Empagliflozin and Dapagliflozin in Improving Ejection Fraction: A Prospective Observational Comparison of Clinical Outcomes and Patient Reported Quality of Life

Empagliflozin and Dapagliflozin in Improving Ejection Fraction: A Prospective Observational Comparison of Clinical Outcomes and Patient Reported Quality of Life | 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 Empagliflozin and Dapagliflozin in Improving Ejection Fraction: A Prospective Observational Comparison of Clinical Outcomes and Patient Reported Quality of Life Sandeep Reddy Ambati, Bhargava Reddy C, Daisy Joyce Sharon Midthuri, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9290020/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background SGLT2 inhibitors have transformed heart failure treatment, especially empagliflozin and dapagliflozin. Although both have proven cardiovascular benefits in clinical trials, there is little data comparing them in real-world settings. This study aims to address that by comparing their effects on heart function, lab tests, echocardiography, and quality of life using routine clinical data and the Kansas City Cardiomyopathy Questionnaire-12. Methods We conducted a prospective observational study with 70 heart failure patients, evenly divided between those taking empagliflozin 10 mg daily and those on dapagliflozin 10 mg daily as part of their usual care. We collected vital signs, blood tests, echocardiographic measures, including left ventricular ejection fraction, and KCCQ-12 scores. Statistical analysis included t-tests, Mann-Whitney U, Chi-square, ANOVA, Shapiro-Wilk for normality, Levene’s test for equal variances, and Pearson correlation. Results with p-values under 0.005 were considered significant. Results The groups were similar in age, gender, rates of hypertension, HbA1c, and lipid levels. However, more patients in the dapagliflozin group had diabetes (68.6% vs. 31.4%, p = 0.007). Both drugs significantly lowered systolic blood pressure and improved ejection fraction. Dapagliflozin caused a larger drop in sodium levels (2.51 vs 0.83 mEq/L, p = 0.007). Among diabetic patients, empagliflozin reduced blood glucose by a greater amount (-75.00 vs -42.33 mg/dL). Patients on empagliflozin also had higher overall KCCQ-12 scores (66.22 vs. 56.06), with a significant difference in symptom frequency (p = 0.033). Improvements in ejection fraction, kidney function, hemoglobin, and lipids were similar for both drugs. Conclusions Empagliflozin and dapagliflozin showed similar effects in heart failure patients, though some important differences emerged. Empagliflozin was associated with greater reductions in KCCQ-12 symptom frequency scores and blood sugar levels in patients with diabetes, whereas dapagliflozin was associated with a larger drop in serum sodium levels. These results suggest that although both drugs are generally equivalent, individual differences might guide personalized treatment choices. Empagliflozin Dapagliflozin SGLT2 inhibitors Heart Failure with reduced ejection fraction Quality of Life KCCQ-12 Comparative effectiveness INTRODUCTION Heart failure is arguably the most burdensome of all the major clinical syndromes we treat in the modern era of medicine. It has remained so for decades, despite major advances in its management, to the extent that it still leads to hospitalization, premature death, and diminished quality of life on a large scale 1 . The pharmacological management of heart failure, as recommended by guidelines, has, over the years, progressively incorporated the use of angiotensin-converting enzyme inhibitors, angiotensin receptor neprilysin inhibitors, beta blockers, and mineralocorticoid receptor antagonists, among the drugs, but the prognosis of patients with heart failure has remained poor, especially despite the optimal use of the above drugs 2 , 3 . The introduction of SGLT2 inhibitors has revolutionized the management of heart failure to a large extent, as these drugs were originally intended for the management of type 2 diabetes mellitus but were surprisingly found to have a positive impact on the heart, independent of glucose metabolism 4 . The key evidence comes from two landmark clinical trials. The DAPA-HF trial found that the drug reduced the composite risk of worsening heart failure and death by 26% compared with the placebo, and the effect was seen regardless of the presence or absence of diabetes 5 . The EMPEROR-Reduced trial yielded similar results for the drug empagliflozin, showing significant reductions in heart failure hospitalizations and cardiovascular death 6 . The DELIVER and EMPEROR-Preserved trials were conducted to show the effect of the drugs even when the ejection fraction was not severely reduced, but only mildly or preserved 7 . Yet, despite this solid body of evidence supporting both drugs, the two have never been compared prospectively. In real-life settings, the choice between them has to be made mostly based on indirect evidence, cost, and availability. The South Asian population also has its own set of complexities in terms of its cardiovascular risk profile and high prevalence of diabetes and associated comorbid conditions. In addition to hard clinical endpoints, there has been a growing realization that how patients feel is as important as their performance on objective parameters. This is where questionnaires like the KCCQ-12 have become important. The KCCQ-12 is a patient-reported measure that reflects the four aspects of heart failure that patients consider most important 8 . Thus, this study was designed to compare and evaluate these two SGLT-2 inhibitors head-to-head in a tertiary care cardiology setting in India, including hemodynamic responses, metabolic and renal laboratory parameters, echocardiographic ejection fraction, lipid profiles, medication regimens, and patient-reported outcomes using the KCCQ-12 tool 9 , 10 . This study represents one of the first head-to-head prospective studies in this context. MATERIALS AND METHODS Study Design and Setting A prospective observational study was conducted in the department of cardiology in a tertiary care teaching hospital between September 2025 and March 2026. The Institutional Ethics Committee approved the study before patient enrolment, and all patients provided informed consent. This study is not a clinical trial but an observational study in which the drugs of interest were not prescribed explicitly for the study. The idea for the study emerged during routine ward rounds, when both SGLT2 inhibitors were being prescribed to the patients. Study Population We included 70 patients who were admitted with heart failure based on the inclusion criteria. This included patients with HFrEF, and some patients with right-sided heart failure. The cardiologist in charge of each patient prescribed an SGLT2 inhibitor as part of routine care. We included 35 patients who received empagliflozin (Group I) and 35 who received dapagliflozin (Group II). The study included patients aged 18 years or older with heart failure, as defined by ESC/ACC/AHA criteria, who were prescribed an SGLT2 inhibitor as part of routine care during their hospitalization solely to improve ejection fraction, and who had baseline and follow-up information available. We excluded patients who had an eGFR of less than 30 ml/min/1.732 m 2 , had type 1 diabetes, had prior use of an SGLT2 inhibitor, had active malignancy, had severe hepatic impairment, were pregnant or lactating, or were unable to give consent. Data Collection Demographic data, comorbid history, and concomitant medications were noted at the time of admission. Vital parameters, including systolic and diastolic blood pressure, pulse rate, respiratory rate, oxygen saturation, and temperature, were noted at the time of initiation of the SGLT2 inhibitor and at the time of discharge. Blood parameters included a full blood count, blood urea, serum creatinine, uric acid, serum electrolytes (sodium, potassium, chloride), HbA1c, and random blood sugar. Ejection fraction was measured by 2D echocardiogram using the biplane Simpson method at baseline and at discharge. Quality of Life Assessment – KCCQ-12 The KCCQ-12 was administered to all patients after 3 months of initiation of SGLT2 inhibitor therapy. The KCCQ-12 has four domains. These domains are Physical Limitation, Symptom Frequency, Quality of Life, and Social Limitation. Each item has a score ranging from 0 to 100. Higher scores indicate better health. The score for each domain is the mean of the individual item scores. The Overall Summary Score is the equally weighted mean of the four domains. The KCCQ-12 has been well-validated in heart failure patients and demonstrates high