Comprehensive management workflow of atrial fibrillation raises the compliance of patients: An observational cross-sectional study

Weizhuo Liu; Jian Li; Bangwei Wu; Nanqing Xiong; Peng Zhou; Liwen Bao; Kun Xie; Xiufang Gao; Yutao Wang; Haiming Shi; Xinping Luo

doi:10.4103/ijhr.ijhr_12_20

ORIGINAL ARTICLE

Year : 2020 | Volume : 5 | Issue : 2 | Page : 25-29

Comprehensive management workflow of atrial fibrillation raises the compliance of patients: An observational cross-sectional study

Weizhuo Liu, Jian Li, Bangwei Wu, Nanqing Xiong, Peng Zhou, Liwen Bao, Kun Xie, Xiufang Gao, Yutao Wang, Haiming Shi, Xinping Luo
Department of Cardiology, Huashan Hospital, Fudan University, Shanghai, China

Date of Submission	31-Oct-2020
Date of Decision	12-Nov-2020
Date of Acceptance	17-Nov-2020
Date of Web Publication	28-Jan-2021

Correspondence Address:
Dr. Jian Li
Huashan Hospital, Fudan University, 12 Wulumuqizhong Road, Shanghai 200040
China

Source of Support: None, Conflict of Interest: None

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DOI: 10.4103/ijhr.ijhr_12_20

Abstract

Background: Low compliance works as a significant limitation to the treatment effect of patients with atrial fibrillation (AF) during clinical practice. This study aimed to evaluate the influence of a recently established AF management platform on AF workflow, including diagnosis, treatment, and follow-up. Participants and Methods: Patients diagnosed with AF from January 2015 to February 2019 in Huashan Hospital, Fudan University, were enrolled and classified into two groups depending on their hospital visits before (Group 1) or after (Group 2) platform establishment in this observational cross-sectional study. Clinical characteristics, including clinical comorbidities, medications, and clinical events, were compared before and after the establishment of AF management platform. This study was approved by the Institutional Review Board of Huashan Hospital, Fudan University (approval No. KY2019-552), in May 2019. Results: In this study, 8343 hospital visits by patients were involved in Group 1 and 13,294 records in Group 2, from which several key points could be shown: (1) a downward trend in various comorbidities; (2) the follow-up frequency distinctly increased, especially among outpatients with low CHA₂DS₂-VASc scores; (3) a significant increase in anticoagulant use in patients with different stroke risks; and (4) the main indicators that affected anticoagulant use differed between the two groups. Conclusion: This comprehensive management platform can raise the compliance of AF patients, which shows superiority to conventional workflow in overall effect.

Keywords: Anticoagulation, atrial fibrillation, compliance of patients, management workflow, stroke risk level

How to cite this article:
Liu W, Li J, Wu B, Xiong N, Zhou P, Bao L, Xie K, Gao X, Wang Y, Shi H, Luo X. Comprehensive management workflow of atrial fibrillation raises the compliance of patients: An observational cross-sectional study. Int J Heart Rhythm 2020;5:25-9

How to cite this URL:
Liu W, Li J, Wu B, Xiong N, Zhou P, Bao L, Xie K, Gao X, Wang Y, Shi H, Luo X. Comprehensive management workflow of atrial fibrillation raises the compliance of patients: An observational cross-sectional study. Int J Heart Rhythm [serial online] 2020 [cited 2023 Jun 3];5:25-9. Available from: https://www.ijhronline.org/text.asp?2020/5/2/25/308170

Introduction

Comprehensive management is crucial for improving clinical outcome in patients with atrial fibrillation (AF),^[1] but its effect is contemporary limited by the low treatment compliance of the patients in China.^[2],[3],[4] In September 2017, we established an AF management platform based on a computer decision model, including AF electrocardiograph (ECG) screening, patient guidance, record of medication use, evaluation of thrombosis risk, and anticoagulation recommendation, to modify the existing workflow. This study aimed to evaluate the effect of the platform establishment on the patient outcome.

