Sex Based Disparities in Glycemic Control and Hospitalization Outcomes of Medical Patients with Diabetes Mellitus - A Historical Cohort Study

Sex Based Disparities in Glycemic Control and Hospitalization Outcomes of Medical Patients with Diabetes Mellitus - A Historical Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sex Based Disparities in Glycemic Control and Hospitalization Outcomes of Medical Patients with Diabetes Mellitus - A Historical Cohort Study Ronit Koren, Matan Elkan, Arielle Barouch, Tomer Ziv-Baran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7114518/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Nov, 2025 Read the published version in Internal and Emergency Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Introduction While sex differences in type 2 diabetes mellitus (T2DM) are well-documented in outpatient settings, data on inpatient disparities remain limited. This study examines the relationship between sex and glycemic control, chronic treatment patterns, and hospitalization outcomes in patients with T2DM. Methods A historical cohort of 5,133 adult T2DM patients hospitalized for > 24 hours at a tertiary Israeli medical center in 2023 was analyzed. Outcomes included hypoglycemia, hyperglycemia, length of stay, 30-day readmission, and mortality. Propensity score matching and multivariable regression were applied to adjust for confounding variables. Results Men were more frequently treated with chronic aspirin therapy than women (35.8% vs. 29.4%; p < 0.001), a difference that remained significant after multivariable adjustment (aOR 1.31; 95% CI: 1.13–1.52; p < 0.001). Male sex was also associated with higher rates of hyperglycemia (76.4% vs. 73.3%; p = 0.009) and severe hyperglycemia (48.6% vs. 43.7%; p < 0.001), with these associations persisting after adjustment (hyperglycemia: aOR 1.23; 95% CI: 1.03–1.46; p = 0.02; severe hyperglycemia: aOR 1.28; 95% CI: 1.09–1.49; p = 0.002) and confirmed in propensity score-matched analyses. One-month rehospitalization rates were also higher in men (12.7% vs. 11.1%; p = 0.088), reaching statistical significance after multivariable adjustment (aOR 1.27; 95% CI: 1.03–1.55; p = 0.02). Conclusions Sex-based disparities persist in inpatient glycemic control and chronic treatment patterns. Men are more prone to hyperglycemia and rehospitalization, while women are less likely to receive guideline-recommended cardioprotective therapies. A sex-specific approach may improve inpatient diabetes management and long-term outcomes. Diabetes Mellitus Sex hospitalization glycemic control chronic treatment patterns Figures Figure 1 Introduction Sex plays a crucial role in precision medicine, influencing nearly all aspects of disease pathophysiology, including mechanism, prevalence, progression, treatment response, and prognosis.[1] In diabetes, well-documented sex differences exist in metabolic regulation and cardiovascular risk.[2] Men have a higher prevalence of type 2 diabetes mellitus (T2DM) in young and middle-aged populations, whereas women exhibit a higher prevalence of undiagnosed diabetes and an increased diabetes risk after the age of 70.[3, 4] Men more frequently present with impaired fasting glucose, while women more commonly exhibit impaired glucose tolerance.[5] Disparities in clinical care and treatment patterns parallel these sex-based biological differences. In a large outpatient cohort, diabetes care was of poorer quality in women compared with men, including in achieving glucose and lipid targets and diabetic foot monitoring. Furthermore, sex-based disparities exist in medication prescriptions. It seems that the perception of cardiovascular risk is lower for women; hence, preventive measures are recommended less often.[6] For example, men with cardiovascular disease or heart failure (HF) receive sodium-glucose co-transporter-2 (SGLT2) inhibitors more often and at an earlier stage than women.[7, 8] Similarly, women are less frequently prescribed cardioprotective medications such as statins, aspirin, and angiotensin-converting enzyme (ACE) inhibitors.[9, 10] Patient adherence to prescribed therapies may represent an additional factor contributing to sex-based differences in care. Despite well-established sex differences in outpatient diabetes care, data on disparities among hospitalized patients with diabetes remain limited. Compared to the general population, individuals with diabetes face an increased risk of hospitalization,[11] and readmission.[12] Sex-based differences have been reported in hospitalization and readmission rates, causes of admission, risk of hypoglycemia, and discharge destination.[13, 14] In the inpatient setting, both hyperglycemia and hypoglycemia are associated with adverse outcomes, including infections, prolonged hospital stay, post-discharge morbidity, and mortality.[15] Most guidelines recommend initiating insulin therapy in critically ill and non-critically ill patients with glucose levels > 180 mg/dL, aiming for glycemic targets of 100–180 mg/dL.[16] However, data on sex differences in inpatient glycemic control are scarce, with limited evidence such as a small study reporting greater insulin resistance in critically ill women.[17] This study aims to evaluate sex-based differences in glycemic control, hospital length of stay, and short- and long-term mortality among patients with T2DM, adjusting for baseline comorbidities and clinical characteristics. Methods Study Design and participants A historical cohort study of all consecutive adult (> 18 years) patients with T2DM who were hospitalized in medical wards for more than 24 hours between January 1, 2023, and December 31, 2023, at Shamir Medical Center (SMC). SMC is a 904-bed university-affiliated tertiary medical center located in Israel’s central region and serves urban and rural populations. SMC comprises 7 internal medicine departments with a total of 269 beds. Hospitalization outcomes were assessed through February 2024. Patients were excluded if they were admitted for diabetes-related complications (diabetic ketoacidosis or hyperosmolar state), were pregnant, or had type 1 diabetes mellitus. Data collection : Data was accessed via the Israeli Ministry of Health’s Kineret Platform. Kineret is a cloud-based service that facilitates secure, anonymized analysis of electronic health records structured within the Observational Medical Outcomes Partnership (OMOP) Common Data Model.[18] The cohort was designed and characterized using ATLAS,[19] an open-source tool developed by the Observational Health Data Sciences and Informatics (OHDSI) community. [20] Ethical approval was obtained from the local institutional ethics committee before study initiation (approval number 0224-24ASF). Study variables, measurements, and definitions : Comorbidities were identified using International Classification of Diseases (ICD-9-CM) codes, while medications were classified according to the Anatomical Therapeutic Chemical (ATC) system ( Supplementary Tables 1 and 2 ). Laboratory indices, including hemoglobin, albumin, creatinine, electrolytes, and C-reactive protein (CRP), were collected from the first available post-admission results up to 24 hours from arrival. Glomerular filtration rate (GFR) was calculated using the “CKD-EPI” equation. Acute kidney injury (AKI) was defined as an increase in serum creatinine of ≥ 0.3 mg/dL during hospitalization. We also collected data on chronic cardioprotective treatment such as the use of antiplatelets (aspirin, clopidogrel), ACE inhibitors, angiotensin receptor blockers (ARBs), statins, and SGLT2 inhibitors before hospitalization. The primary outcomes were based on 4 levels of Glycemic Control During Hospitalization: hypoglycemia (≤ 70 mg/dL), severe hypoglycemia (≤ 54 mg/dL), hyperglycemia (> 180 mg/dL), and severe hyperglycemia (> 250 mg/dL). Data were derived from blood tests and point-of-care glucose monitoring. Additional outcomes included length of stay, rehospitalization within one month, and all-cause mortality during hospitalization and within 30-days since hospital admission. Mortality data were obtained from the Israeli Ministry of Interior’s national registry. Sample size A two-group design was employed to investigate whether the proportions of the studied outcome differ between men and women. The sample size calculation was made using a two-sided, two-sample Z-test, with a Type I error rate (α) of 0.01, a power of 90%, and an equal number of patients in each group. To detect a small effect (effect size = 0.2) of sex on the studied outcomes, the number of subjects needed in each group was 744. Statistics Categorical variables were described as frequencies and percentages. Continuous variables were evaluated for normal distribution using histograms and reported as means and standard deviations or as medians and interquartile ranges (IQR). The chi-square test was used to compare categorical variables between the sex groups, and the independent samples t-test and Mann-Whitney tests were applied to compare continuous variables. Multivariable logistic regression was applied to evaluate the association between sex and the studied outcomes while controlling for possible known confounders. Each regression contained two blocks. In the first block, sex and age were forced into the regression. In the second block, the following variables were considered for inclusion using the forward selection method (the Wald test was used and p < 0.05 was the criterion for inclusion): Chronic Obstructive Pulmonary disease (COPD)/asthma, pulmonary embolism, liver disease, rheumatic disease, chronic infection, hypertension (HTN), HF, ischemic heart Disease, atrial fibrillation/ flutter, cardiac arrhythmias, peripheral artery disease (PVD), dyslipidemia, past stroke\transient ischemic attack (TIA), dementia, malignancy, systemic steroids, white blood cells (WBC), lymphocytes, neutrophils, hemoglobin, platelets, C-reactive protein (CRP), albumin, estimated GFR (eGFR), calcium, corrected calcium level, potassium, sodium, diastolic blood pressure (BP), systolic BP, pulse, temperature, glucose levels, high density lipoprotein (HDL), triglycerides, low density lipoprotein (LDL), and body mass index (BMI). The two sex groups were matched according to the probability of a patient being a male. The probability (propensity score) was calculated using a logistic regression model. The following parameters were used to calculate the propensity score: age, BMI, systemic steroids, COPD/asthma, liver disease, chronic infection, HTN, HF, ischemic heart disease (IHD), atrial fibrillation flutter, acute arrhythmia, PVD, dyslipidemia, past stroke/TIA, dementia, and malignancy. Propensity score matching was performed using 1:1 nearest neighbor matching with a 0.05 caliper width to balance covariates between groups. Standardized differences were calculated to compare the two sex groups, before and after matching. A standardized difference < 0.1 was considered a negligible difference, and a difference between 0.1 and 0.2 was considered a small difference ( Supplementary Table 3 ). The matched groups were compared using the McNamar test for categorical variables, and the paired t-test or Wilcoxon test for the continuous variables. All the statistical tests were two-sided, and p < 0.05 was considered statistically significant. Statistical analysis was performed using R (version 4.1.2, R Foundation for Statistical Computing, Austria, 2023). Results Study population – unmatched cohort Overall, 5133 patients met the criteria and were included in the study. Of them, 2,845 were men and 2,288 were women. Their demographic characteristics, comorbidities, and chronic medications are presented in Table 1 . Before matching, women were older (median 77 vs. 74 years, p < 0.001) and had a higher BMI (median 28.1 vs. 27.3 kg/m 2 , p < 0.001). Women had higher rates of HTN (52.7% vs. 47.0%), while men had more IHD (26.6% vs. 13.6%), cerebrovascular disease (11.7% vs. 8.7%), CKD (17.2% vs. 14.1%), and PVD (5% vs. 1.8%), p < 0.001 for all comparisons. Men were more frequently treated with aspirin (35.8% vs. 29.5%, p < 0.001) and clopidogrel (11% vs. 7.2%, p < 0.001). Overall, few people were treated with GLP1 agonists or SGLT2 inhibitors. Men were more likely to receive SGLT2 inhibitors (7.8% vs. 5% p < 0.001) and long-acting insulin (40% vs. 35.4%, p 0.05) in the use of ACE inhibitors or ARBs. Table 1 Baseline characteristics, before and after matching: Unmatched cohort Matched cohort