Dual antiplatelet treatment has been shown to lower the risk of recurrent stroke as compared with aspirin alone when treatment is initiated early (≤24 hours) after an acute mild stroke. The effect of clopidogrel plus aspirin as compared with aspirin alone administered within 72 hours after the onset of acute cerebral ischemia from atherosclerosis has not been well studied.
Methods
In 222 hospitals in China, we conducted a double-blind, randomized, placebo-controlled, two-by-two factorial trial involving patients with mild ischemic stroke or high-risk transient ischemic attack (TIA) of presumed atherosclerotic cause who had not undergone thrombolysis or thrombectomy. Patients were randomly assigned, in a 1:1 ratio, within 72 hours after symptom onset to receive clopidogrel (300 mg on day 1 and 75 mg daily on days 2 to 90) plus aspirin (100 to 300 mg on day 1 and 100 mg daily on days 2 to 21) or matching clopidogrel placebo plus aspirin (100 to 300 mg on day 1 and 100 mg daily on days 2 to 90). There was no interaction between this component of the factorial trial design and a second part that compared immediate with delayed statin treatment (not reported here). The primary efficacy outcome was new stroke, and the primary safety outcome was moderate-to-severe bleeding — both assessed within 90 days.
Results
A total of 6100 patients were enrolled, with 3050 assigned to each trial group. TIA was the qualifying event for enrollment in 13.1% of the patients. A total of 12.8% of the patients were assigned to a treatment group no more than 24 hours after stroke onset, and 87.2% were assigned after 24 hours and no more than 72 hours after stroke onset. A new stroke occurred in 222 patients (7.3%) in the clopidogrel–aspirin group and in 279 (9.2%) in the aspirin group (hazard ratio, 0.79; 95% confidence interval [CI], 0.66 to 0.94; P=0.008). Moderate-to-severe bleeding occurred in 27 patients (0.9%) in the clopidogrel–aspirin group and in 13 (0.4%) in the aspirin group (hazard ratio, 2.08; 95% CI, 1.07 to 4.04; P=0.03).
Conclusions
Among patients with mild ischemic stroke or high-risk TIA of presumed atherosclerotic cause, combined clopidogrel–aspirin therapy initiated within 72 hours after stroke onset led to a lower risk of new stroke at 90 days than aspirin therapy alone but was associated with a low but higher risk of moderate-to-severe bleeding.
Dual antiplatelet treatment has been shown to lower the risk of recurrent stroke as compared with aspirin alone when treatment is initiated early (≤24 hours) after an acute mild stroke. The effect of clopidogrel plus aspirin as compared with aspirin alone administered within 72 hours after the onset of acute cerebral ischemia from atherosclerosis has not been well studied.
METHODS
In 222 hospitals in China, we conducted a double-blind, randomized, placebo-controlled, two-by-two factorial trial involving patients with mild ischemic stroke or high-risk transient ischemic attack (TIA) of presumed atherosclerotic cause who had not undergone thrombolysis or thrombectomy. Patients were randomly assigned, in a 1:1 ratio, within 72 hours after symptom onset to receive clopidogrel (300 mg on day 1 and 75 mg daily on days 2 to 90) plus aspirin (100 to 300 mg on day 1 and 100 mg daily on days 2 to 21) or matching clopidogrel placebo plus aspirin (100 to 300 mg on day 1 and 100 mg daily on days 2 to 90). There was no interaction between this component of the factorial trial design and a second part that compared immediate with delayed statin treatment (not reported here). The primary efficacy outcome was new stroke, and the primary safety outcome was moderate-to-severe bleeding — both assessed within 90 days.
RESULTS
A total of 6100 patients were enrolled, with 3050 assigned to each trial group. TIA was the qualifying event for enrollment in 13.1% of the patients. A total of 12.8% of the patients were assigned to a treatment group no more than 24 hours after stroke onset, and 87.2% were assigned after 24 hours and no more than 72 hours after stroke onset. A new stroke occurred in 222 patients (7.3%) in the clopidogrel–aspirin group and in 279 (9.2%) in the aspirin group (hazard ratio, 0.79; 95% confidence interval [CI], 0.66 to 0.94; P=0.008). Moderate-to-severe bleeding occurred in 27 patients (0.9%) in the clopidogrel–aspirin group and in 13 (0.4%) in the aspirin group (hazard ratio, 2.08; 95% CI, 1.07 to 4.04; P=0.03).
CONCLUSIONS
Among patients with mild ischemic stroke or high-risk TIA of presumed atherosclerotic cause, combined clopidogrel–aspirin therapy initiated within 72 hours after stroke onset led to a lower risk of new stroke at 90 days than aspirin therapy alone but was associated with a low but higher risk of moderate-to-severe bleeding.
Adults who reported taking frequent daily naps had greater risk for essential hypertension and stroke compared with those who never or rarely nap, researchers reported.