Participants and Methods

Populations

Patients diagnosed with AF in Huashan Hospital, Fudan University, were enrolled from January 2015 to February 2019, who were divided into two groups, according to their diagnosis that was made before (Group 1) or after (Group 2) the establishment of AF management platform, in this observational cross-sectional study. We excluded patients with insufficient clinical data, for example, ECG or written history regarding cardiovascular diseases. Those with missing data pertaining to major laboratory results or medication were also ruled out, and so were the patients who lacked contact details for personal identification and follow-up. The study included the hospital visits which did not meet the exclusion criteria and involved the specific diagnosis of AF by a qualified physician. This study was approved by the Institutional Review Board of Huashan Hospital, Fudan University, in May 2019 (Approval No. KY2019-552). This study was performed in line with the Declaration of Helsinki, and patients gave written informed consent before enrollment.

Follow-up and data collection

Baseline characteristics, clinical comorbidities, medications, and clinical events were recorded, and CHA₂DS_2-VASc scores (Congestive Heart Failure, Hypertension, Age 75 [Doubled], Diabetes Mellitus, Prior Stroke or Transient Ischemic Attack [Doubled], Vascular Disease, Age 65–74, Female) were automatically calculated based on the acquired history using the comprehensive AF management platform, where clinical information in hospital information system will be transferred, structured, and stored. Data were retrieved immediately when patients were diagnosed with AF, and thrombosis risk stratification and suggestions for further treatment were provided.

We also set up a self-management App for patients, which could send follow-up reminders and was connected to the platform to update information, including adverse events and laboratory results from an outside hospital, as well. At the same time, an online appointment with a physician was available. In addition, online education on different topics with regard to AF was provided for patients and noncardiologists on the platform.

Definitions

AF: Absolutely irregular RR intervals without discernible, distinct P waves, which is replaced by irregular fibrillatory waves^[1]
CHA₂DS₂-VASc score: C, congestive heart failure, 1 point; H, hypertension, 1 point; A₂, age ≥75 years, 2 points, D, diabetes, 1 point; S₂, history of stroke or transient ischemic attack or systemic thrombosis, 2 points; V, vascular diseases, 1 point; A, age 65–74 years, 1 point; Sc, sex category-female, 1 point. Maximum 9 points^[1]
Stroke: Neurologists diagnose ischemic/hemorrhagic stroke based on computed tomography or magnetic resonance imaging^[5]
Transient ischemic attack: Transient neurological dysfunction caused by brain/spinal cord/retina ischemia without acute infarction, duration <24 h.^[6]

Sample size calculation

According to epidemiological statistics, the prevalence of AF in the natural population is 0.4%–1%,^[7] and it can reach 4% in people over 40 years old;^[8] the annual incidence of AF patients with thrombosis is about 5%, who were 2–7 times than that of non-AF.^[9] Based on a Type I error of 0.05 and a reliability of 80%, we calculated an estimated sample size of 650 patients in both the groups. Considering the exclusion rate was about 20% for missing data, the sample size should be 780 patients. If the results of the provisional analysis of the two comparison groups were significantly different from the initial expectations, the sample size could be adjusted appropriately.

Statistical analysis

Statistical analysis was performed with SPSS 19.0 software (IBM, Armonk, NY, USA). Clinical variables were expressed as a percentage (%) for categorical variables, mean with standard deviation for normally distributed continuous variables, and median with interquartile rate for discontinuous variables. To compare categorical variables, Chi-square test or Fisher's exact test was used; to compare continuous variables, paired-samples t-test was used. P < 0.05 was accepted as statistically significant. The model of decision-making tree was used to analyze major indicators that were probably related to the use of anticoagulants.

Results

Demographics

There were 21,637 records of visits from 6923 AF patients before and after the establishment of AF management platform in total, which matched the inclusion criteria and did not meet the exclusion criteria [Figure 1].

Figure 1: Flowchart of the study procedure. AF=Atrial fibrillation, ECG=Electrocardiogram; N=The number of records of visits; n=The number of AF patients. Groups 1 and 2: Before or after the establishment of comprehensive AF management platform

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Group 1 included 8343 records from 3538 patients with a mean age of 74.3 ± 11.2 years, while Group 2 had 13,294 records from 3385 patients with a mean age of 73.9 ± 14.8 years (P > 0.05). 48.1% visiting records from Group 1 and 48.7% from Group 2 were female (P > 0.05).

