Evidence Based Medicine I. Article appraisal 2. Answer questions after reading articles.
Article 1 Instructions: The purpose of the first article appraisal is to specifically look at EBM measures related clinical intervention related to older patients with hypertension to monitor for sequelae of the disease. You will be answering some specific questions to apply knowledge, reading the entire study, and appraising the author’s conclusions.You will then make an informed decision on how you would apply this to your practice.It is recommended for you to read the questions alongside of the article to respond to each.
Article 2: The purpose of the first article appraisal is to specifically look at EBM measures related the prevalence of disease among a specific patient population. You will be answering some specific questions to apply knowledge, reading the entire study, and appraising the author’s conclusions.You will then make an informed decision on how you would apply this to your practice.It is recommended for you to read the questions alongside of the article to respond to each.
HPPA 6294 Evidence Based Medicine I
Article Appraisal 3 & 4– Treatment and Epidemiology Articles
Name:
For this assignment, you will begin to practice article appraisal of the results and author’s
conclusions for clinical intervention and epidemiology articles. It is recognized you have basic
overview of the concepts. You are encouraged to use higher order thinking with the concepts
presented and practical application to patient care to answer the questions provided.
Grading: Refer to the rubric tab connected to the assignment on Sakai.
Article 1 Instructions: The purpose of the first article appraisal is to specifically look at EBM
measures related clinical intervention related to older patients with hypertension to monitor
for sequelae of the disease. You will be answering some specific questions to apply knowledge,
reading the entire study, and appraising the author’s conclusions. You will then make an
informed decision on how you would apply this to your practice. It is recommended for you to
read the questions alongside of the article to respond to each.
Article for Appraisal –
Gladstone DJ, Wachter R, Schmalstieg-Bahr K, Quinn FR, Hummers E, Ivers N, Marsden T,
Thornton A, Djuric A, Suerbaum J, von Grünhagen D, McIntyre WF, Benz AP, Wong JA, Merali F,
Henein S, Nichol C, Connolly SJ, Healey JS; SCREEN-AF Investigators and Coordinators. Screening
for Atrial Fibrillation in the Older Population: A Randomized Clinical Trial. JAMA Cardiol. 2021
May 1;6(5):558-567. doi: 10.1001/jamacardio.2021.0038. PMID: 33625468; PMCID:
PMC7905702.
Answer the following questions:
Study Type: Randomized Controlled Trial
1. Identify the aim of the study using the questions below.
Complete the following information:
* Population studied?
* Intervention given?
* Comparator chosen?
* Outcomes measured?
2. Briefly state the study methodology (design) and selection of participants.
3. Looking at the statistical analysis methods in this study, what was the sample size? How are
the treatment effect results expressed? (ex. ARR, p-values, OR, CI, etc.)
HPPA 6294 Evidence Based Medicine I
Article Appraisal 3 & 4– Treatment and Epidemiology Articles
4. Regarding the primary outcome of AF detection, explain the meaning of the RR, CI, ARR
(absolute difference); and number needed to treat (number needed to screen) related to atrial
fibrillation detection at 6 months.
5. Related to question 4, were the authors conclusions consistent with the EBM measures
provided?
6. Read through the entire study and discussion, keeping in mind the EBM measures presented
in the results. Based on critical analysis of the study, would you regularly use cECG monitoring
for AF detection in your practice? Why or Why not?
Article 2: The purpose of the first article appraisal is to specifically look at EBM measures
related the prevalence of disease among a specific patient population. You will be answering
some specific questions to apply knowledge, reading the entire study, and appraising the
author’s conclusions. You will then make an informed decision on how you would apply this to
your practice. It is recommended for you to read the questions alongside of the article to
respond to each.
Article for appraisal: Mahendran DC, Hamilton G, Weiss J, Churilov L, Lew J, Khoo K, Lam Q,
Robbins R, Hart GK, Johnson D, Hare DL, Farouque O, Zajac JD, Ekinci EI. Prevalence of preexisting dysglycaemia among inpatients with acute coronary syndrome and associations with
outcomes. Diabetes Res Clin Pract. 2019 Aug;154:130-137. doi: 10.1016/j.diabres.2019.07.002.
Epub 2019 Jul 4. PMID: 31279958.
1. Identify the aim of the study using the questions below.
Complete the following information:
* Population studied?
* Intervention given?
* Comparator chosen?
* Outcomes measured?
2. Briefly state the study methodology (design) and selection of participants.
3. Looking at the statistical analysis methods in this study, what was the sample size? How are
the statistical results expressed as EBM measures? (ex. ARR, p-values, OR, CI, etc.)
4. Based on the study results, explain the EBM measures presented (section 4.2) related to
acute pulmonary edema (APO in the study) of those with diabetes vs. those without diabetes.
5. Related to question 4, were the authors conclusions consistent with the EBM measures
provided?
HPPA 6294 Evidence Based Medicine I
Article Appraisal 3 & 4– Treatment and Epidemiology Articles
6. Read through the entire study and discussion, keeping in mind the EBM measures presented
in the results. Based on critical analysis of the study, what is your take-away and how would
you apply it to your clinical practice?
Research
JAMA Cardiology | Original Investigation
Screening for Atrial Fibrillation in the Older Population
A Randomized Clinical Trial
David J. Gladstone, MD, PhD; Rolf Wachter, MD; Katharina Schmalstieg-Bahr, MD; F. Russell Quinn, MD, PhD;
Eva Hummers, MD, PhD; Noah Ivers, MD; Tamara Marsden, MSc; Andrea Thornton, BSc; Angie Djuric;
Johanna Suerbaum, MD; Doris von Grünhagen; William F. McIntyre, MD; Alexander P. Benz, MD;
Jorge A. Wong, MD, MPH; Fatima Merali, MSc, MD; Sam Henein, MD; Chris Nichol, MD; Stuart J. Connolly, MD;
Jeff S. Healey, MD, MSc; for the SCREEN-AF Investigators and Coordinators
Editorial page 495
IMPORTANCE Atrial fibrillation (AF) is a major cause of preventable strokes. Screening
asymptomatic individuals for AF may increase anticoagulant use for stroke prevention.
Multimedia
Supplemental content
OBJECTIVE To evaluate 2 home-based AF screening interventions.
DESIGN, SETTING, AND PARTICIPANTS This multicenter randomized clinical trial recruited
individuals from primary care practices aged 75 years or older with hypertension and without
known AF. From April 5, 2015, to March 26, 2019, 856 participants were enrolled from 48
practices.
INTERVENTIONS The control group received standard care (routine clinical follow-up plus a
pulse check and heart auscultation at baseline and 6 months). The screening group received a
2-week continuous electrocardiographic (cECG) patch monitor to wear at baseline and at 3
months, in addition to standard care. The screening group also received automated home
blood pressure (BP) machines with oscillometric AF screening capability to use twice-daily
during the cECG monitoring periods.
MAIN OUTCOMES AND MEASURES With intention-to-screen analysis, the primary outcome was
AF detected by cECG monitoring or clinically within 6 months. Secondary outcomes included
anticoagulant use, device adherence, and AF detection by BP monitors.
RESULTS Of the 856 participants, 487 were women (56.9%); mean (SD) age was 80.0 (4.0)
years. Median cECG wear time was 27.4 of 28 days (interquartile range [IQR], 18.4-28.0 days).
In the primary analysis, AF was detected in 23 of 434 participants (5.3%) in the screening
group vs 2 of 422 (0.5%) in the control group (relative risk, 11.2; 95% CI, 2.7-47.1; P = .001;
absolute difference, 4.8%; 95% CI, 2.6%-7.0%; P < .001; number needed to screen, 21). Of
those with cECG-detected AF, median total time spent in AF was 6.3 hours (IQR, 4.2-14.0
hours; range 1.3 hours-28 days), and median duration of the longest AF episode was 5.7 hours
(IQR, 2.9-12.9 hours). Anticoagulation was initiated in 15 of 20 patients (75.0%) with
cECG-detected AF. By 6 months, anticoagulant therapy had been prescribed for 18 of 434
participants (4.1%) in the screening group vs 4 of 422 (0.9%) in the control group (relative
risk, 4.4; 95% CI, 1.5-12.8; P = .007; absolute difference, 3.2%; 95% CI, 1.1%-5.3%; P = .003).
