Incidence and associated risk factors of fecal incontinence in stroke patients: From a clinical observational study to meta-analysis

Abstract

Background:

Stroke patients are prone to gastrointestinal dysfunction, with a significantly higher risk of fecal incontinence (FI), which severely affects their quality of life.

Aims:

We investigated the incidence and associated risk factors of FI in stroke patients.

Summary of review:

Stroke patients who met the inclusion and exclusion criteria were consecutively recruited at one university medical center from January 2022 to December 2024. FI occurred in 220 of 1359 stroke patients (16.19%). Logistic regression analysis revealed that age, gender, cognitive impairment, National Institutes of Health Stroke Scale (NIHSS) admission score, antibiotic use, and gavage feeding were closely associated with FI incidence in stroke patients. Subsequently, a systematic review and meta-analysis of observational studies followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Studies were included if they reported the incidence or risk factors of FI in stroke patients. Subgroup analyses were conducted according to the stage of stroke and survey site. The forest plot showed that the incidence of post-stroke FI was 24.42% (95% confidence interval (CI) = 15.11–33.72). On subgroup analysis, the incidence of FI was 25.73% (95% CI = 14.61–36.86) in the acute stage and 15.99% (95% CI = 9.32–22.67) in the rehabilitation stage. Meanwhile, the incidence of FI in the hospital-based stroke patients was 25.31% (95% CI = 11.37–39.26), which was higher than that in the community-dwelling stroke patients (15.14%, 95% CI = 2.26–28.02).

Conclusion:

The incidence of post-stroke FI is relatively high. Age, gender, cognitive impairment, NIHSS score, antibiotic use, and gavage feeding are closely associated with FI occurrence in stroke patients.

Graphical abstract

Keywords

Stroke fecal incontinence incidence risk factor clinical observational study meta-analysis

Introduction

Stroke has long been a significant public health issue, causing enormous economic and social burdens.^1–3 In addition, it is a major contributor to morbidity and mortality. It is reported that it is the second leading cause of death and the third most prominent cause of disability-adjusted life years.⁴

Stroke is often accompanied by various medical complications, leading to the deterioration of the disease. Gastrointestinal dysfunction, such as fecal incontinence (FI), is a common and painful manifestation after stroke.⁵ There is evidence to suggest that gastrointestinal complications may reduce quality of life, leading to prolonged hospitalization, malnutrition, pressure ulcers, and poor neurological prognosis.^6,7 These consequences pose significant challenges to stroke patient care, public health systems, and rehabilitation services.

Compared with the general population, patients with central nervous system diseases have a significantly increased risk of gastrointestinal complications.⁸ The bidirectional communication between the central nervous system and the gastrointestinal tract, known as the brain–gut axis, plays a crucial role.^9–11 Cerebrovascular diseases can damage various components of the brain–gut axis, including the gut and autonomic nervous system.^12,13

Currently, gastrointestinal complications receive much less attention, but as many as 50% of stroke patients may experience FI, diarrhea, constipation, or other gastrointestinal complications.¹⁴ The risk of stroke patients losing intestinal control is much higher than that of others.¹⁵ Although FI is common after stroke, the incidence and associated risk factors of FI remain inadequately investigated.

This study focuses on the incidence and risk factors of FI after stroke. The purpose is to draw attention to the development of effective management strategies. We initially conducted a clinical observational study to describe the FI characteristics of stroke patients. Subsequently, a meta-analysis was conducted to synthesize studies from different sources and validate the generalizability of our findings.

Methods

Clinical observational study

This section followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, conformed to the Declaration of Helsinki, and was approved by the Ethics Committee of Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine (No. KY2022270).

Study design and participants

This retrospective study enrolled stroke inpatients admitted to Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine from January 2022 to December 2024. Based on the presence or absence of FI, the participants were categorized into the observation group (FI group) and the control group (fecal continence group, FC group).

Data sample and patient selection

Patients meeting the following criteria were included in this study: all stroke diagnoses comply with the International Classification of Diseases 11th revision (ICD-11) criteria; irrespective of age and gender, those with brain injury due to ischemic or hemorrhagic stroke, without considering the post-injury time span, cognitive impairment, and language function changes. For patients without cognitive and/or language dysfunction, researchers conducted direct interviews. For those with cognitive and/or language dysfunction, interviews were carried out by caregivers familiar with their bowel function characteristics.

The exclusion criteria were as follows: patients with transient ischemic attack (TIA) or no evidence of ischemia or hemorrhage on imaging examination; pre-stroke history of FI; concomitant spinal cord injury of any type; incomplete or unclear medical information, precluding accurate disease assessment; and refusal to provide relevant information.

According to the symptomatic diagnostic criteria of the Rome IV, FI was defined as the recurrent uncontrolled passage of solid or liquid fecal material. Defecation status was recorded daily during hospitalization for all stroke patients. Data on fecal leakage were routinely reported by nurses in the stroke ward during hospitalization, and the diagnosis was confirmed by two trained and qualified physicians through joint assessment. New-onset FI during hospitalization was defined as post-stroke FI.

Data collection

Information of eligible stroke patients was collected, including (1) demographic data: age and gender; (2) medical history: hypertension, diabetes mellitus, hyperlipidemia, coronary heart disease, atrial fibrillation, chronic renal failure, chronic obstructive pulmonary disease (diagnosed according to ICD-11 criteria); cognitive impairment (assessed by the Mini-Mental State Examination, score <24); gastrointestinal diseases (including peptic ulcer, chronic gastritis, inflammatory bowel disease, irritable bowel syndrome, diagnosed according to ICD-11 criteria); (3) disease-related information: National Institutes of Health Stroke Scale (NIHSS) admission score for stroke severity; stroke subtype (ischemic stroke, hemorrhagic stroke, hemorrhagic infarction); stroke stage (acute stage: first 7 days following stroke onset; rehabilitation stage: more than 7 days after stroke onset); affected cerebral hemisphere (left, right, bilateral); arterial lesions (anterior circulation, posterior circulation, bilateral circulation); and (4) treatment-related information: antibiotic use and gavage feeding.

