The Prognosis Prediction in Non-Traumatic Hemorrhagic Stroke with Base Excess Levels in the Emergency Department

The Prognosis Prediction in Non-Traumatic Hemorrhagic Stroke with Base Excess Levels in the Emergency Department | 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 The Prognosis Prediction in Non-Traumatic Hemorrhagic Stroke with Base Excess Levels in the Emergency Department Fatma Ak, Umit Unal, Agit Olgun, Mehmet Yildiz, Turkay Simsek, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8410165/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 The base excess (BE) parameter reflects the body’s acid-base imbalance and compensatory capacity. It can predict the prognosis of various clinical conditions in the emergency department (ED). This study aims to evaluate the prognostic value of BE in patients admitted to the ED with non-traumatic hemorrhage. Methods This study involved 453 adult patients with non-traumatic hemorrhage who presented to our ED. We recorded data on demographics, gender, comorbidities, Glasgow Coma Scale (GCS) scores, history of antiaggregant and anticoagulant use, type of hemorrhage, arterial blood gas (ABG) values, 30-day mortality rates, length of hospitalization, and surgical requirements. Results The mean age of the study group was 64.99 ± 15.18 years, with a range of 20 to 99 years. Among patients who survived for 30 days, the mean BE level was + 2.88 ± 4.47 mEq/L. In contrast, the mean BE level was + 0.09 ± 4.97 mEq/L among those who did not survive beyond 30 days. There was a statistically significant relationship between BE categories and 30-day survival (p < 0.001). Furthermore, BE level was significantly associated with both discharge and death outcomes (p < 0.001). ROC analysis for BE and 30-day mortality yielded an area under the curve (AUC) of 0.688 (p < 0.001). For the relationship between lactate level and 30-day mortality, the ROC analysis showed an AUC of 0.654. Conclusion Our study demonstrated that admission BE levels could be a significant predictor of 30-day mortality in patients diagnosed with non-traumatic hemorrhage, compared to lactate levels. Non-traumatic Hemorrhage base excess arterial blood gas prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Intracerebral hemorrhages (ICH) constitute 10% of all stroke cases and represent the most prevalent form of hemorrhagic stroke. Hypertension (HT) is the primary cause of these hemorrhages, while the use of oral anticoagulants is also a significant contributing factor [ 1 ]. Epidural hematomas most commonly result from traumatic brain injuries, occurring in approximately 85% of cases due to the rupture of the middle meningeal artery or its branches [ 2 ]. While subdural hemorrhages are generally related to trauma, other factors such as cerebral aneurysm rupture, long-term anticoagulant and antiplatelet therapy, thrombocytopenia, intracranial tumors, vascular malformations, and certain surgical interventions may contribute to their etiology. The primary cause of subarachnoid hemorrhage (SAH) is traumatic bleeding; however, about 80% of non-traumatic hemorrhage cases are associated with spontaneous cerebral aneurysm rüptüre [ 2 – 4 ]. Blood gas analysis includes parameters such as pH, PO₂, PCO₂, estimated bicarbonate (HCO₃) levels, lactate levels, serum electrolytes, and hemoglobin. This method is particularly preferred for the rapid assessment of electrolyte and hemoglobin levels. [ 5 ]. Base excess (BE) is a crucial blood gas parameter reflecting the metabolic acid-base balance. It provides insight into a patient’s overall physiological condition. BE quantifies the amount of titratable hydrogen ions needed to maintain blood pH at 7.4, highlighting the role of non-volatile fixed acids – those not excretable by the lungs – in the buffering capacity of extracellular fluid [ 6 ]. Blood gas serves as an important indicator, capable of reflecting ischemic damage at the cellular level. In patients experiencing hemorrhagic stroke, blood gas analysis can offer valuable insights into prognosis and survival by indirectly indicating the duration of perfusion deficiency. The literature contains limited information about BE, and this study seeks to contribute new insights to this area. Methods Our research is retrospective and began after receiving approval from the Clinical Research Ethics Committee of our hospital. The approval, dated November 9, 2023, corresponds to meeting number 139 and ethics approval number 2929. The study adheres to the Declaration of Helsinki and Good Clinical Practice guidelines. We included a total of 453 patients, all over the age of 18, who presented to our emergency department (ED) and were diagnosed with non-traumatic hemorrhagic stroke between August 1, 2021, and August 1, 2023. We established exclusion criteria beforehand: patients with an unclear diagnosis, those who could not be followed up, those with missing data in their medical records, patients under the age of 18 and intubated patients were excluded from the study. In the standard data collection form, several variables were recorded, including patients’ age, gender, comorbidities, Glasgow Coma Scale (GCS) [ 7 ], and use of anticoagulant or antiplatelet agents. Additionally, types of hemorrhage, blood gas values, mortality status in the ED, direct transfer to the intensive care unit (ICU) or direct admission to surgery, ICU length of stay (if hospitalized), and 30-day survival information were documented. Deaths occurring during hospitalization were identified using the hospital information system and discharge summaries (epicrises). For patients transferred to another hospital, prognosis information was obtained by contacting the relevant institution and reviewing their discharge summaries. Furthermore, 30-day mortality and outcomes were monitored and recorded through the Public Health Directorate Death Notification System. The blood gas values assessed at the time of the patient’s admission to the ED included pH, pCO₂, pO₂, HCO₃, lactate, sodium, potassium, and BE values. Table 1 presents the categorical classification of BE values. Table 1 Categorical classification of base excess (BE) values Category BE Range (mEq/L) Clinical Interpretation 1- Severe Acidosis BE ≤ − 6 Severe metabolic acidosis 2- Mild to Moderate Acidosis –6 < BE < − 2 Mild to moderate metabolic acidosis 3- Normal Value –2 ≤ BE ≤ + 2 Normal acid-base balance within physiological limits 4- Mild to Moderate Alkalosis + 2 < BE < + 6 Mild to moderate metabolic alkalosis 5- Severe Alkalosis BE ≥ + 6 Severe metabolic alkalosis Statistical Analyses The statistical evaluation of the study data was conducted using SPSS 22.0 software (SPSS Inc., Chicago, Illinois, USA). Continuous variables were presented as mean and standard