Abstract
Intraduction
Heart failure (HF) outcomes may vary by gender and geographic region, yet data from middle-income countries remain limited. This study aimed to evaluate the impact of gender and urban-rural residence on clinical outcomes and quality of life in HF patients from Tokat, Turkey.
Methods
In this retrospective study, 200 HF patients (54% male, 50% rural) were analyzed. Baseline characteristics, including comorbidities and laboratory parameters, were compared by gender and region. Primary outcomes were mortality and hospital readmission at one year. Quality of life was assessed using the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Cox regression analysis identified risk factors for adverse outcomes.
Results
Females exhibited higher mortality (17.4% vs. 13.0%, p=0.04) and hospital readmission rates (40.2% vs. 28.7%, p=0.02) than males, alongside worse MLHFQ scores (53.4 ± 15.4 vs. 46.0 ± 13.2, p<0.01). Rural patients had elevated mortality (20.0% vs. 10.0%, p=0.02) and ACCEPTED MANUSCRIPT Accepted manuscript readmission (43.0% vs. 25.0%, p<0.01) compared to urban counterparts, with poorer MLHFQ scores (54.8 ± 15.0 vs. 44.2 ± 13.0, p<0.001). Cox analysis identified low education (HR=1.85, 95% CI: 1.05-3.26, p=0.03) and healthcare access distance >20 km (HR=1.90, 95% CI: 1.15-3.14, p=0.02) as mortality predictors. SGLT-2 inhibitor use was associated with reduced readmission risk (HR=0.40, 95% CI: 0.18-0.89, p=0.02).
Conclusion
Gender and regional disparities significantly influence HF outcomes in Tokat, with females and rural patients facing worse prognosis. Interventions targeting education, healthcare access, and optimized therapies may mitigate these inequities.
Keywords: Heart failure, Gender differences, Rural-Urban disparities, Mortality, Hospital readmission, Quality of life, Socioeconomic factors
Intraduction
Heart failure (HF) is a leading cause of morbidity and mortality worldwide, and the impact of gender-specific differences on disease presentation, management and outcomes is increasingly recognized [1]. While ischemic etiologies usually predominate in men, heart failure with preserved ejection fraction (HFpEF) is more common in women, which is associated with hormonal, genetic and sociocultural factors [2]. The diagnosis of HF relies on a combination of clinical assessment, imaging, and biomarker analysis, with echocardiography and NT-proBNP levels being the cornerstone diagnostic tools in most settings. Additionally, right heart catheterization, though less commonly used due to its invasive nature, provides critical hemodynamic data for confirming HF diagnosis in complex cases, particularly when echocardiography is inconclusive [3]. The burden of cardiovascular disease is increasing and regional inequalities in access to health services further complicate these disparities [4]. However, data specific to transition regions where urban and rural areas coexist is still limited. Tokat, located in the Black Sea Region of Turkey, offers a unique opportunity to study this interaction; the city combines urban infrastructure with rural access challenges.
In the literature, it is increasingly recognized that gender-related outcomes of HF are shaped by regional factors. In particular, the impact of urban-rural disparities on access to health care and patient outcomes is an important area of research [5]. Our hospital serves both urban residents and the surrounding rural communities (e.g., Niksar, Erbaa), providing an ideal setting to investigate whether gender-based differences-especially those that may be exacerbated by limited health access in rural areas-translate into measurable clinical outcomes (mortality, hospital readmissions, symptom severity). In this context, preliminary observations from our cardiology practice in Tokat suggest that women with HF present later and with more advanced symptoms compared to men. This may be associated with delays in careseeking behavior or underrecognition of HFpEF. However, these observations have not yet been systematically validated.
Our study aims to assess the impact of geographical and socioeconomic factors (access to health care, education level, lifestyle) in addition to gender. This approach aims to provide a basis for more personalized strategies in HF management by addressing regional diversity and gender together.
