Abstract
Background
Multimorbidity is common in patients with atrial fibrillation (AF); however, the impact of the number and type of comorbid conditions on outcomes remains uncertain.
Methods
This cohort study included patients with new‐onset AF from a Midwest region between 2013 and 2017. Eighteen chronic conditions at the time of AF were classified into groups: cardiometabolic, other somatic, and mental health. Cox regression determined associations between the number of each condition type with death, ischemic stroke/transient ischemic attack, and congestive heart failure, stratified by age.
Results
Among 16 509 patients with AF (mean age, 74 years; 43% women), the mean number of cardiometabolic, other somatic, and mental health conditions was 2.7, 1.4, and 0.5, respectively. The number and type of conditions had a varying impact on outcomes and differed by age. A higher number of cardiometabolic conditions were associated with increased risk of death within 90 days only in people aged ≥85 years (≥4 conditions versus 0: hazard ratio [HR], 1.74 [95% CI, 1.05–2.87]), whereas for death after 90 days, associations were strongest in the youngest age group (<65 years: HR, 1.83 [95% CI, 1.35–2.46]; 65–74 years: HR, 1.34 [95% CI, 1.04–1.73]; 75–84 years: HR, 1.32 [95% CI, 1.09–1.60]; ≥85 years: HR, 1.14 [95% CI, 0.96–1.35]). Associations with outcomes were generally strongest in the youngest age group and attenuated with older age for higher number of other somatic conditions, whereas the pattern was less consistent for mental health conditions.
Conclusions
Along with cardiometabolic‐related conditions, other somatic and mental health conditions are important predictors of outcomes in AF, with effects differing by age, and should be considered when caring for these patients.
Keywords: atrial fibrillation, chronic conditions, multimorbidity, outcomes
Subject Categories: Epidemiology
Nonstandard Abbreviation and Acronym
- GLORIA‐AF
Global Registry on Long‐Term Antithrombotic Treatment in Patients With Atrial Fibrillation
Clinical Perspective.
What Is New?
In >16 000 patients with atrial fibrillation from the Midwest region of the United States, we investigated the impact of the number and type of comorbid conditions on outcomes, including ischemic stroke or transient ischemic attack, congestive heart failure, and death.
The impact of the number and type of comorbid chronic conditions (cardiometabolic, other somatic, and mental health) differed by age group and across outcomes; having ≥4 cardiometabolic conditions was associated with an increased risk of death within 90 days only in those aged ≥85 years, whereas for other outcomes the associations were generally strongest in the youngest age group and attenuated with older age.
What Are the Clinical Implications?
Our findings underscore that along with cardiometabolic‐related conditions, other somatic and mental health conditions are also important predictors of adverse outcomes, with associations varying across outcomes and age, and should be considered when caring for patients with atrial fibrillation.
More than 1 in 4 adults in the United States has multimorbidity. 1 The prevalence of multimorbidity increases with age, with nearly 3 of 4 adults aged >65 years having multiple chronic conditions. 2 , 3 , 4 With cardiovascular disease disproportionately affecting older people, multimorbidity is common among these patients, including those with atrial fibrillation (AF). 5 , 6 , 7 Multimorbidity increases the risk of adverse outcomes such as declining functional status, hospitalizations, and death, as well as other consequences such as challenges related to disease management. 6 , 7 , 8 , 9 , 10 , 11
We recently reported that patients with AF have a higher prevalence of most comorbidities compared with those without AF. 12 Approximately 90% of patients with AF have ≥2 chronic conditions, while 67% of patients without AF have ≥2 chronic conditions. 13 The median number of conditions was 5 among patients with AF and 3 among those without AF. Furthermore, we found that some cardiometabolic conditions, especially hypertension, hyperlipidemia, and diabetes, clustered together in both patients with and without AF. However, patients with AF more commonly had clustering of the cardiometabolic conditions of coronary artery disease, congestive heart failure (CHF), and stroke, as well as clustering of other somatic conditions. 13 These findings raise the question of which set of comorbid conditions are most likely to adversely impact outcomes in AF. Indeed, patients with AF have been shown to be at an increased risk of adverse outcomes, including death, CHF, and stroke. 14 , 15 , 16 , 17 , 18 , 19 Therefore, we undertook this study to investigate, in a large, geographically defined community, the impact of the number and types of comorbid chronic conditions (cardiometabolic, other somatic, and mental health conditions) on outcomes including death, ischemic stroke/transient ischemic attack (TIA), and CHF, in patients with new‐onset AF.
Methods
Study Setting
The expanded Rochester Epidemiology Project was used to identify patients residing in a 27‐county region in southern Minnesota and western Wisconsin, with new‐onset AF from January 1, 2013, through December 31, 2017. 20 The expanded Rochester Epidemiology Project is a medical records–linkage system for the residents of 19 counties in southern Minnesota and 8 counties in western Wisconsin. The data captured are available starting in 2010 and include comprehensive medical information such as demographics, procedure codes, diagnosis, laboratory measurements, medications, health care use, and vital status. Because of the sensitive nature of the data collected for this study, requests to access the data set from qualified researchers trained in human subject confidentiality protocols may be sent to info@rochesterproject.org.