internal consistency. Statistical Analysis Data were analyzed using R (version 4.5.0). Continuous variables are presented as mean plus or minus standard deviation; categorical variables as counts and percentages. All continuous outcomes were first tested for normality using the Shapiro-Wilk test, and Levene’s test was used to assess variance equality between groups. When data were normally distributed, we used independent-samples t-tests for between-group comparisons and paired t-tests for within-group pre-post changes. For variables that violated normality assumptions, we used the Mann-Whitney U test for between-group comparisons. Categorical variables were compared with the chi-square test, applying Fisher’s exact correction when any expected cell count fell below 5. One-way ANOVA compared EF change across age strata. Pearson correlation coefficients were calculated for all relevant continuous variable pairs. All significance tests were two-tailed, with p < 0.05 considered significant. RESULTS Baseline Demographics and Clinical Characteristics The two groups were closely matched at baseline across most characteristics. Mean age was 58.63 ± 14.59 years in the dapagliflozin group and 56.49 ± 15.87 years in the empagliflozin group, a non-significant difference (t = 0.588, p = 0.558). Both groups had an identical sex distribution, with 28 men (80%) and 7 women (20%) each (p = 1.000). Hypertension was more prevalent in the dapagliflozin group (62.9% vs 45.7%), though the difference did not reach significance (p = 0.230). The one meaningful baseline imbalance was in diabetes prevalence. Nearly 69% of dapagliflozin patients had diabetes mellitus, compared with 31% in the empagliflozin group (p = 0.007). This difference is an important confounder for glucose-related outcomes and is discussed accordingly. HbA1c values were numerically higher in the dapagliflozin group (7.26 ± 2.04% vs 6.70 ± 2.33%), though this did not achieve significance (p = 0.290). Baseline ejection fraction, renal parameters, hemoglobin, and lipid profiles were comparable. Concomitant Medications Several concomitant drug classes differed significantly between groups. Anticoagulant use was substantially higher in the empagliflozin group (74.3% vs 45.7%; chi 2 =4.821, p = 0.028), possibly reflecting a greater burden of atrial fibrillation or a higher thromboembolic risk. Anti-anginal agents were also more frequently prescribed in the empagliflozin group (57.1% vs 28.6%, p = 0.030), pointing to a higher underlying ischaemic disease burden. over 85% of patients in both groups were receiving statin therapy. Antiplatelet agents, diuretics, anti-hypertensives, corticosteroids, non-SGLT2 anti-diabetics, and nebulizers were distributed equally. Data are summarised in Table 1 . Table 1 Concomitant Medication Distribution Concomitant Medication DAPA Yes DAPA No EMPA Yes EMPA No p-value Anti-coagulants 16 (45.7%) 19 (54.3%) 26 (74.3%) 9 (25.7%) 0.028* Anti-platelet 31 (88.6%) 4 (11.4%) 31 (88.6%) 4 (11.4%) 1.000 Statins 31 (88.6%) 4 (11.4%) 30 (85.7%) 5 (14.3%) 0.011* Diuretics 31 (88.6%) 4 (11.4%) 29 (82.9%) 6 (17.1%) 0.733 Anti-anginal 10 (28.6%) 25 (71.4%) 20 (57.1%) 15 (42.9%) 0.030* Anti-hypertensives 23 (65.7%) 12 (34.3%) 20 (57.1%) 15 (42.9%) 0.623 Corticosteroids 5 (14.3%) 30 (85.7%) 3 (8.6%) 32 (91.4%) 0.707 Anti-diabetes (non-SGLT2) 14 (40%) 21 (60%) 6 (17.1%) 29 (82.9%) 0.064 *p < 0.05. Chi-square test with continuity correction applied throughout. Hemodynamic Parameters Both drugs significantly lowered systolic blood pressure in their respective groups. In the dapagliflozin group, mean systolic BP fell from 123.83 to 112.86 mmHg (paired t = 2.920, p = 0.006); in the empagliflozin group, the drop was from 124.29 to 113.71 mmHg (paired t = 4.493, p < 0.001). When the magnitude of change was compared between groups, the difference was negligible (ΔDAPA 10.40 ± 22.51 vs ΔEMPA 10.57 ± 13.92 mmHg; t = 0.038, p = 0.970). It is not significant, however, that Levene’s test revealed significantly greater variability in the systolic BP response in dapagliflozin patients (W = 4.699, p + 0.034), suggesting that some individuals in that group had considerably larger or smaller responses than others. Diastolic blood pressure fell significantly only with empagliflozin (from 77.43 to 72.57 mmHg, p = 0.045), with the dapagliflozin group showing a similar but non-significant trend (p = 0.056). The between-group comparison was not remarkable (p = 0.895). Heart rate did not change meaningfully in either group throughout the admission period. These data are presented in Table 2 . Table 2 Hemodynamic parameters before and after treatment Parameter DAPA Before DAPA After EMPA Before EMPA After Inter-group p-value Systolic BP (mm Hg) 123.83 19.5 112.86 ± 17.9 124.29 ± 18.0 113.71 ± 15.9 0.970 Paired t-test (p) 0.006* 0.0001* Diastolic BP (mm Hg) 76.71 11.8 72.29 ± 11.7 77.43 ± 12.0 72.57 ± 10.1 0.895 Paired t-test (p) 0.056 0.045* Heart rate (bpm) 84.40 14.4 84.43 ± 9.8 88.89 ± 15.2 85.74 ± 16.9 0.467 Paired t-test (p) 0.991 0.364 *p < 0.05. Paired t-test for within-group comparisons, independent t-test for between-group Δ comparisons. Laboratory Parameters Serum Creatinine rose modestly in both groups over the admission period: from 1.19 to 1.36 mg/dL in the dapagliflozin group (p = 0.024) and from 1.24 to 1.50 mg/dL with empagliflozin (p = 0.001). This early rise reflects the expected hemodynamic effect of SGLT2 inhibition on intraglomerular pressure and is well recognized in the literature. The magnitude of the rise was not significantly different between groups (ΔDAPA 0.17 ± 0.42 vs ΔEMPA 0.26 ± 0.42; p = 0.399; Mann-Whitney p = 0.424). Blood urea also increased in both groups, consistent with the same mechanism (DAPA p = 0.044; EMPA p = 0.038), again without any between-group difference. Serum uric acid rose significantly in the empagliflozin group alone (p = 0.007). Random blood sugar fell substantially in the dapagliflozin group (from 161.23 to 131.17 mg/dL; p = 0.006), while the empagliflozin group showed a more modest, non-significant within-group change (p = 0.434). On first glance, this might suggest that dapagliflozin is the better glucose-lowering agent. Still, Mann-Whitney testing of the actual change scores told a more nuanced story: the glucose reduction in the dapagliflozin group was driven primarily by its much higher proportion of diabetic patients rather than by greater intrinsic efficacy (U = 373.5, p = 0.005). When only diabetic patients were analyzed, empagliflozin achieved a meaningfully larger absolute reduction in blood glucose (ΔRBS: -75.00 vs -42.33 mg/dL in the dapagliflozin diabetic subgroup, p < 0.001), consistent with its higher reported SGLT2 receptor affinity. The most clinically interesting electrolyte finding was in serum sodium. In the dapagliflozin group, sodium dropped from 140.37 to 137.89 mEq/L (p < 0.001), and the between-group comparison of change scores was statistically significant (ΔDAPA 2.51 ± 2.66 vs ΔEMPA 0.83 ± 2.42 mEq/L; t = 2.773, p = 0.007). Potassium declined in both groups, though more steeply with empagliflozin (from 4.18 to 3.73 mEq/L; p < 0.001 vs dapagliflozin: 4.21 to 3.99; p = 0.018), with the between-group comparison trending toward significance (p = 0.086). Hemoglobin, white cell count, and platelet count showed no meaningful change in either group (Table 3 ). Table 3 Laboratory Parameters Before and After Treatment Parameter DAPA Pre DAPA Post EMPA Pre EMPA Post Δp MWU P Hemoglobin (g/dL) 13.02 ± 2.3 12.66 ± 1.9 12.73 ± 2.5 12.68 ± 2.2 0.293 0.290 TWBC (cells/µL) 8607 ± 3346 9045 ± 3924 8525 ± 2832 10423 ± 9837 0.420 0.879 Platelet Count (x10 3) 255.8 ± 106.9 260.5 ± 88.7 210.9 ± 58.6 212.9 ± 52.1 0.848 0.716 Blood Urea (mg/dL) 31.97 ± 16.6 39.20 ± 22.6 30.91 ± 15.5 37.63 ± 21.1 0.912 0.585 Sr.Creatinine (mg/dL) 1.19 ± 0.35 1.36 ± 0.53 1.24 ± 0.43 1.50 ± 0.56 0.399 0.424 Uric Acid (mg/dL) 5.95 ± 1.86 6.41 ± 2.07 5.45 ± 1.75 6.41 ± 2.33 0.257 0.318 RBS (mg/dL) 161.23 ± 58.6 131.17 ± 43.6 145.23 ± 66.9 136.26 ± 12.1 0.174 0.005* Sodium (mEq/L) 140.37 ± 3.3 137.89 ± 3.8 135.69 ± 2.97 134.86 ± 2.9 0.007* 0.010 Potassium (mEq/L) 4.21 ± 0.49 3.99 ± 0.52 4.18 ± 0.46 3.73 ± 0.52 0.086 0.070 *p < 0.05. Δp: independent t-test on change scores; MWU p: Mann-Whitney U test. Ejection Fraction (EF) Ejection Fraction improved significantly in both groups during the admission period. In the dapagliflozin group, mean EF rose from 35% to 39% (mean Δ4%±4%; paired t = -5.36, p < 0.001); in the empagliflozin group, it increased from 35% to 40% (mean Δ5%±5%; paired t=-6.528, p < 0.001). The inter-group difference in EF change did not reach significance (t = -1.655, p = 0.102; Mann-Whitney, p = 