deviation, while categorical variables were represented as frequency and percentage. To compare categorical variables, the chi-square test was employed. The normality of the distribution of continuous variables was assessed using the Kolmogorov-Smirnov test. For normally distributed variables, comparisons between two groups were conducted using Student’s t-test. In contrast, the Mann-Whitney U test was applied for non-normally distributed variables. Descriptive statistics were expressed as mean ± standard deviation (SD) and median (interquartile range, IQR). The study evaluated the accuracy of BE in predicting 30-day mortality and mortality at the final outcome using the receiver operating characteristic (ROC) curve analysis. Calculations were performed with MedCalc version 23.1.6 statistical software (Acacialaan 22, 8400 Ostend, Belgium). For significant threshold values, the sensitivity and specificity of these cut-off points were determined. A Type I error rate below 5% in the area under the curve (AUC) analysis indicated a statistically significant diagnostic value of the test. A p-value less than 0.05 was considered statistically significant. Results The mean age of patients in the study was 64.99 ± 15.18 years, ranging from 20 to 99 years. The cohort included 270 male and 183 female patients. Male patients had a mean age of 63.53 ± 14.76 years, while female patients had a mean age of 67.07 ± 15.57 years. The overall mean GCS score was 10.87 ± 3.90. Specifically, male patients had a mean GCS score of 11.37 ± 3.58, compared to 10.14 ± 4.38 for female patients. Table 4 delineates the distribution of hemorrhage types by gender. Epidural hemorrhages were exclusively observed in male patients, whereas subdural hemorrhages were more frequently detected in female patients. Intraparenchymal hemorrhage (IPH) was the most common type, occurring predominantly in males (183 males, 132 females). SAH appeared at similar rates across genders, with 45 cases in males and 41 in females. Table 4 Distribution of base excess categories according to clinical outcomes. Base Excess Category 30-Day Mortality Final Outcome (Discharge/Exitus) Death in ED Surgical Intervention Transferred to ICU Total 1- Severe Acidosis 22 (81.5) 23 (85.1) 3 (11.1) - 24 (88.9) 27 2- Mild to Moderate Acidosis 41 (87.2) 41 (87.2) 8 (17) 2 (4.3) 38 (80.9) 47 3-Normal 86 (54.8) 93 (59.2) 7 (4.9) 5 (3.2) 142 (90.5) 157 4- Mild to Moderate Alkalosis 57 (32.9) 57 (32.9) 6 (3.5) 2 (1.2) 165 (95.4) 173 5- Severe Alkalosis 21 (42.9) 22 (44.9) 2 (4.1) - 47 (95.9) 49 p-value p < 0.001 p < 0.001 p: 0.051 p: 0.336 p: 0.015 A statistically significant relationship was identified between mortality and hypertension (HT) (p = 0.021) as well as chronic kidney disease (CKD) (p = 0.003). Among patients with HT, 55.6% died within 30 days, and 76.7% of those with CKD died within the same timeframe. Conversely, no significant relationship was observed between 30-day survival and diabetes mellitus (DM), coronary artery disease (CAD), malignancy, cerebrovascular disease, or anticoagulant use (p > 0.05). Table 2 presents a summary of the blood gas and electrolyte parameters for the 453 patients included in the study. The mean pH was 7.38 ± 0.08, while the mean partial pressures of carbon dioxide (pCO₂) and oxygen (pO₂) were 44.13 ± 8.70 mmHg and 60.04 ± 34.27 mmHg, respectively. The mean lactate level was notably elevated at 21.05 ± 15.42 mg/dL. Additionally, the mean BE was + 1.49 ± 4.93 mEq/L, with some patients exhibiting severe acidosis (minimum: − 23.1) and others demonstrating marked alkalosis (maximum: +39). Table 2 Blood gas and electrolyte parameters. ParameterMean Standard Deviation Minimum Maximum N PH 7.3809 0.08086 6.90 7.96 453 PCO₂ (mmHg) 44.13 8.697 14 90 453 PO₂ (mmHg) 60.04 34.266 11 505 453 HCO₃ (mmol/l) 25.098 10.6083 9.6 239.0 453 Lactate (mg/dL) 21.05 15.416 1 131 453 Na⁺ (mmol/l) 139.70 6.621 41 164 453 K⁺ (mmol/l) 4.1994 2.87279 0.80 39.00 453 BE (mEq/L) 1.4909 4.92770 -23.10 39.00 453 This study statistically analyzed the relationship between patients’ BE classifications and their 30-day survival status, as detailed in Table 3 . The analysis revealed that 81.5% of patients in the severe acidosis group (BE ≤ − 6 mEq/L) and 87.2% of those in the mild-to-moderate acidosis group (–6 < BE < − 2 mEq/L) died within 30 days. Conversely, the survival rates were higher in the alkalosis groups, with 67.1% survival in the mild-to-moderate alkalosis group (+ 2 < BE < + 6 mEq/L) and 57.1% in the severe alkalosis group (BE ≥ + 6 mEq/L). Patients with a normal BE range showed a more balanced survival distribution of 45.2%. The chi-square test confirmed that this distribution was statistically significant (χ² = 59.320, p < 0.001). Notably, patients with low (acidotic) BE values exhibited higher mortality rates. Table 3 Distribution of 30-day survival outcomes according to BE categories. Base Excess Category Deceased within 30 Days (n, %) Survived at 30 Days (n, %) Total (n, %) 1 - Severe Acidosis 22 (81.5) 5 (18.5) 27 (6.0) 2 - Mild to Moderate Acidosis 41 (87.2) 6 (12.8) 47 (10.4) 3 - Normal 86 (54.8) 71 (45.2) 157 (34.7) 4 - Mild to Moderate Alkalosis 57 (32.9) 116 (67.1) 173 (38.2) 5 - Severe Alkalosis 21 (42.9) 28 (57.1) 49 (10.8) Total 227 (50.1) 226 (49.9) 453 (100) p-value p < 0.001 The mean BE value for patients who died in the ED was − 0.71 ± 4.77 mEq/L. For patients taken directly to surgery, the mean BE value was + 0.22 mEq/L. Meanwhile, those admitted to the ICU had a mean BE value of + 1.64 ± 4.85 mEq/L. The mean BE was + 2.88 ± 4.47 mEq/L in patients who survived within 30 days, compared to + 0.09 ± 4.97 mEq/L in those who died. In discharged patients, the mean BE was + 2.89 ± 4.52 mEq/L, whereas it was + 0.20 ± 4.93 mEq/L in patients who died. A statistically significant relationship was identified between BE categories and 30-day survival status (p < 0.001), as summarized in Table 4 . The highest mortality rates occurred in the severe acidosis (81.5%) and mild-to-moderate acidosis (87.2%) groups. Conversely, the mild-to-moderate alkalosis group exhibited the highest survival rate (67.1%). Additionally, a significant relationship was found between BE categories and patients’ discharge or exitus status (p < 0.001). The mild-to-moderate acidosis group had the highest exitus rate (85.1%), while the mild-to-moderate alkalosis (67.1%) and severe alkalosis (55.1%) groups showed the highest discharge rates. There was no statistically significant relationship between BE categories and exitus status in the ED (p = 0.05). When comparing BE categories regarding patients taken directly to surgery, no statistically significant difference was identified between the groups (p = 0.336). The highest surgery rates occurred in the mild-to-moderate