Method
This study was a retrospective cohort design in which the medical records of patients diagnosed with HF in the Cardiology Clinic of Tokat Gaziosmanpasa University Hospital between January 1, 2023 and March 31, 2025 were analyzed. The study population consisted of 200 patients aged ≥ 18 years with a diagnosis of HF regardless of ejection fraction (including heart failure reduced ejection fraction (HFrEF) and HFpEF); 100 were recruited from urban centers and 100 from rural districts (Niksar and Erbaa). Inclusion criteria were confirmation of HF diagnosis by clinical findings, echocardiography or biomarkers. Exclusion criteria were acute myocardial infarction (< 1 month), incomplete medical records and terminal illness. Patients were selected in equal numbers from urban (central Tokat) and rural (Niksar and Erbaa) areas by retrospective screening of all eligible cases diagnosed with heart failure at the Cardiology Clinic of Tokat Gaziosmanpaşa University Hospital between January 1, 2023 and March 31, 2025.
This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the local Ethics Committee (Protocol No: 25-MOBAEK-143). Due to the retrospective design, individual informed consent was not required; patient data were analyzed anonymized.
Data collection
Data collection will be performed by three main methods: echocardiography, biomarker analysis and patient questionnaires.
Echocardiography: Transthoracic echocardiography performed at the time of diagnosis or at follow-up visits were reviewed, and left ventricular ejection fraction (LVEF), diastolic function parameters (E/A ratio, E/e’ ratio) and right ventricular function were assessed. HFpEF (LVEF ≥ 50%) and HFrEF (LVEF < 50%) were classified, and significant valvular disease (moderate or severe stenosis/regurgitation) was noted. Measurements were reported according to the American Society of Echocardiography guidelines [6].
Biomarker Analysis: NT-proBNP levels measured at diagnosis or follow-up were obtained from hospital laboratory records. Data were obtained by immunochemical method using standard protocols. Creatinine clearance (CrCl) was calculated using the Cockcroft-Gault formula based on serum creatinine, age, weight, and gender. Patient Information and Questionnaires: Socioeconomic factors (education level, distance to health care) and lifestyle (smoking, physical activity) were excluded from the patient files, and symptom severity was assessed by Minnesota Living with Heart Failure Quality of Life Questionnaire (MLHFQ) scores, if available [7]. For the 40% of patients lacking MLHFQ scores, clinical notes on symptom severity (e.g., shortness of breath, fatigue) were systematically reviewed to ensure comprehensive data collection for primary outcomes, minimizing the impact of missing questionnaire data on our findings. Clinical notes in patient files were used for missing data. Guideline-based treatments (e.g., beta-blockers, ACE inhibitors/ARBs, SGLT-2 inhibitors, spironolactone, furosemide) were extracted from medical records and compared by gender and region to assess treatment patterns.
Measurement parameters and results
In the study, gender-based outcome differences will be evaluated on three main clinical parameters:
Mortality: All-cause mortality rates within 12 months of diagnosis were verified through hospital records and death notification systems.
Hospital Readmission: The frequency of hospitalization due to HF was calculated from patient files within 12 months of diagnosis.
Symptom Severity: It was assessed on the basis of MLHFQ scores or symptom records in clinical notes (shortness of breath, severity of fatigue).
These parameters were compared between urban and rural areas and gender subgroups, and the impact of socioeconomic factors such as access to health care, education level and lifestyle were analyzed.
Statistical analysis
Data were analyzed with SPSS (version 26.0). Continuous variables (NT-proBNP, LVEF) were presented as mean ± standard deviation, and gender and region differences were evaluated by Student’s t-test or Mann-Whitney U test. Categorical variables (mortality, hospitalization) were reported as percentages and analyzed by chi-square test. Cox proportional hazard model was used for mortality and hospitalization risk; age, gender, region, education level and NT-proBNP were included as covariates. Differences in symptom severity were compared by t-test if MLHFQ scores were available. Significance level was accepted as p < 0.05.To ensure robust primary outcome analyses (mortality and hospital readmission) despite the modest sample size (n = 200), covariates in the Cox regression models were carefully selected to avoid overfitting. However, the sample size may limit the power for detecting differences in subgroup analyses (e.g., specific comorbidities or therapies), which is acknowledged as a limitation in the study.
Results
In this retrospective analysis, 200 HF patients from Tokat were evaluated; 100 urban, 100 rural (Niksar and Erbaa), 54% male (n = 108), 46% female (n = 92). Baseline characteristics are presented in Table 1, clinical outcomes in Table 2. Females had higher LVEF (44.0 ± 13.8 vs. 39.3 ± 12.1, p = 0.03) and NT-proBNP (1880 ± 930 vs. 1560 ± 850 pg/mL, p = 0.02); rural patients had lower education (66% vs. 41%, p < 0.01) and higher creatinine (1.3 ± 0.4 vs. 1.1 ± 0.3 mg/dL, p = 0.02) (Table 1).
