Abstract
Introduction
Acute ischemic stroke (AIS) is the most prevalent type of stroke, associated with a significant burden of functional impairments. About 94.2% of AIS patients present with multiple comorbidities, but how they affect AIS prognosis remains largely unknown. This study aimed to comprehensively assess the associations of a wide range of AIS-related comorbidities, their patterns, with functional impairments in AIS patients.
Methods
This study utilized data from the China National Stroke Registry 3 (CNSR3), a prospective registry encompassing 201 Chinese hospitals from August 2015 to March 2018. A total of 10,508 AIS patients were included, with a median age of 62.0 years (IQR: 54.0–70.0), and 65% were female. Eighteen AIS-related comorbidities were considered in the analysis and frequent pattern mining was employed to identify potential comorbidity patterns among AIS patients. Functional outcomes at 1 year after an AIS event were assessed using the modified Rankin Scale. Logistic regression models were utilized to evaluate associations of comorbidities, their patterns with AIS prognosis. Furthermore, association rule mining was applied to explore the hidden comorbidity combinations and their relationship with functional outcomes based on the identified patterns.
Results
Comorbidity was observed in 88.9% of AIS patients. The majority of AIS patients exhibited one to 3 comorbidities. Eight patterns of main comorbidities among AIS patients were identified. The pattern of common metabolic disorders, coronary heart disease, and atrial fibrillation demonstrated the strongest association (OR = 2.49, 1.59–3.89) with the development of poor functional outcomes. The further combination of heart failure and arthritis significantly increases the probability of poor functional outcomes, with lifts of 3.11 and 5.52, respectively.
Conclusions
Our study revealed that comorbidity is highly prevalent among AIS patients in China, encompassing diverse patterns. Specific comorbidities and comorbidity patterns are closely associated with poor functional outcomes. Our findings emphasized the importance of prioritizing comprehensive management of AIS and AIS-related comorbidities to reduce the risk of disability among AIS patients.
Keywords: Comorbidity, Comorbidity pattern, Acute ischemic stroke, Functional outcomes, Healthcare strategy
Introduction
Stroke is the primary cause of disability and the second leading cause of death globally [1]. Acute ischemic stroke (AIS) is the most common type, comprising approximately 87% of all strokes [2]. It is estimated that 70%–80% of AIS patients suffer from functional impairments, which hinder their ability to live independently and place a considerable strain on the healthcare system, the economy, and families [2]. The impact of AIS is significantly heavier in developing countries, including China, compared to developed nations [2, 3]. The disability rate resulting from AIS in China is 2.5 times higher, and the mortality rate is five times greater, than those in European and American countries [3–5]. Therefore, exploring potential factors that may affect the prognosis of AIS and improving the healthcare strategies for AIS patients are of clinical and public health relevance.
Comorbidity refers to the occurrence of two or more diseases or unfavorable health conditions in the same person, and the presence of these additional conditions has a combined influence on the index disease and its prognosis [6–9]. AIS is a chronic disease characterized by prolonged progression, complex development, and diverse subtypes. It has been reported that 94.2% of AIS patients commonly have more than one comorbidity [10], which can worsen their prognosis [11–14]. With the aging population and improved survival rates [15, 16], the prevalence of comorbidity in AIS patients is expected to continue rising. Although several studies have examined the impact of individual comorbidity on AIS prognosis [17–20], few studies have thoroughly examined the associations of a wide range of AIS-related comorbidities, particularly their patterns, with the prognosis of AIS. Addressing this gap is essential for effective treatment of AIS and comprehensive management of AIS comorbid disorders, as well as for improving the functional outcomes of AIS patients.
This study aimed to comprehensively assess the associations of individual comorbid disease with functional outcomes among AIS patients. Furthermore, we aimed to identify potential comorbidity patterns and evaluate how these patterns are associated with functional outcomes.
Methods
Study Population
Data were derived from the China National Stroke Registry 3 (CNSR3) which was a nationwide prospective registry that included patients with AIS or transient ischemic attacks (TIAs) from 201 hospitals between August 2015 and March 2018 in China. Participants were consecutively enrolled if they met the following criteria: (1) age >18 years, (2) diagnosis of AIS or TIA within 7 days, and (3) informed consent from the participant or a legally authorized representative. The detailed design and main results of the CNSR3 [21] study were described previously. It enrolled 15,166 patients diagnosed with AIS or TIA within 7 days of the onset of symptoms. Among the 15,166 patients in CNSR3, 14,146 patients were finally diagnosed with AIS. In our study, 3,369 patients with a history of TIA, intracerebral hemorrhage, AIS, or subarachnoid hemorrhage were excluded. Additionally, 269 patients without available measurements for evaluating functional outcomes at 1 year were also excluded. Thus, a total of 10,508 patients were included in this study.
Baseline Characteristics
The baseline data of the included patients were systematically collected by trained research coordinators in accordance with a standard data collection protocol [21, 22]. Upon admission, the coordinators from the hospitals conducted face-to-face interviews, respectively, to ascertain patients’ ages and to evaluate the National Institutes of Health Stroke Scale (NIHSS). Additionally, the other data extracted from medical records by the research coordinators includes gender, body mass index (kg/m2), medical history, previous medication, diagnoses, and treatments. The medical history of the patients includes dyslipidemia, hypertension, diabetes, different types of strokes (ischemic, intracerebral, and subarachnoid hemorrhages), TIA, heart disease, peripheral arterial disease, cancer, dementia, mental disorders, obstructive sleep apnea-hypopnea syndrome, arthritis, and other diseases. The patients’ previous medications use includes antiplatelet agents, anticoagulant agents, dyslipidemia medications, anti-lipid oxidants, hypoglycemic agents, and antihypertensive agents. AIS or TIA is the final diagnosis. The etiological evaluation for AIS adhered to the TOAST (Trial of Org 10172 in Acute Stroke Treatment) criteria [23]. Other discharge diagnoses encompass hypertension, diabetes, dyslipidemia, heart disease, chronic respiratory diseases, liver diseases, urinary system diseases, peripheral arterial disease, and epilepsy. Acute phase thrombolysis and endovascular treatments included intravenous thrombolysis, intra-arterial catheter-directed thrombolysis, mechanical thrombectomy, and stent therapy.
Comorbidities of AIS
Guided by clinical knowledge and the current literature on comorbidity and multimorbidity related to AIS [10, 14], we have compiled a list of diseases that are considered related to the etiological mechanism of AIS, complications caused by AIS, or affecting the prognosis of AIS. Along with the collection of the CNSR3 database, 18 chronic diseases were selected in our study, including hypertension, dyslipidemia, diabetes, coronary heart disease, atrial fibrillation, liver diseases, peripheral arterial disease, obstructive sleep apnea-hypopnea syndrome, arthritis, digestive system disease, chronic respiratory diseases, renal insufficiency, mental disorders, cancer, heart failure, cognitive impairment, epilepsy, and pulmonary embolism.