AF Identification
The methods of this study have been previously described in detail. 13 In brief, between January 1, 2010, and December 31, 2017, all adults (aged ≥18 years) with an inpatient or outpatient encounter with an International Classification of Diseases, Ninth Revision (ICD‐9) and Tenth Revision (ICD‐10) code for AF or atrial flutter (codes 427.3 and I48) were identified. ECG and Holter monitor reports within 30 days before or 365 days after AF diagnosis were identified via an electronic database for Mayo Clinic and Mayo Clinic Health System. AF status was considered negative (no AF) if the reports indicated possible, probable, likely, or cannot rule out AF/atrial flutter, or if no ECG or Holter monitor reports were available. Trained nurse abstractors reviewed ECGs or Holter reports with missing or inconclusive interpretations to determine AF status. Current Procedural Terminology codes for ECGs (codes 93000 and 93010) and Holter monitors (codes 93224–93227, 93230–93233, and 93236) were used to identify these procedures from Olmsted Medical Center, and all ECGs and Holter monitors were reviewed by trained nurse abstractors to determine the rhythm using the same rules (excluding possible, probable, likely, and cannot‐rule‐out diagnoses).
To confirm the diagnosis of AF, a diagnostic code plus evidence of AF or atrial flutter on either an ECG or Holter monitor was required. The diagnostic code date was assigned as the incident AF date. If AF was not confirmed at first diagnosis date, we used subsequent diagnoses and used the same rules on timing and evidence of AF on an ECG or Holter monitor. Patients were excluded if their AF incidence date occurred before January 1, 2013, or they were nonresidents of our region at the time of AF diagnosis.
Comorbid Conditions
Comorbid chronic conditions were defined according to the US Department of Health and Human Services. 21 , 22 These conditions were ascertained electronically by retrieving inpatient and outpatient ICD‐9 and ICD‐10 codes. Any occurrence of a code occurring within 3 years before the incident AF date or during the same hospital stay (if hospitalized at index) was required for diagnosis. Since all patients had AF, arrhythmia was not included. Very few individuals in our cohort had autism or HIV, and thus these were also excluded. However, we added anxiety as a condition, resulting in 18 chronic conditions. The conditions were further classified into 3 comorbidity groups: cardiometabolic, other somatic, and mental health. The cardiometabolic conditions included hypertension, hyperlipidemia, diabetes, coronary artery disease, CHF, and stroke. Patients with a diagnosis of CHF before AF or within 90 days after AF diagnosis were excluded from the CHF outcome analysis; patients with a stroke diagnosis were retained and followed for subsequent diagnoses for the ischemic stroke/TIA outcome analysis. The other somatic conditions included arthritis, cancer, chronic kidney disease, chronic obstructive pulmonary disease, asthma, osteoporosis, and hepatitis, whereas the mental health conditions included depression, anxiety, substance abuse, schizophrenia, and dementia/delirium.
Outcomes
Patients were followed through September 30, 2023, for the outcomes of ischemic stroke/TIA, CHF, or death. The ischemic stroke/TIA and CHF outcomes were ascertained by code lists defined by the Department of Health and Human Services. 21 , 22 Any inpatient or outpatient diagnosis was used to determine CHF. The following modifications were used for the ascertainment of ischemic stroke/TIA. Because the Department of Health and Human Services definition of stroke included hemorrhagic strokes, we restricted to the codes for ischemic stroke or TIA (ICD‐9, 433–436; ICD‐10, G45 and I63) and also restricted to only primary or secondary inpatient diagnoses. A single diagnostic code in the code set for the given outcome was used to ascertain the presence of the outcome. Deaths were identified by the Rochester Epidemiology Project, which compiles death information from medical records, death certificates received from the state of Minnesota, and data from the National Death Index Plus.
Statistical Analysis
Baseline characteristics are presented as frequency (percentage), mean±SD or median (interquartile range). The proportion of patients with chronic conditions stratified by sex, age group, and condition type was determined. For time‐to‐event analyses, survival curves were estimated using the Kaplan–Meier method. Cox proportional hazards regression was used to determine associations between each individual condition and each outcome (death within 90 days and landmark analyses for death after 90 days, ischemic stroke/TIA among 90‐day survivors, and CHF among 90‐day survivors), adjusted for age, sex, race and ethnicity, and all other conditions. Due to the high rate of death within 90 days (≈10%), we presented the results for death both within 90 days and after 90 days and presented the other outcomes only among the 90‐day survivors. Patients with missing race and ethnicity data (n=46) were excluded, and patients with ≤90 days of follow‐up (n=1793) were excluded from analyses among 90‐day survivors. For CHF, subjects with diagnoses before AF or within 90 days after AF diagnosis were also excluded (n=5959). Cox proportional hazards regression was also used to determine associations between the number of conditions within each comorbidity group and each outcome, adjusted for age, sex, race and ethnicity, and the number of conditions in each of the other 2 comorbid condition groups. Comorbidity groups were modeled as categorical variables. For the cardiometabolic and somatic condition groups, which contain a total of 6 conditions, the categories were: 0, 1, 2, 3 and ≥4, and for the mental health group, which contains a total of 5 conditions, the categories were: 0, 1, 2, and ≥3. For all models, the reference level used was 0 conditions. The proportional hazards assumption for all Cox regression models were assessed using visual inspection of scaled Schoenfeld residual plots and interaction tests with time. In addition, effect modification by age was assessed by including interaction tests with age and number of conditions for each of the models. Because age was found to have a nonproportional effect on the outcome of death and significant effect modification by age (P for interaction<0.05) was observed for most of the models, all results were stratified by age groups (age <65, 65–74, 75–84, and ≥85 years), and robust standard errors were estimated. All analyses were performed using SAS statistical software version 9.4 (SAS Institute Inc., Cary, NC) or R 4.4.1 or higher (R Foundation for Statistical Computing, Vienna, Austria). This study was approved by the Mayo Clinic and Olmsted Medical Center institutional review boards. The study was considered minimal risk by both institutional review boards; therefore, the requirement for informed consent was waived. However, patients who did not provide authorization to use their medical records for research were excluded.
