Quantifying the Benefit of Prehospital Rapid Treatment in Acute Stroke
Background and Purpose—
In acute ischemic stroke, time from onset to tissue-type plasminogen activator treatment (OTT) is a major determinant of outcome. To reduce OTT, clinical trials have been undertaken evaluating prehospital cerebral imaging with mobile computed tomographic scanners. Furthermore, blood biomarkers may allow rapid differentiation between ischemic stroke and intracerebral hemorrhage before hospital admission. How such treatment strategies translate into clinical benefit has not been specifically evaluated.
We constructed decision models to estimate the net clinical benefit yielded by shorter OTT. In different scenarios, we estimated the proportion of patients with favorable outcome and the average quality of life.
An OTT reduction of 60 minutes increases the probability of favorable outcome by 6.6% in a mixed stroke population. For comparison, the average effect of tissue-type plasminogen activator itself is 7.0%. Prehospital mobile computed tomography gaining 25 to 40 minutes increases the probability of favorable outcome by 3.0% to 4.6%. The additional benefit of prehospital computed tomography to deliver patients with large vessel occlusion directly to endovascular treatment centers increases the probability of favorable outcome by another 0.2% to 1.0%. A blood test discriminating ischemic stroke and intracerebral hemorrhage may beneficially substitute brain scan before tissue-type plasminogen activator if >32 to 40 minutes are gained and if sensitivity for intracerebral hemorrhage is >75% to 80%.
Reducing the OTT has robust beneficial effects for acute stroke patients. Prehospital tissue-type plasminogen activator treatment without brain imaging may become conceivable under several preconditions, including a point-of-care test with >75% to 80% sensitivity to detect intracerebral hemorrhage and a time gain of >32 to 40 minutes. Ethical implications remain to be addressed.
Tissue-type plasminogen activator (tPA) improves the outcome in acute ischemic stroke (IS) if it is administered within 4.5 h after stroke onset.1,2 Even within this time window, the earlier tPA is given, the better is the prognosis.1 This fact has motivated multiple attempts to shorten the time to tPA treatment, both in hospital (door-to-needle-time)3 and in the prehospital phase.
Prehospital delay can be reduced by a mobile computed tomographic (CT) scanner, as has been demonstrated in 2 clinical trials.4,5 On average, 40 minutes have been gained in a rural environment4 and 25 minutes in an urban infrastructure.5 In the large endovascular thrombectomy (EVT) trials, between 7% and 21% of tPA patients were eligible for EVT.6–9 A preclinical CT scan—when it includes angiography—may bear the additional option to deliver patients who are eligible for EVT directly to interventional stroke centers, resulting in additional benefit.9
Another approach constitutes the search for blood biomarkers of acute stroke. Meanwhile, a considerable body of evidence from prospective clinical studies and pathophysiological considerations is available, indicating that plasma glial fibrillary acidic protein (GFAP) discriminates between IS and intracerebral hemorrhage (ICH) within the first hours after symptom onset with high diagnostic accuracy.10–14 GFAP is abundantly present in astrocytes. It is rapidly released into blood in case of ICH because of immediate structural disintegration of cells, but is released with delay in IS. A recent meta-analysis has estimated the sensitivity of GFAP to detect ICH as 80% (95% confidence interval 71% to 88%).14 Because only a minority of all acute stroke patients has ICH, the negative predictive value of such a test is high (ie, 96% of GFAP-negative patients do not have ICH).12 This example has motivated the hypothetical concept of a point-of-care (POC) blood test used to screen for ICH before prehospital tPA administration. On the one hand, many patients may benefit from being treated with much shorter onset to tPA treatment (or being treated at all if no brain imaging is readily available); on the other hand, misclassified ICH patients may develop potentially lethal bleeding complications under tPA. Thus, the time gain–associated benefits need to well outweigh the risks. Even standard tPA treatment bears the risk of harming few patients by inducing hemorrhagic complications, which overall is compensated by the benefit of many other patients.1 Even more so, a blood test–based prehospital tPA treatment strategy would be a double-edged sword. The objective of this research article is to determine the benchmark for innovative rapid treatment options in acute stroke to set the standards for future clinical trials.
