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People with paroxysmal atrial fibrillation who explored potential triggers of their arrhythmia, and used them to make lifestyle changes, went on to show a 40% decline in subjectively experienced bouts of AFib in a randomized trial with an unusual design.
But the study didn’t provide evidence that the drop in self-reported AFib necessarily improved their quality of life, its primary endpoint. Nor was there any apparent relationship between potential triggers and AFib episodes detected less subjectively using a handheld electrocardiography monitor.
Although the study – called I-STOP-AFib – has limitations, its results jibe with alcohol intake’s increasingly appreciated status as a potential AFib trigger. It was alone among many possible triggers tested in showing a consistent association with self-reported AFib.
As a result, the study offers no support for such a link between the arrhythmia and caffeine intake, sleep deprivation, dehydration, exercise, or other conditions sometimes perceived as triggers, observed principal investigator Gregory M. Marcus, MD, MAS, University of California, San Francisco, when presenting results at the American Heart Association scientific sessions. He is also lead author on the study’s simultaneous publication in JAMA Cardiology.
The I-STOP-AFib trial was unusual in part for its virtual design, in which participants followed instructions and tracked AFib episodes – both perceived and detected by the handheld ECG device – through a smartphone application. It also featured an N-of-1 randomized comparisons of different weeks in which individuals were or were not exposed to their self-selected trigger.
Such patients following their own weekly personalized randomization were compared to an entirely separate randomized control arm of the trial, in which patients simply tracked any ECG-monitored and self-perceived AFib episodes.
Current use in patients
Although wearable and smartphone-based ECG recorders are increasingly popular for AFib screening, Dr. Marcus said the devices may be especially helpful for validating whether a person’s symptoms are actually caused by AFib.
“I have actually suggested to some of my patients that they run some of these experiments,” he said at a media briefing on I-STOP-AFib before his main presentation of the trial. The demonstration might help patients recognize that some perceived triggers actually do not induce AFib.
Allowing patients to determine on their own whether a substance indeed triggers their AFib “is an efficient use of these devices,” Dr. Marcus said. Such N-of-1 exploration of possible triggers “might help free patients up to enjoy substances – caffeine or coffee is one example – that they otherwise might not, and may help actually reassure them that certain exposures –like certain exercises, which can also be beneficial – might actually not be harmful.”
Dr. Marcus and the other authors on the report noted – as he did at the AHA sessions – that the study has several limitations, such as the subjectivity of self-reported AFib, dropouts from the trial that shrank the randomization arms, and a population that may not be very representative.
There is also the potential for detection bias in the group assigned to track their selected triggers, as Dr. Marcus and some observers have noted.
It follows that conscious avoidance of a potential AFib trigger might well lead to a reduction in AFib subjectively identified by symptoms, proposed David Conen, MD, MPH, Population Health Research Institute, McMaster University, Hamilton, Ont. But perhaps there would have been no reduction in AFib had it been objectively documented with the handheld ECG device, he said in an interview.
“If I were to redesign the study,” he said, “I think the primary endpoint should be confirmed atrial fibrillation, because we would have to show first that the specific trigger actually reduced objective AFib events before we then try to address the question whether reducing that trigger improves quality of life.”
Unrepresentative sample
The trial entered 446 overwhelmingly White and college-educated adults known to have symptomatic paroxysmal AFib who were “interested in testing a presumed AFib trigger they could readily introduce or withhold” and who owned a smartphone; the average age was 58 years, and 58% were men. The cohort was randomly assigned to the trigger-testing group or the control group, charged only with tracking their AFib.
Of the total, 320, or about 72%, completed the study; those who did not were mostly from the trigger-testing arm, leaving 136 in that group versus 184 patients in the control group.
Potential triggers that participants selected for tracking included, foremost, caffeine, alcohol, reduced sleep, and exercise, followed by lying on the left side, dehydration, large meals, and cold food or drink, the report noted.
Patients in the control group used the smartphone app and handheld ECG monitor (KardiaMobile, AliveCor) to document the duration and severity of AFib episodes daily and received data summary reports through the app weekly for 10 weeks. They then had the option to follow the trigger-testing protocol at least once.
Those in the trigger-testing group conducted their N-of-1 trials by exposing themselves to their chosen potential trigger during 3 separate weeks and avoiding the trigger during 3 other weeks, alternating each of the 6 weeks of trigger exposure or avoidance. They were instructed through the app to start the 6-week sequence with one or the other strategy randomly and to regularly track their AFib.
At the end of 6 weeks, each participant in the trigger-testing group had the opportunity to review their data for any potential trigger-AFib associations. They were then to use the next 4 weeks to enact lifestyle changes based on what they learned – as described in the report and on clinicaltrials.gov. They had the option of repeating the entire N-of-1 sequence at least one more time.