Diagnostic criteria for hypertension include two resting blood pressure tests with a reading of ≥140/90 mm Hg or the use of antihypertensive medication [24, 25]. Dyslipidemia was diagnosed as having a total serum cholesterol level greater than 6.22 mmol/L, triglycerides of 2.26 mmol/L or above, low-density lipoprotein cholesterol of 4.14 mmol/L or above, or using hypolipidemic drugs [26]. Diabetes was diagnosed as having a fasting plasma glucose level of 7.0 mmol/L or higher, or being treated with oral antidiabetic agents or insulin injection [27]. At discharge, liver diseases were identified as a final diagnosis, including abnormal liver function, virus carriage, liver cirrhosis, undergoing liver surgery, alcoholic liver disease, and other liver diseases. Coronary heart disease, atrial fibrillation, peripheral arterial disease, renal insufficiency, pulmonary embolism, and epilepsy were also determined from the final diagnosis in the inpatient records. A medical history of peptic ulcers and gastrointestinal bleeding, as recorded in medical documents, defines digestive system disease. Chronic respiratory diseases encompass chronic obstructive pulmonary disease and asthma and were identified through medical documentation. Additionally, mental disorders, including psychogenic conditions, poststroke depression, or the utilization of antidepressant treatment, are also identified through medical records. Furthermore, obstructive sleep apnea-hypopnea syndrome, arthritis, cancer, and cognitive impairment are also documented in medical records.
Outcomes Assessment
The modified Rankin Scale (mRS) was used to evaluate patients’ functional outcomes at 1 year after an AIS event through a telephone interview. The mRS is the most frequently employed tool for assessing functional outcomes after stroke [28, 29], consistently applied in clinical trials and large-scale studies [29–32]. In our analysis, the mRS score of 3–6 is defined as the poor functional outcomes [32, 33].
Statistical Analysis
Descriptive statistics were employed to summarize the demographics, clinical characteristics of AIS at baseline, functional outcomes at 1-year follow-up, as well as the number of comorbidities and medication history. Sex differences were analyzed across participant characteristics, utilizing Student’s t tests for continuous variables and chi-square tests for categorical variables. Furthermore, descriptive statistics were also used to analyze the prevalence of comorbidities among patients with AIS. The association between each comorbidity and functional outcomes in AIS patients was assessed using univariate logistic regression.
Frequent pattern mining was applied to identify main comorbidity patterns among AIS patients. Multiple logistic regression models were then used to investigate the associations between these comorbidity patterns and functional outcomes, adjusted for age, sex, NIHSS score at admission, medication history, and thrombolysis or endovascular treatment during the acute phase. Additionally, association rule mining was used to improve comprehension and interpretability of the hidden comorbidity combinations and their relationships with functional outcomes based on the identified patterns. This is significant for understanding and exploring the synergistic impact of comorbidity combinations on functional outcomes. Both frequent pattern mining and association rule mining were implemented using the Apriori algorithm. In 1993, R. Agrawal from IBM’s Almaden Research Center presented the concept of association rules mining, analyzing item sets in a customer transaction database [34]. The Apriori algorithm, which was developed based on this pioneering work, has become a conventional approach for identifying frequent items and examining association rules. It is widely used in clinical practice. The association rule algorithm consists of two steps. First, the set contains a listing of all frequently occurring objects. Then, based on these high-frequency items, frequent association rules are constructed [35]. The determination of interesting or beneficial rules relies on three fundamental values: support, confidence, and lift. Support is defined as the probability of the co-occurrence of events A and B, without implying any causal relationship between them. Confidence represents the conditional probability that B occurs, given that A is present. The lift is defined as the ratio of the joint probability of A and B (P [A, B]) to the product of the individual probabilities of A and B (P[A] *P[B]). It is used to quantify the level of reliance between A and B. When the lift is below one, A and B are less likely to co-occur than expected by chance. A lift of one indicates that A and B are independent. If the lift is greater than one, it indicates a positive relationship or dependency between A and B. Statistical significance was assessed at two-tailed p value threshold of <0.05. The analyses were performed using SAS software (version 9.4) and R software (version 4.3.1).
Results
Characteristics of the Study Participants
The characteristics of the study participants are listed in Table 1. The median age of the patients was 62 years, ranging from 54 to 70. The median NIHSS score on admission was 3, ranging from 2 to 6. Among AIS subtypes, 24.6% patients were categorized as large artery atherosclerosis, followed by small-vessel occlusion (22.5%) and cardio-embolism (6.1%). Additionally, 1.3% had another determined cause, and 45.4% had more than one cause. The majority of AIS patients (88.9%) had at least one comorbid disease. Specifically, 80.8% AIS patients had 1–3 comorbidities. Furthermore, 51.6% of them had a history of medication use. After 1-year follow-up, 1,268 (12.1%) patients experienced a poor functional outcome and 320 (3%) patients died.
Table 1.
Characteristics of the study patients
| Variables | Total (N = 10,508) | Male (N = 7,157) | Female (N = 3,351) | p value |
|---|---|---|---|---|
| Sociodemographic and lifestyle characteristics | ||||
| Age, years | 62.0 (54.0–70.0) | 61.0 (53.0–68.0) | 65.0 (57.0–72.0) | <0.01 |
| BMI | 24.5 (22.5–26.5) | 24.5 (22.6–26.3) | 24.4 (22.3–26.8) | 0.1723 |
| Current smoker | 3,457 (32.9) | 3,331 (46.5) | 126 (3.8) | <0.01 |
| Heavy drinkera | 1,577 (15.0) | 1,566 (21.9) | 11 (0.3) | <0.01 |
| Clinical characteristics | ||||
| NIHSS score on admission | 3.0 (2.0–6.0) | 3.0 (1.0–6.0) | 4.0 (2.0–6.0) | <0.01 |
| AIS subtypes | <0.01 | |||
| Large artery atherosclerosis | 2,589 (24.6) | 1,789 (25.0) | 800 (23.9) | |
| Cardio-embolism | 644 (6.1) | 402 (5.6) | 242 (7.2) | |
| Small-vessel occlusion | 2,369 (22.5) | 1,677 (23.4) | 692 (20.7) | |
| Other determined cause | 133 (1.3) | 75 (1.0) | 58 (1.7) | |
| Undetermined cause | 4,773 (45.4) | 3,214 (44.9) | 1,559 (46.5) | |
| Treatment in acute phase | 1,079 (10.3) | 750 (10.5) | 329 (9.8) | 0.29 |
| Comorbidities, n | <0.01 | |||
| 0 | 1,166 (11.1) | 866 (12.1) | 300 (9.0) | |
| 1 | 3,279 (31.2) | 2,321 (32.4) | 958 (28.6) | |
| 2 | 3,315 (31.5) | 2,263 (31.6) | 1,052 (31.4) | |
| 3 | 1,905 (18.1) | 1,205 (16.8) | 700 (20.9) | |
| 4 | 624 (5.9) | 372 (5.2) | 252 (7.5) | |
| ≥5 | 219 (2.1) | 130 (1.8) | 89 (2.7) | |
| Medication history | 5,422 (51.6) | 3,413 (47.7) | 2,009 (60.0) | <0.01 |
| Antiplatelet agents | 993 (9.4) | 630 (8.8) | 363 (10.8) | <0.01 |
| Anticoagulant agents | 88 (0.8) | 53 (0.7) | 35 (1.0) | 0.11 |
| Dyslipidemia medications | 692 (6.6) | 461 (6.4) | 231 (6.9) | 0.38 |
| Anti-lipid oxidants | 26 (0.2) | 18 (0.3) | 8 (0.2) | 0.90 |
| Hypoglycemic agents | 1,789 (17.0) | 1,074 (15.0) | 715 (21.3) | <0.01 |
| Antihypertensive agents | 4,305 (41.0) | 2,672 (37.3) | 1,633 (48.7) | <0.01 |
| Outcome | ||||
| One-year death | 320 (3.0) | 193 (2.7) | 127 (3.8) | <0.01 |
| mRS score at 1 year | <0.01 | |||
| 0–2 | 9,240 (87.9) | 6,394 (89.3) | 2,846 (84.9) | |
| 3–6 | 1,268 (12.1) | 763 (10.7) | 505 (15.1) | |
Continuous variables are expressed as median with interquartile range. Categorical variables are expressed as frequency with percentage.