To assess the benefit of prehospital time gain, we built decision models with one decision node (treatment decision, eg, standard treatment versus prehospital tPA). The population of interest was a mixed cohort of stroke patients, including IS, ICH, subdural hematoma, subarachnoid hemorrhage, and stroke mimics. All patients were assumed to reach medical care within 4.5 h after stroke onset, to fulfil severity criteria for tPA (NIHSS 4–22), and to have no tPA contraindications in their medical history (such as recent surgery or ongoing anticoagulation). After the decision node, the probability of every stroke type (IS, ICH, subdural hematoma, subarachnoid hemorrhage, and stroke mimics) was modeled, then—for every stroke type—the probability to reach the outcome categories favorable outcome (modified Rankin scale [mRS] 0–1 at day 90), minor stroke (mRS 2–3), major stroke (mRS 4–5), or death (mRS 6). An example decision tree for prehospital mobile CT-guided tPA is shown in Figure 1; other decision trees can be found in the online-only Data Supplement (Figures I–IV in the online-only Data Supplement). When the time between symptom onset and evaluation for tPA treatment is reduced, patients can benefit in 2 ways: first, all patients receiving tPA have a higher probability of a better outcome because of shorter onset to tPA treatment (see Table I in the online-only Data Supplement); second, those near the end of the 4.5 h time window have a higher probability to receive tPA treatment at all.
Another decision model to estimate the benefit of prehospital CT by increasing the proportion of primary rather than secondary EVT used data from the Solitaire FR With the Intention for Thrombectomy as Primary Endovascular Treatment for Acute Ischemic Stroke (SWIFT PRIME) trial,9 where the outcome distributions of tPA alone, tPA+EVT in the primary center, and tPA+EVT in a secondary center were displayed. This analysis could be done only for the end point favorable outcome but not for quality-adjusted life years (QALY) because there was insufficient information on the mRS categories 5 and 6. Details of the underlying assumptions for this model are shown in the online-only Data Supplement (Table II in the online-only Data Supplement).
To assess the quality of life, we added the average survival time and the estimated quality of life for every outcome category. The average survival time by outcome category was estimated with simulations based on UK lifetables15 and several assumptions supported by literature. First, we estimated the natural life expectancy of the respective subpopulation based on UK lifetables15 projected into 2015 (index date) and the known age and sex distribution. Then, we modeled premature death because of the stroke; therefore, we chose absolute death rates per outcome category in a way that both the relative risks between the outcome categories and the absolute event rates were matching published data of long-term survival after stroke (for details, see Table I in the online-only Data Supplement).
The quality of life associated with a specific outcome was derived from a study from Hallan et al16 who asked healthy people, nonstroke medical patients, and stroke survivors how they might perceive different stroke outcomes and estimated a utility of living in such a state (details see Table I in the online-only Data Supplement). As simplification, we assumed that the quality of life remained constant until the end of the life expectancy. For every branch of the decision tree, the number of QALY was derived as follows:
The outcome (mRS) distribution at day 90 and how it is affected by the treatment decision was derived from large stroke trials and other publications. In particular, we conservatively assumed that all patients with ICH, subdural hematoma, or subarachnoid hemorrhage receiving tPA died. All calculations relied on published estimates, public lifetables, and well-founded assumptions, not on own patient data. The specific assumptions met for the decision models are shown in Table 1 and (in full detail) in the online-only Data Supplement (Table I in the online-only Data Supplement). The assumptions of the primary model were chosen a priori, before beginning to evaluate the decision models.
