Author + information
- Received August 25, 2016
- Revision received December 20, 2016
- Accepted December 22, 2016
- Published online March 29, 2017.
- Rajeev K. Pathak, MBBS, PhDa,
- Michelle Evans, MHlthEc&Pola,
- Melissa E. Middeldorpa,
- Rajiv Mahajan, MD, PhDa,
- Abhinav B. Mehta, M Act Stb,
- Megan Mereditha,
- Darragh Twomey, MBBSa,
- Christopher X. Wong, MBBS, MSc, PhDa,
- Jeroen M.L. Hendriks, PhDa,
- Walter P. Abhayaratna, MBBS, PhDc,
- Jonathan M. Kalman, MBBS, PhDd,
- Dennis H. Lau, MBBS, PhDa and
- Prashanthan Sanders, MBBS, PhDa,∗ ()
- aCentre for Heart Rhythm Disorders, South Australian Health and Medical Research Institute, University of Adelaide and Royal Adelaide Hospital, Adelaide, Australia
- bResearch School of Finance, Actuarial Studies and Applied Statistics, Australian National University, Canberra, Australia
- cCollege of Medicine, Biology and Environment, Australian National University and Canberra Hospital, Canberra, Australia
- dDepartment of Cardiology, Royal Melbourne Hospital and the Department of Medicine, University of Melbourne, Melbourne, Australia
- ↵∗Address for Correspondence:
Dr. Prashanthan Sanders, Centre for Heart Rhythm Disorders, Department of Cardiology, Royal Adelaide Hospital, L5 McEwin Building, North Terrace, Adelaide, South Australia 5000, Australia.
Background Atrial fibrillation (AF) imposes a substantial cost burden on the healthcare system. Weight and risk factor management (RFM) reduces AF burden and improves the outcomes of AF ablation.
Objectives This study sought to evaluate the cost and clinical effectiveness of integrating RFM into the overall management of AF.
Methods Of 1,415 consecutive patients with symptomatic AF, 825 patients had body mass index ≥27 kg/m2. After screening for exclusion criteria, the final cohort comprised 355 patients: 208 patients who opted for RFM and 147 control subjects and were followed by 3 to 6 monthly clinic review, 7-day Holter monitoring, and AF Symptom Score. A decision analytical model calculated the incremental cost-effectiveness ratios of cost per unit of global well-being gained and unit of AF burden reduced.
Results There were no differences in baseline characteristics or follow-up duration (p = NS). Arrhythmia-free survival was better in the RFM compared with control subjects (Kaplan-Meier: 79% vs. 44%; p < 0.001). At follow-up, RFM group had less unplanned specialist visits (0.19 ± 0.4 vs. 1.94 ± 2.0; p < 0.001), hospitalizations (0.74 ± 1.3 vs. 1.05 ± 1.6; p = 0.03), cardioversions (0.89 ± 1.5 vs. 1.51 ± 2.3; p = 0.002), emergency presentations (0.18 ± 0.5 vs. 0.76 ± 1.2; p < 0.001), and ablation procedures (0.60 ± 0.69 vs. 0.72 ± 0.86; p = 0.03). Antihypertensive (0.53 ± 0.7 vs. 0.78 ± 0.6; p = 0.04) and antiarrhythmic (0.26 ± 0.5 vs. 0.91 ± 0.6; p = 0.003) use declined in RFM. The RFM group had an increase of 0.1930 quality-adjusted life years and a cost saving of $12,094 (incremental cost-effectiveness ratios of $62,653 saved per quality-adjusted life years gained).
Conclusions A structured physician-directed RFM program is clinically effective and cost saving.
Recently reported epidemiological data confirm the emergence of atrial fibrillation (AF) as a global epidemic (1). This has significant and progressive impact on health care costs because of its association with increased cardiovascular morbidity, reduced quality of life, stroke, and mortality (2–4). The incremental cost of AF in the United States is estimated to range between $6.0 billion and $26.0 billion per year (5). Hospitalization, increased medication use, and procedural requirements constitute the major contributors to the total treatment cost of patients with AF (6–9). Ageing populations are an important contributor to the growing burden of AF. Recent data have also implicated the increasing prevalence of risk factors, such as obesity, hypertension, diabetes mellitus, and obstructive sleep apnea (10,11). Therefore, there is an urgent need for improved and cost-effective primary and secondary prevention strategies to reduce the impact of this enormous health burden.