Participants in both the trigger-tracking and control arms were tested at baseline and at 10 weeks using the validated Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire.
AFEQT scores didn’t change significantly over the 10 weeks in either arm, nor were they significantly different in one arm, compared with the other.
On the other hand, patients in the trigger-tracking arm reported significantly fewer daily AFib episodes during the final 4-week period of lifestyle changes based on their N-of-1 trial results, compared to the monitoring-only control group’s final 4 weeks.
The adjusted relative risk in the trigger-tracking arm was 0.60 (95% confidence interval, 0.43-0.83; P < .001), the difference driven by patients who selected alcohol, dehydration, or exercise for their trigger, Dr. Marcus reported.
Only alcohol intake emerged consistently as a significant predictor of risk for self-reported AFib episodes in a series of meta-analyses conducted using all of the individual N-of-1 trials that provided per-protocol data. The odds ratio was 1.77 (95% CI, 1.20-2.69).
I-STOP-AFib explored an important subject “that has been understudied,” Dr. Conen said. “The trial has some limitations that the authors address themselves, but hopefully it opens the path to future studies that can build upon this experience.”
Dr. Marcus reported receiving personal fees and equity interest from InCarda Therapeutics; personal fees from Johnson & Johnson; and grants from Baylis Medical, Medtronic, the National Institutes of Health, the Patient-Centered Outcomes Research Institute, and the California Tobacco-Related Disease Research Program.
A version of this article first appeared on Medscape.com.
People with paroxysmal atrial fibrillation who explored potential triggers of their arrhythmia, and used them to make lifestyle changes, went on to show a 40% decline in subjectively experienced bouts of AFib in a randomized trial with an unusual design.
But the study didn’t provide evidence that the drop in self-reported AFib necessarily improved their quality of life, its primary endpoint. Nor was there any apparent relationship between potential triggers and AFib episodes detected less subjectively using a handheld electrocardiography monitor.
Although the study – called I-STOP-AFib – has limitations, its results jibe with alcohol intake’s increasingly appreciated status as a potential AFib trigger. It was alone among many possible triggers tested in showing a consistent association with self-reported AFib.
As a result, the study offers no support for such a link between the arrhythmia and caffeine intake, sleep deprivation, dehydration, exercise, or other conditions sometimes perceived as triggers, observed principal investigator Gregory M. Marcus, MD, MAS, University of California, San Francisco, when presenting results at the American Heart Association scientific sessions. He is also lead author on the study’s simultaneous publication in JAMA Cardiology.
The I-STOP-AFib trial was unusual in part for its virtual design, in which participants followed instructions and tracked AFib episodes – both perceived and detected by the handheld ECG device – through a smartphone application. It also featured an N-of-1 randomized comparisons of different weeks in which individuals were or were not exposed to their self-selected trigger.
Such patients following their own weekly personalized randomization were compared to an entirely separate randomized control arm of the trial, in which patients simply tracked any ECG-monitored and self-perceived AFib episodes.
Current use in patients
Although wearable and smartphone-based ECG recorders are increasingly popular for AFib screening, Dr. Marcus said the devices may be especially helpful for validating whether a person’s symptoms are actually caused by AFib.
“I have actually suggested to some of my patients that they run some of these experiments,” he said at a media briefing on I-STOP-AFib before his main presentation of the trial. The demonstration might help patients recognize that some perceived triggers actually do not induce AFib.
Allowing patients to determine on their own whether a substance indeed triggers their AFib “is an efficient use of these devices,” Dr. Marcus said. Such N-of-1 exploration of possible triggers “might help free patients up to enjoy substances – caffeine or coffee is one example – that they otherwise might not, and may help actually reassure them that certain exposures –like certain exercises, which can also be beneficial – might actually not be harmful.”
Dr. Marcus and the other authors on the report noted – as he did at the AHA sessions – that the study has several limitations, such as the subjectivity of self-reported AFib, dropouts from the trial that shrank the randomization arms, and a population that may not be very representative.
There is also the potential for detection bias in the group assigned to track their selected triggers, as Dr. Marcus and some observers have noted.
It follows that conscious avoidance of a potential AFib trigger might well lead to a reduction in AFib subjectively identified by symptoms, proposed David Conen, MD, MPH, Population Health Research Institute, McMaster University, Hamilton, Ont. But perhaps there would have been no reduction in AFib had it been objectively documented with the handheld ECG device, he said in an interview.
“If I were to redesign the study,” he said, “I think the primary endpoint should be confirmed atrial fibrillation, because we would have to show first that the specific trigger actually reduced objective AFib events before we then try to address the question whether reducing that trigger improves quality of life.”