acidosis group (4.3%) and the normal BE group (3.2%). In contrast, no patients in the severe acidosis and severe alkalosis groups were taken directly to surgery. However, the power of the chi-square test may be limited by the numerical imbalance between the groups. A statistically significant relationship was identified between BE categories and admission to the ICU (p = 0.015). In the severe acidosis group, 88.9% of patients were admitted to intensive care, compared to 80.9% in the mild-to-moderate acidosis group. The highest transfer rates to intensive care were observed in the alkalosis groups, with 95.4% in the mild-to-moderate alkalosis group and 95.9% in the severe alkalosis group. The predictive power of the BE value for 30-day mortality was assessed using ROC analysis. The analysis yielded an AUC value of 0.688, which was statistically significant (p < 0.001), indicating a moderate level of discriminative performance. “ discharged, consequently leading to exitus. In comparing the predictive values of BE and lactate levels for 30-day mortality using ROC analysis, the AUC for BE was 0.688, while the AUC for lactate was 0.654. Both AUC values were statistically significant (p < 0.001), with BE demonstrating slightly greater discriminative power than lactate (Fig. 1 ). The optimal cut-off value for BE was determined to be − 0.5 mEq/L, with a sensitivity of 91.15% for patients below this threshold. At this point, the specificity was 40.53%. In contrast, a threshold value of > 17 mmol/L for lactate resulted in a sensitivity of 62.11% and a specificity of 65.04%. According to the Youden index, BE (J = 0.315) exhibits higher diagnostic performance compared to lactate (J = 0.2716). The analysis results are summarized in Table 5 (Fig. 2 ). Table 5 Comparison of the predictive power of base excess and lactate levels for 30-day mortality. Indicators Base Excess Lactate AUC 0.688 0.654 95% Confidence Interval 0.641–0.728 0.609–0.698 p-value < 0.001 17 mg/dL Sensitivity 91.15% 62.11% Specificity 40.53% 65.04% Youden Index (J) 0.315 0.2716 The prognostic values of BE and lactate levels on patient outcomes (discharge or exitus) were evaluated using ROC analysis. The AUC was 0.686 (95% CI: 0.641–0.728) for BE and 0.643 (95% CI: 0.597–0.687) for lactate. Both AUC values were statistically significant (p < 0.001), although the higher AUC for BE suggests it has a stronger discriminative ability than lactate (Fig. 3 ). A summary of the analysis results can be found in Table 6 . Table 6 Comparison of the predictive power of base excess and lactate levels for clinical outcomes (discharge/ exitus). Parameters Base Excess Lactate AUC 0.686 0.643 95% Confidence Interval 0.641–0.728 0.597–0.687 p-value < 0.001 17 mg/dL Sensitivity 61.86% 61.02% Specificity 70.51% 64.98% Youden Index (J) 0.324 0.2599 The prognostic values of BE and lactate levels on outcome (discharge/exitus) status were evaluated using ROC analysis. The AUC was 0.686 (95% CI: 0.641–0.728) for BE and 0.643 (95% CI: 0.597–0.687) for lactate. Both AUC values were statistically significant (p < 0.001). However, the AUC for BE was higher than that for lactate, indicating BE’s stronger discriminative ability (Fig. 4 ). Analysis results are summarized in Table 6 . The optimal threshold values were established as ≤ 1.5 mEq/L for BE and > 17 mg/dL for lactate. At these thresholds, BE demonstrated a sensitivity of 61.86% and a specificity of 70.51%. In contrast, lactate exhibited a sensitivity of 61.02% and a specificity of 64.98%. Based on the Youden index, BE (J = 0.324) exhibited superior diagnostic performance compared to lactate (J = 0.2599). The relationship between exitus time and both negative and positive BE values was evaluated using Spearman correlation analysis. In the BE-negative group, the correlation coefficient was (r = -0.053), which was not statistically significant (p = 0.556). Similarly, in the BE-positive group, the correlation coefficient was (r = -0.096), and this relationship also lacked statistical significance (p = 0.084). The relationship between BE value and ICU stay length was evaluated using Spearman correlation analysis. For patients with negative BE (BE < 0; n = 128), a statistically significant but very weak positive correlation was observed between BE value and ICU stay duration (r = 0.193, p = 0.029). This finding suggests that as the severity of acidosis diminishes, the length of ICU stay increases. Conversely, in patients with positive BE (BE > 0; n = 325), the correlation was weak and not statistically significant (r = 0.022, p = 0.697). Discussion In this study, we investigated the relationship between BE, a blood gas parameter, and prognosis in non-traumatic hemorrhagic strokes. Our findings indicate that BE is crucial in predicting patient prognosis in such cases. Specifically, we identified a significant relationship between negative BE values and poor clinical outcomes, including high mortality, neurological deficits, and severe complications. In instances where BE was negative, the recovery process tended to be prolonged, and patients experienced extended stays in the ICU. Recent studies indicate that in stress conditions, such as ischemic and hemorrhagic strokes, the brain utilizes lactate produced from anaerobic metabolism as an energy source [ 8 – 10 ]. However, the effect of elevated lactate levels on prognosis remains uncertain. While numerous studies suggest an association between higher lactate levels and favorable outcomes, others report potential adverse effects [ 11 – 14 ]. In a study investigating the relationship between venous blood lactate levels and prognosis, a lactate value exceeding 2 mmol/L upon admission was classified as hyperlactatemia. This condition was associated with increased 3-month mortality and poor prognosis [ 15 ]. While a limited number of clinical studies have highlighted the association between SAH and elevated lactate levels, [ 16 ]. our study found that BE demonstrated superior diagnostic performance compared to lactate levels. The discriminative power of BE was notably higher than that of lactate. This difference is attributed to the numerous parameters influencing lactate levels, as well as the potential for an increase in lactate alone to lead to an elevation in BE. Youn-Jung Kim and colleagues identified hypoxemia, hypercapnic acidosis, and lactic acidosis as primary abnormalities in blood gas analysis. [ 17 ]. In this study, the patients’ mean pH was 7.38 ± 0.08, with pCO₂ at 44.13 ± 8.70 mmHg and pO₂ at 60.04 ± 34.27 mmHg. The mean lactate level was 21.05 ± 15.42 mg/dL. The mean pO₂ indicates that the patient group was generally hypoxemic, suggesting significant tissue hypoperfusion. Some patients also exhibited severe acidosis (minimum: − 23.1) and