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BMC Public Health
BMC Public Health
BMC Public Health
1471-2458
BioMed Central London

39266983
20006
10.1186/s12889-024-20006-w
Research
Females with diabetes have a higher risk of ischemic stroke readmission: a retrospective cohort study
Meng Hua 12
Pan Ting 12
Pan Dongfeng 3
Su Xinya 12
Lu Wenwen 12
Wang Xingtian 12
Liu Zhuo 12
Geng Yuhui 12
Ma Xiaojuan 12
Liang Peifeng doctor_pf@126.com

4
1 https://ror.org/02h8a1848 grid.412194.b 0000 0004 1761 9803 School of Public Health, Ningxia Medical University, Yinchuan, China
2 Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, China
3 grid.412194.b 0000 0004 1761 9803 Department of Emergency Medicine, People’s Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, China
4 grid.412194.b 0000 0004 1761 9803 Public Health Center, People’s Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, 301 Zhengyuan North Street, Yinchuan, Ningxia 750002 China
12 9 2024
12 9 2024
2024
24 248817 4 2024
6 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

There are significant sex differences in the incidence of stroke or diabetes mellitus. However, little is known about sex differences in stroke rehospitalization among diabetic patients.

Object

To explore the sex differences in short-term and long-term rehospitalization of ischemic stroke patients with Type 2 diabetes mellitus.

Methods

A retrospective cohort study was conducted from 2017 to 2021. The rehospitalization events of ischemic stroke patients with diabetes mellitus were identified by the national unified Electronic Health Record. Propensity score matching was applied to adjust for multiple covariates, and LASSO regression was used to screen for independent variables. Cox proportional hazards model was utilized to analyze the different sex in short-term (90 days, 1 year) and long-term (5 years) rehospitalization in ischemic stroke patients with type 2 diabetes mellitus.

Result

A total of 10,724 ischemic stroke patients were included in this study, of whom 5,952 (55.5%) were males. After a 1:1 propensity score matching, there were 3,460 males and 2,772 females. After adjusting for confounding factors, female patients with type 2 diabetes had an increased risk of ischemic stroke rehospitalization at 90 days (HR: 1.94, 95%CI: 1.13–3.33, P < 0.05), 1 year (HR: 1.65, 95%CI:1.22–2.23, P = 0.001), and 5 years (HR: 1.58, 95%CI: 1.26–1.97, P < 0.001). However, there was no significant relationship between male patients with type 2 diabetes and the risk of ischemic stroke rehospitalization, either in the short or long term.

Conclusion

Females with type 2 diabetes mellitus have a higher risk of ischemic stroke rehospitalization in both the short-term and long-term.

Keywords

Type 2 diabetes
Ischemic stroke
Rehospitalization
Sex differences
http://dx.doi.org/10.13039/501100004772 Natural Science Foundation of Ningxia Province 2023AAC03445 2023AAC03445 2023AAC03445 2023AAC03445 2023AAC03445 2023AAC03445 2020AAC03354 2023AAC03445 2023AAC03445 2023AAC03445 http://dx.doi.org/10.13039/501100012166 National Key Research and Development Program of China 2021BEG03099 2021BEG03099 2021BEG03099 2021BEG03099 2021BEG03099 2021BEG03099 2021BEG03099 2021BEG03099 2021BEG03099 2021BEG03099 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Stroke is the third leading cause of death and disability globally [1]. It is also a disease with high recurrence rates and a significant economic burden [2, 3]. Meta-analysis findings indicate that the 30-day readmission rate for acute stroke is 12.4% (95% CI: 10.8-14.1%), and the one-year readmission rate is 53% (95% CI: 49.7-56.2%) [4], significantly increasing the burden experienced by patients.