Sinus rhythm is associated with better quality of life (12). Detrimental effects of antiarrhythmic agents offset the benefit from sinus rhythm maintenance (13,14). Catheter ablation of AF has evolved as an effective therapy for drug-refractory symptomatic AF (15). However, it is resource intensive and has significant upfront costs. Furthermore, reports of long-term outcomes demonstrate attrition in success with time (16–18). The cost-effectiveness of AF ablation is greatly influenced by the number of procedures, their success rate, and procedural complications (19,20). Studies have associated cardiac risk factors with the more frequent recurrence of AF, increased risk of complications, and direct medical costs (21–24). Aggressive management of these risk factors in a dedicated physician-led clinic has been shown to reduce the burden of AF and improve the long-term success of ablation (25–27).
In the LEGACY (Long-Term Effect of Goal Directed Weight Management in an Atrial Fibrillation Cohort: A Long-Term Follow-Up Study) study, progressive weight-loss had a dose-dependent effect on long-term freedom from AF (28). However, it is not clear if a dedicated risk factor management (RFM) clinic is cost-effective. In this study, we aim to evaluate the cost and clinical effectiveness of a dedicated RFM clinic in overall management of AF.
The impact of weight loss and its effects on AF outcomes from our registry were presented in the LEGACY Study (28). In the LEGACY study, all suitable patients (with body mass index ≥27 kg/m2 and ≥1 risk factor) were offered RFM in a dedicated physician-directed clinic at the time of initial assessment (Figure 1). Here we compare the clinical and cost-effectiveness of a dedicated RFM clinic for long-term results of patients diagnosed with AF. Patients were dichotomized based on whether they accepted this strategy and formed the intervention group (RFM group), whereas those who declined formed the control group. The study protocol was approved by the Human Research Ethics Committee of the Royal Adelaide Hospital and University of Adelaide, Adelaide, Australia.
Risk factor management
Patients in the RFM group attended a physician-directed RFM clinic (in addition to their arrhythmia follow-up) at least every 3 months and were encouraged to use support counselling and to schedule more frequent reviews as required. Risk factors were managed according to American College of Cardiology/American Heart Association guidelines. The details of our RFM have been previously presented (28). In brief, a structured motivational, goal-directed program using face-to-face counseling was used to achieve behavioral change for weight management and increasing physical activity. Weight, hypertension, glucose intolerance, dyslipidemia, sleep apnea, and alcohol and tobacco use were screened and managed individually according to American College of Cardiology/American Heart Association guidelines. The control group was given information on management of risk factors and encouraged to begin RFM under the direction of their treating physician.
Management of AF was undertaken in a separate arrhythmia clinic by physicians blinded to the patients’ study group. The use of rate and rhythm control strategies was at the discretion of the treating physician. In patients who remained symptomatic despite the use of antiarrhythmic agents, AF ablation was offered. The ablation technique used at our institution has been previously described (29). Arrhythmia occurrence was determined by clinical review, 12-lead electrocardiogram, and 7-day Holter monitoring at yearly intervals before ablation. AF was taken as any atrial arrhythmia ≥30 s. Following ablation, patients were similarly reviewed every 3 months for the first year and every 6 months thereafter. If patients developed recurrent symptomatic arrhythmia after the blanking period (3 months), repeat ablation was offered. All patients were anticoagulated for CHADS2 score >1. Amiodarone was not used routinely and if required only for 3- to 6-month periods. No patient continued on amiodarone after ablation.
Outcomes for clinical effectiveness
The primary outcome was AF symptom burden as determined by the AF Severity Scale (AFSS, University of Toronto), which quantifies 3 domains of AF-related symptoms: 1) frequency; 2) duration; and 3) severity. The AFSS has been clinically validated and used for assessment of AF burden (30). The AFSS questionnaire was administered at baseline and final follow-up. Freedom from AF was ascertained with 7-day Holter monitoring. Secondary outcomes included healthcare use (hospitalization, emergency department presentation, unscheduled specialist clinic presentation), medication use (antihypertensive medication, lipid therapy, antiarrhythmic use), sleep apnea device requirement, and procedural requirements (cardioversion, echocardiography, transesophageal echocardiography, ablation procedure).