Participants who napped more frequently were also more likely to have poorer social determinants of health, more comorbidities and more self-reported sleep problems such as insomnia, snoring or evening chronotype, according to data published in Hypertension.
Data were derived from Yang M, et al. Hypertension. 2022;doi:10.1161/HYPERTENSIONAHA.122.19120.
“These results are especially interesting since millions of people might enjoy a regular, or even daily, nap,” E. Wang, PhD, MD, professor and chair of the department of anesthesiology at Xiangya Hospital Central South University in Hunan, China, said in a press release.
“This may be because, although taking a nap itself is not harmful, many people who take naps may do so because of poor sleep at night. Poor sleep at night is associated with poorer health, and naps are not enough to make up for that,” Michael A. Grandner, PhD, MTR, director of the Sleep and Health Research Program and the Behavioral Sleep Medicine Clinic, associate professor of psychiatry at the University of Arizona in Tucson and co-author of the American Heart Association’s new Life’s Essential 8 CV health score, said in the release. “This study echoes other findings that generally show that taking more naps seems to reflect increased risk for problems with heart health and other issues.”
Sleep duration was added to the AHA’s Life’s Simple 7 tool in June as the eighth metric for optimal CV and brain health.
As Healio previously reported, the updated checklist that now includes sleep health metrics showed about 80% of Americans have low to moderate CV health, with the largest gaps noted in diet, physical activity and BMI.
Daily napping and risk
To better understand the relationship between the frequency of daytime napping and the incidence of essential hypertension or stroke, researchers in China assessed the data of 358,451 participants in the UK Biobank free of hypertension or stroke at baseline (mean age, 55 years, 43% men). The median follow-up duration was 11.16 years.
Daytime napping was self-reported with the following responses: never/rarely, sometimes, usually or prefer not to answer.
The researchers observed individuals who reported usually napping had higher risk for essential hypertension (HR = 1.12; 95% CI, 1.08-1.17), stroke (HR = 1.24; 95% CI, 1.1-1.39) and ischemic stroke (HR = 1.2; 95% CI, 1.05-1.36) compared with individuals who reported never or rarely napping.
Risk for essential hypertension, stroke and ischemic stroke was slightly lower among those who reported sometimes napping compared with usually napping but remained elevated compared with never/rarely napping.
The researchers noted that people who reported daytime napping were more likely to be men, older, non-European, less educated; to have lower income, higher BMI, higher waist-hip ratio, higher Townsend deprivation index; and to have a history of smoking, psychiatric disorder, high cholesterol and diabetes. Individuals who reported daytime napping were also more likely to sleep longer at nighttime and reported sleep problems, including insomnia, snoring or evening chronotype.
Validation using Mendelian randomizations
To validate these results, researchers conducted a two-sample Mendelian randomization for the association between daytime napping frequency and essential hypertension using the FinnGen Biobank, and stroke and ischemic stroke were validated using the MEGASTROKE consortium and a corresponding one-sample Mendelian randomization.
In both the one- and two-sample Mendelian randomizations, researchers reported that increased daytime napping frequency was linked in a causal manner to risk for essential hypertension in the FinnGen Biobank (OR = 1.43; 95% CI, 1.06-1.92) and UK Biobank (OR = 1.4; 95% CI, 1.28-1.58).
Moreover, the results of the two-sample Mendelian randomization in the MEGATROKE also validated daytime napping frequency as a potential causal risk factor for ischemic stroke (OR = 1.29; 95% CI, 1.04-1.62).
“The specific biological mechanism for the effect of daytime napping on BP regulation or stroke has not yet been discovered. The underlying mechanisms are poorly understood but may include increased inflammatory indices or the long-term effect of a BP peak after a daytime nap,” the researchers wrote. “Our study, along with previous clinical studies, suggests that further examination of the mechanistic basis of the association between a healthy sleep pattern, including daytime napping, and cardiovascular disease is necessary.”
In adults aged 35 years or younger, women were 44% more likely to have an ischemic stroke compared with men, according to a systematic review.
The study was published in a Go Red for Women spotlight issue of Stroke.
Data were derived from Leppert M, et al. Stroke. 2022;doi:10.1161/STROKEAHA.121.037117.
Sharon N. Poisson
“Our finding suggests that strokes in young adults may be happening for different reasons than strokes in older adults. This emphasizes the importance of doing more studies of stroke in younger age groups so that we can better understand what puts young women at a higher risk of stroke,” Sharon N. Poisson, MD, MAS, an associate professor of neurology at the University of Colorado, Denver, said in a press release. “Better understanding which young adults are at risk for stroke can help us to do a better job of preventing and treating strokes in young people.”
Poisson and colleagues conducted a systematic review to determine sex differences among young adults with ischemic stroke.