Objective laboratory markers were also collected in this study, including (1) blood cell tests: white blood cells, neutrophils, lymphocytes, monocytes, and platelets; (2) renal function tests: urea, creatinine, uric acid, cystatin C; (3) lipid indices: total cholesterol, triglycerides, high-density lipoprotein, lipoprotein alpha; (4) D-dimer; (5) glycated hemoglobin; (6) high-sensitivity C-reactive protein (hs-CRP); and (7) interleukin-6 (IL-6).

During data collection, all investigators involved in retrospective data extraction received standardized training on the study protocols. A uniform data collection form was used to extract key study information from electronic medical records (EMRs), with the accuracy and consistency of all extracted data further verified.

Statistical analysis

All the statistical analyses were performed using SPSS Statistics (version 25; IBM Corp., NY, USA). Measurement data with normal distribution were presented as the means ± standard deviations, while non-normally distributed measurement data were presented as medians (interquartile range). The count data were expressed as percentages (%). A Student’s t-test was performed to analyze normally distributed measurement data between two groups. The rank-sum test was used for non-normally distributed measurement data analysis. The chi-square test or Fisher’s exact test was applied for count data. Binary logistic regression was utilized to explore the factors associated with FI. All candidate variables were force-entered into the multivariate model simultaneously using SPSS’s default “Enter” method, without prior univariate screening. For variables with missing data, complete case analysis was uniformly adopted, with no data imputation conducted, to minimize potential bias. Statistical significance was defined as a two-sided P-value < 0.05.

Following the clinical observational study, a systematic review and meta-analysis was conducted to validate the generalizability of our findings regarding the incidence and risk factors of FI in stroke patients. This approach enabled us to compare clinical data with the pooled evidence from existing literature, addressing potential biases in regional patient demographics.

Systematic review and meta-analysis

The study design followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Since this section was a systematic review and meta-analysis, ethics approval and informed patient consent were not required.

Search strategy

A literature search was conducted for studies published between January 1, 1980, and December 31, 2024. The relevant literature was identified through the following steps: (1) searching pertinent articles in the electronic databases: MEDLINE, Embase, Cochrane Database, and Web of Science; (2) using electronic search terms included stroke, cerebrovascular accident/disorder, ischemic stroke, cerebral/brain infarction, brain ischemia, intracerebral/cerebral hemorrhage, bowel dysfunction/disorder, and FI; (3) carefully reviewing the abstract of each article to identify appropriate publications; (4) retrieving and thoroughly reading full-text articles, and also examining all reference lists of relevant articles to identify additional eligible publications; and (5) manually searching references from previously retrieved articles and all eligible studies. The detailed full search strategy is provided in the Supplemental Material.

Study selection

In the process of including and excluding studies, the source of selection and measurement bias was considered in terms of study design, context, and participants.

Studies were included if they reported the incidence of FI in stroke patients. The inclusion criteria were as follows: (1) prospective studies, retrospective cohort studies, or cross-sectional studies; (2) original research on adult human stroke survivors; (3) studies that recruited both stroke and non-stroke participants were included if separate stroke data were provided; (4) the incidence of FI was reported in the study; (5) the study population had a history of stroke; and (6) FI was defined as recurrent uncontrolled, involuntary, or unplanned passage of fecal material.

The following articles were excluded: (1) patients with a prior history of FI before the stroke diagnosis; (2) patients who had suffered from gastrointestinal tract disorders or undergone gastrointestinal surgery in the past; (3) studies with an unclear definition of FI; (4) studies involving a non-adult population; (5) highly selected studies or treatment studies without incidence data; (6) commentaries, single case reports, editorials, and reviews; and (7) non-English articles or articles without full text available.

Data extraction and quality assessment

The following general descriptive information was extracted from each study: (1) first author and year of publication; (2) country of participants, survey date, and survey sites; (3) sample, age, and gender; (4) subtype of stroke; (5) various stages following a stroke; and (6) associated risk factors of FI, including age, gender, type of stroke (ischemia/hemorrhage), stroke severity factors (NIHSS, Barthel Index score, Glasgow Coma Scale, initial urinary incontinence, visual field defect, dysphasia, sensory or visual neglect, dysphagia, assistance with toilet access, assistance with transfers, assistance with stairs), clinical characteristics (Mini-Mental score, urinary incontinence, medication use, lesion size, cortical involvement, Scandinavian Stroke Scales score), psychosocial factors (living situation, district nurse since stroke, home help since stroke, poor social support network, general health perception), and medical history (hypertension, diabetes mellitus, dyslipidemia, coronary heart diseases, atrial fibrillation, preceding TIA, current smoking, former stroke, other disabling disease).

Three authors (X.H., W.Q., and Y.B.) independently selected the studies to be included in the review by carefully reading each article, extracting the data independently, and cross-checking the information. Any disagreement was resolved through discussion until a consensus was reached. Two reviewers (J.L. and T.W.) were consulted if disagreement persisted.

The quality of the included studies was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for studies, which evaluates methodological quality across nine core domains.^16,17 The data from all included studies were clearly tabulated, and deviations were identified and considered during the quality assessment stage.

Statistical analysis

For each included study, information on the first author, year of publication, sample size, and the incidence of FI was extracted and recorded before the pooled incidence calculation. The statistical validity of aggregating the studies was assessed with the I² statistic for heterogeneity. Pooled incidence estimates and 95% confidence intervals (CIs) were determined by the random-effects model. Forest plots were used to present the pooled effect sizes and their 95% CI. Funnel plots were constructed to evaluate publication bias and other potential small-study effects in the meta-analysis. The meta-analysis was performed using Stata MP software (version 18; Stata Corp., Texas, USA). Subgroup analyses were conducted based on stroke stage (acute stage or rehabilitation stage) and survey sites (hospital or community).

Results

Phase I

Incidence of post-stroke FI

A total of 1359 patients were enrolled in this study. Among them, 220 patients (16.19%) experienced FI and were assigned to the FI group, while 1139 patients (83.81%) formed the FC group.

Clinical characteristics associated with post-stroke FI

Demographic data, medical history, disease-related information, and treatment-related information are presented in Table 1. Due to incomplete medical and imaging records, non-randomly distributed missing data were noted in the clinical variables: stroke subtype (46 missing, 3.38%), stroke stage (332 missing, 24.43%), affected cerebral hemisphere (314 missing, 23.11%), and arterial lesions (125 missing, 9.20%).

Table 1.

Clinical characteristics of stroke patients with versus without FI.

