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Exploring the Role of In-patient Magnetic Resonance Imaging Use Among Admitted Ischemic Stroke Patients in Improving Patient Outcomes and Reducing Healthcare Resource Utilization

Kumar M, Beyea SD, Hu S, & Kamal N. (2024, March). Exploring the Role of In-patient Magnetic Resonance Imaging Use Among Admitted Ischemic Stroke Patients in Improving Patient Outcomes and Reducing Healthcare Resource Utilization. Frontiers in Neurology. 14; 1-9. https://doi.org/10.3389/fneur.2024.1305514 

  • Purpose: Despite the diagnostic and etiological significance of in-patient MRI in ischemic stroke (IS), its utilization is considered resource-intensive, expensive, and thus limiting feasibility and relevance. This study investigated the utilization of in-patient MRI for IS patients and its impact on patient and healthcare resource utilization outcomes

    Methods: This retrospective registry-based study analyzed 1,956 IS patients admitted to Halifax’s QEII Health Centre between 2015 and 2019. Firstly, temporal trends of MRI and other neuroimaging utilization were evaluated. Secondly, we categorized the cohort into two groups (MRI vs. No MRI; in addition to a non-contrast CT) and investigated adjusted differences in patient outcomes at admission, discharge, and post-discharge using logistic regression. Additionally, we analyzed healthcare resource utilization using Poisson log-linear regression. Furthermore, patient outcomes significantly associated with MRI use underwent subgroup analysis for stroke severity (mild stroke including transient ischemic attack vs. moderate and severe stroke) and any acute stage treatment (thrombolytic or thrombectomy or both vs. no treatment) subgroups, while using an age and sex-adjusted logistic regression model.

    Results: MRI was used in 40.5% patients; non-contrast CT in 99.3%, CT angiogram in 61.8%, and CT perfusion in 50.3%. Higher MRI utilization was associated with male sex, younger age, mild stroke, wake-up stroke, and no thrombolytic or thrombectomy treatment. MRI use was independently associated with lower in-hospital mortality (adjusted OR, 0.23; 95% CI, 0.15–0.36), lower symptomatic neurological status changes (0.64; 0.43–0.94), higher home discharge (1.32; 1.07–1.63), good functional outcomes at discharge (mRS score 0–2) (1.38; 1.11–1.72), lower 30-day stroke re-admission rates (0.48; 0.26–0.89), shorter hospital stays (regression coefficient, 0.92; 95% CI, 0.90–0.94), and reduced direct costs of hospitalization (0.90; 0.89–0.91). Subgroup analysis revealed a significantly positive association of MRI use with most patient outcomes in the moderate and severe strokes subgroup and non-acutely treated subgroup. Conversely, outcomes in mild strokes (including TIAs) subgroup and acute treatment subgroup were comparable regardless of MRI use.

    Conclusion: A substantial proportion of admitted IS patients underwent MRI, and MRI use was associated with improved patient outcomes and reduced healthcare resource utilization. Considering the multifactorial nature of IS patient outcomes, further randomized controlled trials are suggested to investigate the role of increased MRI utilization in optimizing in-patient IS management.

Discrete event simulation model of an acute stroke treatment process at a comprehensive stroke center: Determining the ideal improvement strategies for reducing treatment times 

Koca G, Blake J, Gubitz G, Kamal N. (2025). Discrete event simulation model of an acute stroke treatment process at a comprehensive stroke center: Determining the ideal improvement strategies for reducing treatment times. Journal of the Neurological Sciences. 468: 123369. https://doi.org/10.1016/j.jns.2024.123369

  • Background

    Fast treatment is crucial for ischemic stroke patients; the probability of good patient outcomes increases with faster treatment. Treatment times can be improved by making changes to the treatment process. However, it is challenging to identify the benefits of changes prior to implementation. Simulation modelling, which mimics the treatment process, can be used to evaluate changes without patient involvement. This study models the acute stroke treatment process using discrete event simulation (DES) and identifies improvement strategies to reduce treatment times.

    Method

    The model was developed for a comprehensive stroke center in Nova Scotia, using Python. All treatment pathways and sub-tasks were identified via an observational time and motion study conducted in the center. Nine process change scenarios were tested individually and in combinations. The primary outcome measures were door-to-CT time (DTCT), door-to-needle time (DNT), and door-to-groin puncture time (DGPT). The model simulated 500 patients 30 times.

    Results

    Collecting patient history on the way to the radiology department (rather than in ED) showed the highest reduction among individual scenarios for DTCT (14.2 vs 12.4 min, p < 0.001). Combining all scenarios in the door-to-CT process resulted in a reduction of the DTCT by approximately 28 %. Thrombolysing patients in the imaging department's waiting area resulted in the lowest DNT (39.4 vs 34.8 min, p < 0.001) among all individual scenarios. The highest reduction in DGPT, among all individual scenarios, was achieved by implementing Rapid angiosuite preparation (67.7 vs 51.4 min, p < 0.001). The combinations of all scenarios resulted in the lowest DTCT (14.2 vs 10.1 min, p < 0.001), DNT (39.4 vs 23.0 min, p < 0.001), and DGPT (67.9 vs 38.5 min, p < 0.001).