Diabetes is not only positively correlated with the occurrence and death from ischemic stroke (IS) [5], but also associated with stroke rehospitalization [6, 7]. Studies have shown that the risk of stroke in diabetic patients is two to four times higher than in the non-diabetic population [8]. On the other hand, there were significant sex differences in stroke occurrence and outcome. Women had a lower incidence of stroke than men (1.54 vs. 1.59/1000) [9], but a higher lifetime risk of stroke [10]. A stroke case-control study across 32 countries found that self-reported diabetes or HbA1C ≥ 6.5% was associated with stroke in men (OR = 1.16, 99%CI: 1.01 to 1.34), but not in women (OR = 1.16, 99%CI:0.98 to 1.38) [11]. However, little is known about sex differences in stroke rehospitalization in diabetic patients. Understanding sex differences in stroke rehospitalization can help guide prevention efforts to reduce stroke incidence in both men and women.

Our study aimed to explore whether the population of sex differences with diabetes has a different impact on IS rehospitalization, as well as on short- and long-term effects. We collected data from IS patients over a five-year period using national unified Electronic Health Records (EHR). The findings will contribute to understanding the risk of rehospitalization in IS patients with diabetes, support effective personalized prevention, and improve patients’ quality of life through early intervention, thereby reducing the social and family burden of stroke.

Methods

Data resources

In this retrospective cohort study, we used data from People’s Hospital of Ningxia Hui Autonomous Region. We included 12,782 hospitalizations recorded over a 5-year period (January 2017 to December 2021) collected from national unified EHR data. The EHR data was anonymized and accessed in a secure environment. The data has a hierarchical structure, including medical record numbers, demographic characteristics, primary and secondary diagnoses, procedures, method of payment, and others, totaling 642 variables.

The study was approved by the People’s Hospital of Ningxia Hui Autonomous Region (Approval number: 2020-KY-053).

Patient selection

The extraction criteria were patients over 18 years old with a primary diagnosis of IS (International Classification of Diseases, Tenth Edition [ICD-10]: I63). Patients discharged from the hospital who were deceased, under the age of 18, with non-type 2 diabetes, or had incorrect ID numbers and encoding formats were excluded from the study.

The identification of type 2 diabetes was based on the ICD-10 code: E11.

Outcome measures

Rehospitalization refers to being readmitted to the hospital after being discharged. The rehospitalization rate refers to the number of patients readmitted after hospitalization divided by the total number of patients who were discharged alive during the same period [12].

Patients readmitted to any hospital after discharge due to an IS condition. The initial occurrence of IS was considered as the index event and IS readmission was considered the endpoint event. If a patient has multiple rehospitalization records, only the first two hospitalization records are retained to avoid data dependency, enhance the representativeness and randomness of the analysis results. Rehospitalizations were identified using ID numbers and the primary diagnostic code for the disease from the EHD data in five districts of Ningxia Hui Autonomous Region from 2017 to 2021. According to the length of the interval between readmissions, rehospitalizations were regrouped into categories, including those within five years, one year, ninety days, and thirty days.

The primary outcome was rehospitalization within a 5-year period. The secondary outcomes included rehospitalization at 1-year, 90-day, and 30-day intervals.

Variables collected

Published relevant literature was reviewed to summarize and integrate the factors involved [6, 13–18]. Covariates considered for confounding adjustment included age (< 40, 40–49, 50–59, 60–69, ≥ 70), admission route, length of stay (LOS), anemia (D50-64), thyroid disease (E00-07), dementia (F00-03), parkinsonism (G20), transient ischemic attack and related syndrome (G45), hypertension (I10-15), coronary heart disease (CHD) (I20-25), paroxysmal tachycardia (I47), atrial fibrillation and flutter (I48), heart failure (I50), arteriosclerosis (I70), embolism and thrombosis (I74), acute upper respiratory tract infection (J00-06), pneumonia (J12-18), renal failure (N17-19), and urinary tract infection (N39.000), treatment (anticoagulants, thrombectomy, thrombolysis, thrombectomy and thrombolysis), and NIH Stroke Scale (NIHSS) score.