Results
Between 2013 and 2017, 16 509 patients with new‐onset AF were identified (mean age, 74±13; 43% women; Table 1). The most common comorbid condition was hypertension (79%), followed by hyperlipidemia (68%), diabetes (45%), and arthritis (45%). The overall mean number of conditions per individual was 5.1±2.8; the mean number of conditions per comorbidity group was 2.7±1.5 for cardiometabolic conditions, 1.4±1.2 for other somatic conditions, and 0.5±0.9 for mental health conditions. The number of conditions by type was similar for men and women, and the number of conditions increased with age, with the exception of mental health conditions (Table 1; Figure 1).
Table 1.
Characteristics of Patients With Atrial Fibrillation, Overall and Stratified by Age Group
| Total (N=16 509) | Age <65 y (n=3557) | Age 65–74 y (n=3987) | Age 75–84 y (n=4931) | Age ≥85 y (n=4034) | |
|---|---|---|---|---|---|
| Demographics | |||||
| Age, y, mean±SD | 74.4±13.2 | 55.0±8.8 | 69.8±2.9 | 79.6±2.9 | 89.5±3.7 |
| Male sex | 9335 (56.5) | 2543 (71.5) | 2550 (64.0) | 2575 (52.2) | 1667 (41.3) |
| Race and ethnicity | |||||
| Non‐Hispanic White | 15 944 (96.9) | 3325 (93.9) | 3845 (96.8) | 4809 (97.7) | 3965 (98.5) |
| Hispanic, any race | 212 (1.3) | 90 (2.5) | 54 (1.4) | 40 (0.8) | 28 (0.7) |
| Other race* | 307 (1.9) | 126 (3.6) | 75 (1.9) | 73 (1.5) | 33 (0.8) |
| Unknown | 46 | 16 | 13 | 9 | 8 |
| Cardiometabolic conditions | |||||
| Hypertension | 12 992 (78.7) | 2125 (59.7) | 3148 (79.0) | 4191 (85.0) | 3528 (87.5) |
| Hyperlipidemia | 11 164 (67.6) | 1862 (52.4) | 2880 (72.2) | 3734 (75.7) | 2688 (66.6) |
| Diabetes | 7399 (44.8) | 1351 (38.0) | 2071 (51.9) | 2440 (49.5) | 1537 (38.1) |
| Coronary artery disease | 6916 (41.9) | 954 (26.8) | 1648 (41.3) | 2394 (48.6) | 1920 (47.6) |
| Congestive heart failure | 5751 (34.8) | 825 (23.2) | 1261 (31.6) | 1785 (36.2) | 1880 (46.6) |
| Stroke | 3066 (18.6) | 309 (8.7) | 613 (15.4) | 1122 (22.8) | 1022 (25.3) |
| Number of cardiometabolic conditions | |||||
| Mean±SD | 2.7±1.5 | 2.1±1.6 | 2.9±1.5 | 3.2±1.5 | 3.1±1.5 |
| Median (IQR) | 3 (2–4) | 2 (1–3) | 3 (2–4) | 3 (2–4) | 3 (2–4) |
| Other somatic conditions | |||||
| Arthritis | 7483 (45.3) | 1062 (29.9) | 1745 (43.8) | 2499 (50.7) | 2177 (54.0) |
| Cancer | 5316 (32.2) | 532 (15.0) | 1202 (30.2) | 1922 (39.0) | 1660 (41.2) |
| Chronic kidney disease | 6047 (36.6) | 870 (24.5) | 1358 (34.1) | 1920 (39.0) | 1899 (47.1) |
| Chronic obstructive pulmonary disease | 4116 (24.9) | 695 (19.5) | 1047 (26.3) | 1379 (28.0) | 995 (24.7) |
| Asthma | 1499 (9.1) | 389 (10.9) | 393 (9.9) | 435 (8.8) | 282 (7.0) |
| Osteoporosis | 1980 (12.0) | 130 (3.7) | 311 (7.8) | 702 (14.2) | 837 (20.8) |
| Hepatitis | 233 (1.4) | 113 (3.2) | 63 (1.6) | 42 (0.9) | 15 (0.4) |
| Number of other somatic conditions | |||||
| Mean±SD | 1.4±1.2 | 1.0±1.1 | 1.5±1.2 | 1.8±1.2 | 1.9±1.3 |
| Median (IQR) | 1 (0–2) | 1 (0–2) | 1 (1–2) | 2 (1–3) | 2 (1–3) |
| Mental health conditions, n (%) | |||||
| Depression | 3389 (20.5) | 881 (24.8) | 813 (20.4) | 893 (18.1) | 802 (19.9) |
| Anxiety | 2872 (17.4) | 748 (21.0) | 673 (16.9) | 784 (15.9) | 667 (16.5) |