severe alkalosis (maximum: +39). This wide range of blood gas parameters demonstrates a high degree of metabolic and respiratory heterogeneity and reflects a critical patient profile. This study is the first to analyze the BE value specifically in hemorrhagic strokes. Previously, there has been limited data on how acid-base imbalances affect the central nervous system. Blood and cerebrospinal gas evaluations have mainly focused on patients with SAH. Cerebrovascular acid-base balance is known to influence cerebral blood flow, and conditions such as COPD, pulmonary disease, and diabetic ketoacidosis can impact both cerebral blood flow and cerebrospinal fluid pH. An examination of data from the past 75 years reveals diverse descriptions of the relationships among cerebrospinal fluid, pCO₂, HCO₃⁻, and pH. These factors significantly modify the dynamics of acid-base compensation and the cerebrovascular responses involving arterial–extracellular fluid kinetics and extracellular fluid–intracellular fluid kinetics. Variations in arterial [HCO₃⁻], pCO₂, and pH, caused by respiratory acidosis or respiratory alkalosis, influence the cerebrovascular acid-base balance [ 18 ]. Hypocapnic alkalosis, resulting from hyperventilation and its subsequent effects on cerebrospinal fluid (CSF), is commonly observed in patients experiencing acute cerebrovascular events. Although the precise pathophysiological mechanisms remain unclear, a study by Langer et al. investigated patients with SAH and found that SAH correlates with a reduced strong ion difference in CSF and elevated lactate levels. This localized acidifying effect is counterbalanced by hypocapnia in the CSF, maintaining normal CSF pH values and leading to pronounced arterial hypocapnic alkalosis. Conducted under intensive care with 45 participants, the study assessed CSF and arterial pH, pCO₂, lactate, and strong ion difference. In SAH patients, a lower strong ion difference (23.1 ± 2.3 vs. 26.5 ± 1.4 mmol/L) and decreased pCO₂ (40 ± 4 vs. 46 ± 3 mm Hg) were noted compared to the control group. No significant difference was found between the groups concerning weak non-carbonic acids and pH (7.34 ± 0.06 vs. 7.35 ± 0.02). The study also reported that a low strong ion difference in CSF was linked to high lactate levels (3.3 ± 1.3 vs. 1.4 ± 0.2 mmol/L) and that the strong ion difference in CSF correlated with arterial pH (r = 0.71, p < 0.001) [ 19 ]. In a study by Lehman et al. on patients with SAH, the authors investigated changes in fluid, electrolyte, and acid-base balance. Hypernatremia and hyponatremia are commonly observed in SAH patients and are linked to poor outcomes; thus, managing fluid balance is crucial for these patients’ electrolyte and acid-base stability. The double-blind study included 36 SAH patients and compared normal saline, Voluven, and colloids (Ringer’s Fundin and Tetraspan). Samples were collected at 24 and 48 hours post-administration. The saline group alone showed increases in serum sodium, chloride, osmolality, and a positive fluid balance (> 1500 mL). Additionally, in the saline group, 12 patients exhibited a BE value of < − 2, compared to two patients in the other groups. [ 20 ]. Our study found that patients with severe acidosis had lower 30-day survival rates than those with BE values in the alkalotic range. Notably, a higher mortality rate was associated with low (acidotic) BE values, while a shift toward alkalosis correlated with significantly improved survival rates. This suggests that severe metabolic acidosis at admission negatively affects prognosis. Furthermore, when BE values surpassed the normal range, indicating alkalosis, survival rates increased significantly, whereas patients with normal BE values had more balanced survival rates. These findings suggest the BE level, especially when it moves into negative values, is a key predictor of 30-day mortality and serves as an early clinical warning. Patients with severe acidosis were transferred to the ICU at a higher rate. Conversely, as the severity of acidosis decreased, the length of stay in the ICU increased, which may be associated with a prolonged time to death. However, when compared with the time to death, the BE value alone was not a significant marker. These findings suggest that the BE value influences the severity of the clinical condition and the need for intensive care. Our study demonstrates that BE, especially in the context of acidosis, provides a limited yet significant contribution to intensive care prognosis. Furthermore, the study highlights that BE serves as an important physiological marker reflected in the clinical presentation and suggests that disturbances in BE can affect the immediate prognosis of patients. Limitations Our study has several limitations. First, its retrospective design may have introduced systematic errors during both clinical management and data collection. Additionally, as data were collected from a single center, larger-scale, multi-center studies could enhance the generalizability of our findings. Despite these limitations, our results offer valuable insights into the prognostic value of BE in non-traumatic hemorrhagic strokes. Another limitation is that BE was examined as a standalone parameter. When combined with other clinical indicators, BE may provide more robust prognostic information. For instance, analyzing BE alongside the GCS, lactate levels, and other blood gas parameters could offer a more accurate prediction of patient response to treatment. Conclusion The data from this study suggest that the BE value, especially when measured at initial admission, may serve as a clinically significant biomarker for predicting 30-day survival. CKD and hypertension (HT) were identified as factors that negatively influence the clinical course following a stroke. The study determined that the discriminative power of BE for predicting 30-day mortality slightly exceeds that of lactate. Disturbances in BE potentially affect patient prognosis. Specifically, a shift in BE toward alkalosis was associated with a significant increase in survival rates. This finding indicates that severe metabolic acidosis at the time of admission adversely affects prognosis. As the BE value decreases, indicating increased acidosis, clinical deterioration becomes more apparent. This suggests that the BE value may hold prognostic significance. Utilizing the BE value in clinical practice could serve as a guide, particularly for early risk stratification and decision-making in intensive care units. Declarations Ethics approval and consent to participate: Our research is retrospective and began after receiving approval from the Adana City Hospital Clinical Research Ethics Committee of our hospital. The approval, dated November 9, 2023, corresponds