Item	Total sampleN = 1359	FI groupN = 220 (16.19%)	FC groupN = 1139 (83.81%)	Z/x ²	P
Demographic data
Age (years)	68.00 (60.00, 77.00)	75.50 (66.25, 83.00)	67.00 (59.00, 75.00)	−8.813	<0.001
⩽64	528 (38.85)	48 (21.81)	480 (42.14)	61.644	<0.001
65–79	565 (41.57)	90 (40.91)	475 (41.70)
⩾80	266 (19.57)	82 (37.27)	184 (16.15)
Gender, male	895 (65.86)	119 (54.09)	776 (68.13)	16.162	<0.001
Female	464 (34.14)	101 (45.91)	363 (31.87)
Medical history
Hypertension	760 (55.92)	133 (60.45)	627 (55.05)	2.186	0.139
Diabetes mellitus	368 (27.08)	68 (30.91)	300 (26.34)	1.950	0.163
Hyperlipidemia	40 (2.94)	5 (2.27)	35 (3.07)	0.413	0.520
Coronary heart disease	111 (8.17)	29 (13.18)	82 (7.20)	8.798	0.003
Atrial fibrillation	67 (4.93)	25 (11.36)	42 (3.69)	23.180	<0.001
Chronic renal failure	33 (2.43)	7 (3.18)	26 (2.28)	0.629	0.428
Chronic obstructive pulmonary disease	20 (1.47)	5 (2.27)	15 (1.32)	1.162	0.281
Cognitive impairment	46 (3.38)	17 (7.72)	29 (2.55)	15.136	<0.001
Gastrointestinal disease	41 (3.02)	8 (3.63)	33 (2.90)	0.344	0.557
Disease-related information
NIHSS admission score
⩽4	364 (26.8)	35 (15.9)	329 (28.9)	16.927	<0.001
5–15	911 (67.0)	166 (75.5)	745 (65.4)
⩾16	84 (6.2)	19 (8.6)	65 (5.7)
Stroke subtype
Ischemic stroke	1182 (86.98)	186 (84.55)	996 (87.45)	1.369	0.242
Hemorrhagic stroke	35 (2.58)	7 (3.18)	28 (2.46)	0.385	0.535
Hemorrhagic infarction	96 (7.06)	20 (9.09)	76 (6.67)	1.643	0.200
Stroke stage
Acute stage	842 (61.96)	123 (55.91)	719 (63.13)	4.074	0.044
Rehabilitation stage	185 (13.61)	31 (14.09)	154 (13.52)	0.051	0.821
Affected cerebral hemisphere
Left	459 (33.77)	65 (29.55)	394 (34.59)	2.099	0.147
Right	482 (35.47)	73 (33.18)	409 (35.91)	0.599	0.439
Bilateral	104 (7.65)	17 (7.73)	87 (7.64)	0.011	0.918
Arterial lesions
Anterior circulation	606 (44.59)	89 (40.45)	517 (45.39)	1.818	0.178
Posterior circulation	430 (31.64)	68 (30.91)	362 (31.78)	0.065	0.799
Bilateral circulation	198 (14.57)	39 (17.73)	159 (13.96)	2.103	0.147
Treatment-related information
Antibiotic use	220 (16.19)	91 (41.36)	129 (11.33)	122.619	<0.001
Gavage feeding	145 (10.67)	80 (36.36)	65 (5.71)	181.817	<0.001

FI: fecal incontinence; FC: fecal continence. Some data on stroke subtype, stroke stage, affected cerebral hemisphere, and arterial lesions were missing due to incomplete medical and imaging records.

A series of significant between-group differences was identified across multiple clinical dimensions. Specifically, there were significant differences in age, gender, coronary heart disease, atrial fibrillation, cognitive impairment, NIHSS admission score, antibiotic use, and gavage feeding (P < 0.01). Patients also showed significant differences between groups in their status in the acute stage (P < 0.05).

Conversely, several other clinical parameters did not demonstrate significant between-group variation. These included medical history such as hypertension, diabetes mellitus, hyperlipidemia, chronic renal failure, chronic obstructive pulmonary disease, and gastrointestinal disease (P > 0.05). In addition, there were no significant differences in the stroke subtype, rehabilitation stage, affected cerebral hemisphere, or arterial lesions (P > 0.05).

Objective laboratory markers associated with post-stroke FI

Results of blood cell tests, renal function markers, lipid profile indices, D-dimer, glycated hemoglobin, hs-CRP, and IL-6 are presented in Table 2.

Table 2.

Univariate analysis of factors associated with FI in stroke patients.

Item	FI groupN = 220 (16.19%)	FC groupN = 1139 (83.81%)	Z	P
Blood cell tests
White blood cells, 10⁹/L	7.57 (6.10, 9.52)	7.00 (5.80, 8.49)	−3.258	0.001
Neutrophil, 10⁹/L	5.37 (4.13, 7.39)	4.60 (3.57, 6.16)	−4.730	<0.000
Lymphocyte, 10⁹/L	1.48 (1.02, 1.98)	1.70 (1.29, 2.21)	−4.416	<0.000
Monocyte, 10⁹/L	0.44 (0.32, 0.58)	0.40 (0.32, 0.51)	−1.978	0.048
Platelet, 10⁹/L	209.26 ± 4.50	207.00 (171.00, 252.00)	−0.879	0.379
Renal function markers
Urea, mmol/L	5.66 (4.66, 7.40)	5.57 (4.59, 6.79)	−1.239	0.216
Creatinine, μmol/L	65.70 (54.88, 80.75)	69.00 (57.00, 85.80)	−1.834	0.067
Uric acid, μmol/L	318.00 (253.98, 395.05)	331.00 (263.15, 401.50)	−1.376	0.169
Cystatin C, mg/L	0.99 (0.78, 1.26)	0.91 (0.76, 1.14)	−2.540	0.011
Lipid profile indices
Total cholesterol, mmol/L	4.49 (3.76, 5.37)	4.69 ± 0.05	−0.737	0.461
Triglyceride, mmol/L	1.25 (0.89, 1.70)	1.35 (0.95, 1.93)	−1.931	0.053
High-density lipoprotein, mmol/L	1.18 ± 0.03	1.12 (0.96, 1.33)	−1.082	0.279
Lipoprotein alpha, mg/L	189.00 (122.00, 373.00)	186.00 (111.00, 346.00)	−0.639	0.523
D-dimer, mg/L FEU	0.83 (0.46, 1.82)	0.48 (0.31, 0.93)	−6.371	<0.000
Glycated hemoglobin, %	6.28 (5.66, 8.18)	6.10 (5.62, 7.62)	−1.655	0.098
High-sensitivity C-reactive protein, μg/mL	5.00 (2.00, 13.20)	3.00 (2.00, 7.00)	−4.134	<0.000
Interleukin-6, pg/mL	28.81 (9.57, 74.16)	6.12 (3.15, 9.87)	−4.009	<0.000

FI: fecal incontinence; FC: fecal continence.