Outcomes for cost effectiveness
Quality-adjusted life years
At each annual follow-up, freedom from AF was recorded and converted into a utility value in accordance with previously validated and published data (7,31,32). The utility value for “Not AF Free” was 0.725. Using an increase in utility value of 0.065 for the successful treatment for AF, we generated a utility value of 0.79 for the health state “AF Free.” A 3% discount rate was applied to the annual utility values. Details of the utility value calculation and the discount rate used for the study is provided in Online Appendix Sections 6.1 to 6.3.
Model structure and modeling framework
Short-term analysis was undertaken using a decision tree (Figure 2). Study data provided the probabilities of patients undergoing ablation procedures and the likelihood that, for each pathway, the patient would achieve “AF Freedom” at Year 4. Incremental cost-effectiveness ratios (ICER) were calculated for cost per quality-adjusted life years (QALY) gained (Figure 3). Sensitivity analyses were performed and plotted in a tornado diagram (Figure 4). Bootstrapping of 1,000 pair-wise comparisons improved estimates of the sampling distribution and provided data for the cost-effectiveness utility plane.
A Markov model estimated the long-term cost-effectiveness of RFM over 10 years, corresponding to the longest follow-up of ablation outcomes available (17). The health states used were “AF Free” and “Not AF Free.” There were no deaths or strokes in patients, and the health state “Dead” was not included. All patients entered the Markov model in the health state “Not AF Free.” Using the Markovian assumption, annual transition probabilities were calculated using an average of recorded results in the study for each cohort. In each annual cycle, patients could remain “Not AF Free” (with transition probabilities of RFM, 48%; control, 72%), or transition to “AF Free” (RFM, 52%; control, 28%). Once “AF Free” patients could remain in this state (RFM, 75%; control, 34%) or transition back to “Not AF Free” (RFM, 25%; control, 66%).
Using a bottom-up costing method, all costs were calculated in year 2010 Australian dollars. Costs were classified into 5 categories: 1) interventional procedures; 2) diagnostic procedures; 3) inpatient care; 4) outpatient visits; and 5) medication. Hospitalization costs were calculated using “Average Length of Stay” for AF with the standard price per Occupied Bed Day, provided by the South Australian Department of Health and Ageing. For each patient, the total number of drugs prescribed for arrhythmia, hypertension, and lipid disorder was recorded at baseline and at follow-up. The price of specific brands of medications, at standard dosages, was averaged to provide an average annual cost for each drug (Online Tables 1 and 2).
For the Markov model the annual health cost of each health state was developed using the costs recorded in the decision tree analysis. For each health state, the probability of a patient requiring a clinic service annually was estimated, based on study data and standard clinical knowledge. The annual probability of a clinical service was multiplied by the annual cost to create a total annual cost per patient, and then summed for each health state (Online Figure 2). The annual cost for a patient in the state “AF Free” was $1,135 and “Not AF Free” was $5,207. Details are provided in Online Appendix Section 6.4 (Online Tables 3–7).
Categorical variables are represented by frequencies and percentages. Continuous variables are summarized by mean ± SD. The differences in baseline characteristics between groups were assessed using analysis of variance procedures for continuous variables and chi-square test for categorical variables. A repeated measure analysis of variance was used to assess change over time. For categorical variables, change in status at follow-up was compared between groups using a chi-square test. Time-to-recurrence and event-free survival curves following the last ablation procedure were estimated by the Kaplan-Meier product-limit method. Differences between curves were tested with the log-rank test. Predictors of recurrent AF were assessed using proportional hazards Cox regression models. The proportional hazards assumption met for all the multivariable models that have been fit. The assumption was assessed using the Cox-Snell and Schoenfeld residuals. Exploratory analysis of the univariate predictors was carried out to understand the association between recurrent AF and the covariates. Multiples Cox regression models were fit and assessed using Wald statistic and Akaike information criterion (AIC) to arrive at the final multivariate regression model. Two-tailed p < 0.05 was considered statistically significant. Statistical analysis was performed with SPSS version 21.0 (SPSS, Inc., Chicago, Illinois).