MEN (n = 2845) WOMEN (n = 2288) P-value Men (n = 1755) Women (n = 1755) P-value Age, years, median [IQR] 74 [66–81] 77 [70–85] < 0.001 75 [68–82] 75 [68–83] 0.243 BMI, kg/m^2, median [IQR] 27.3 [24.5–30.9] 28.1 [24.8–32.4] < 0.001 27.7 [24.7–31.2] 27.7 [24.2–31.6] 0.981 Comorbidities HTN, n (%) 1337 (47.0%) 1206 (52.7%) 0.99 Dyslipidemia, n (%) 1331 (46.8%) 1118 (48.9%) 0.138 857 (48.8%) 839 (47.8%) 0.555 CHF, n (%) 280 (9.8%) 224 (9.8%) 0.951 159 (9.1%) 160 (9.1%) > 0.99 IHD, n (%) 758 (26.6%) 312 (13.6%) < 0.001 296 (16.9%) 277 (15.8%) 0.297 Atrial fibrillation/flutter, n (%) 369 (13.0%) 359 (15.7%) 0.005 230 (13.1%) 250 (14.2%) 0.358 Past CVA/TIA, n (%) 334 (11.7%) 198 (8.7%) < 0.001 167 (9.5%) 161 (9.2%) 0.764 CKD, n (%) 488 (17.2%) 322 (14.1%) 0.003 286 (16.3%) 232 (13.2%) 0.012 COPD/Asthma, n (%) 300 (10.5%) 242 (10.6%) 0.97 183 (10.4%) 179 (10.2%) 0.866 Liver disease, n (%) 83 (2.9%) 99 (4.3%) 0.007 61 (3.5%) 63 (3.6%) 0.924 Connective tissue disease, n (%) 36 (1.3%) 110 (4.8%) < 0.001 25 (1.4%) 85 (4.8%) < 0.001 PVD, n (%) 143 (5.0%) 42 (1.8%) < 0.001 43 (2.5%) 40 (2.3%) 0.812 Dementia, n (%) 51 (1.8%) 72 (3.1%) 0.002 40 (2.3%) 37 (2.1%) 0.820 Malignancy, n (%) 260 (9.1%) 218 (9.5%) 0.633 174 (9.9%) 169 (9.6%) 0.819 ^Charlson Index, median [IQR] 5 [4–7] 5 [4–7] < 0.001 5 [4–7] 5 [4–7] 0.201 Chronic Medications Metformin, n (%) 1257 (44.2%) 1043 (45.6%) 0.315 787 (44.8%) 814 (46.4%) 0.36 Long-acting Insulin, n (%) 379 (13.3%) 281 (12.3%) 0.268 229 (13.0%) 210 (12.0%) 0.332 Short-acting Insulin, n (%) 153 (5.4%) 116 (5.1%) 0.623 79 (4.5%) 91 (5.2%) 0.345 SU, n (%) 141 (5.0%) 87 (3.8%) 0.046 89 (5.1%) 70 (4.0%) 0.123 Non-SU, n (%) 151 (5.3%) 100 (4.4%) 0.122 96 (5.5%) 65 (3.7%) 0.012 DPP4, n (%) 154 (5.4%) 143 (6.2%) 0.202 95 (5.4%) 102 (5.8%) 0.608 GLP1 agonists, n (%) 147 (5.2%) 111 (4.9%) 0.607 100 (5.7%) 95 (5.4%) 0.761 SGLT2 inhibitor, n (%) 223 (7.8%) 115 (5.0%) < 0.001 135 (7.7%) 97 (5.5%) 0.013 Any BP Medication n (%) * 1181 (41.5%) 1022 (44.7%) 0.023 734 (41.8%) 745 (42.5%) 0.707 Ace inhibitors /ARB 1310 (46.0%) 1091 (47.7%) 0.242 831 (47.4%) 814 (46.4%) 0.581 Plavix, n (%) 313 (11.0%) 165 (7.2%) < 0.001 168 (9.6%) 136 (7.7%) 0.059 Aspirin, n (%) 1019 (35.8%) 674 (29.5%) < 0.001 609 (34.7%) 515 (29.3%) < 0.001 Statins, n (%) 1532 (53.8%) 1150 (50.3%) 0.011 931 (53.0%) 879 (50.1%) 0.085 Thiazolidinediones, n (%) 36 (1.3%) 31 (1.4%) 0.779 20 (1.1%) 27 (1.5%) 0.304 Diuretics, n (%) 404 (14.2%) 394 (17.2%) 0.003 253 (14.4%) 278 (15.8%) 0.239 Systemic Steroids, n(%) 271 (9.5%) 231 (10.1%) 0.494 182 (10.4%) 170 (9.7%) 0.5 Long-acting Insulin, n (%) 1137 (40.0%) 809 (35.4%) < 0.001 697 (39.7%) 639 (36.4%) 0.044 Short-acting Insulin, n (%) 1066 (37.5%) 815 (35.6%) 0.172 657 (37.4%) 656 (37.4%) 0.972 ACE angiotensin converting enzyme, ARB -angiotensin receptor blockers, BMI- body mass index, CHF- congestive heart failure, CKD chronic kidney disease, COPD- chronic obstructive pulmonary disease, CVA- cerebrovascular accident, DPP4- Dipeptidyl peptidase-4, GLP-1 Glucagon-like peptide-1, HTN- hypertension, IHD- ischemic heart disease, PVD- peripheral vascular disease, SU-sulfonylurea, SGLT2- sodium-glucose transport protein 2, TIA- transient ischemic stroke. *including- ACE inhibitors, ARB, thiazide diuretics, alpha antagonists, beta blockers ^Charlson index [41] The cause of admission differed between men and women. Acute infection was slightly more common in men than in women (33.6% vs. 30.1%, p = 0.007). Chest pain and acute coronary syndrome (ACS) were more frequent in men (17.3% vs. 12.8%, p < 0.001, and 32.9% vs. 18.4% p < 0.001, respectively), manifested in higher troponin levels in men (median 25 vs. 21ng/L, p < 0.001). Women had higher rates of pulmonary embolism (2.1% vs 0.7% p < 0.001). LDL levels were higher in women (median 73 vs. 62mg/dL p 0.001). (Table 2 ) Table 2 Hospitalization characteristics, before and after matching: Unmatched cohort Matched cohort MEN (n = 2845) WOMEN (n = 2288) P-value Men (n = 1755) Women (n = 1755) P-value Cause of admission Acute infection, n (%) 956 (33.6%) 688 (30.1%) 0.007 585 (33.3%) 521 (29.7%) 0.019 Chest pain, n (%) 491 (17.3%) 294 (12.8%) < 0.001 293 (16.7%) 241 (13.7%) 0.016 Dyspnea, n (%) 273 (9.6%) 282 (12.3%) 0.002 171 (9.7%) 205 (11.7%) 0.071 COPD/Asthma decompensation, n (%) 326 (11.5%) 261 (11.4%) 0.954 198 (11.3%) 193 (11.0%) 0.829 ADHF, n (%) 465 (16.3%) 386 (16.9%) 0.614 285 (16.2%) 286 (16.3%) > 0.99 ACS, n (%) 936 (32.9%) 420 (18.4%) < 0.001 430 (24.5%) 364 (20.7%) 0.002 Acute atrial fibrillation, n (%) 457 (16.1%) 442 (19.3%) 0.002 300 (17.1%) 308 (17.5%) 0.755 Acute TIA/CVA, n (%) 489 (17.2%) 332 (14.5%) 0.009 271 (15.4%) 248 (14.1%) 0.295 AKI*, n (%) 468 (16.5%) 340 (14.9%) 0.112 286 (16.4%) 252 (14.4%) 0.116 Acute arrhythmia, n (%) 114 (4.0%) 89 (3.9%) 0.83 82 (4.7%) 72 (4.1%) 0.465 Pulmonary embolism, n (%) 21 (0.7%) 47 (2.1%) < 0.001 14 (0.8%) 29 (1.7%) 0.031 Laboratory results upon admission : WBC, count/µL, median [IQR] 9.2 [7.2–12.2] 9.3 [7.2–12.4] 0.703 9.1 [7.2–12.1] 9.3 [7.2–12.4] 0.212 Lymphocytes, count/µL, median [IQR] 1.3 [0.8–1.9] 1.4 [0.9-2.0] < 0.001 1.3 [0.8–1.8] 1.4 [0.9-2.0] < 0.001 Neutrophils, count/µL, median [IQR] 6.7 [5.0-9.5] 6.7 [4.9–9.8] 0.946 6.6 [4.9–9.4] 6.7 [4.9–9.8] 0.653 Hemoglobin, g/dL, mean ± SD 12.8 (± 2.3) 11.8 (± 2.0) < 0.001 12.8 (± 2.3) 11.8 (± 2.0) < 0.001 Platelets, count/µL, median [IQR] 214 [168–274] 247 [197–310] < 0.001 213 [167–270] 247 [199–312] < 0.001 CRP, mg/L, median [IQR] 17 [4–77] 14 [4–58] 0.003 17 [4–77] 13 [4–57] 0.003 Albumin, g/dL, mean ± SD 3.65 (± 0.54) 3.61 (± 0.54) 0.012 3.66 (± 0.54) 3.63 (± 0.55) 0.1 Creatinine, mg/dL, median [IQR] 1.15 [0.89–1.67] 0.96 [0.72–1.39] < 0.001 1.15 [0.90–1.64] 0.93 [0.71–1.37] < 0.001 eGFR, mL/min/1.73m², median [IQR] 67 [42–89] 61 [38–87] < 0.001 66 [42–89] 64 [40–88] 0.087 Glucose, mg/dL, median [IQR] 160 [123–221] 155 [121–214] 0.102 157 [122–212] 155 [121–214] 0.961 Potassium, mmol/L, median [IQR] 4.30 [4.00-4.70] 4.30 [3.90–4.70] < 0.001 4.30 [4.00-4.70] 4.30 [3.90–4.70] 0.007 Sodium, mmol/L, median [IQR] 136 [134–139] 137 [133–139] 0.563 137 [134–139] 137 [133–139] 0.554 HbA1c (%), %, median [IQR] # 6.9 [6.2–8.1] 6.8 [6.1-8.0] 0.1 6.9 [6.1-8.0] 6.9 [6.1–8.1] 0.841 HbA1c(mmol/mol) median [IQR]# 52.0 [43.5–64.0] 49.0 [43.0–61.0] 0.042 50.0 [43.0-62.2] 49.0 [42.0–60.0] 0.206 Troponin, ng/L, median [IQR]# 25.0 [13.8–42.6] 21.0 [6.5–38.6] < 0.001 25.8 [14.9–42.5] 19.3 [6.5–36.0] < 0.001 LDL cholesterol Admission, mg/dL, median [IQR] 62 [45–85] 73 [52–99] < 0.001 63 [45–87] 73 [53–99] < 0.001 HDL cholesterol Admission, mg/dL, median [IQR] 36 [29–44] 42 [33–53] < 0.001 37 [30–45] 42 [33–52] < 0.001 TG Admission, mg/dL, median [IQR] 120.0 [88.0-169.0] 123.0 [92.0-172.2] 0.031 118 [86.0-163.0] 126 [94.0-179.0] < 0.001 Vital signs upon admission Systolic BP, mmHg, mean ± SD 138 (± 26) 140 (± 27) 0.004 138 (± 25) 140 (± 27) 0.031 Diastolic BP, mmHg, mean ± SD 75 (± 15) 74 (± 15) 0.006 75 (± 15) 74 (± 15) 0.049 Pulse, beats/min, median [IQR] 82 [71–95] 82 [71–96] 0.992 82 [71–96] 82 [71–95] 0.548 Temperature, °C, median [IQR] 36.8 [36.6–37.1] 36.8 [36.6–37.0] 0.678 36.8 [36.6–37.1] 36.8 [36.6–37.0] 0.651 ACS- acute coronary syndrome, ADHF- acute decompensated heart failure, AKI- acute kidney injury, BP-blood pressure, COPD- chronic obstructive pulmonary disease, CRP- C-reactive protein, CVA- cerebrovascular accident, eGFR- estimated glomerular filtration rate (using CKD-EPI equation), HDL- high density lipoprotein, PVD- peripheral vascular disease, LDL- low density lipoprotein, TG-triglycerides, TIA- transient ischemic stroke, WBC- white blood cells *AKI- acute kidney injury, was estimated as an increase in laboratory creatinine levels by > 0.3 mg/dL, #Troponin and Hba1C levels were available for 60.1% of men and 57.7% of women, and after matching for 60.5% of men and 58% of women and HbA1c levels were available in 16.5% of men and 16.8% of women and after matching in 15.8% of men and 16.9% of women. Glucose levels upon admission were not statistically different between women and men (p = 0.102), nor were HbA1c levels (p = 0.1, available for only 21.5% and 22.9% of patients, respectively). In-hospital glucose control and hospitalization outcomes During hospitalization, men suffered more often from hyperglycemic episodes (76.4% vs. 73.3%, p = 0.009), and severe hyperglycemic episodes (48.6% vs. 43.7%, p < 0.001) compared with women. Overall, women had more measurements in range during hospitalization (60% vs. 57.1%, p = 0.036). Rehospitalization after 1 month was higher in men − 12.8% vs. 11.1%, though this difference was not statistically significant (p = 0.088). There were no differences in mortality outcomes )Table 3 . Figure 1). Table 3 In Hospital glucose control and outcomes before and after matching: Unmatched cohort Matched cohort Men (n = 2845) Women (n = 2288) P-value Men ( n = 1755) Women (n = 1755) P-value Hypoglycemia*, n (%) 378 (13.3%) 320 (14.0%) 0.467 234 (13.3%) 233 (13.3%) > 0.99 Severe hypoglycemia*, n (%) 144 (5.1%) 136 (5.9%) 0.166 91 (5.2%) 94 (5.4%) 0.879 Hyperglycemia*, n (%) 2175 (76.4%) 1676 (73.3%) 0.009 1340 (76.4%) 1288 (73.4%) 0.049 Severe hyperglycemia*, n (%) 1384 (48.6%) 999 (43.7%) < 0.001 842 (48.0%) 776 (44.2%) 0.031 Measurements in range* (%) 57.1 [33.3–80.0] 60.0 [36.1–80.0] 0.036 58.3 [35.4–80.0] 60.0 [36.0–80.0] 0.328 Hospitalization Length (days), median [IQR] 5 [4–9] 6 [4–10] 0.123 6 [4–10] 6 [4–9] 0.459 Rehospitalization 1M, n (%) 353 (12.7%) 247 (11.1%) 0.088 229 (13.3%) 194 (11.4%) 0.061 In hospital Mortality, n (%) 64 (2.2%) 66 (2.9%) 0.15 39 (2.2%) 49 (2.8%) 0.337 1M mortality, n (%) 137 (4.8%) 113 (4.9%) 0.838 84 (4.8%) 83 (4.7%) > 0.99 hypoglycemia ≤ 70mg/dL, severe hypoglycemia ≤ 54mg/dL, normal glucose values- 71-180mg/dL, hyperglycemia 181<, and severe hyperglycemia 250 < mg/dL Range- 100–180 mg/dL, 1M- 1 month, 3M- 3 months m− P value using McNemar after propensity score matching for: BMI, age, gender, steroid intake, COPD asthma, liver disease, chronic infection, HTN, CHF, IHD, Atrial fibrillation flutter, cardiac arrhythmias, PVD, dyslipidemia, past stroke TIA, dementia, and malignancy. Table 4 The association between male sex and main hospitalization characteristics and outcomes: OR 95% Confidence interval P value aOR# 95% Confidence interval P value Chronic medications : Aspirin 1.33 1.18–1.50 < 0.001 1.31 1.13–1.52 < 0.001 Plavix 1.59 1.3–1.93 0.001 1.25 0.94–1.64 0.113 Ace inhibitors 1.33 1.18–1.50 < 0.001 1.31 1.13–1.52 0.001 1.28 1.09–1.49 0.002 Hypoglycemia* 0.94 0.8–1.1 0.467 1.108 0.913–1.344 0.298 Severe hypoglycemia* 0.84 0.66–1.07 0.166 1.049 0.791–1.39 0.741 Hospitalization outcomes : Rehospitalization 1M 1.16 0.97–1.38 0.088 1.27 1.03–1.55 0.02 In hospital mortality 0.77 0.54–1.09 0.151 0.889 0.6–1.317 0.558 30 day mortality 0.97 0.75–1.25 0.838 1.152 0.851–1.558 0.36 Mortality or prolonged stay** 0.9 0.8–1.0 0.07 1.12 0.971–1.291 0.119 ACE angiotensin converting enzyme, GLP-1 Glucagon-like peptide-1, SGLT2- sodium-glucose transport protein 2 *hypoglycemia ≤ 70mg/dL, severe hypoglycemia ≤ 54mg/dL, normal glucose values- 71-180mg/dL, hyperglycemia 181<, and severe hyperglycemia 250 7days M- month #aOR adjusted odds ratio- adjusted for male sex, age and variables with p < 0.1 using a stepwise forward regression analysis The association between sex and main hospitalization characteristics and outcomes Men were treated more often with aspirin both in univariate analysis [OR 1.33 (95%CI 1.18–1.5), p < 0.001 and after adjustment in multivariable analysis [aOR 1.31 (95%CI 1.13–1.52) p < 0.001]. SGLT treatment was not more common in men after multivariable analysis aOR 1.25 (95%CI (0.94–1.64), p = 0.113]. Male sex was associated with hyperglycemia [OR 1.18 (95% CI 1.04–1.34), p = 0.008] and severe hyperglycemia [OR 1.22 (95%CI (1.09–1.36 p < 0.001)]. It remained independently associated with hyperglycemia [aOR 1.23 (95%CI 1.03–1.46 p = 0.02)] and severe hyperglycemia [aOR 1.28 (95% CI (1.09–1.49, p = 0.002)] after adjustment in multivariable analysis. 