Twice-daily AF screening using the home BP monitor had a sensitivity of 35.0% (95% CI,
15.4%-59.2%), specificity of 81.0% (95% CI, 76.7%-84.8%), positive predictive value of 8.9%
(95% CI, 4.9%-15.5%), and negative predictive value of 95.9% (95% CI, 94.5%-97.0%).
Adverse skin reactions requiring premature discontinuation of cECG monitoring occurred in 5
of 434 participants (1.2%).
CONCLUSIONS AND RELEVANCE In this randomized clinical trial, among older
community-dwelling individuals with hypertension, AF screening with a wearable cECG
monitor was well tolerated, increased AF detection 10-fold, and prompted initiation of
anticoagulant therapy in most cases. Compared with continuous ECG, intermittent
oscillometric screening with a BP monitor was an inferior strategy for detecting paroxysmal
AF. Large trials with hard clinical outcomes are now needed to evaluate the potential benefits
and harms of AF screening.
TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02392754
JAMA Cardiol. 2021;6(5):558-567. doi:10.1001/jamacardio.2021.0038
Published online February 24, 2021.
558
Author Affiliations: Author
affiliations are listed at the end of this
article.
Group Information: The SCREEN-AF
Investigators and Coordinators are
listed at the end of this article.
Corresponding Author: David J.
Gladstone, MD, PhD, Sunnybrook
Research Institute, University of
Toronto Department of Medicine,
2075 Bayview Ave, Toronto, ON M4N
3M5, Canada (david.gladstone@
sunnybrook.ca).
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Screening for Atrial Fibrillation in the Older Population
A
trial fibrillation (AF) is one of the most common treatable risk factors for stroke, and its prevalence is
increasing.1,2 By age 55 years, the lifetime risk of developing AF in the US is 1 in 3.3 When AF is identified, initiation of oral anticoagulant therapy (OAC) can prevent twothirds of strokes. However, AF often goes undetected and
untreated because it is frequently short-lasting and asymptomatic, and stroke can be its first manifestation.4-6 Approximately 10% to 20% of ischemic strokes are attributed to previously undiagnosed AF,7 and AF-associated strokes tend to
be more disabling and more often fatal compared with other
types of ischemic strokes.8 If screening can effectively detect
sufficient numbers of individuals with AF and trigger initiation of OAC, many strokes could potentially be prevented.
Interest in AF screening has increased with advances in
wearable technologies for arrhythmia detection9,10 and the availability of safer and highly effective OAC medications for stroke
prevention.11 In the secondary prevention context, among patients with recent ischemic stroke, ambulatory electrocardiographic (ECG) monitoring with wearable or implanted devices
detects AF in 15% to 30% of patients.12-15 However, the concept of primary prevention screening for AF in asymptomatic
individuals remains controversial.16-18 Randomized clinical trials
are lacking to determine which patients merit screening, how
best to screen, and whether screening will prevent stroke. In primary care, routine screening for AF is either not performed or
is limited to a random pulse check or single opportunistic ECG.
According to European guidelines, “systematic ECG screening
should be considered to detect AF in individuals aged 75 years
or older or those at high risk of stroke.”19(p3) However, the US
Preventive Services Task Force recommended against routine
ECG screening for AF, citing insufficient evidence.20
We conducted a randomized clinical trial of AF screening
in older community-dwelling individuals without known AF
using a wearable continuous ECG (cECG) patch monitor vs standard care. The secondary objective assessed whether AF detection led to OAC treatment. Within the screening group, we
also evaluated an automated home blood pressure (BP) device
for oscillometric AF screening.
Methods
Key Points
Question Will screening older individuals for atrial fibrillation with a
wearable electrocardiographic monitor be feasible, detect a high rate
of atrial fibrillation, and lead to anticoagulation for most patients?
Findings In a randomized clinical trial of 856 participants aged 75
years or older with hypertension from outpatient primary care
practices, new atrial fibrillation was detected in 5.3% of the
screening group vs 0.5% of the control group. Median atrial
fibrillation duration on continuous electrocardiographic
monitoring was 6.3 hours, and anticoagulation was prescribed to
75.0% of the participants with screen-detected atrial fibrillation.
Meaning In this trial, a wearable electrocardiogram-based
screening intervention increased atrial fibrillation detection
10-fold and prompted anticoagulation in most cases; this strategy
warrants evaluation to prevent stroke.
dards of Reporting Trials (CONSORT) reporting guideline for
randomized clinical trials.
Participants
We recruited community-dwelling individuals aged 75 years
or older without known AF who were not receiving OAC but
were potential OAC candidates if AF was diagnosed (CHADS2
[congestive heart failure, hypertension, age ≥75 years, diabetes, stroke]) score ≥2, indicating moderate or high risk for
stroke, and no OAC contraindications). All participants had a
history of hypertension requiring antihypertensive medication and were in sinus rhythm at enrollment as assessed by 30second pulse palpation and heart auscultation by enrolling physicians (eTable 1 in Supplement 2). Key exclusion criteria were
previously documented AF or atrial flutter, pacemaker, defibrillator, or implanted loop recorder.
At Canadian sites, patients attending regularly scheduled
outpatient visits were screened for eligibility and offered study
participation. Study visits were conducted by local investigators and coordinators. German sites screened electronic records for potentially eligible patients and sent invitation letters. A mobile study team (physician, nurse, and medical
student) performed study procedures; 6-month visits were conducted by the patients’ family physician.
Randomization
Trial Design, Setting, and Oversight
SCREEN-AF was an investigator-initiated, multicenter, openlabel, randomized clinical trial investigating noninvasive homebased AF screening interventions. Participants were recruited
from primary care clinics in Canada and Germany. Participants provided written informed consent and were not paid to
participate. The study protocol and prespecified statistical analysis plan are available in Supplement 1. The study was funded
by peer-reviewed national government grants, coordinated at
the Population Health Research Institute, Hamilton, Canada, and
approved by the national regulatory authorities and research
ethics committees representing all sites. The list of site principal investigators and individuals who provided statistical assistance and study coordination is available in the eAppendix
in Supplement 2. This study followed the Consolidated Stanjamacardiology.com
Original Investigation Research
Eligible participants were randomly allocated (1:1) to the screening group or control group via web-based randomization using
computer-generated random block sizes of 4 and 6, stratified by
center. The control group received standard clinical care and follow-up for 6 months, including pulse check and heart auscultation by a physician at baseline and 6 months. The screening
group received a 2-week ambulatory cECG patch monitor, one
at baseline and another at 3 months, in addition to standard
care. The screening group also received an automated home BP
monitor with an AF screening algorithm to be used twice daily
during each of the 2-week cECG monitoring periods.
Study Interventions
We studied an adhesive patch cECG (Zio XT; iRhythm Technologies), a miniature, single-lead Holter-type device worn on
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Research Original Investigation
Screening for Atrial Fibrillation in the Older Population
the chest that provides up to 2 weeks of continuous ECG
recording.21-25 It was applied by study personnel immediately after randomization, with instructions to wear it for 2
weeks, including sleep and showering. At 3 months, participants returned to receive another 2-week cECG. Devices were
mailed back for central interpretation. Results were sent to each
participant’s primary care physician who was responsible for
clinical treatment decisions.
The home BP monitor (WatchBP-Home A; Microlife Corp)
has been endorsed as an AF screening tool.26-29 Participants
in the screening group were instructed to record their home
BP twice daily (morning and evening) during each of the cECG
monitoring periods. Each assessment consisted of 3 sequential BP measurements; a positive AF screen was indicated if 2
or more of the 3 consecutive measurements were positive for
AF. Participants and clinicians were advised not to act upon
the home BP monitor AF screening results.