Of the 1,415 consecutive patients with symptomatic AF, 825 patients had body mass index ≥27 kg/m2. After screening for exclusion criteria, the final cohort comprised 355 patients; 208 RFM and 147 control subjects (Figure 1). Mean follow-up in the RFM and control groups were 47.03 ± 17.9 months and 49.01 ± 17.6 months, respectively (p = 0.30). Baseline characteristics were similar in the 2 groups (Table 1).
Risk factor modification
Table 2 shows the impact of RFM on various cardiac risk factors. There was a greater decline in systolic blood pressure with RFM compared with control subjects (-10.1 ± 1.2 mm Hg vs. -3.3 ± 1 mm Hg; p < 0.001). Weight and body mass index decreased in both groups, but significantly more with RFM compared with control subjects (-10.1 ± 8.8 kg vs. -3.3 ± 8.4 kg; p < 0.0001; Table 2). At baseline, 49% of RFM and 44% of control subjects had dyslipidemia (p = 0.35). With diet and lifestyle modification, low-density lipoprotein cholesterol and non-high-density lipoprotein cholesterol were well controlled in 47% of RFM and 15% of control subjects (p = 0.02). At baseline, 30% of RFM and 27% of control subjects had a history of diabetes mellitus (p = 0.36). At final follow-up, patients with diabetes mellitus in the RFM group had significantly better glycemic control compared with control subjects (hemoglobin A1c <7% in 71% vs. 52%, respectively; p = 0.003). At baseline, 53% of RFM and 48% of control subjects had severe obstructive sleep apnea (apnea-hypopnea index ≥30; p = 0.35). At final follow up, 31% in the RFM group and 39% in the control group had severe obstructive sleep apnea (p = 0.003) suggesting significant improvement in the RFM group.
Effect of RFM clinic on AF symptom burden
At baseline, both groups had comparable and high AFSS subscale scores (Table 2). AF frequency, duration, symptom, and symptom severity were reduced at final follow-up in both groups with a significantly greater reduction seen in the RFM group (p < 0.001).
Freedom from AF without the use of rhythm control strategies
Figure 5A demonstrates the “ablation and drug free” AF freedom based on groups. At final follow-up, 35% of patients in the RFM group remained free from arrhythmia without antiarrhythmic drugs or ablation, compared with 18% of patients in the control group (p < 0.001). Univariate predictors of AF recurrence without antiarrhythmic drugs or ablation were as follows: control group (hazard ratio [HR]: 1.6; 95% confidence interval [CI]: 1.2 to 2.0; p < 0.001) and diastolic dysfunction (HR: 1.5; 95% CI: 1.2 to 1.7; p < 0.001). On multivariate analysis: control group (HR: 1.7; 95% CI: 1.3 to 1.8; p < 0.001) and diastolic dysfunction (HR: 1.4; 95% CI: 1.2 to 1.8; p < 0.001) were independently associated with increased risk of AF recurrence.
Total arrhythmia-free survival
Figure 5B demonstrates the arrhythmia-free survival after multiple procedures with a significant attrition in control subjects compared with RFM. At final follow-up, the arrhythmia-free survival rate following the last catheter ablation procedure was 79% with RFM compared with 44% for control subjects (p < 0.001). Univariate predictors of AF recurrence after multiple procedures were as follows: control group (HR: 3.3; 95% CI: 2.3 to 4.9; p < 0.001); diabetes (HR: 1.8; 95% CI: 1.2 to 2.6; p = 0.002), and tobacco (HR: 2.1; 95% CI: 1.12 to 4.25; p = 0.034). Control group (HR: 3.6; 95% CI: 2.4 to 5.2; p < 0.001) remained the most significant predictor of recurrent AF in multivariate analyses.
Effect of RFM clinic on health care use
Table 3 shows the details of the healthcare use between groups over the follow-up period.