to meeting number 139 and ethics approval number 2929. Informed consent to participate was obtained from all of the participants in the study. Consent for publication: Written informed consent for publication was obtained from all of the patients for this study. Declarations of Generative AI and AI-assisted technologies in the writing process During the preparation of this work the authors used ChatGPT for languadge editing. But the authors checked and corrected the manuscript after ChatGPT. Funding No funding Funding: None to declare Our research is retrospective and began after receiving approval from the Clinical Research Ethics Committee of our hospital. The approval, dated November 9, 2023, corresponds to meeting number 139 and ethics approval number 2929. The study adheres to the Declaration of Helsinki and Good Clinical Practice guidelines. Author Contribution FA, AA, SY conceived of the presented idea. UA, AO, MY, TS, AK, UI, MO developed the methods and performed the patient data. AA, AO and SY verified the analytical methods and made the statistical analyse. AA and SY supervised the findings of this work. All authors discussed the results and contributed to the final manuscript. Acknowledgments: All authors declare that they have no conflict of interest. Declaration of Helsinki and Good Clinical Practice : The study adheres to the Declaration of Helsinki and Good Clinical Practice guidelines. Data Avaibility : All the data will be available from the corresponding author upon reasonable request. Data Availability All the data will be available from the corresponding author upon reasonable request. References Alerhand S, Lay C. Spontaneous Intracerebral Hemorrhage. Emerg Med Clin North Am. 2017;35(4):825–45. Brazzelli M, Sandercock PA, Chappell FM, Celani MG, Righetti E, Arestis N, et al. Magnetic resonance imaging versus computed tomography for detection of acute vascular lesions in patients presenting with stroke symptoms. Cochrane Database Syst Rev. 2009;4:7424. Bradac GB, Bergui M, Ferrio MF, Fontanella M, Stura G. False-negative angiograms in subarachnoid haemorrhage due to intracranial aneurysms. Neuroradiology. 1997;39(11):772–6. Kolcun JPG, Gernsback JE, Richardson AM, Jagid JR, Flow. Liver, Flow: A Retrospective Analysis of the Interplay of Liver Disease and Coagulopathy in Chronic Subdural Hematoma. World Neurosurg. 2017;102:246–52. Sahyouni R, Goshtasbi K, Mahmoodi A, Tran DK, Chen JW. Chronic Subdural Hematoma: A Historical and Clinical Perspective. World Neurosurg. 2017;108:948–53. Bloom BM, Grundlingh J, Bestwick JP, Harris T. The role of venous blood gas in the emergency department: a systematic review and meta-analysis. Eur J Emerg Med. 2014;21(2):81–8. Kompanje EJ. De Glascow coma schaal [The Glasgow coma scale]. Tijdschr Ziekenverpl. 1982;35(4):107 – 13. Dutch. PMID: 6917585. Seo MH, Choa M, You JS, Lee HS, Hong JH, Park YS, et al. Hypoalbuminemia, Low Base Excess Values, and Tachypnea Predict 28-Day Mortality in Severe Sepsis and Septic Shock Patients in the Emergency Department. Yonsei Med J. 2016;57(6):1361–9. Berthet C, Lei H, Thevenet J, Gruetter R, Magistretti PJ, Hirt L. Neuroprotective role of lactate after cerebral ischemia. J Cereb Blood Flow Metab. 2009;29(11):1780–9. Wyss MT, Jolivet R, Buck A, Magistretti PJ, Weber B. In vivo evidence for lactate as a neuronal energy source. J Neurosci. 2011;31(20):7477–85. Oddo M, Levine JM, Frangos S, Maloney-Wilensky E, Carrera E, Daniel RT, et al. Brain lactate metabolism in humans with subarachnoid hemorrhage. Stroke. 2012;43(5):1418–21. Brouns R, Sheorajpanday R, Wauters A, De Surgeloose D, Mariën P, De Deyn PP. Evaluation of lactate as a marker of metabolic stress and cause of secondary damage in acute ischemic stroke or TIA. Clin Chim Acta. 2008;397(1–2):27–3. Berthet C, Castillo X, Magistretti PJ, Hirt L. New evidence of neuroprotection by lactate after transient focal cerebral ischaemia: extended benefit after intracerebroventricular injection and efficacy of intravenous administration. Cerebrovasc Dis. 2012;34(5–6):329–35. Dienel GA. Brain lactate metabolism: the discoveries and the controversies. J Cereb Blood Flow Metab. 2012;32(7):1107–38. Jo S, Jeong T, Lee JB, Jin YH, Yoon J, Jun YK, et al. Initial hyperlactatemia in the ED is associated with poor outcome in patients with ischemic stroke. Am J Emerg Med. 2012;30(3):449–55. Parsons MW, Li T, Barber PA, Yang Q, Darby DG, Desmond PM, et al. Combined (1)H MR spectroscopy and diffusion-weighted MRI improves the prediction of stroke outcome. Neurology. 2000;55(4):498–505. Kim YJ, Lee YJ, Ryoo SM, Sohn CH, Ahn S, Seo DW, et al. Role of blood gas analysis during cardiopulmonary resuscitation in out-of-hospital cardiac arrest patients. Med (Baltim). 2016;95(25):3960. Caldwell HG, Carr J, Minhas JS, Swenson ER, Ainslie PN. Acid-base balance and cerebrovascular regulation. J Physiol. 2021;599(24):5337–59. Langer T, Zadek F, Carbonara M, Caccioppola A, Brusatori S, Zoerle T, et al. Cerebrospinal Fluid and Arterial Acid-Base Equilibrium of Spontaneously Breathing Patients with Aneurismal Subarachnoid Hemorrhage. Neurocrit Care. 2022;37(1):102–10. Lehmann L, Bendel S, Uehlinger DE, Takala J, Schafer M, Reinert M, et al. Randomized, double-blind trial of the effect of fluid composition on electrolyte, acid-base, and fluid homeostasis in patients early after subarachnoid hemorrhage. Neurocrit Care. 2013;18(1):5–12. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8410165","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596638881,"identity":"f7d13c20-08df-48eb-b18d-eb2c979b2d3b","order_by":0,"name":"Fatma Ak","email":"","orcid":"","institution":"Health Science University","correspondingAuthor":false,"prefix":"","firstName":"Fatma","middleName":"","lastName":"Ak","suffix":""},{"id":596638882,"identity":"1c8c976a-bd35-4e8e-bd60-f3ab5794c7e9","order_by":1,"name":"Umit Unal","email":"","orcid":"","institution":"Health Science University","correspondingAuthor":false,"prefix":"","firstName":"Umit","middleName":"","lastName":"Unal","suffix":""},{"id":596638883,"identity":"69b4c0aa-8362-4b37-a2f7-7030294a184f","order_by":2,"name":"Agit Olgun","email":"","orcid":"","institution":"Health Science University","correspondingAuthor":false,"prefix":"","firstName":"Agit","middleName":"","lastName":"Olgun","suffix":""},{"id":596638884,"identity":"3840da77-6b1c-41cd-98de-b09964a102cc","order_by":3,"name":"Mehmet Yildiz","email":"","orcid":"","institution":"Health Science University","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"","lastName":"Yildiz","suffix":""},{"id":596638885,"identity":"5336fb8f-951a-4d7f-90d3-207c33445778","order_by":4,"name":"Turkay Simsek","email":"","orcid":"","institution":"Health Science