A quality improvement project integrating in-laboratory point-of-care testing to shorten turnaround time for international normalized ratio in emergency department stroke patients

Introduction

Stroke remains a leading cause of death and disability worldwide, with an estimated 93.8 million cases. According to World Health Organization, the lifetime risk of stroke has increased by 50% over the past 20 years, with 1 in 4 adults predicted to experience a stroke in their lifetime. Ischemic strokes account for 87% of all stroke cases (1), with associated costs estimated to tip US$1 trillion by 2030 including health care services, medications, and lost productivity (2-4). Timely thrombolytic therapy like tissue plasminogen activator (tPA) measured by door-to-needle (DTN) time significantly reduces stroke-related mortality and morbidity (5,6) and is associated with better in-hospital outcomes. Without treatment, two million neurons are estimated to be lost each minute (7). In 2003, American Heart Association emphasized the quality of care by introducing “Get with the Guidelines-Stroke” Program (8,9). The guidelines provide a set of crucial strategies for the hospitals to enhance DTN, including fast laboratory testing. Most hospitals have adopted the “stroke alert protocol” that would dedicate the hospital’s resources to immediately recognizing and managing these patients. To standardize, The Joint Commission (TJC) has established quality measures for stroke centers certification (10,11). According to the American Stroke Association guidelines (12) and our institutional stroke policy, thrombolytic therapy should not be delayed while waiting for the lab results unless the patient has been taking warfarin, heparin or anticoagulation use is uncertain and/or a bleeding abnormality or thrombocytopenia is suspected. Approximately 29.7% of ischemic stroke patients with atrial fibrillation in the United States are already on anticoagulation at the time of presentation (13), which makes coagulation lab results especially INR TAT a clinically significant component of thrombolysis decisions.

Despite the recommended quality measures, existing guidelines and published literature primarily emphasize clinical workflow improvements and overall DTN targets, with little focus on laboratory-specific issues. Multicenter quality studies (14) have shown that combined interventions are effective in reducing DTN. However, the interventions mainly targeted emergency department processes, faster imaging and bedside point-of-care testing. Laboratory delays remain under-characterized.

In 2021, a multidisciplinary review of stroke quality data at our hospital revealed that although the institution met the TJC recommendation of 40-minute order-to-result mean TAT, the outlier rate was high. After analyzing the process, we identified key areas in the clinical laboratory workflow that contributed to delays in the INR results. Delays were mainly in the pre-analytic phase involving staff communication, specimen transport, handoff, and centrifugation. Published evidence shows that combining multiple strategies can have a synergistic effect on TAT improvement. A decision was made to implement a quality improvement project based on a multi-phase Plan-Do-Study-Act (PDSA) framework. The plan was to improve the current process, assess the impact, identify potential opportunities and act accordingly. We initiated the project with phase 1, focused on targeting problem areas in the existing workflow. Plan was to review data after phase 1 and proceed with phase 2 if further enhancements were deemed necessary. Post-phase 1 data revealed a persistent gap in pre-analytics, especially sample processing. Since traditional laboratory INR testing requires specimen centrifugation, it was proposed to continue with phase 2 to introduce a point-of-care (POC) INR system at bedside in the ED which would eliminate the need to centrifuge (POC INR testing is performed on whole blood). However, nursing staff in the ED was resistant to performing an added task to an already overloaded workflow, which led to a unified decision to place the POC INR system in central laboratory. The existing literature cites several studies on process improvements to shorten stroke INR TAT including bedside POC INR implementation, but data on a laboratory-based POC INR in acute stroke workflow is scarce. This study aimed to evaluate the INR TAT at the emergency department of stroke, identify areas of inefficiency, implement measures to reduce the TAT, and improve compliance with the benchmark standards. This was an institutional operational quality improvement project, exempted by the Institutional Review Board. We present this article in accordance with the SQUIRE reporting checklist (available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-2025-1-63/rc).