AIS, acute ischemic stroke; BMI, body mass index; NIHSS, National Institutes of Health Stroke Scale score; mRS, modified Rankin Scale.
aHeavy drinker was defined as ≥2 standard alcohol consumption/per day.
Comorbidity and Its Risk for Poor Functional Outcomes among AIS Patients
The analysis included a total of 18 chronic diseases, with the prevalence of comorbidities listed in Table 2. The three most prevalent comorbidities in AIS patients were hypertension (71.93%), dyslipidemia (36.31%), and diabetes (31.38%), which are also considered common metabolic disorders of AIS. Following these were coronary heart disease (13.95%), atrial fibrillation (6.53%), liver diseases (6.40%), peripheral arterial disease (5.22%), obstructive sleep apnea-hypopnea syndrome (2.49%), arthritis (1.83%), and digestive system disease (1.44%), etc.
Table 2.
Comorbidity prevalence in patients with AIS
| Rank | Comorbidity | N | Prevalence, % |
|---|---|---|---|
| 1 | Hypertension | 7,558 | 71.93 |
| 2 | Dyslipidemia | 3,815 | 36.31 |
| 3 | Diabetes | 3,297 | 31.38 |
| 4 | Coronary heart disease | 1,466 | 13.95 |
| 5 | Atrial fibrillation | 686 | 6.53 |
| 6 | Liver diseases | 672 | 6.40 |
| 7 | Peripheral arterial disease | 549 | 5.22 |
| 8 | Obstructive sleep apnea-hypopnea syndrome | 262 | 2.49 |
| 9 | Arthritis | 192 | 1.83 |
| 10 | Digestive system disease | 151 | 1.44 |
| 11 | Chronic respiratory diseases | 131 | 1.25 |
| 12 | Renal insufficiency | 123 | 1.17 |
| 13 | Mental disorders | 119 | 1.13 |
| 14 | Cancer | 95 | 0.90 |
| 15 | Heart failure | 61 | 0.58 |
| 16 | Cognitive impairment | 41 | 0.39 |
| 17 | Epilepsy | 38 | 0.36 |
| 18 | Pulmonary embolism | 8 | 0.08 |
The odds ratios (ORs) of comorbidities in patients with AIS versus patients with AIS alone were compared for poor functional outcomes (online suppl. Table 1; for all online suppl. material, see https://doi.org/10.1159/000544170). Notably, no AIS patients had heart failure or pulmonary embolism alone. Additionally, AIS patients who suffered only from obstructive sleep apnea-hypopnea syndrome or cancer did not experience poor functional outcomes. As a result, 14 comorbidities were included in the analysis. In particular, patients with coronary heart disease, atrial fibrillation, peripheral arterial disease, chronic respiratory diseases, and epilepsy were more likely to experience poor functional outcomes. Furthermore, the risk of poor functional outcomes increased with the number of comorbidities, and the OR was 1.13 (1.06–1.19) per additional comorbidity (Fig. 1).
Fig. 1.
ORs (with 95% Cl) for each comorbidity in relation to functional outcome in AIS patients. *Adjusted for age, gender, NIHSS score at admission, medication history, and thrombolysis or endovascular treatment during the acute phase. OSAHS, obstructive sleep apnea-hypopnea syndrome; OR, odds ratio; CI, confidence interval.
Comorbidity Patterns and Its Risk for Poor Functional Outcomes among AIS Patients
The minimum support degree was set at 0.01, and the minimum confidence degree was set at 0, indicating that the prevalence of the disease or disease combination should be at least 1% and neglecting the conditional probability between disease combinations. Among AIS patients, a total of 35 combinations of comorbidities were identified, involving 18 different disorders. These combinations are listed in Table 3, ordered the combination prevalence from highest to lowest. The four most common combinations of comorbidities were combinations of three major risk factors, with prevalence rates of 27.63%, 24.58%, 14.60%, and 11.63%, respectively. The combination of hypertension and coronary heart disease ranked fifth, with an 11.06% prevalence rate.
Table 3.