Statistically significant differences were found between the FI group and FC group in white blood cell count, neutrophil count, lymphocyte count, monocyte count, cystatin C, D-dimer, hs-CRP, and IL-6 (P < 0.01, P < 0.05). However, no significant between-group differences were observed in platelet count, urea, creatinine, uric acid, total cholesterol, triglycerides, high-density lipoprotein cholesterol, lipoprotein α, or glycated hemoglobin (P > 0.05).

Binary logistic regression analysis of FI in stroke patients

A logistic regression model revealed that age, gender, cognitive impairment, antibiotic use, and gavage feeding were closely associated with the incidence of FI in the stroke patients.

More specifically, age (odds ratio (OR) = 1.418; 95% CI = 1.119–1.796), the gender (OR = 0.515; 95% CI = 0.363–0.732), cognitive impairment (OR = 2.967; 95% CI = 1.418–6.210), NIHSS admission score (OR = 1.766; 95% CI = 1.337–2.334), antibiotic use (OR = 2.521; 95% CI = 1.654–3.844), and gavage feeding (OR = 5.779; 95% CI = 3.675–9.088) were significantly associated with FI among patients with stroke (Table 3).

Table 3.

Multivariate analysis of factors associated with FI in stroke patients.

Item	B	Exp (B)	95% CI of Exp (B)	P
Age	0.349	1.418	1.119–1.796	0.004
Gender	−0.663	0.515	0.363–0.732	0.000
Cognitive impairment	1.088	2.967	1.418–6.210	0.004
NIHSS admission score	0.569	1.766	1.337–2.334	0.000
Antibiotic use	0.925	2.521	1.654–3.844	0.000
Gavage feeding	1.754	5.779	3.675–9.088	0.000

FI: fecal incontinence; CI: confidence interval.

Phase II

Literature review

The literature search yielded 11,809 articles, of which 11,783 were excluded after title and abstract screening. A total of 26 full-text articles were assessed for eligibility. Of these, nine were review articles, one was a duplicate, one lacked sufficient baseline data, two provided insufficient outcome data, and three contained unextractable data. After exclusions, 10 eligible studies were selected, with an additional three studies retrieved from manual searching and screening of the reference lists of the included studies. After detailed evaluations, 13 studies were included in the final meta-analysis.^18–30 Detailed methodological quality appraisal scores for all included studies (JBI tool) are presented in Table S1, with all studies rated moderate or high quality.

Data from the 13 studies were systematically tabulated with all key elements listed. The flow diagram of the literature search process is illustrated in Figure 1.

Figure 1.

The flow diagram of the literature search process.

Study characteristics

The characteristics of the included studies are presented, including the first author, publication year, country, survey date, survey site, total sample size of stroke, gender, age, stroke stage, and samples of FI (Table 4).

Table 4.

Characteristics of included studies investigating the incidence of FI in stroke patients.

Study	Country	Survey date	Survey site	Total samples (n) (stroke subtype)	Male/Female (n)	Age, years	Stroke stage	Samples of FI, %
Cruz et al.¹⁸	Australia	May 2021–July 2022	Acute Stroke Unit	440 (IS 332; HS 108)Analyzed 418	256/184	76 (66–84)	Acute	103 (24.64)
Lucente et al.¹⁹	Spain	May 2018–December 2019	Hospital-Stroke unit	359 (IS 302; HS 57)	229/130	65.9 ± 10	Acute	23 (6.41)
							Rehabilitation	7 (1.95)
Pérez et al.²⁰	Spain	2008	Hospital-Post-Acute Care Unit	879	417/462	77.48 ± 10.18	Rehabilitation	435 (49.49)
Engler et al.²¹	Brazil	December 2009–May 2010	Hospital-Rehabilitation unit	98 (IS 87)	49/49	58.13 ± 12.64	Rehabilitation	1 (1.02)
Kovindha et al.²²	Thailand	NA	Hospital-Stroke rehabilitation registry	185 (IS 145; HS 40)	106/79	62.3 ± 12.7	Rehabilitation	75 (40.54)
Bracci et al.²³	Italy	NA	Hospital-Rehabilitation center	90	43/47	68 (27–95)	Rehabilitation	5 (5.56)
Otegbayo et al.²⁴	Nigeria	NA	Hospital	54 (IS 31; HS 16)	25/29	58.7 ± 10.6	Rehabilitation	3 (5.56)
Brittain et al.²⁵	UK	October 1996–July 1998	Community	1483	802/681	72 (64–79)	Rehabilitation	103 (6.95)
Baztan et al.²⁶	Spain	October 2000–December 2001	Hospital-Rehabilitation unit	166	66/100	NA	Rehabilitation	92 (55.42)
Harari et al.²⁷	UK	January 1995–January 2000	Community	1069	NA	NA	Acute	317 (29.65)
				846	NA	NA	Rehabilitation	91 (10.76)
Nakayama et al.²⁸	Denmark	March 1992–September 1993	Hospital-Neurological department	867 (IS 654; HS 50)	389/478	NA	Acute	347 (40.02)
			Community	493	NA	NA	Rehabilitation	44 (8.92)
Wade et al.²⁹	UK	March 1981–June 1983	NA	531	NA	NA	Acute	163 (30.70)
				494	NA	NA	Rehabilitation	32 (6.48)
Brocklehurst et al.³⁰	UK	NA	NA	135	72/63	NA	Acute	31 (22.96)
				119	NA	NA	Rehabilitation	5 (4.20)

FI: fecal incontinence; IS: ischemic stroke; HS: hemorrhagic stroke; NA: not available.