1-month rehospitalization rates tended to be higher in men ]OR 1.16 (95% CI 0.97–1.38, p = 0.088] in univariate analysis, and became significant after adjustment in multivariable analysis [aOR 1.27 (95%CI 1.03–1.55, p = 0.02)]. Matched cohorts Study population – matched cohort The matched cohort consisted of two similar groups, comprising 1,755 men and women (Table 1 and Supplementary Table 3 ). Men had higher rates of CKD (16.3% vs. 13.2%, p = 0.012) and women had higher rates of connective tissue disease (4.8% vs. 1.4%, p < 0.001). Men were more frequently treated with aspirin (34.7% vs. 29.3%, p < 0.001) and SGLT2 inhibitors (7.7% vs. 5.5%, p = 0.013). LDL levels were higher in women (median 73mg/dL vs. 63mg/dL, p < 0.001) despite similar statin use. The cause of admission differed between men and women: Acute infection was more common in men than in women (33.3% vs. 29.7%, p = 0.019). Chest pain and ACS were also more common in men (16.7% vs. 13.7%, p = 0.016, and 24.5% vs. 20.7%, p = 0.002, respectively), which was manifested in higher troponin levels in men (25.8 ng/L vs. 19.3ng/L p < 0.001). Women were more often diagnosed with pulmonary embolism (1.7% vs. 0.8%, p = 0.031). In-hospital glucose control and hospitalization outcomes During hospitalization, men suffered more often from hyperglycemic episodes (76.4% vs. 73.4%, p = 0.049), and severe hyperglycemic episodes (48% vs. 44.2%, p = 0.031) compared with women. Overall, women had more measurements in the desirable range during hospitalization (60% vs. 57.1%, p = 0.036). Rehospitalization after 1 month was higher in men (13.3% vs. 11.4%), though this difference was not statistically significant (p = 0.061). No significant differences in mortality were observed post-matching (Table 3 , Fig. 1). Discussion This study highlights sex-based differences in hospitalized patients with T2DM, particularly in baseline cardiovascular risk, chronic treatment patterns, glycemic control, and hospitalization outcomes. Men had a higher burden of cardiovascular disease (IHD, CKD, and PVD) and were more frequently prescribed cardioprotective medications (statins, aspirin, and SGLT2 inhibitors). Ischemic complications were a more common cause of hospitalization in men. Importantly, male sex was independently associated with worse outcomes, including an increased risk of hyperglycemia, severe hyperglycemia, and 1-month rehospitalization. Hyperglycemia during hospitalization is related to worse outcomes across a range of clinical conditions, in both diabetic and non-diabetic patients.[21–23] However, the interaction between sex and hyperglycemia-related outcomes remains less clearly defined. For example, in patients hospitalized with acute coronary syndrome, admission hyperglycemia was independently associated with increased mortality in men but not in women.[24, 25] Conversely, another study reported that elevated HBA1c levels were associated with rehospitalization in women with CVD but not in men.[26] In our study, men experienced a higher frequency of hyperglycemia events during hospitalization. In general, in-hospital treatment protocols do not differ between men and women and typically involve initiating insulin treatment when blood glucose levels are > 180mg/dL in ≥ 2 measurements, using weight-based dosages. Despite this standardized approach, previous studies suggest that glycemic control may differ between hospitalized men and women with diabetes. For example, a study focusing on anthropometric and body composition measurements found men to have higher blood glucose fluctuations.[27] Sex hormones play a key role in glucose regulation and may contribute to these differences. Testosterone deficiency in older men is related to metabolic syndrome, visceral adiposity and increased insulin resistance,[28] while androgen excess in women is related to increased risk of diabetes. Elevated levels of sex hormone-binding globulins appear to have a protective effect, particularly in women.[29] In contrast, a clinical study of critically ill patients reported greater insulin resistance in women than in men, highlighting the variability of sex-based metabolic responses under different physiological conditions.[30] Beyond hospitalization characteristics and outcomes, our study also revealed sex-based differences in chronic treatment, co-morbidities and reasons for acute admission. Notably, men were more likely to receive cardioprotective therapies, such as antiplatelet agents, even after propensity score matching and multivariable adjustment. Previous studies have shown that men with diabetes are more likely than women to receive guideline-directed treatment for cardiovascular risk and complications. [6, 7, 31] It is well established that treatment with SGLT2 inhibitors reduces the risk of major cardiovascular events and all-cause mortality in patients with T2DM when added to standard care[32], [33]. Accordingly, current diabetes treatment guidelines recommend SGLT2 as a standard therapy for adults with T2DM and established or high risk of atherosclerotic cardiovascular disease, HF, or CKD.[34]A large meta-analysis, found no significant sex differences in HbA1c reduction or major adverse cardiovascular outcomes (MACE) with either SGLT2 inhibitors or GLP1 agonists.[35] Consistent with previous research, this study demonstrates low overall SGLT2 inhibitors or GLP1 agonists treatment. However, men were more frequently prescribed with SGLT2 inhibitors than women [OR 1.39 (95%CI 1.061–1.828), p = 0.0169]. Similar results were reported in a large retrospective study involving 934,737 patients, where women were less likely than men to receive SGLT2 inhibitors; aOR, 0.84; 95% CI, 0.82–0.85. [8] Statins have the same effectiveness in men and women with similar cardiovascular risk factors.[36]Yet, women used statins less often than men (RR 0.90; 95% CI 0.86, 0.93).[37] In our study, women had higher LDL levels (p < 0.001) and men were more often chronically treated with statins (p = 0.011), though this was not significant after matching and multivariable regression. Women have lower in-hospital mortality rate than men for a wide variety of medical conditions, both infectious [38] and non-communicable diseases.[39] Yet, diabetic women have similar or even higher mortality rates compared to men.[40] This was also demonstrated in our study, though we could not address the causes of death. Treatment disparities may partly explain the lack of observed sex-based differences in mortality. Study limitations: This retrospective, large-scale study has inherent limitations due to its observational nature and is subject to residual confounding. We relied on coded data, and it is possible that some information was not recorded. For example, physicians may not have coded all the medications a patient received or all comorbid conditions. Nevertheless, such underreporting is unlikely to differ between men and women. Therefore, if present, it would most likely attenuate the observed association (i.e., non-differential misclassification bias). Finally, the follow-up was limited to 30 days and different sex related outcomes may appear with longer follow up. Further research should explore sex-specific responses to inpatient glucose management interventions. Conclusion Our findings support the development of hospital protocols that integrate sex-specific glucose monitoring strategies and equitable medication prescribing practices. Since men are more prone to hyperglycemia, they may require closer monitoring and more aggressive insulin adjustment during hospitalization. In addition, optimizing guideline-directed cardioprotective therapy, especially in women, is crucial. Given the persistent sex gap in statin and SGLT2 inhibitor prescriptions, further efforts are needed to ensure equitable treatment. Declarations Acknowledgments : This study has been conducted using Kineret medical data platform of the Directorate of Government Medical Centers at the Israeli Ministry of Health. (https://kineret.health.gov.il/en) Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Shamir medical center, approval number 0224-24ASF. Compliance with Ethical Standards: Funding : This research was supported by Grant No. 0006865 awarded as part of the 2023 Kinneret Platform and was jointly funded by the Ministry of Innovation, Science and Technology and the Ministry of Health. Author contributions Conceptualization: Ronit Koren, Matan Elkan ; Methodology: Arielle Barouch, Tomer Ziv-Baran ; Formal analysis and investigation: Arielle Barouc ; Writing - original draft preparation: Ronit Koren, Matan Elkan ; Writing - Ronit Koren, Matan Elkan, Tomer Ziv-Baran , Funding acquisition: Ronit Koren , Tomer Ziv-Baran , Code availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Shang D, Wang L, Klionsky DJ, Cheng H, Zhou R (2021) Sex differences in autophagy-mediated diseases: toward precision medicine. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7114518","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486980849,"identity":"1e663509-d487-401a-874c-bdbcd17e05f3","order_by":0,"name":"Ronit Koren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYBACCSgtw8DAfABVio0dvxYeoJLEBohSmBZmglp4DFG1MODQItne+/Bx4Q4bHn7+Nd8f/GyzSWyQb34mwVBjx8CHQ4s0z3Fj45ln0ngkZ7zd2NjblmbMwMZmJsFwLBmnw+Qk0tikedsO8xjcOLuxAciQAzoMqIXtAD4t7L952/7z2N8487DxL5ABDKlvEgz/cGuRBtrCzNt2gMeAv4exGcgA2sJjJsHYhluLZM8xZmneM8k8EjfYDGfLnEs2ZmPLKbZI7EvmwRnIx9sYP/PusJPj7z/84OObMrvEfubjG298+GYnJ9/egF0PCDCC5CQSIBxwtCSAYwoPAGvhP4BXzSgYBaNgFIxgAACtn0yzGeTw6AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7236-5354","institution":"Shamir Medical Center Assaf Harofeh","correspondingAuthor":true,"prefix":"","firstName":"Ronit","middleName":"","lastName":"Koren","suffix":""},{"id":486980850,"identity":"b6bc258e-7b3a-4c51-a147-2d86a69218c9","order_by":1,"name":"Matan Elkan","email":"","orcid":"","institution":"Shamir Medical Center: Shamir Medical Center Assaf Harofeh","correspondingAuthor":false,"prefix":"","firstName":"Matan","middleName":"","lastName":"Elkan","suffix":""},{"id":486980851,"identity":"f4ea69a4-97f1-4a73-a221-dd19cd0b0ff3","order_by":2,"name":"Arielle Barouch","email":"","orcid":"","institution":"Shamir Medical Center: Shamir Medical Center Assaf Harofeh","correspondingAuthor":false,"prefix":"","firstName":"Arielle","middleName":"","lastName":"Barouch","suffix":""},{"id":486980852,"identity":"5b39589a-7a0d-4eaf-b612-edf9146efe6f","order_by":3,"name":"Tomer Ziv-Baran","email":"","orcid":"","institution":"Tel Aviv University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tomer","middleName":"","lastName":"Ziv-Baran","suffix":""}],"badges":[],"createdAt":"2025-07-13 16:29:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7114518/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7114518/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11739-025-04170-4","type":"published","date":"2025-11-01T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87379542,"identity":"d2ad60c6-469e-41c9-a388-61263d69ccae","added_by":"auto","created_at":"2025-07-23 08:30:20","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228157,"visible":true,"origin":"","legend":"\u003cp\u003eIn Hospital glucose control and hospitalization outcomes for matched and unmatched population\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7114518/v1/b2a357462274ae22389bc55e.jpeg"},{"id":95039925,"identity":"26fc8161-62ff-4e93-b0c6-94a73d67a416","added_by":"auto","created_at":"2025-11-03 16:05:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1391877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7114518/v1/8df94c06-4453-4d3f-9b11-af41500b6392.pdf"},{"id":87379543,"identity":"a51fb238-000f-4ca5-b11d-e5c82fcd586e","added_by":"auto","created_at":"2025-07-23 08:30:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39553,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7114518/v1/9b8f3acc03feab8c66abdbf9.docx"}],"financialInterests":"","formattedTitle":"Sex Based Disparities in Glycemic Control and Hospitalization Outcomes of Medical Patients with Diabetes Mellitus - A Historical Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSex plays a crucial role in precision medicine, influencing nearly all aspects of disease pathophysiology, including mechanism, prevalence, progression, treatment response, and prognosis.[1] In diabetes, well-documented sex differences exist in metabolic regulation and cardiovascular risk.[2] Men have a higher prevalence of type 2 diabetes mellitus (T2DM) in young and middle-aged populations, whereas women exhibit a higher prevalence of undiagnosed diabetes and an increased diabetes risk after the age of 70.[3, 4] Men more frequently present with impaired fasting glucose, while women more commonly exhibit impaired glucose tolerance.