Comorbidity combination groups in AIS patients
| Rank | Comorbidity combination group | Prevalence, % |
|---|---|---|
| 1 | Hypertension, dyslipidemia | 27.63 |
| 2 | Hypertension, diabetes | 24.58 |
| 3 | Diabetes, dyslipidemia | 14.60 |
| 4 | Hypertension, diabetes, dyslipidemia | 11.63 |
| 5 | Hypertension, CHD | 11.06 |
| 6 | Dyslipidemia, CHD | 5.51 |
| 7 | Diabetes, CHD | 5.34 |
| 8 | Hypertension, liver diseases | 4.94 |
| 9 | Hypertension, diabetes, CHD | 4.54 |
| 10 | Hypertension, dyslipidemia, CHD | 4.40 |
| 11 | Hypertension, AF | 4.16 |
| 12 | Hypertension, PAD | 3.99 |
| 13 | Dyslipidemia, liver diseases | 3.15 |
| 14 | Diabetes, dyslipidemia, CHD | 2.66 |
| 15 | Hypertension, dyslipidemia, liver diseases | 2.57 |
| 16 | Diabetes, liver diseases | 2.40 |
| 17 | Dyslipidemia, PAD | 2.38 |
| 18 | Diabetes, PAD | 2.24 |
| 19 | Hypertension, diabetes, dyslipidemia, CHD | 2.21 |
| 20 | CHD, AF | 2.12 |
| 21 | Hypertension, diabetes, liver diseases | 2.00 |
| 22 | Hypertension, OSAHS | 1.96 |
| 23 | Dyslipidemia, AF | 1.82 |
| 24 | Hypertension, dyslipidemia, PAD | 1.82 |
| 25 | Hypertension, diabetes, PAD | 1.81 |
| 26 | Hypertension, CHD, AF | 1.54 |
| 27 | Diabetes, AF | 1.43 |
| 28 | Hypertension, dyslipidemia, AF | 1.43 |
| 29 | Hypertension, arthritis | 1.35 |
| 30 | Diabetes, dyslipidemia, PAD | 1.28 |
| 31 | Diabetes, dyslipidemia, liver diseases | 1.27 |
| 32 | Dyslipidemia, OSAHS | 1.24 |
| 33 | Hypertension, diabetes, AF | 1.12 |
| 34 | Hypertension, diabetes, dyslipidemia, liver diseases | 1.09 |
| 35 | Hypertension, diabetes, dyslipidemia, PAD | 1.03 |
CHD, coronary heart disease; AF, atrial fibrillation; PAD, peripheral arterial disease; OSAHS, obstructive sleep apnea-hypopnea syndrome.
We consider hypertension, dyslipidemia, and diabetes to be the common metabolic disorders in AIS patients, and all disease patterns consist of common metabolic disorders and combinations of common metabolic disorders with other comorbidities. Thus, we have identified eight distinct patterns of chronic comorbidity among AIS patients. Pattern 1, referred to as the common metabolic disorders pattern, includes any combination of hypertension, dyslipidemia, and diabetes. Pattern 2 is characterized by the combination of common metabolic disorders and coronary heart disease. Pattern 3 is a combination of common metabolic disorders and atrial fibrillation. Pattern 4 involves the combination of common metabolic disorders and liver diseases. Pattern 5 is made up of a combination of common metabolic disorders and peripheral arterial disease. Pattern 6 represents the combination of common metabolic disorders and obstructive sleep apnea-hypopnea syndrome. Pattern 7 encompasses the combination of common metabolic disorders and arthritis. Lastly, pattern 8 entails the combination of common metabolic disorders, coronary heart disease, and atrial fibrillation.
Table 4 presents associations between these identified comorbidity patterns and poor functional outcomes. Six patterns were found to be associated with an increased risk of poor functional outcomes in AIS patients compared to those with isolated AIS. Notably, the pattern of common metabolic disorders, coronary heart disease, and atrial fibrillation indicates the highest risk (OR = 2.49, 1.59–3.89) for poor functional outcomes among the listed patterns. Subsequently, patterns of common metabolic disorders and atrial fibrillation, as well as common metabolic disorders, peripheral arterial disease, follow with ORs of 1.79 and 1.72, respectively. In addition, compared to isolated hypertension, dyslipidemia, and diabetes, which individually are not related to the risk of functional outcomes (Fig. 1), the pattern of common metabolic disorders is associated with a moderately increased risk (OR = 1.29) of developing poor functional outcomes, suggesting that the combination of common metabolic disorders elevates the risk.
Table 4.
Associations of comorbidity pattern and functional outcomes in AIS patients
| Pattern | Comorbidity combinations | Subjects, n | Events, n (%) | p value | OR (95% CI) |
|---|---|---|---|---|---|
| 1 | Common metabolic disorders | 5,805 | 596 (10.3%) | <0.05 | 1.29 (1.00–1.67) |
| 2 | Common metabolic disorders, CHD | 924 | 122 (13.2%) | 0.04 | 1.40 (1.02–1.92) |
| 3 | Common metabolic disorders, AF | 346 | 87 (25.1%) | <0.01 | 1.79 (1.24–2.59) |
| 4 | Common metabolic disorders, liver diseases | 437 | 45 (10.3%) | 0.03 | 1.58 (1.06–2.38) |
| 5 | Common metabolic disorders, PAD | 331 | 43 (13.0%) | 0.01 | 1.72 (1.14–2.6) |
| 6 | Common metabolic disorders, OSAHS | 156 | 10 (6.4%) | 0.98 | 0.99 (0.49–2.01) |
| 7 | Common metabolic disorders, arthritis | 110 | 12 (10.9%) | 0.24 | 1.51 (0.77–2.97) |
| 8 | Common metabolic disorders, CHD, AF | 169 | 57 (33.7%) | <0.01 | 2.49 (1.59–3.89) |
Odds ratio and 95% confidence interval were derived from logistic regression models that were adjusted for age, sex, NIHSS score on admission, medication history, treatment in acute phase. Participants suffered isolated AIS without the other comorbidity (n = 1,166) were considered the reference group in estimating odds ratios (95% confidence intervals) of poor functional outcome.
CI, confidence interval; CHD, coronary heart disease; AF, atrial fibrillation; PAD, peripheral arterial disease; OSAHS, obstructive sleep apnea-hypopnea syndrome.
Furthermore, based on observed patterns, the association rule analysis was performed with the comorbidity combination as the antecedent and the poor functional result as the consequent. The analysis was performed with the support rate greater than 0.0001 and the lift rate greater than 1. Detailed results are presented in Table 5. Based on pattern 1, further combining common metabolic disorders with chronic respiratory diseases, digestive system disease, heart failure, renal insufficiency, cognitive impairment, cancer, epilepsy, and pulmonary embolism increases the probability of poor functional outcomes. Based on pattern 2, the subsequent combination with other diseases such as liver disease, arthritis, chronic respiratory disease, and heart failure raises the probability of poor functional outcomes, with the lifts ranging from 1.38 to 8.29. Based on pattern 3, the additional combinations of digestive system diseases and arthritis substantially increase the correlation with poor functional outcomes. Based on pattern 4, the additional combination of diseases such as coronary heart disease, atrial fibrillation, renal insufficiency increases the probability of poor functional outcomes with lifts greater than 1. Based on pattern 5, the additional combination of chronic respiratory diseases, arthritis, renal insufficiency, heart failure, and cancer increases the probability of poor functional outcomes. Based on pattern 8, the additional combination with heart failure, as well as the additional combination with arthritis, significantly increases the probability of poor functional outcomes, with lifts of 3.11 and 5.52, respectively. Specifically, the combination of common metabolic disorders, coronary heart disease, cognitive impairment, and chronic respiratory diseases, as well as the combination of common metabolic disorders, atrial fibrillation, arthritis, and digestive system disease among AIS patients, achieve the highest lift (8.29), indicating their strongest association with poor functional outcomes.