An obvious increase was shown in the number and proportion of initiative outpatient clinic visits, which possessed 75.4% (6293/8343) and 86.1% (11,439/13,294) in all follow-up numbers in Groups 1 and 2, respectively (P < 0.001), while the hospitalization has distinctly decreased, taking up 12.5% versus 6.1%, and the percentage of emergency department visits declined from 12.1% in Group 1 to 7.8% in Group 2 (P < 0.001). In addition, several important clinical departments owned different ratios of AF patients, such as the department of cardiology (65.7% vs. 74.2%, P < 0.01) and neurology [11.9% vs. 10.9%, P = 0.016; [Table 1]].

Table 1: Demographics of atrial fibrillation patients before and after the application of the platform

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Follow-up visits with different comorbidities and CHA₂DS₂-VASc scores

Significant differences were shown in the proportion of comorbidities between both the groups, including hypertension (Group 1: 32.8%, Group 2: 21.7%, P < 0.001), coronary heart disease (Group 1: 13.4%, Group 2: 8.2%, P < 0.001), and diabetes mellitus [Group 1: 12.5%, Group 2: 11.3%, 1, P < 0.001; [Figure 2]a].

Figure 2: Proportions of different comorbidities and CHA₂DS₂-VASc scores in AF patients. (a) Proportions of comorbidities in AF patients compared in Group 1 and Group 2, showing a downward trend in various comorbidities. (b) Mean annual follow-up visits per person with different CHA₂DS₂-VASc scores (Congestive Heart Failure, Hypertension, Age ≥75 [Doubled], Diabetes Mellitus, Prior Stroke or Transient Ischemic Attack [Doubled], Vascular Disease, Age 65–74, Female) in two groups, indicating that the visit frequency has distinctly increased. AF=Atrial fibrillation, HTN=Hypertension, DM=Diabetes mellitus, CAD=Coronary hear t disease, TIA=Transient ischemic attack, HF=Hear t failure, CKD=Chronic kidney disease. Groups 1 and 2: Before or after the establishment of comprehensive AF management platform

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Moreover, great increases were shown in the annual follow-up visit per person with different CHA₂DS_2-VASc scores. Median CHA₂DS_2-VASc scores were 3 in Group 1 and 2 in Group 2, and the mean annual follow-up for each person increased significantly with a greater growth in patients with lower CHA₂DS_2-VASc scores [CHA₂DS_2-VASc = 1, Group 1: 1.8 times/person-year, Group 2: 3.7 times/person-year, P < 0.001; CHA₂DS_2-VASc = 2, Group 1: 2.1 times/person-year, Group 2: 3.6 times/person-year, P < 0.001; [Figure 2]b].

Medication: Proper anticoagulation and adjuvant agents

The total anticoagulation was given in 22.6% follow-ups in Group 1 and 67.0% follow-ups in Group 2, while there were approximately accordant decreases in the use of other adjuvant agents, including the lipid-lowering agents (20.4% vs. 12.9%, P < 0.001), antiarrhythmic agents (37.6% vs. 24.7%, P < 0.001), and angiotensin-converting enzyme inhibitor/angiotensin receptor blocker [22.1% vs. 12.6%, P < 0.001; [Figure 3]a].

Figure 3: Medication use in patients with AF. (a) Major medication before and after the platform establishment. (b) Anticoagulation rates with different CHA²DS²-VASc scores (Congestive Heart Failure, Hypertension, Age =75 [Doubled], Diabetes Mellitus, Prior Stroke or Transient Ischemic Attack [Doubled], Vascular Disease, Age 65–74, Female) between two groups. Anticoagulation rates were rapidly increasing in patients with different CHA2DS2-VASc scores, while the numbers of other adjunct medicines are decreasing. AAA=Antiarrhythmic agent, ACEI=Angiotensin-converting enzyme inhibitor, ARB=Angiotensin receptor blocker. Groups 1 and 2: Before or after the establishment of comprehensive AF management platform

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Significant raises of anticoagulant use were shown in patients with different stroke risk levels. 23.6% of patients from Group 1 and 47.8% of patients from Group 2 with CHA₂DS_2-VASc score = 1 received anticoagulation. For patients whose CHA₂DS_2-VASc score equaled 2, the rate was 24.6% versus 53.1%. 23.8% of Group 1 and 52.4% of Group 2 received anticoagulation with CHA₂DS_2-VASc score from 3 to 5. What's more, in general, stroke occurred in 11.9% and 6.5% of Groups 1 and 2, respectively [Figure 3]b.

