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Females with diabetes have a higher risk of ischemic stroke readmission: a retrospective cohort study

国产情侣 Public Health volume听24, Article听number:听2488 (2024) Cite this article

Abstract

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鈥�3.33, P鈥�<鈥�0.05), 1 year (HR: 1.65, 95%CI:1.22鈥�2.23, P鈥�=鈥�0.001), and 5 years (HR: 1.58, 95%CI: 1.26鈥�1.97, P鈥�<鈥�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.

Peer Review reports

Introduction

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鈥夆墺鈥�6.5% was associated with stroke in men (OR鈥�=鈥�1.16, 99%CI: 1.01 to 1.34), but not in women (OR鈥�=鈥�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鈥� 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鈥檚 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鈥檚 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, 13,14,15,16,17,18]. Covariates considered for confounding adjustment included age (<鈥�40, 40鈥�49, 50鈥�59, 60鈥�69, 鈮モ��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 鈥淢atchit鈥� 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鈥�=鈥�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 [26,27,28]. 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 鈥済lmnet鈥� package was used to analyze the LASSO regression model [29]. The Kaplan-Meier curve (K-M curve) was performed using the 鈥渟urvminer鈥� 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.听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听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
figure 1

The follow chart of patient selection

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

As shown in Fig.听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
figure 2

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

Full size image

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听2). LOS鈥夆墺鈥�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鈥�5.59), respectively. The results were shown in Fig.听3.

Table 2 Cox regression analysis for diabetes (propensity score-matching)
Full size table
Fig. 3
figure 3

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

Full size image

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鈥�3.33, P鈥�<鈥�0.05), 1 year (HR: 1.65, 95%CI: 1.22鈥�2.23, P鈥�=鈥�0.001), and 5 years (HR: 1.58, 95%CI: 1.26鈥�1.97, P鈥�<鈥�0.001)] (Table听2). The results were shown in Fig.听4.

Fig. 4
figure 4

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

Full size image

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听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鈥檚 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.

Data availability

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

References

  1. Collaborators GBDS. Global, regional, and national burden of stroke and its risk factors, 1990鈥�2019: a systematic analysis for the global burden of Disease Study 2019. Lancet Neurol. 2021;20(10):795鈥�820.

    听 听

  2. Wen T, Liu B, Wan X, Zhang X, Zhang J, Zhou X, Lau AYL, Zhang Y. Risk factors associated with 31-day unplanned readmission in 50,912 discharged patients after stroke in China. 国产情侣 Neurol. 2018;18(1):218.

    听 听 听 听

  3. Sun Y, Lee SH, Heng BH, Chin VS. 5-year survival and rehospitalization due to stroke recurrence among patients with hemorrhagic or ischemic strokes in Singapore. 国产情侣 Neurol. 2013;13:133.

    听 听 听 听

  4. Zhong W, Geng N, Wang P, Li Z, Cao L. Prevalence, causes and risk factors of hospital readmissions after acute stroke and transient ischemic attack: a systematic review and meta-analysis. Neurol Sci. 2016;37(8):1195鈥�202.

    听 听 听

  5. Chen R, Ovbiagele B, Feng W. Diabetes and stroke: Epidemiology, Pathophysiology, Pharmaceuticals and outcomes. Am J Med Sci. 2016;351(4):380鈥�6.

    听 听 听 听

  6. Qiu X, Xue X, Xu R, Wang J, Zhang L, Zhang L, Zhao W, He L. Predictors, causes and outcome of 30-day readmission among acute ischemic stroke. Neurol Res. 2021;43(1):9鈥�14.

    听 听 听

  7. Echouffo-Tcheugui JB, Xu H, Matsouaka RA, Xian Y, Schwamm LH, Smith EE, Bhatt DL, Hernandez AF, Heidenreich PA, Fonarow GC. Diabetes and long-term outcomes of ischaemic stroke: findings from get with the guidelines-Stroke. Eur Heart J. 2018;39(25):2376鈥�86.

    听 听 听 听

  8. Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E, Ingelsson E, Lawlor DA, Selvin E, Stampfer M, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215鈥�22.

