%)19 (46.3)31 (59.6)NS
Diagnosis
  Stroke, No. (%)38 (92.7)49 (94.2)NS
  TIA, No. (%)3 (7.3)3 (5.8)NS
Type of access
  Directly to ED, No. (%)37 (90.2)41 (78.8)NS
  Spoke, No. (%)4 (9.8)11 (21.2)NS
Integrated care pathway performance indicators
  Onset-to-door time, min, median (range)161 (31–6042)387 (35–9496)0.001 (U = 1277.500)
  Door-to-CT time, min, median (range)45 (5–720)54 (13–502)NS
  Thrombolysis, No. (%)13 (31.7)7 (13.5)0.033 (χ2 = 4.521)
  Door-to-needle time, min, median (range)58 (27–132)63 (41–70)NS
  Thrombectomy, No. (%)9 (22.0)15 (28.8)NS
  Door-to-groin time, min, median, (range)93 (69–122)120 (83–271)0.048 (U = 101.000)
  Thrombolysis + thrombectomy, No.6.0 (14.6)6.0 (11.5)NS
Length of stay in ED, min, median (range)195 (0–3395)134 (3–1078)NS
Hospitalization, days, median (range)6 (1–30)4 (0–26)0.007 (U = 622.000)
Death, No. (%)3 (7.3)5 (9.6)NS
Stroke team evaluation, No. (%)36 (87.8)50 (96.2)NS
Clinical features
  NIHSS at admission, median (range)4 (0–24)5 (0–25)NS
  NIHSS at discharge, median (range)1 (0–18)2 (0–23)NS
  Fever during hospitalization, No. (%)9 (22.0)17 (32.7)NS
  Pneumonia during hospitalization, No. (%)4 (9.8)12 (23.1)NS
Demographic, clinical features, and performance indicators: 2020 vs 2019 Comparing s-COVID with n-COVID patients, DGT was significantly longer in s-COVID (median = 168 vs 105, p = 0.004). It was not possible to make a comparison between groups for the DNT because in the s-COVID group, the only thrombolytic treatment was performed in a SPOKE center. The total number of deaths was significantly higher in the s-COVID group (2 vs 3, p = 0.015). Detailed results between s-COVID and n-COVID are shown in Table 2.
Table 2

Demographic, clinical features, and performance indicators: s-COVID vs n-COVID

n-COVID (n = 42)s-COVID (n = 10)p value
Age, years, mean (SD)70.5 (12.9)76.1 (7.5)NS
Gender, male, No. (%)26 (61.9)5 (50.0)NS
Diagnosis
  Stroke, No. (%)39 (92.9)10 (100)NS
  TIA, No. (%)3 (7.1)0 (0)NS
Type of access
  Directly to ED, No. (%)34 (81.0)8 (80)NS
  Spoke, No. (%)8 (19.0)2 (20)NS
Integrated care pathway performance indicators
  Onset-to-door, min, median (range)387 (35–9496)632 (75–4725)NS
  Door-to-CT, min, median (range)53 (13–366)70 (39–502)NS
  Thrombolysis, No. (%)6 (14.3)1 (10.0)NS
  Door-to-needle time, min, median (range)63 (41–70)
  Thrombectomy, No. (%)12 (28.6)3 (30.0)NS
  Door-to-groin time, min, median (range)105 (83–123)168 (129–271)0.004 (U = 36.000)
  Thrombolysis + thrombectomy, No. (%)5.0 (11.9)1.0 (10.0)NS
  Length of stay in ED, min, median (range)131 (3–1078)233 (16–567)NS
  Hospitalization, days, median (range)4 (0–26)6 (1–10)NS
Death, No. (%)2 (4.8)3 (30.0)0.015 (χ2 = 5.920)
  Stroke team evaluation, No. (%)41 (97.6)9 (90.0)NS
Clinical features
  NIHSS at admission, median (range)4 (0–24)7 (1–25)NS
  NIHSS at discharge, median (range)2 (0–23)2 (0–5)NS
  Fever during hospitalization, No. (%)10 (23.8)7 (70.0)0.006 (χ2 = 7.526)
  Pneumonia during hospitalization, No. (%)6 (14.3)5 (50.0)0.025 (χ2 = 5.056)
  Confirmed diagnosis of COVID-19, No. (%)2 (4.8)1 (10.0)NS
Demographic, clinical features, and performance indicators: s-COVID vs n-COVID