| Substance abuse | 1802 (10.9) | 773 (21.7) | 536 (13.4) | 438 (7.1) | 145 (3.6) |
| Schizophrenia | 776 (4.7) | 110 (3.1) | 128 (3.2) | 225 (4.6) | 313 (7.8) |
| Dementia/Delirium | 1814 (11.0) | 104 (2.9) | 210 (5.3) | 573 (11.6) | 927 (23.0) |
| Number of mental health conditions | |||||
| Mean±SD | 0.5±0.9 | 0.7±1.0 | 0.6±0.9 | 0.6±0.9 | 0.7±1.0 |
| Median (IQR) | 0 (0–1) | 0 (0–1) | 0 (0–1) | 0 (0–1) | 0 (0–1) |
| Total number of chronic conditions | |||||
| Mean±SD | 5.1±2.8 | 3.9±2.8 | 5.0±2.7 | 5.5±2.6 | 5.8±2.6 |
| Median (IQR) | 5 (3–7) | 3 (2–6) | 5 (3–7) | 5 (4–7) | 6 (4–8) |
Values are presented as number (percentage) unless otherwise specified. IQR indicates interquartile range.
Other race includes the following self‐reported categories: Black, Asian, Hawaiian/Pacific Islander, American Indian/Alaska Native, other, and mixed.
Figure 1. Proportion of patients with chronic conditions, stratified by age, sex, and type of condition.
Within 90 days, 1621 deaths occurred. Overall, mortality increased with increasing number of conditions for all 3 chronic condition types (Figure S1). Individual cardiometabolic predictors of death within 90 days included CHF for those aged ≥65 years and diabetes for those aged <65 years (Figure 2). For other somatic conditions, chronic kidney disease was associated with an increased risk of death within 90 days across all age groups, while cancer was also a predictor among those aged <85 years. Mental health predictors of death within 90 days included dementia/delirium among patients aged ≥65 years and substance abuse among those aged <75 years. After adjustment for age, sex, race and ethnicity, and the number of conditions in each of the other 2 comorbid condition groups, the risk of death (within 90 days) was not associated with the number of cardiometabolic conditions across any age group, with the exception of patients aged ≥85 years with ≥4 conditions (adjusted hazard ratio [aHR], 1.74 [95% CI, 1.05–2.87]; Table 2). There was an association between the number of other somatic conditions and death within 90 days across all age groups (Table 2). The association increased with increasing number of conditions and was strongest in the younger age groups (≥4 other somatic conditions versus none, age <65 years: aHR, 13.50 [95% CI, 6.31–28.87]; age 65–74 years: aHR, 7.86 [95% CI, 4.33–14.26]; age 75–84 years: aHR, 4.54 [95% CI, 2.82–7.30]; age ≥85 years: aHR, 1.58 [95% CI, 1.14–2.20]). There was also an association between the number of mental health conditions and risk of death within 90 days that was similar across age groups (≥3 mental health conditions versus none, age <65 years: aHR, 1.99 [95% CI, 1.26–3.15]; age 65–74 years: aHR, 2.17 [95% CI, 1.47–3.21]; age 75–84 years: aHR, 2.62 [95% CI, 1.87–3.67]; age ≥85 years: aHR, 2.44 [95% CI, 1.88–3.18]; Table 2).
Figure 2. Adjusted* hazard ratio (95% CI) for (A) death within 90 days, (B) death after 90 days, (C) ischemic stroke/transient ischemic attack, and (D) congestive heart failure, according to individual conditions, stratified by age group.
COPD indicates chronic obstructive pulmonary disease. *Adjusted for all other conditions, age, sex, and race and ethnicity.
Table 2.