A total of 6334 stroke patients were investigated, with 1698 patients having suffered FI. The study samples ranged from 54²⁴ to 1483²⁵ patients. In terms of survey location, nine studies^{19,20,23,25–30} were conducted in Europe, one¹⁸ in Oceania, one²¹ in America, one²² in Asia, and one²⁴ in Africa.

Six studies (3379 patients)^{18,19,27–30} focused on the acute stroke stage, while 12 studies (5266 patients)^{19,20,21–30} examined the rehabilitation stage. Nine studies^{18–24,26,28} involving hospital inpatients included 3116 patients, whereas 3 studies^25,27,28 involving community participants included 3045 patients.

Incidence of post-stroke FI and subgroup analysis

The incidence of FI varied significantly, ranging from 1.02%²¹ to 55.42%.²⁶ The forest plot in Figure 2(a) demonstrates an overall FI incidence of 24.42% (95% CI = 15.11–33.72) among stroke patients.

Figure 2.

Incidence of FI in stroke patients. (a) The forest plot of the incidence of FI in stroke patients. (b) The forest plot of the incidence of FI in the acute stage of stroke. (c) The forest plot of the incidence of FI in the rehabilitation stage of stroke. (d) The forest plot of the incidence of FI in hospital-based stroke patients. (e) The forest plot of the incidence of FI in community-dwelling stroke patients. FI: fecal incontinence; CI: confidence interval.

Subgroup analysis (Figure 2(b) and (c)) revealed stage-specific incidences of FI. In the acute stage, the incidence of FI ranged from 6.41%¹⁹ to 40.02%,²⁸ and in the rehabilitation stage, it ranged from 1.02%²¹ to 55.42%.²⁶ The forest plot showed that the incidence of FI was 25.73% (95% CI = 14.61–36.86) in the acute stage and 15.99% (95% CI 9.32–22.67) in the rehabilitation stage.

Figure 2(d) and (e) showed that the incidence of FI in the hospital ranged from 1.02%²¹ to 55.42%,²⁶ and it ranged from 6.95%²⁵ to 29.65%²⁷ in community patients. The incidence of FI in the hospital-based stroke patients was 25.31% (95% CI = 11.37–39.26), which was higher than that in the community-dwelling stroke patients (15.14%, 95% CI = 2.26–28.02).

Associated risk factors of FI in stroke patients with statistical significance

Six studies^{18,19,22,26–28} reported associated risk factors for FI in stroke patients. These factors were categorized into four main types: demographic data, medical history, disease-related information, and treatment-related information.

Table 5 lists the statistically significant risk factors, which fall into four main categories. The first is demographic data, such as age. The second is medical history, including the previous modified Rankin Scale (mRS) and Barthel Index, Charlson Comorbidity Index (CCI), diabetes mellitus, and other disabling diseases. The third is disease-related information, encompassing NIHSS, hemorrhagic stroke (location), Barthel Index, Glasgow Coma Scale (GCS), urinary incontinence, visual field defect, dysphasia, visual and/or sensory neglect, need for assistance with toilet access, Scandinavian Stroke Scale, and lesion size. The fourth is treatment-related information, which covers factors such as anticholinergic medication use.

Table 5.

Associated risk factors for FI in stroke patients.

Study	Total samples/FI samples	Types of related factors	Associated risk factors
Cruz et al.¹⁸	418/103	Demographic data	Age
		Medical history	Previous mRS
		Disease-related information	NIHSSIntraventricular hemorrhageLobar hemorrhage
Lucente et al.¹⁹	359/23	Disease-related information	HSNIHSS admission > 12
Kovindha et al.²²	185/75	Demographic data	Age (years) >60
		Disease-related information	Barthel Index ⩽ 12
Baztan et al.²⁶	166/92	Medical history	Previous Barthel Index < 90Charlson Comorbidity Index
Harari et al.²⁷	1069/317	Demographic data	Age (years) ⩾65
		Disease-related information	Initial urinary incontinenceGCS < 15Visual field defectDysphagia
	846/91	Disease-related information	Initial urinary incontinenceVisual and/or sensory neglectNeed for assistance with toilet access
		Treatment-related information	Anticholinergic medications use
Nakayama et al.²⁸	867/347	Demographic data	Age
		Medical history	Diabetes mellitusOther disabling disease
		Disease-related information	Scandinavian Stroke ScaleLesion size

FI: fecal incontinence; mRS: modified Rankin Scale; NIHSS: National Institutes of Health Stroke Scale; HS: hemorrhagic stroke; GCS: Glasgow Coma Scale.

FI after stroke occurs more frequently in aged individuals, those with a medical history of multiple pre-existing comorbidities, diabetes mellitus, or disabling disease. It is also associated with stroke severity, as well as treatment-related factors.

Discussion

The current research aims to investigate the incidence and associated risk factors of FI in stroke patients. In the clinical observational study, the data of stroke patients were analyzed to assess the incidence of FI. Furthermore, multivariate regression analyses were performed to identify associated risk factors. The meta-analysis presented the pooled incidence of FI after stroke, including stroke patients in the acute stage, rehabilitation stage, and those investigated in hospitals and communities. Moreover, the analysis summarized and categorized the associated risk factors from four distinct aspects. This enables a comprehensive comparison between clinical findings and existing evidence. This integrated approach helps to situate the single-center results within the wider literature and provides a more complete overview of post-stroke FI. To clarify the link between the two components, we present the core findings and compare consistencies and differences.

The clinical study showed the incidence of post-stroke FI was 16.19%. This meta-analysis reported its overall incidence was 24.42% (95% CI = 15.11–33.72). The above results suggest that the incidence of post-stroke FI differs across the analyzed studies. This difference may be attributed to multiple potential reasons. For one thing, this clinical study was from a hospital, including acute and rehabilitation patients admitted to a general ward. These patients may have more stable conditions and less severe nerve damage. In contrast, the studies included in the meta-analysis included more patients admitted to stroke units or intensive care units.^19,20 These patients were more likely to have more serious neurological damage, which led to a higher incidence of FI. For another thing, the clinical observation study was limited to hospitalized patients, and all patients had received professional and standardized treatment. Meta-analysis included community-based studies with a more representative sample.^25,27,28 It included cases with obvious symptoms that did not seek medical assistance, leading to an increase in the overall incidence.