Discussion

The massive spread of COVID-19 and the ‘lockdown’ strategy in Italy have impacted procedures of time-dependent disease management [8]. The Gemelli Hospital, one of the 4 Hubs in the Stroke Network of Regione Lazio, serves a population of around 1.7 million and is equipped with a specific ICP for IS management. Due to the outbreak of COVID-19 pandemic, the Gemelli Hospital was appointed as a COVID Hospital and the local stroke ICP underwent some changes with the identification of s-COVID-19 and n-COVID-19 pathways. As primary endpoint, we observed a significant increase in the ODT, the key performance indicator of pre-hospital stroke care related to the behavior of patients and bystanders and the efficiency of the emergency medical service (EMS) [9]. Psychological factors such as fear of exposure to COVID-19 could have contributed to a detrimental wait-and-see behavior. The dangerous interpretation of “stay at home” probably is due to the lack of specific education campaigns explaining the serious health consequences of a delay in diagnosis of IS and the erroneous perception that COVID-19 is more severe than stroke. Less likely, the increase in the ODT could be due to a delay in a COVID-oriented EMS. Besides, we observed in 2020 a significant reduction in the number of IVT which could be due to the ODT delay and to an increase in the number of patients accessing to ED outside the time window for IVT. We observed a significant increase of DGT, essentially due to the delay in EVT observed in s-COVID patients since their DGT is significantly higher than DGT of n-COVID patients. The increase of DGT in s-COVID patients is widely expected and strictly linked to the complexity of the clinical care pathway of the s-COVID patient that includes the time needed to wear recommended personal protection equipment, safe transport with bio-containment measures, preparation, and cleaning of the CT or angiography rooms. In apparent conflict with other authors [8, 10, 11], we did not observe a reduction in the overall number of IS patients admitted to our ED when comparing the current lockdown period with the same period of 2019, which is in line with the number of monthly patients admitted throughout the whole year. The reason for the lack of the expected reduction of admitted stroke patients could be linked to the role of hub of our hospital within the Lazio region network, which means a stroke physician 24 h a day and a interventional neuroradiologist on call 24/7. In order to avoid time-wasting and to reduce the risk of diffusion of SARS-CoV-2 between hospitals, emergency medical services could have opted for a primary centralization of stroke patients at our institution. However, Hub-and-Spoke model was not modified during lockdown period in our area. Further studies, involving spokes and other hubs of the stroke regional network, are warranted with the aim to clarify the overall number of stroke patients admitted in regional hospitals during lockdown. We observed a significantly shorter hospitalization in 2020, due to the need to reduce the risk of exposure to SARS-CoV-2 and to a shorter waiting list for radiological or laboratory tests, due to the interruption of all non-urgent activities. Moreover, an interruption of non-essential activities at the internal and external rehabilitation facilities could have resulted in a higher availability of beds and, in turn, in a faster transfer of patients to rehabilitation units. Comparing s-COVID and n-COVID patients, we observed an increase in mortality in patients who entered the s-COVID care pathway. This result, with the limit of the sample size, could mean that the management of a patient within the s-COVID pathway is independently associated with an increased risk of death probably due to previous respiratory comorbidity, signs of current infection, and to a more complex medical and nursing management. The main limitations of this study are the sample size and the short observation period, due to the choice to describe the management of acute stroke during the first month of Italian lockdown as a single-center experience, in a COVID hospital. The study highlights how during the COVID-19 pandemic and for the adoption of the lockdown strategy, the stroke care pathway changed, involving the management of patients both with and without SARS-CoV-2 infection. Furthermore, our data point out that it is crucial to preserve the integrity of stroke network and to continue sensitization campaigns on time-dependent pathologies during the COVID-19 pandemic.
  9 in total

1.  Contribution of Onset-to-Alarm Time to Prehospital Delay in Patients with Ischemic Stroke.

Authors:  Alejandro Gonzalez-Aquines; Adolfo C Cordero-Pérez; Mario Cristobal-Niño; Gil Pérez-Vázquez; Fernando Góngora-Rivera
Journal:  J Stroke Cerebrovasc Dis       Date:  2019-09-10       Impact factor: 2.136

2.  Ischemic stroke: clinical pathway impact.

Authors:  Antonio Giulio de Belvis; Franziska Michaela Lohmeyer; Andrea Barbara; Gabriele Giubbini; Carmen Angioletti; Giovanni Frisullo; Walter Ricciardi; Maria Lucia Specchia
Journal:  Int J Health Care Qual Assur       Date:  2019-04-15

3.  On being a neurologist in Italy at the time of the COVID-19 outbreak.

Authors:  Anna Bersano; Leonardo Pantoni
Journal:  Neurology       Date:  2020-04-03       Impact factor: 9.910

Review 4.  Streamlining of prehospital stroke management: the golden hour.