The review included 19 studies of adults aged 45 years or younger, 16 of which concerned ischemic stroke. Of those, nine found no difference by sex in ischemic stroke rates in people aged 45 years or younger, three found a higher rate of ischemic stroke in men among people aged 30 to 35 years and four found a higher rate of ischemic stroke in women among people aged 35 years or younger.
When the researchers analyzed adults from all 16 studies aged 35 years or younger, they determined that women were 44% more likely to have an ischemic stroke compared with men (incidence rate ratio [IRR] = 1.44; 95% CI, 1.18-1.76; I2 = 82%), but when they analyzed adults from all 16 studies aged 35 to 45 years, they found no difference by sex in ischemic stroke likelihood by sex (IRR = 1.08; 95% CI, 0.85-1.38; I2 = 95%).
“The sex difference in the incidence of ischemic strokes was the greatest and most evident among adults younger than age 35 years, with an estimated 44% more women, though there is notable heterogeneity in this effect,” Poisson and colleagues wrote. “This sex difference narrows among adults ages 35 to 45 years, and there is contradictory evidence whether young men may be more at risk of ischemic strokes in this age group.”
The key message is that women under 35 are having strokes when generally women of childbearing age are considered healthy and low risk for such events. The awareness of strokes in young women is limited, and physicians need to be aware to look for signs of strokes in women at reproductive age as well as postmenopause.
As the authors pointed out, migraine headache, oral contraceptives and complications of pregnancy may be to blame for some of the strokes in women of this age. I would also add to the list that untreated hypertension, clotting disorders, autoimmune disorders and rheumatologic disorders are also common in women of these ages and associated with strokes.
The data on sex differences in ischemic stroke risk for people aged 35 to 45 were not more contradictory. I believe they were more limited. There were eight studies done in women under age 35 and only four studies done in women aged 35 to 45. Where there are less data, there is more room for error of interpretation. Definitely, more studies need to be done in this age group.
The key component missing here is what were the comorbidities and other circumstances surrounding the strokes. We do not have any data on recent pregnancy, delivery or miscarriage. There are no data on underlying hypertension and other comorbidities. This is essential to understanding why younger women have strokes. It is wonderful that attention has been drawn to the data that women of these young ages are having more strokes than men. What we need to understand now is the why of it, which can help lead us to the intervention to correct it.Gina Lundberg, MD, FACCCardiology Today Editorial Board Member Emory University School of Medicine
Stroke is a very common and serious medical problem. Ischemic strokes happen when a blood clot blocks blood flow to the brain, causing brain cells to die. A hemorrhagic stroke happens when a brain artery breaks and blood flows freely into the brain. Telling the difference between the two types of stroke is very important as their treatments are quite different, and the longer a stroke goes without treatment, the more brain cells die. Artificial intelligence (AI), a computerised form of learning, has allowed great progress in identifying situations or items in the technological world, but it has rarely been used in medicine. Recently, however, there have been a few reports. For example, AI has been used to detect bone fractures from X-ray images. For this project, AI was tested on computed tomography (CT) images of ischemic and hemorrhage strokes to see whether AI could tell the difference between the two types. The online AI program Google Teachable Machine was used for these experiments. First, multiple images of the two types of strokes were selected from freely-available online sources. Then the images were cleaned to focus on the brain only. The images were organised into categories: ischemic vs. hemorrhagic, large vs. small, and training vs. validation sets. The training set was used to teach the AI program, while the validation set was used to test it. AI correctly identified the type of stroke 77.4% of the time in the validation set. AI incorrectly identified the stroke type 22.6% of the time. AI did very well at telling the difference between the two stroke types, but for this approach to be useful in the hospital, the error rate would need to improve. If the AI program trained on more images, and its error rate went down, then it has potential to be used as a verification method for stroke diagnosis for doctors in the future.
Introduction
Every 40 seconds someone in the United States has a stroke.[1] Each year about 795,000 people in the US suffer a stroke, and 140,000 of those people die. Knowing the difference between stroke types is a life or death situation. There are two types of strokes: ischemic and hemorrhagic.[2] Ischemic strokes happen when an artery in the brain gets blocked, usually by a blood clot preventing blood from reaching a part of the brain. As blood carries oxygen and nutrients to the cells in the body; without these the brain cells die. This is the most common type of stroke, occurring in 87% of cases.[3] Hemorrhagic strokes happen when a blood artery in the brain bursts, causing blood to spill out into the brain.[2] This can be caused by high blood pressure and/or weak arteries.
Both ischemic and hemorrhagic strokes can be life threatening. Ischemic strokes are treated with a clot-busting drug called alteplase (also known as tissue plasminogen activator, or tPA).[2] However, if this drug was given to someone with a hemorrhagic stroke, it would make things much worse by increasing bleeding. Hemorrhagic strokes are often treated with medicine to lower blood pressure to stop bleeding. If this was done for an ischemic stroke patient, it could reduce the blood flow to the brain even more, making the stroke worse. Therefore, it is important to know the difference between these types of strokes as the wrong treatment for either could lead to a worse prognosis. Artificial intelligence (AI) could help with this.