    Conclusions

    The study identified various improvement strategies in the acute stroke treatment process through a discrete-event simulation model. Combining all scenarios resulted in significant reductions for all outcome measures.

Selected Publications

Koca G, Blake J, Gubitz G, Kamal N. (2025). Discrete event simulation model of an acute stroke treatment process at a comprehensive stroke center: Determining the ideal improvement strategies for reducing treatment times. Journal of the Neurological Sciences. 468: 123369. https://doi.org/10.1016/j.jns.2024.123369

Forward A, Koca G, Kamal N. Identification of design requirements and user needs for a software application that collects acute stroke treatment data during clinical workflow. JMIR – Medical Informatics. Preprint. http://doi.org/10.2196/preprints.6480 0

Forward A, Sahli A, Kamal N. (2024, Sep). Streamlining acute stroke processes and data collection: a narrative review. Healthcare. 12:1-15 https://doi.org/10.3390/healthcare12191920

Aljendi S, Mrklas K, Kamal, N. (2024, Sep) Qualitative evaluation of a quality improvement collaborative implementation to improve acute ischemic stroke treatment in Nova Scotia, Canada. Healthcare. 12: 1-25 https://doi.org/10.3390/healthcare12181801

Kumar M, Holodinsky JK, Yu AYX, McNaughton CD, Austin PC, Chu A, Hill MD, Norris C, Lee DS, Kapral MK, Khan N, Kamal N. (2024, Sep). Rising out-of-hospital mortality in Canada during 2020-2022: a striking impact observed amoung young adults. Canadian Journal of Public Health. 1-13. https://doi.org/10.17269/s41997-024-00934-1

Holodinsky JK, Kumar M, NcNaughton CD, Austin PC, Chu A, Hill MD, Norris C, Field TS, Lee DS, Kapral MK, Kamal N, & Yu AYX. (2024, May). An interrupted time-series analysis of the impact of COVID-19 on hospitalizations for vascular events in three Canadian provinces. CJC Open. https://doi.org/10.1016/j.cjco.2024.04.010 Online In-Press.

Kumar M, Beyea SD, Hu S, & Kamal N. (2024, March). Exploring the Role of In-patient Magnetic Resonance Imaging Use Among Admitted Ischemic Stroke Patients in Improving Patient Outcomes and Reducing Healthcare Resource Utilization. Frontiers in Neurology. 14; 1-9. https://doi.org/10.3389/fneur.2024.1305514

Koca G, Kumar M & Kamal N. (2024, March). A Systematic Review of Computer Simulation Modelling Methods in Optimizing Acute Ischemic Stroke Treatment Services. IISE Transactions on Health care Systems Engineering. https://doi.org/10.1080/24725579.2024.2322959 

Kumar M, Beyea S, Hu S & Kamal N. (2024, February). Impact of early MRI in ischemic strokes beyond hyper-acute stage to improve patient outcomes, enable early discharge, and realize cost savings. Journal of Stroke & Cerebrovascular Diseases. 33. https://doi.org/10.1016/j.jstrokecerebrovasdis.2024.107662  

Paydarfar DA, Holodinsky JK, Mazya MV, Hill MD, Menon B, Jayaraman M, Kamal N. Modeling the decay in probability of receiving endovascular thrombectomy on the basis of time from stroke onset. Stroke: Vascular and Interventional Neurology. 2023; 3:6, 1-9. https://doi.org/10.1161/SVIN.123.000932 

Koca G, Kumar M, Gubitz G, Kamal N. Optimizing acute stroke treatment process: insights from sub-tasks duration in a prospective observational time and motion study. Frontiers in Neurology. 2023; 14:1253065. https://doi.org/10.3389/fneur.2023.1253065

Mirpouya M, Kamal N. The application of data envelopment analysis to emergency departments and management of emergency conditions: a narrative review. Healthcare. 2023; 11:18, 1-28. https://doi.org/10.3390/healthcare11182541 

Kumar M, Hu S, Beyea S, Kamal N. Is improved access to magnetic resonance imaging imperative for optimal ischemic stroke care?. Journal of the Neurological Sciences. 2023 Feb 18:120592. https://doi.org/10.1016/j.jns.2023.120592

Kamal, N., Yu, A.Y.Z. (2023). Addressing Access to Stroke Treatment for Patients with Pre-Existing Disabilities. Canadian Journal of Neurological Sciences. https://doi.org/10.1017/cjn.2023.5 

Paydarfar, D.A., Holodinsky, J.K., Abbas, H., Field, T.S., Zhou, L.W., Kamal, N. (2022). Quantifying Improved Outcomes, Cost Saving, and Hospital Volume Changes From Optimized Emergency Stroke Transport. Stroke. 53(12). 3644-3651. https://doi.org/10.1161/STROKEAHA.122.039172