|Probability that patient had dropped out of time window since prehospital evaluation||Derived from OTT distribution, as reported by Hacke 20041|
|Probability of IS/ICH/SDH/SAH/stroke mimics in stroke cohort <4.5 h, NIHSS 4–22||71:11:0:0:18%* (Walter4)|
|Probability of mRS 0-1/2–3/4–5/6 in IS under standard thrombolysis†||40.1:22.6:21.9:15.4% (Hacke1)|
|Expected long-term survival of subjects with mRS 0-1/2–3/4–5/6 in IS†||Mean survival IS mRS 0–1: 17.0 y|
|Mean survival IS mRS 2–3: 15.5 y|
|Mean survival IS mRS 4–5: 8.4 y|
|Mean survival IS mRS 6: 0.0 y|
|Derived from simulations based on UK lifetables,15 age and sex distribution, and event rates from literature (Hacke,1 Petty,19 Sacco,20 Petty,21 Hartmann,22 Reggiani,23 Andersen24).|
|Probability of mRS 0-1/2–3/4–5/6 in ICH under standard therapy†||26:35:27:12% (Anderson25)|
|Expected long-term survival of subjects with mRS 0-1/2–3/4–5/6 in ICH†||Mean survival IS mRS 0–1: 23.6 y|
|Mean survival IS mRS 2–3: 19.0 y|
|Mean survival IS mRS 4–5: 10.9 y|
|Mean survival IS mRS 6: 0.0 y|
|Derived from simulations based on UK lifetables, age and sex distribution, and event rates from literature (Saloheimo26).|
|Probability of mRS 0-1/2–3/4–5/6 in SDH under standard therapy†||11:32:22:35% (Li27)|
|Expected long-term survival of subjects with mRS 0-1/2–3/4–5/6 in SDH†||Mean survival IS mRS 0–1: 33.2 y|
|Mean survival IS mRS 2–3: 22.5 y|
|Mean survival IS mRS 4–5: 11.4 y|
|Mean survival IS mRS 6: 0.0 y|
|Derived from simulations based on UK lifetables, age and sex distribution, and event rates from literature (Saloheimo26 and Li27).|
|Probability of mRS 0-1/2–3/4–5/6 in SAH under standard therapy†||17:20:13:50%* (Hop28)|
|Expected long-term survival of subjects with mRS 0-1/2–3/4–5/6 in SAH†||Mean survival IS mRS 0–1: 31.4 y|
|Mean survival IS mRS 2–3: 21.9 y|
|Mean survival IS mRS 4–5: 11.3 y|
|Mean survival IS mRS 6: 0.0 y|
|Derived from simulations based on UK lifetables, age and sex distribution, and event rates from literature (Hop,28 Saloheimo,26 Mathys,29 Abla30).|
|Probability of mRS 0-1/2–3/4–5/6 in stroke mimics†||77:11:6:6% (Collated from Scott,31 Winkler,32, Chernyshev,33 Chen,34 Sarikaya35)|
|Expected long-term survival of subjects with mRS 0-1/2–3/4–5/6 in stroke mimics†||Mean survival IS mRS 0–1: 28.3 y|
|Mean survival IS mRS 2–3: 20.1 y|
|Mean survival IS mRS 4–5: 11.0 y|
|Mean survival IS mRS 6: 0.0 y|
|Derived from simulations based on UK lifetables,15 age and sex distribution, and event rates from literature (Scott,31 Winkler,32 Chernyshev,33 Chen,34 Sarikaya35).|
|Probability of mRS 0-1/2–3/4–5/6 in IS without thrombolysis†||30.2:26.4:28.7:14.7% (Hacke1)|
|Distribution of GFAP test in unselected stroke cohort||p(GFAP+IS)=0.03 (0.02–0.06) (Sun14)|
|p(GFAP+ICH)=0.80 (0.71–0.88) (Sun14)|
|p(GFAP+SAH)=0.00 (conservative assumption)|
|p(GFAP+SM)=0.00 (conservative assumption)|
|Probability of mRS 0-1/2–3/4–5/6 in IS under early thrombolysis†||Time gain–dependent probabilities were derived from the estimated adjusted OR by time to treatment (OTT) and weighted by OTT distribution, as reported by Hacke1|
|Probability of mRS 0-1/2–3/4–5/6 in ICH under thrombolysis†||0:0:0:100%* (conservative assumption)|
|22:45:22:11% (Foerch10 and Foerch37)|
|Probability of mRS 0-1/2–3/4–5/6 in SDH under thrombolysis†||0:0:0:100% (conservative assumption)|
|Probability of mRS0-1/2–3/4–5/6 in SAH under thrombolysis†||0:0:0:100% (conservative assumption)|
|Probability of IS/ICH/SDH/SAH/stroke mimics in GFAP-positive stroke cohort <4.5 h NIHSS 4–22||Derived from distribution of diagnoses in unselected cohort and GFAP test distributions|
|Utility of mRS 0-1/2–3/4–5/6, as perceived by patients||1/0.91/0.61/0* (Hallan16)|
The decision trees were evaluated with the software package TreeAge Pro (Version 2014; TreeAge Software Inc, Williamstown, MA). The simulation of survival times was done with SAS (Version 9.3; SAS Institute Inc, Cary, NC).