Hazard Ratios* (95% CIs) for Outcomes by Number of Conditions in Each Comorbidity Group; Stratified by Age Group
| Age <65 | Age 65–74 | Age 75–84 | Age ≥85 | |
|---|---|---|---|---|
| Death within 90 d | ||||
| No. at risk | 3541 | 3974 | 4922 | 4026 |
| Cardiometabolic conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 0.99 (0.53–1.85) | 0.51 (0.28–0.94) | 0.81 (0.44–1.51) | 1.19 (0.69–2.05) |
| 2 | 1.03 (0.57–1.86) | 0.65 (0.38–1.12) | 0.75 (0.43–1.32) | 1.15 (0.68–1.93) |
| 3 | 0.93 (0.50–1.73) | 0.40 (0.23–0.70) | 0.65 (0.37–1.14) | 1.13 (0.68–1.89) |
| ≥4 | 1.27 (0.71–2.29) | 0.64 (0.39–1.07) | 0.91 (0.53–1.56) | 1.74 (1.05–2.87) |
| Other somatic conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 3.53 (1.93–6.48) | 1.85 (1.05–3.24) | 1.61 (1.01–2.54) | 1.08 (0.80–1.46) |
| 2 | 8.43 (4.59–15.48) | 4.29 (2.48–7.41) | 2.58 (1.66–4.02) | 1.29 (0.97–1.73) |
| 3 | 13.47 (7.04–25.74) | 6.97 (3.97–12.24) | 3.85 (2.45–6.05) | 1.37 (1.01–1.84) |
| ≥4 | 13.50 (6.31–28.87) | 7.86 (4.33–14.26) | 4.54 (2.82–7.30) | 1.58 (1.14–2.20) |
| Mental health conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 1.18 (0.79–1.77) | 1.67 (1.26–2.23) | 1.96 (1.57–2.45) | 1.86 (1.55–2.23) |
| 2 | 1.42 (0.90–2.24) | 1.54 (1.09–2.17) | 2.03 (1.54–2.68) | 1.91 (1.54–2.36) |
| ≥3 | 1.99 (1.26–3.15) | 2.17 (1.47–3.21) | 2.62 (1.87–3.67) | 2.44 (1.88–3.18) |
| Death after 90 d | ||||
| No. at risk | 3305 | 3670 | 4438 | 3307 |
| Cardiometabolic conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 1.12 (0.82–1.52) | 0.88 (0.66–1.18) | 0.94 (0.75–1.17) | 0.84 (0.69–1.02) |
| 2 | 0.98 (0.72–1.34) | 0.82 (0.63–1.08) | 0.94 (0.77–1.15) | 0.89 (0.75–1.06) |
| 3 | 1.18 (0.87–1.60) | 0.87 (0.67–1.13) | 0.92 (0.76–1.13) | 0.91 (0.77–1.08) |
| ≥4 | 1.83 (1.35–2.46) | 1.34 (1.04–1.73) | 1.32 (1.09–1.60) | 1.14 (0.96–1.35) |
| Other somatic conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 1.43 (1.16–1.78) | 1.26 (1.07–1.49) | 0.92 (0.82–1.04) | 0.96 (0.86–1.08) |
| 2 | 2.30 (1.82–2.91) | 1.59 (1.34–1.89) | 1.06 (0.94–1.19) | 1.05 (0.93–1.18) |
| 3 | 2.51 (1.91–3.29) | 2.18 (1.80–2.64) | 1.24 (1.09–1.42) | 1.10 (0.96–1.25) |
| ≥4 | 3.68 (2.60–5.20) | 2.74 (2.18–3.45) | 1.55 (1.31–1.82) | 1.11 (0.95–1.30) |
| Mental health conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 1.53 (1.26–1.87) | 1.39 (1.21–1.59) | 1.35 (1.23–1.48) | 1.27 (1.16–1.39) |
| 2 | 2.08 (1.68–2.57) | 1.55 (1.31–1.84) | 1.75 (1.54–1.98) | 1.45 (1.29–1.63) |
| ≥3 | 2.44 (1.88–3.17) | 2.33 (1.85–2.92) | 2.58 (2.15–3.09) | 1.65 (1.42–1.92) |
| Ischemic stroke/TIA in 90‐d survivors | ||||
| No. at risk | 3305 | 3670 | 4438 | 3307 |
| Cardiometabolic conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 1.56 (0.75–3.25) | 1.02 (0.43–2.41) | 1.02 (0.50–2.10) | 0.95 (0.47–1.94) |
| 2 | 1.37 (0.67–2.82) | 0.91 (0.40–2.06) | 1.30 (0.67–2.51) | 1.22 (0.64–2.32) |
| 3 | 2.51 (1.26–5.01) | 1.50 (0.71–3.15) | 1.40 (0.74–2.65) | 1.22 (0.65–2.31) |
| ≥4 | 2.15 (1.05–4.38) | 1.99 (0.96–4.16) | 2.12 (1.14–3.95) | 1.50 (0.81–2.79) |
| Other somatic conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 1.28 (0.82–2.01) | 1.08 (0.71–1.66) | 0.69 (0.49–0.97) | 0.93 (0.64–1.35) |
| 2 | 1.16 (0.66–2.02) | 1.19 (0.75–1.88) | 0.93 (0.67–1.28) | 0.87 (0.59–1.28) |
| 3 | 2.75 (1.52–5.00) | 1.02 (0.57–1.83) | 0.60 (0.39–0.90) | 0.54 (0.34–0.85) |
| ≥4 | 3.04 (1.36–6.81) | 2.07 (1.18–3.66) | 0.45 (0.25–0.80) | 0.68 (0.39–1.18) |
| Mental health conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 1.53 (0.99–2.39) | 1.38 (0.95–1.99) | 1.18 (0.90–1.57) | 0.88 (0.63–1.23) |
| 2 | 1.83 (1.10–3.06) | 1.02 (0.60–1.74) | 1.11 (0.72–1.72) | 0.98 (0.64–1.51) |
| ≥3 | 1.27 (0.63–2.57) | 1.93 (1.05–3.54) | 3.16 (2.01–4.96) | 1.18 (0.65–2.15) |
| Congestive heart failure in 90‐d survivors | ||||
| No. at risk | 2340 | 2296 | 2560 | 1565 |