0.104), though the numerical direction consistently favored empagliflozin. A one-way ANOVA examining EF improvement across age groups yielded an F-statistic of 3.101 (p = 0.052), with patients under 40 years showing a mean ΔEF of 7%, compared to 3.1% in those over 60, suggesting a possible age-related gradient in cardiac recovery that warrants exploration in larger studies. KCCQ-12 Quality of Life Both groups reported moderate impairment across all KKCQ-12 domains, which is consistent with symptomatic heart failure. The empagliflozin group scored numerically higher on every domain, though most of these differences did not reach conventional statistical significance by independent t-test. The Physical Limitation Score was 55.18 ± 20.54 with dapagliflozin versus 60.36 ± 19.41 with empagliflozin (p = 0.282). The Symptom Frequency Score was 51.90 ± 18.25 versus 58.04 ± 16.44; the parametric t-test was non-significant at p = 0.144, but the non-parametric Mann-Whitney U test found a significant distributional difference (p = 0.033). This means the distribution of symptom frequency responses was shifted meaningfully towards higher scores in empagliflozin patients, a finding with clinical relevance even without a large mean difference. Quality of Life scores were identical across groups (63.21 in both, p = 1.000). The Social Limitation Score (53.93 vs 59.29, p = 0.290) and the Overall KCCQ Score (56.06 vs 60.22, p = 0.278) both favored empagliflozin numerically, but did not reach significance. Table 4 contains the full KCCQ data. Table 4 KCCQ-12 Domain and Overall Scores by Treatment Group KCCQ Domain Dapagliflozin Mean ± SD Empagliflozin Mean ± SD t-test p Mann-Whitney p Physical Limitation Score 55.1820.54 60.3619.41 0.282 0.275 Symptom Frequency Score 51.9018.25 58.0416.44 0.144 0.033* Quality of Life Score 63.2117.92 63.2115.14 1.000 0.730 Social Limitation Score 53.9319.59 59.2922.35 0.290 0.381 KCCQ Overall Score 56.0615.93 60.2215.97 0.278 0.252 *p < 0.05. Shapiro-Wilk normality testing was applied to all domains before analysis. Correlation Analyses HbA1c and pre-treatment RBS correlated almost perfectly in both groups (DAPA r = 1.000; EMPA r = 0.999, both p < 0.001). This near-perfect relationship validates the glycemic data and confirms the internal consistency of our measurements. Serum creatinine and blood urea were strongly correlated at baseline (DAPA r = 0.701; EMPA r = 0.582, both p < 0.001), behaving exactly as parallel renal biomarkers should. Age showed a consistent, albeit non-significant, negative association with EF improvement in both groups (DAPA r = 0.328, p = 0.054; EMPA r = 0.221, p = 0.202). The trend suggests younger patients may recover more cardiac function with SGLT2 inhibitors, though our sample size was insufficient to establish this definitively. Glycemic control, as measured by HbA1c, did not predict the magnitude of EF improvement in either group, nor did glucose change predict EF change. In the empagliflozin group, there was a borderline positive association between post-treatment EF and RBS (r = 0.318, p = 0.063), suggesting a metabolic-cardiac interaction worthy of further study, and all correlation data in Tables 5 and 6 . Table 5 Pearson Correlation Coefficients for Clinical Parameter Pairs Variable Pair DAPA r DAPA p EMPA r EMPA p Interpretation HbA1c vs RBS (pre-treatment) 1.000 < 0.001* 0.999 < 0.001* Strong positive Creatinine vs BUN (pre-treatment) 0.701 < 0.001* 0.582 < 0.001* Moderate positive Age vs EF -0.328 0.054 -0.221 0.202 Weak negative Age vs ΔSystolic BP -0.236 0.172 -0.116 0.508 Weak negative HbA1c vs ΔEF -0.112 0.522 0.028 0.875 Not significant ΔEF vs ΔEF 0.195 0.262 0.004 0.980 Not significant EF-After vs RBS after 0.035 0.840 0.318 0.063 Trend in EMPA r=Pearson correlation coefficient Table 6 KCCQ-12 Inter-Domain Pearson Correlation Analysis Domain Pair DAPA r DAPA p EMPA r EMPA p Physical Limitation vs Symptom Frequency 0.670 < 0.001* 0.866 < 0.001* Physical Limitation vs QoL 0.522 0.001* 0.662 < 0.001* Physical Limitation vs Social Limitation 0.468 0.005* 0.619 < 0.001* Symptom Frequency vs QoL 0.710 < 0.001* 0.733 < 0.001* Symptom Frequency vs QoL 0.613 < 0.001* 0.654 < 0.001* QoL vs Social Limitation 0.620 < 0.001* 0.564 < 0.001* Overall Score vs Physical Limitation 0.805 < 0.001* 0.900 < 0.001* Overall score vs Symptom Frequency 0.890 < 0.001* 0.923 < 0.001* Overall Score vs QoL 0.844 < 0.001* 0.824 < 0.001* Overall Score vs Social Limitation 0.808 < 0.001* 0.840 < 0.001* *p < 0.05. Strong and consistent inter-domain correlations in both groups confirm the construct validity of the KCCQ-12 in this population. DISCUSSION This study compared dapagliflozin and empagliflozin at a single institution using consistent methods across clinical, biochemical, echocardiographic, and patient-reported measures. Both drugs had generally similar effects: ejection fraction improved, blood pressure decreased, and creatinine rose slightly. However, there were clear differences in electrolyte balance, blood sugar response, and patient-reported symptoms 11 . The improvement in ejection fraction with both drugs during a short hospital stay suggests that SGLT2 inhibitors benefit the heart quickly through mechanisms beyond glucose lowering. They reduce preload by promoting natriuresis and osmotic diuresis 12 . They also directly affect the heart muscle by reducing sodium and calcium overload, thereby improving contractility 13 . Additionally, they reduce sympathetic activity and partly decrease inflammation and fibrosis 14 . Although empagliflozin showed a slightly larger increase in ejection fraction (0.05 versus 0.04), this difference was not statistically significant here but aligns with the EMPEROR-Reduced findings and warrants further study 15 . The sodium results are especially noteworthy. Dapagliflozin caused a much larger drop in serum sodium (2.51 versus 0.83 mEq/L, p = 0.007), which hasn’t been widely reported before. It’s unclear whether this is due to differences in natriuretic strength, initial sodium levels, or other factors 16 . This matters clinically because many heart failure patients have dilutional hyponatremia, a condition linked to worse outcomes 16 . Dapagliflozin’s effect on sodium levels may be important, depending on a patient’s baseline levels. Meanwhile, potassium dropped further with empagliflozin, so patients on high-dose diuretics (over 80%) in both groups require careful monitoring. The glucose results require careful explanation. Initially, dapagliflozin appeared to lower glucose levels overall, but this was because more diabetic patients were in that group 17 . When looking only at diabetic patients, empagliflozin reduced blood sugar nearly twice as much (-75.00 versus − 42.33 mg/dl). This aligns with evidence that empagliflozin binds SGLT2 receptors more strongly 18 . This difference would be missed if we only considered combined data, highlighting why the composition of study groups matters in observational research. The KCCQ-12 results are among the most clinically relevant, capturing patient-reported outcomes beyond lab values and imaging. The empagliflozin group scored higher across all areas. However, most differences were modest and not statistically significant by parametric tests; the symptom frequency domain showed significant improvement using Mann-Whitney testing (p = 0.033). This non-parametric result reflects a meaningful shift in symptom scores, with empagliflozin patients reporting fewer episodes of breathlessness, leg swelling, chest pain, and fatigue over the past two weeks 9 . This finding is clinically important regardless of differences in lab values 19 . The KCCQ-12 domains showed strong correlations (0.47 to 0.92, all p < 0.001), confirming the tool’s reliability for this group. The strongest connection in both groups was between Symptom Frequency and the Overall Score (DAPA r = 0.890; EMPA r = 0.923), showing that how often symptoms occur largely shapes patients’ overall health ratings. Because symptom frequency drives the overall KCCQ score, empagliflozin’s better results here carry important clinical significance. This study has several strengths: equal group sizes, a prospective design, standardized measurement methods, a validated quality-of-life tool, and a thorough statistical approach that didn’t rely on just one test for any result. However, there are limitations 20 . Since it’s observational, we can’t say the drugs alone caused the differences. Other factors, like more diabetics and higher use of anticoagulants and anti-anginals in the empagliflozin group, might have influenced the results. Follow-up only lasted until hospital discharge, so long-term effects are unknown. Also, being a single-center study may introduce bias. Future research should use random assignment, longer follow-up, and analyze heart failure types separately. 