Table 5.
Analysis of association rules between comorbidity patterns and poor functional outcomes in AIS patients
| Comorbidity combinations | Support, % | Lift |
|---|---|---|
| Pattern 1 | ||
| Common metabolic disorders, mental disorders | 0.11 | 0.97 |
| Common metabolic disorders | 10.06 | 0.99 |
| Common metabolic disorders, chronic respiratory diseases | 0.14 | 1.14 |
| Common metabolic disorders, digestive system disease | 0.16 | 1.15 |
| Common metabolic disorders, heart failure | 0.08 | 1.38 |
| Common metabolic disorders, renal insufficiency | 0.20 | 1.53 |
| Common metabolic disorders, cognitive impairment | 0.09 | 2.13 |
| Common metabolic disorders, cancer | 0.22 | 2.24 |
| Common metabolic disorders, epilepsy | 0.08 | 2.65 |
| Common metabolic disorders, cognitive impairment, chronic respiratory diseases | 0.02 | 2.76 |
| Common metabolic disorders, pulmonary embolism | 0.03 | 3.11 |
| Common metabolic disorders, pulmonary embolism, chronic respiratory diseases | 0.02 | 4.14 |
| Pattern 2 | ||
| Common metabolic disorders, CHD, OSAHS | 0.03 | 0.62 |
| Common metabolic disorders, CHD, digestive system disease | 0.02 | 0.66 |
| Common metabolic disorders, CHD, renal insufficiency | 0.03 | 0.99 |
| Common metabolic disorders, CHD, PAD | 0.13 | 1.29 |
| Common metabolic disorders, CHD | 1.97 | 1.30 |
| Common metabolic disorders, CHD, liver diseases | 0.14 | 1.38 |
| Common metabolic disorders, CHD, arthritis | 0.07 | 1.49 |
| Common metabolic disorders, CHD, chronic respiratory diseases | 0.06 | 1.71 |
| Common metabolic disorders, CHD, heart failure | 0.06 | 1.84 |
| Common metabolic disorders, CHD, liver diseases, PAD | 0.05 | 2.44 |
| Common metabolic disorders, CHD, cognitive impairment | 0.03 | 2.49 |
| Common metabolic disorders, CHD, cancer | 0.04 | 2.76 |
| Common metabolic disorders, CHD, cognitive impairment, chronic respiratory diseases | 0.02 | 8.29 |
| Pattern 3 | ||
| Common metabolic disorders, AF, liver diseases | 0.06 | 1.38 |
| Common metabolic disorders, AF, OSAHS | 0.02 | 1.66 |
| Common metabolic disorders, AF, cancer | 0.03 | 1.78 |
| Common metabolic disorders, AF, renal insufficiency | 0.02 | 1.84 |
| Common metabolic disorders, AF, heart failure | 0.04 | 2.07 |
| Common metabolic disorders, AF, chronic respiratory diseases | 0.02 | 2.07 |
| Common metabolic disorders, AF, PAD | 0.06 | 2.16 |
| Common metabolic disorders, AF | 1.29 | 2.25 |
| Common metabolic disorders, AF, digestive system disease | 0.03 | 3.55 |
| Common metabolic disorders, AF, arthritis | 0.07 | 3.87 |
| Common metabolic disorders, AF, arthritis, digestive system disease | 0.02 | 8.29 |
| Pattern 4 | ||
| Common metabolic disorders, liver diseases | 0.66 | 0.95 |
| Common metabolic disorders, liver diseases, PAD | 0.08 | 0.96 |
| Common metabolic disorders, CHD, liver diseases | 0.14 | 1.38 |
| Common metabolic disorders, AF, liver diseases | 0.06 | 1.38 |
| Common metabolic disorders, liver diseases, renal insufficiency | 0.05 | 2.18 |
| Common metabolic disorders, CHD, liver diseases, PAD | 0.05 | 2.44 |
| Common metabolic disorders, liver diseases, mental disorders | 0.02 | 4.14 |
| Pattern 5 | ||
| Common metabolic disorders, PAD | 0.63 | 1.10 |
| Common metabolic disorders, PAD, chronic respiratory diseases | 0.02 | 1.38 |
| Common metabolic disorders, PAD, arthritis | 0.02 | 1.84 |
| Common metabolic disorders, PAD, renal insufficiency | 0.04 | 2.07 |
| Common metabolic disorders, PAD, heart failure | 0.02 | 3.31 |
| Common metabolic disorders, PAD, cancer | 0.03 | 3.55 |
| Pattern 6 | ||
| Common metabolic disorders, OSAHS | 0.18 | 0.66 |
| Common metabolic disorders, CHD, OSAHS | 0.03 | 0.62 |
| Common metabolic disorders, OSAHS, mental disorders | 0.03 | 0.69 |
| Common metabolic disorders, AF, OSAHS | 0.02 | 1.66 |
| Common metabolic disorders, OSAHS, arthritis | 0.02 | 1.84 |
| Pattern 7 | ||
| Common metabolic disorders, arthritis | 0.27 | 1.40 |
| Common metabolic disorders, CHD, arthritis | 0.07 | 1.49 |
| Common metabolic disorders, OSAHS, arthritis | 0.02 | 1.84 |
| Common metabolic disorders, PAD, arthritis | 0.02 | 1.84 |
| Common metabolic disorders, arthritis, digestive system disease | 0.03 | 2.76 |
| Common metabolic disorders, arthritis, cancer | 0.02 | 2.76 |
| Common metabolic disorders, AF, arthritis | 0.07 | 3.87 |
| Common metabolic disorders, AF, arthritis, digestive system disease | 0.02 | 8.29 |
| Pattern 8 | ||
| Common metabolic disorders, CHD, AF | 0.59 | 2.84 |
| Common metabolic disorders, CHD, AF, liver diseases | 0.03 | 2.49 |
| Common metabolic disorders, CHD, AF, heart failure | 0.03 | 3.11 |
| Common metabolic disorders, CHD, AF, arthritis | 0.04 | 5.52 |
CHD, coronary heart disease; AF, atrial fibrillation; PAD, peripheral arterial disease; OSAHS, obstructive sleep apnea-hypopnea syndrome.
Discussion
This hospital-based registry involved 10,508 patients with first-ever AIS, and comorbidity was present in 88.9% of these patients. The majority of AIS patients had 1–3 comorbidities. Eight patterns of main comorbidities among AIS patients were identified, and the potential association with functional outcomes was further explored. In addition, subsequent combinations linked to poor functional outcomes were examined based on these patterns. This study used data from the largest nationwide prospective registry of AIS patients in China, allowing for a comprehensive identification of comorbidity patterns. It offers a highly representative and accurate reflection of real-world scenarios. To our knowledge, this is the first study to assess the associations between comorbidity patterns and long-term functional outcomes. Our findings highlighted the importance of prioritizing comprehensive management of AIS and AIS-related comorbidities, with the potential to improve healthcare strategies for these patients.