Unrepresentative sample
The trial entered 446 overwhelmingly White and college-educated adults known to have symptomatic paroxysmal AFib who were “interested in testing a presumed AFib trigger they could readily introduce or withhold” and who owned a smartphone; the average age was 58 years, and 58% were men. The cohort was randomly assigned to the trigger-testing group or the control group, charged only with tracking their AFib.
Of the total, 320, or about 72%, completed the study; those who did not were mostly from the trigger-testing arm, leaving 136 in that group versus 184 patients in the control group.
Potential triggers that participants selected for tracking included, foremost, caffeine, alcohol, reduced sleep, and exercise, followed by lying on the left side, dehydration, large meals, and cold food or drink, the report noted.
Patients in the control group used the smartphone app and handheld ECG monitor (KardiaMobile, AliveCor) to document the duration and severity of AFib episodes daily and received data summary reports through the app weekly for 10 weeks. They then had the option to follow the trigger-testing protocol at least once.
Those in the trigger-testing group conducted their N-of-1 trials by exposing themselves to their chosen potential trigger during 3 separate weeks and avoiding the trigger during 3 other weeks, alternating each of the 6 weeks of trigger exposure or avoidance. They were instructed through the app to start the 6-week sequence with one or the other strategy randomly and to regularly track their AFib.
At the end of 6 weeks, each participant in the trigger-testing group had the opportunity to review their data for any potential trigger-AFib associations. They were then to use the next 4 weeks to enact lifestyle changes based on what they learned – as described in the report and on clinicaltrials.gov. They had the option of repeating the entire N-of-1 sequence at least one more time.
Participants in both the trigger-tracking and control arms were tested at baseline and at 10 weeks using the validated Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire.
AFEQT scores didn’t change significantly over the 10 weeks in either arm, nor were they significantly different in one arm, compared with the other.
On the other hand, patients in the trigger-tracking arm reported significantly fewer daily AFib episodes during the final 4-week period of lifestyle changes based on their N-of-1 trial results, compared to the monitoring-only control group’s final 4 weeks.
The adjusted relative risk in the trigger-tracking arm was 0.60 (95% confidence interval, 0.43-0.83; P < .001), the difference driven by patients who selected alcohol, dehydration, or exercise for their trigger, Dr. Marcus reported.
Only alcohol intake emerged consistently as a significant predictor of risk for self-reported AFib episodes in a series of meta-analyses conducted using all of the individual N-of-1 trials that provided per-protocol data. The odds ratio was 1.77 (95% CI, 1.20-2.69).
I-STOP-AFib explored an important subject “that has been understudied,” Dr. Conen said. “The trial has some limitations that the authors address themselves, but hopefully it opens the path to future studies that can build upon this experience.”
Dr. Marcus reported receiving personal fees and equity interest from InCarda Therapeutics; personal fees from Johnson & Johnson; and grants from Baylis Medical, Medtronic, the National Institutes of Health, the Patient-Centered Outcomes Research Institute, and the California Tobacco-Related Disease Research Program.
A version of this article first appeared on Medscape.com.
People with paroxysmal atrial fibrillation who explored potential triggers of their arrhythmia, and used them to make lifestyle changes, went on to show a 40% decline in subjectively experienced bouts of AFib in a randomized trial with an unusual design.
But the study didn’t provide evidence that the drop in self-reported AFib necessarily improved their quality of life, its primary endpoint. Nor was there any apparent relationship between potential triggers and AFib episodes detected less subjectively using a handheld electrocardiography monitor.
Although the study – called I-STOP-AFib – has limitations, its results jibe with alcohol intake’s increasingly appreciated status as a potential AFib trigger. It was alone among many possible triggers tested in showing a consistent association with self-reported AFib.
As a result, the study offers no support for such a link between the arrhythmia and caffeine intake, sleep deprivation, dehydration, exercise, or other conditions sometimes perceived as triggers, observed principal investigator Gregory M. Marcus, MD, MAS, University of California, San Francisco, when presenting results at the American Heart Association scientific sessions. He is also lead author on the study’s simultaneous publication in JAMA Cardiology.
The I-STOP-AFib trial was unusual in part for its virtual design, in which participants followed instructions and tracked AFib episodes – both perceived and detected by the handheld ECG device – through a smartphone application. It also featured an N-of-1 randomized comparisons of different weeks in which individuals were or were not exposed to their self-selected trigger.
Such patients following their own weekly personalized randomization were compared to an entirely separate randomized control arm of the trial, in which patients simply tracked any ECG-monitored and self-perceived AFib episodes.