21 . CONCLUSION In 70 patients with heart failure treated over a single hospital admission, both dapagliflozin and empagliflozin significantly reduced systolic blood pressure and improved ejection fraction. Across most laboratory, hemodynamic, and echocardiographic outcomes, the two drugs were equivalent. Where they diverged: dapagliflozin produced a notably larger reduction in serum sodium, while empagliflozin achieved greater blood glucose lowering in diabetic patients, caused a steeper decline in potassium, and was associated with significantly better symptom frequency scores on the KCCQ-12. Taken together, these results support the practical view that dapagliflozin and empagliflozin are therapeutically interchangeable for most heart failure patients, while also identifying specific clinical situations where one agent may offer an edge over the other. Empagliflozin appears better suited for patients in whom rapid symptom relief is the priority, particularly in the context of diabetes. Dapagliflozin may be preferable in patients with low serum sodium or those in whom additional natriuresis is desirable. These remain hypothesis-generating conclusions that need confirmation in adequately powered randomized trials. Declarations Conflict of Interest The authors have no conflicts of interest to declare Ethical Approval The study was conducted in a tertiary care center, and approval was obtained from the institutional ethics committee (IEC/SRMC/SRCP/RESEARCH/159/2025). The study was conducted to ensure that informed consent was obtained from all participants and that anonymity was maintained throughout the analysis. Data Availability Data supporting these findings are available on reasonable request from the corresponding author. Artificial Intelligence The authors used Grammarly for proofreading and refining the writing and declare that no generative text or results are included. 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Effect of Dapagliflozin on Worsening Heart Failure and Cardiovascular Death in Patients With Heart Failure With and Without Diabetes. JAMA. 2020;323(14):1353. 10.1001/jama.2020.1906 . Spertus JA, Jones PG. Development and Validation of a Short Version of the Kansas City Cardiomyopathy Questionnaire. Circ Cardiovasc Qual Outcomes. 2015;8(5):469–76. 10.1161/CIRCOUTCOMES.115.001958 . Talha KM, Anker SD, Butler J. SGLT-2 Inhibitors in Heart Failure: A Review of Current Evidence. Int J Heart Fail. 2023;5(2):82. 10.36628/ijhf.2022.0030 . Palmer BF, Clegg DJ. Kidney-Protective Effects of SGLT2 Inhibitors. Clin J Am Soc Nephrol. 2023;18(2):279–89. 10.2215/CJN.09380822 . Mitoff P. Evolution of natriuretic peptides testing in heart failure — Impact of novel therapies. Clin Biochem. 2016;49(9):643–4. 10.1016/j.clinbiochem.2016.03.005 . Fujiki S. Antiarrhythmic potential of SGLT2 inhibitors: Mechanistic insights and clinical evidence. J Cardiol. 2026;87(2):128–35. 10.1016/j.jjcc.2025.09.018 . Grigoriou K, Karakasis P, Nasoufidou A, Stachteas P, Klisic A, Karagiannidis E, et al. SGLT2 Inhibitors in the Management of Cardio-Renal-Metabolic Syndrome: A New Therapeutic Era. Med (Mex). 2025;61(11):1903. 10.3390/medicina61111903 . Zannad F, Ferreira JP, Pocock SJ, Zeller C, Anker SD, Butler J, et al. Cardiac and Kidney Benefits of Empagliflozin in Heart Failure Across the Spectrum of Kidney Function: Insights From EMPEROR-Reduced. Circulation. 2021;143(4):310–21. 10.1161/CIRCULATIONAHA.120.051685 . Tee SL, Sindone A, Roger S, Atherton J, Amerena J, D’Emden M, et al. Hyponatremia in heart failure. Intern Med J. 2020;50(6):659–66. 10.1111/imj.14624 . O’Hara DV, Lam CSP, McMurray JJV, Yi TW, Hocking S, Dawson J, et al. Applications of SGLT2 inhibitors beyond glycaemic control. Nat Rev Nephrol. 2024;20(8):513–29. 10.1038/s41581-024-00836-y . Frampton JE, Empagliflozin. A Review in Type 2 Diabetes. Drugs. 2018;78(10):1037–48. 10.1007/s40265-018-0937-z . Zoccali C, Mallamaci F. Moderator’s view: Vitamin D deficiency treatment in advanced chronic kidney disease: a close look at the emperor’s clothes. Nephrol Dial Transpl. 2016;31(5):714–6. 10.1093/ndt/gfw081 . Vaduganathan M, Docherty KF, Claggett BL, Jhund PS, De Boer RA, Hernandez AF, et al. SGLT2 inhibitors in patients with heart failure: a comprehensive meta-analysis of five randomised controlled trials. Lancet. 2022;400(10354):757–67. 10.1016/S0140-6736(22)01429-5 . Zannad F, Ferreira JP, Butler J, Filippatos G, Januzzi JL, Sumin M, et al. Effect of empagliflozin on circulating proteomics in heart failure: mechanistic insights into the EMPEROR programme. Eur Heart J. 2022;43(48):4991–5002. 10.1093/eurheartj/ehac495 . Additional Declarations No competing interests reported. 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It has remained so for decades, despite major advances in its management, to the extent that it still leads to hospitalization, premature death, and diminished quality of life on a large scale\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The pharmacological management of heart failure, as recommended by guidelines, has, over the years, progressively incorporated the use of angiotensin-converting enzyme inhibitors, angiotensin receptor neprilysin inhibitors, beta blockers, and mineralocorticoid receptor antagonists, among the drugs, but the prognosis of patients with heart failure has remained poor, especially despite the optimal use of the above drugs\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The introduction of SGLT2 inhibitors has revolutionized the management of heart failure to a large extent, as these drugs were originally intended for the management of type 2 diabetes mellitus but were surprisingly found to have a positive impact on the heart, independent of glucose metabolism\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe key evidence comes from two landmark clinical trials. The DAPA-HF trial found that the drug reduced the composite risk of worsening heart failure and death by 26% compared with the placebo, and the effect was seen regardless of the presence or absence of diabetes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The EMPEROR-Reduced trial yielded similar results for the drug empagliflozin, showing significant reductions in heart failure hospitalizations and cardiovascular death\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The DELIVER and EMPEROR-Preserved trials were conducted to show the effect of the drugs even when the ejection fraction was not severely reduced, but only mildly or preserved\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eYet, despite this solid body of evidence supporting both drugs, the two have never been compared prospectively. In real-life settings, the choice between them has to be made mostly based on indirect evidence, cost, and availability. The South Asian population also has its own set of complexities in terms of its cardiovascular risk profile and high prevalence of diabetes and associated comorbid conditions. In addition to hard clinical endpoints, there has been a growing realization that how patients feel is as important as their performance on objective parameters. This is where questionnaires like the KCCQ-12 have become important. The KCCQ-12 is a patient-reported measure that reflects the four aspects of heart failure that patients consider most important\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThus, this study was designed to compare and evaluate these two SGLT-2 inhibitors head-to-head in a tertiary care cardiology setting in India, including hemodynamic responses, metabolic and renal laboratory parameters, echocardiographic ejection fraction, lipid profiles, medication regimens, and patient-reported outcomes using the KCCQ-12 tool\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This study represents one of the first head-to-head prospective studies in this context.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003e A prospective observational study was conducted in the department of cardiology in a tertiary care teaching hospital between September 2025 and March 2026. The Institutional Ethics Committee approved the study before patient enrolment, and all patients provided informed consent. This study is not a clinical trial but an observational study in which the drugs of interest were not prescribed explicitly for the study. The idea for the study emerged during routine ward rounds, when both SGLT2 inhibitors were being prescribed to the patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eWe included 70 patients who were admitted with heart failure based on the inclusion criteria. This included patients with HFrEF, and some patients with right-sided heart failure. The cardiologist in charge of each patient prescribed an SGLT2 inhibitor as part of routine care. We included 35 patients who received empagliflozin (Group I) and 35 who received dapagliflozin (Group II).