The estimates of the net clinical benefit depending on the time gain is shown in Table 2, expressed as probability to reach a favorable outcome and as the gain of QALYs. For example, in a mixed stroke population, a time gain of 60 minutes increased the probability of a favorable outcome by 6.6%, whereas the average benefit of tPA (versus placebo, as derived from tPA trials1) increased this probability by 7.0%.
|Time Gain, min||Analysis Restricted to Ischemic Stroke||Analysis of Mixed Population, Including Hemorrhagic Stroke and Stroke Mimics|
|Additional Probability of Favorable Outcome (mRS 0–1 at Day 90)||Average Gain of Life Quality per Person (QALY)||Additional Probability of Favorable Outcome (mRS 0–1 at Day 90)||Average Gain of Life Quality per Person (QALY)|
|Effect of tPA vs placebo in 4.5 h time window||0.098||0.800||0.070||0.570|
The probability of having a favorable outcome increased by 3.0% when onset to tPA treatment was reduced by 25 minutes (as yielded by mobile prehospital CT imaging in an urban environment5) and 4.6% with a time gain of 40 minutes (as prehospital CT imaging in rural environment4). A hypothetical blood test–based prehospital tPA algorithm increased the probability of favorable outcome by 3.9% when onset to tPA treatment was reduced by 40 minutes and 5.9% with a time gain of 60 minutes. The distribution of patients among the outcome categories is shown in Figure 2. Figure 2A shows the outcome change induced by tPA versus placebo, as derived from tPA trials,1 but translated into a mixed stroke population. Figure 2B shows the additional yield by prehospital CT with a time gain of 25 minutes. Figure 2C and 2D shows the outcome distributions of a blood test–based prehospital tPA algorithm with 40 and 60 minutes time gain, respectively, as compared with standard CT-based in-hospital tPA administration.
With extensive sensitivity analyses, we studied the influence of different variables on the benefit–risk ratio of a hypothetical blood test–based prehospital tPA scenario compared with standard CT-based in-hospital tPA treatment. The critical variables were time gain and sensitivity of the blood test to detect ICH.
When we assumed 80% sensitivity of the blood test to detect ICH, a time gain of only 5 minutes sufficed to compensate for the risk of overlooking ICH, considering the end point favorable outcome (Figure 3A). However, when the end point QALY (that accounts for shifts between the upper mRS categories) was used, a time gain of at least 32 minutes was required to compensate for the risk (Figure 3B). When we assumed a fixed time gain of 40 minutes and varied the sensitivity for ICH, the end point favorable outcome was always favored of the blood test–guided tPA scenario (Figure 4A); for the end point QALY, a sensitivity of at least 74.5% was required to favor blood test–guided tPA (Figure 4B). With a fixed time gain of 60 minutes, blood test–guided tPA was favored at 63.5% sensitivity or more. A bivariate sensitivity analysis including these 2 key variables can be found in the online-only Data Supplement (Figure V in the online-only Data Supplement).
Specifically, when we assume a sensitivity of the blood test to detect ICH of 80%, a time gain of 25 minutes would cause harm, losing 0.060 QALYs per person, although the probability of favorable outcome is 2.3% higher. A time gain of 40 minutes would increase the chances of favorable outcome by 3.9% and cause a gain of 0.080 QALYs per person. This corresponds to a number-needed-to-treat to save one QALY of 12.5 and—assuming costs of 100 € per blood test—a cost of 1250€ per QALY. A time gain of 60 minutes increases the probability of favorable outcome by 5.9% and gains 0.250 QALYs per person, with a number-needed-to-treat of 4 and costs of 400€ per QALY.