Assessments
Study visits were conducted in the clinic at baseline, 3 months,
and 6 months. Baseline assessments collected data via patient interview and medical records review on demographics, medical history, and medications, and measured pulse and
BP. Follow-up assessments recorded medications and any interim outcome events; specifically, any new clinical diagnosis of AF, ischemic or hemorrhagic stroke, transient ischemic
attack, systemic embolism, major bleeding, death, and physician/hospital visits. For all outcome events, original source
documents were requested for central adjudication (ECG tracings, hospital records).
Outcomes
560
ables are presented as means (SDs) or medians and interquartile ranges (IQRs), and groups were compared using the t tests
or Wilcoxon rank sum tests if normality was questionable. Missing values were treated as missing and no imputation was done
for any analyses.
The primary analysis compared the proportion of participants in each group achieving the primary outcome of AF detection using the χ2 test and is presented with relative risk and
corresponding 95% CI from a modified Poisson regression model
with robust error variances. The absolute risk difference is also
reported along with 95% CI. A 2-sided significance level of
P < .05 was used for all analyses. A planned per-protocol analysis evaluated the primary outcome among patients who wore
2 cECG monitors for 12 or more days each.
For patients with the primary outcome of AF detected by
cECG, we summarized the time to first AF detection, number
and longest duration of AF episodes, total time in AF, and AF
burden (percentage of analyzable time spent in AF per cECG
monitor). Adherence was measured by cECG wear times (automatically time-stamped by the device). Tolerability was
measured by patient satisfaction surveys and incidence of
adverse skin reactions.
For the BP monitor analysis, we estimated sensitivity,
specificity, and positive and negative predictive values at the
patient level using the simultaneously acquired cECG results
as the standard for AF detection. Post hoc, we separately considered AF duration of more than 24 hours and AF duration
of less than 24 hours. Details are available in the eMethods in
Supplement 2. We compared the number of physician visits,
hospitalizations, and emergency department visits at 6 months
between groups using the Poisson regression model with log
as the link function.
The primary outcome was detection of AF within 6 months
postrandomization, either by study cECG monitors or as part
of routine clinical care. We defined AF for the primary outcome as 1 or more episode of continuous AF or atrial flutter lasting more than 5 minutes on cECG or diagnosed clinically (12lead ECG or other source documentation). All AF outcome
events (from cECG and site-reported clinical AF diagnoses) underwent central adjudication by 2 arrhythmia physicians
(F.R.Q., W.F.M., A.P.B., and J.A.W.) blinded to randomization
group.
The secondary outcome was OAC use at 6 months. Additional outcomes included device adherence, tolerability, detection of other prespecified arrhythmias, health care use, and
AF screening performance of the home BP monitor. Sitereported clinical events (stroke, transient ischemic attack, and
systemic embolism) underwent central blinded adjudication
by 2 neurologists.
From April 5, 2015, to March 26, 2019, 856 participants (555
from Canada, 301 from Germany) underwent randomization:
434 were assigned to the screening group and 422 to the control group. Follow-up was complete in 95.6% of the participants at 3 months and 92.6% at 6 months (Figure 1). There were
no interim analyses. Baseline patient characteristics were balanced between groups (Table 1). Mean (SD) age was 80.0 (4.0)
years, 487 participants (56.9%) were women, 369 were men
(43.1%), with a median score of 4 points (IQR, 4-5 points) on
the CHA2DS2-VASc (congestive heart failure, hypertension, age
≥75 years, diabetes, stroke or transient ischemic attack, vascular disease, age 65-74 years, sex category) scale.
Statistical Analysis
Adherence to cECG Monitoring
Sample size calculations are reported in the eMethods in Supplement 2. All analyses were performed using SAS, version 9.4 (SAS
Institute Inc), using an intention-to-screen principle of all randomized patients, regardless of device use, adherence, or duration of participation. Categorical variables are presented as
numbers and percentages, and groups were compared using χ2
tests or Fisher exact tests for small samples. Continuous vari-
The first cECG monitor was worn by 423 participants (97.5%),
and a second monitor was worn by 344 individuals (79.3%).
Overall, 93.8% of the participants completed at least 1 week
of monitoring, 85.9% completed at least 2 weeks, and 74.2%
completed at least 3 weeks. Median wear times were 14.0 days
(IQR, 13.4-14.0 days) for the first monitoring period, 13.9 days
(IQR, 9.8-14.0 days) for the second monitoring period, and 27.5
Results
Patient Characteristics
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Screening for Atrial Fibrillation in the Older Population
Original Investigation Research
Table 1. Patient Characteristics
Figure 1. Flow Diagram
No. (%)
856 Participants enrolled
856 Randomized
Characteristic
Screening group
(n = 434)
Control group
(n = 422)
Age, mean (SD), y
79.8 (3.8)
80.1 (4.1)
Sex
434 Assigned to screening group
423 Received at least 1
adhesive ECG patch
422 Assigned to control group
422 Received standard care
386 Completed 6-mo follow-up
48 Did not have 6-mo follow-up
0 Died
13 Lost to follow-up
35 Withdrew
402 Completed 6-mo follow-up
20 Did not have 6-mo follow-up
1 Died
11 Lost to follow-up
8 Withdrew
434 Included in the intentionto-screen analysis
255 (58.8)
232 (55.0)
Male
179 (41.2)
190 (45.0)
Canada
283 (65.2)
272 (64.4)
Germany
151 (34.8)
150 (35.5)
White
409 (94.2)
397 (94.1)
Black
7 (1.6)
6 (1.4)
Asian
14 (3.2)
13 (3.1)
Othera
4 (0.9)
6 (1.4)
Location
Race/ethnicity
422 Included in the intentionto-screen analysis
Baseline blood pressure, mean
(SD), mm Hg
ECG indicates electrocardiograhic.
days (IQR, 20.4-28.0 days) in total. The ECG quality (signalto-noise) was high, yielding a median analyzable time of 13.7
days (IQR, 12.9-14.0 days) for the first monitoring period, 13.5
days (IQR, 8.0-13.9 days) for the second monitoring period, and
26.8 days (IQR, 15.6-27.7 days) in total.
In the intention-to-screen analysis, the primary outcome of AF
detection at 6 months occurred in 23 of 434 participants (5.3%)
in the screening group vs 2 of 422 (0.5%) in the control group
(relative risk, 11.2; 95% CI, 2.7-47.1; P = .001; absolute difference, 4.8%; 95% CI, 2.6%-7.0%; P < .001; number-needed-toscreen, 21). Sensitivity analysis using a different AF definition yielded similar results (eResults in Supplement 2). Results
did not differ substantially by country. In the screening group,
20 of 23 of the AF cases (87.0%) were detected by the cECG
monitors and 3 of 23 (13.0%) were diagnosed clinically (these
3 patients presented to the hospital with symptomatic ECGdocumented AF).
In a per-protocol analysis restricted to patients with the
highest level of adherence (wearing both cECG monitors for
at least 12 days each, n = 294), the primary outcome was detected in 17 of 294 individuals (5.8%) in the screening group
vs 2 of 422 (0.5%) in the control group (relative risk, 12.2; 95%
CI, 2.8-52.4; P < .001; absolute difference, 5.3%; 95% CI, 2.6%8.1%; P < .001, number-needed-to-screen, 19). The 3-month
rate of AF detection (a secondary outcome, based on only a
single 2-week cECG) was significantly higher in the screening
group (4.6% vs 0.2%; relative risk, 19.5; 95% CI, 2.6-144.3;
P = .004; absolute difference, 4.4%; 95% CI, 2.3% to 6.4%;
P < .001; number-needed-to-screen, 23).