\u003c/p\u003e \u003cp\u003eThe study included patients aged 18 years or older with heart failure, as defined by ESC/ACC/AHA criteria, who were prescribed an SGLT2 inhibitor as part of routine care during their hospitalization solely to improve ejection fraction, and who had baseline and follow-up information available. We excluded patients who had an eGFR of less than 30 ml/min/1.732 m\u003csup\u003e2\u003c/sup\u003e, had type 1 diabetes, had prior use of an SGLT2 inhibitor, had active malignancy, had severe hepatic impairment, were pregnant or lactating, or were unable to give consent.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eDemographic data, comorbid history, and concomitant medications were noted at the time of admission. Vital parameters, including systolic and diastolic blood pressure, pulse rate, respiratory rate, oxygen saturation, and temperature, were noted at the time of initiation of the SGLT2 inhibitor and at the time of discharge. Blood parameters included a full blood count, blood urea, serum creatinine, uric acid, serum electrolytes (sodium, potassium, chloride), HbA1c, and random blood sugar. Ejection fraction was measured by 2D echocardiogram using the biplane Simpson method at baseline and at discharge.\u003c/p\u003e\n\u003ch3\u003eQuality of Life Assessment – KCCQ-12\u003c/h3\u003e\n\u003cp\u003eThe KCCQ-12 was administered to all patients after 3 months of initiation of SGLT2 inhibitor therapy. The KCCQ-12 has four domains. These domains are Physical Limitation, Symptom Frequency, Quality of Life, and Social Limitation. Each item has a score ranging from 0 to 100. Higher scores indicate better health. The score for each domain is the mean of the individual item scores. The Overall Summary Score is the equally weighted mean of the four domains. The KCCQ-12 has been well-validated in heart failure patients and demonstrates high internal consistency.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using R (version 4.5.0). Continuous variables are presented as mean plus or minus standard deviation; categorical variables as counts and percentages. All continuous outcomes were first tested for normality using the Shapiro-Wilk test, and Levene\u0026rsquo;s test was used to assess variance equality between groups. When data were normally distributed, we used independent-samples t-tests for between-group comparisons and paired t-tests for within-group pre-post changes. For variables that violated normality assumptions, we used the Mann-Whitney U test for between-group comparisons. Categorical variables were compared with the chi-square test, applying Fisher\u0026rsquo;s exact correction when any expected cell count fell below 5. One-way ANOVA compared EF change across age strata. Pearson correlation coefficients were calculated for all relevant continuous variable pairs. All significance tests were two-tailed, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Demographics and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eThe two groups were closely matched at baseline across most characteristics. Mean age was 58.63\u0026thinsp;\u0026plusmn;\u0026thinsp;14.59 years in the dapagliflozin group and 56.49\u0026thinsp;\u0026plusmn;\u0026thinsp;15.87 years in the empagliflozin group, a non-significant difference (t\u0026thinsp;=\u0026thinsp;0.588, p\u0026thinsp;=\u0026thinsp;0.558). Both groups had an identical sex distribution, with 28 men (80%) and 7 women (20%) each (p\u0026thinsp;=\u0026thinsp;1.000). Hypertension was more prevalent in the dapagliflozin group (62.9% vs 45.7%), though the difference did not reach significance (p\u0026thinsp;=\u0026thinsp;0.230).\u003c/p\u003e \u003cp\u003eThe one meaningful baseline imbalance was in diabetes prevalence. Nearly 69% of dapagliflozin patients had diabetes mellitus, compared with 31% in the empagliflozin group (p\u0026thinsp;=\u0026thinsp;0.007). This difference is an important confounder for glucose-related outcomes and is discussed accordingly. HbA1c values were numerically higher in the dapagliflozin group (7.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04% vs 6.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33%), though this did not achieve significance (p\u0026thinsp;=\u0026thinsp;0.290). Baseline ejection fraction, renal parameters, hemoglobin, and lipid profiles were comparable.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConcomitant Medications\u003c/h3\u003e\n\u003cp\u003eSeveral concomitant drug classes differed significantly between groups. Anticoagulant use was substantially higher in the empagliflozin group (74.3% vs 45.7%; chi\u003csup\u003e2\u003c/sup\u003e=4.821, p\u0026thinsp;=\u0026thinsp;0.028), possibly reflecting a greater burden of atrial fibrillation or a higher thromboembolic risk. Anti-anginal agents were also more frequently prescribed in the empagliflozin group (57.1% vs 28.6%, p\u0026thinsp;=\u0026thinsp;0.030), pointing to a higher underlying ischaemic disease burden. over 85% of patients in both groups were receiving statin therapy. Antiplatelet agents, diuretics, anti-hypertensives, corticosteroids, non-SGLT2 anti-diabetics, and nebulizers were distributed equally. Data are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eConcomitant Medication Distribution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcomitant Medication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAPA Yes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDAPA No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEMPA Yes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEMPA No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-coagulants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-platelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (88.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31 (88.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (88.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (88.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-anginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-hypertensives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (65.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (34.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32 (91.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-diabetes (non-SGLT2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29 (82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Chi-square test with continuity correction applied throughout.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHemodynamic Parameters\u003c/h2\u003e \u003cp\u003eBoth drugs significantly lowered systolic blood pressure in their respective groups. In the dapagliflozin group, mean systolic BP fell from 123.83 to 112.86 mmHg (paired t\u0026thinsp;=\u0026thinsp;2.920, p\u0026thinsp;=\u0026thinsp;0.006); in the empagliflozin group, the drop was from 124.29 to 113.71 mmHg (paired t\u0026thinsp;=\u0026thinsp;4.493, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When the magnitude of change was compared between groups, the difference was negligible (ΔDAPA 10.40\u0026thinsp;\u0026plusmn;\u0026thinsp;22.51 vs ΔEMPA 10.57\u0026thinsp;\u0026plusmn;\u0026thinsp;13.92 mmHg; t\u0026thinsp;=\u0026thinsp;0.038, p\u0026thinsp;=\u0026thinsp;0.970). It is not significant, however, that Levene\u0026rsquo;s test revealed significantly greater variability in the systolic BP response in dapagliflozin patients (W\u0026thinsp;=\u0026thinsp;4.699, p\u0026thinsp;+\u0026thinsp;0.034), suggesting that some individuals in that group had considerably larger or smaller responses than others.