The sensitivity analyses of the other parameters showed that the primary model was the most conservative one. For example, in the POC test–based prehospital tPA model, the conservative assumption that all false-negative tested ICH patients would die under tPA resulted in a cutoff of 32 minutes time gain (see above) where a prehospital blood test equals standard treatment. Alternative assumptions included an outcome of tPA-treated primary ICH similar to tPA-associated hemorrhagic complications after IS.36 For these scenarios, the resulting cutoff time gain was between 25 and 30 minutes. The optimistic alternative assumption that ICH patients would suffer no additional harm by tPA resulted in a cutoff time gain near zero (Figure VI in the online-only Data Supplement).
The distribution of stroke diagnosis, in particular the percentage of ICH in the unselected stroke cohort, had some influence on the results. A QALY benefit of blood test–guided prehospital tPA over standard tPA was found when the time gain was between 17 and 35 minutes when other diagnosis distributions were assumed (Figure VII in the online-only Data Supplement), instead of 32 minutes in the principal model.
The utility of poststroke handicap used in this analysis had been investigated by Hallan et al,16 who suggested different methods to determine subjects’ opinions on how they rated quality of life in different handicap situations. The time gain cutoff for blood test prehospital tPA was 32 minutes (see above) with the principal method (standard gamble), 28 minutes with the time trade-off approach, and 20 minutes with direct scaling16 (Figure VIII in the online-only Data Supplement).
The decision models were robust to variations of other assumptions.
Benefit of Prehospital CT in EVT
Prehospital CT may bear additional benefit when large-vessel occlusions are detected and the patient is brought directly to a stroke center capable of EVT. However, under the baseline model (13.7% of all tPA patients assumed eligible for EVT; 31% of EVT patients assumed to shipped for EVT; both derived from large EVT trials; assumed shipping was avoided completely with prehospital CT), only 0.2% additional patients resulted in a favorable outcome when preclinical CT was compared with standard treatment. In sensitivity analyses, the maximal additional benefit by avoiding secondary EVT was 1.2% (30% tPA patients assumed eligible for EVT; 61% of EVT patients assumed being shipped), but when we assumed that even with prehospital CT, 10% of patients would not primarily reach an EVT center, this benefit shrinked to 1.0%.
Hitherto, published interventions to save time before tPA mainly focus on improving details in the sequence from preclinical management by an ambulance team to tPA administration in the hospital. Although the single detail may not have great effect, a package of measures has the potential to gain 1 hour and more.3 Such measures, including education of personnel, improved communication, prioritization, and better organization of the procedures, raise little or moderate cost and imply no additional risk for the patient.
An even more radical idea is the use of a POC blood test instead of brain imaging to select patients eligible for prehospital tPA treatment. This concept has the positive spin of low tech; it comes at low cost, but implies additional risk for the patient. Today, such an approach is hardly thinkable because of the risk of harming the patient, in the case where tPA would be applied in primary ICH that was misclassified by the blood test.
Our decision models allow estimating the clinical benefit for the individual patient in all these strategies, including additional risk if applicable. However, considering the consequences, the preconditions of these models have to be determined carefully.
First, our results show that a time gain of 1 hour nearly doubles the average benefit of tPA. This emphasizes the demand to take every possible action to minimize the local door-to-needle-time to every stroke physician.
Second, we can number the benefit of a mobile CT-based treatment algorithm. With this concept, a time gain of 25 minutes as has been demonstrated in an urban environment was associated with about half of the average tPA benefit. A time gain that was yielded in a more rural region with longer transportation times was 40 minutes, corresponding to approximately two thirds of the average tPA effect. In countries where even longer transportation to a stroke center is required, the time gain might be even greater; however, the deployment of a mobile CT scanner may take more time as well. Furthermore, the lower the population density, the more expensive the provision of these vehicles may be. In addition, prehospital CT may enable faster EVT in eligible patients, which may create an additional virtue in the dimension of 3% to 15% of the tPA benefit.