[5]\u003c/p\u003e\u003cp\u003eDisparities in clinical care and treatment patterns parallel these sex-based biological differences. In a large outpatient cohort, diabetes care was of poorer quality in women compared with men, including in achieving glucose and lipid targets and diabetic foot monitoring. Furthermore, sex-based disparities exist in medication prescriptions. It seems that the perception of cardiovascular risk is lower for women; hence, preventive measures are recommended less often.[6] For example, men with cardiovascular disease or heart failure (HF) receive sodium-glucose co-transporter-2 (SGLT2) inhibitors more often and at an earlier stage than women.[7, 8] Similarly, women are less frequently prescribed cardioprotective medications such as statins, aspirin, and angiotensin-converting enzyme (ACE) inhibitors.[9, 10] Patient adherence to prescribed therapies may represent an additional factor contributing to sex-based differences in care.\u003c/p\u003e\u003cp\u003eDespite well-established sex differences in outpatient diabetes care, data on disparities among hospitalized patients with diabetes remain limited. Compared to the general population, individuals with diabetes face an increased risk of hospitalization,[11] and readmission.[12] Sex-based differences have been reported in hospitalization and readmission rates, causes of admission, risk of hypoglycemia, and discharge destination.[13, 14]\u003c/p\u003e\u003cp\u003eIn the inpatient setting, both hyperglycemia and hypoglycemia are associated with adverse outcomes, including infections, prolonged hospital stay, post-discharge morbidity, and mortality.[15] Most guidelines recommend initiating insulin therapy in critically ill and non-critically ill patients with glucose levels\u0026thinsp;\u0026gt;\u0026thinsp;180 mg/dL, aiming for glycemic targets of 100\u0026ndash;180 mg/dL.[16] However, data on sex differences in inpatient glycemic control are scarce, with limited evidence such as a small study reporting greater insulin resistance in critically ill women.[17]\u003c/p\u003e\u003cp\u003eThis study aims to evaluate sex-based differences in glycemic control, hospital length of stay, and short- and long-term mortality among patients with T2DM, adjusting for baseline comorbidities and clinical characteristics.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA historical cohort study of all consecutive adult (\u0026gt;\u0026thinsp;18 years) patients with T2DM who were hospitalized in medical wards for more than 24 hours between January 1, 2023, and December 31, 2023, at Shamir Medical Center (SMC). SMC is a 904-bed university-affiliated tertiary medical center located in Israel\u0026rsquo;s central region and serves urban and rural populations. SMC comprises 7 internal medicine departments with a total of 269 beds. Hospitalization outcomes were assessed through February 2024. Patients were excluded if they were admitted for diabetes-related complications (diabetic ketoacidosis or hyperosmolar state), were pregnant, or had type 1 diabetes mellitus.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eData collection\u003c/span\u003e:\u003c/p\u003e\u003cp\u003eData was accessed via the Israeli Ministry of Health\u0026rsquo;s Kineret Platform. Kineret is a cloud-based service that facilitates secure, anonymized analysis of electronic health records structured within the Observational Medical Outcomes Partnership (OMOP) Common Data Model.[18] The cohort was designed and characterized using ATLAS,[19] an open-source tool developed by the Observational Health Data Sciences and Informatics (OHDSI) community. [20] Ethical approval was obtained from the local institutional ethics committee before study initiation (approval number 0224-24ASF).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStudy variables, measurements, and definitions\u003c/span\u003e:\u003c/p\u003e\u003cp\u003eComorbidities were identified using International Classification of Diseases (ICD-9-CM) codes, while medications were classified according to the Anatomical Therapeutic Chemical (ATC) system (\u003cem\u003eSupplementary Tables\u0026nbsp;1 and 2\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eLaboratory indices, including hemoglobin, albumin, creatinine, electrolytes, and C-reactive protein (CRP), were collected from the first available post-admission results up to 24 hours from arrival. Glomerular filtration rate (GFR) was calculated using the \u0026ldquo;CKD-EPI\u0026rdquo; equation. Acute kidney injury (AKI) was defined as an increase in serum creatinine of \u0026ge;\u0026thinsp;0.3 mg/dL during hospitalization.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe also collected data on chronic cardioprotective treatment such as the use of antiplatelets (aspirin, clopidogrel), ACE inhibitors, angiotensin receptor blockers (ARBs), statins, and SGLT2 inhibitors before hospitalization.\u003c/p\u003e\u003cp\u003eThe primary outcomes were based on 4 levels of Glycemic Control During Hospitalization: hypoglycemia (\u0026le;\u0026thinsp;70 mg/dL), severe hypoglycemia (\u0026le;\u0026thinsp;54 mg/dL), hyperglycemia (\u0026gt;\u0026thinsp;180 mg/dL), and severe hyperglycemia (\u0026gt;\u0026thinsp;250 mg/dL). Data were derived from blood tests and point-of-care glucose monitoring.\u003c/p\u003e\u003cp\u003eAdditional outcomes included length of stay, rehospitalization within one month, and all-cause mortality during hospitalization and within 30-days since hospital admission. Mortality data were obtained from the Israeli Ministry of Interior\u0026rsquo;s national registry.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSample size\u003c/span\u003e\u003c/p\u003e\u003cp\u003eA two-group design was employed to investigate whether the proportions of the studied outcome differ between men and women. The sample size calculation was made using a two-sided, two-sample Z-test, with a Type I error rate (α) of 0.01, a power of 90%, and an equal number of patients in each group. To detect a small effect (effect size\u0026thinsp;=\u0026thinsp;0.2) of sex on the studied outcomes, the number of subjects needed in each group was 744.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStatistics\u003c/span\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCategorical variables were described as frequencies and percentages. Continuous variables were evaluated for normal distribution using histograms and reported as means and standard deviations or as medians and interquartile ranges (IQR). The chi-square test was used to compare categorical variables between the sex groups, and the independent samples t-test and Mann-Whitney tests were applied to compare continuous variables. Multivariable logistic regression was applied to evaluate the association between sex and the studied outcomes while controlling for possible known confounders. Each regression contained two blocks. In the first block, sex and age were forced into the regression. In the second block, the following variables were considered for inclusion using the forward selection method (the Wald test was used and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was the criterion for inclusion): Chronic Obstructive Pulmonary disease (COPD)/asthma, pulmonary embolism, liver disease, rheumatic disease, chronic infection, hypertension (HTN), HF, ischemic heart Disease, atrial fibrillation/ flutter, cardiac arrhythmias, peripheral artery disease (PVD), dyslipidemia, past stroke\\transient ischemic attack (TIA), dementia, malignancy, systemic steroids, white blood cells (WBC), lymphocytes, neutrophils, hemoglobin, platelets, C-reactive protein (CRP), albumin, estimated GFR (eGFR), calcium, corrected calcium level, potassium, sodium, diastolic blood pressure (BP), systolic BP, pulse, temperature, glucose levels, high density lipoprotein (HDL), triglycerides, low density lipoprotein (LDL), and body mass index (BMI).\u003c/p\u003e\u003cp\u003eThe two sex groups were matched according to the probability of a patient being a male. The probability (propensity score) was calculated using a logistic regression model. The following parameters were used to calculate the propensity score: age, BMI, systemic steroids, COPD/asthma, liver disease, chronic infection, HTN, HF, ischemic heart disease (IHD), atrial fibrillation flutter, acute arrhythmia, PVD, dyslipidemia, past stroke/TIA, dementia, and malignancy.\u003c/p\u003e\u003cp\u003ePropensity score matching was performed using 1:1 nearest neighbor matching with a 0.05 caliper width to balance covariates between groups. Standardized differences were calculated to compare the two sex groups, before and after matching. A standardized difference\u0026thinsp;\u0026lt;\u0026thinsp;0.1 was considered a negligible difference, and a difference between 0.1 and 0.2 was considered a small difference (\u003cem\u003eSupplementary Table\u0026nbsp;3\u003c/em\u003e). The matched groups were compared using the McNamar test for categorical variables, and the paired t-test or Wilcoxon test for the continuous variables.\u003c/p\u003e\u003cp\u003eAll the statistical tests were two-sided, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Statistical analysis was performed using R (version 4.1.2, R Foundation for Statistical Computing, Austria, 2023).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStudy population \u0026ndash; unmatched cohort\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOverall, 5133 patients met the criteria and were included in the study. Of them, 2,845 were men and 2,288 were women. Their demographic characteristics, comorbidities, and chronic medications are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Before matching, women were older (median 77 vs. 74 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had a higher BMI (median 28.1 vs. 27.3 kg/m\u003csup\u003e2\u003c/sup\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women had higher rates of HTN (52.7% vs. 47.0%), while men had more IHD (26.6% vs. 13.6%), cerebrovascular disease (11.7% vs. 8.7%), CKD (17.2% vs. 14.1%), and PVD (5% vs. 1.8%), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all comparisons. Men were more frequently treated with aspirin (35.8% vs. 29.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and clopidogrel (11% vs. 7.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Overall, few people were treated with GLP1 agonists or SGLT2 inhibitors. Men were more likely to receive SGLT2 inhibitors (7.8% vs. 5% p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and long-acting insulin (40% vs. 35.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women were more likely to be treated for HTN (44.7% vs. 41.5%, p\u0026thinsp;=\u0026thinsp;0.024). There were no significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the use of ACE inhibitors or ARBs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics, before and after matching:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eUnmatched cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c12\" namest=\"c7\"\u003e\u003cp\u003eMatched cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMEN (n\u0026thinsp;=\u0026thinsp;2845)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWOMEN (n\u0026thinsp;=\u0026thinsp;2288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;1755)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;1755)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e74 [66\u0026ndash;81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 [70\u0026ndash;85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e75 [68\u0026ndash;82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e75 [68\u0026ndash;83]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m^2, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e27.3 [24.5\u0026ndash;30.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.1 [24.8\u0026ndash;32.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e27.7 [24.7\u0026ndash;31.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27.7 [24.2\u0026ndash;31.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTN, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1337 (47.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1206 (52.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e881 (50.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e882 (50.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1331 (46.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1118 (48.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e857 (48.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e839 (47.