The overall prevalence of comorbidity in AIS patients was generally comparable to that observed in previous studies [10, 14]. This study replicated findings of multiple previous studies that reported independent risk factors for poor functional outcomes, including atrial fibrillation [36, 37], peripheral arterial disease [38], chronic respiratory diseases [39], and epilepsy [19]. An increased risk of a poor functional result was linked to a higher number of comorbidities, which is consistent with previous studies [14]. We identified eight main patterns of comorbidity among the AIS patients and assessed their relationships with functional outcomes. Specifically, six of these patterns showed significant associations with functional outcomes.
Pattern 1, as the pattern of common metabolic disorders, encompasses any combination of hypertension, dyslipidemia, and diabetes. This pattern is the most prevalent in our study, which is consistent with prior literature regarding the clustering of common metabolic disorders among AIS patients [14, 40]. Extensive literature has indicated that smoking, hypertension, diabetes, and dyslipidemia are well-recognized risk factors for cardiovascular disease. The prevalence of these risk factors, particularly smoking and hypertension, is higher in China than in numerous countries, such as the USA [41, 42]. Moreover, hypertension poses a greater risk ratio for stroke in the Chinese population compared to Europeans [43]. Additionally, we found that compared to isolated hypertension, dyslipidemia, and diabetes, the combination of these diseases increases the risk for poor functional outcomes. This emphasizes the necessity for comprehensive, multi-targeted primary prevention and management strategies for AIS in China, particularly focusing on managing hypertension [42].
Patterns 2, 3, and 8 are characterized by the combination of common metabolic disorders with coronary heart disease and atrial fibrillation. Notably, these patterns have been linked to a heightened risk of unfavorable functional outcomes among AIS patients, exhibiting ORs of 1.40, 1.79, and 2.49, respectively (Table 4). These findings are consistent with earlier studies. Numerous studies have consistently reported a strong association between coronary heart disease, atrial fibrillation, and AIS [44, 45]. Moreover, atrial fibrillation (35.30%), hypertension (32.75%), and coronary heart disease (16.48%) were the most common comorbidities among AIS patients, according to a retrospective study in nine Chinese cities [46]. Atrial fibrillation was a significant predictor of mortality in AIS patients [47]. Moreover, our observations indicate that the subsequent combinations of other diseases, including heart failure, digestive system disease, arthritis, cognitive impairment, and chronic respiratory diseases, significantly increase the probability for poor functional outcomes, with lift greater than 3 (Table 4). The co-occurrence of these diseases is possibly explained by the shared risk of aging and physical limitations [48]. Additionally, gastroesophageal reflux disease has been reported as a potential cause of atrial fibrillation, involving mechanisms such as inflammation, autoimmune responses, and autonomic dysfunction [49]. Recent studies have revealed the role of gut microbiota-derived molecules in the development of cardiovascular diseases and their risk factors, as well as their impact on the prognosis of cardiovascular diseases [17, 50]. This could also explain why the combinations of common metabolic disorders, atrial fibrillation, arthritis, and digestive disorders among AIS patients holds the largest probability of poor functional outcomes (lift = 8.29), as detailed in Table 4. Future research should focus on the potential pathogenesis between comorbidities as well as their shared risk factors and biomarkers.
Pattern 4 was represented by common metabolic disorders and liver diseases. AIS causes systemic alterations across peripheral organs. These alterations include autonomic and endocrine dysregulation, as well as brain-derived pro-inflammatory mediators, which trigger peripheral immune responses and systemic inflammation [51, 52]. As a crucial metabolic organ, the disruption in autonomic and endocrine functions resulting from AIS has been demonstrated to adversely impact hepatic functions [53]. Liver function is a valuable indicator of IS prognosis [20], aligning with the findings of our study.
Pattern 5 was distinguished by common metabolic disorders and peripheral arterial disease. A subsequent combination with heart failure significantly increased the likelihood of poor functional outcomes. Individuals with peripheral arterial disease, such as carotid artery stenosis, could substantially increase their likelihood of developing an AIS and worsening prognosis [38]. Patients with heart failure are at an elevated risk of developing AIS [18]. And patients who suffer both heart failure and AIS simultaneously often experience more severe neurological impairments and face a higher risk of mortality [36, 54]. The co-occurrence of heart failure, peripheral arterial disease, and AIS can be explained by the shared metabolic risk factors and vascular risk factors (e.g., atherosclerosis). This suggests that future studies should focus on optimizing the comprehensive management of comorbidities and their common risk factors.
However, our study also has limitations. First, we were not able to assess the impact of certain individual diseases, such as heart failure, pulmonary embolism, obstructive sleep apnea-hypopnea syndrome and cancer, on functional outcomes due to the relatively low prevalence among AIS patients. And we did not adjust for multiple covariables in the models due to the constraints imposed by the limited sample size [55]. Second, comorbidities were extracted from medical records. Some comorbidities may have been missed or underestimated if they were inadequately coded or not documented. Specifically, the prevalence of arthritis might have been underestimated because the misdiagnosis and underdiagnosis of arthritis are common in China [56]. Third, our data were collected over 6 years ago, potentially not fully capturing the current situation. Nevertheless, our dataset remains the most comprehensive and recent registry cohort available for AIS to date. Additionally, the present study was based on participants from China, which may potentially limit the interethnic extrapolation of the findings. Further studies involving multiethnic participants are needed. Furthermore, our study mainly focuses on examining associations between comorbidities and functional outcomes in AIS patients. It should also concentrate on elucidating the potential pathogenesis between these comorbidities and AIS, as well as identifying their shared risk factors and biomarkers.
Conclusion
Our study revealed that comorbidity is highly prevalent among AIS patients in China, encompassing diverse patterns. The specific comorbidities and comorbidity patterns identified in this study are closely associated with poor functional outcomes. Our findings emphasize the importance of prioritizing comprehensive management of AIS and AIS-related comorbidities to mitigate the risk of disability among AIS patients.
Acknowledgments
We thank all the participants in the China National Stroke Registry 3 study.
Statement of Ethics
The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of Beijing Tiantan Hospital (No. KY2015-001-01), and all study centers gave ethical approval of the study protocol. Written consents were obtained from all participants or their legal representatives.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
The study is supported by the nonprofit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No. 2021-RC330-004), the nonprofit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No. 2022-ZHCH330-01), and the Disciplines Construction Project: Population Medicine (Grant No. WH10022022010).
Author Contributions
Xinying Huang was mainly involved in study design, data analysis, data interpretation, and manuscript preparation. Xia Meng completed the data collection and management. Juan Zhang, Yachen Wang, Weihao Shao, and Xiaoxia Wei were mainly involved in data analysis. Zuolin Lu, Tianqi Li, Yong Jiang, and Ruitai Shao were mainly involved in study design, data interpretation, and manuscript preparation. All authors revised and approved the final version of the manuscript.