Of the patients who required AF ablation, the mean number of AF ablation procedures performed in the RFM group was 0.60 ± 0.69 and in control subjects was 0.72 ± 0.86 (p = 0.09). Of the patients who were AF free at final follow up, 35% of RFM patients had no procedure versus 18% in the control group (p < 0.001), 41% of RFM patients required only a single procedure versus 29% of control subjects (p = 0.02), and 9% of RFM patients required multiple procedures versus 29% of control subjects (p = 0.005).
The average number of cardioversions per patient was 0.89 ± 1.5 in the RFM group and 1.51 ± 2.3 in the control group (p = 0.002). Forty-eight (23%) patients in the RFM group had >1 cardioversion versus 54 (37%) in the control group (p = 0.02).
Mean antihypertensive medication use in the RFM group was 0.53 ± 0.7 and in the control group 0.78 ± 0.6 (p = 0.04). At final follow-up, mean antiarrhythmic use in the RFM group was 0.26 ± 0.5 and in the control group was 0.91 ± 0.6 (p = 0.003). At final follow up, 11% of the RFM patients versus 35% in control group were on antilipid therapy at final follow-up (p = 0.008).
The average number of emergency department presentations caused by an episode of AF was 0.18 ± 0.5 in the RFM group and 0.76 ± 1.2 among the control subjects (p < 0.001). In the RFM group 30 (14%) had 1 presentation and 6 (3%) had 2 or more presentations, whereas 26 (18%) in the control group had 1 and 29 (20%) had 2 or more presentations.
The mean number of hospital admissions caused by AF was 0.74 ± 1.3 in the RFM group and 1.05 ± 1.6 (p = 0.03) in the control group.
Unscheduled specialists visit
The mean number of unscheduled visits (caused by arrhythmia and associated symptoms) to specialist clinics in the RFM group was 0.19 ± 0.4 and in the control group was 1.94 ± 2.0 (p < 0.001).
Quality of life
In the RFM group the QALY gain over 4 years was 2.74 ± 0.07 and in the control group this was 2.68 ± 0.05 (p = 0.001). The decrease in AF burden in the RFM group was 9.54 ± 5.49 and in control subjects this was 6.31 ± 4.79 (p < 0.001).
The estimates of health care costs are presented in the Online Table 3. The mean expected health care cost per patient over the 4-year study was lower in the RFM group ($17,421 ± $9,073), compared with the control group ($20,388 ± $7,870).
Based on the decision tree model, the RFM group had a QALY gain of 0.06 (p = 0.001) and a cost saving of $2,968 ± $438 per patient, in comparison with the control group. This equates to an ICER of $52,305 saved per QALY gained. Using bootstrap replication, RFM had an increase of 0.0568 QALYs and a cost saving of $3,012 ± $1,599 ($53,452 saved per QALY gained). RFM was a dominant intervention, with ICERs predominantly in the South-Eastern quadrant of the cost-effective plane (Figure 3). Projecting over a 10-year model, RFM would have an increase of 0.1930 QALYs and a cost saving of $12,094 (ICER of $62,653 saved per QALY gained).
Sensitivity analyses were undertaken on factors that could potentially affect the cost-effectiveness of RFM, including procedural costs, the number and cost of clinics attended, the number of hospital admissions for AF-related causes, and the probability of undergoing initial and repeat procedures (Figure 4). Cost-effectiveness was most susceptible to ablation procedure cost, with an increase in price resulting in higher savings per QALY gained; and the probability of a patient requiring AF ablation, with a higher probability resulting in lower savings.
This study demonstrates that in overweight and obese individuals with highly symptomatic AF, a structured physician-directed risk factor and weight management program was not only clinically effective, improving AF outcomes, quality of life measures, and health care use, but was also cost saving. These findings underscore the clinical importance and significant cost-effectiveness of treating the underlying causes of AF to achieve rhythm control and maintenance of sinus rhythm.