Intelligence is the ability to take memories and do something useful with them. AI is the ability of a computer program to gain and apply knowledge, and to use this knowledge to make decisions.[4-6] To do this, AI needs inputs, which consists of data from many possible sources. The term AI was coined by John McCarthy in 1995 and implementations of AI include Siri on the Apple iPhone and Alexa on Amazon Echo. AI has also been used recently in the field of medicine. For example, AI has been used in studies to help doctors find dangerous infections[7] and identify bone fractures on X-ray images.[8] In the second of these studies, AI improved doctors’ ability to detect bone fractures. In this study, AI was applied to stroke.
Ischemic and hemorrhagic strokes can be distinguished by magnetic resonance imaging (MRI) or computerised tomography (CT) scans[9], two ways of forming images of the inside of the head or body. In this project, only CT scan images were used. Although a trained professional can tell the difference between an ischemic and hemorrhagic stroke on a CT scan, there may not always be someone immediately available with that knowledge, hence the potential of the use of AI. It could also be used as a verification method even when a trained doctor is available.
A CT is a scan that uses X-rays to look inside the head or body taking many pictures from different angles to create a 3D image.[9] CT scans are fast, painless, and accurate, and can reveal internal injuries and bleeding, as well as being used to determine what kind of stroke a person is having. In an ischemic stroke, the brain gets darker (hypodense) where the stroke occurred (Figure 1). In a hemorrhagic stroke, the blood that spills into the brain appears brighter than the rest of the brain (hyperdense).
Figure 1: Appearance of ischemic and hemorrhagic strokes on CT scan. An ischemic stroke[10] appears dark compared to the rest of the brain, while a hemorrhagic stroke[11] appears bright. In both cases above, the stroke appears on the left side of the brain.
The objective of this project is to inverstigate whether AI can determine the difference between the two types of strokes based on CT scan pictures. Based on the ability of AI to correctly identify pictures in common technology applications, the hypothesis or expected outcome is that AI will be able to tell the difference between CT scan images of ischemic and hemorrhagic strokes at least 75% of the time after training on >25 pictures of each stroke type.
Methods
In this project, the internet will be used to find CT images of stroke and brain hemorrhage. A subset of these will be input into an AI program while telling the program which type of stroke each image represents (AI training). Then the AI program will be tested on a new set of images to see whether it has learned the difference between these types of strokes (AI validation).
Variables and Constants
The independent variable is the number of CT images of each brain stroke and brain hemorrhage used to train the AI program. The dependent variable is AI’s accuracy at telling the difference between new CT scan images of brain stroke and brain hemorrhage. Some constants include: the same kind of image (CT scan), all images in same orientation (axial; Figure 2), and the conditions (lighting, positioning of camera and image) in the room when showing the AI program the images for training and validation.
Figure 2: The three different imaging orientations.[12] The axial orientation was used for all experiments.
Selecting the Images
First, CT scan images were found through the use of the search engine “Google.com” by searching terms ‘CT stroke,’ and ‘CT large stroke’. This was then repeated while using the terms ‘CT hemorrhage’ to find images of CT scans of hemorrhagic stroke. Examples of each are in Figure 1. A neurologist helped to identify CT images, ensuring they were correctly identified.
Cleaning up the Images
Images were cleaned up by splitting any images that had multiple panels into separate images and removing anything outside the head using image processing software (Adobe Photoshop). The goal was to make the only differences the strokes themselves; not objects outside of the head or labels.
Organising the Images for training and validation
The images were organised into ischemic and hemorrhagic stroke by splitting them into separate folders. These folders were then each split into ‘big’ and ‘small’ based on the size of the stroke. The small strokes were less than approximately 20% of a brain hemisphere (one side of the brain). The big strokes were larger than this approximate cut off. These four sets were then divided into training and validation sets. The training set was 90% of the images (of each stroke type and size), and the validation set was 10% of the images. The training and validation sets of each stroke were visually compared to make sure that they looked similar. In the final training set, there were 127 images, 56 images of ischemic stroke (45 large and 11 small) and 71 images of hemorrhagic stroke (39 large and 32 small). In the final validation set there were 41 images, 20 images of ischemic stroke (18 large and 2 small) and 21 images of hemorrhagic stroke (10 large and 11 small).
Training AI
The free website for Google Teachable Machine (https://teachablemachine.withgoogle.com/) was used for these experiments. The training and testing was done on a laptop with a camera (to input the images). The images for training and testing were downloaded onto an iPad to display to the camera. The iPad and laptop were set up so that the input frame for the AI program was completely filled with the iPad display (Figure 3). To minimise error in the methodology, masking tape was used to carefully keep the computer and the iPad in the same position throughout the experiments. Training images on the iPad were shown one after the other to the AI program. First, the AI program was trained with ischemic stroke images, then with hemorrhagic stroke images. Next the AI program was tested with new images of each type of stroke type, and its confidence percentage was recorded for each, along with the minimum and maximum confidence displayed (this varied somewhat over time). Several screenshots of this process were recorded (Figure 4).