University","correspondingAuthor":false,"prefix":"","firstName":"Turkay","middleName":"","lastName":"Simsek","suffix":""},{"id":596638886,"identity":"2c702d86-3527-4c63-8e5a-94dff98bd218","order_by":5,"name":"Alihan Kutur","email":"","orcid":"","institution":"Health Science University","correspondingAuthor":false,"prefix":"","firstName":"Alihan","middleName":"","lastName":"Kutur","suffix":""},{"id":596638887,"identity":"298d9c0a-43f0-43a4-876e-822ed53cbcfe","order_by":6,"name":"Ugur Isik","email":"","orcid":"","institution":"Health Science University","correspondingAuthor":false,"prefix":"","firstName":"Ugur","middleName":"","lastName":"Isik","suffix":""},{"id":596638888,"identity":"23ec99e6-2a3f-4372-950a-f86ba5d2890c","order_by":7,"name":"Muhammed Oguz","email":"","orcid":"","institution":"Health Science University","correspondingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"","lastName":"Oguz","suffix":""},{"id":596638889,"identity":"101ea221-c417-4f2f-aebe-1e017c6e458c","order_by":8,"name":"Akkan Avci","email":"","orcid":"","institution":"Health Science University","correspondingAuthor":false,"prefix":"","firstName":"Akkan","middleName":"","lastName":"Avci","suffix":""},{"id":596638890,"identity":"63bd3a09-7300-4064-9aa9-73b6da6bec31","order_by":9,"name":"Sadiye Yolcu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYHACgwMgko+BgfEBA8MBErSwMTAwGxCthQGqhU2CKC3y7Yc3HuapOCzHxn7GrJqn5o4cPwPzw0c38FlxJq3gMM+Zw8ZsPDlmt3mOPTOWbGAzNs7B66ocg8O8bYcT2yR4gFrYDiduOMDDJo1Pi3z/G6CWfxAtxTz/iNDCcANkSwNECzPIOoJaDG48Kzg451g60C9pxZJz+w4bSzYT8It8f/LmD29qrOX42Q9v/PDm22Ego/nhY7wOAwImHoZmGAMImAkoBwHGHwx1MMYoGAWjYBSMAkwAAGBSTTjyjTsyAAAAAElFTkSuQmCC","orcid":"","institution":"Health Science University","correspondingAuthor":true,"prefix":"","firstName":"Sadiye","middleName":"","lastName":"Yolcu","suffix":""}],"badges":[],"createdAt":"2025-12-20 07:08:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8410165/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8410165/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103728594,"identity":"bb80f87d-3ae5-4137-bdbf-9421723fcecc","added_by":"auto","created_at":"2026-03-02 08:43:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14453,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic value of base excess in predicting 30-day mortality.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8410165/v1/ba51395f756ac79703d31455.png"},{"id":103728583,"identity":"ac4ff600-a5da-48cc-8c42-db284a1c8b6b","added_by":"auto","created_at":"2026-03-02 08:43:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14643,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive value of base excess for discharge/exitus outcome: ROC curve analysis.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8410165/v1/cfa7d21a862122fe31d3eb19.png"},{"id":103728585,"identity":"c705e1b0-d5bc-4ba0-aff2-04c3994e7e40","added_by":"auto","created_at":"2026-03-02 08:43:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122860,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive power of BE and lactate levels for 30-Day mortality.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8410165/v1/e2e82ddf6e5d7c77e4202526.png"},{"id":103728581,"identity":"dafcd700-bb24-4a76-9222-7c7383e2d59e","added_by":"auto","created_at":"2026-03-02 08:43:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125770,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive power of BE and lactate levels for outcome (discharge/death).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8410165/v1/a896e0cfad5f2793a017ae42.png"},{"id":105034023,"identity":"eae9d38f-687c-480e-92bb-e874c9b77fbf","added_by":"auto","created_at":"2026-03-20 07:22:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1024824,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8410165/v1/d9d40f77-268c-495e-bfdf-72df3f754a23.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Prognosis Prediction in Non-Traumatic Hemorrhagic Stroke with Base Excess Levels in the Emergency Department","fulltext":[{"header":"Background","content":"\u003cp\u003eIntracerebral hemorrhages (ICH) constitute 10% of all stroke cases and represent the most prevalent form of hemorrhagic stroke. Hypertension (HT) is the primary cause of these hemorrhages, while the use of oral anticoagulants is also a significant contributing factor [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEpidural hematomas most commonly result from traumatic brain injuries, occurring in approximately 85% of cases due to the rupture of the middle meningeal artery or its branches [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While subdural hemorrhages are generally related to trauma, other factors such as cerebral aneurysm rupture, long-term anticoagulant and antiplatelet therapy, thrombocytopenia, intracranial tumors, vascular malformations, and certain surgical interventions may contribute to their etiology. The primary cause of subarachnoid hemorrhage (SAH) is traumatic bleeding; however, about 80% of non-traumatic hemorrhage cases are associated with spontaneous cerebral aneurysm r\u0026uuml;pt\u0026uuml;re [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBlood gas analysis includes parameters such as pH, PO₂, PCO₂, estimated bicarbonate (HCO₃) levels, lactate levels, serum electrolytes, and hemoglobin. This method is particularly preferred for the rapid assessment of electrolyte and hemoglobin levels. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Base excess (BE) is a crucial blood gas parameter reflecting the metabolic acid-base balance. It provides insight into a patient\u0026rsquo;s overall physiological condition. BE quantifies the amount of titratable hydrogen ions needed to maintain blood pH at 7.4, highlighting the role of non-volatile fixed acids \u0026ndash; those not excretable by the lungs \u0026ndash; in the buffering capacity of extracellular fluid [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBlood gas serves as an important indicator, capable of reflecting ischemic damage at the cellular level. In patients experiencing hemorrhagic stroke, blood gas analysis can offer valuable insights into prognosis and survival by indirectly indicating the duration of perfusion deficiency. The literature contains limited information about BE, and this study seeks to contribute new insights to this area.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eOur research is retrospective and began after receiving approval from the Clinical Research Ethics Committee of our hospital. The approval, dated November 9, 2023, corresponds to meeting number 139 and ethics approval number 2929. The study adheres to the Declaration of Helsinki and Good Clinical Practice guidelines.