This study was an attempt to determine the cost-effectiveness of aggressive RFM as a concurrent treatment strategy in patients with AF. Our results did not compare the clinical and cost-effectiveness of RFM and AF ablation as treatment options exclusive of each other. Moreover, we did not focus on cost-effectiveness regarding rate versus rhythm control. However, this study does suggest the order in which treatment should be offered. In the study, successful RFM was associated with a 38% reduction in need for initial AF ablation procedure and a 20% reduction in need for redo ablation. In sensitivity analyses, costs were most sensitive to the need for initial and redo AF ablation procedures. RFM strategy is associated with higher upfront cost because of increased clinic visits. In the long term, reducing the number of patients requiring ablation procedures will reduce overall costs substantially. However, if ablation cannot be avoided, RFM should still be offered to reduce the need for redo procedures by improving the ablation outcomes. Thus, our results show that RFM should be offered as first-line therapy to patients with AF. This study also indicates that RFM in patients with AF is a cost-saving measure irrespective of the AF management strategy adopted.
Hospitalizations represent the major cost driver in AF care. Recent studies have found a 23% to 125% growth in AF hospitalization (8,9). The greater incidence of AF with increasing age is an obvious contributor. However, the age-specific rate of AF hospitalizations is also increasing, possibly because of a worsening risk factor profile (33,34) and the cost of AF hospitalizations increases proportionately with an increase in CHADS2 score (9). In this study, we found that RFM was associated with a 36% reduction in hospitalization. In addition, even if managed with rate control strategies the cost of AF care is still driven by medication use, emergency department presentation, and specialist appointments. We found RFM improved the overall health care use with a 20% reduction in medication use, a 58% reduction in emergency department presentation, and a 1.8-fold reduction in unscheduled specialist reviews.
Catheter ablation is an effective therapy for rhythm control in patients with drug-refractory or intolerant AF and is increasingly used (35). Although it is associated with upfront costs it is more effective in maintaining sinus rhythm with a downstream cost saving, because of avoided hospitalizations, reduced antiarrhythmic drugs use, and greater improvement in quality of life (36,37). However, clinical and cost-effectiveness studies of catheter ablation of AF are sensitive to the time horizon for the analysis (38). Short time horizons bias against ablation because of the high initial costs associated with the procedure. In contrast, there is gradual attrition of success after ablation with time, reducing the clinical effectiveness and also the cost-effectiveness (7). From a long-term perspective, the cost-effectiveness of catheter ablation of AF depends on single-procedure efficacy. Cardiac risk factors are associated with increased risk of AF recurrence post-ablation, increasing need for redo procedures (21,22,39–41). RFM improves the long-term freedom from AF and thus improves the cost and clinical effectiveness of the AF ablation. We used quality-of-life adjustment as it pertains to sinus rhythm maintenance, which permits the expression of results in dollars per QALY. RFM was associated with a gain in QALYs at a cost saving of $53,452.
These results were insensitive to changes in the cost estimates explored in the sensitivity analyses. The results of the sensitivity analysis in some way enable us to compare the cost-effectiveness of the RFM in different health systems. The cost-effectiveness of RFM was evaluated for the Australian health system, where average cost of AF ablation is calculated at $13,847. Sensitivity analysis was undertaken, with the cost of the procedure taken from $6,000 to $20,000. RFM became incrementally cost-effective with higher cost of AF ablation. In the United States, there is a wide site level variation in cost of AF ablation. Although there is paucity of published data in this regards, it is estimated that it ranges from $16,200 to ≥$77,300 (42,43). Resultantly, RFM may be even more cost-effective in the U.S. health care system.
In this study, aggressive RFM was more clinically effective and less costly than usual care. However, the total costs of AF care based on the previously mentioned factors are probably an underestimation of the true costs of AF. The indirect societal costs related to lost productivity and the time costs associated with primary care and specialist visits are considerable. Although not assessed in the current study, if the reduced AF burden seen in this study over the long term is associated with reduced risk of dementia, stroke, and institutionalization of elderly, the cost-effectiveness of RFM may be even more favorable.