Table 1.
Baseline characteristics of the study population by gender and region
| Parameter | Total (n = 200) |
Men (n = 108) |
Women (n:92) |
P-value | Urban (n = 100) |
Rural (n = 100) |
P-value |
|---|---|---|---|---|---|---|---|
| Age | 66.3 ± 10.5 | 65.8 ± 10.2 | 66.9 ± 10.8 | 0.47 |
65.5 ± 10.0 |
67.1 ± 11.0 |
0.29 |
| BMI (kg/m² | 27.8 ± 4.5 | 27.5 ± 4.3 | 28.2 ± 4.7 | 0.33 | 28.1 ± 4.4 | 27.5 ± 4.6 | 0.38 |
|
Education ≤ Primary |
53.5 | 50.0 | 57.6 | 0.31 | 41.0 | 66.0 | < 0.01 |
| Diabetes Mellitus | 70 (35%) | 34 (31.5%) | 36 (39.1%) | 0.26 | 32 (32%) | 38 (38%) | 0.37 |
| Hypertension | 140 (70%) | 72 (66.7%) | 68 (73.9%) | 0.28 | 66 (66%) | 74 (74%) | 0.54 |
| Hyperlipidemia | 90 (45%) | 50 (46.3%) | 40 (43.5%) | 0.68 | 48 (48%) | 42 (42%) | 0.39 |
| Prior MI | 60 (30%) | 38 (35.2%) | 22 (23.9%) | 0.08 | 28 (28%) | 32 (32%) | 0.54 |
| Prior stroke | 24 (12%) | 12 (11.1%) | 12 (13.0%) | 0.69 | 10 (10%) | 14 (14%) | 0.39 |
| Smoking | 40.0 | 51.9 | 26.1 | < 0.01 | 38.0 | 42.0 | 0.57 |
| Glucose (mg/dL) | 128 ± 45 | 125 ± 42 | 132 ± 48 | 0.31 | 126 ± 43 | 130 ± 47 | 0.52 |
| Creatinine Clearance (mL/min) | 70.5 ± 20.1 | 72.3 ± 21.0 | 68.5 ± 19.0 | 0.22 | 73.2 ± 19.8 | 67.8 ± 20.3 | 0.08 |
| BUN | 22 ± 8 | 23 ± 9 | 21 ± 7 | 0.12 | 21 ± 7 | 23 ± 9 | 0.14 |
| Sodium (mmol/L) | 138 ± 4 | 139 ± 4 | 137 ± 5 | 0.07 | 138 ± 4 | 137 ± 5 | 0.25 |
| Potassium (mmol/L) | 4.3 ± 0.6 | 4.4 ± 0.6 | 4.2 ± 0.5 | 0.09 | 4.3 ± 0.5 | 4.3 ± 0.6 | 0.81 |
| Albumin(g/dL) | 3.8 ± 0.5 | 3.9 ± 0.5 | 3.7 ± 0.5 | 0.06 | 3.9 ± 0.4 | 3.7 ± 0.5 | 0.03 |
| Hemoglobin (g/dL | 12.5 ± 1.8 | 12.8 ± 1.9 | 12.1 ± 1.6 | 0.04 | 12.7 ± 1.7 | 12.3 ± 1.9 | 0.18 |
| AST (U/L | 25.4 ± 8.2 | 26.1 ± 8.5 | 24.6 ± 7.8 | 0.25 | 24.8 ± 7.9 | 26 ± 8.4 | 0.36 |
| ALT (U/L | 22.8 ± 7.5 | 23.5 ± 7.8 | 22.0 ± 7.1 | 0.22 | 22.3 ± 7.3 | 23.3 ± 7.7 | 0.41 |
| TSH (mIU/L) | 2.8 ± 1.4 | 2.7 ± 1.3 | 2.9 ± 1.5 | 0.038 | 2.6 ± 1.2 | 3.0 ± 1.6 | 0.09 |
| NT-proBNP (pg/mL) | 1710 ± 900 | 1560 ± 850 | 1880 ± 930 | 0.02 | 1650 ± 870 | 1770 ± 920 | 0.33 |
| LVEF (%) | 41.5 ± 13 | 39.3 ± 12.1 | 44 ± 13.8 | 0.03 | 42.1 ± 12.8 | 40.9 ± 13.2 | 0.51 |
| HFpEF (LVEF ≥ 50%) | 40 (20%) | 16 (14.8%) | 24 (26.1%) | 0.04 | 22 (22%) | 18 (18%) | 0.49 |
| HFrEF (LVEF < 50%) | 160 (80%) | 92 (85.2%) | 68 (73.9%) | 0.04 | 78 (78%) | 82 (82%) | 0.49 |