Third, we investigated the overall consequences of the hypothetical and radical idea of substituting brain imaging before tPA with a POC blood test. As expected, we found that the key variables are (1) the sensitivity of the blood test to detect ICH and (2) the time gain that can be achieved compared with standard treatment. With the given informed assumptions, we set benchmarks for this approach. As long as the sensitivity of the blood test for ICH is at least 80% across the whole tPA time window of 0 to 4.5h, a time gain of >32 minutes is required to result in an overall benefit for the patient. With a time gain of at least 40 minutes, the sensitivity is required to be 75% or higher.
How far away are we from these benchmarks? In prospective studies, a GFAP test reached 80% sensitivity to detect ICH.14 In a setting where mobile CT saves 25 to 40 minutes, a POC test available on the ambulance car may readily save 40 minutes or more. Once we have assured that the additional risk is outweighed by additional benefit, the best argument to prefer prehospital blood test–based tPA over mobile CT is the cost. Our calculations show that if 40 to 60 minutes are gained, one QALY can be obtained at 400 to 1250 € with a POC blood test at 100 € per piece. In comparison, one QALY gained with mobile CT has been estimated to cost 32 456 €.38
Our analysis shows in principle that the rapid delivery of thrombolysis in acute stroke care may be beneficial even if ICH is not ruled out with 100% certainty. To date, performing a brain scan before tPA therapy is considered an unalterable dogma.
How dangerous tPA is exactly for the misclassified (POC test false-negative) ICH may be partially answered with animal studies.37,39 However, final certainty will only be created by a clinical trial. Such a trial may only be ethical when the additional risk for the patient can be overbalanced with a benefit; the above mentioned clinical trial with POC test–based prehospital tPA may be one of few opportunities that seem ethical. A thrombolysis agent with comparable efficiency, but less bleeding risk, compared with tPA, may further promote this concept.40
Before starting further studies and trials, important ethical aspects must be considered. Current standard tPA treatment of stroke implies jeopardy for a minority of patients (those who suffer hemorrhagic complications) while improving the prognosis of the majority. The idea of POC test–based prehospital tPA treatment increases this dilemma: more patients may benefit and more patients may suffer harm, whereas the overall benefit may be increased. The treating physician has to balance these risks and benefits and may feel challenged by this to ethical controversy: to save time and increase the chances of recovery of the average patient, we would substitute a reliable safety measure (CT brain scan) with a less reliable one (POC test). From the perspective of the individual patient, the question is even more tangible: am I willing to risk death to get healthy? To research patient preferences on this elementary question seems mandatory.
Still, there are open questions that leave work to do. A suitable POC test for GFAP remains to be developed. The dynamics of GFAP in the early time window has to be corroborated with larger samples in a real-life scenario. This may best be done with an ambulance-based study design, doing the POC test in the first contact with the acute stroke patient.
Our decision models relied on multiple assumptions that were well informed. However, despite comprehensive research of the literature and realistic estimation, some of our assumptions may turn out to be imprecise. For example, the sensitivity of the GFAP test may decrease in the early time window.11 Furthermore, there may be additional effects unforeseen or difficult to put in a decision model, like, for example, the influence of time to (neurosurgical) treatment of ICH. Again, the only way to get realistic evidence is to set up an ambulance-based clinical trial; a design with simulated tPA treatment may yield accurate estimates of the distribution of diagnoses that hide under the acute stroke syndrome, the GFAP dynamics in the particular diagnoses, and the real-time gain that is yielded. Finally, if all these gaps of certitude are closed, a clinical trial with POC test–based real prehospital tPA may become thinkable.
Reducing the time from stroke onset to treatment deserves great attention because the benefit of tPA treatment can be nearly doubled with early delivery. Important benchmark for a hypothetical blood test to detect ICH without brain imaging is 75% to 80% sensitivity across the whole time window 0 to 4.5h after stroke onset; the benchmark for the treatment setting is a reduction of the time from stroke onset to tPA of >32 to 40 minutes. If these benchmarks were fulfilled, the concept of rapid prehospital tPA treatment based on POC blood tests instead of brain imaging might become thinkable in the future. A careful ethical discussion of potential implications, however, is mandatory.
Dr Foerch holds a patent for the use of glial fibrillary acidic protein (GFAP) for differentiating intracerebral hemorrhage (ICH) and ischemic stroke. The other authors report no conflicts.
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