Probing the Effective Treatment Thresholds for Alteplase in Acute Ischemic Stroke With Regression Discontinuity Designs

The whole problem I have here is the definition of effective. Removing the clot is effective but the endpoint to be measuring is 100% recovery. None of these half-assed endpoints like reperfusion.

Probing the Effective Treatment Thresholds for Alteplase in Acute Ischemic Stroke With Regression Discontinuity Designs

Andrew M. Naidech^1*†, Patrick N. Lawlor^2†, Haolin Xu³, Gregg C. Fonarow⁴, Ying Xian^3,5, Eric E. Smith⁶, Lee Schwamm⁷, Roland Matsouaka^8,9, Shyam Prabhakaran¹, Ioana Marinescu¹⁰ and Konrad P. Kording¹¹

• ¹Department of Neurology, Northwestern University, Chicago, IL, United States
• ²Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, United States
• ³Duke Clinical Research Institute, Duke University, Durham, NC, United States
• ⁴Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, United States
• ⁵Department of Neurology, Duke University Medical Center, Durham, NC, United States
• ⁶Department of Neurology, University of Calgary, Calgary, AB, Canada
• ⁷Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
• ⁸Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States
• ⁹Program for Comparative Effectiveness Methodology, Duke Clinical Research Institute, Duke University, Durham, NC, United States
• ¹⁰School of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, United States
• ¹¹Departments of Neuroscience and Bioengineering, University of Pennsylvania, Philadelphia, PA, United States

Randomized Controlled Trials (RCTs) are considered the gold standard for measuring the efficacy of medical interventions. However, RCTs are expensive, and use a limited population. Techniques to estimate the effects of stroke interventions from observational data that minimize confounding would be useful. We used regression discontinuity design (RDD), a technique well-established in economics, on the Get With The Guidelines-Stroke (GWTG-Stroke) data set. RDD, based on regression, measures the occurrence of a discontinuity in an outcome (e.g., odds of home discharge) as a function of an intervention (e.g., alteplase) that becomes significantly more likely when crossing the threshold of a continuous variable that determines that intervention (e.g., time from symptom onset, since alteplase is only given if symptom onset is less than e.g., 3 h). The technique assumes that patients near either side of a threshold (e.g., 2.99 and 3.01 h from symptom onset) are indistinguishable other than the use of the treatment. We compared outcomes of patients whose estimated onset to treatment time fell on either side of the treatment threshold for three cohorts of patients in the GWTG-Stroke data set. This data set spanned three different treatment thresholds for alteplase (3 h, 2003–2007, N = 1,869; 3 h, 2009–2016, N = 13,086, and 4.5 h, 2009–2016, N = 6,550). Patient demographic characteristics were overall similar across the treatment thresholds. We did not find evidence of a discontinuity in clinical outcome at any treatment threshold attributable to alteplase. Potential reasons for failing to find an effect include violation of some RDD assumptions in clinical care, large sample sizes required, or already-well-chosen treatment threshold.

Introduction

Randomized controlled trials (RCTs) are considered the gold standard in clinical investigation because, ideally, RCTs remove both known and unknown imbalances in groups that could lead investigators to wrongly conclude a treatment is efficacious (1). Data from RCTs are generally required before regulatory approval is granted to market a drug treatment (e.g., alteplase for acute ischemic stroke) (2), and new interventions typically require data from RCTs on efficacy before they are widely accepted.

Therapies that have a treatment threshold lead to challenging problems about the choice of those thresholds. In the case of alteplase, only patients whose symptoms began before the threshold time (e.g., 3 h prior to presentation) are eligible for treatment. The time window curtails treatment in clinical practice, and off-label use beyond approved time windows introduces legal and ethical concerns. If a treatment is effective within a narrow time window (e.g., 3 h for alteplase), there is typically a desire to extend it further to increase the number of patients who might be treated. Yet, each new time window typically requires another RCT, with the associated time and expenses of planning and conducting the trial. In the case of alteplase, data from other clinical trials was utilized to propose additional RCTs. However, extending the time window for fibrinolytic treatment expressly carried an increased risk of intracranial hemorrhage, which was borne out in a RCT with an extended time window (3). The concern for symptomatic hemorrhage has guided the design and conduct of RCTs for ischemic stroke generally (4, 5). Publication of an RCT showing that treatment up to 4.5 h after symptom onset was efficacious required several more years (6). Clinicians are often hesitant to wait years for new RCTs, and may treat patients outside of rigorously applied clinical trial protocols (7). Each new therapy (e.g., endovascular therapy for large vessel occlusion) brings a similar invitation to extend the window as long as it is efficacious. Conversely, some RCTs of time-limited therapies are negative, leading to the testing of more stringent time windows in hopes of finding efficacy [e.g., shortening the window of recombinant Factor VII for intracerebral hemorrhage from 3 h of symptom onset (8) to 2.5 h (9)]. Methods to hasten the determination of effective time windows for treatments with a threshold are needed.

New analytic techniques may improve our ability to determine the optimal treatment window for time-limited treatments. Regression Discontinuity Design (RDD), well-validated in economics and epidemiology (10–13), could be particularly helpful for determining if treatment thresholds are correctly set. RDD uses observational data to examine whether patients just above and just below the treatment threshold have different outcomes. RDD depends on the assumption that patients within a small window on either side of a threshold are no different other than being eligible for a treatment. We hypothesized that RDD would be a useful technique to evaluate alteplase treatment thresholds using observational data (clinical data), and that we could compare the results with those from already-conducted RCTs (6). RDD could eventually supplement RCTs in clinical decision making.