Methods

Prior to implementation, we performed a literature review of TAT benchmarks for acute stroke protocols. The College of American Pathologists (CAP) in 2012 in a QT8 study (15) and TJC recommended a target mean receipt-to-result TAT of 25 minutes and order-to-result of 40 minutes for stroke lab protocols. Using these benchmarks, we performed a pre-intervention review of the INR TAT for emergency department (ED) stroke alerts from August 2021 to October 2021 at Jackson Memorial Hospital, Miami Florida. Metrics included “receipt-to-result” TAT: the time from sample registration in the lab to the result reported in the EMR; “order-to-result” TAT: the time from the order placement to the time result is reported in the electronic medical record (EMR) system; and TAT outlier rate: percent of samples exceeding benchmark TAT thresholds. Data was collected using the laboratory information system (LIS) and EMR systems. The inclusion criteria were consecutive INR samples obtained from ED patients with a clinical suspicion of stroke, first-time admission for stroke, or stroke alert protocol was activated via emergency medical service or triage. The exclusion criteria included INR samples from in-patients who had a stroke during hospital stay, patients transferred from another facility, canceled orders/stroke alerts, and untimed samples. Initial diagnoses of patient cohorts in all phases are listed in Table 1. This study was an institutional operational quality improvement project. No patient-identifying information or detailed baseline data were collected. We observed the existing process flow (Figure 1) from the time an alert was initiated from ED to within the laboratory process. Several inefficiencies were discovered in all phases with the pre-analytic phase contributing a larger role:

• Inefficient communication system: when a stroke alert was initiated, at times the pager system was found to be technically unreliable.
• Delays in the sample chain of custody: the lab received stroke samples via pneumatic tube. There was a delay between the time sample arrived in the receptacle and was noticed, picked up and accessioned by a lab technologist.
• Unclear roles: there were no clear role assignments among laboratory personnel to process stroke samples.
• Analytic and post-analytic delays: conventional PT/INR testing in central laboratory requires centrifugation to obtain platelet-poor plasma. Prothrombin time (PT)/INR testing is an automated clot-based assay performed on Stago platform, STA-R Max^®. Sample was centrifuged and manually loaded on the analyzer with manual result verification, both steps added unnecessary delays.

Figure 1 Pre-intervention in-lab workflow for stroke INR. INR, international normalized ratio; MT, medical technologist.

We implemented targeted interventions in phase 1 from November to December 2021; no TAT data was collected during this period.

Phase 1 interventions (Figure 2) included the following:

• Implementation of a dual-pager alert: use two pagers by the lab with alerts being received simultaneously on both pagers, one for all system-wide stroke alerts, assigned to the Person-In-Charge “PIC”, while the other for hospital ED stroke alerts, assigned to the laboratory Pathology Assistant “PA2.”.
• Upon alert, the PIC verified patient location and notified the lab via intercom.
• PIC initiated a 25-minute timer clock on the SpectraLink phone.
• Sample continued to be transported via pneumatic tube system.
• If the sample was not received within 25 minutes, the PIC immediately contacted the ED.
• PIC documented all delays (>25 min) on a dedicated log sheet.
• PA2 received the stroke sample accompanying the green stroke form and delivered it directly to medical technologist (MT) at the coagulation bench.
• MT centrifuged the sample with the minimum time “2 min” setting as per assay protocol (instead of the 3 minutes default setting) and proceeded with testing.
• To further improve efficiency, auto-verification of INR results was implemented. Auto-verification allows laboratory results cross over to the LIS and patient chart automatically without human intervention, provided the results fulfill the predetermined criteria.

Figure 2 In-lab process flow for stroke INR after two-phase interventions. ETA, extra time in arrival of sample; INR, international normalized ratio; MT, medical technologist; PA, pathology assistant; PIC, person in-charge; POCT, point-of-care test.