Current use in patients
Although wearable and smartphone-based ECG recorders are increasingly popular for AFib screening, Dr. Marcus said the devices may be especially helpful for validating whether a person’s symptoms are actually caused by AFib.
“I have actually suggested to some of my patients that they run some of these experiments,” he said at a media briefing on I-STOP-AFib before his main presentation of the trial. The demonstration might help patients recognize that some perceived triggers actually do not induce AFib.
Allowing patients to determine on their own whether a substance indeed triggers their AFib “is an efficient use of these devices,” Dr. Marcus said. Such N-of-1 exploration of possible triggers “might help free patients up to enjoy substances – caffeine or coffee is one example – that they otherwise might not, and may help actually reassure them that certain exposures –like certain exercises, which can also be beneficial – might actually not be harmful.”
Dr. Marcus and the other authors on the report noted – as he did at the AHA sessions – that the study has several limitations, such as the subjectivity of self-reported AFib, dropouts from the trial that shrank the randomization arms, and a population that may not be very representative.
There is also the potential for detection bias in the group assigned to track their selected triggers, as Dr. Marcus and some observers have noted.
It follows that conscious avoidance of a potential AFib trigger might well lead to a reduction in AFib subjectively identified by symptoms, proposed David Conen, MD, MPH, Population Health Research Institute, McMaster University, Hamilton, Ont. But perhaps there would have been no reduction in AFib had it been objectively documented with the handheld ECG device, he said in an interview.
“If I were to redesign the study,” he said, “I think the primary endpoint should be confirmed atrial fibrillation, because we would have to show first that the specific trigger actually reduced objective AFib events before we then try to address the question whether reducing that trigger improves quality of life.”
Unrepresentative sample
The trial entered 446 overwhelmingly White and college-educated adults known to have symptomatic paroxysmal AFib who were “interested in testing a presumed AFib trigger they could readily introduce or withhold” and who owned a smartphone; the average age was 58 years, and 58% were men. The cohort was randomly assigned to the trigger-testing group or the control group, charged only with tracking their AFib.
Of the total, 320, or about 72%, completed the study; those who did not were mostly from the trigger-testing arm, leaving 136 in that group versus 184 patients in the control group.
Potential triggers that participants selected for tracking included, foremost, caffeine, alcohol, reduced sleep, and exercise, followed by lying on the left side, dehydration, large meals, and cold food or drink, the report noted.
Patients in the control group used the smartphone app and handheld ECG monitor (KardiaMobile, AliveCor) to document the duration and severity of AFib episodes daily and received data summary reports through the app weekly for 10 weeks. They then had the option to follow the trigger-testing protocol at least once.
Those in the trigger-testing group conducted their N-of-1 trials by exposing themselves to their chosen potential trigger during 3 separate weeks and avoiding the trigger during 3 other weeks, alternating each of the 6 weeks of trigger exposure or avoidance. They were instructed through the app to start the 6-week sequence with one or the other strategy randomly and to regularly track their AFib.
At the end of 6 weeks, each participant in the trigger-testing group had the opportunity to review their data for any potential trigger-AFib associations. They were then to use the next 4 weeks to enact lifestyle changes based on what they learned – as described in the report and on clinicaltrials.gov. They had the option of repeating the entire N-of-1 sequence at least one more time.
Participants in both the trigger-tracking and control arms were tested at baseline and at 10 weeks using the validated Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire.
AFEQT scores didn’t change significantly over the 10 weeks in either arm, nor were they significantly different in one arm, compared with the other.
On the other hand, patients in the trigger-tracking arm reported significantly fewer daily AFib episodes during the final 4-week period of lifestyle changes based on their N-of-1 trial results, compared to the monitoring-only control group’s final 4 weeks.
The adjusted relative risk in the trigger-tracking arm was 0.60 (95% confidence interval, 0.43-0.83; P < .001), the difference driven by patients who selected alcohol, dehydration, or exercise for their trigger, Dr. Marcus reported.
Only alcohol intake emerged consistently as a significant predictor of risk for self-reported AFib episodes in a series of meta-analyses conducted using all of the individual N-of-1 trials that provided per-protocol data. The odds ratio was 1.77 (95% CI, 1.20-2.69).
I-STOP-AFib explored an important subject “that has been understudied,” Dr. Conen said. “The trial has some limitations that the authors address themselves, but hopefully it opens the path to future studies that can build upon this experience.”
Dr. Marcus reported receiving personal fees and equity interest from InCarda Therapeutics; personal fees from Johnson & Johnson; and grants from Baylis Medical, Medtronic, the National Institutes of Health, the Patient-Centered Outcomes Research Institute, and the California Tobacco-Related Disease Research Program.
A version of this article first appeared on Medscape.com.