Funding Statement
The study is supported by the nonprofit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No. 2021-RC330-004), the nonprofit Central Research Institute Fund of Chinese Academy of Medical Sciences (Grant No. 2022-ZHCH330-01), and the Disciplines Construction Project: Population Medicine (Grant No. WH10022022010).
Data Availability Statement
The data that support the findings of this study are available from the co-corresponding author (Dr. Yong Jiang, jiangyong@ncrcnd.org.cn) on reasonable request. Interested parties can apply for data access requests from the website of China National Clinical Research Center for Neurological Diseases at https://www.ncrcnd.org.cn.
Supplementary Material.
References
- 1. Feigin VL, Stark BA, Johnson CO, Roth GA, Bisignano C, Abady GG, et al. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021;20(10):795–820. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Wang YJ, Li ZX, Gu HQ, Zhai Y, Jiang Y, Zhao XQ, et al. China stroke statistics 2019: a report from the national center for healthcare quality management in neurological diseases, China national clinical research center for neurological diseases, the Chinese stroke association, national center for chronic and non-communicable disease control and prevention, Chinese center for disease control and prevention and Institute for global neuroscience and stroke collaborations. Stroke Vasc Neurol. 2020;5(3):211–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Johnston SC, Mendis S, Mathers CD. Global variation in stroke burden and mortality: estimates from monitoring, surveillance, and modelling. Lancet Neurol. 2009;8(4):345–54. [DOI] [PubMed] [Google Scholar]
- 4. GBD 2016 Neurology Collaborators . Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(5):459–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Feinstein AR. The pre-therapeutic classification of co-morbidity in chronic disease. J Chronic Dis. 1970;23(7):455–68. [DOI] [PubMed] [Google Scholar]
- 7. Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. Ann Fam Med. 2009;7(4):357–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Meghani SH, Buck HG, Dickson VV, Hammer MJ, Rabelo-Silva ER, Clark R, et al. The conceptualization and measurement of comorbidity: a review of the interprofessional discourse. Nurs Res Pract. 2013;2013(1):192782. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Nicholson K, Makovski TT, Griffith LE, Raina P, Stranges S, van den Akker M. Multimorbidity and comorbidity revisited: refining the concepts for international health research. J Clin Epidemiol. 2019;105:142–6. [DOI] [PubMed] [Google Scholar]
- 10. Gallacher KI, Batty GD, McLean G, Mercer SW, Guthrie B, May CR, et al. Stroke, multimorbidity and polypharmacy in a nationally representative sample of 1,424,378 patients in Scotland: implications for treatment burden. BMC Med. 2014;12:151. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Gallacher KI, Jani BD, Hanlon P, Nicholl BI, Mair FS. Multimorbidity in stroke. Stroke. 2019;50(7):1919–26. [DOI] [PubMed] [Google Scholar]
- 12. Kabboord AD, van Eijk M, Fiocco M, van Balen R, Achterberg WP. Assessment of comorbidity burden and its association with functional rehabilitation outcome after stroke or hip fracture: a systematic review and meta-analysis. J Am Med Dir Assoc. 2016;17(11):1066.e13–21. [DOI] [PubMed] [Google Scholar]
- 13. Schmidt M, Jacobsen JB, Johnsen SP, Bøtker HE, Sørensen HT. Eighteen-year trends in stroke mortality and the prognostic influence of comorbidity. Neurology. 2014;82(4):340–50. [DOI] [PubMed] [Google Scholar]
- 14. She R, Yan Z, Hao Y, Zhang Z, Du Y, Liang Y, et al. Comorbidity in patients with first-ever ischemic stroke: disease patterns and their associations with cognitive and physical function. Front Aging Neurosci. 2022;14:887032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Liu LP, Liu J, Wang Y, Wang D, Wang Y. Substantial improvement of stroke care in China. Stroke. 2018;49(12):3085–91. [DOI] [PubMed] [Google Scholar]
- 16. Feigin VL, Norrving B, Mensah GA. Global burden of stroke. Circ Res. 2017;120(3):439–48. [DOI] [PubMed] [Google Scholar]
- 17. Xu J, Zhao M, Wang A, Xue J, Cheng S, Cheng A, et al. Association between plasma trimethyllysine and prognosis of patients with ischemic stroke. J Am Heart Assoc. 2021;10(23):e020979. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Witt BJ, Brown RD Jr, Jacobsen SJ, Weston SA, Ballman KV, Meverden RA, et al. Ischemic stroke after heart failure: a community-based study. Am Heart J. 2006;152(1):102–9. [DOI] [PubMed] [Google Scholar]
- 19. Lee DA, Jang T, Kang J, Park S, Park KM. Correction to: functional connectivity alterations in patients with post-stroke epilepsy based on source-level EEG and graph theory. Brain Topogr. 2024;37(5):921–30. [DOI] [PubMed] [Google Scholar]
- 20. Eto F, Nezu T, Aoki S, Kuzume D, Hosomi N, Maruyama H. Liver fibrosis index is associated with functional outcome among acute ischemic stroke patients. J Stroke Cerebrovasc Dis. 2024;33(2):107537. [DOI] [PubMed] [Google Scholar]
- 21. Wang Y, Jing J, Meng X, Pan Y, Wang Y, Zhao X, et al. The Third China National Stroke Registry (CNSR-III) for patients with acute ischaemic stroke or transient ischaemic attack: design, rationale and baseline patient characteristics. Stroke Vasc Neurol. 2019;4(3):158–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Ding L, Mane R, Wu Z, Jiang Y, Meng X, Jing J, et al. Data-driven clustering approach to identify novel phenotypes using multiple biomarkers in acute ischaemic stroke: a retrospective, multicentre cohort study. EClinicalMedicine. 2022;53:101639. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Adams HP Jr, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke. 1993;24(1):35–41. [DOI] [PubMed] [Google Scholar]
- 24. Song A, Liang Y, Yan Z, Sun B, Cai C, Jiang H, et al. Highly prevalent and poorly controlled cardiovascular risk factors among Chinese elderly people living in the rural community. Eur J Prev Cardiol. 2014;21(10):1267–74. [DOI] [PubMed] [Google Scholar]
- 25. Wang R, Fratiglioni L, Liang Y, Welmer AK, Xu W, Mangialasche F, et al. Prevalence, pharmacological treatment, and control of cardiometabolic risk factors among older people in central Stockholm: A population-based study. PLoS One. 2015;10(3):e0119582. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) . Third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation. 2002;106(25):3143–421. [PubMed] [Google Scholar]