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e280 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e224 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e159 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e160 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIHD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e758 (26.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e312 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e296 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e277 (15.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial fibrillation/flutter, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e369 (13.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e359 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e230 (13.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e250 (14.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePast CVA/TIA, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e334 (11.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e198 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e167 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e161 (9.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e488 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e322 (14.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e286 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e232 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD/Asthma, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e300 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e242 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e183 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e179 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e83 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e61 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e63 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConnective tissue disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e36 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e25 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e85 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e143 (5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e43 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e40 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDementia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e51 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e40 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e37 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignancy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e260 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e218 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e174 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e169 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e^Charlson Index, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e5 [4\u0026ndash;7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 [4\u0026ndash;7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e5 [4\u0026ndash;7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5 [4\u0026ndash;7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic Medications\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetformin, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1257 (44.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1043 (45.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e787 (44.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e814 (46.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong-acting Insulin, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e379 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e281 (12.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e229 (13.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e210 (12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShort-acting Insulin, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e153 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e116 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e79 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e91 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSU, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e141 (5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e89 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e70 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-SU, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e151 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100 (4.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e96 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e65 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPP4, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e154 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e143 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e95 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e102 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGLP1 agonists, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e147 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e100 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e95 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGLT2 inhibitor, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e223 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115 (5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e135 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e97 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny BP Medication n (%) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1181 (41.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1022 (44.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e734 (41.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e745 (42.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAce inhibitors /ARB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1310 (46.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1091 (47.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e831 (47.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e814 (46.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlavix, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e313 (11.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e165 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e168 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e136 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspirin, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1019 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e674 (29.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e609 (34.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e515 (29.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatins, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1532 (53.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1150 (50.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e931 (53.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e879 (50.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThiazolidinediones, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e36 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e20 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiuretics, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e404 (14.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e394 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e253 (14.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e278 (15.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystemic Steroids, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e271 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e231 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e182 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e170 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong-acting Insulin, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1137 (40.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e809 (35.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e697 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e639 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShort-acting Insulin, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1066 (37.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e815 (35.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e657 (37.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e656 (37.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c13\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eACE angiotensin converting enzyme, ARB -angiotensin receptor blockers, BMI- body mass index, CHF- congestive heart failure, CKD chronic kidney disease, COPD- chronic obstructive pulmonary disease, CVA- cerebrovascular accident, DPP4- Dipeptidyl peptidase-4, GLP-1 Glucagon-like peptide-1, HTN- hypertension, IHD- ischemic heart disease, PVD- peripheral vascular disease, SU-sulfonylurea, SGLT2- sodium-glucose transport protein 2, TIA- transient ischemic stroke.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e*including- ACE inhibitors, ARB, thiazide diuretics, alpha antagonists, beta blockers\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e^Charlson index [41]\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe cause of admission differed between men and women. Acute infection was slightly more common in men than in women (33.6% vs. 30.1%, p\u0026thinsp;=\u0026thinsp;0.007). Chest pain and acute coronary syndrome (ACS) were more frequent in men (17.3% vs. 12.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and 32.9% vs. 18.4% p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively), manifested in higher troponin levels in men (median 25 vs. 21ng/L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women had higher rates of pulmonary embolism (2.1% vs 0.7% p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). LDL levels were higher in women (median 73 vs. 62mg/dL p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which correlated with lower statin use in women (50.3% vs. 53.8%, p\u0026thinsp;=\u0026thinsp;0.012). Men were also treated more often with SGLT2 inhibitors (7.8% vs. 5% p\u0026thinsp;\u0026gt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHospitalization characteristics, before and after matching:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnmatched cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMatched cohort\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMEN (n\u0026thinsp;=\u0026thinsp;2845)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWOMEN (n\u0026thinsp;=\u0026thinsp;2288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;1755)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;1755)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCause of admission\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute infection, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e956 (33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e688 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e585 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e521 (29.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChest pain, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e491 (17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e294 (12.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e293 (16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e241 (13.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyspnea, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e273 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e282 (12.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e171 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e205 (11.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD/Asthma decompensation, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e326 (11.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e261 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e198 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e193 (11.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.829\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADHF, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e465 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e386 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e285 (16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e286 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACS, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e936 (32.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e420 (18.