\u003c/p\u003e \u003cp\u003eWe included a total of 453 patients, all over the age of 18, who presented to our emergency department (ED) and were diagnosed with non-traumatic hemorrhagic stroke between August 1, 2021, and August 1, 2023. We established exclusion criteria beforehand: patients with an unclear diagnosis, those who could not be followed up, those with missing data in their medical records, patients under the age of 18 and intubated patients were excluded from the study.\u003c/p\u003e \u003cp\u003eIn the standard data collection form, several variables were recorded, including patients\u0026rsquo; age, gender, comorbidities, Glasgow Coma Scale (GCS) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and use of anticoagulant or antiplatelet agents. Additionally, types of hemorrhage, blood gas values, mortality status in the ED, direct transfer to the intensive care unit (ICU) or direct admission to surgery, ICU length of stay (if hospitalized), and 30-day survival information were documented. Deaths occurring during hospitalization were identified using the hospital information system and discharge summaries (epicrises). For patients transferred to another hospital, prognosis information was obtained by contacting the relevant institution and reviewing their discharge summaries. Furthermore, 30-day mortality and outcomes were monitored and recorded through the Public Health Directorate Death Notification System.\u003c/p\u003e \u003cp\u003eThe blood gas values assessed at the time of the patient\u0026rsquo;s admission to the ED included pH, pCO₂, pO₂, HCO₃, lactate, sodium, potassium, and BE values. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the categorical classification of BE values.\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\u003eCategorical classification of base excess (BE) values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBE Range (mEq/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClinical Interpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1- Severe Acidosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBE \u0026le; \u0026minus;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere metabolic acidosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2- Mild to Moderate Acidosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;6\u0026thinsp;\u0026lt;\u0026thinsp;BE \u0026lt; \u0026minus;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild to moderate metabolic acidosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3- Normal Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;2\u0026thinsp;\u0026le;\u0026thinsp;BE\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal acid-base balance within physiological limits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4- Mild to Moderate Alkalosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;2\u0026thinsp;\u0026lt;\u0026thinsp;BE\u0026thinsp;\u0026lt;\u0026thinsp;+\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild to moderate metabolic alkalosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5- Severe Alkalosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBE\u0026thinsp;\u0026ge;\u0026thinsp;+\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere metabolic alkalosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eThe statistical evaluation of the study data was conducted using SPSS 22.0 software (SPSS Inc., Chicago, Illinois, USA). Continuous variables were presented as mean and standard deviation, while categorical variables were represented as frequency and percentage. To compare categorical variables, the chi-square test was employed. The normality of the distribution of continuous variables was assessed using the Kolmogorov-Smirnov test. For normally distributed variables, comparisons between two groups were conducted using Student\u0026rsquo;s t-test. In contrast, the Mann-Whitney U test was applied for non-normally distributed variables. Descriptive statistics were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and median (interquartile range, IQR).\u003c/p\u003e \u003cp\u003eThe study evaluated the accuracy of BE in predicting 30-day mortality and mortality at the final outcome using the receiver operating characteristic (ROC) curve analysis. Calculations were performed with MedCalc version 23.1.6 statistical software (Acacialaan 22, 8400 Ostend, Belgium). For significant threshold values, the sensitivity and specificity of these cut-off points were determined. A Type I error rate below 5% in the area under the curve (AUC) analysis indicated a statistically significant diagnostic value of the test. A p-value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean age of patients in the study was 64.99\u0026thinsp;\u0026plusmn;\u0026thinsp;15.18 years, ranging from 20 to 99 years. The cohort included 270 male and 183 female patients. Male patients had a mean age of 63.53\u0026thinsp;\u0026plusmn;\u0026thinsp;14.76 years, while female patients had a mean age of 67.07\u0026thinsp;\u0026plusmn;\u0026thinsp;15.57 years. The overall mean GCS score was 10.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90. Specifically, male patients had a mean GCS score of 11.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58, compared to 10.14\u0026thinsp;\u0026plusmn;\u0026thinsp;4.38 for female patients. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4\u003c/span\u003e delineates the distribution of hemorrhage types by gender. Epidural hemorrhages were exclusively observed in male patients, whereas subdural hemorrhages were more frequently detected in female patients. Intraparenchymal hemorrhage (IPH) was the most common type, occurring predominantly in males (183 males, 132 females). SAH appeared at similar rates across genders, with 45 cases in males and 41 in females.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of base excess categories according to clinical outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase Excess Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30-Day Mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinal Outcome (Discharge/Exitus)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeath in ED\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurgical Intervention\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTransferred to ICU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1- Severe Acidosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2- Mild to Moderate Acidosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38 (80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-Normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e142 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4- Mild to Moderate Alkalosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165 (95.