Variables mostly related to the obedience of anticoagulation

With the help of decision-making tree model, we explored the main indicators mostly associated with the prescription of anticoagulation. For Group 1, the top three were “department of cardiology” (χ² = 192.874, df = 1, P < 0.001), “age” (χ² = 100.157, df = 2, P < 0.001), and “vascular disease” [χ² = 29.964, df = 1, P < 0.001; [Figure 4]a]. Although the label of “cardiology” also occupied the first place (χ² = 1652.527, df = 1, P < 0.001) in Group 2, “department of neurology” (χ² = 441.871, df = 1, P < 0.001) and “HTN” (χ² = 455.623, df = 1, P < 0.001) were the following two variables [Figure 4]b.

Figure 4: Main indicators that affect whether to use anticoagulants in Groups 1 (a) and 2 (b), using decision-making tree model, showing the index affecting anticoagulation have changed from some unmodifiable elements such as one's “age” and “vascular events” already happened to some adjustable factors such as “HTN.” HTN=Hypertension

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Discussion

AF damages life quality by causing stroke, heart failure, and other complications.^{[10],[11],[12]} Low compliance is an important limitation of treatment during clinical practice.^[3] Long-time comprehensive management is crucial for improving clinical outcome,^{[13],[14],[15]} which could highly improve the efficiency of traditional AF workflow.

Raised initiative awareness of follow-ups

Outpatient clinic and telephone calls are two traditional models for patient follow-up but are related to high prevalence of loss of follow-up.^[16],[17] After the platform was established, the follow-up frequency has distinctly increased with different CHA₂DS_2-VASc scores. The increase of outpatient follow-ups and the decrease in hospitalization and emergency department visit frequency also reflected the raise of patients' compliance, which probably implied that close follow-up decreased the risk of disease deterioration in AF as well.

Greater compliance improvement of patients with lower CHA₂DS₂-VASc scores

Moreover, AF patients with lower CHA₂DS_2-VASc scores had a greater increase of clinical visits, suggesting that these patients have an increased recognition of early diagnosis and convention, standard treatment, and monitoring system, compared to those with high scores. In addition, the fact that difference in various comorbidities was shown, but no significant difference was found in baseline characteristics, for example, age and sex, also demonstrated this changing situation in some way.

Enhanced recognition of anticoagulation in health-care workers

Early recognition of AF, especially those with high stroke risk, is the premise of anticoagulation.^[18] The platform could automatically retrieve the patients' information from the database in the hospital and calculate CHA₂DS_2-VASc scores which significantly encourage proper anticoagulation treatment. After intervention with the platform, anticoagulation rates were rapidly increasing in patients with different CHA₂DS_2-VASc scores. In contrast, the numbers of medications other than anticoagulants are decreasing, indicating the need for emphasizing upstreaming therapy mentioned in the latest guideline as well.^{[1],[19],[20]}

In addition, the decision-making tree model shows main factors affecting anticoagulation have changed from “age” to “hypertension” which can be intervened. This reflects the increased awareness of thrombosis prevention in earlier stage.

Limitations

This is an observational cross-sectional study with relatively short observation period. We cannot actually infer the relationship between compliance and the prognosis of patients with AF, as well as other risk factors.

Conclusion

The computer-assisted management provided multidimensional support in patients' health management. It has demonstrated superiority to conventional workflow in overall effect during follow-up, which can work as a new model for the management of AF.

Institutional review board statement

This study was approved by the Institutional Review Board of Huashan Hospital, Fudan University, in May 2019 (approval No. KY2019-552) and conducted in accordance with the Declaration of Helsinki.

Declaration of patient consent

The authors certify that they have obtained all appropriate consent from patients. In the forms, the patients have given their consent for their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity.

Financial support and sponsorship

This work was supported by the Natural Science Foundation of Shanghai (No. 17ZR1403700).

Conflicts of interest

There are no conflicts of interest.

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