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e430 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e364 (20.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute atrial fibrillation, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e457 (16.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e442 (19.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e308 (17.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.755\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute TIA/CVA, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e489 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e332 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e271 (15.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e248 (14.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAKI*, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e468 (16.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e340 (14.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e286 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e252 (14.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcute arrhythmia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89 (3.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82 (4.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulmonary embolism, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory results upon admission\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, count/\u0026micro;L, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.2 [7.2\u0026ndash;12.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.3 [7.2\u0026ndash;12.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.1 [7.2\u0026ndash;12.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.3 [7.2\u0026ndash;12.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes, count/\u0026micro;L, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3 [0.8\u0026ndash;1.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4 [0.9-2.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3 [0.8\u0026ndash;1.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.4 [0.9-2.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils, count/\u0026micro;L, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.7 [5.0-9.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.7 [4.9\u0026ndash;9.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.6 [4.9\u0026ndash;9.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.7 [4.9\u0026ndash;9.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, g/dL, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.8 (\u0026plusmn;\u0026thinsp;2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.8 (\u0026plusmn;\u0026thinsp;2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.8 (\u0026plusmn;\u0026thinsp;2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.8 (\u0026plusmn;\u0026thinsp;2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets, count/\u0026micro;L, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214 [168\u0026ndash;274]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e247 [197\u0026ndash;310]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e213 [167\u0026ndash;270]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e247 [199\u0026ndash;312]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP, mg/L, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 [4\u0026ndash;77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 [4\u0026ndash;58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 [4\u0026ndash;77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13 [4\u0026ndash;57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin, g/dL, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.65 (\u0026plusmn;\u0026thinsp;0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.61 (\u0026plusmn;\u0026thinsp;0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.66 (\u0026plusmn;\u0026thinsp;0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.63 (\u0026plusmn;\u0026thinsp;0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine, mg/dL, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15 [0.89\u0026ndash;1.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96 [0.72\u0026ndash;1.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.15 [0.90\u0026ndash;1.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.93 [0.71\u0026ndash;1.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR, mL/min/1.73m\u0026sup2;, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 [42\u0026ndash;89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 [38\u0026ndash;87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66 [42\u0026ndash;89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64 [40\u0026ndash;88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose, mg/dL, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e160 [123\u0026ndash;221]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155 [121\u0026ndash;214]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e157 [122\u0026ndash;212]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e155 [121\u0026ndash;214]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.961\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium, mmol/L, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.30 [4.00-4.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.30 [3.90\u0026ndash;4.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.30 [4.00-4.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.30 [3.90\u0026ndash;4.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium, mmol/L, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 [134\u0026ndash;139]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137 [133\u0026ndash;139]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e137 [134\u0026ndash;139]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e137 [133\u0026ndash;139]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c (%), %, median [IQR]\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.9 [6.2\u0026ndash;8.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.8 [6.1-8.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.9 [6.1-8.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.9 [6.1\u0026ndash;8.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c(mmol/mol) median [IQR]#\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.0 [43.5\u0026ndash;64.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.0 [43.0\u0026ndash;61.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.0 [43.0-62.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49.0 [42.0\u0026ndash;60.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTroponin, ng/L, median [IQR]#\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.0 [13.8\u0026ndash;42.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.0 [6.5\u0026ndash;38.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.8 [14.9\u0026ndash;42.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.3 [6.5\u0026ndash;36.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL cholesterol Admission, mg/dL, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 [45\u0026ndash;85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73 [52\u0026ndash;99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63 [45\u0026ndash;87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73 [53\u0026ndash;99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL cholesterol Admission, mg/dL, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 [29\u0026ndash;44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 [33\u0026ndash;53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 [30\u0026ndash;45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42 [33\u0026ndash;52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG Admission, mg/dL, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120.0 [88.0-169.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123.0 [92.0-172.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e118 [86.0-163.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e126 [94.0-179.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVital signs upon admission\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic BP, mmHg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138 (\u0026plusmn;\u0026thinsp;26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (\u0026plusmn;\u0026thinsp;27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e138 (\u0026plusmn;\u0026thinsp;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140 (\u0026plusmn;\u0026thinsp;27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic BP, mmHg, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (\u0026plusmn;\u0026thinsp;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (\u0026plusmn;\u0026thinsp;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75 (\u0026plusmn;\u0026thinsp;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74 (\u0026plusmn;\u0026thinsp;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulse, beats/min, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 [71\u0026ndash;95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82 [71\u0026ndash;96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82 [71\u0026ndash;96]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82 [71\u0026ndash;95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature, \u0026deg;C, median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.8 [36.6\u0026ndash;37.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.8 [36.6\u0026ndash;37.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.8 [36.6\u0026ndash;37.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.8 [36.6\u0026ndash;37.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eACS- acute coronary syndrome, ADHF- acute decompensated heart failure, AKI- acute kidney injury, BP-blood pressure, COPD- chronic obstructive pulmonary disease, CRP- C-reactive protein, CVA- cerebrovascular accident, eGFR- estimated glomerular filtration rate (using CKD-EPI equation), HDL- high density lipoprotein, PVD- peripheral vascular disease, LDL- low density lipoprotein, TG-triglycerides, TIA- transient ischemic stroke, WBC- white blood cells\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*AKI- acute kidney injury, was estimated as an increase in laboratory creatinine levels by \u0026gt;\u0026thinsp;0.3 mg/dL,\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e#Troponin and Hba1C levels were available for 60.1% of men and 57.7% of women, and after matching for 60.5% of men and 58% of women and HbA1c levels were available in 16.5% of men and 16.8% of women and after matching in 15.8% of men and 16.9% of women.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGlucose levels upon admission were not statistically different between women and men (p\u0026thinsp;=\u0026thinsp;0.102), nor were HbA1c levels (p\u0026thinsp;=\u0026thinsp;0.1, available for only 21.5% and 22.9% of patients, respectively).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn-hospital glucose control and hospitalization outcomes\u003c/span\u003e\u003c/p\u003e\u003cp\u003eDuring hospitalization, men suffered more often from hyperglycemic episodes (76.4% vs. 73.3%, p\u0026thinsp;=\u0026thinsp;0.009), and severe hyperglycemic episodes (48.6% vs. 43.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with women. Overall, women had more measurements in range during hospitalization (60% vs. 57.1%, p\u0026thinsp;=\u0026thinsp;0.036). Rehospitalization after 1 month was higher in men \u0026minus;\u0026thinsp;12.8% vs. 11.1%, though this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.088). There were no differences in mortality outcomes )Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Figure\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIn Hospital glucose control and outcomes before and after matching:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnmatched cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMatched cohort\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2845)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003cp\u003e( n\u0026thinsp;=\u0026thinsp;1755)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1755)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypoglycemia*, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e378 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e320 (14.