As part of PDSA cycle, post-phase 1 data was collected from January to March 2022 which showed an improvement in the TAT central tendency but persistence of receipt-to-result TAT outliers (refer to results section for specific data). Like pre-intervention results, the outliers were mostly attributable to pre-analytic variability of sample centrifugation and batching. In the interdisciplinary discussions, POC INR testing, although less precise compared to laboratory INR was considered since it eliminates the need for centrifugation and offers almost immediate results. Studies evaluating POC INR performance in acute ischemic stroke settings have shown that Hemochron Signature Elite^® demonstrates strong agreement with plasma INR at the decision threshold of 1.7, with 95.4% cases showing no discrepancy in treatment eligibility determination (16). An interdisciplinary discussion was held to introduce a POC INR device at bedside in the ED as phase 2 of this project. However, positioning of Hemochron^® in the ER was not feasible due to resistance from nursing team. Their concerns were responsibility, quality control oversight, staff competency and overall supervision of maintaining regulatory compliance. Therefore, decision was made to place the Hemochron^® Signature Elite, in the core lab (Figure 2). We also discussed POC INR’s known limitations in conditions such as DOAC use, supratherapeutic INR, and extremes of hematocrit in which the results are inappropriate or unreliable for clinical use. Stroke patients are likely to be on prior anticoagulation, particularly on DOACs in non-valvular atrial fibrillation. The problem is compounded by the fact that most stroke patients are in altered mental status at the time of presentation to provide medication history. Although intrigued by the potential of improving DTN, stroke teams shared concerns about POC INR limitations in this patient population. Laboratory leadership and stroke teams decided to implement POC INR to evaluate the operational impact on TAT because this learning experience could potentially be useful in other high acuity situations. To ensure patient safety and prevent treatment delays in stroke, we defined a clear pathway in which sample was immediately run on Hemochron^®, followed by centrifugation of the same sample for plasma INR on the laboratory instrument. Stroke teams preferred to wait for the central laboratory INR results before deciding for thrombolysis. As a result, impact of POC INR on DTN time or clinical outcomes was irrelevant and TAT impact was purely operational.

From a technical aspect, Hemochron^® is Food and Drug Administration (FDA)-approved for use in clinical settings that require assessment of hemostasis. This is a microcoagulation system of PT assay that performs PT/INR testing on citrated whole blood, hence eliminating the need to centrifuge with a mechanical clot detection as the endpoint. Clot formation occurs inside a disposable citrate PT cuvette. The citrate PT cuvette is a self-contained test chamber with pre-loaded dried thromboplastin, calcium, buffers and stabilizers. Whole blood PT results are reported by the instrument with a mathematical conversion to INR as well as a plasma equivalent value (17). Two devices were validated, calibrated and strategically placed near lab entrance. Special consideration was given to the lab layout for Hemochron^® implementation. We continued to receive citrate tube via pneumatic tube system in phase 2. The laboratory MT on the coagulation bench manually loaded the sample and verified the results.

Each sample received for POC INR was immediately run on Hemochron^®, followed by centrifugation and a concurrent plasma INR on the laboratory instrument. Data for POC INR vs. plasma INR was analyzed on a continuous basis for correlation using least-squares linear regression and Bland-Altman analysis (Figures 3,4). In addition, any results with discrepant INR (INR difference of >0.5) at clinical decision point of >1.7, in which clinical decision would have been different were investigated. One (1/40) such sample had POC INR of 2.4 and 1.36 on lab INR; patient was found to be on DOAC therapy at the time of presentation. A prior sample size calculation was not performed as this study was conducted as a time bound quality improvement initiative with a predefined implementation and reporting timeline. Instead, consecutive eligible cases were included during each study phase to reflect real-world workflow performance. The limited sample size in phase 2 (n=40) was therefore determined by the duration of the post-implementation observation window rather than statistical considerations. To address concerns regarding statistical power, we supplemented our analysis with effect size estimation and post-hoc power considerations. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Publication of this project was waived from ethical approval and patient consent by Jackson Health System.

Figure 3 Correlation between laboratory INR and POC INR measurements. The dotted line represents the least-squares regression fit, with regression equation and R2 displayed. Laboratory INR is plotted on the x-axis and POC INR on the y-axis. INR, international normalized ratio; POC, point-of-care.

Figure 4 Bland-Altman plot comparing POC INR and laboratory INR. The solid line represents mean bias, and dashed lines represent the 95% limits of agreement. A proportional bias trendline is included. INR, international normalized ratio; POC, point-of-care.

Statistical analysis

Statistical analyses were performed using R (R Foundation for Statistical Computing, Vienna, Austria)/SPSS (IBM Corp., Armonk, NY, USA). Continuous variables were assessed for normality using the Shapiro-Wilk test, along with visual inspection of histograms and Q-Q plots. As turnaround time (TAT) data were non-normally distributed, results are presented primarily as median [interquartile range (IQR)], with means reported for completeness.

Comparisons were conducted across three study phases: pre-intervention (n=289), phase 1 (n=284), and phase 2 (n=40). Overall group differences were assessed using the Kruskal-Wallis test. Since the results were non-normal in distribution, comparisons (pre-intervention vs. phase 1, pre-intervention vs. phase 2, and phase 1 vs. phase 2) were performed using the non-parametric method, Mann-Whitney U test. Data with corrupted/missing values, collect-to-receipt time of >4 hours were excluded. Effect sizes were calculated using Cliff’s delta, with corresponding 95% confidence intervals (CIs). Categorical variables, including the proportion of outliers, were summarized as counts and percentages. Outliers were retained in the primary analysis to reflect real-world workflow variability. All tests were two-tailed, and a P value <0.05 was considered statistically significant.