Anticoagulant Therapy
Oral anticoagulant therapy for any indication increased from
0 at baseline in both groups to 18 of 434 (4.1%) in the screening group vs 4 of 422 (0.9%) in the control group at 6 months
(relative risk, 4.4; 95% CI, 1.5-12.8; P = .007; absolute differ-
Systolic
140 (17.1)
141 (17.9)
Diastolic
75.1 (9.5)
74.6 (9.9)
Height, mean (SD), cm
165 (10.6)
165 (9.7)
Weight, mean (SD), kg
76.9 (16.4)
76.1 (16.2)
BMI, mean (SD)
28.1 (5.4)
27.8 (5.4)
CHA2DS2-VASc score, median
(IQR)
4.0 (4.0-5.0)
4.0 (4.0-5.0)
Diabetes
102 (23.7)
103 (24.4)
Congestive heart failure
16 (3.7)
19 (4.5)
Ischemic stroke, TIA, or
systemic embolism
40 (9.3)
43 (10.2)
Coronary artery disease
72 (16.7)
80 (19.0)
Coronary angioplasty/coronary
stent
47 (10.9)
46 (10.9)
Medical history
AF Detection
jamacardiology.com
Female
Myocardial infarction
42 (9.7)
38 (9.0)
Hyperlipidemia
241 (56.0)
234 (55.5)
Severe aortic or mitral valve
disease
4 (0.9)
4 (0.9)
Rheumatic valve disease
3 (0.7)
1 (0.2)
Prosthetic heart valve
3 (0.7)
1 (0.2)
Sleep apnea
31 (7.2)
23 (5.5)
Dialysis
0
0
CABG
20 (4.6)
29 (6.9)
Valve surgery
4 (0.9)
3 (0.7)
Peripheral artery disease
(aortic plaque)
26 (6.0)
24 (5.7)
Dementia
6 (1.4)
2 (0.5)
Current
26 (6.0)
20 (4.7)
Past
153 (38.0)
154 (38.3)
73 (16.9)
75 (17.8)
Cardiac surgery
Smoker
History of any palpitations in
past year (patient self-report)
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided
by height in meters squared); CABG, coronary artery bypass graft;
CHA2DS2-VASc, congestive heart failure, hypertension, age ⱖ75 years,
diabetes, stroke or transient ischemic attack, vascular disease, age 65-74 years,
sex category; TIA, transient ischemic attack.
a
Other includes the following categories: Hispanic/Latino, Middle Eastern,
Aboriginal, and Other.
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Research Original Investigation
Screening for Atrial Fibrillation in the Older Population
Figure 2. Duration of Longest Episode of Atrial Fibrillation (AF)
Detected by Continuous Electrocardiographic Monitoring
12
Table 2. Details of the AF Cases Detected by Continuous
Electrocardiographic Patch Monitoring in the Screening Group
Characteristic
Median (IQR) [range]
Total time in AF per patient
6.3 h (4.2-14.0 h) [1.3 h-28 d]
AF per patient
10
Maximum burden per 2-wk patch, %
No. of patients
8
1.9 (1.3-17.1) [0.4-17.1]
No. of episodes
3 (2-13) [1-110]
Duration of longest episode
5.7 h (2.9-12.9 h) [50 min-14 d]
Time to first detected episode, d
10.2 (2.8-15.2) [0-24.3]
6
Abbreviations: AF, atrial fibrillation; IQR, interquartile range.
4
ond cECG finding AF was 5 of 329 (1.5%). Detection of other
arrhythmias was rare (eTable 2 in Supplement 2).
2
0
>5 min-6 h
>6-24 h
>24 h
Duration of longest AF episode
ence, 3.2%; 95% CI, 1.1%-5.3%; P = .003). Atrial fibrillation was
the only indication for OAC in the screening group. In the control group, reasons for OAC were AF (2 patients), venous thromboembolism (1 patient), and lower limb angioplasty/stenting
(1 patient). The 6-month rate of OAC use for AF was 18 of 434
(4.1%) in the screening group vs 2 of 422 (0.5%) in the control
group (relative risk, 8.8; 95% CI, 2.0-37.5; P = .004; absolute
difference, 3.7%; 95% CI, 1.7%-5.7%; P < .001).
In the screening group, OAC was prescribed by 6 months
for 15 of 20 (75.0%) patients who had cECG-detected AF and
for all 3 patients who had AF diagnosed clinically. There were
no major bleeding events within 6 months among patients with
AF or those prescribed OAC.
Details of AF Detected by cECG Monitors
Of the 20 patients with cECG-detected AF duration of more
than 5 minutes, 19 had AF and 1 had atrial flutter. Atrial fibrillation was paroxysmal in 18 of 20 patients (90.0%) and continuous throughout the recording in 2 of 20 (10.0%). Atrial fibrillation was asymptomatic in 17 of 20 patients (85.0%); only
1 of 20 patients (5.0%) presented to the hospital with symptomatic AF during the 6-month follow-up.
The median time in AF per patient was 6.3 hours (IQR, 4.214.0 hours; range, 1.3 hours-28 days). Atrial fibrillation episodes lasted more than 1 hour in 95% of the participants, more
than 4 hours in 70%, more than 6 hours in 50%, more than 9
hours in 30%, more than 12 hours in 25%, and more than 24
hours in 15%. The median duration of the longest AF episode
was 5.7 hours (IQR, 2.9-12.9 hours) (Figure 2 and Table 2).
The time course of AF detection is shown in Figure 3. Of
the 20 AF cases, 8 (40%) were detected within the first week
of cECG monitoring, 15 (75%) were detected within 2 weeks,
17 (85%) were detected within 3 weeks, and 3 (15%) were first
detected only during the fourth week; 18 AF cases (90%) were
first detected after the first 24 hours of monitoring. Among patients who wore 2 cECG monitors (baseline and 3 months), if
the first cECG was negative for AF, the probability of the sec562
cECG Tolerability
Adverse skin reactions requiring premature discontinuation
of cECG monitoring occurred in 5 of 434 participants (1.2%).
On a 5-point scale, most participants agreed or strongly agreed
that the cECG monitor was comfortable (daytime: 289 of 356
[81.2%]; sleeping: 285 of 357 [79.8%]); 33 of 355 participants
(9.3%) reported that the monitor hindered daily activities and
134 of 354 (37.9%) reported pruritus.
Clinical Outcomes
During the 6-month study period, 1 participant died (control
group; cardiovascular death) and 2 participants had an ischemic stroke (both in the screening group) (eMethods in Supplement 2). One patient had a transient ischemic attack (screening group); this patient’s cECG was negative for AF, but AF was
subsequently diagnosed in the hospital at the time of presentation with transient ischemic attack. There were no cases of
intracranial hemorrhage or systemic embolism. There were no
significant differences between the screening and control
groups in total physician visits (1233 vs 1251; P = .83), emergency department visits (5 vs 2; P > .99), hospitalizations (5 vs
3; P = .48), or pacemaker implantations (3 vs 2; P > .99).
AF Screening by Home BP Monitors
In the screening group, 412 of 434 participants (94.9%) used
the BP monitor for a median of 27.4 days (IQR, 14-28 days), with
a median of 55 measurements (IQR, 28-56 measurements) recorded per patient. Among 399 patients with concurrent BP
monitor and cECG recordings, the BP monitor had a sensitivity of 35.0% (95% CI, 15.4%-59.2%), specificity of 81.0% (95%
CI, 76.7%-84.8%), positive predictive value of 8.9% (95% CI,
4.9%-15.5%), and negative predictive value of 95.9% (95% CI,
94.5%-97.0%) (eTable 3 in Supplement 2). Sensitivity increased to 66.7% for detecting AF episodes lasting more than
24 hours. Specificity increased to 93.4% when BP monitoring
was positive for AF at both the morning and evening measurement times in a given day (additional details in the eMethods
in Supplement 2).