\u003c/p\u003e \u003cp\u003eDiastolic blood pressure fell significantly only with empagliflozin (from 77.43 to 72.57 mmHg, p\u0026thinsp;=\u0026thinsp;0.045), with the dapagliflozin group showing a similar but non-significant trend (p\u0026thinsp;=\u0026thinsp;0.056). The between-group comparison was not remarkable (p\u0026thinsp;=\u0026thinsp;0.895). Heart rate did not change meaningfully in either group throughout the admission period. These data are presented in 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\u003eHemodynamic parameters before and after treatment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAPA Before\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDAPA After\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEMPA Before\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEMPA After\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInter-group p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.83 19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.86\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124.29\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113.71\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaired t-test (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.71 11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.29\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.43\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.57\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaired t-test (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.045*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.40 14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.43\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.89\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.74\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaired t-test (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Paired t-test for within-group comparisons, independent t-test for between-group Δ comparisons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory Parameters\u003c/h2\u003e \u003cp\u003eSerum Creatinine rose modestly in both groups over the admission period: from 1.19 to 1.36 mg/dL in the dapagliflozin group (p\u0026thinsp;=\u0026thinsp;0.024) and from 1.24 to 1.50 mg/dL with empagliflozin (p\u0026thinsp;=\u0026thinsp;0.001). This early rise reflects the expected hemodynamic effect of SGLT2 inhibition on intraglomerular pressure and is well recognized in the literature. The magnitude of the rise was not significantly different between groups (ΔDAPA 0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 vs ΔEMPA 0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42; p\u0026thinsp;=\u0026thinsp;0.399; Mann-Whitney p\u0026thinsp;=\u0026thinsp;0.424). Blood urea also increased in both groups, consistent with the same mechanism (DAPA p\u0026thinsp;=\u0026thinsp;0.044; EMPA p\u0026thinsp;=\u0026thinsp;0.038), again without any between-group difference. Serum uric acid rose significantly in the empagliflozin group alone (p\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e \u003cp\u003eRandom blood sugar fell substantially in the dapagliflozin group (from 161.23 to 131.17 mg/dL; p\u0026thinsp;=\u0026thinsp;0.006), while the empagliflozin group showed a more modest, non-significant within-group change (p\u0026thinsp;=\u0026thinsp;0.434). On first glance, this might suggest that dapagliflozin is the better glucose-lowering agent. Still, Mann-Whitney testing of the actual change scores told a more nuanced story: the glucose reduction in the dapagliflozin group was driven primarily by its much higher proportion of diabetic patients rather than by greater intrinsic efficacy (U\u0026thinsp;=\u0026thinsp;373.5, p\u0026thinsp;=\u0026thinsp;0.005). When only diabetic patients were analyzed, empagliflozin achieved a meaningfully larger absolute reduction in blood glucose (ΔRBS: -75.00 vs -42.33 mg/dL in the dapagliflozin diabetic subgroup, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with its higher reported SGLT2 receptor affinity.\u003c/p\u003e \u003cp\u003eThe most clinically interesting electrolyte finding was in serum sodium. In the dapagliflozin group, sodium dropped from 140.37 to 137.89 mEq/L (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the between-group comparison of change scores was statistically significant (ΔDAPA 2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66 vs ΔEMPA 0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42 mEq/L; t\u0026thinsp;=\u0026thinsp;2.773, p\u0026thinsp;=\u0026thinsp;0.007). Potassium declined in both groups, though more steeply with empagliflozin (from 4.18 to 3.73 mEq/L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs dapagliflozin: 4.21 to 3.99; p\u0026thinsp;=\u0026thinsp;0.018), with the between-group comparison trending toward significance (p\u0026thinsp;=\u0026thinsp;0.086). Hemoglobin, white cell count, and platelet count showed no meaningful change in either group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eLaboratory Parameters Before and After Treatment\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 \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAPA Pre\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDAPA Post\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEMPA Pre\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEMPA Post\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMWU P\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWBC (cells/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8607\u0026thinsp;\u0026plusmn;\u0026thinsp;3346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9045\u0026thinsp;\u0026plusmn;\u0026thinsp;3924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8525\u0026thinsp;\u0026plusmn;\u0026thinsp;2832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10423\u0026thinsp;\u0026plusmn;\u0026thinsp;9837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.420\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\u003ePlatelet Count (x10\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255.8\u0026thinsp;\u0026plusmn;\u0026thinsp;106.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260.5\u0026thinsp;\u0026plusmn;\u0026thinsp;88.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210.9\u0026thinsp;\u0026plusmn;\u0026thinsp;58.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e212.9\u0026thinsp;\u0026plusmn;\u0026thinsp;52.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood Urea (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.97\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.20\u0026thinsp;\u0026plusmn;\u0026thinsp;22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.91\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.63\u0026thinsp;\u0026plusmn;\u0026thinsp;21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr.Creatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric Acid (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBS (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161.23\u0026thinsp;\u0026plusmn;\u0026thinsp;58.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.17\u0026thinsp;\u0026plusmn;\u0026thinsp;43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145.23\u0026thinsp;\u0026plusmn;\u0026thinsp;66.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136.26\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Δp: independent t-test on change scores; MWU p: Mann-Whitney U test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEjection Fraction (EF)\u003c/h2\u003e \u003cp\u003eEjection Fraction improved significantly in both groups during the admission period. In the dapagliflozin group, mean EF rose from 35% to 39% (mean Δ4%\u0026plusmn;4%; paired t = -5.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); in the empagliflozin group, it increased from 35% to 40% (mean Δ5%\u0026plusmn;5%; paired t=-6.528, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The inter-group difference in EF change did not reach significance (t = -1.655, p\u0026thinsp;=\u0026thinsp;0.102; Mann-Whitney, p\u0026thinsp;=\u0026thinsp;0.104), though the numerical direction consistently favored empagliflozin. A one-way ANOVA examining EF improvement across age groups yielded an F-statistic of 3.101 (p\u0026thinsp;=\u0026thinsp;0.052), with patients under 40 years showing a mean ΔEF of 7%, compared to 3.1% in those over 60, suggesting a possible age-related gradient in cardiac recovery that warrants exploration in larger studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eKCCQ-12 Quality of Life\u003c/h2\u003e \u003cp\u003eBoth groups reported moderate impairment across all KKCQ-12 domains, which is consistent with symptomatic heart failure. The empagliflozin group scored numerically higher on every domain, though most of these differences did not reach conventional statistical significance by independent t-test.