Figure 3: Setup of AI training and testing. Images were shown on the iPad, and the AI was trained on the laptop.
Results
AI correctly identified the two stroke types in the validation images the majority of the time, as shown in Table 1. AI’s few failures in identification were spread throughout, independent of the type of stroke or stroke size. When AI misclassified the stroke type, its confidence was generally lower than when it classified the stroke correctly. There was also a tendency when AI misclassified the stroke type for its confidence to fluctuate more than when it was correct, as shown in the range column. In one case of a large ischemic stroke validation image (image 5), AI could not reach a conclusion, reporting a confidence of 50% for each stroke type.
Table 1: Performance of AI on the validation set
Figure 4 shows examples of the performance of the trained AI program when tested on validation images. The top example shows AI correctly identifying a small ischemic stroke, with a confidence of 99%. In the middle, is an example of AI, with a confidence of 100%, correctly identifying a large hemorrhagic stroke. Occasionally, AI incorrectly classified a stroke. The bottom example is one such instance. In this example, AI incorrectly identified a hemorrhagic stroke as an ischemic one, with a confidence of 85%.
Figure 4: Examples of stroke classification from the validation set of images. The top two examples were correctly identified, and the bottom example was incorrectly identified.
Table 2 shows AI’s error rates on top and the correct rates on the bottom. The lowest percentage correct was 72.7% on small hemorrhagic strokes. The highest percentage correct was 100% on small ischemic strokes, but there were only two validation examples. It is difficult to distinguish whether this encouraging result is due to chance or is a result of significance. The performance of AI was otherwise similar in every stroke type and size. Overall, AI correctly identified the stroke type 77.4% of the time.
Table 2: The error rate and correct rate of the trained AI program. On average, AI was correct 77.4% of the time.
Discussion
This project is important because it shows that AI could be helpful in telling the difference between brain stroke and brain hemorrhage, a life or death decision. AI did very well in telling the difference between the two types of strokes, supporting the hypothesis that a correct rate of >75% could be achieved. AI did slightly better when injuries were large versus small, but the results were extremely close, and more examples would be needed to figure out if this is a true difference.
AI could be applicable to doctors as a verification method in the future. For example, following the reading of a stroke CT scan by a trained doctor, AI could be used to make an independent check. If AI disagrees with the doctor’s read, then the image could be passed on to another doctor for further confirmation. This might help to eliminate potential human errors and prevent patients from getting the wrong treatment. AI could instead be used in place of a doctor (as a first check). To do this, however, the error rate would have to be much lower. One way to work towards lowering the error rate, would be to give AI more images to train. In order to do this more images would be needed, not just from Google but from hospital and doctors’ databases.
If this study was repeated, there are some areas that could be improved. First, as AI is trained on the second type of stroke (hemorrhagic), it would be interesting to note its decisions on the images as it learns. This could show how many training images it takes before the percentage correct starts to improve. This could help to understand how much data is required for AI to learn. It would also be interesting to give the AI program a few training images of each type, then run the validation set (without learning), give a few more training images, run the validation set again, and repeat until it exceeds the goal accuracy. This would also help to understand how AI learns. There are also limitations related to the use of images from the internet. Hospital records may provide better reliability and may be more relevant than internet images. Such images were not available for this study, but could be used, providing patient privacy was protected. In the future, AI may be an important part of how doctors diagnose patients more often, or even help to suggest treatments.
Conclusion
In this project, AI was tested on how accurately it could tell the difference between CT scan images of brain stroke and brain hemorrhage. It is critical to know the difference between the two main types of brain strokes because the treatments for them are very different. This project is important because a computer program that can identify stroke type from CT scan images could save lives. Overall, AI did very well at identifying the type of brain stroke that was shown. AI correctly identified the type of stroke 77.4% of the time. This confirmed the study’s hypothesis, exceeding the goal of 75% correct. AI could be useful as a verification method for doctors in the future, catching errors to make sure patients get the correct medications. It could also be a first check in situations where a trained doctor is not available, providing the error rate could be lowered.
Acknowledgements
I would like to thank my family for encouraging me throughout the whole time I was doing this project. I would especially like to thank my dad for helping me, including providing identification of the type of stroke in the images used, as a neurologist. I would not have been able to do this without him.
References
“Stroke”, Centers for Disease Control and Prevention, last modified November 5, 2018, https://www.cdc.gov/stroke/.
Matthieu Komorowski, Leo A. Celi, Omar Badawi, Anthony C. Gordon and A. Aldo Faisal, “The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care,” Nature Medicine 24, no. 11 (November 2018): 1716–1720, https://doi.org/10.1038/s41591-018-0213-5.
