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Siskou O, Galanis P, Konstantakopoulou O, Stafylas P, Karagkouni I, Tsampalas E, Garefou D, Alexopoulou H, Gamvroula A, Lypiridou M, Kalliontzakis I, Fragkoulaki A, Kouridaki A, Tountopoulou A, Kouzi I, Vassilopoulou S, Manios E, Mavraganis G, Vemmou A, Karagkiozi E, Savopoulos C, Dimas G, Myrou A, Milionis H, Siopis G, Evaggelou H, Protogerou A, Samara S, Karapiperi A, Kakaletsis N, Papastefanatos G, Papastefanatos S, Sourtzi P, Ntaios G, Vemmos K, Korompoki E, Kaitelidou D. The Cost and the Value of Stroke Care in Greece: Results from the SUN4P Study. Healthcare (Basel) 2023; 11:2545. [PMID: 37761742 PMCID: PMC10530928 DOI: 10.3390/healthcare11182545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/06/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
The aim of this study was to measure the one-year total cost of strokes and to investigate the value of stroke care, defined as cost per QALY. The study population included 892 patients with first-ever acute strokes, hemorrhagic strokes, and ischemic strokes, (ICD-10 codes: I61, I63, and I64) admitted within 48 h of symptoms onset to nine public hospitals located in six cities. We conducted a bottom-up cost analysis from the societal point of view. All cost components including direct medical costs, productivity losses due to morbidity and mortality, and informal care costs were considered. We used an annual time horizon, including all costs for 2021, irrespective of the time of disease onset. The average cost (direct and indirect) was extrapolated in order to estimate the national annual burden associated with stroke. We estimated the total cost of stroke in Greece at EUR 343.1 mil. a year in 2021, (EUR 10,722/patient or EUR 23,308 per QALY). Out of EUR 343.1 mil., 53.3% (EUR 182.9 mil.) consisted of direct healthcare costs, representing 1.1% of current health expenditure in 2021. Overall, productivity losses were calculated at EUR 160.2 mil. The mean productivity losses were estimated to be 116 work days with 55.1 days lost due to premature retirement and absenteeism from work, 18.5 days lost due to mortality, and 42.4 days lost due to informal caregiving by family members. This study highlights the burden of stroke and underlines the need for stakeholders and policymakers to re-organize stroke care and promote interventions that have been proven cost-effective.
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Affiliation(s)
- Olga Siskou
- Center for Health Services Management and Evaluation, Department of Nursing National and Kapodistrian, University of Athens, 115 27 Athens, Greece (D.K.)
- Department of Tourism Studies University of Piraeus, 185 34 Piraeus, Greece
| | - Petros Galanis
- Center for Health Services Management and Evaluation, Department of Nursing National and Kapodistrian, University of Athens, 115 27 Athens, Greece (D.K.)
| | - Olympia Konstantakopoulou
- Center for Health Services Management and Evaluation, Department of Nursing National and Kapodistrian, University of Athens, 115 27 Athens, Greece (D.K.)
| | | | - Iliana Karagkouni
- Center for Health Services Management and Evaluation, Department of Nursing National and Kapodistrian, University of Athens, 115 27 Athens, Greece (D.K.)
| | - Evangelos Tsampalas
- Department of Neurology, Panarkadikon General Hospital, 221 00 Tripoli, Greece
| | - Dafni Garefou
- Department of Neurology, Panarkadikon General Hospital, 221 00 Tripoli, Greece
| | - Helen Alexopoulou
- Department of Neurology, Panarkadikon General Hospital, 221 00 Tripoli, Greece
| | - Anastasia Gamvroula
- Department of Neurology, Panarkadikon General Hospital, 221 00 Tripoli, Greece
| | - Maria Lypiridou
- Department of Neurology, Panarkadikon General Hospital, 221 00 Tripoli, Greece
| | | | | | - Aspasia Kouridaki
- Department of Neurology, General Hospital of Chania, 733 00 Creta, Greece
| | - Argyro Tountopoulou
- 1st Department of Neurology, Eginition Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece; (A.T.)
| | - Ioanna Kouzi
- 1st Department of Neurology, Eginition Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece; (A.T.)
| | - Sofia Vassilopoulou
- 1st Department of Neurology, Eginition Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece; (A.T.)