    听 CAS听 听 听

  9. Feigin VL, Forouzanfar MH, Krishnamurthi R, Mensah GA, Connor M, Bennett DA, Moran AE, Sacco RL, Anderson L, Truelsen T, et al. Global and regional burden of stroke during 1990鈥�2010: findings from the global burden of Disease Study 2010. Lancet. 2014;383(9913):245鈥�54.

    听 听 听 听

  10. Feigin VL, Nguyen G, Cercy K, Johnson CO, Alam T, Parmar PG, Abajobir AA, Abate KH, Abd-Allah F, Abejie AN, et al. Global, Regional, and Country-Specific Lifetime risks of Stroke, 1990 and 2016. N Engl J Med. 2018;379(25):2429鈥�37.

    听 听 听 听

  11. O鈥橠onnell MJ, Chin SL, Rangarajan S, Xavier D, Liu L, Zhang H, Rao-Melacini P, Zhang X, Pais P, Agapay S, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. Lancet. 2016;388(10046):761鈥�75.

    听 听 听

  12. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418鈥�28.

    听 CAS听 听 听

  13. Zhou LW, Lansberg MG, de Havenon A. Rates and reasons for hospital readmission after acute ischemic stroke in a US population-based cohort. PLoS ONE. 2023;18(8):e0289640.

    听 CAS听 听 听 听

  14. Loebel EM, Rojas M, Wheelwright D, Mensching C, Stein LK. High risk features contributing to 30-Day readmission after Acute ischemic stroke: a single Center Retrospective Case-Control Study. Neurohospitalist. 2022;12(1):24鈥�30.

    听 听 听

  15. Bjerkreim AT, Naess H, Khanevski AN, Thomassen L, Waje-Andreassen U, Logallo N. One-year versus five-year hospital readmission after ischemic stroke and TIA. 国产情侣 Neurol. 2019;19(1):15.

    听 听 听 听

  16. Jin P, Matos Diaz I, Stein L, Thaler A, Tuhrim S, Dhamoon MS. Intermediate risk of cardiac events and recurrent stroke after stroke admission in young adults. Int J Stroke. 2018;13(6):576鈥�84.

    听 听 听

  17. Nouh AM, McCormick L, Modak J, Fortunato G, Staff I. High mortality among 30-Day readmission after stroke: predictors and etiologies of Readmission. Front Neurol. 2017;8:632.

    听 听 听 听

  18. Mu帽oz-Rivas N, M茅ndez-Bail贸n M, Hern谩ndez-Barrera V, de Miguel-Yanes JM, Jim茅nez-Garc铆a R, Esteban-Hern谩ndez J, Jim茅nez-Trujillo I, Alvaro-Meca A, Carrasco-Garrido P, de Miguel-D铆ez J, et al. Time trends in ischemic stroke among type 2 Diabetic and non-diabetic patients: analysis of the Spanish National Hospital Discharge Data (2003鈥�2012). PLoS ONE. 2015;10(12):e0145535.

    听 听 听 听

  19. Roz茅 JC, Cambonie G, Marchand-Martin L, Gournay V, Durrmeyer X, Durox M, Storme L, Porcher R, Ancel PY. Association between Early Screening for Patent Ductus Arteriosus and In-Hospital mortality among extremely Preterm infants. JAMA. 2015;313(24):2441鈥�8.

    听 听 听

  20. Austin PC. An introduction to Propensity score methods for reducing the effects of confounding in Observational studies. Multivar Behav Res. 2011;46(3):399鈥�424.

    听 听

  21. Brookhart MA, Schneeweiss S, Rothman KJ, Glynn RJ, Avorn J, St眉rmer T. Variable selection for propensity score models. Am J Epidemiol. 2006;163(12):1149鈥�56.

    听 听 听

  22. Austin PC. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Stat Med. 2014;33(7):1242鈥�58.

    听 听 听

  23. Sterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, Wood AM, Carpenter JR. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393.

    听 听 听 听

  24. van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45(3):1鈥�67.

    听 听

  25. Deng Jianxin SL, He Deqiang T, Rui. Handling methods of missing data and their development trends. Stat Decis vol. 2019;35:28鈥�34.

    听

  26. Le VH, Kha QH, Hung TNK, Le NQK. Risk score generated from CT-Based Radiomics signatures for overall survival prediction in Non-small Cell Lung Cancer. Cancers (Basel) 2021, 13(14).