Robert Lindsey, Aaron Daluiski, Sumit Chopra, Alexander Lachapelle, Michael Mozer, Serge Sicular, Douglas Hanel, et al., “Deep neural network improves fracture detection by clinicians,” PNAS 115, no. 45 (November 6, 2018): 11591-6, https://doi.org/10.1073/pnas.1806905115.
Stephan A. Mayer, Nikolai C. Brun, Kamilla Begtrup, Joseph Broderick, Stephen Davis, Michael N. Diringer, Brett E. Skolnick, Thorsten Steiner and Recombinant Activated Factor VII Intracerebral Hemorrhage Trial Investigators, “Recombinant activated factor VII for acute intracerebral hemorrhage,”The New England Journal of Medicine 352, no. 8 (February 24, 2005): 777-85, https://doi.org/10.1056/NEJMoa042991.
Ulf Nestler, Daniel Memia-Zolo, Nidal Salloum, Mehdi Mejdoubi, François Lengelle, Raoul Santiago, William Cécile, Remus Stegaru and Norbert Manzo, “Sinogenic Subdural Empyema in a Ten-Year-Old Boy with Sickle Cell Anemia,” Open Journal of Modern Neurosurgery 3, no. 4 (October 2013): 53-58, http://doi.org/10.4236/ojmn.2013.34012.
The thiazolidinedione class of peroxisome proliferator–activated receptor γ (PPAR-γ) agonists are among the most potent insulin-sensitizing drugs available. One medication in this class, pioglitazone, may reduce the risk of cardiovascular events, including stroke, in patients with type 2 diabetes, for whom the drug is currently approved as a glucose-lowering agent. Kernan et al. designed the multicenter, double-blind Insulin Resistance Intervention after Stroke (IRIS) trial to test the hypothesis that pioglitazone would reduce the rates of stroke and myocardial infarction after ischemic stroke or transient ischemic attack (TIA) in patients without diabetes who have insulin resistance.
In this trial in nondiabetic patients with insulin resistance and a recent ischemic stroke or transient ischemic attack, pioglitazone was associated with a lower risk of stroke and MI than was placebo but with a higher risk of weight gain, edema, and bone fracture. A new Original Article summarizes.
Clinical Pearl
• Insulin resistance is present in what percentage of patients without diabetes who have had an ischemic stroke or a TIA?
Insulin resistance is nearly universal in patients with type 2 diabetes but is also present in more than 50% of patients without diabetes who have had an ischemic stroke or a TIA. Treatment of insulin resistance represents a potential new preventive strategy that could be added to standard care after ischemic stroke or TIA.
Clinical Pearl
• Does pioglitazone reduce the rate of new stroke or myocardial infarction in patients without diabetes who have already had an ischemic stroke or TIA?
In the study by Kernan et al., the primary outcome was a first fatal or nonfatal stroke or fatal or nonfatal myocardial infarction. In the study, the rate of the primary outcome was lower among patients who received pioglitazone than among those who received placebo. The incidence of a new diagnosis of diabetes was also lower with pioglitazone.
Morning Report Questions
Q:What adverse effects are associated with pioglitazone?
A: Weight gain with PPAR-γ agonists, such as pioglitazone, reflects an increase in adipose tissue mass and a tendency for fluid accumulation owing to renal sodium retention. Sodium retention, if unchecked, can also increase the risk of heart failure. In the IRIS trial, patients in the pioglitazone group had more weight gain, edema, and shortness of breath than did patients in the placebo group. However, the authors did not observe a greater incidence of heart failure in the pioglitazone group than in the placebo group, which was probably because the study excluded patients with a history of heart failure and used safety algorithms that triggered dose reduction for excessive weight gain or edema. Pioglitazone has also been associated with an increased risk of bone fracture. In the IRIS study, rates of serious bone fracture (i.e., requiring hospitalization or surgery) were higher in the pioglitazone group than in the placebo group, which were reported in 99 patients and 62 patients, respectively (5.1% vs. 3.2%, P=0.003).
Q:Did the study by Kernan et al. support a possible link between pioglitazone and an increased risk of bladder cancer?
A: Observational research conducted in 2011 and 2012 suggested that pioglitazone may increase the risk of bladder cancer. However, more recent studies showed no significant association for any dose or duration of therapy. Other research suggests that PPAR-γ agonists might prevent certain cancers. Although the study by Kernan et al. did not observe a significant effect of treatment on the incidence of total or any specific cancer, the study was not powered to address these questions.
A review of the revised ACEP Clinical Policy – IV tPA downgraded to Level B evidence
Nothing seems to stir the emotions of an emergency physician more than a discussion of tPA for stroke: who, why, and when. Skeptics cite problems in the data, and conflicts of interest of the authors. Supporters trust the literature that passed peer review and became policy of emergency medicine and neurology professional societies around the world.