| | - Efstathios Manios
- Department of Clinical Therapeutics, Alexandra Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece (E.K.)
| | - Georgios Mavraganis
- Department of Clinical Therapeutics, Alexandra Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece (E.K.)
| | - Anastasia Vemmou
- Department of Clinical Therapeutics, Alexandra Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece (E.K.)
| | - Efstathia Karagkiozi
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, 413 34 Larissa, Greece (G.N.)
| | - Christos Savopoulos
- 1st Medical Propedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA Hospital, 546 36 Thessaloniki, Greece
| | - Gregorios Dimas
- 1st Medical Propedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA Hospital, 546 36 Thessaloniki, Greece
| | - Athina Myrou
- 1st Medical Propedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, AHEPA Hospital, 546 36 Thessaloniki, Greece
| | - Haralampos Milionis
- Department of Internal Medicine, School of Medicine, University of Ioannina, 455 00 Ioannina, Greece
| | - Georgios Siopis
- Department of Internal Medicine, School of Medicine, University of Ioannina, 455 00 Ioannina, Greece
| | - Hara Evaggelou
- Department of Internal Medicine, School of Medicine, University of Ioannina, 455 00 Ioannina, Greece
| | - Athanasios Protogerou
- Cardiovascular Prevention & Research Unit, Laiko General Hospital of Athens at the Medical School, National & Kapodistrian University of Athens, 115 27 Athens, Greece
| | - Stamatina Samara
- Cardiovascular Prevention & Research Unit, Laiko General Hospital of Athens at the Medical School, National & Kapodistrian University of Athens, 115 27 Athens, Greece
| | - Asteria Karapiperi
- Cardiovascular Prevention & Research Unit, Laiko General Hospital of Athens at the Medical School, National & Kapodistrian University of Athens, 115 27 Athens, Greece
| | - Nikolaos Kakaletsis
- Second Department of Internal Medicine, Aristotle University of Thessaloniki, Hippokrateion General Hospital of Thessaloniki, 546 42 Thessaloniki, Greece
| | - George Papastefanatos
- Center for Health Services Management and Evaluation, Department of Nursing National and Kapodistrian, University of Athens, 115 27 Athens, Greece (D.K.)
- Information Management Systems Institute, ATHENA Research Center, 151 25 Athens, Greece
| | - Stefanos Papastefanatos
- Center for Health Services Management and Evaluation, Department of Nursing National and Kapodistrian, University of Athens, 115 27 Athens, Greece (D.K.)
| | - Panayota Sourtzi
- Center for Health Services Management and Evaluation, Department of Nursing National and Kapodistrian, University of Athens, 115 27 Athens, Greece (D.K.)
| | - George Ntaios
- Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, 413 34 Larissa, Greece (G.N.)
- Hellenic Stroke Organization, 115 28 Athens, Greece;
| | | | - Eleni Korompoki
- Department of Clinical Therapeutics, Alexandra Hospital, National and Kapodistrian University of Athens, 115 28 Athens, Greece (E.K.)
- Hellenic Stroke Organization, 115 28 Athens, Greece;
| | - Daphne Kaitelidou
- Center for Health Services Management and Evaluation, Department of Nursing National and Kapodistrian, University of Athens, 115 27 Athens, Greece (D.K.)
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Wood RM, Moss SJ, Murch BJ, Vasilakis C, Clatworthy PL. Optimising acute stroke pathways through flexible use of bed capacity: a computer modelling study. BMC Health Serv Res 2022; 22:1068. [PMID: 35987642 PMCID: PMC9392305 DOI: 10.1186/s12913-022-08433-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 08/10/2022] [Indexed: 11/11/2022] Open
Abstract
Background Optimising capacity along clinical pathways is essential to avoid severe hospital pressure and help ensure best patient outcomes and financial sustainability. Yet, typical approaches, using only average arrival rate and average lengths of stay, are known to underestimate the number of beds required. This study investigates the extent to which averages-based estimates can be complemented by a robust assessment of additional ‘flex capacity’ requirements, to be used at times of peak demand. Methods The setting was a major one million resident healthcare system in England, moving towards a centralised stroke pathway. A computer simulation was developed for modelling patient flow along the proposed stroke pathway, accounting for variability in patient arrivals, lengths of stay, and the time taken for transfer processes. The primary outcome measure was flex capacity utilisation over the simulation period. Results For the hyper-acute, acute, and rehabilitation units respectively, flex capacities of 45%, 45%, and 36% above the averages-based calculation would be required to ensure that only 1% of stroke presentations find the hyper-acute unit full and have to wait. For each unit some amount of flex capacity would be required approximately 30%, 20%, and 18% of the time respectively. Conclusions This study demonstrates the importance of appropriately capturing variability within capacity plans, and provides a practical and economical approach which can complement commonly-used averages-based methods. Results of this study have directly informed the healthcare system’s new configuration of stroke services.
Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-08433-0.
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Study on the Mechanism of Üstikuddus Sherbiti in Ischemic Cerebrovascular Diseases: Based on Network Pharmacology. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:5581864. [PMID: 35432563 PMCID: PMC9012636 DOI: 10.1155/2022/5581864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 01/12/2022] [Indexed: 11/17/2022]
Abstract
This paper aims to study the potential biological mechanism of Üstikuddus Sherbiti (ÜS) in the treatment of ischemic cerebrovascular diseases (ICVD) by the network pharmacology method. Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to obtain effective constituents of ÜS by screening eligible oral utilization, drug similarity, and blood-brain barrier permeability threshold. By drug target prediction and stroke treatment target mining, 2 target data sets were analyzed to find intersection targets and the corresponding constituents were used as active constituents. An active constituent target network and an effective constituent target network were constructed by using Cytoscape 3.7.2 software. Degree parameters of the effective constituent target network were analyzed to find important effective constituents and targets. Through protein-protein interaction (PPI) analysis/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, potential signaling pathways of ÜS in ischemic stroke were found out. AutoDock was used for molecular docking verification. A total of 90 active constituents of ÜS were screened out. There were 10 active constituents against ICVD, including quercetin, luteolin, kaempferol, and naringenin, and 10 important targets for anticerebral ischemia, namely, PIK3CA, APP, PIK3R1, MAPK1, MAPK3, AKT1, PRKCD, Fyn, RAC1, and NF-κB1. Based on the protein interaction network, the important targets of ÜS were significantly enriched in PI3K-Akt signaling pathway, neuroactive ligand-receptor interaction pathway, Ras signaling pathway, etc. ÜS in ICVD has characteristics like multiple targets, multiple approaches, and multiple pathways. Results of molecular docking showed that the active components in ICVD had a good binding ability with the key targets. Its main biological mechanism may be related to the PI3K-Akt and Ras-MAPK centered signaling pathway. Our study demonstrated that ÜS exerted the effect of treating ICVD by regulating multiple targets and multiple channels with multiple components through the method of network pharmacology and molecular docking.
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Bulmer T, Volders D, Blake J, Kamal N. Discrete-Event Simulation to Model the Thrombolysis Process for Acute Ischemic Stroke Patients at Urban and Rural Hospitals. Front Neurol 2021; 12:746404. [PMID: 34777215 PMCID: PMC8586711 DOI: 10.3389/fneur.2021.746404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 09/30/2021] [Indexed: 11/22/2022] Open
Abstract
Background: Effective treatment with tissue plasminogen activator (tPA) critically relies on rapid treatment. Door-to-needle time (DNT) is a key measure of hospital efficiency linked to patient outcomes. Numerous changes can reduce DNT, but they are difficult to trial and implement. Discrete-event simulation (DES) provides a way to model and determine the impact of process improvements. Methods: A conceptual framework was developed to illustrate the thrombolysis process; allowing for treatment processes to be replicated using a DES model developed in ARENA. Activity time duration distributions from three sites (one urban and two rural) were used. Five scenarios, three process changes, and two reductions in activity durations, were simulated and tested. Scenarios were tested individually and in combinations. The primary outcome measure is median DNT. The study goal is to determine the largest improvement in DNT at each site. Results: Administration of tPA in the imaging area resulted in the largest median DNT reduction for Site 1 and Site 2 for individual test scenarios (12.6%, 95% CI 12.4–12.8%, and 8.2%, 95% CI 7.5–9.0%, respectively). Ensuring that patients arriving via emergency medical services (EMS) remain on the EMS stretcher to imaging resulted in the largest median DNT improvement for Site 3 (9.2%, 95% CI 7.9–10.5%). Reducing both the treatment decision time and tPA preparation time by 35% resulted in a 11.0% (95% CI 10.0–12.0%) maximum reduction in median DNT. The lowest median and 90th percentile DNTs were achieved by combining all test scenarios, with a maximum reduction of 26.7% (95% CI 24.5–28.9%) and 17.1% (95% CI 12.5–21.7%), respectively. Conclusions: The detailed conceptual framework clarifies the intra-hospital logistics of the thrombolysis process. The most significant median DNT improvement at rural hospitals resulted from ensuring patients arriving via EMS remain on the EMS stretcher to imaging, while urban sites benefit more from administering tPA in the imaging area. Reducing the durations of activities on the critical path will provide further DNT improvements. Significant DNT improvements are achievable in urban and rural settings by combining process changes with reducing activity durations.