Results

The intervention led to a statistically and operationally meaningful reduction in receipt-to-result TAT across study phases (Table 2). Compared with the pre-intervention period, phase 1 implementation resulted in a modest but statistically significant reduction in median receipt-to-result TAT (17 vs. 15 minutes. The Hodges-Lehman estimator indicated a median reduction of 2 minutes (95% CI: 1.5–6 minutes), with an outlier rate reduction from 28.3% to 11.6%. This shift was statistically significant (Mann-Whitney U two-sided P=1.69×10⁻²⁰) and supported by a moderate-to-large effect size (Cliff’s s =0.45), indicating that post-intervention results were consistently lower across the distribution. The wide pre-intervention IQR (15.00–35.06) highlights operational variability, commonly seen when prioritization of critical samples disrupts the usual lab workflow. The post intervention narrowing of the IQR (12.88–23.05) suggests a stable workflow, which is important in acute stroke care and situations requiring consistent response times. The order-to-result TAT decreased from a median of 34 (IQR, 34.8–58.9) pre-intervention to 31 (IQR, 28.3–53.5) post phase 1 (Mann-Whitney U two-sided P=7.97×10⁻⁵, Cliff’s s =0.25), a small effect size and a median reduction of 3 minutes (95% CI: 2.0–7.5 minutes) phase 2, which introduced Hemochron^® system produced the most significant gains. The median receipt-to-result INR TAT showed further improvement from 15 minutes phase 1 to 8 minutes post phase 2 (P<0.001, Cliff’s s =0.85, 95% CI: 5–12 min) with an elimination of outliers. The impact on order-to-result TAT outliers was not as dramatic, with persistence of outliers at 15%. To address the concerns regarding sample size adequacy, a post-hoc power analysis was performed using the Mann-Whitney U framework, leveraging the relationship between Cliff’s δ and the probability of superiority. The observed effect size for receipt-to-result TAT was Cliff’s δ =0.45, corresponding to a probability of superiority of 0.725. Using study group sizes, the estimated statistical power for the primary comparison between the pre-intervention and Phase 1 cohorts (n=289 vs. n=284) exceeded 99.9%, indicating that the study was more than adequately powered to detect the observed effect. Even under more conservative assumptions comparing phase 1 and phase 2 cohorts (n=284 vs. n=40), power remained high at approximately 99.6%. Importantly, under the most conservative scenario assuming equal group sizes of n=40, power remained approximately 93%, demonstrating robustness of the findings despite the limited phase 2 sample size. These analyses confirm that the study was sufficiently powered to detect moderate-to-large effects in TAT reduction. However, smaller incremental differences particularly in secondary endpoints such as order-to-result TAT may remain susceptible to type II error, consistent with known limitations of post-hoc power estimation and the constrained phase 2 sample size.

Linear correlation between POC and lab INR demonstrated a strong association, R²=0.893 (Figure 3). Bland-Altman analysis (Figure 4) showed consistent positive bias in POC INR across most of the clinical range, with wider divergence at higher INR values. This finding is consistent with prior published evidence of variability in sensitivity of POC INR reagents from various manufacturers.

Discussion

Door-to-needle time is an important quality metric in acute stroke management with global efforts focused on optimizing each step of the stroke protocols. Emergency department management of acute stroke includes immediate imaging and laboratory studies such as PT/INR, activated partial thromboplastin time (aPTT) and complete blood count (CBC). Laboratory delays in coagulation testing have implications for both clinical care and laboratory operations. The findings of this study are consistent with prior literature demonstrating that targeted workflow interventions can reduce TATs in acute stroke care; however, the magnitude of improvement appears to depend on the level of system integration. Our results align with the meta-analysis by Siarkowski et al. (14), which emphasized comprehensive multidisciplinary interventions. Although a few minutes reduction may seem modest in ordinary situations, the clinical relevance of this improvement may be substantial in high acuity scenarios.