Propensity score-matching

All eligible patients were divided into male and female cohorts and then further divided into patients with diabetes and without diabetes. The propensity score matching (PSM) analysis was performed to balance confounding factors that might influence both group assignment and outcome [19]. The matching was performed using the “Matchit” package in R studio with the nearest neighbor algorithm, a 1:1 matching ratio, no replacement, and a caliper value of 0.2 [20]. The propensity score was calculated using a logistic regression model that included the following variables: age, hypertension, coronary heart disease, length of hospital stay, renal failure, and heart failure (in the male cohort); age, hypertension, coronary heart disease, length of hospital stay, renal failure, NIHSS, embolism and thrombosis (in the female cohort) [21].

The baseline balance of potential confounders was assessed using the absolute standardized difference (SMD). An SMD greater than 0.1 indicated a significant difference in potential confounders between the two groups [22].

Statistical analysis

The variables of admission route and NIHSS score had missing values. The missing mechanism was determined to be missing completely at random based on the correlation coefficient matrix between the missing variable and other variables [23]. Therefore, the incomplete data for admission route and NIHSS score were simultaneously imputed using multiple imputations (n = 25) with the R package MICE [24, 25]. Based on the Akaike Information Criterion value, one of the interpolation data sets was selected and analyzed.

Some previous studies have demonstrated that the Least Absolute Shrinkage and Selection Operator (LASSO) method is superior to traditional methods [26–28]. LASSO regression is used to avoid overfitting and collinearity [28]. Therefore, LASSO analysis was used to select variables to be included in the COX model. The “glmnet” package was used to analyze the LASSO regression model [29]. The Kaplan-Meier curve (K-M curve) was performed using the “survminer” package.

Sensitivity analysis

The regression analysis for Model 1 only included diabetes. In Model 2, we adjusted for age. In Model 3, we adjusted for age and factors related to nursing treatment, such as admission route, LOS, NIHSS, and treatment. In Model 4, all variables were included. Forest maps were used to show the findings.

A two-sided P-value of less than 0.05 indicates statistical significance. Data screening and extraction were performed using Excel version 2016, and other analyses were conducted using R version 4.2.2.

Results

Baseline characteristics

Based on the screening process depicted in Fig. 1, 10-724 IS patients were included in our study. Among them, 5,952 were males, with 29.4% having type 2 diabetes. Out of the total, 4,772 were females with 29.3% having type 2 diabetes. There were differences in age, LOS, hypertension, CHD, heart failure, and renal failure between male IS patients without type 2 diabetes and male IS patients with type 2 diabetes. Among females, there were imbalances between groups in terms of age, LOS, NIHSS score, hypertension, coronary heart disease, embolism and thrombosis, and renal failure. After 1:1 PSM, there were 3,460 men and 2,772 women with IS, respectively. None of the SMDs exceeded 0.1 (Table 1), indicating that the matching process effectively reduced the differences between IS patients with type 2 diabetes and those without type 2 diabetes groups in both the male and female cohorts, thereby enhancing the accuracy of estimating causal effects.

Fig. 1 The follow chart of patient selection

Table 1 Baseline characteristics of patients in the total population and in the propensity score matching