| Cardiometabolic conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 1.33 (1.02–1.75) | 1.14 (0.84–1.53) | 0.81 (0.63–1.05) | 0.98 (0.73–1.33) |
| 2 | 1.63 (1.25–2.11) | 1.31 (1.00–1.71) | 0.81 (0.64–1.03) | 1.04 (0.79–1.37) |
| 3 | 1.94 (1.49–2.52) | 1.56 (1.20–2.03) | 1.04 (0.83–1.31) | 0.90 (0.68–1.19) |
| ≥4 | 2.42 (1.76–3.33) | 1.77 (1.34–2.34) | 1.26 (1.01–1.59) | 1.07 (0.80–1.42) |
| Other somatic conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 1.07 (0.88–1.30) | 0.90 (0.76–1.07) | 1.00 (0.86–1.16) | 0.97 (0.80–1.19) |
| 2 | 1.00 (0.78–1.29) | 1.18 (0.98–1.41) | 1.01 (0.86–1.18) | 0.98 (0.80–1.21) |
| 3 | 1.68 (1.25–2.28) | 1.20 (0.96–1.51) | 1.15 (0.95–1.39) | 1.16 (0.92–1.48) |
| ≥4 | 1.60 (0.93–2.76) | 1.73 (1.28–2.35) | 1.46 (1.13–1.90) | 1.43 (1.06–1.93) |
| Mental health conditions | ||||
| 0 | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) | 1.00 (reference) |
| 1 | 1.09 (0.89–1.34) | 0.99 (0.83–1.17) | 1.12 (0.98–1.29) | 0.99 (0.84–1.18) |
| 2 | 1.42 (1.10–1.82) | 1.04 (0.82–1.31) | 1.09 (0.89–1.34) | 0.86 (0.68–1.08) |
| ≥3 | 1.48 (1.08–2.04) | 1.44 (0.99–2.10) | 0.90 (0.63–1.29) | 0.70 (0.48–1.01) |
In nonstratified models, the interaction terms between number of conditions and age were significant (P<0.05) for most models. Nonsignificant interaction terms were observed between number of cardiometabolic conditions and age for the outcomes of death within 90 days (P=0.63) and ischemic stroke/TIA (P=0.15); between number of other somatic conditions and age for the outcome of congestive heart failure (P=0.068); and between number of mental health conditions and age for the outcome of death within 90 days (P=0.057). TIA indicates transient ischemic attack.
Each hazard ratio is adjusted for age, sex, race and ethnicity, and the number of conditions in each of the other 2 types of comorbid conditions.
After a median follow‐up of 5.9 years, 7846 deaths occurred after 90 days. Overall, mortality increased with increasing number of conditions for all 3 types (Figure S1). Cardiometabolic predictors of death after 90 days included CHF among all age groups and diabetes and stroke among those aged ≥65 years (Figure 2). Other somatic conditions associated with death after 90 days included chronic kidney disease and chronic obstructive pulmonary disease among all age categories and cancer among those aged <75 years. For mental health conditions, depression was associated with death among all ages, while substance abuse was for those aged <85 years and dementia/delirium for those aged ≥65 years. After adjustment for age, sex, race and ethnicity, and the number of conditions in each of the other 2 comorbid condition groups, the association between number of cardiometabolic conditions with death after 90 days was limited to those with ≥4 conditions and strongest among the younger age groups (≥4 cardiometabolic conditions versus none, age <65 years: aHR, 1.83 [95% CI, 1.35–2.46]; age 65–74 years: aHR, 1.34 [95% CI, 1.04–1.73]; age 75–84 years: aHR, 1.32 [95% CI, 1.09–1.60]; age ≥85 years: aHR, 1.14 [95% CI, 0.96–1.35]; Table 2). The association between the number of somatic conditions and death after 90 days was also attenuated in the older age groups (Table 2). Among those aged <75 years, the association increased with increasing number of conditions (≥4 other somatic conditions versus none, age <65 years: aHR, 3.68 [95% CI, 2.60–5.20]; age 65–74 years: aHR, 2.74 [95% CI, 2.18–3.45]). There was an association between the number of mental health conditions and risk of death after 90 days across all age groups, and the risk increased with a higher number of conditions (≥3 mental health conditions versus none, age <65 years: aHR, 2.44 [95% CI, 1.88–3.17]; age 65–74 years: aHR, 2.33 [95% CI, 1.85–2.92]; age 75–84 years: aHR, 2.58 [95% CI, 2.15–3.09]; age ≥85 years: aHR, 1.65 [95% CI, 1.42–1.92]; Table 2).