In particular, the marked reduction in TAT observed in phase 2 is consistent with the American Heart Association/American Stroke Association (AHA/ASA) guidelines, which highlight the importance of streamlined processes, rapid laboratory or POC testing, and coordinated stroke systems of care to minimize door-to-needle times (18,19). A key observation in our study is the differential impact of phase 1 on receipt-to-result versus order-to-result TAT. While phase 1 produced a modest improvement in laboratory processing intervals, the effect on overall order-to-result TAT was not significant. This finding suggests that early interventions primarily targeted analytical processes within the laboratory, such as specimen prioritization or instrument throughput, without addressing pre-analytical delays. This interpretation is supported by prior laboratory workflow studies, including the CAP Q-Probes analysis by Volmar et al., which demonstrated that system-level factors rather than isolated laboratory processes are the dominant determinants of overall TAT performance (15).

The substantial improvement observed in phase 2 is mechanistically consistent with studies evaluating the integration of POC INR testing and streamlined stroke workflows. Bedside POC testing has been shown to significantly reduce delays associated with specimen transport and processing, thereby improving time-sensitive clinical decision-making in acute stroke (5,20). In contrast to prior studies which describe decentralized bedside POC testing, we implemented a laboratory-based POC INR device for a better TAT while maintaining quality of results. We encountered some barriers during POC implementation such as historically known resistance to POC adoption by clinical and nursing teams due to issues like involvement of non-laboratory personnel, training requirements, periodic competency assessment, running quality control and interruption of the existing patient-care workflow. Placement of Hemochron^® in central laboratory not only addressed these issues but also offered advantages from an operational perspective such as test results being quickly available without the need to centrifuge while maintaining laboratory oversight. Also, the value of evaluating TAT distributions rather than relying solely on mean TAT is critical in stroke protocols, since the impact on patient outcomes is significant. The large effect size and narrow confidence interval observed in our phase 2 analysis suggest a near-complete shift in the TAT distribution, indicating not only faster results but also improved consistency. This reduction in variability aligns with prior reports indicating that standardized stroke pathways and POC testing strategies enhance reliability and reduce process inefficiencies (16,18). Notably, our findings highlight an inconsistency across the literature regarding the effectiveness of partial interventions. While some studies report moderate improvements with isolated process changes, our data suggest that such interventions yield only limited benefits unless integrated into a broader system redesign. Differences in baseline workflow efficiency, institutional infrastructure, and the degree of multidisciplinary coordination likely contribute to these discrepancies. The AHA/ASA guidelines similarly emphasize that optimal stroke care requires coordinated, system-wide approaches rather than isolated improvements (18,19). Collectively, these results support a nonlinear, phase-dependent model of improvement in laboratory TAT, in which incremental optimization produces modest gains, whereas comprehensive workflow integration particularly incorporation of POC testing and streamlined stroke pathways yields substantial and clinically meaningful reductions. These findings provide an insight into how laboratory and system-level factors interact to influence performance and underscore the importance of targeting both pre-analytical and analytical components when designing quality improvement initiatives in acute stroke care.

A major setback was limited clinical utility of POC INR results for stroke patients on direct oral anticoagulant (DOAC) therapy. Workflow improvements need to be evaluated in the context of patient population and expected clinical utility. DOAC use for stroke prevention in non-valvular atrial fibrillation has increased over recent years and it is well known that POC INR results are unreliable in patients on DOAC therapy. Even though POC INR was an operational success, clinical teams preferred reliability over speed and decided to use laboratory INR results for determining tPA eligibility. Therefore, impact of improved TAT on DTN and other clinical outcomes was deemed irrelevant. A key lesson learned is that without interdisciplinary goal prioritization and alignment, even well-executed process improvements may not translate into better clinical outcomes.