	Male	Female	
	Pre- Propensity score-matching	After- Propensity score-matching	Pre- Propensity score-matching	After-P Propensity score-matching	
No-diabetes	Diabetes	SMD	No-diabetes	Diabetes	SMD	No-diabetes	Diabetes	SMD	No-diabetes	Diabetes	SMD	
N	4202	1750		1730	1730		3373	1399		1386	1386		
Age (%)	
 <40	83 (2.0)	11 (0.6)	0.163	11 (0.6)	11 (0.6)	0.021	24 (0.7)	0 (0.0)	0.170	0 (0.0)	0 (0.0)	0.049	
 40-49	325 (7.7)	91 (5.2)		86 (5.0)	91 (5.3)		139 (4.1)	33 (2.4)		25 (1.8)	33 (2.4)		
 50-59	803 (19.1)	355 (20.3)		345 (19.9)	353 (20.4)		462 (13.7)	167 (11.9)		155 (11.2)	166 (12.0)		
 60-69	1148 (27.3)	518 (29.6)		524 (30.3)	512 (29.6)		954 (28.3)	405 (28.9)		415 (29.9)	404 (29.1)		
 ≥70	1843 (43.9)	775 (44.3)		764 (44.2)	763 (44.1)		1794 (53.2)	794 (56.8)		791 (57.1)	783 (56.5)		
Admission route (%)	
 emergency	2118 (50.4)	880 (50.3)	0.055	850 (49.1)	865 (50.0)	0.043	1536 (45.5)	672 (48.0)	0.051	679 (49.0)	667 (48.1)	0.028	
 transferred	3 (0.1)	5 (0.3)		2 (0.1)	5 (0.3)		4 (0.1)	2 (0.1)		1 (0.1)	2 (0.1)		
 outpatient	2080 (49.5)	865 (49.4)		878 (50.8)	860 (49.7)		1833 (54.3)	725 (51.8)		706 (50.9)	717 (51.7)		
 others	1 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)		
 Treatment (%)													
 Anticoagulants	3935 (93.6)	1660 (94.9)	0.060	1622 (93.8)	1641 (94.9)	0.052	3239 (96.0)	1338 (95.6)	0.074	1330 (96.0)	1325 (95.6)	0.051	
 Thrombectomy	7 (0.2)	2 (0.1)		4 (0.2)	2 (0.1)		10 (0.3)	1 (0.1)		3 (0.2)	1 (0.1)		
 Thrombolysis	237 (5.6)	83 (4.7)		97 (5.6)	82 (4.7)		114 (3.4)	58 (4.1)		50 (3.6)	58 (4.2)		
 Thrombectomy and thrombolysis	23 (0.5)	5 (0.3)		7 (0.4)	5 (0.3)		10 (0.3)	2 (0.1)		3 (0.2)	2 (0.1)		
Length of hospital stay (%)	
< Q1(<8)	1053 (25.1)	349 (19.9)	0.133	341 (19.7)	346 (20.0)	0.011	863 (25.6)	290 (20.7)	0.186	286 (20.6)	288 (20.8)	0.014	
 Q1-Q2(8-10)	898 (21.4)	365 (20.9)		371 (21.4)	365 (21.1)		752 (22.3)	276 (19.7)		268 (19.3)	274 (19.8)		
 Q2-Q3(10-13)	1194 (28.4)	541 (30.9)		537 (31.0)	534 (30.9)		742 (22.0)	294 (21.0)		292 (21.1)	292 (21.1)		
 ≥Q3(≥13)	1057 (25.2)	495 (28.3)		537 (31.0)	534 (30.9)		1016 (30.1)	539 (38.5)		540 (39.0)	532 (38.4)		
NIH Stroke Scale score (%)	
 Q1(≤1)	1113 (26.5)	487 (27.8)	0.034	445 (25.7)	481 (27.8)	0.050	797 (23.6)	269 (19.2)	0.108	264 (19.0)	267 (19.3)	0.049	
 Q1-Q2(1-2)	1491 (35.5)	598 (34.2)		598 (34.6)	591 (34.2)		964 (28.6)	418 (29.9)		422 (30.4)	416 (30.0)		