2012 and 2015
The 2012 ACEP Clinical Policy on the use of tissue plasminogen activator for acute ischemic stroke [1] raised plenty of questions. It resulted in concerns about the effectiveness of the drug, the magnitude of its effectiveness, the risks of cerebral hemorrhage, effects of the drug on stroke mimics, legal implications of the policy, the policy’s impact in different practice settings, hospital differences in resources for stroke diagnosis and emergency care, and the lack of tools for patient/family shared decision making [2,3,4,5]. The key question asked was ‘Is IV tPA safe and effective for acute ischemic stroke if given within 3, and within 3-4.5 hours after symptom onset? The level A recommendation stated: IVtPA should be offered …to patients who meet …(NINDS) inclusion/exclusion criteria and can be treated within 3 hours after symptom onset. The level B recommendation stated: IVtPA should be considered in patients who meet…(ECASS III) inclusion/exclusion criteria and can be treated between 3 to 4.5 hours after symptom onset.
Criticism of this policy was swift, in these pages and elsewhere. Level A recommendations imply generally accepted principles of care and as such seemed to ignore or shut down years of well-intentioned, well-reasoned debate on tPA’s safety and efficacy. Subsequently, ACEP took the unprecedented step of reconsidering their policy, which had been co-authored with the American Academy of Neurology.
In June 2015, the ACEP Board approved a revised clinical policy on the use of tPA for ischemic stroke [6]. The question asked was the same as in 2012 – whether IV tPA is safe and effective within 3 hours of symptom onset, and within 3-4.5 hours of symptom onset.
2015 Recommendations
Is IV tPA safe and effective for patients with acute ischemic stroke if given within 3 hours, and within 3-4.5 hours of symptom onset? Recommendations for ❤ and 3-4.5 hours are combined below for simplicity.
Level A recommendation: None.
Note that the 2015 policy no longer provides level A recommendations for IVtPA.
Level B recommendation for ❤ hr administration: IV tPA should be offered and may be given …within 3 hours after symptom onset at institutions where systems are in place to safely administer the medication. Consider the increased risk of sICH.
Level B recommendation for 3-4.5 hr administration: despite the known risk of sICH and the variability in the degree of benefit in functional outcomes, IV tPA may be offered and may be given to carefully selected patients…within 3-4.5 hours after symptom onset at institutions where systems are in place to safely administer the medication.
Level C recommendation: When feasible, shared decisionmaking…should include a discussion of potential benefits and harms…
Risks and Benefits
Here are selected comments from Appendix D and text, using the most robust analyses that were provided for risk-benefit analysis:
IVtPA given < 3 hrs
NNT = 8 (95% CI 4-31) NINDS
NNH=17 (95% CI 12-34) NINDS
sICH 5-7% sICH overall (could be lower with adherence to protocol; sICH definitions also vary with study)
Proportion who improved: 13% achieving Rankin Scale 0-1 (NINDS 39% IVtPA vs 26% control)
IVtPA given 3-4.5 hrs
NNT = 14 (95% CI 7-244) ECASS III
NNH= 23 (95% 31-78) ECASS III
sICH 3-8% and 2-6% depending on NINDS definition of sICH
Proportion who improved: 7% achieving Rankin Scale 0-1, ECASS III, 52% for IVtPA vs 45% for control)
Who benefits and when?
Published data is somewhat population-based and not at all individual-based, so we are still in the dark about the patients most likely to benefit from IVtPA. If only 13% benefit from IVtPA if given within 3 hours, or 7% in 3-4.5 hours, what about the rest? What about the cost and resources used? Who exactly are the ‘carefully-selected’ patients who may receive IVtPA at 3-4.5 hours? The benefits of giving tPA in ❤ hours, or ≤90 minutes also needs discussion [8].
Who is most at risk for sICH?
Several scoring systems for assessing the likelihood of sICH after IVtPA have been developed [9]. Predictive variables and scoring methods vary with the tool, but typically include early infarct signs on non-contrast CT scan; prestroke Rankin scale score; age; antithrombotic therapy; hyperglycemia; and NIHSS on admission.
What about the ED, the hospital and the hospital system?
Stroke diagnosis and emergency management is not a one-man emergency physician show. It requires a team to make what is often a difficult diagnosis and treatment plan. All members of the team should use the same unified protocol: indications for activating Code Stroke; NIHSS reporting; imaging protocols; scoring systems for risk-benefit analysis; and well-articulated and well-presented information and consent forms.
What about tools for shared decision-making?
Involving patients and family in decision-making is more complex than asking ‘do you want the clot-buster’? Patients who elect a treatment, whether a stent for STEMI, or IVtPA for stroke, assume they will personally benefit from the treatment. This is not the case with IVtPA where most do not benefit. Only a few do. And a small number are harmed. Graphs and pictorial images are an excellent way to communicate a risk-benefit analysis, but are in various stages of development [10,11]. The challenge for tool developers is to convey information simply, clearly, and quickly given a variety of clinical scenarios.