| Valvular Disease (Moderate/Severe) | 20 (10%) | 8 (7.4%) | 12 (13.0%) | 0.20 | 9 (9%) | 11 (11%) | 0.65 |
| Furosemide | 150 (75%) | 78 (72.2%) | 72 (78.3%) | 0.33 | 74 (74%) | 76 (76%) | 0.74 |
| SGLT-2 Inhibitor | 40 (20%) | 20 (18.5%) | 20 (21.7%) | 0.58 | 22 (22%) | 18 (18%) | 0.49 |
| Beta Bloker | 160 (80%) | 88 (81.5%) | 72 (78.3%) | 0.58 | 84 (84%) | 76 (76%) | 0.15 |
| ACE Inh/ARB | 130 (65%) | 70 (64.8%) | 60 (65.2%) | 0.95 | 68 (68%) | 62 (62%) | 0.37 |
| Spironolakton | 80 (40%) | 42 (38.9%) | 38 (41.3%) | 0.73 | 38 (38%) | 42 (42%) | 0.57 |
| AF Rhythm | 50 (25%) | 24 (22.2%) | 26 (28.3%) | 0.33 | 22 (22%) | 28 (28%) | 0.33 |
BUN Blood urea nitrogen, ACE Inh/ARB Angiotensin-converting enzyme inhibitor/Angiotensin receptor blocker, AF Atrial fibrillation
Table 2.
Clinical outcomes
| Parameter | Total (n = 200) |
Men (n = 108) |
Women (n:92) |
p- value |
Urban (n = 100) |
Rural (n = 100) |
p-value |
|---|---|---|---|---|---|---|---|
| Mortality | 30 (15%) | 14 (13.0%) | 16 (17.4%) | 0.04 | 10 (10%) | 20 (20%) | 0.02 |
| Hospital readmission | 68 (34%) | 31 (28.7%) | 37 (40.2%) | 0.02 | 25 (25%) | 43 (43%) | < 0.01 |
| MLHFQ Score | 49.5 ± 14.6 | 46.0 ± 13.2 | 53.4 ± 15.4 | < 0.01 | 44.2 ± 13 | 54.8 ± 15 | < 0.001 |
MLHFQ Minnesota Quality of Life with Heart Failure Questionnaire
Mortality was 15% (n = 30), higher in females (17.4% vs. 13.0%, p = 0.04) and rural areas (20% vs. 10%, p = 0.02) (Table 2; Fig. 1). Hospital readmission was 34% (n = 68), more frequent in females (40.2% vs. 28.7%, p = 0.02) and rural areas (43% vs. 25%, p < 0.01) (Table 2; Fig. 1). MLHFQ scores were higher in females (53.4 ± 15.4 vs. 46.0 ± 13.2, p < 0.01) and rural areas (54.8 ± 15.0 vs. 44.2 ± 13.0, p < 0.001), with temporal changes shown in Fig. 2.
Fig. 1.
Comparison of Mortality and Hospital Readmission Rates by Gender and Region
Fig. 2.
Change in MLHFQ Scores over Time (by Gender and Region)
Factors influencing mortality and readmission are detailed in Table 3. Cox analysis showed low education (HR = 1.85, 95% CI: 1.05–3.26, p = 0.03) and health service access distance > 20 km (HR = 1.90, 95% CI: 1.15–3.14, p = 0.02) increased mortality risk, while SGLT-2 inhibitor use reduced readmission risk (HR = 0.40, 95% CI: 0.18–0.89, p = 0.02) (Table 3). Comorbidities like diabetes and elevated creatinine showed trends toward higher rates (Table 3; Fig. 3).
Table 3.