 Q2-Q3(2-4)	751 (17.9)	313 (17.9)		317 (18.3)	310 (17.9)		836 (24.8)	370 (26.4)		343 (24.7)	368 (26.6)		
 > Q3(>4)	847 (20.2)	352 (20.1)		370 (21.4)	348 (20.1)		776 (23.0)	342 (24.4)		357 (25.8)	335 (24.2)		
Parkinsonism (%)	49 (1.2)	27 (1.5)	0.033	22 (1.3)	27 (1.6)	0.024	42 (1.2)	18 (1.3)	0.004	29 (2.1)	17 (1.2)	0.068	
Anemia (%)	145 (3.5)	51 (2.9)	0.031	72 (4.2)	50 (2.9)	0.069	154 (4.6)	44 (3.1)	0.074	55 (4.0)	43 (3.1)	0.047	
Thyroid disease (%)	372 (8.9)	195 (11.1)	0.076	146 (8.4)	193 (11.2)	0.091	635 (18.8)	236 (16.9)	0.051	264 (19.0)	235 (17.0)	0.054	
Dementia (%)	84 (2.0)	36 (2.1)	0.004	32 (1.8)	36 (2.1)	0.017	58 (1.7)	39 (2.8)	0.072	35 (2.5)	39 (2.8)	0.018	
Transient ischemic attack and related syndrome (%)	271 (6.4)	77 (4.4)	0.091	107 (6.2)	76 (4.4)	0.080	301 (8.9)	101 (7.2)	0.063	122 (8.8)	101 (7.3)	0.056	
Hypertension (%)	2793 (66.5)	1370 (78.3)	0.267	1346 (77.8)	1350 (78.0)	0.006	2271 (67.3)	1215 (86.8)	0.477	1202 (86.7)	1202 (86.7)	<0.001	
Coronary heart disease (%)	798 (19.0)	538 (30.7)	0.274	528 (30.5)	523 (30.2)	0.006	707 (21.0)	472 (33.7)	0.290	457 (33.0)	463 (33.4)	0.009	
Paroxysmal tachycardia (%)	291 (6.9)	114 (6.5)	0.016	142 (8.2)	111 (6.4)	0.069	243 (7.2)	80 (5.7)	0.060	102 (7.4)	79 (5.7)	0.067	
Atrial fibrillation and flutter (%)	227 (5.4)	99 (5.7)	0.011	94 (5.4)	96 (5.5)	0.005	224 (6.6)	86 (6.1)	0.020	104 (7.5)	86 (6.2)	0.051	
Heart failure (%)	286 (6.8)	184 (10.5)	0.132	170 (9.8)	179 (10.3)	0.017	248 (7.4)	139 (9.9)	0.092	142 (10.2)	136 (9.8)	0.014	
Arteriosclerosis (%)	2351 (55.9)	1046 (59.8)	0.077	969 (56.0)	1034 (59.8)	0.076	1799 (53.3)	805 (57.5)	0.085	801 (57.8)	799 (57.6)	0.003	
Embolism and thrombosis (%)	80 (1.9)	40 (2.3)	0.027	37 (2.1)	39 (2.3)	0.008	29 (0.9)	34 (2.4)	0.124	27 (1.9)	28 (2.0)	0.005	
Acute upper respiratory tract infection (%)	45 (1.1)	17 (1.0)	0.010	16 (0.9)	17 (1.0)	0.006	56 (1.7)	20 (1.4)	0.019	25 (1.8)	20 (1.4)	0.029	
Pneumonia (%)	143 (3.4)	55 (3.1)	0.015	56 (3.2)	53 (3.1)	0.010	109 (3.2)	44 (3.1)	0.005	48 (3.5)	44 (3.2)	0.016	
Renal failure (%)	51 (1.2)	60 (3.4)	0.147	36 (2.1)	40 (2.3)	0.016	27 (0.8)	30 (2.1)	0.112	19 (1.4)	23 (1.7)	0.024	
Urinary tract infection (%)	66 (1.6)	31 (1.8)	0.016	27 (1.6)	31 (1.8)	0.018	167 (5.0)	91 (6.5)	0.067	70 (5.1)	91 (6.6)	0.065	