\u003c/p\u003e \u003cp\u003eThe Physical Limitation Score was 55.18\u0026thinsp;\u0026plusmn;\u0026thinsp;20.54 with dapagliflozin versus 60.36\u0026thinsp;\u0026plusmn;\u0026thinsp;19.41 with empagliflozin (p\u0026thinsp;=\u0026thinsp;0.282). The Symptom Frequency Score was 51.90\u0026thinsp;\u0026plusmn;\u0026thinsp;18.25 versus 58.04\u0026thinsp;\u0026plusmn;\u0026thinsp;16.44; the parametric t-test was non-significant at p\u0026thinsp;=\u0026thinsp;0.144, but the non-parametric Mann-Whitney U test found a significant distributional difference (p\u0026thinsp;=\u0026thinsp;0.033). This means the distribution of symptom frequency responses was shifted meaningfully towards higher scores in empagliflozin patients, a finding with clinical relevance even without a large mean difference. Quality of Life scores were identical across groups (63.21 in both, p\u0026thinsp;=\u0026thinsp;1.000). The Social Limitation Score (53.93 vs 59.29, p\u0026thinsp;=\u0026thinsp;0.290) and the Overall KCCQ Score (56.06 vs 60.22, p\u0026thinsp;=\u0026thinsp;0.278) both favored empagliflozin numerically, but did not reach significance. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e contains the full KCCQ data.\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\u003eKCCQ-12 Domain and Overall Scores by Treatment Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCCQ Domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDapagliflozin Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmpagliflozin Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-test p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMann-Whitney p\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Limitation Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.1820.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.3619.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom Frequency Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.9018.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.0416.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of Life Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.2117.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.2115.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Limitation Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.9319.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.2922.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCCQ Overall Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.0615.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.2215.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Shapiro-Wilk normality testing was applied to all domains before analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analyses\u003c/h2\u003e \u003cp\u003eHbA1c and pre-treatment RBS correlated almost perfectly in both groups (DAPA r\u0026thinsp;=\u0026thinsp;1.000; EMPA r\u0026thinsp;=\u0026thinsp;0.999, both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This near-perfect relationship validates the glycemic data and confirms the internal consistency of our measurements. Serum creatinine and blood urea were strongly correlated at baseline (DAPA r\u0026thinsp;=\u0026thinsp;0.701; EMPA r\u0026thinsp;=\u0026thinsp;0.582, both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), behaving exactly as parallel renal biomarkers should.\u003c/p\u003e \u003cp\u003eAge showed a consistent, albeit non-significant, negative association with EF improvement in both groups (DAPA r\u0026thinsp;=\u0026thinsp;0.328, p\u0026thinsp;=\u0026thinsp;0.054; EMPA r\u0026thinsp;=\u0026thinsp;0.221, p\u0026thinsp;=\u0026thinsp;0.202). The trend suggests younger patients may recover more cardiac function with SGLT2 inhibitors, though our sample size was insufficient to establish this definitively. Glycemic control, as measured by HbA1c, did not predict the magnitude of EF improvement in either group, nor did glucose change predict EF change. In the empagliflozin group, there was a borderline positive association between post-treatment EF and RBS (r\u0026thinsp;=\u0026thinsp;0.318, p\u0026thinsp;=\u0026thinsp;0.063), suggesting a metabolic-cardiac interaction worthy of further study, and all correlation data in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson Correlation Coefficients for Clinical Parameter Pairs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Pair\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAPA r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDAPA p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEMPA r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEMPA p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c vs RBS (pre-treatment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrong positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine vs BUN (pre-treatment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerate positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge vs EF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeak negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge vs ΔSystolic BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeak negative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c vs ΔEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔEF vs ΔEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.262\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\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF-After vs RBS after\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTrend in EMPA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003er=Pearson correlation coefficient\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=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKCCQ-12 Inter-Domain Pearson Correlation Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain Pair\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAPA r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDAPA p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEMPA r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEMPA p\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Limitation vs Symptom Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003ePhysical Limitation vs QoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003ePhysical Limitation vs Social Limitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eSymptom Frequency vs QoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eSymptom Frequency vs QoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eQoL vs Social Limitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eOverall Score vs Physical Limitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eOverall score vs Symptom Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eOverall Score vs QoL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eOverall Score vs Social Limitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Strong and consistent inter-domain correlations in both groups confirm the construct validity of the KCCQ-12 in this population.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study compared dapagliflozin and empagliflozin at a single institution using consistent methods across clinical, biochemical, echocardiographic, and patient-reported measures. Both drugs had generally similar effects: ejection fraction improved, blood pressure decreased, and creatinine rose slightly. However, there were clear differences in electrolyte balance, blood sugar response, and patient-reported symptoms\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe improvement in ejection fraction with both drugs during a short hospital stay suggests that SGLT2 inhibitors benefit the heart quickly through mechanisms beyond glucose lowering. They reduce preload by promoting natriuresis and osmotic diuresis\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. They also directly affect the heart muscle by reducing sodium and calcium overload, thereby improving contractility\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Additionally, they reduce sympathetic activity and partly decrease inflammation and fibrosis\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Although empagliflozin showed a slightly larger increase in ejection fraction (0.05 versus 0.04), this difference was not statistically significant here but aligns with the EMPEROR-Reduced findings and warrants further study\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe sodium results are especially noteworthy. Dapagliflozin caused a much larger drop in serum sodium (2.51 versus 0.83 mEq/L, p\u0026thinsp;=\u0026thinsp;0.007), which hasn\u0026rsquo;t been widely reported before. It\u0026rsquo;s unclear whether this is due to differences in natriuretic strength, initial sodium levels, or other factors\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. This matters clinically because many heart failure patients have dilutional hyponatremia, a condition linked to worse outcomes\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Dapagliflozin\u0026rsquo;s effect on sodium levels may be important, depending on a patient\u0026rsquo;s baseline levels. Meanwhile, potassium dropped further with empagliflozin, so patients on high-dose diuretics (over 80%) in both groups require careful monitoring.