  27. Zeng Y, Cao W, Wu C, Wang M, Xie Y, Chen W, Hu X, Zhou Y, Jing X, Cai X. Survival prediction in 国产情侣 Hospice Care patients with Lung Cancer based on LASSO Algorithm. Cancer Control. 2022;29:10732748221124519.

    听 听 听 听

  28. Xu Y, Wang X, Huang Y, Ye D, Chi P. A LASSO-based survival prediction model for patients with synchronous colorectal carcinomas based on SEER. Transl Cancer Res. 2022;11(8):2795鈥�809.

    听 听 听 听

  29. Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385鈥�95.

    听 CAS听 听 听

  30. Henry RM, Kostense PJ, Spijkerman AM, Dekker JM, Nijpels G, Heine RJ, Kamp O, Westerhof N, Bouter LM, Stehouwer CD. Arterial stiffness increases with deteriorating glucose tolerance status: the Hoorn Study. Circulation. 2003;107(16):2089鈥�95.

    听 听 听

  31. van Sloten TT, Sedaghat S, Laurent S, London GM, Pannier B, Ikram MA, Kavousi M, Mattace-Raso F, Franco OH, Boutouyrie P, et al. Carotid stiffness is associated with incident stroke: a systematic review and individual participant data meta-analysis. J Am Coll Cardiol. 2015;66(19):2116鈥�25.

    听 听 听

  32. van Sloten TT, Sedaghat S, Carnethon MR, Launer LJ, Stehouwer CDA. Cerebral microvascular complications of type 2 diabetes: stroke, cognitive dysfunction, and depression. Lancet Diabetes Endocrinol. 2020;8(4):325鈥�36.

    听 听 听 听

  33. Luitse MJ, Biessels GJ, Rutten GE, Kappelle LJ. Diabetes, hyperglycaemia, and acute ischaemic stroke. Lancet Neurol. 2012;11(3):261鈥�71.

    听 听 听

  34. Palacio S, McClure LA, Benavente OR, Bazan C 3rd, Pergola P, Hart RG. Lacunar strokes in patients with diabetes mellitus: risk factors, infarct location, and prognosis: the secondary prevention of small subcortical strokes study. Stroke. 2014;45(9):2689鈥�94.

    听 听 听 听

  35. Allen A, Barron T, Mo A, Tangel R, Linde R, Grim R, Mingle J, Deibert E. Impact of neurological Follow-Up on Early Hospital Readmission Rates for Acute ischemic stroke. Neurohospitalist. 2017;7(3):127鈥�31.

    听 听 听 听

  36. Shao X, Liu H, Hou F, Bai Y, Cui Z, Lin Y, Jiang X, Bai P, Wang Y, Zhang Y, et al. Development and validation of risk prediction models for stroke and mortality among patients with type 2 diabetes in northern China. J Endocrinol Invest. 2023;46(2):271鈥�83.

    听 CAS听 听 听

  37. Khan MM, Roberson S, Reid K, Jordan M, Odoi A. Prevalence and predictors of stroke among individuals with prediabetes and diabetes in Florida. 国产情侣 Public Health. 2022;22(1):243.

    听 听 听 听

  38. Chen Y, Wright N, Guo Y, Turnbull I, Kartsonaki C, Yang L, Bian Z, Pei P, Pan D, Zhang Y, et al. Mortality and recurrent vascular events after first incident stroke: a 9-year community-based study of 0路5 million Chinese adults. Lancet Glob Health. 2020;8(4):e580鈥�90.

    听 听 听 听

  39. Bailey RR, Singleton JR, Majersik JJ. Association of obesity and diabetes with physical activity and fruit and vegetable consumption in stroke survivors. Fam Pract. 2021;38(1):56鈥�61.

    听 听 听

  40. Rexrode KM, Madsen TE, Yu AYX, Carcel C, Lichtman JH, Miller EC. The impact of sex and gender on stroke. Circ Res. 2022;130(4):512鈥�28.

    听 CAS听 听 听 听

  41. Petrea RE, Beiser AS, Seshadri S, Kelly-Hayes M, Kase CS, Wolf PA. Gender differences in stroke incidence and poststroke disability in the Framingham heart study. Stroke. 2009;40(4):1032鈥�7.