Authors:  Klaus Fassbender; Clotilde Balucani; Silke Walter; Steven R Levine; Anton Haass; James Grotta
Journal:  Lancet Neurol       Date:  2013-06       Impact factor: 44.182

5.  Time to Treatment With Endovascular Thrombectomy and Outcomes From Ischemic Stroke: A Meta-analysis.

Authors:  Jeffrey L Saver; Mayank Goyal; Aad van der Lugt; Bijoy K Menon; Charles B L M Majoie; Diederik W Dippel; Bruce C Campbell; Raul G Nogueira; Andrew M Demchuk; Alejandro Tomasello; Pere Cardona; Thomas G Devlin; Donald F Frei; Richard du Mesnil de Rochemont; Olvert A Berkhemer; Tudor G Jovin; Adnan H Siddiqui; Wim H van Zwam; Stephen M Davis; Carlos Castaño; Biggya L Sapkota; Puck S Fransen; Carlos Molina; Robert J van Oostenbrugge; Ángel Chamorro; Hester Lingsma; Frank L Silver; Geoffrey A Donnan; Ashfaq Shuaib; Scott Brown; Bruce Stouch; Peter J Mitchell; Antoni Davalos; Yvo B W E M Roos; Michael D Hill
Journal:  JAMA       Date:  2016-09-27       Impact factor: 56.272

6.  Emergency medical services for acute ischemic stroke: Hub-and-spoke model versus exclusive care in comprehensive centers.

Authors:  Kimon Bekelis; Symeon Missios; Shannon Coy; Bruce Mayerson; Todd A MacKenzie
Journal:  J Clin Neurosci       Date:  2018-10-19       Impact factor: 1.961

7.  Acute stroke management pathway during Coronavirus-19 pandemic.

Authors:  Claudio Baracchini; Alessio Pieroni; Federica Viaro; Vito Cianci; Anna M Cattelan; Ivo Tiberio; Marina Munari; Francesco Causin
Journal:  Neurol Sci       Date:  2020-04-09       Impact factor: 3.307

8.  Stroke integrated care pathway during COVID-19 pandemic.

Authors:  Giovanni Frisullo; Antonio Giulio De Belvis; Giacomo Della Marca; Carmen Angioletti; Paolo Calabresi
Journal:  Neurol Sci       Date:  2020-06-03       Impact factor: 3.307

9.  The Baffling Case of Ischemic Stroke Disappearance from the Casualty Department in the COVID-19 Era.

Authors:  Nicola Morelli; Eugenia Rota; Chiara Terracciano; Paolo Immovilli; Marco Spallazzi; Davide Colombi; Domenica Zaino; Emanuele Michieletti; Donata Guidetti
Journal:  Eur Neurol       Date:  2020-04-14       Impact factor: 1.710

  9 in total
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1.  Impact of COVID-19 on ischemic stroke care in Hungary.

Authors:  Péter Pál Böjti; Géza Szilágyi; Balázs Dobi; Rita Stang; István Szikora; Balázs Kis; Ákos Kornfeld; Csaba Óváry; Lóránd Erőss; Péter Banczerowski; Wojciech Kuczyński; Dániel Bereczki
Journal:  Geroscience       Date:  2021-08-18       Impact factor: 7.713

2.  Evaluation of Workflow Delays in Stroke Reperfusion Therapy: A Comparison between the Year-Long Pre-COVID-19 Period and the with-COVID-19 Period.

Authors:  Takeshi Yoshimoto; Masayuki Shiozawa; Junpei Koge; Manabu Inoue; Masatoshi Koga; Masafumi Ihara; Kazunori Toyoda
Journal:  J Atheroscler Thromb       Date:  2021-08-13       Impact factor: 4.394

Review 3.  Global burden of the COVID-19 associated patient-related delay in emergency healthcare: a panel of systematic review and meta-analyses.

Authors:  Vahid Mogharab; Mahshid Ostovar; Jakub Ruszkowski; Syed Zohaib Maroof Hussain; Rajeev Shrestha; Uzair Yaqoob; Poorya Aryanpoor; Amir Mohammad Nikkhoo; Parasta Heidari; Athar Rasekh Jahromi; Esmaeil Rayatdoost; Anwar Ali; Farshid Javdani; Roohie Farzaneh; Aref Ghanaatpisheh; Seyed Reza Habibzadeh; Mahdi Foroughian; Sayyed Reza Ahmadi; Reza Akhavan; Bita Abbasi; Behzad Shahi; Arman Hakemi; Ehsan Bolvardi; Farhad Bagherian; Mahsa Motamed; Sina Taherzadeh Boroujeni; Sheida Jamalnia; Amir Mangouri; Maryam Paydar; Neda Mehrasa; Dorna Shirali; Francesco Sanmarchi; Ayesha Saeed; Narges Azari Jafari; Ali Babou; Navid Kalani; Naser Hatami
Journal:  Global Health       Date:  2022-06-08       Impact factor: 10.401