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e234 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e233 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere hypoglycemia*, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperglycemia*, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2175 (76.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1676 (73.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1340 (76.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1288 (73.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere hyperglycemia*, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1384 (48.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e999 (43.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e842 (48.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e776 (44.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasurements in range* (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.1 [33.3\u0026ndash;80.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.0 [36.1\u0026ndash;80.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.3 [35.4\u0026ndash;80.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60.0 [36.0\u0026ndash;80.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospitalization Length (days), median [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 [4\u0026ndash;9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 [4\u0026ndash;10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 [4\u0026ndash;10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 [4\u0026ndash;9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.459\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRehospitalization 1M, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e353 (12.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e247 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e229 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e194 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIn hospital Mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1M mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e83 (4.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003ehypoglycemia\u0026thinsp;\u0026le;\u0026thinsp;70mg/dL, severe hypoglycemia\u0026thinsp;\u0026le;\u0026thinsp;54mg/dL, normal glucose values- 71-180mg/dL, hyperglycemia 181\u0026lt;, and severe hyperglycemia 250\u0026thinsp;\u0026lt;\u0026thinsp;mg/dL Range- 100\u0026ndash;180 mg/dL, 1M- 1 month, 3M- 3 months\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003em\u0026minus;\u003c/sup\u003e P value using McNemar after propensity score matching for: BMI, age, gender, steroid intake, COPD asthma, liver disease, chronic infection, HTN, CHF, IHD, Atrial fibrillation flutter, cardiac arrhythmias, PVD, dyslipidemia, past stroke TIA, dementia, and malignancy.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe association between male sex and main hospitalization characteristics and outcomes:\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% Confidence interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eaOR#\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% Confidence interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic medications\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspirin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.18\u0026ndash;1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.13\u0026ndash;1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlavix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3\u0026ndash;1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.01\u0026ndash;1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03\u0026ndash;1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.96\u0026ndash;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGLP1 agonist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83\u0026ndash;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61\u0026ndash;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGLT2 inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27\u0026ndash;2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94\u0026ndash;1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAce inhibitors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.18\u0026ndash;1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.13\u0026ndash;1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIn-hospital glucose control*\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperglycemia*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04\u0026ndash;1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03\u0026ndash;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere hyperglycemia*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09\u0026ndash;1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.09\u0026ndash;1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypoglycemia*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8\u0026ndash;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.913\u0026ndash;1.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere hypoglycemia*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.66\u0026ndash;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.791\u0026ndash;1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.741\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHospitalization outcomes\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRehospitalization 1M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u0026ndash;1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03\u0026ndash;1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIn hospital mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.54\u0026ndash;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6\u0026ndash;1.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30 day mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u0026ndash;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.851\u0026ndash;1.558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality or prolonged stay**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8\u0026ndash;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.971\u0026ndash;1.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eACE angiotensin converting enzyme, GLP-1 Glucagon-like peptide-1, SGLT2- sodium-glucose transport protein 2\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*hypoglycemia\u0026thinsp;\u0026le;\u0026thinsp;70mg/dL, severe hypoglycemia\u0026thinsp;\u0026le;\u0026thinsp;54mg/dL, normal glucose values- 71-180mg/dL, hyperglycemia 181\u0026lt;, and severe hyperglycemia 250\u0026thinsp;\u0026lt;\u0026thinsp;mg/dLRange- 100\u0026ndash;180 mg/dL\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e** prolonged stay-hospitalization\u0026thinsp;\u0026gt;\u0026thinsp;7days\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eM- month\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e#aOR adjusted odds ratio- adjusted for male sex, age and variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 using a stepwise forward regression analysis\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe association between sex and main hospitalization characteristics and outcomes\u003c/span\u003e\u003c/p\u003e\u003cp\u003eMen were treated more often with aspirin both in univariate analysis [OR 1.33 (95%CI 1.18\u0026ndash;1.5), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and after adjustment in multivariable analysis [aOR 1.31 (95%CI 1.13\u0026ndash;1.52) p\u0026thinsp;\u0026lt;\u0026thinsp;0.001]. SGLT treatment was not more common in men after multivariable analysis aOR 1.25 (95%CI (0.94\u0026ndash;1.64), p\u0026thinsp;=\u0026thinsp;0.113]. Male sex was associated with hyperglycemia [OR 1.18 (95% CI 1.04\u0026ndash;1.34), p\u0026thinsp;=\u0026thinsp;0.008] and severe hyperglycemia [OR 1.22 (95%CI (1.09\u0026ndash;1.36 p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)]. It remained independently associated with hyperglycemia [aOR 1.23 (95%CI 1.03\u0026ndash;1.46 p\u0026thinsp;=\u0026thinsp;0.02)] and severe hyperglycemia [aOR 1.28 (95% CI (1.09\u0026ndash;1.49, p\u0026thinsp;=\u0026thinsp;0.002)] after adjustment in multivariable analysis. 1-month rehospitalization rates tended to be higher in men ]OR 1.16 (95% CI 0.97\u0026ndash;1.38, p\u0026thinsp;=\u0026thinsp;0.088] in univariate analysis, and became significant after adjustment in multivariable analysis [aOR 1.27 (95%CI 1.03\u0026ndash;1.55, p\u0026thinsp;=\u0026thinsp;0.02)].\u003c/p\u003e\u003cp\u003e\u003cb\u003eMatched cohorts\u003c/b\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStudy population \u0026ndash; matched cohort\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe matched cohort consisted of two similar groups, comprising 1,755 men and women (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cem\u003eSupplementary Table\u0026nbsp;3\u003c/em\u003e). Men had higher rates of CKD (16.3% vs. 13.2%, p\u0026thinsp;=\u0026thinsp;0.012) and women had higher rates of connective tissue disease (4.8% vs. 1.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Men were more frequently treated with aspirin (34.7% vs. 29.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SGLT2 inhibitors (7.7% vs. 5.5%, p\u0026thinsp;=\u0026thinsp;0.013). LDL levels were higher in women (median 73mg/dL vs. 63mg/dL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) despite similar statin use.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe cause of admission differed between men and women: Acute infection was more common in men than in women (33.3% vs. 29.7%, p\u0026thinsp;=\u0026thinsp;0.019). Chest pain and ACS were also more common in men (16.7% vs. 13.7%, p\u0026thinsp;=\u0026thinsp;0.016, and 24.5% vs. 20.7%, p\u0026thinsp;=\u0026thinsp;0.002, respectively), which was manifested in higher troponin levels in men (25.8 ng/L vs. 19.3ng/L p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Women were more often diagnosed with pulmonary embolism (1.7% vs. 0.8%, p\u0026thinsp;=\u0026thinsp;0.031).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn-hospital glucose control and hospitalization outcomes\u003c/span\u003e\u003c/p\u003e\u003cp\u003eDuring hospitalization, men suffered more often from hyperglycemic episodes (76.4% vs. 73.4%, p\u0026thinsp;=\u0026thinsp;0.049), and severe hyperglycemic episodes (48% vs. 44.2%, p\u0026thinsp;=\u0026thinsp;0.031) compared with women. Overall, women had more measurements in the desirable range during hospitalization (60% vs. 57.1%, p\u0026thinsp;=\u0026thinsp;0.036). Rehospitalization after 1 month was higher in men (13.3% vs. 11.4%), though this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.061). No significant differences in mortality were observed post-matching (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights sex-based differences in hospitalized patients with T2DM, particularly in baseline cardiovascular risk, chronic treatment patterns, glycemic control, and hospitalization outcomes. Men had a higher burden of cardiovascular disease (IHD, CKD, and PVD) and were more frequently prescribed cardioprotective medications (statins, aspirin, and SGLT2 inhibitors). Ischemic complications were a more common cause of hospitalization in men. Importantly, male sex was independently associated with worse outcomes, including an increased risk of hyperglycemia, severe hyperglycemia, and 1-month rehospitalization.\u003c/p\u003e\u003cp\u003eHyperglycemia during hospitalization is related to worse outcomes across a range of clinical conditions, in both diabetic and non-diabetic patients.[21\u0026ndash;23] However, the interaction between sex and hyperglycemia-related outcomes remains less clearly defined. For example, in patients hospitalized with acute coronary syndrome, admission hyperglycemia was independently associated with increased mortality in men but not in women.[24, 25] Conversely, another study reported that elevated HBA1c levels were associated with rehospitalization in women with CVD but not in men.[26]\u003c/p\u003e\u003cp\u003eIn our study, men experienced a higher frequency of hyperglycemia events during hospitalization. In general, in-hospital treatment protocols do not differ between men and women and typically involve initiating insulin treatment when blood glucose levels are \u0026gt;\u0026thinsp;180mg/dL in \u0026ge;\u0026thinsp;2 measurements, using weight-based dosages. Despite this standardized approach, previous studies suggest that glycemic control may differ between hospitalized men and women with diabetes. For example, a study focusing on anthropometric and body composition measurements found men to have higher blood glucose fluctuations.