    听 听 听 听

  42. Christensen H, Cordonnier C. Age at Menopause: a female risk factor of stroke? Stroke 2021, 52(8):2592鈥�3.

  43. Khan M, Wasay M, Menon B, Saadatnia M, Venketasubramanian N, Gunaratne P, Mehndiratta MM, Dai A, Kaul S. Pregnancy and puerperium-related strokes in Asian women. J Stroke Cerebrovasc Dis. 2013;22(8):1393鈥�8.

    听 听 听

  44. Poorthuis MH, Algra AM, Algra A, Kappelle LJ, Klijn CJ. Female- and male-specific risk factors for stroke: a systematic review and Meta-analysis. JAMA Neurol. 2017;74(1):75鈥�81.

    听 听 听

  45. Ohkuma T, Iwase M, Fujii H, Kitazono T. Sex differences in cardiovascular risk, lifestyle, and psychological factors in patients with type 2 diabetes: the Fukuoka Diabetes Registry. Biol Sex Differ. 2023;14(1):32.

    听 CAS听 听 听 听

  46. Monterrosa AE, Haffner SM, Stern MP, Hazuda HP. Sex difference in lifestyle factors predictive of diabetes in mexican-americans. Diabetes Care. 1995;18(4):448鈥�56.

    听 CAS听 听 听

  47. Sialino LD, Picavet HSJ, Wijnhoven HAH, Loyen A, Verschuren WMM, Visser M, Schaap LS, van Oostrom SH. Exploring the difference between men and women in physical functioning: how do sociodemographic, lifestyle- and health-related determinants contribute? 国产情侣 Geriatr. 2022;22(1):610.

    听 听 听 听

  48. Lichtman JH, Leifheit-Limson EC, Jones SB, Watanabe E, Bernheim SM, Phipps MS, Bhat KR, Savage SV, Goldstein LB. Predictors of hospital readmission after stroke: a systematic review. Stroke. 2010;41(11):2525鈥�33.

    听 听 听 听

  49. Larsson SC, Scott RA, Traylor M, Langenberg CC, Hindy G, Melander O, Orho-Melander M, Seshadri S, Wareham NJ, Markus HS. Type 2 diabetes, glucose, insulin, BMI, and ischemic stroke subtypes: mendelian randomization study. Neurology. 2017;89(5):454鈥�60.

    听 CAS听 听 听 听

Acknowledgements

Not applicable.

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).

Author information

Author notes

  1. Hua Meng and Ting Pan contributed equally to this work.

Authors and Affiliations

  1. School of Public Health, Ningxia Medical University, Yinchuan, China

    Hua Meng,听Ting Pan,听Xinya Su,听Wenwen Lu,听Xingtian Wang,听Zhuo Liu,听Yuhui Geng听&听Xiaojuan Ma

  2. Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, China

    Hua Meng,听Ting Pan,听Xinya Su,听Wenwen Lu,听Xingtian Wang,听Zhuo Liu,听Yuhui Geng听&听Xiaojuan Ma

  3. Department of Emergency Medicine, People鈥檚 Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, Yinchuan, China

    Dongfeng Pan

  4. Public Health Center, People鈥檚 Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University, 301 Zhengyuan North Street, Yinchuan, Ningxia, 750002, China

    Peifeng Liang

Authors
  1. Hua Meng

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  2. Ting Pan

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  3. Dongfeng Pan

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  4. Xinya Su

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  5. Wenwen Lu

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  6. Xingtian Wang

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  7. Zhuo Liu

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  8. Yuhui Geng

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  9. Xiaojuan Ma

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  10. Peifeng Liang

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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.

Corresponding author

Correspondence to Peifeng Liang.

Ethics declarations

Ethics approval and consent to participate

This study protocol was reviewed and approved by [People鈥檚 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.

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Cite this article

Meng, H., Pan, T., Pan, D. et al. Females with diabetes have a higher risk of ischemic stroke readmission: a retrospective cohort study. 国产情侣 Public Health 24, 2488 (2024). https://doi.org/10.1186/s12889-024-20006-w

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  • DOI: https://doi.org/10.1186/s12889-024-20006-w

Keywords

国产情侣 Public Health

ISSN: 1471-2458

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