As shown in Fig. 2, there was no difference between the combined type 2 diabetes and no type 2 diabetes groups for male IS patients, both before and after matching. However, there was already a difference between groups for female IS patients before matching. Additionally, after matching, the difference between the groups became even more pronounced.

Fig. 2 Kaplan-Meier curve of male cohort (A, B) and female cohort (C, D) (PSM: Propensity score matching)

LASSO regression

Among males, age, admission route, diabetes, hypertension, coronary heart disease, heart failure, acute upper respiratory tract infection, paroxysmal tachycardia, arteriosclerosis, embolism and thrombosis, renal failure, anemia, transient ischemic attack and related syndrome, urinary tract infection, parkinsonism, treatment, LOS, NIHSS were indicated for inclusion from a five-year Lasso Regression. LOS was selected from one-year Lasso Regression and ninety-day Lasso Regression. No factors were identified by the thirty-day Lasso Regression.

Among females, age, admission route, diabetes, hypertension, coronary heart disease, paroxysmal tachycardia, atrial fibrillation and flutter, arteriosclerosis, pneumonia, anemia, transient ischemic attack and related syndrome, urinary tract infection, parkinsonism, and LOS were indicated for inclusion from a five-year Lasso Regression. Age, diabetes, hypertension, paroxysmal tachycardia, arteriosclerosis, pneumonia, anemia, transient ischemic attack and related syndrome, parkinsonism, and LOS were identified by one-year Lasso Regression. Age, diabetes, embolism and thrombosis were selected from the ninety-day Lasso Regression. No factors were screened for the thirty-day Lasso Regression.

Cox regression

For male patients, diabetes was not found to be a significant factor for IS rehospitalization in both univariate and multivariate analyses (Table 2). LOS ≥ 13 days was identified as an independent risk factor for IS rehospitalization at five years, one year, and 90 days, with HRs of 2.84 (1.99-4.05), 2.23 (1.41-3.53), 2.41 (1.04–5.59), respectively. The results were shown in Fig. 3.

Table 2 Cox regression analysis for diabetes (propensity score-matching)

	Model 1	Model 2	Model 3	Model 4	
Male		
 Five-year	1.17 (0.97-1.40) #	1.17 (0.97-1.40) #	1.15 (0.96-1.39) #	1.15 (0.95-1.38) #	
Female		
 Five-year	1.57 (1.26-1.97) ***	1.59 (1.27-1.98) ***	1.59 (1.28-1.99) ***	1.58 (1.26-1.97) ***	
 One-year	1.66 (1.23-2.24) **	1.68 (1.24-2.26) **	1.69 (1.25-2.27) **	1.65 (1.22-2.23) **	
 Ninety-day	1.92 (1.12-3.30) *	1.94 (1.13-3.33) *		1.94 (1.13-3.33) *	
Model 1 only included diabetes. Model 2: adjusted for age. Model 3: adjusted for age and factors related to nursing treatment. Model 4: all variables were included

#: P>0.05; *: P<0.05; **: P=0.001; ***: P<0.001

Fig. 3 Forest plot of influencing factors of rehospitalization in male ischemic stroke patients at different periods

For female patients, there was a significant association between diabetes and IS rehospitalization in the univariate model. Multivariate analysis showed that type 2 diabetes mellitus increased the short- and long-term risk of rehospitalization for IS [at 90 days (HR: 1.94, 95%CI: 1.13–3.33, P < 0.05), 1 year (HR: 1.65, 95%CI: 1.22–2.23, P = 0.001), and 5 years (HR: 1.58, 95%CI: 1.26–1.97, P < 0.001)] (Table 2). The results were shown in Fig. 4.

Fig. 4 Forest plot of influencing factors of rehospitalization in female ischemic stroke patients at different periods

Discussion

In this study, we analyzed the long-term and short-term risk of IS rehospitalization in IS patients with comorbid diabetes by sex. Female IS patients with comorbid type 2 diabetes mellitus was confirmed as an independent risk factor for both long-term and short-term rehospitalization in female IS patients through PSM, LASSO regression, univariate, and multifactorial Cox analyses.

This study is consistent with some previous studies [2].Evidence from experimental studies has shown that type 2 diabetes increases arterial stiffness [30], which is also a risk factor for stroke [31]. Patients with diabetes may experience microvascular dysfunction [32]. Hyperglycemia can lead to recirculation disorders and worsen reperfusion injury [33]. Population studies have also shown that compared to patients without type 2 diabetes, those with type 2 diabetes and IS have poor functional recovery and a higher risk of stroke recurrence [34]. In the 30-day rehospitalization analysis, this study found no correlation between diabetes and rehospitalization, which is inconsistent with some previous studies [35]. In addition to the differences in analysis methods, it is likely that different characteristics exist in various different countries or regions [36]. There is evidence that approximately 14% to 46% of stroke patients have diabetes [37]. In China, the prevalence of diabetes in stroke patients is about 34% [38], while in the United States, it is 22.6% [39].