Regarding the comparison of different stroke stages, the clinical study and meta-analysis showed that the incidence of the acute stage is different, while the incidence of the rehabilitation stage is consistent. Compared with the clinical study, the meta-analysis report of FI incidence after acute stroke is higher, which may be due to the above-mentioned similar reasons. As for the consistent results during the rehabilitation phase, there may be the following reasons. First, during the rehabilitation stage, patients had already passed the critical stage of the acute stage, and the degree of neurological impairment remained stable. The recovery or residual state of intestinal function was also more stable.^31,32 Second, patients in the rehabilitation stage usually received standardized rehabilitation treatment. This reduced the difference in intervention measures between different studies, and the incidence of FI was more consistent. Third, during the rehabilitation stage, the intestinal diagnosis of FI symptoms was clear. The differences in the definition of rehabilitation stages among different studies have decreased. This reduces the result bias caused by heterogeneity of evaluation criteria.

The analysis of this clinical study showed that post-stroke FI was statistically correlated with demographic data (age, gender), medical history (coronary heart disease, atrial fibrillation, cognitive impairment), disease-related information (NIHSS admission score, acute stage), and treatment-related information (antibiotic use, gavage feeding). There are similarities and differences between the findings of the clinical observation study and the risk factors summarized in the meta-analysis. Both consistently identify age and NIHSS score as key risk factors for post-stroke FI.

This consistency may be attributed to the influence of these reasons. As individuals grow older, the mechanism of gastrointestinal regulation is weakened. Aging was related to the decline of gastrointestinal motility, the reduction in digestive enzyme secretion, the change in intestinal nerve control, and the significant change in intestinal flora.^33,34 Notably, three studies^22,27,28 observed a significant correlation between age and post-stroke FI, further supporting the role of age in intestinal control disorders. These changes collectively damage the normal function of the intestinal tract, leading to impaired intestinal control. Higher NIHSS scores reflect greater stroke severity, which correlates closely with poor bowel control. Severe neurological impairment from central nervous system damage disrupts brain regulation of intestinal function, leading to loss of voluntary control. In the clinical observational study, additional risk factors were identified. Females are associated with a higher risk of post-stroke FI, possibly due to pelvic floor muscle strength and hormonal effects on gastrointestinal motility. Cognitive impairment also elevates FI risk, as impaired decision-making and reduced awareness of defecatory signals hinder timely bowel management. The use of antibiotics would seriously disrupt the internal environment of the intestinal tract.³⁵ Antibiotics can also damage the intestinal mucosal barrier function, making harmful substances penetrate the intestinal wall and trigger inflammation.^36,37 The gavage feeding method could lead to abnormal intestinal motility patterns, changes in nutrient absorption, and overall intestinal imbalance. All of these collectively contribute to impaired intestinal control.³⁸

In terms of medical history, the clinical observation study found that coronary heart disease and atrial fibrillation were associated with FI after stroke, while the meta-analysis emphasized that the CCI was a related factor. CCI gathers a variety of comorbidities, reflecting the cumulative burden of systemic diseases.³⁹ This difference may be because the clinical study focused on specific cardiovascular and neurocognitive conditions, which were directly related to autonomic nerve or vascular dysfunction affecting intestinal motility. The meta-analysis focused on the impact of cumulative scores of multiple diseases on intestinal incontinence after stroke. In addition, diabetes mellitus and previous disability, identified as risk factors in the meta-analysis, did not show a correlation in the clinical study. This difference might be attributed to differences in the study population.

With regard to disease-related factors, the clinical study emphasized the admission NIHSS score and the high incidence of FI in the acute stage. The meta-analysis focused on mRS, hemorrhagic stroke (intraventricular or lobar hemorrhage), Barthel Index, GCS, and specific symptoms, which usually reflect more severe nervous system impairment in the acute phase.^40,41 This indicated that the underlying mechanism was consistent. The neurological deficit caused by acute stroke was heavier than that in the rehabilitation stage. Acute stroke directly disrupted intestinal function, leading to intestinal incontinence.⁴² Besides, the clinical study did not evaluate the potential effects of anticholinergic medications or length of hospital stay on post-stroke FI.

Objective laboratory markers provide a new insight into studying the associated risk factors of FI in stroke patients. In the clinical study, it was found that there was a correlation between post-stroke FI and white blood cell count with differential counts, as well as cystatin C, D-dimer, hs-CRP, and IL-6.

White blood cell count and differential counts (neutrophils, lymphocytes, monocytes) reflect the activation of systemic immunity. Stroke can trigger a systemic inflammatory cascade. Neutrophils release proinflammatory cytokines^43–45 and reactive oxygen species, destroy the intestinal mucosal barrier, and impair intestinal motility. The disorder of lymphocytes and monocytes further indicates an immune imbalance and aggravates the disruption of intestinal homeostasis. Meanwhile, elevated hs-CRP and IL-6 as key markers of systemic inflammation strengthen the occurrence of FI after stroke.^46,47 The activation of these inflammatory markers aggravates intestinal dysfunction and leads to loss of intestinal control. Cystatin C is a marker of renal function that can reflect systemic endothelial integrity. Evidence showed that impaired renal clearance in stroke patients could lead to the accumulation of uremic toxins.⁴⁸ This damages intestinal epithelial cells and disrupts their movement. D-dimer is an indicator of hypercoagulable and fibrinolytic states, which is elevated due to endothelial injury and prethrombotic state in acute stroke.⁴⁹ Intestinal microthrombosis can reduce intestinal mucosal perfusion, cause intestinal ischemia-reperfusion injury, and thereby damage intestinal function.⁵⁰ In addition, a hypercoagulable state can destroy lymphatic drainage, leading to intestinal edema and intestinal motility disorders. These findings showed that the associated risk factors of post-stroke FI were correlated with inflammation, renal endothelium, and coagulation.

Although the clinical study and meta-analysis provide complementary insights, both approaches have limitations. The clinical study provided real-time data, but it was limited by its single-center design and restricted survey location. Furthermore, potential temporal variability in assessment and documentation practices may have resulted in inconsistent data recording and introduced misclassification bias. In addition, the simplified stroke stage classification and the lack of data on rehabilitation exposure may be a potential limitation. Although the meta-analysis integrated different studies, it was limited by publication bias. In the future, multi-center research could be carried out in combination with real-world data to improve these findings.