[27] Sex hormones play a key role in glucose regulation and may contribute to these differences. Testosterone deficiency in older men is related to metabolic syndrome, visceral adiposity and increased insulin resistance,[28] while androgen excess in women is related to increased risk of diabetes. Elevated levels of sex hormone-binding globulins appear to have a protective effect, particularly in women.[29] In contrast, a clinical study of critically ill patients reported greater insulin resistance in women than in men, highlighting the variability of sex-based metabolic responses under different physiological conditions.[30]\u003c/p\u003e\u003cp\u003eBeyond hospitalization characteristics and outcomes, our study also revealed sex-based differences in chronic treatment, co-morbidities and reasons for acute admission. Notably, men were more likely to receive cardioprotective therapies, such as antiplatelet agents, even after propensity score matching and multivariable adjustment. Previous studies have shown that men with diabetes are more likely than women to receive guideline-directed treatment for cardiovascular risk and complications. [6, 7, 31]\u003c/p\u003e\u003cp\u003eIt is well established that treatment with SGLT2 inhibitors reduces the risk of major cardiovascular events and all-cause mortality in patients with T2DM when added to standard care[32], [33]. Accordingly, current diabetes treatment guidelines recommend SGLT2 as a standard therapy for adults with T2DM and established or high risk of atherosclerotic cardiovascular disease, HF, or CKD.[34]A large meta-analysis, found no significant sex differences in HbA1c reduction or major adverse cardiovascular outcomes (MACE) with either SGLT2 inhibitors or GLP1 agonists.[35] Consistent with previous research, this study demonstrates low overall SGLT2 inhibitors or GLP1 agonists treatment. However, men were more frequently prescribed with SGLT2 inhibitors than women [OR 1.39 (95%CI 1.061\u0026ndash;1.828), p\u0026thinsp;=\u0026thinsp;0.0169]. Similar results were reported in a large retrospective study involving 934,737 patients, where women were less likely than men to receive SGLT2 inhibitors; aOR, 0.84; 95% CI, 0.82\u0026ndash;0.85. [8]\u003c/p\u003e\u003cp\u003eStatins have the same effectiveness in men and women with similar cardiovascular risk factors.[36]Yet, women used statins less often than men (RR 0.90; 95% CI 0.86, 0.93).[37] In our study, women had higher LDL levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and men were more often chronically treated with statins (p\u0026thinsp;=\u0026thinsp;0.011), though this was not significant after matching and multivariable regression.\u003c/p\u003e\u003cp\u003eWomen have lower in-hospital mortality rate than men for a wide variety of medical conditions, both infectious [38] and non-communicable diseases.[39] Yet, diabetic women have similar or even higher mortality rates compared to men.[40] This was also demonstrated in our study, though we could not address the causes of death. Treatment disparities may partly explain the lack of observed sex-based differences in mortality.\u003c/p\u003e\u003cp\u003eStudy limitations:\u003c/p\u003e\u003cp\u003eThis retrospective, large-scale study has inherent limitations due to its observational nature and is subject to residual confounding. We relied on coded data, and it is possible that some information was not recorded. For example, physicians may not have coded all the medications a patient received or all comorbid conditions. Nevertheless, such underreporting is unlikely to differ between men and women. Therefore, if present, it would most likely attenuate the observed association (i.e., non-differential misclassification bias). Finally, the follow-up was limited to 30 days and different sex related outcomes may appear with longer follow up. Further research should explore sex-specific responses to inpatient glucose management interventions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings support the development of hospital protocols that integrate sex-specific glucose monitoring strategies and equitable medication prescribing practices. Since men are more prone to hyperglycemia, they may require closer monitoring and more aggressive insulin adjustment during hospitalization.\u003c/p\u003e\u003cp\u003e In addition, optimizing guideline-directed cardioprotective therapy, especially in women, is crucial. Given the persistent sex gap in statin and SGLT2 inhibitor prescriptions, further efforts are needed to ensure equitable treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has been conducted using Kineret medical data platform of the Directorate of Government Medical Centers at the Israeli Ministry of Health. (https://kineret.health.gov.il/en)\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Shamir medical center, approval number 0224-24ASF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research was supported by Grant No. 0006865 awarded as part of the 2023 Kinneret Platform and was jointly funded by the Ministry of Innovation, Science and Technology and the Ministry of Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u0026nbsp;\u003c/strong\u003eRonit Koren, Matan Elkan\u003cstrong\u003e; Methodology:\u0026nbsp;\u003c/strong\u003eArielle Barouch, Tomer Ziv-Baran\u003cstrong\u003e; Formal analysis and investigation:\u0026nbsp;\u003c/strong\u003eArielle Barouc\u003cstrong\u003e; Writing - original draft preparation:\u0026nbsp;\u003c/strong\u003eRonit Koren, Matan Elkan\u003cstrong\u003e; Writing -\u0026nbsp;\u003c/strong\u003eRonit Koren, Matan Elkan, Tomer Ziv-Baran\u003cstrong\u003e, Funding acquisition:\u0026nbsp;\u003c/strong\u003eRonit Koren\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eTomer Ziv-Baran\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShang D, Wang L, Klionsky DJ, Cheng H, Zhou R (2021) Sex differences in autophagy-mediated diseases: toward precision medicine. Autophagy 17:1065\u0026ndash;1076. https://doi.org/10.1080/15548627.2020.1752511\u003c/li\u003e\n\u003cli\u003eKautzky-Willer A, Leutner M, Harreiter J (2023) Sex differences in type 2 diabetes. Diabetologia 66:986\u0026ndash;1002. https://doi.org/10.1007/s00125-023-05891-x\u003c/li\u003e\n\u003cli\u003eDECODE Study Group (2003) Age- and Sex-Specific Prevalences of Diabetes and Impaired Glucose Regulation in 13 European Cohorts. Diabetes Care 26:61\u0026ndash;69. https://doi.org/10.2337/diacare.26.1.61\u003c/li\u003e\n\u003cli\u003eSaeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, Shaw JE, Bright D, Williams R (2019) Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 157:107843. https://doi.org/10.1016/j.diabres.2019.107843\u003c/li\u003e\n\u003cli\u003eSicree RA, Zimmet PZ, Dunstan DW, Cameron AJ, Welborn TA, Shaw JE (2008) Differences in height explain gender differences in the response to the oral glucose tolerance test\u0026mdash; the AusDiab study. Diabetic Medicine 25:296\u0026ndash;302. https://doi.org/10.1111/j.1464-5491.2007.02362.x\u003c/li\u003e\n\u003cli\u003eMosca L, Linfante AH, Benjamin EJ, Berra K, Hayes SN, Walsh BW, Fabunmi RP, Kwan J, Mills T, Simpson SL (2005) National Study of Physician Awareness and Adherence to Cardiovascular Disease Prevention Guidelines. 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Available online: https://www.ohdsi.org/software-tools.\u003c/li\u003e\n\u003cli\u003eHripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, Suchard MA, Park RW, Wong ICK, Rijnbeek PR, van der Lei J, Pratt N, Nor\u0026eacute;n GN, Li Y-C, Stang PE, Madigan D, Ryan PB (2015) Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers. Stud Health Technol Inform 216:574\u0026ndash;8\u003c/li\u003e\n\u003cli\u003eStraumann E, Kurz DJ, Muntwyler J, Stettler I, Furrer M, Naegeli B, Frielingsdorf J, Schuiki E, Mury R, Bertel O, Spinas GA (2005) Admission glucose concentrations independently predict early and late mortality in patients with acute myocardial infarction treated by primary or rescue percutaneous coronary intervention. 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Maturitas 128:81\u0026ndash;86. https://doi.org/10.1016/j.maturitas.2019.07.025\u003c/li\u003e\n\u003cli\u003eWang Y, O\u0026rsquo;Neil A, Jiao Y, Wang L, Huang J, Lan Y, Zhu Y, Yu C (2019) Sex differences in the association between diabetes and risk of cardiovascular disease, cancer, and all-cause and cause-specific mortality: a systematic review and meta-analysis of 5,162,654 participants. BMC Med 17:136. https://doi.org/10.1186/s12916-019-1355-0\u003c/li\u003e\n\u003cli\u003eElixhauser A, Steiner C, Harris DR, Coffey RM (1998) Comorbidity Measures for Use with Administrative Data. Med Care 36:8\u0026ndash;27. https://doi.org/10.1097/00005650-199801000-00004\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Diabetes Mellitus, Sex, hospitalization, glycemic control, chronic treatment patterns","lastPublishedDoi":"10.21203/rs.3.rs-7114518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7114518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile sex differences in type 2 diabetes mellitus (T2DM) are well-documented in outpatient settings, data on inpatient disparities remain limited. This study examines the relationship between sex and glycemic control, chronic treatment patterns, and hospitalization outcomes in patients with T2DM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA historical cohort of 5,133 adult T2DM patients hospitalized for \u0026gt; 24 hours at a tertiary Israeli medical center in 2023 was analyzed. Outcomes included hypoglycemia, hyperglycemia, length of stay, 30-day readmission, and mortality. Propensity score matching and multivariable regression were applied to adjust for confounding variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMen were more frequently treated with chronic aspirin therapy than women (35.8% vs. 29.4%; p \u0026lt; 0.001), a difference that remained significant after multivariable adjustment (aOR 1.31; 95% CI: 1.13–1.52; p \u0026lt; 0.001). Male sex was also associated with higher rates of hyperglycemia (76.4% vs. 73.3%; p = 0.009) and severe hyperglycemia (48.6% vs. 43.7%; p \u0026lt; 0.001), with these associations persisting after adjustment (hyperglycemia: aOR 1.23; 95% CI: 1.03–1.46; p = 0.02; severe hyperglycemia: aOR 1.28; 95% CI: 1.09–1.49; p = 0.002) and confirmed in propensity score-matched analyses. One-month rehospitalization rates were also higher in men (12.7% vs. 11.1%; p = 0.088), reaching statistical significance after multivariable adjustment (aOR 1.27; 95% CI: 1.03–1.55; p = 0.02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSex-based disparities persist in inpatient glycemic control and chronic treatment patterns. Men are more prone to hyperglycemia and rehospitalization, while women are less likely to receive guideline-recommended cardioprotective therapies. A sex-specific approach may improve inpatient diabetes management and long-term outcomes.\u003c/p\u003e","manuscriptTitle":"Sex Based Disparities in Glycemic Control and Hospitalization Outcomes of Medical Patients with Diabetes Mellitus - A Historical Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 08:30:15","doi":"10.21203/rs.3.rs-7114518/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-07-21T06:21:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-17T15:17:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-16T04:28:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Internal and Emergency Medicine","date":"2025-07-15T15:52:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"92579530-35e0-420f-b6b5-0f4ded4f01a9","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:00:34+00:00","versionOfRecord":{"articleIdentity":"rs-7114518","link":"https://doi.org/10.1007/s11739-025-04170-4","journal":{"identity":"internal-and-emergency-medicine","isVorOnly":false,"title":"Internal and Emergency Medicine"},"publishedOn":"2025-11-01 15:57:20","publishedOnDateReadable":"November 1st, 2025"},"versionCreatedAt":"2025-07-23 08:30:15","video":"","vorDoi":"10.1007/s11739-025-04170-4","vorDoiUrl":"https://doi.org/10.1007/s11739-025-04170-4","workflowStages":[]},"version":"v1","identity":"rs-7114518","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7114518","identity":"rs-7114518","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}