The BP monitors had at least 1 positive screen for AF in 79
of 434 patients (18.2%), of which 72 (91.1%) were falsepositives. When BP monitoring was positive for AF on both the
morning and evening measurements in a single day (28 of 434
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Screening for Atrial Fibrillation in the Older Population
Original Investigation Research
Figure 3. Time Course of Atrial Fibrillation (AF) Detection by Continuous Electrocardiographic (cECG) Monitoring in the Screening Group
First adhesive ECG patch (baseline)
Second adhesive ECG patch (3 mo)
100
7
6
80
No. of patients
60
4
3
40
2
Cumulative % of first AF events
5
20
1
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 1
2
3
4
5
6
7
8
9 10 11 12 13 14
Time to primary outcome of AF detection by adhesive ECG patch since start of monitoring, d
The bars show when AF was first detected for each of the 20 patients with
cECG-detected AF during the first (A) and second (B) monitoring periods. The
curve shows the cumulative incidence of AF. Of the 20 AF cases, 8 (40%) were
detected within the first week of cECG monitoring, 15 (75%) were detected
within 2 weeks, 17 (85%) were detected within 3 weeks, and 3 (15%) were first
detected only during the fourth week; 18 (90%) of AF cases were first detected
after the first 24 hours of monitoring.
patients [6.5%]), 3 of 28 (10.7%) were true-positives and 25
(89.3%) were false-positives. In total, BP monitoring yielded
497 false-positive screens, of which 39.2% were associated with
frequent atrial or ventricular premature complexes (>60/h) on
cECG monitoring. The median AF duration among patients with
a true-positive AF screen by BP monitoring was 15.8 hours; the
median AF duration among episodes missed by BP monitoring was 4.9 hours.
3).29 Our AF detection rate with 2- or 4-week noninvasive cECG
monitoring was similar to the yield of implanted cardiac loop
recorders after 2 weeks (3.1%) and 1 month (6.2%)31,32 and it
was lower, as expected, than in patients with a recent stroke
undergoing a similar duration of ECG monitoring.12,13 Our results in an older population complement the Apple Heart
Study33 and Huawei Heart Study,34 which both focused on
younger patients, lending support for further testing of wearables for screening of older individuals who are at risk for AF.
Our findings highlight several advantages of ambulatory
cECG monitoring over other types of AF screening interventions. First, cECG monitoring yields higher AF detection rates
than either single time point screening (eg, pulse palpation,
BP monitoring, or handheld ECG device) or brief intermittent
screening.35-39 Single random screening primarily detects nonparoxysmal AF, whereas cECG monitoring detects both paroxysmal and nonparoxysmal AF. Thus, it is not surprising that
2 recent trials found that single time point AF screening was
not superior to usual care in primary care clinics in the
Netherlands.40,41 Second, unlike handheld ECGs, watches, and
BP monitors in which false-positive AF detection is an issue,
wearable cECG devices serve as both the screening tool and
the diagnostic test, essentially eliminating the need for confirmatory testing. Third, compared with implanted cardiac
monitors, wearable ECG devices are noninvasive, less costly,
more accessible, can be self-applied by patients at home, and
have fewer false-positive results.42 Although 2- or 4-week cECG
monitoring will miss infrequent paroxysmal AF compared with
long-term implanted devices,43 AF detected within only 2
weeks by a wearable monitor is of higher burden and likely
more clinically significant than the same amount of AF that is
detectable only after longer monitoring durations (months to
years) with implanted devices.
Discussion
SCREEN-AF investigated 2 technologies for home-based AF
screening in older primary care patients with hypertension. A
wearable cECG strategy for up to 4 weeks (1) detected a substantial prevalence (5%) of subclinical AF, (2) was superior to
6 months of standard clinical care for AF detection (numberneeded-to-screen, 21), and (3) resulted in more patients prescribed OAC for stroke prevention. Most AF cases were paroxysmal, with episodes lasting many hours. Patient adherence
to cECG was high, three-quarters of AF cases were detected
within the first 2 weeks of ECG monitoring, and 90% of cases
would have been missed using a 24-hour Holter monitor.
Our trial strengthens the evidence supporting the effectiveness of screening for early detection of AF. Most previous
AF screening studies have been single-group observational
studies. Only 1 other completed randomized clinical trial
(mSTOPS) investigated screening interventions beyond pulse
palpation or random 30-second ECGs in asymptomatic
individuals30; our results are consistent with that trial, which
found a 3.9% AF detection rate within 4 months among patients randomized to two 2-week cECG patch monitors vs 0.9%
in controls (mean age, 74 years; median CHA2DS2-VASc score,
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563
Research Original Investigation
Screening for Atrial Fibrillation in the Older Population
Twice-daily AF screening by a home BP monitor had a high
false-positive rate and missed most AF episodes lasting less
than 24 hours. The low sensitivity and specificity of BP monitoring in this study, as compared with previous reports,27,44,45
may reflect patient adherence to the BP monitor, a different
standard compared with other studies,38-40 the patient population, and our detection of relatively brief episodes of paroxysmal AF rather than persistent AF.
Strengths of our study include the randomized multicenter trial design and broad eligibility criteria to maximize
generalizability. We selected a population at risk for both AF
and stroke based on older age and hypertension. Our findings
should not be generalized to younger individuals who will have
a lower prevalence of AF. In our patients, with a median
CHA2DS2-VASc score of 4, the finding of many hours of AF is
potentially clinically important. The minimum clinically significant amount of subclinical device-detected AF is currently debated.46 An AF burden of greater than 11% during
2-week cECG monitoring has been associated with a 3-fold increase in stroke risk without OAC.47 In patients with a pacemaker or cardioverter-defibrillator, AF durations associated
with significantly increased stroke risk range from greater than
6 minutes48 to greater than 1 hour,49 greater than 5.5 hours,50-52
and greater than 24 hours.53,54 A subset of patients with brief
AF progress to longer AF episodes over time.55-58
This work has implications for primary stroke prevention, as the prevalence of AF and AF-associated strokes is increasing with an aging population. The primary goal of screening is to identify patients with a sufficiently high burden of AF
(eg, >24 continuous hours) for whom OAC is likely to provide
net benefit.59 For subclinical AF of less than 24 hours, clinical
equipoise for OAC exists.60 Trials underway should help define the role of OAC for brief subclinical AF.61,62 Until such results are available, use of OAC is empirical and treatment
decisions must be individualized taking into account the
CHA2DS2-VASc score and likelihood of recurrent AF (eg, left
atrial enlargement). If the efficacy of OAC for patients with
low-burden, device-detected AF is similar to that of clinically
detected AF, then future AF screening programs could have
ARTICLE INFORMATION
Accepted for Publication: December 22, 2020.
Published Online: February 24, 2021.
doi:10.1001/jamacardio.2021.0038
Author Affiliations: Hurvitz Brain Sciences
Program, Sunnybrook Research Institute, and
Division of Neurology, Sunnybrook Health Sciences
Centre, Toronto, Ontario, Canada (Gladstone);
Division of Neurology, Department of Medicine,
University of Toronto, Toronto, Ontario, Canada
(Gladstone); Clinic and Policlinic for Cardiology,
University Hospital, Leipzig, Germany (Wachter);
Department of Cardiology, University Medical
Center Göttingen, Göttingen, Germany (Wachter,
Suerbaum); DZHK (German Centre for
Cardiovascular Research), partner site Göttingen,
Göttingen, Germany (Wachter, Hummers,
Suerbaum); Department of General Practice,
University Medical Center Göttingen, Göttingen,
Germany (Schmalstieg-Bahr, Hummers);
Department of General Practice and Primary Care,
564
major public health benefits for improving stroke prevention
at a population level.63
Limitations
The main limitation of our trial is that it was underpowered to
detect differences in clinical outcomes. Follow-up duration was
short and we cannot exclude a lead-time bias effect. Extended
follow-up is ongoing to explore these questions. Larger trials and
pooled analyses will be necessary to determine the effect of AF
screening on stroke prevention, and several such trials are
underway.64-68 Screening for AF, like screening for other conditions, has potential harms.69 The main concerns are overdiagnosis and overtreatment of low-risk patients for whom OAC
may be unwarranted (eg, AF lasting seconds only), increasing
bleeding risk without benefit. Screening may also cause patient anxiety, increase health care use, or affect health insurance eligibility.70 We found no signal of increased bleeding, although our follow-up duration was short, and we have not
assessed cost-effectiveness. Preliminary estimates suggest wearable cECG strategies are potentially cost-effective,71,72 but further work is needed. Given the rapid proliferation of wearable
technologies, we are entering an uncharted new era of consumer-driven screening and direct-to-consumer marketing, and
consumers must be informed of the potential benefits and
risks.73-75 We caution against premature or inappropriate uptake of screening until its effect on hard clinical outcomes and
cost-effectiveness have been established.