- 27. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2012. 35 Suppl 1(Suppl 1):S64–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. National Medical Products Administration . (2015, November 3). Technical guidelines for clinical research on new traditional Chinese medicine in the treatment of stroke (No. 83) [In Chinese]. National Medical Products Administration. https://www.nmpa.gov.cn/xxgk/ggtg/ypggtg/ypqtggtg/20151103120001444.html
- 29. Quinn TJ, Dawson J, Walters MR, Lees KR. Functional outcome measures in contemporary stroke trials. Int J Stroke. 2009;4(3):200–5. [DOI] [PubMed] [Google Scholar]
- 30. Rankin J. Cerebral vascular accidents in patients over the age of 60. II. Prognosis. Scott Med J. 1957;2(5):200–15. [DOI] [PubMed] [Google Scholar]
- 31. van Swieten JC, Koudstaal PJ, Visser MC, Schouten HJ, van Gijn J. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. 1988;19(5):604–7. [DOI] [PubMed] [Google Scholar]
- 32. Zhang G, Pan Y, Zhang R, Wang M, Meng X, Li Z, et al. Prevalence and prognostic significance of malnutrition risk in patients with acute ischemic stroke: results from the third China national stroke registry. Stroke. 2022;53(1):111–9. [DOI] [PubMed] [Google Scholar]
- 33. Banks JL, Marotta CA. Outcomes validity and reliability of the modified Rankin scale: implications for stroke clinical trials: a literature review and synthesis. Stroke. 2007;38(3):1091–6. [DOI] [PubMed] [Google Scholar]
- 34. Agrawal R. Mining association rules between sets of items in large databases. In: Acm sigmod conference on management of data; 1993. [Google Scholar]
- 35. Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases; 1994. [Google Scholar]
- 36. Haeusler KG, Laufs U, Endres M. Chronic heart failure and ischemic stroke. Stroke. 2011;42(10):2977–82. [DOI] [PubMed] [Google Scholar]
- 37. Doehner W, Böhm M, Boriani G, Christersson C, Coats AJS, Haeusler KG, et al. Interaction of heart failure and stroke: a clinical consensus statement of the ESC council on stroke, the heart failure association (HFA) and the ESC working group on thrombosis. Eur J Heart Fail. 2023;25(12):2107–29. [DOI] [PubMed] [Google Scholar]
- 38. Eisenberg RL, Nemzek WR, Moore WS, Mani RL. Relationship of transient ischemic attacks and angiographically demonstrable lesions of carotid artery. Stroke. 1977;8(4):483–6. [DOI] [PubMed] [Google Scholar]
- 39. Tsung TH, Huang KH, Chien WC, Chen YH, Yen IC, Chung CH, et al. Uveitis increases the risk of stroke among patients with ankylosing spondylitis: a nationwide population-based longitudinal study. Front Immunol. 2022;13:959848. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Wang R, Yan Z, Liang Y, Tan ECK, Cai C, Jiang H, et al. Prevalence and patterns of chronic disease pairs and multimorbidity among older Chinese adults living in a rural area. Plos One. 2015;10(9):e0138521. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. National Health Commission of the People’s Republic of China . Report on the nutrition and chronic disease status of Chinese residents 2015. People’s Medical Publishing House; 2015. [Google Scholar]
- 42. Liu M, Wu B, Wang WZ, Lee LM, Zhang SH, Kong LZ. Stroke in China: epidemiology, prevention, and management strategies. Lancet Neurol. 2007;6(5):456–64. [DOI] [PubMed] [Google Scholar]
- 43. Zhang XF, Attia J, D’Este C, Yu XH. Prevalence and magnitude of classical risk factors for stroke in a cohort of 5092 Chinese steelworkers over 13.5 years of follow-up. Stroke. 2004;35(5):1052–6. [DOI] [PubMed] [Google Scholar]
- 44. Daoud EG, Glotzer TV, Wyse DG, Ezekowitz MD, Hilker C, Koehler J, et al. Temporal relationship of atrial tachyarrhythmias, cerebrovascular events, and systemic emboli based on stored device data: a subgroup analysis of TRENDS. Heart Rhythm. 2011;8(9):1416–23. [DOI] [PubMed] [Google Scholar]
- 45. Singer DE, Ziegler PD, Koehler JL, Sarkar S, Passman RS. Temporal association between episodes of atrial fibrillation and risk of ischemic stroke. JAMA Cardiol. 2021;6(12):1364–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Wu M, Jiang H, Yu K, Zhao Z, Zhu B. The Prescription trends and dosing appropriateness analysis of novel oral anticoagulants in ischemic stroke patients: a retrospective study of 9 cities in China. Front Pharmacol. 2024;15:1304139. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Addisu ZD, Mega TA. Clinical characteristics and treatment outcomes of acute ischemic stroke with atrial fibrillation among patients admitted to tertiary care hospitals in amhara regional state: retrospective-cohort study. Vasc Health Risk Manag. 2023;19:837–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Gimeno-Miguel A, Gracia Gutiérrez A, Poblador-Plou B, Coscollar-Santaliestra C, Pérez-Calvo JI, Divo MJ, et al. Multimorbidity patterns in patients with heart failure: an observational Spanish study based on electronic health records. BMJ Open. 2019;9(12):e033174. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49. Martins J, Mendes L, Durães S. Cardiovascular complications of gastrointestinal diseases. CCCM. 2015;102:1–4. [Google Scholar]
- 50. Ahmadmehrabi S, Tang WHW. Gut microbiome and its role in cardiovascular diseases. Curr Opin Cardiol. 2017;32(6):761–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51. Balch MHH, Nimjee SM, Rink C, Hannawi Y. Beyond the brain: the systemic pathophysiological response to acute ischemic stroke. J Stroke. 2020;22(3):424–172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52. Cui P, McCullough LD, Hao J. Brain to periphery in acute ischemic stroke: mechanisms and clinical significance. Front Neuroendocrinol. 2021;63:100932. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53. Inderhees J, Schwaninger M. Liver metabolism in ischemic stroke. Neuroscience. 2024;550:62–8. [DOI] [PubMed] [Google Scholar]
- 54. Ois A, Gomis M, Cuadrado-Godia E, Jiménez-Conde J, Rodríguez-Campello A, Bruguera J, et al. Heart failure in acute ischemic stroke. J Neurol. 2008;255(3):385–9. [DOI] [PubMed] [Google Scholar]
- 55. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9. [DOI] [PubMed] [Google Scholar]
- 56. Tian X, Li M, Zeng X. The current status and challenges in the diagnosis and treatment of rheumatoid arthritis in China: an annual report of 2019. Rheumatol Immunol Res. 2021;2(1):49–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the co-corresponding author (Dr. Yong Jiang, jiangyong@ncrcnd.org.cn) on reasonable request. Interested parties can apply for data access requests from the website of China National Clinical Research Center for Neurological Diseases at https://www.ncrcnd.org.cn.