In conclusion, this study combines clinical observation data and systematic evidence from published studies. It emphasizes the incidence and associated risk factors of FI in stroke patients. The incidence of post-stroke FI is high, which is associated with demographic data, medical history, disease-related information, treatment-related information, and objective laboratory markers. In particular, age, gender, cognitive impairment, antibiotic use, and gavage feeding are important factors. It aims to deepen the understanding of intestinal complications and risk factors after stroke. These findings provide the basis for optimizing the clinical management strategies of patients with stroke.

Supplemental Material

sj-doc-1-wso-10.1177_17474930261452825 – Supplemental material for Incidence and associated risk factors of fecal incontinence in stroke patients: From a clinical observational study to meta-analysis

Supplemental material, sj-doc-1-wso-10.1177_17474930261452825 for Incidence and associated risk factors of fecal incontinence in stroke patients: From a clinical observational study to meta-analysis by Jianxiang Li, Xiangyi Han, Wenzhe Qiu, Yuanzhu Bao, Yuanze He, Shengbo Zhang, Yingying Sun, Yuan Yuan, Tianchen Wu, Dejing Ding and Weifeng Guo in International Journal of Stroke

Supplemental Material

sj-docx-2-wso-10.1177_17474930261452825 – Supplemental material for Incidence and associated risk factors of fecal incontinence in stroke patients: From a clinical observational study to meta-analysis

Supplemental material, sj-docx-2-wso-10.1177_17474930261452825 for Incidence and associated risk factors of fecal incontinence in stroke patients: From a clinical observational study to meta-analysis by Jianxiang Li, Xiangyi Han, Wenzhe Qiu, Yuanzhu Bao, Yuanze He, Shengbo Zhang, Yingying Sun, Yuan Yuan, Tianchen Wu, Dejing Ding and Weifeng Guo in International Journal of Stroke

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Scientific Research Project of Jiangsu Commission of Health (Z2024022) and “Double Hundred Plan” for Young Teachers’ Development of Nanjing University of Chinese Medicine (NJUCM Renzi [2024] 48). The funder of the study had no role in study design, data collection, data analysis, or writing of the report.

ORCID iD

Weifeng Guo

Supplemental material

Supplemental material for this article is available online.

References

Martinez

Muñoz-Venturelli

Ordunez

, et al. Risk and impact of stroke across 38 countries and territories of the Americas from 1990 to 2021: a population-based trends analysis from the Global Burden of Disease Study 2021. Lancet Reg Health Am 2025; 43: 101017.

Katan

Luft

Global burden of stroke. Semin Neurol 2018; 38: 208–211.

Song

Zhang

, et al. Stroke mortality risk factors: global trends and regional variations (1990-2021). J Am Heart Assoc 2025; 14: e042107.

GBD 2017 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: 1859–1922.

Ozkan

Ambler

Esmail

Banerjee

Simister

Werring

DJ.

Prevalence, trajectory, and factors associated with patient-reported nonmotor outcomes after stroke: a systematic review and meta-analysis. JAMA Netw Open 2025; 8: e2457447.

Tuz

Hasenberg

Hermann

Gunzer

Singh

Ischemic stroke and concomitant gastrointestinal complications—a fatal combination for patient recovery. Front Immunol 2022; 13: 1037330.

Fan

Man

Effectiveness and safety of digital rectal stimulation and abdominal massage for neurogenic bowel dysfunction in stroke patients: a randomized controlled trial protocol. Trials 2023; 24: 633.

Todd

Johnson

Stewart

, et al. Conservative, physical and surgical interventions for managing faecal incontinence and constipation in adults with central neurological diseases. Cochrane Database Syst Rev 2024; 10: CD002115.

Zhang

Yang

Hou

Xia

Wang

Y-Q.

Influence of the brain-gut axis on neuroinflammation in cerebral ischemia-reperfusion injury (Review). Int J Mol Med 2024; 53: 30.

10.

Dinan

Cryan

JF.

The Microbiome-gut-brain axis in health and disease. Gastroenterol Clin North Am 2017; 46: 77–89.

11.

Arya

Brain-gut axis after stroke. Brain Circ 2018; 4: 165–173.

12.

The microbiota-gut-brain axis and central nervous system diseases: from mechanisms of pathogenesis to therapeutic strategies. Front Microbiol 2025; 16: 1583562.

13.

Arneth

Gut-brain axis and brain microbiome interactions from a medical perspective. Brain Sci 2025; 15: 167.

14.

Camara-Lemarroy

Ibarra-Yruegas

Gongora-Rivera

Gastrointestinal complications after ischemic stroke. J Neurol Sci 2014; 346: 20–25.

15.

Coggrave

Norton

Cody

JD.

Management of faecal incontinence and constipation in adults with central neurological diseases. Cochrane Database Syst Rev 2014; 2014: CD002115.

16.

Munn

Moola

Lisy

, et al. Chapter 5: systematic reviews of prevalence and incidence. In: Aromataris

Munn

(eds) JBI manual for evidence synthesis. Adelaide: Joanna Briggs Institute, 2020, pp.177–217.

17.

Tivadar

Popit

Locatelli

Stuhec

Critical appraisal of studies evaluating prevalence of attention deficit hyperactivity disorder. Front Psychiatry 2025; 16: 1646618.

18.

Cruz

Wells

Miller

Lannin

Cloud

GC.

Incidence of and risk factors for new-onset urinary and fecal incontinence after acute stroke. Continence 2024; 9: 101067.

19.

Lucente

Corral

Rodríguez-Esparragoza

, et al. Current incidence and risk factors of fecal incontinence after acute stroke affecting functionally independent people. Front Neurol 2021; 12: 755432.

20.

Pérez

Inzitari

Roqué

, et al. Change in cognitive performance is associated with functional recovery during post-acute stroke rehabilitation: a multi-centric study from intermediate care geriatric rehabilitation units of Catalonia. Neurol Sci 2015; 36: 1875–1880.

21.

Engler

Dourado

Amâncio

Farage

de Mello

Padula

MPC

. Stroke: bowel dysfunction in patients admitted for rehabilitation. Open Nurs J 2014; 8: 43–47.

22.

Kovindha

Wattanapan

Dejpratham

Permsirivanich

Kuptniratsaikul

Prevalence of incontinence in patients after stroke during rehabilitation: a multi-centre study. J Rehabil Med 2009; 41: 489–491.