Conclusions
This randomized clinical trial provides evidence that a wearable continuous ECG strategy is well tolerated and effective
for early detection of AF in older primary care patients, often
leading to OAC treatment with the potential to avert future
strokes. Intermittent oscillometric screening with a BP monitor is an inferior strategy for detecting paroxysmal AF. Future
studies need to determine the effect of AF screening on clinical outcomes.
University Medical Center Hamburg-Eppendorf,
Hamburg-Eppendorf, Germany (Schmalstieg-Bahr);
Libin Cardiovascular Institute, University of Calgary,
Calgary, Alberta, Canada (Quinn); Women’s College
Hospital, Department of Family and Community
Medicine, University of Toronto, Toronto, Ontario,
Canada (Ivers); Population Health Research
Institute, McMaster University, Hamilton, Ontario,
Canada (Marsden, Thornton, Djuric, McIntyre,
Benz, Wong, Connolly, Healey); Clinic for Cardiology
and Pneumology, University Medicine Göttingen,
Göttingen, Germany (von Grünhagen); LMC Manna
Research, Burlington, Ontario, Canada (Merali);
Southlake Regional Health Centre, Newmarket,
Ontario, Canada (Henein); Camrose Primary Care
Network, Camrose, Alberta, Canada (Nichol).
Author Contributions: Drs Gladstone and Marsden
had full access to all of the data in the study and
take responsibility for the integrity of the data and
the accuracy of the data analysis. Drs Gladstone and
Wachter are co–principal authors.
Concept and design: Gladstone, Quinn, Hummers,
Ivers, Healey.
Acquisition, analysis, or interpretation of data:
All authors.
Drafting of the manuscript: Gladstone, Wachter,
Schmalstieg-Bahr, Ivers, Thornton, Djuric, Merali.
Critical revision of the manuscript for important
intellectual content: Gladstone, Wachter,
Schmalstieg-Bahr, Quinn, Hummers, Ivers,
Marsden, Suerbaum, von Grunhagen, McIntyre,
Benz, Wong, Henein, Nichol, Connolly, Healey.
Statistical analysis: Gladstone, Marsden.
Obtained funding: Gladstone, Wachter, Hummers,
Healey.
Administrative, technical, or material support:
Wachter, Schmalstieg-Bahr, Hummers, Thornton,
Djuric, Suerbaum, von Grunhagen, Wong, Healey.
Supervision: Wachter, Hummers, Merali, Henein,
Nichol, Connolly.
Conflict of Interest Disclosures: Dr Gladstone
reported receiving an operating grant from the
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Screening for Atrial Fibrillation in the Older Population
Canadian Stroke Prevention Intervention Network
(C-SPIN) for the SCREEN-AF trial (C-SPIN is a
peer-reviewed national network grant funded by
the Canadian Institutes of Health Research [CIHR])
during the conduct of the study; a Heart and Stroke
Foundation of Canada Mid-Career Investigator
Award, grants from Ontario Genomics
peer-reviewed operating grant, and grants from
CIHR-funded C-SPIN Network operating grant for
the Ontario Holter/Echo Database Study outside
the submitted work; in addition, he is chair of the
Secondary Stroke Prevention Guidelines
Committee for the Canadian Stroke Best Practice
Recommendations (uncompensated); member of
the Canadian Cardiovascular Society Atrial
Fibrillation Guidelines Committee
(uncompensated); he served as a site principal
investigator for the NAVIGATE ESUS and
NASPAF-ICH trials (all site fees paid to his
institution); and he is a Canadian national coleader
of the National Institute of Neurological Disorders
and Stroke–sponsored ARCADIA trial. He previously
served as an independent medical safety monitor
for ARCADIA (uncompensated). Dr Gladstone had
no personal financial relationships with
pharmaceutical companies or cardiac monitoring
device manufacturers in the past more than 4
years. Dr Wachter reported receiving grants from
Deutsches Zentrum für Herz-/Kreislaufforschung
during the conduct of the study, Deutsche
Forschungsgemeinschaft, European Union,
Bundesministerium für Bildung und Forschung, and
Boehringer Ingelheim; personal fees from Bayer,
Boehringer Ingelheim, CVRx, Daiichi, BMS,
Medtronic, Novartis, Pfizer, Pharmacosmos, and
Servier outside the submitted work.
Dr Schmalstieg-Bahr reported receiving grants from
German Centre for Cardiovascular Research
(DZHK), which financed her position within the
research team during the conduct of the study.
Dr Quinn reported receiving personal fees from
BMS-Pfizer and grants from Bayer outside the
submitted work. Dr Hummers reported receiving
peer-reviewed grants from the German Centre for
Cardiovascular Research and Canadian Stroke
Prevention Intervention Network, and nonfinancial
support from iRhythm technologies during the
conduct of the study. Dr Suerbaum reported the
German Centre of Cardiovascular Research
Scholarship allowed her to participate in the
project. Dr McIntyre reported speaking fees from
Servier and Bayer outside the submitted work.
Dr Healey reported receiving grants from
Boehringer-Ingelheim C-SPIN network during the
conduct of the study; grants from BMS/Pfizer,
Bayer, Medtronic, and Abbott outside the
submitted work; and personal fees from Servier. No
other disclosures were reported.
Funding/Support: The trial was funded by C-SPIN,
a peer-reviewed national network grant from the
CIHR; a peer-reviewed operating grant (FKZ
81X1300111) from the German Centre for
Cardiovascular Research (DZHK); Boehringer
Ingelheim; and in-kind support from Microlife Corp,
ManthaMed, and iRhythm. In Germany, the trial
was carried out using the Clinical Research Platform
of the DZHK.
Role of the Funder/Sponsor: The device
manufacturers and funders had no role in study
design, data collection, analysis, interpretation, or
manuscript preparation. Zio patch reports and ECG
tracings provided by iRhythm underwent central
study adjudication and analysis by the trial
jamacardiology.com
Original Investigation Research
statistician and steering committee, independent of
industry.
Lancet. 2014;383(9921):955-962. doi:10.1016/
S0140-6736(13)62343-0
Group Information: The SCREEN-AF Steering
Committee members are David J. Gladstone, MD,
PhD, Jeff S. Healey, MD, MSc, F. Russell Quinn, MD,
PhD, Noah Ivers, Rolf Wachter, MD, Katharina
Schmalstieg-Bahr, MD, Eva Hummers, MD, PhD,
Andrea Thornton, BSc, and Angie Djuric; the clinical
events adjudication committee comprised
Demetrios J. Sahlas, MD, Ashkan Shoamanesh, MD,
and Mukul Sharma, MD; and the AF adjudication
committee included F. Russell Quinn, MD, PhD,
Alexander P. Benz, MD, William F. McIntyre, MD,
and Jorge A. Wong, MD, MPH.
12. Gladstone DJ, Spring M, Dorian P, et al;
EMBRACE Investigators and Coordinators. Atrial
fibrillation in patients with cryptogenic stroke.
N Engl J Med. 2014;370(26):2467-2477.
doi:10.1056/NEJMoa1311376
Data Sharing Statement: See Supplement 3.