Our study has several limitations: The project is a single institutional experience, and the lack of clinical outcome data and the single-center nature limit the findings to this specific institutional context. The pre-/post-intervention cohorts are independent and unpaired; therefore, the effect of time cannot be ruled out. Although we did collect data on type of stroke at the time of initial presentation, severity of stroke was not documented. In addition, key baseline characteristics such as age, sex, and anticoagulant use were not collected, the potential confounding effects of these factors on TAT outcomes cannot be ruled out. . The interventions could not eliminate order-to-result TAT outliers. Since the proportion of receipt-to-result times exceeding 25 minutes, which is related to internal laboratory factors, has successfully dropped to 0% after efforts in both phases, persistence of order-to-result TAT outliers can be attributable to factors external to the laboratory such as delays in sample collection, sample handoff and transport. A previous study demonstrated enhanced laboratory TAT without improving the door-to-needle time, as many other factors contribute to it, and laboratory TAT is just one of them (21). Currently, our institution does not maintain an electronic record of sample chain of custody. A quality improvement project encompassing these factors provides an excellent opportunity for future. Also, in March 2022, 3 months after implementation of the phase I improvement, the order-to-result TAT rebounded to nearly the pre-intervention level of August 2021. Although a definitive retrospective root cause analysis is limited by the absence of contemporaneous process-level data, we revisited the workflow and operational context during that period to identify plausible contributing factors. Potential explanations that may account for this temporary regression include variability in staff adherence to the newly implemented workflow, onboarding of new personnel with limited familiarity with the optimized process, and fluctuations in workload or competing clinical priorities. Analysis of these factors can enhance robustness of future process improvements. Data analysis focused on overall TAT and did not segment delays into discrete steps. A detailed analysis could identify which components of the workflow derived the greatest benefit and could inform targeted follow up interventions.

The project was implemented with a preset completion date; the post-phase 2 data was only assessed over two months, and short-term data may not reflect the long-term sustainability of the improvement. We recognize that phase 2 sample size restricts generalizability, and larger prospective studies are warranted to confirm these findings and to more precisely quantify the impact of POC INR implementation. Additionally, while the study was well powered to detect moderate-to-large effects as determined by post hoc analysis, smaller differences particularly in secondary outcomes may remain underpowered.

Collection times were not considered a metric in calculating performance indicators due to the non-availability of accurate data in POC setting. Collection times were often identical to receipt times if the collecting nurse did not record the event on the chart. Lastly, data associated with cost-effectiveness of POC INR was not collected.

Conclusions

Implementation of targeted coagulation workflow optimizations and a structured integration of POC INR in the clinical laboratory led to TAT improvements and strengthened operational variability in acute stroke at our institution. Strategic placement of POC INR in central lab preserved laboratory oversight on quality of testing. Although these improvements proved to be of great operational value, results were not translatable to clinical outcomes due to unreliable POC INR results in DOAC treated patients. Future multi-center prospective studies may be a potential area of research to explore the clinical impact of shortened TAT in stroke. Although POC INR results had a good correlation with laboratory INR with occasional discordance at thrombolysis cutoff, POC INR showed systematic bias. Due to single institutional nature and a very limited data set, these findings remain poorly characterized. POC INR accuracy and analytic performance at thrombolysis cutoff needs further research on larger cohorts.

Acknowledgments

Devin Prescott Fitzmaurice (Biology/College of Arts and Sciences, University of Miami), Omar Al Juboori MD, MD (Pathology and Laboratory Medicine, University of Miami), Alin Lacroix (Pathology and Laboratory Medicine, Jackson Memorial Hospital), Selina Rebecca Ancheta (Quality and Patient Safety, Jackson Memorial Hospital), Sallie-anne R. Wright (Pathology and Laboratory Medicine, Jackson Memorial Hospital), Solange M. Narcisse (Pathology and Laboratory Medicine, Jackson Memorial Hospital). Devin and Omar assisted with editing and formatting. Alin, Sallie, Solange and Selina facilitated data collection.

The project was selected among top 4 quality projects for verbal presentation at the 2022 Quality improvement and patient safety showcase, University of Miami/Jackson Health System, Miami Florida, USA.

Footnote

Reporting Checklist: The authors have completed the SQUIRE reporting checklist. Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-2025-1-63/rc

Data Sharing Statement: Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-2025-1-63/dss