After a median follow‐up of 5.8 years, 862 ischemic strokes/TIAs occurred among 90‐day survivors. The difference in risk of stroke across the number of conditions was less clear; however, an increasing risk of stroke was observed for increasing number of cardiometabolic conditions (Figure S1). The individual cardiometabolic predictors of stroke/TIA included prior stroke among all ages and hypertension among those aged <65 years (Figure 2). Other somatic and mental health predictors included cancer and schizophrenia, respectively, among patients aged 65–74 years. After adjustment for age, sex, race and ethnicity, and the number of conditions in each of the other 2 comorbid condition groups, the association between the number of cardiometabolic conditions and stroke was strongest in those aged <65 years with 3 or 4 conditions versus none (aHR, 2.51 [95% CI, 1.26–5.01]; aHR, 2.15 [95% CI, 1.05–4.38], respectively; Table 2). The association between the number of other somatic conditions and risk of stroke followed a similar trend (3 other somatic conditions versus none, age <65 years: aHR, 2.75 [95% CI, 1.52–5.00]; ≥4 other somatic conditions versus none, age <65: aHR, 3.04 [95% CI, 1.36–6.81]; Table 2). The association between the number of mental health conditions and risk of stroke was also stronger in the younger age groups (Table 2).
After a median follow‐up of 4.5 years, 3836 cases of CHF occurred among 90‐day survivors. Overall, there was an increasing risk of CHF with increasing number of cardiometabolic and other somatic conditions, but no difference with number mental health conditions (Figure S1). The cardiometabolic conditions associated with CHF included coronary artery disease and hypertension among those aged <85 years and stroke among those aged <65 years. Other somatic conditions associated with CHF included chronic obstructive pulmonary disease among all age groups and chronic kidney disease among those aged 65 to 84 years. For mental health conditions, substance abuse was associated with CHF among those aged ≥85 years. After adjustment for age, sex, race and ethnicity, and the number of other conditions, there was an association between the number of cardiometabolic conditions and risk of CHF, and it was strongest in the younger age groups and among those with ≥4 conditions (≥4 cardiometabolic conditions versus none, age <65 years: aHR, 2.42 [95% CI, 1.76–3.33]; age 65–74 years: aHR, 1.77 [95% CI, 1.34–2.34]; age 75–84 years: aHR, 1.26 [95% CI, 1.01–1.59]; age ≥85 years: aHR, 1.07 [95% CI, 0.80–1.42]; Table 2). The association between other somatic conditions and CHF was strongest among those with more conditions, across all age groups (Table 2). There was an association between the number of mental health conditions and CHF among those aged <65 years, with ≥3 conditions (≥3 mental health conditions versus none, age <65: aHR, 1.48 [95% CI, 1.08–2.04]; Table 2).
Discussion
Our findings from this large, population‐based study indicate that, on average, individuals with new‐onset AF have 5 additional comorbid conditions (out of a list of 18 conditions). Although the most common comorbid conditions are cardiometabolic related, the burden of other somatic and mental health conditions are also important predictors of adverse outcomes. The associations with outcomes varied by both type of condition and age at index. The association between the number of cardiometabolic conditions and death within 90 days was significant among those aged ≥85 years with ≥4 conditions, while for death after 90 days the association was significant for those aged <85 years with ≥4 conditions. An increasing number of other somatic conditions were associated with death within and after 90 days and was strongest in the younger age groups. There was also an association between the number of mental health conditions and risk of death both within and after 90 days across all age groups, and the risk increased along with number of conditions. The association between stroke and the number of cardiometabolic, other somatic, and mental health conditions followed a similar trend, with the association being strongest among those in the younger age groups. Furthermore, the association between the number of cardiometabolic conditions and risk of CHF was strongest in those aged <75 years. The association between the number of other somatic conditions and CHF was strongest among those with several conditions across all age groups, while for mental conditions it was strongest for those aged <75 years with ≥3 conditions. Overall, these results highlight that the number and type of comorbid chronic conditions have a varying impact on adverse outcomes in patients with AF, and it differs by age. The results also underscore that not only cardiometabolic but also noncardiometabolic conditions have an impact on outcomes.
Several studies have previously described the burden of multimorbidity in patients with AF. 12 , 13 , 23 , 24 , 25 However, while it is known that comorbidities are prevalent in patients with AF, less is known about how these conditions impact outcomes and whether the type of condition may play a role in prognosis. In brief, in a study of UK Biobank participants (aged 37–73 years), those with self‐reported AF that had ≥4 comorbidities had a 6‐fold higher risk of death compared with participants without any long‐term condition. 25 CHF was associated with higher risk of death in people with AF, while among noncardiometabolic conditions, chronic obstructive pulmonary disease and osteoporosis were associated with a higher risk of death. The presence of a single cardiometabolic condition was associated with an 83% increased risk of death, while the presence of any noncardiometabolic condition was associated with a 45% higher risk of death. 25 Furthermore, a recent study from GLORIA‐AF (Global Registry on Long‐Term Antithrombotic Treatment in Patients With Atrial Fibrillation) identified the following AF phenotypes according to comorbidities (based on 18 conditions) through latent class analysis 26 : low complexity (39%), cardiovascular risk factors (28%), atherosclerotic (10%), thromboembolic (8%), cardiometabolic (8%), and high complexity (6%). Compared with the low‐complexity phenotype, all other groups were associated with higher risk of the primary outcome (compositive of all‐cause death and major adverse coronary events) except for the cardiovascular risk factor class. The greatest association was observed for the cardiometabolic and high‐complexity phenotypes. Similar results were found for all‐cause death. 26
Our current study extends previous findings by studying in a large, population‐based cohort of patients with AF, the impact of the types of comorbid conditions on death, ischemic stroke/TIA, and CHF, by age groups. In addition, we separated noncardiometabolic conditions into other somatic and mental health conditions to investigate the specific effect of these types of conditions. We found that the associations with chronic condition number and type differed by age group and were different across outcomes. For example, having ≥4 cardiometabolic conditions was associated with increased risk of death within 90 days only among those aged ≥85 years, whereas associations were generally strongest in the youngest age groups and attenuated with older age for other outcomes.