\u003c/p\u003e \u003cp\u003eThe glucose results require careful explanation. Initially, dapagliflozin appeared to lower glucose levels overall, but this was because more diabetic patients were in that group\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. When looking only at diabetic patients, empagliflozin reduced blood sugar nearly twice as much (-75.00 versus \u0026minus;\u0026thinsp;42.33 mg/dl). This aligns with evidence that empagliflozin binds SGLT2 receptors more strongly\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This difference would be missed if we only considered combined data, highlighting why the composition of study groups matters in observational research.\u003c/p\u003e \u003cp\u003eThe KCCQ-12 results are among the most clinically relevant, capturing patient-reported outcomes beyond lab values and imaging. The empagliflozin group scored higher across all areas. However, most differences were modest and not statistically significant by parametric tests; the symptom frequency domain showed significant improvement using Mann-Whitney testing (p\u0026thinsp;=\u0026thinsp;0.033). This non-parametric result reflects a meaningful shift in symptom scores, with empagliflozin patients reporting fewer episodes of breathlessness, leg swelling, chest pain, and fatigue over the past two weeks\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This finding is clinically important regardless of differences in lab values\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe KCCQ-12 domains showed strong correlations (0.47 to 0.92, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming the tool\u0026rsquo;s reliability for this group. The strongest connection in both groups was between Symptom Frequency and the Overall Score (DAPA r\u0026thinsp;=\u0026thinsp;0.890; EMPA r\u0026thinsp;=\u0026thinsp;0.923), showing that how often symptoms occur largely shapes patients\u0026rsquo; overall health ratings. Because symptom frequency drives the overall KCCQ score, empagliflozin\u0026rsquo;s better results here carry important clinical significance.\u003c/p\u003e \u003cp\u003eThis study has several strengths: equal group sizes, a prospective design, standardized measurement methods, a validated quality-of-life tool, and a thorough statistical approach that didn\u0026rsquo;t rely on just one test for any result. However, there are limitations\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Since it\u0026rsquo;s observational, we can\u0026rsquo;t say the drugs alone caused the differences. Other factors, like more diabetics and higher use of anticoagulants and anti-anginals in the empagliflozin group, might have influenced the results. Follow-up only lasted until hospital discharge, so long-term effects are unknown. Also, being a single-center study may introduce bias. Future research should use random assignment, longer follow-up, and analyze heart failure types separately.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn 70 patients with heart failure treated over a single hospital admission, both dapagliflozin and empagliflozin significantly reduced systolic blood pressure and improved ejection fraction. Across most laboratory, hemodynamic, and echocardiographic outcomes, the two drugs were equivalent. Where they diverged: dapagliflozin produced a notably larger reduction in serum sodium, while empagliflozin achieved greater blood glucose lowering in diabetic patients, caused a steeper decline in potassium, and was associated with significantly better symptom frequency scores on the KCCQ-12.\u003c/p\u003e \u003cp\u003eTaken together, these results support the practical view that dapagliflozin and empagliflozin are therapeutically interchangeable for most heart failure patients, while also identifying specific clinical situations where one agent may offer an edge over the other. Empagliflozin appears better suited for patients in whom rapid symptom relief is the priority, particularly in the context of diabetes. Dapagliflozin may be preferable in patients with low serum sodium or those in whom additional natriuresis is desirable. These remain hypothesis-generating conclusions that need confirmation in adequately powered randomized trials.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in a tertiary care center, and approval was obtained from the institutional ethics committee (IEC/SRMC/SRCP/RESEARCH/159/2025). The study was conducted to ensure that informed consent was obtained from all participants and that anonymity was maintained throughout the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting these findings are available on reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArtificial Intelligence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors used Grammarly for proofreading and refining the writing and declare that no generative text or results are included.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePonikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. 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Eur Heart J. 2022;43(48):4991\u0026ndash;5002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehac495\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehac495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Empagliflozin, Dapagliflozin, SGLT2 inhibitors, Heart Failure with reduced ejection fraction, Quality of Life, KCCQ-12, Comparative effectiveness","lastPublishedDoi":"10.21203/rs.3.rs-9290020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9290020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSGLT2 inhibitors have transformed heart failure treatment, especially empagliflozin and dapagliflozin. Although both have proven cardiovascular benefits in clinical trials, there is little data comparing them in real-world settings. This study aims to address that by comparing their effects on heart function, lab tests, echocardiography, and quality of life using routine clinical data and the Kansas City Cardiomyopathy Questionnaire-12.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a prospective observational study with 70 heart failure patients, evenly divided between those taking empagliflozin 10 mg daily and those on dapagliflozin 10 mg daily as part of their usual care. We collected vital signs, blood tests, echocardiographic measures, including left ventricular ejection fraction, and KCCQ-12 scores. Statistical analysis included t-tests, Mann-Whitney U, Chi-square, ANOVA, Shapiro-Wilk for normality, Levene\u0026rsquo;s test for equal variances, and Pearson correlation. Results with p-values under 0.005 were considered significant.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe groups were similar in age, gender, rates of hypertension, HbA1c, and lipid levels. However, more patients in the dapagliflozin group had diabetes (68.6% vs. 31.4%, p\u0026thinsp;=\u0026thinsp;0.007). Both drugs significantly lowered systolic blood pressure and improved ejection fraction. Dapagliflozin caused a larger drop in sodium levels (2.51 vs 0.83 mEq/L, p\u0026thinsp;=\u0026thinsp;0.007). Among diabetic patients, empagliflozin reduced blood glucose by a greater amount (-75.00 vs -42.33 mg/dL). Patients on empagliflozin also had higher overall KCCQ-12 scores (66.22 vs. 56.06), with a significant difference in symptom frequency (p\u0026thinsp;=\u0026thinsp;0.033). Improvements in ejection fraction, kidney function, hemoglobin, and lipids were similar for both drugs.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEmpagliflozin and dapagliflozin showed similar effects in heart failure patients, though some important differences emerged. Empagliflozin was associated with greater reductions in KCCQ-12 symptom frequency scores and blood sugar levels in patients with diabetes, whereas dapagliflozin was associated with a larger drop in serum sodium levels. These results suggest that although both drugs are generally equivalent, individual differences might guide personalized treatment choices.\u003c/p\u003e","manuscriptTitle":"Empagliflozin and Dapagliflozin in Improving Ejection Fraction: A Prospective Observational Comparison of Clinical Outcomes and Patient Reported Quality of Life","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 06:18:01","doi":"10.21203/rs.3.rs-9290020/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9cf03e22-ae15-4874-810a-ffa05ea452f4","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T03:25:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 06:18:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9290020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9290020","identity":"rs-9290020","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}