14. Sanna T, Diener HC, Passman RS, et al; CRYSTAL
AF Investigators. Cryptogenic stroke and
underlying atrial fibrillation. N Engl J Med. 2014;370
(26):2478-2486. doi:10.1056/NEJMoa1313600
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d i a b e t e s r e s e a r c h a n d c l i n i c a l p r a c t i c e 1 5 4 ( 2 0 1 9 ) 1 3 0 –1 3 7
Contents available at ScienceDirect
Diabetes Research
and Clinical Practice
journa l home page: www.e lse vier.com/locate/diabres
Prevalence of pre-existing dysglycaemia among
inpatients with acute coronary syndrome and
associations with outcomes q
Dinesh C. Mahendran a, Garry Hamilton b, Jeremy Weiss a, Leonid Churilov c,
Jeremy Lew a, Kaylyn Khoo a, Que Lam d, Raymond Robbins e, Graeme K. Hart f,g,
Douglas Johnson h,i, David L. Hare b,i, Omar Farouque b,i, Jeffrey D. Zajac a,i,
Elif I. Ekinci a,i,*
a
Department of Endocrinology, Austin Health, Melbourne, Victoria, Australia
Department of Cardiology, Austin Health, Melbourne, Victoria, Australia
c
The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
d
Department of Pathology, Austin Health, Melbourne, Victoria, Australia
e
Department of Administrative Informatics, Austin Health, Melbourne, Victoria, Australia
f
Department of Intensive Care, Austin Health, Melbourne, Victoria
g
Health and Biomedical Informatics Centre, University of Melbourne, Melbourne, Victoria, Australia
h
Department of General Medicine, Austin Health, Melbourne, Victoria, Australia
i
Department of Medicine, Austin Health, The University of Melbourne, Melbourne, Victoria, Australia
b
A R T I C L E I N F O
A B S T R A C T
Article history:
Aims: We aimed to confirm the hypothesis that dysglycaemia including in the pre-diabetes
Received 21 November 2018
range affects a majority of patients admitted with acute coronary syndrome (ACS) and is
Received in revised form
associated with worse outcomes.
8 March 2019
Methods: In
Accepted 1 July 2019
aged 54 years with ACS were uniformly tested and categorised into diabetes (prior diag-
Available online 4 July 2019
nosis/ HbA1c 6.5%, 48 mmol/mol), pre-diabetes (HbA1c 5.7–6.4%, 39–47 mmol/mol)
this
prospective
observational
cohort
study,
consecutive
inpatients
and no diabetes (HbA1c 5.6%, 38 mmol/mol) groups.
Keywords:
Diabetes
Pre-diabetes
Cardiovascular
Myocardial infarction
Unstable angina
Heart failure
Results: Over two years, 847 consecutive inpatients presented with ACS. 313 (37%) inpatients had diabetes, 312 (37%) had pre-diabetes and 222 (25%) had no diabetes. Diabetes,
compared with no diabetes, was associated with higher odds of acute pulmonary oedema
(APO, odds ratio, OR 2.60, p < 0.01), longer length of stay (LOS, incidence rate ratio, IRR 1.18,
p = 0.02) and, 12-month ACS recurrence (OR 1.86, p = 0.046) after adjustment, while no significant associations were identified for pre-diabetes. Analysed as a continuous variable,
every 1% (11 mmol/mol) increase in HbA1c was associated with increased odds of APO
(OR 1.28, P = 0.002) and a longer LOS (IRR 1.05, P = 0.03).
Conclusions: The high prevalence of dysglycaemia and association with poorer clinical
outcomes justifies routine HbA1c testing to identify individuals who may benefit from
q This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
* Corresponding author at: Sir Edward Dunlop Medical Research Foundation Principal Research Fellow in Metabolic Medicine, The
University of Melbourne, Department of Medicine, Austin Health, Director of Diabetes Austin Health, Heidelberg, VIC 3081, Australia.
E-mail address: elif.ekinci@unimelb.edu.au (E.I. Ekinci).
https://doi.org/10.1016/j.diabres.2019.07.002
0168-8227/Ó 2019 Elsevier B.V. All rights reserved.
diabetes research and clinical practice
1 5 4 ( 2 0 1 9 ) 1 3 0 –1 3 7
131
cardioprotective anti-hyperglycaemic agents and, lifestyle modification to prevent progression of pre-diabetes.
Ó 2019 Elsevier B.V. All rights reserved.
1.
Introduction
Cardiovascular disease is the primary cause of excess mortality in individuals with diabetes mellitus [1]. Endothelial dysfunction, reduction in nitrous oxide mediated vasodilation,
increased inflammatory cytokines and coagulopathy together
contribute to unstable fibrous caps in the atherosclerotic
lesions of patients with diabetes, resulting in increased risk
of clot formation and subsequent cardiovascular events [2].
This increased cardiovascular risk begins prior to the
development of diabetes, affecting patients with prediabetes. An HbA1c of 5.7% (39 mmol/mol) is associated with
13.3% risk of cardiovascular disease over 10 years [3]. An
HbA1c of greater or equal to 5.7% (39 mmol/mol) has also
been associated with an increased risk of coronary heart disease in a prospective case-cohort study of individuals without
diabetes. In that study, there was an increase in relative risk
of coronary heart disease of 2.36 for every 1 percentage
increase in HbA1c [4]. In those without diabetes but with insulin resistance and a mean HbA1c of 5.8% (40 mmol/mol), the
Insulin Resistance Intervention after Stroke (IRIS) trial in
patients who had a stroke demonstrated that pioglitazone
reduced the risk of fatal or non-fatal recurrence of stroke or
myocardial infarction [5]. The American Diabetes Association
guidelines recommend the use of HbA1c as a screening tool
for pre-diabetes, defined by an HbA1c of 5.7% to 6.4% (39 to
46 mmol/mol), identifying individuals with a high risk of
developing diabetes and, who may be amenable to lifestyle
intervention [6]. Diagnosis of pre-diabetes in patients presenting with acute coronary syndrome through routine HbA1c
testing may identify a high-risk group of patients who may
benefit from lifestyle modification, weight loss and in the
future pharmacotherapy.
The EMPA-REG OUTCOME trial (EMPA-REG) [7], the Canagliflozin Cardiovascular Assessment Study (CANVAS) [8], the
Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) [9] and the Trial to
Evaluate Cardiovascular and Other Long-term Outcomes with
Semaglutide in Subjects with Type 2 Diabetes (SUSTAIN-6)
[10],
demonstrated
that
Sodium
Glucose
Linked
co-Transporter-2 (SGLT-2) inhibitors and Glucagon Like
Peptide-1 (GLP-1) receptor analogues were effective in reducing cardiovascular events when used in patients with a long
duration of diabetes mellitus and a high prevalence of established macrovascular disease. In the case of empagliflozin
and liraglutide, subsequent cardiovascular death rates were
reduced. Therefore, for individuals presenting with acute
coronary syndrome with a background of diabetes, we now
have cardioprotective anti-hyperglycaemic agents which can
be used to prevent further cardiovascular events. Routine
HbA1c testing of patients presenting with acute coronary syndrome therefore provides an opportunity to optimise therapy
in line with recent evidence without the need for prior fasting.
In patients presenting with acute coronary syndrome,
HbA1c testing is not affected by stress-induced hyperglycaemia which may result in a false positive result when fasting plasma glucose is used instead [11]. Hyperglycaemia in
acute coronary syndrome has been extensively investigated,
but previous literature has mainly focused on stressinduced hyperglycaemia being a potential poor prognostic
factor. In a large population of consecutive patients admitted
with ST-segment elevation myocardial infarction, those with
elevated admission glucose and HbA1c but without diabetes
had higher one year and total mortality [12]. In a study examining a small number of consecutive admissions with
myocardial infarction, admission plasma glucose was associated with 28-day mortality after adjustment for HbA1c [13]. A
meta-analysis of 15 studies examining the rates of in-hospital
mortality and congestive heart failure in patients admitted
with myocardial infarction found adverse outcomes in those
whose glucose concentrations were over 6.0 mmol/L compared to those whose glucose concentrations less than or
equal to 6.0 mmol/L [14]. Another study found a high prevalence of diabetes in patients with acute coronary syndrome
and HbA1c testing at admission [15]. Prior literature has failed
to explore pre-diabetes and, its association with clinical outcomes in acute coronary syndrome. The relationship between
HbA1c and acute pulmonary oedema, an important complication of heart failure after acute coronary syndrome has not
previously been studied.