Factors associated with mortality and hospital readmission
| Factor | Mortality (%) | HR (95% CI) | p Value | Hospital Readmission (%) | HR (95% CI) | p Value |
|---|---|---|---|---|---|---|
| Education Level | ||||||
| - ≤ Primary (n = 107) | 19.5 (21/107) | 1.85 (1.05–3.26) | 0.03 | 41.2 (44/107) |
1.60 (1.12- 2.29) |
< 0.01 |
| - >Primary (n = 93) | 10.5 (10/93) | Ref | 26.8 (25/93) | Ref | ||
| Health Service Access Distance | ||||||
| - >20 km (n = 80) | 21.3 (17/80) | 1.90 (1.15–3.14) | 0.02 | 46.3 (37/80) |
1.75 (1.20- 2.55) |
< 0.01 |
| - ≤20 km (n = 120) | 10.8 (13/120) | Ref | 25.8 (31/120) | Ref | ||
| Diabetes Mellitus | ||||||
| - Present (n = 70) | 20.0 (14/70) | 1.65 (0.92–2.96) | 0.09 | 44.3 (31/70) |
1.50 (1.04- 2.17) |
0.03 |
| - Absent (n = 130) | 12.3 (16/130) | Ref | 28.5 (37/130) | Ref | ||
| SGLT-2 Inhibitor Use | ||||||
| - Yes (n = 40) | 12.5 (5/40) | 0.80 (0.35–1.83) | 0.58 | 15.0 (6/40) |
0.40 (0.18- 0.89) |
0.02 |
| - No (n = 160) | 15.6 (25/160) | Ref | 38.8 (62/160) | Ref | ||
| Creatinine | ||||||
| - ≥1.5 mg/dL (n = 45) | 22.2 (10/45) | 1.70 (0.94–3.07) | 0.09 | 48.9 (22/45) |
1.65 (1.10- 2.47) |
0.02 |
| - <1.5 mg/dL (n = 155) | 12.9 (20/155) | Ref | 29.7 (46/155) | Ref |
Percentages are calculated as (number of events/subgroup size) × 100
HR Hazard Ratio, 95% CI 95% Confidence Interval, Ref Reference category
Fig. 3.
Distribution of Comorbidities by Gender and Region
Discussion
Our study provides a comprehensive analysis of gender and regional differences in HF outcomes and highlights important differences in mortality, hospital readmission and quality of life. By integrating clinical, demographic and laboratory data, our study demonstrates a worse prognosis in women and rural residents, highlighting the need for specific strategies to improve HF management in underserved populations and providing a rationale for addressing disparities in similar settings.
Previous studies have consistently reported gender differences in HF outcomes, with women generally experiencing worse quality of life and higher hospitalization rates [8]. Sotomi et al. found that women with HFpEF exhibited greater symptom burden and risk of hospitalization compared to men, which is consistent with our observation of higher hospitalization rates in women [9]. Our observation of worse outcomes in women is in line with these reports, potentially reflecting diagnostic delays or higher prevalence of comorbidities such as hypertension, as noted in larger cohorts [10, 11]. Similarly, our finding of higher NT-proBNP levels in women supports reports of higher biomarker levels in female HF patients, potentially reflecting higher disease severity or diagnostic delays [12]. Regarding regional disparities, studies have shown that rural HF patients face higher mortality and readmission risk due to limited healthcare access [13–15]. In parallel with this situation, in our study, rural patients were found to have higher mortality levels than patients in urban areas. Wang et al. Reported similar trends in rural patients in Taiwan and attributed the worse prognosis to socioeconomic barriers [16]. Furthermore, low education and distance to health care services were found to be risk factors in our study, reflecting studies emphasizing socioeconomic determinants of HF prognosis [17, 18]. However, our rural-urban classification relied on geographic residence rather than detailed socioeconomic indices (e.g., income, occupation, or healthcare infrastructure), which may oversimplify the interplay of social determinants in HF outcomes. Women in our study had higher mortality and readmission rates than men, likely due to higher HFpEF prevalence, delayed diagnosis, and sociocultural barriers, consistent with Colombo et al. [19]. Future studies incorporating granular socioeconomic data could provide deeper insights into the mechanisms driving these disparities. Additionally, the study period coincided with evolving heart failure management practices, particularly the increased adoption of SGLT-2 inhibitors, which may have been implemented heterogeneously across urban and rural regions due to differences in healthcare infrastructure and access. This variability could contribute to the observed regional disparities in outcomes, notably the lower use of SGLT-2 inhibitors in rural areas. To mitigate the impact of this heterogeneity, our Cox regression analyses included treatment use as a covariate, ensuring that differences in therapeutic approaches were accounted for in our risk assessments. These results are also consistent with the findings of Lawson et al. regarding socioeconomic determinants of HF prognosis [20].