Kamal N., Aljendi S., Cora E.A, Chandler T., Clift F., Fok P.T., Goldstein J., Gubitz G., Hill M.D., Menon B.K., Metcalfe B., Mrklas K.J., Phillips S., Theriault S., Van Der Linde E., Volders D., Williams H., ACTEAST Collaborators. (2022). Improving Access and Efficiency of Ischemic Stroke Treatment Across Four Canadian Provinces Using a Stepped Wedge Trial: Methodology. Frontiers in Stroke. 1, 1-12. https://doi.org/10.3389/fstro.2022.1014480

Kamal, N. Lakshminarayan, K. (2022). Simulation and Machine Learning Provide New Approaches to Examine Quality of Acute Stroke Management. Stroke. 53(9), 2768-2769. https://doi.org/10.1161/STROKEAHA.122.039954

Wheaton, A., For, P.T., Holodinsky, J.K., Vanberkel, P., Volders, D., Kamal, N. (2021). Optimal Transport Scenario with Rotary Air Transport for Access to Endovascular Therapy Considering Patient Outcomes and Cost: A Modelling Study. Frontiers in Neurology. 2021; 17:article769381. https://doi.org/10.3389/fneur.2021.768381 

Bulmer, T., Volders, D., Blake, J., Kamal, N. (2021). Discrete-event simulation to model the thrombolysis process for acute ischemic stroke patients at urban and rural hospitals. Frontiers in Neurology. 2021;12:article746404. https://doi.org/10.3389/fneur.2021.746404

Gillis, M., Saif, A., Murphy, M., Kamal, N. (2021). Effects of Various Policy Options on Covid-19 Cases in Nova Scotia Including Vaccination Rollout Schedule: A Modelling Study, MedRxIV, July. https://doi.org/10.1101/2021.07.28.21261219

Bulmer, T., Volders, D., & Kamal, N. (2021). Analysis of thrombolysis process for acute ischemic stroke in urban and rural hospitals in Nova Scotia Canada. Frontiers in Neurology, 12. doi:https://doi.org/10.3389/fneur.2021.645228

Kamal, N., Jeerakathil, T., Stang, J., Liu, M., Rogers, E., Smith, E. E., ... & Hill, M. D. (2020). Provincial Door-to-Needle Improvement Initiative Results in Improved Patient Outcomes Across an Entire Population. Stroke, 51(8), 2339-2346. doi:https://doi.org/10.1161/STROKEAHA.120.029734

Holodinsky JK, Kamal N, Zerna C, Ospel JM, Zhu L, Wilson LT, Hill MD, and Goyal M. (2020). In what scenarios does a mobile stroke unit predict better patient outcomes? A modelling study. Stroke, 51(6), 1805-1812. doi:https://doi.org/10.1161/STROKEAHA.119.028474

Kamal N, Rogers E, Stang J, Mann B, Butcher KS, Rempel J, Jeerakathil T, Shuaib A, Goyal M, Menon BK, Demchuk AM, Hill MD. (2019). 1-Year Healthcare Utilization for Patients that Received Endovascular Treatment Compared to Control. Stroke, 50, 1883-1886. doi:https://doi.org/10.1161/STROKEAHA.119.024870​

Kamal N, Shand E, Swanson R, Hill MD, Jeerakthil T, Imoukhuede O, Heinrichs I, Bakker J, Stoyberg C, Fowler L, Duckett S, Holsworth S, Mann B, Valaire S, Bestard J. (2019). Reducing door-to-needle times for acute ischemic stroke to a median of 30 minutes at a community hospital: a cohort study. Canadian Journal of Neurosciences, 46(1), 51-56. doi:https://doi.org/10.1017/cjn.2018.368

Holodinsky JK, Williamson TS, Demchuk AM, Zhao H, Zhu L, Francis MJ, Goyal M, Hill MD, Kamal N. (2018). Drip ‘n ship vs. mothership: modelling stroke patient transport for all suspected large vessel occlusion patients. JAMA Neurology, 75(12), 1477-1486. doi:https://doi.org/10.1001/jamaneurol.2018.2424

Kamal N, Wiggam MI, Holodinsky JK, Francis MJ*, Hopkins E, Frei D, Baxter B, Williams M, Nygren A, Goyal M, Hill MD, Jayaraman M. (2018). Geographic Modeling of Best Transport Options for Treatment of Acute Ischemic Stroke Patients: Applied to Influencing Health Policy in the USA and Northern Ireland. IISE Transactions on Healthcare Systems Engineering, 8(3), 220-226. doi:https://doi.org/10.1080/24725579.2018.1501623

Kamal N, Sheng S, Xian Y, Matsoualka R, Hill MD, Bhatt D, Saver J, Reeves M, Fonarow GC, Schwamm LH, and Smith EE. (2017). Delays in door-to-needle times and their impacton treatment time and outcomes in Get With the Guidelines Stroke. Stroke, 48, 946-954. doi:https://doi.org/10.1161/STROKEAHA.116.015712

Kamal N, Smith EE, Jeerakathil T, Hill MD. (2017). Thrombolysis: Improving door-to-needle times for ischemic stroke treatment, a narrative review. International Journal of Stroke, 13(3), 268–276. doi:https://doi.org/10.1177/1747493017743060