4.  Effect of the COVID-19 pandemic on acute stroke reperfusion therapy: data from the Lyon Stroke Center Network.

Authors:  Cécile Plumereau; Tae-Hee Cho; Marielle Buisson; Camille Amaz; Matteo Cappucci; Laurent Derex; Elodie Ong; Julia Fontaine; Lucie Rascle; Roberto Riva; David Schiavo; Axel Benhamed; Marion Douplat; Thomas Bony; Karim Tazarourte; Célia Tuttle; Omer Faruk Eker; Yves Berthezène; Michel Ovize; Norbert Nighoghossian; Laura Mechtouff
Journal:  J Neurol       Date:  2020-09-09       Impact factor: 4.849

Review 5.  COVID-19 and cerebrovascular diseases: a comprehensive overview.

Authors:  Georgios Tsivgoulis; Lina Palaiodimou; Ramin Zand; Vasileios Arsenios Lioutas; Christos Krogias; Aristeidis H Katsanos; Ashkan Shoamanesh; Vijay K Sharma; Shima Shahjouei; Claudio Baracchini; Charalambos Vlachopoulos; Rossetos Gournellis; Petros P Sfikakis; Else Charlotte Sandset; Andrei V Alexandrov; Sotirios Tsiodras
Journal:  Ther Adv Neurol Disord       Date:  2020-12-08       Impact factor: 6.570

Review 6.  Stroke Care during the COVID-19 Pandemic: International Expert Panel Review.

Authors:  Narayanaswamy Venketasubramanian; Craig Anderson; Hakan Ay; Selma Aybek; Waleed Brinjikji; Gabriel R de Freitas; Oscar H Del Brutto; Klaus Fassbender; Miki Fujimura; Larry B Goldstein; Roman L Haberl; Graeme J Hankey; Wolf-Dieter Heiss; Isabel Lestro Henriques; Carlos S Kase; Jong S Kim; Masatoshi Koga; Yoshihiro Kokubo; Satoshi Kuroda; Kiwon Lee; Tsong-Hai Lee; David S Liebeskind; Gregory Y H Lip; Stephen Meairs; Roman Medvedev; Man Mohan Mehndiratta; Jay P Mohr; Masao Nagayama; Leonardo Pantoni; Panagiotis Papanagiotou; Guillermo Parrilla; Daniele Pastori; Sarah T Pendlebury; Luther Creed Pettigrew; Pushpendra N Renjen; Tatjana Rundek; Ulf Schminke; Yukito Shinohara; Wai Kwong Tang; Kazunori Toyoda; Katja E Wartenberg; Mohammad Wasay; Michael G Hennerici
Journal:  Cerebrovasc Dis       Date:  2021-03-23       Impact factor: 2.762

7.  Consequences of COVID-19 pandemic lockdown on emergency and stroke care in a German tertiary stroke center.

Authors:  Robin Jansen; John-Ih Lee; Bernd Turowski; Marius Kaschner; Julian Caspers; Michael Bernhard; Hans-Peter Hartung; Sebastian Jander; Tobias Ruck; Sven G Meuth; Michael Gliem
Journal:  Neurol Res Pract       Date:  2021-03-31

8.  Impact of COVID-19 on the rate of stroke cases at a tertiary hospital in Makkah, Saudi Arabia.

Authors:  Ebaa T Samkari; Amal M Alkhotani; Mohammed I Siddiqui
Journal:  Neurosciences (Riyadh)       Date:  2021-04       Impact factor: 0.906

9.  Impact of COVID-19 on stroke admissions, treatments, and outcomes at a comprehensive stroke centre in the United Kingdom.

Authors:  Nishita Padmanabhan; Indira Natarajan; Rachel Gunston; Marko Raseta; Christine Roffe
Journal:  Neurol Sci       Date:  2020-10-06       Impact factor: 3.307

10.  The Effect of the 2019 Novel Coronavirus Pandemic on Stroke and TIA Patient Admissions: Perspectives and Risk Factors.

Authors:  Luke Carson; Christopher Kui; Gemma Smith; Anand K Dixit
Journal:  J Clin Med       Date:  2021-03-25       Impact factor: 4.241

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