Clinical Implications
Our results underscore that multimorbidity is common in patients with AF. Furthermore, along with cardiometabolic‐related conditions, non–cardiometabolic‐related conditions also negatively impact outcomes in patients with AF, and importantly, the associations vary by the number and type of conditions and by age. Thus, these patients are complex, and it is important to holistically consider the impact of multimorbidity, and not only each individual condition, in the care of patients with AF. 27 Indeed, the burden of multimorbidity may have a negative impact on disease management. 5 , 7 , 28 , 29 An American Heart Association/American College of Cardiology/US Department of Health and Human Services Clinical Practice Guideline for patients with cardiovascular disease and comorbid conditions indicates that the presence of multiple conditions increases challenges for health care providers and patients and can affect patient safety if recommendations for 1 condition conflict with those of another condition. 5 Further, a recent systematic review aimed to assess the evidence on the prevalence of potentially inappropriate prescribing in multimorbid patients with AF. 28 One study described a 68% prevalence of potentially inappropriate prescribing for all prescribed medications. Potentially inappropriate prescribing of direct oral anticoagulants specifically had a pooled prevalence of 35% in multimorbid and polymedicated older adults with AF. In addition, an association was found between potentially inappropriate prescribing of direct oral anticoagulants and major bleeding events. However, as the review noted, more research is needed on the prevalence of potentially inappropriate prescribing and its association with adverse outcomes in multimorbid patients with AF. 28
Our results highlight the complexity of patients with AF and the need to better understand the role of multimorbidity, including the number and type of conditions (cardiometabolic, other somatic, or mental health conditions) and age, in the management and outcomes of patients with AF. Indeed, a recent project is working to improve the identification of comorbidities and further define multimorbidity in patients with AF through “interdisciplinary, patient‐centered, systematic care pathways.” 30 A better understanding of the role of multimorbidity in AF and outcomes could lead to improve patient‐centered care for these complex patients; however, more research is needed.
Limitations and Strengths
Some limitations should be acknowledged to aid in the interpretation of our findings. First, there may have been some misclassification of the chronic conditions due to possible errors in coding. We also recognize that additional ailments beyond those studied herein, including geriatric conditions, peripheral vascular disease, and sleep apnea, contribute to multimorbidity in patients with AF. In addition, we did not include information on severity, duration, or treatment for the chronic conditions. Furthermore, new diagnoses of these conditions over follow‐up could impact outcomes; however, our analyses did not account for this. Moreover, the impact of comorbidities on outcomes could vary according to the type of AF, such as paroxysmal versus persistent, and provoked after acute illness or surgery versus nonprovoked AF; however, this was not examined in the current study. We acknowledge that by requiring both a diagnostic code and evidence of AF on an ECG or Holter monitor, we may have missed some cases of AF. However, we chose this definition to optimize the likelihood that patients identified as AF truly had AF (high positive predictive value) on the basis of results of a prior report that found that code‐based algorithms to identify AF are prone to misclassification. 31 In addition, we did not examine location of AF diagnosis (inpatient versus outpatient), AF treatments, socioeconomic status, health insurance status, or access to primary or specialist care. Finally, because our Midwest population included mostly people of non‐Hispanic White race and ethnicity, our results may not be generalizable to all patient populations. However, our study has many strengths including our ability to assemble a large, population‐based cohort of patients with AF, with comprehensive health care data from multiple health care providers through the Rochester Epidemiology Project records–linkage system. Furthermore, we categorized non–cardiometabolic‐related conditions into other somatic and mental health conditions allowing us to study the impact of these comorbidity groups separately, and we also stratified our results by age.
Conclusions
Our findings from this large, population‐based study indicate that, on average, individuals with new‐onset AF have 5 additional comorbid conditions. Although the most common comorbid conditions are cardiometabolic related, the burden of other somatic and mental health conditions are also important predictors of adverse outcomes, with effects differing by age. This underscores the importance of effectively integrating multimorbidity into the clinical care of adults with AF.
Sources of Funding
This study was funded by the National Institute on Aging (AG 062580) and used the resources of the Rochester Epidemiology Project medical records–linkage system, which is supported by the National Institute on Aging (AG 058738), by the Mayo Clinic Research Committee, and by fees paid annually by Rochester Epidemiology Project users. The funding sources played no role in the design of the study, the analysis, the interpretation of study results, or the writing of the manuscript.
Disclosures
None.
Supporting information
Figure S1
Acknowledgments
The authors thank Deborah S. Strain and Donna Adams for their assistance in formatting the manuscript.
This manuscript was sent to Jose R. Romero, MD, Associate Editor, for review by expert referees, editorial decision, and final disposition.
Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.124.040612
For Sources of Funding and Disclosures, see page 12.
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Associated Data
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Supplementary Materials
Figure S1