Peer Review File: Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-2025-1-63/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-2025-1-63/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Publication of this project was waived from ethical approval and patient consent by Jackson Health System.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Tsao CW, Aday AW, Almarzooq ZI, et al. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation 2022;145:e153-639. [Crossref] [PubMed]
2. Emberson J, Lees KR, Lyden P, et al. Effect of treatment delay, age, and stroke severity on the effects of intravenous thrombolysis with alteplase for acute ischaemic stroke: a meta-analysis of individual patient data from randomised trials. Lancet 2014;384:1929-35. [Crossref] [PubMed]
3. Wardlaw JM, Murray V, Berge E, et al. Thrombolysis for acute ischaemic stroke. Cochrane Database Syst Rev 2014;2014:CD000213. [Crossref] [PubMed]
4. Man S, Xian Y, Holmes DN, et al. Association Between Thrombolytic Door-to-Needle Time and 1-Year Mortality and Readmission in Patients With Acute Ischemic Stroke. JAMA 2020;323:2170-84. [Crossref] [PubMed]
5. Saver JL, Goyal M, van der Lugt A, et al. Time to Treatment With Endovascular Thrombectomy and Outcomes From Ischemic Stroke: A Meta-analysis. JAMA 2016;316:1279-88. [Crossref] [PubMed]
6. Hacke W, Kaste M, Bluhmki E, et al. Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke. N Engl J Med 2008;359:1317-29. [Crossref] [PubMed]
7. Desai SM, Rocha M, Jovin TG, et al. High Variability in Neuronal Loss. Stroke 2019;50:34-7. [Crossref] [PubMed]
8. Fonarow GC, Smith EE, Saver JL, et al. Improving door-to-needle times in acute ischemic stroke: the design and rationale for the American Heart Association/American Stroke Association’s Target: Stroke initiative. Stroke 2011;42:2983-9. [Crossref] [PubMed]
9. Xian Y, Xu H, Lytle B, et al. Use of Strategies to Improve Door-to-Needle Times With Tissue-Type Plasminogen Activator in Acute Ischemic Stroke in Clinical Practice: Findings from Target: Stroke. Circ Cardiovasc Qual Outcomes 2017;10:e003227. [Crossref] [PubMed]
10. The Joint Commission Comprehensive Stroke Center Certification Program (DSDF.3). Chapter: delivering or facilitating clinical care. Available online: https://www.jointcommission.org/en-us/certification/stroke
11. Jadhav B. Stroke diagnostics and therapeutics: global markets. Wellesley, MA, USA: BCC Research; 2015.
12. Adams HP Jr, del Zoppo G, Alberts MJ, et al. Guidelines for the early management of adults with ischemic stroke: a guideline from the American Heart Association/American Stroke Association Stroke Council, Clinical Cardiology Council, Cardiovascular Radiology and Intervention Council, and the Atherosclerotic Peripheral Vascular Disease and Quality of Care Outcomes in Research Interdisciplinary Working Groups; the American Academy of Neurology affirms the value of this guideline as an educational tool for neurologists. Stroke 2007;38:1655-711. [Crossref] [PubMed]
13. Vasquez MA, Lambrakos LK, Velasquez A, et al. Contemporary outcomes of acute ischemic stroke in atrial fibrillation patients on anticoagulation. J Stroke Cerebrovasc Dis 2024;33:107790. [Crossref] [PubMed]
14. Siarkowski M, Lin K, Li SS, et al. Meta-analysis of interventions to reduce door-to-needle times in acute ischaemic stroke patients. BMJ Open Qual 2020;9:e000915. [Crossref] [PubMed]
15. Volmar KE, Wilkinson DS, Wagar EA, et al. Utilization of stat test priority in the clinical laboratory: a College of American Pathologists q-probes study of 52 institutions. Arch Pathol Lab Med 2013;137:220-7. [Crossref] [PubMed]
16. Dolscheid-Pommerich RC, Dolscheid S, Eichhorn L, et al. Thrombolysis in stroke patients: Comparability of point-of-care versus central laboratory international normalized ratio. PLoS One 2018;13:e0190867. [Crossref] [PubMed]
17. International Technidyne Corp. Hemochron Jr. Citrate Prothrombin Time (PT). Edison, NJ, USA: ITC; 2007.
18. Powers WJ, Rabinstein AA, Ackerson T, et al. 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke 2018;49:e46-110. [Crossref] [PubMed]
19. Kleindorfer DO, Towfighi A, Chaturvedi S, et al. 2021 Guideline for the Prevention of Stroke in Patients With Stroke and Transient Ischemic Attack: A Guideline From the American Heart Association/American Stroke Association. Stroke 2021;52:e364-467. [Crossref] [PubMed]
20. Han J, Jang S, Choi M, et al. Point-of-care coagulation testing for reducing in-hospital delay in thrombolysis. Hong Kong J Emerg Med 2019;26:218-24.
21. Trongnit S, Reesukumal K, Kost GJ, et al. Reducing Laboratory Turnaround Time in Patients With Acute Stroke and the Lack of Impact on Time to Reperfusion Therapy. Arch Pathol Lab Med 2023;147:87-93. [Crossref] [PubMed]