This study has the usual potential for selection bias inherent in observational studies. However, measurement bias has been reduced through standardized processes in our clinic and the evaluation by operators blinded to the patient’s weight management strategy. AF burden assessment using 7-day Holter may not detect some AF episodes, especially in patients with low AF burden. However, this was used in both groups and thus unlikely to introduce detection bias. Ascertainment bias was reduced through the collection of outcome via routine data sources. In addition, our population was relatively young, therefore these findings may not be applicable for the elderly population. As is invariably the case with cost-effectiveness modeling studies, simplifying assumptions and a degree of parameter uncertainty exists. We also acknowledge that the studies used for utility value calculation are not a complete match for our study cohort and intervention. However, we think the utility value for the “AF not free state” and “AF-free state” can still be used because both these health states remain relevant irrespective of the type of intervention used to achieve them. Moreover, with the type of intervention (RFM) leading to AF-free state in this study, the gain in utility value may even be a conservative estimate when compared with AF-free state achieved with drug therapy or ablation. A further limitation of this cost analysis is that calculations are based on economic conditions in a single country. Given the absence of such events in our cohort, no assessment was made on the risk of stroke or death and their associated costs.
A structured physician-directed risk factor and weight management program is effective and results in the long-term freedom from AF. This approach is clinically effective and cost saving. With the growing epidemic of AF and the overall health care cost burden, this strategy should be increasingly used.
COMPETENCY IN MEDICAL KNOWLEDGE: Risk factor and weight management are essential components of the management of the patient with AF. This approach is clinically effective and cost saving. A dedicated physician-led clinic that is focused on weight and risk factor management is important to achieve long-term success.
COMPETENCY IN PATIENT CARE: Risk factor and weight management are crucial to a strategy of rhythm control in patients with AF. A dedicated clinic improves patient engagement by promoting treatment adherence and improves long-term outcomes.
TRANSLATIONAL OUTLOOK: Risk factor and weight management are essential components of the management of the patient with AF. Considering the dual epidemic of obesity and AF, primary and secondary prevention strategies should be increasingly used. A dedicated weight and RFM clinic may play a pivotal role in this regard.
For supplemental methods, tables, and figures, please see the online version of this article.
This study was supported by funds from the Centre for Heart Rhythm Disorders at the University of Adelaide, Adelaide, Australia. The sponsor of the study is the University of Adelaide. Several of the authors are employees or students of the University of Adelaide. The sponsor has had no direct involvement in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. Dr. Pathak is supported by a Postgraduate Scholarship from the Lion’s Medical Research Foundation, a Leo J. Mahar Electrophysiology Scholarship from the University of Adelaide, and an Australian Postgraduate Award from the University of Adelaide. Ms. Middeldorp is supported by the Robert J. Craig Scholarship from the University of Adelaide. Dr. Mahajan is supported by the Leo J. Mahar Lectureship from the University of Adelaide. Dr. Twomey is supported by the Leo J. Mahar Electrophysiology Scholarship from the University of Adelaide. Dr. Wong is supported by a Rhodes scholarship and a Postgraduate Medical Scholarship from the National Health and Medical Research Council of Australia. Dr. Hendriks is supported by the Derek Frewin Lectureship from the University of Adelaide. Dr. Abhayaratna is supported by the National Heart Foundation of Australia. Dr. Kalman is supported by Practitioner Fellowships from the National Health and Medical Research Council of Australia; served on the advisory board of Biosense Webster and Boston Scientific; and received research funding from St Jude Medical, Biosense-Webster, Medtronic, and Boston Scientific. Dr. Lau is supported by a Postdoctoral Fellowship from the National Health and Medical Research Council of Australia and by a Robert J. Craig Lectureship from the University of Adelaide. Dr. Sanders is supported by the National Heart Foundation of Australia and by Practitioner Fellowships from the National Health and Medical Research Council of Australia; has served on the advisory board of Biosense-Webster, Medtronic, CathRx, and St Jude Medical; received lecture and/or consulting fees from Biosense-Webster, Medtronic, St Jude Medical, and Boston Scientific; and received research funding from Medtronic, St Jude Medical, Boston Scientific, Biotronik, and Sorin.
- Abbreviations and Acronyms
- atrial fibrillation
- Atrial Fibrillation Severity Scale
- confidence interval
- hazard ratio
- incremental cost-effectiveness ratios
- quality-adjusted life years
- risk factor management
- Received August 25, 2016.
- Revision received December 20, 2016.
- Accepted December 22, 2016.
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