We hypothesised that a high proportion of patients presenting with ACS would have dysglycaemia and that this
would be associated with poorer acute and chronic clinical
outcomes. In the current study, we therefore aimed to use
routine HbA1c measurements, to (i) estimate the prevalence
of diabetes and pre-diabetes and (ii) clinical outcomes over
12 months in successive patients presenting with acute coronary syndromes (ACS). Our pre-specified clinical outcomes
obtained were (i) in-hospital mortality, (ii) acute pulmonary
oedema (APO), (iii) total hospital length of stay (LOS), (iv) 28day readmission rate, (v) 12-month recurrent ACS and, (vi)
12-month all-cause mortality.
2.
Subjects
The study included consecutive hospital inpatients
aged 54 years with acute coronary syndrome where unstable angina, non-ST elevation myocardial infarction and ST
elevation myocardial infarction was the principal diagnosis.
3.
Materials and methods
As part of the Diabetes Discovery Initiative, in this prospective
observational cohort study, routine HbA1c testing was
performed using an automated order through the Cerner
132
diabetes research and clinical practice
Millennium IT Health Platform on all inpatients aged
54 years admitted to Austin Health, a tertiary hospital,
between July 2013 and July 2015 provided there was no HbA1c
result recorded within the preceding 90 days on the CERNER
hospital electronic medical record system. Testing was limited to this age group based on a previous study using HbA1c
in screening inpatients for diabetes. This previous study
found the highest proportion of new diagnosis of diabetes
in those over the age of 54 [16]. All HbA1c results were
reported via electronic medical records and were accessible
to the patients’ treating doctors [17,18].
Ethylenediaminetetraacetic acid (EDTA) whole blood was
obtained from patients for analysis. HbA1c was measured
by turbidimetric inhibition immunoassay (TINIA) on Cobas
Integra 800 (Roche Diagnostics, Mannheim, Germany). This
assay was standardised to the International Federation of
Clinical Chemistry and Laboratory Medicine (IFCC) reference
method. The between run coefficient of variation was 2.5%
for HbA1c 5.6% (30 mmol/mol) and 1.5% for HbA1c 9.7%
(83 mmol/mol).
Hospital admissions where unstable angina, non-ST elevation myocardial infarction and ST elevation myocardial
infarction was the principal diagnosis were included using
the following ICD-10 codes: I20.0, I21.0, I21.1, I21.2, I21.3,
I21.4, I21.9. All included subjects had the accuracy of their
diagnosis confirmed manually. Unstable angina was confirmed in patients presenting with angina at rest, new onset
of severe exertional angina or sudden intensification of previously stable angina and not meeting the definition of myocardial infarction [19]. The diagnosis of myocardial infarction
was confirmed using the third universal definition of myocardial infarction as determined by the Joint Task Force of the
European Society of Cardiology, American College of Cardiology Foundation, the American Heart Association, and the
World Heart Federation [20].
All admission episodes with an HbA1c result within
90 days prior to the admission date or up to 7 days after their
admission date were eligible for inclusion in this study. HbA1c
results, their interpretation and follow-up plans were automatically inserted into patient discharge summaries. As part
of the Diabetes Discovery Initiative, all patients with
HbA1c 8.3% (67 mmol/mol) were automatically reviewed
by the endocrinology team to educate, intensify and optimize
diabetes treatment, screen for complications and, devise
plans for further outpatient management as required [17,18].
Individual inpatient admission episodes were then divided
into three groups according to their dysglycaemic status using
medical records and their HbA1c result as: (i) ‘diabetes’ if a previous diagnosis of diabetes had been documented in the medical record, regardless of HbA1c result or if the HbA1c result
was 6.5% (48 mmol/mol) without a previous diagnosis of diabetes; (ii) ‘pre-diabetes’ if the HbA1c result was between 5.7%
and 6.4% (39 and 47 mmol/mol) without a previous diagnosis
of diabetes and (iii) ‘no diabetes’ if the HbA1c result was 5.6%
(38 mmol/mol) without a previous diagnosis of diabetes.
Pre-specified baseline demographic data, principal admission diagnosis, clinical characteristics and biochemical laboratory values were extracted from electronic medical
records and hospital databases. Estimated glomerular filtra-
1 5 4 ( 2 0 1 9 ) 1 3 0 –1 3 7
tion rate (eGFR) was calculated based on the CKD-EPI formulae using extracted data (age, gender and creatinine levels).
Pre-admission use of beta blockers, Angiotensin Converting Enzyme (ACE) inhibitors or angiotensin II receptor blockers, statins and antiplatelet agents were extracted manually
from medical records and verified by a second investigator.
Pre-existing risk factors such as hypertension, dyslipidaemia,
smoking status and previous myocardial infarction were
ascertained from medical records manually based on the
noted background medical history of the patient on admission and/or through their pre-existing pharmacotherapy.
Pre-specified outcomes ascertained in the current study
were (i) in-hospital mortality, (ii) acute pulmonary oedema
(APO), (iii) total hospital length of stay (LOS), (iv) 28-day readmission rate, (v) 12-month recurrent ACS and, (vi) 12-month
all-cause mortality. In-hospital mortality was defined as
death during the period of inpatient stay. APO was defined
as Killip classification 3 pulmonary oedema [21] within 24 h
of admission. 28-day readmission rate was defined as an
unplanned repeat admission to the same hospital within a
28-day period after the index admission. 12-month recurrent
ACS was defined as formally diagnosed unstable angina or
myocardial infarction within the 12-month period from index
admission date. 12-month all-cause mortality was defined as
death from any cause within 12-months from the index
admission date. All-cause mortality data was available if the
patient had died during any hospital admission or if the hospital had been notified of their death within the 36-month
study period. Vital status was confirmed through the patient’s
general practitioners. Patients where 12-month data could
not be assessed were excluded from the 12-month all-cause
mortality and 12-month recurrent ACS analyses.
Data were analysed using Stata Version 13IC (StataCorp,
College Station, TX, USA). Baseline characteristics were
reported as medians and interquartile ranges (continuous
characteristics) or counts and percentages (categorical characteristics) and compared across the three groups, using
Kruskal-Wallis or chi-squared/Fischer’s exact tests respectively. Multivariable analyses were conducted using negative
binomial regression for length of stay outcome (presented
as a count of days) and logistic regression for binary outcomes. Two analyses were performed: (1) with diabetes status
classified categorically as diabetes, pre-diabetes and, no diabetes; and (2) with HbA1c as a continuous marker. A-priori
chosen adjustment covariates included age, previous myocardial infarction, sex and smoking status. Standard analyses of
collinearity and model fit were performed. A two-sided
p-value < 0.05 was considered statistically significant.
This study was approved by Austin Health Research Ethics
Committee, who waived the need for informed consent for a
planned practice change agreed to by the hospital senior
medical staff as part of the Austin Health Diabetes Discovery
Initiative.
4.
Results
847 consecutive patients were admitted with a primary diagnosis of unstable angina or myocardial infarction. In 16 (1.9%)
patients, post-admission 12-month mortality and ACS
diabetes research and clinical practice
recurrence data were unavailable and these individuals were
excluded from 12-month outcome analyses.
4.1.
Baseline characteristics
The baseline characteristics of patients organised according
to diabetes status are shown in Table 1.
Median age was 73 (IQR 64–82). The study population
included 279 (33%) females and 568 (67%) males. Of these
183 (22%) individuals were diagnosed with ST- elevation
myocardial infarction (STEMI), 537 (63%) patients were diagnosed with Non- ST- elevation myocardial infarction
(NSTEMI) and 127 (15%) patients experienced unstable angina
(UA). The incidence of each type of ACS did not differ between
the no diabetes, pre-diabetes or diabetes groups (p = 0.243).
In the study population, 313 (37%, 95%CI 34%-40%) patients
had diabetes (pre-existing or HbA1c 6.5%, 48 mmol/mol),
312 (37%, 95%CI 34%-40%) had pre-diabetes (HbA1c
5.7%- 6.4%, 39 to 47 mmol/mol) and 222 (25%, 95%CI
23%-29%) had no diabetes (HbA1c < 5.6%,
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