Our study found that females with diabetes have a higher risk of IS rehospitalization, which may involve aspects of lifestyle, societal roles, mental health, anatomy, genetic differences in immunity, hormonal factors, and coagulation [40]. Women tend to experience higher rates of widowhood and solitary living, as well as a greater level of impairment in their daily activities compared to men during the onset of a stroke [41]. Hormonal profiles probably constitute more prominent differences among those differences [42]. During different physiological stages of the menstrual cycle, pregnancy, childbirth, and menopause, a woman’s body undergoes numerous changes, including alterations in hemodynamics and clotting function [43], as well as fluctuations in estrogen levels [44]. These suggest that females may require closer monitoring and preventive measures.

The importance of lifestyle factors cannot be understated [11]. Females require increased dietary fiber intake, physical activity, adequate sleep duration, and relief from constipation [45]. Males may reduce alcohol consumption, smoking, and control their waist circumference [46]. Women may be more sensitive to pain [47] and depression [45] due to the effects of menopause. Therefore, rehabilitation programs also need to take into account these gender-specific physical and psychological factors. Females who are older, lonely, and have other chronic conditions may need more access to clinical care and rehabilitation services [40], which can enhance their physical and mental well-being and alleviate the financial burden on their families.

This study investigated the impact of sex differences of IS patients with type 2 diabetes on different lengths of stroke rehospitalization. PSM was used to reduce bias resulting from residual differences in baseline covariates between the combined diabetes and uncomplicated diabetes groups, thereby enhancing the reliability of the findings. Sensitivity analysis was performed to ensure the stability of the results. Reducing the rate of rehospitalization for IS is important to reduce the enormous economic burden on the healthcare system and enhance the likelihood of avoiding unnecessary costs [48].

There were some limitations to our study. First, the sample was taken from a single medical institution, and it is possible that some local patients may choose to recuperate nearby due to the distance or mild illness. Second, while the confounding factors were balanced by using PSM, the sample size was also reduced. Therefore, the results of this study should be interpreted with caution when applied to other countries or regions. Finally, due to the limited information in the EHR, some variables, such as Body Mass Index, may not be included, which may result in inadequate risk adjustment. And we did not analyze the subtypes of IS. Mendelian randomization demonstrates an association between type 2 diabetes and IS (large artery stroke), but no strong association with small vessel or cardiac embolic stroke [49]. Future studies could incorporate this data, potentially uncovering new associations with rehospitalization for IS.

Conclusion

This study confirmed that female IS patients with type 2 diabetes are at an increased risk of short- and long-term IS readmissions. The findings can provide evidence for personalized clinical prevention of stroke.

Acknowledgements

Not applicable.

Author contributions

LPF and PDF conceived the study. PT, PDF, SXY, LWW, WXT, LZ, GYH and MXJ designed and supervised the study. MH, PT, SXY, LWW, WXT, LZ, GYH and MXJ participated in data collection. MH, PT, SXY and LPF performed the whole data integration and analysis. MH and PDF wrote the first draft of drafted the manuscript. LPF and MH improved the research and edited the manuscript. All authors read and approved the final manuscript. Hua Meng and Ting Pan contributed equally to this work.

Funding

This work was supported by the Ningxia Natural Science Foundation (grant number 2023AAC03445 and grant number 2020AAC03354), and the Key R&D Project of Ningxia Hui Autonomous Region (grant number 2021BEG03099).

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study protocol was reviewed and approved by [People’s Hospital of Ningxia Hui Autonomous Region], approval number [Approval number: 2020-KY-053].

Consent for publication

Not applicable.

Informed consent

All written informed consent were obtained from participants to participate in the study.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Hua Meng and Ting Pan contributed equally to this work.
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