23.

Bracci

Badiali

Pezzotti

, et al. Chronic constipation in hemiplegic patients. World J Gastroenterol 2007; 13: 3967–3972.

24.

Otegbayo

Talabi

Akere

Owolabi

Oguntoye

OO.

Gastrointestinal complications in stroke survivors. Trop Gastroenterol 2006; 27: 127–130.

25.

Brittain

Perry

Shaw

Matthews

Jagger

Potter

Isolated urinary, fecal, and double incontinence: prevalence and degree of soiling in stroke survivors. J Am Geriatr Soc 2006; 54: 1915–1919.

26.

Baztan

Domenech

Gonzalez

New-onset fecal incontinence after stroke: risk factor or consequence of poor outcomes after rehabilitation?

Stroke 2003; 34: e101–102.

27.

Harari

Coshall

Rudd

Wolfe

CDA

. New-onset fecal incontinence after stroke: prevalence, natural history, risk factors, and impact. Stroke 2003; 34: 144–150.

28.

Nakayama

Jørgensen

Pedersen

Raaschou

Olsen

TS.

Prevalence and risk factors of incontinence after stroke. The Copenhagen Stroke Study. Stroke 1997; 28: 58–62.

29.

Wade

Hewer

RL.

Functional abilities after stroke: measurement, natural history and prognosis. J Neurol Neurosurg Psychiatry 1987; 50: 177–182.

30.

Brocklehurst

Andrews

Richards

Laycock

PJ.

Incidence and correlates of incontinence in stroke patients. J Am Geriatr Soc 1985; 33: 540–542.

31.

Shogenji

Yoshida

Sumiya

, et al. Relationship between bowel/bladder function and discharge in older stroke patients in convalescent rehabilitation wards: a retrospective cohort study. Prog Rehabil Med 2022; 7: 20220028.

32.

Chohan

Venkatesh

How

CH.

Long-term complications of stroke and secondary prevention: an overview for primary care physicians. Singapore Med J 2019; 60: 616–620.

33.

Gyriki

Nikolaidis

Bezirtzoglou

Voidarou

Stavropoulou

Tsigalou

The gut microbiota and aging: interactions, implications, and interventions. Front Aging 2025; 6: 1452917.

34.

Borrego-Ruiz

Borrego

JJ.

Influence of human gut microbiome on the healthy and the neurodegenerative aging. Exp Gerontol 2024; 194: 112497.

35.

Schell

Carmody

RN.

An energetic framework for gut microbiome-mediated obesity induced by early-life exposure to antibiotics. Cell Host Microbe 2025; 33: 470–483.

36.

Kester

Brubaker

Velazquez

Wright

Lauffenburger

Griffith

LG.

Clostridioides difficile-associated antibiotics alter human mucosal barrier functions by microbiome-independent mechanisms. Antimicrob Agents Chemother 2020; 64: e01404-19.

37.

Qian

Wang

, et al. Sub-chronic exposure to antibiotics doxycycline, oxytetracycline or florfenicol impacts gut barrier and induces gut microbiota dysbiosis in adult zebrafish (Daino rerio). Ecotoxicol Environ Saf 2021; 221: 112464.

38.

Krishnan

Yeap

Howell

, et al. Improving safety and timeliness around nasogastric tube feeding on an acute stroke unit. BMJ Open Qual 2025; 14: e003353.

39.

Hall

Hansen

Pihlsgard

, et al. The impact of comorbidity burden on outcomes following endovascular thrombectomy for acute ischemic stroke: a nationwide prospective observational study. Eur Stroke J 2026; 11: 23969873251332136.

40.

Padwale

Chivate

Kirnake

Patil

Kumar

Pantbalekundri

Comparative prognostic value of the National Institutes of Health Stroke Scale (NIHSS) and the Glasgow Coma Scale (GCS) in supratentorial and infratentorial stroke patients in western India. Cureus 2024; 16: e65778.

41.

Wang

Chang

Chen

, et al. Comparison of responsiveness of the Barthel Index and modified Barthel Index in patients with stroke. Disabil Rehabil 2023; 45: 1097–1102.

42.

Yang

Han

Yang

Gao

C-H

Cao

Risk factors and predictors of acute gastrointestinal injury in stroke patients. Clin Neurol Neurosurg 2023; 225: 107566.

43.

Pacinella

Ciaccio

Tuttolomondo

Molecular links and clinical effects of inflammation and metabolic background on ischemic stroke: an update review. J Clin Med 2024; 13: 7515.

44.

Mao

Huang

Cheng

Y-M

Qin

F-L

Wang

Y-Q.

Predictive value of lymphocyte-associated inflammation index in post-stroke cognitive impairment: a systematic review and meta-analysis. Front Neurol 2025; 15: 1469152.

45.

Huang

Zhang

Feng

Y-H

Z-P

Yin

X-S.

Systemic inflammation response index as a clinical outcome evaluating tool and prognostic indicator for hospitalized stroke patients: a systematic review and meta-analysis. Eur J Med Res 2023; 28: 474.

46.

Jing

Wang

Meng

Wang

Elevated hs-CRP and symptomatic intracranial/extracranial artery stenosis predict stroke recurrence after acute ischemic stroke or TIA. J Atheroscler Thromb 2023; 30: 601–610.

47.

Ebrahimi

Kalantari

Sarkari

, et al. Serum levels of inflammatory markers, sP-selectin, IL-1β, IL-6, and hsCRP are positively correlated with tissue factor transcript level of peripheral blood mononuclear cells in stroke. Blood Coagul Fibrinolysis 2025; 36: 180–190.

48.

Shang

Han

, et al. Serum cystatin C and stroke risk: a national cohort and Mendelian randomization study. Front Endocrinol (Lausanne) 2024; 15: 1355948.

49.

Zhao

Dai

Liu

Post-procedural plasma D-dimer level may predict futile recanalization in stroke patients with endovascular treatment. J Stroke Cerebrovasc Dis 2025; 34: 108248.

50.

Chen

Yang

, et al. Hypercoagulable state and gut microbiota dysbiosis as predictors of poor functional outcomes in acute ischemic stroke patients. mSystems 2025; 10: e0149224.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.02 MB

0.00 MB