Summary
The 2015 ACEP clinical policy discussion is detailed. Study deficiencies are noted, the discussion uses updated data, and methodology, case definitions, heterogeneity and outcome differences are noted. IVtPA was lowered from a level A to level B recommendation, and qualifications on the proper environment for drug administration were added. However, these qualifications were not spelled out in any detail and key issues relative to decision-making remain unaddressed. The revised policy reminds us that data on risks and benefits are still a moving target. What is clear is that the pressure is on for emergency physicians who must be the initiators and leaders of the emergency stroke care team.
REFERENCES
Clinical Policy: Use of Intravenous tPA for the Management of Acute Ischemic Stroke in the Emergency Department . Annals of Emergency Medicine, 61:2, February 2013, 225- 243
Ellison D, ‘The Lytic Lottery’ Annals of Emergency Medicine, 62:5, October 2013, 432-33
Newman, David, ‘Thrombolytics for Acute Ischemic Stroke’ ,theNNT.com, March 25, 2013 accessed Aug 7, 2015
Hoffman JR and Schriger DL ‘A Graphic Reanalysis of the NINDS Trial’ Annals of Emergency Medicine 54:3 September 2009, 329-336
Klauer, K ‘tPA and the Problems of ‘Indication Creep. Emergency Physicians Monthly, May 29, 2013.
Zinsser, William. On Writing Well. The Classic Guide to Writing Nonfiction’. 7e, 2006, HarperCollins, New York.
Miller JB, Heitsch L, Siket M, et al ‘The Emergency Medicine Debate on tPA for Stroke: What is Best for our Patients? Efficacy in the First Three Hours’ Acad Emerg Med 2015 July; 22(7):852-5
Asuzu D, Nystrom K, Amin H et al ‘Comparison of 8 scores for predicting symptomatic intracerebral hemorhage after IV thrombolysis’ NeurocritCare 2015 Apr; 22 (2):229-33. PMID 25168743
Gadhia J, Starkman S, Ovbiagele B, et al ‘Assessment and Immprovement of Figures to Visually Convey Benefit and Risk of Stroke Thrombolysis’ Stroke 2010 February; 41 (2): 300-306
Flynn D, Ford GA, Stobbart L et al ‘A review of decision support, risk communication and patient information tools for thrombolytic treatment in acute stroke: lessons for tool developers. BMC Health Services Research 2013, 13:225
This study examined dietary potassium effects on different stroke subtypes over 11 years in 90,137 postmenopausal women aged 50 to 79 years who had no stroke history at enrollment. Mean dietary potassium intake was 2,611 mg per day. The highest quartile of potassium intake was associated with lower incidence of ischemic and hemorrhagic stroke and total mortality. Comparing highest to lowest quartile of potassium intake with multivariate analyses yielded a hazard ratio of 0.90 (95% confidence interval [CI], 0.85-0.95) for all-cause mortality, 0.88 (95% CI, 0.79-0.98) for all stroke, and 0.84 (95% CI, 0.74-0.96) for ischemic stroke.
Stroke has devastating consequences for menopausal women, and treating modifiable risk factors should have a large payoff in reduction of morbidity and mortality. Dietary potassium intake has been associated with stroke risk in most but not all previous studies. In the largest study of menopausal women to date, using Women’s Health Initiative observational study data, the association between stroke and dietary potassium intake is confirmed. Interestingly, the association is strongest in nonhypertensive women. Hypertensive women with increasing potassium intake had lowered mortality but no lower incidence of stroke itself. The authors speculate that this may be because of a greater effect on arterial stiffness in the prehypertensive endothelial wall. Higher potassium intake was also associated with lowered all-cause mortality in all women, suggesting effects above and beyond the vascular system. The heterogeneity of “stroke” as a diagnosis was illustrated by the association with reduction in ischemic but not hemorrhagic stroke.
Of course, two major weaknesses of all such studies are our inability to infer causation from association in an observational study and reliance on the human brain to fill out food frequency forms. If potassium’s ability to reduce stoke in menopausal women was confirmed by a randomized clinical trial, public health efforts could be undertaken to increase the US population’s already lower-than-recommended potassium intake (only about 1/6 of these women met US Department of Agriculture potassium intake recommendations.) Such efforts would best take a simple macronutrient/whole foods approach, because counting dietary potassium intake itself would be difficult for patients. However, before considering increasing dietary potassium intake, special attention would have to be given to the potential for hyperkalemia in a population full of risk factors, such as chronic renal failure, and angiotensin-converting enzyme inhibitor use. Forget “an apple a day”—maybe “half a cantaloupe a day (with twice the potassium of a banana) makes the stroke go away” will be the new mantra for menopausal women.
ARYAN'S BLOG 