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Affiliation(s)
- Tessa Bulmer
- Department of Industrial Engineering, Faculty of Engineering, Dalhousie University, Halifax, NS, Canada
| | - David Volders
- Interventional and Diagnostic Neuroradiology, QEII Health Sciences Centre, Nova Scotia Health, Halifax, NS, Canada.,Department of Radiology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada
| | - John Blake
- Department of Industrial Engineering, Faculty of Engineering, Dalhousie University, Halifax, NS, Canada
| | - Noreen Kamal
- Department of Industrial Engineering, Faculty of Engineering, Dalhousie University, Halifax, NS, Canada
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Lutz BJ, Reimold AE, Coleman SW, Guzik AK, Russell LP, Radman MD, Johnson AM, Duncan PW, Bushnell CD, Rosamond WD, Gesell SB. Implementation of a Transitional Care Model for Stroke: Perspectives From Frontline Clinicians, Administrators, and COMPASS-TC Implementation Staff. THE GERONTOLOGIST 2020; 60:1071-1084. [PMID: 32275060 PMCID: PMC7427484 DOI: 10.1093/geront/gnaa029] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Stroke is a chronic, complex condition that disproportionally affects older adults. Health systems are evaluating innovative transitional care (TC) models to improve outcomes in these patients. The Comprehensive Post-Acute Stroke Services (COMPASS) Study, a large cluster-randomized pragmatic trial, tested a TC model for patients with stroke or transient ischemic attack discharged home from the hospital. The implementation of COMPASS-TC in complex real-world settings was evaluated to identify successes and challenges with integration into the clinical workflow. RESEARCH DESIGN AND METHODS We conducted a concurrent process evaluation of COMPASS-TC implementation during the first year of the trial. Qualitative data were collected from 4 sources across 19 intervention hospitals. We analyzed transcripts from 43 conference calls with hospital clinicians, individual and group interviews with leaders and clinicians from 9 hospitals, and 2 interviews with the COMPASS-TC Director of Implementation using iterative thematic analysis. Themes were compared to the domains of the RE-AIM framework. RESULTS Organizational, individual, and community factors related to Reach, Adoption, and Implementation were identified. Organizational readiness was an additional key factor to successful implementation, in that hospitals that were not "organizationally ready" had more difficulty addressing implementation challenges. DISCUSSION AND IMPLICATIONS Multifaceted TC models are challenging to implement. Facilitators of implementation were organizational commitment and capacity, prioritizing implementation of innovative delivery models to provide comprehensive care, being able to address challenges quickly, implementing systems for tracking patients throughout the intervention, providing clinicians with autonomy and support to address challenges, and adequately resourcing the intervention. CLINICAL TRIAL REGISTRATION NCT02588664.
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Affiliation(s)
- Barbara J Lutz
- School of Nursing, University of North Carolina at Wilmington
| | | | - Sylvia W Coleman
- Department of Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Amy K Guzik
- Department of Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Laurie P Russell
- Division of Public Health Sciences, Wake Forest University Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Meghan D Radman
- Department of Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Anna M Johnson
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
| | - Pamela W Duncan
- Department of Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Cheryl D Bushnell
- Department of Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Wayne D Rosamond
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
| | - Sabina B Gesell
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Implementation Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
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