The results from our study expand the understanding of how social determinants interact with clinical factors in HF. While previous studies have often focused on urban-based cohorts or global populations [21] our localized analysis in Tokat reveals unique challenges in a middle income country setting. For example, higher MLHFQ scores in rural areas highlight the compounding effect of geographic isolation and lower levels of education on quality of life, a finding less emphasized in Western cohorts [22]. In addition, the protective effect of SGLT-2 inhibitors on readmission supports recent study data but their underutilization in rural settings suggests the need for targeted pharmacotherapy access [23].
In recent years, the Internet of Things (IoT) has revolutionized HF management. IoT facilitates early intervention by remotely monitoring patients’ vital signs through wearables and telehealth platforms. This is particularly important in reducing disparities identified in our study, such as higher mortality and hospitalization rates of rural patients. IoT-based remote monitoring has shown to reduce hospitalizations [24]. IoT can improve HF care in access-challenged areas such as Tokat.
In contrast to larger registries that may ignore local differences, our focus on Tokat illuminates how gender and rurality intersect with clinical outcomes, providing a model for similar settings globally. Furthermore, our detailed comorbidity profile improves understanding of the distribution of cardiovascular risk, particularly the high prevalence of hypertension and previous MI.
Conclusion
Our findings emphasize the need for tailored interventions to address gender and regional inequities in HF care. The elevated risks in women and rural patients suggest that improving healthcare access, enhancing education, and optimizing SGLT-2 inhibitor use could mitigate adverse outcomes. Clinical practice should prioritize mobile clinics or telemedicine for rural areas and tailored HFpEF management, such as optimized hypertension control, for women to reduce these inequities. Future studies should explore longitudinal interventions, such as telemedicine or community-based education programs, to bridge these gaps. Multicenter trials in similar socioeconomic settings could validate our findings and assess the scalability of targeted therapies. Additionally, integrating genetic or proteomic markers may further elucidate the mechanisms behind gender-specific outcomes observed in our cohort.
Limitations
Despite its strengths, this study has limitations. The retrospective and single-center design introduces inherent biases, such as selection bias and residual confounding, which may limit the reliability of our findings and preclude definitive causal inferences. The sample size (n = 200), while sufficient for primary outcome analyses (mortality and hospital readmission), may restrict generalizability compared to larger registries and could limit the statistical power for subgroup analyses, such as those involving comorbidities (e.g., diabetes) or specific therapies (e.g., SGLT-2 inhibitor use), potentially increasing the risk of Type II errors. Data on MLHFQ scores were available for only 120 patients, potentially introducing selection bias. To mitigate concerns about missing MLHFQ data, we ensured that clinical notes and other outcome measures (e.g., mortality and hospital readmission) were comprehensively collected for all patients, providing a robust basis for our primary analyses. However, the absence of MLHFQ scores in 40% of patients may still affect the precision of quality-of-life comparisons. Rural-urban classifications were based on geographic definitions rather than individual socioeconomic metrics, such as income, occupation, or access to healthcare resources, which may oversimplify the complex nature of regional disparities and limit the granularity of our findings. Finally, unmeasured confounders, such as dietary habits or adherence to therapy, may have influenced outcomes. These limitations highlight the need for larger, prospective, multicenter studies to validate our findings, enhance statistical power for subgroup analyses, and incorporate detailed socioeconomic indices to better characterize rural-urban disparities.
Authors’ contributions
C.Z. contributed to conceptualization, A.C. validation, K.K. resources, G.G. writing – original draft, M.K. writing – review and editing, V.Y. supervision and project administration.
Funding sources
The authors received no financial support for the research, authorship, and/or publication of this article.
Data availability
Data is provided within the manuscript or supplementary information files.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the local Ethics Committee (Protocol No: 25-MOBAEK-143). Due to the retrospective design, individual informed consent was not required; patient data were analyzed anonymized.
Consent for publication
Not applicable
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data Availability Statement
Data is provided within the manuscript or supplementary information files.