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5- Severe Alkalosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47 (95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep: 0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep: 0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep: 0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA statistically significant relationship was identified between mortality and hypertension (HT) (p\u0026thinsp;=\u0026thinsp;0.021) as well as chronic kidney disease (CKD) (p\u0026thinsp;=\u0026thinsp;0.003). Among patients with HT, 55.6% died within 30 days, and 76.7% of those with CKD died within the same timeframe. Conversely, no significant relationship was observed between 30-day survival and diabetes mellitus (DM), coronary artery disease (CAD), malignancy, cerebrovascular disease, or anticoagulant use (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a summary of the blood gas and electrolyte parameters for the 453 patients included in the study. The mean pH was 7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08, while the mean partial pressures of carbon dioxide (pCO₂) and oxygen (pO₂) were 44.13\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70 mmHg and 60.04\u0026thinsp;\u0026plusmn;\u0026thinsp;34.27 mmHg, respectively. The mean lactate level was notably elevated at 21.05\u0026thinsp;\u0026plusmn;\u0026thinsp;15.42 mg/dL. Additionally, the mean BE was +\u0026thinsp;1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;4.93 mEq/L, with some patients exhibiting severe acidosis (minimum: \u0026minus;\u0026thinsp;23.1) and others demonstrating marked alkalosis (maximum: +39).\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBlood gas and electrolyte parameters.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameterMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.3809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCO₂ (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePO₂ (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCO₃ (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e239.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa⁺ (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK⁺ (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.1994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.87279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.4909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.92770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-23.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e453\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\u003eThis study statistically analyzed the relationship between patients\u0026rsquo; BE classifications and their 30-day survival status, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The analysis revealed that 81.5% of patients in the severe acidosis group (BE \u0026le; \u0026minus;\u0026thinsp;6 mEq/L) and 87.2% of those in the mild-to-moderate acidosis group (\u0026ndash;6\u0026thinsp;\u0026lt;\u0026thinsp;BE \u0026lt; \u0026minus;\u0026thinsp;2 mEq/L) died within 30 days. Conversely, the survival rates were higher in the alkalosis groups, with 67.1% survival in the mild-to-moderate alkalosis group (+\u0026thinsp;2\u0026thinsp;\u0026lt;\u0026thinsp;BE\u0026thinsp;\u0026lt;\u0026thinsp;+\u0026thinsp;6 mEq/L) and 57.1% in the severe alkalosis group (BE\u0026thinsp;\u0026ge;\u0026thinsp;+\u0026thinsp;6 mEq/L). Patients with a normal BE range showed a more balanced survival distribution of 45.2%. The chi-square test confirmed that this distribution was statistically significant (χ\u0026sup2; = 59.320, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, patients with low (acidotic) BE values exhibited higher mortality rates.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of 30-day survival outcomes according to BE categories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase Excess Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeceased within 30 Days (n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvived at 30 Days (n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal (n, %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 - Severe Acidosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (6.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 - Mild to Moderate Acidosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (10.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 - Normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157 (34.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 - Mild to Moderate Alkalosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116 (67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173 (38.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 - Severe Alkalosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (10.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e227 (50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e226 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e453 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\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\u003eThe mean BE value for patients who died in the ED was \u0026minus;\u0026thinsp;0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;4.77 mEq/L. For patients taken directly to surgery, the mean BE value was +\u0026thinsp;0.22 mEq/L. Meanwhile, those admitted to the ICU had a mean BE value of +\u0026thinsp;1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85 mEq/L.\u003c/p\u003e \u003cp\u003eThe mean BE was +\u0026thinsp;2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47 mEq/L in patients who survived within 30 days, compared to +\u0026thinsp;0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;4.97 mEq/L in those who died. In discharged patients, the mean BE was +\u0026thinsp;2.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52 mEq/L, whereas it was +\u0026thinsp;0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.93 mEq/L in patients who died.\u003c/p\u003e \u003cp\u003eA statistically significant relationship was identified between BE categories and 30-day survival status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The highest mortality rates occurred in the severe acidosis (81.5%) and mild-to-moderate acidosis (87.2%) groups. Conversely, the mild-to-moderate alkalosis group exhibited the highest survival rate (67.1%). Additionally, a significant relationship was found between BE categories and patients\u0026rsquo; discharge or exitus status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mild-to-moderate acidosis group had the highest exitus rate (85.1%), while the mild-to-moderate alkalosis (67.1%) and severe alkalosis (55.1%) groups showed the highest discharge rates.\u003c/p\u003e \u003cp\u003eThere was no statistically significant relationship between BE categories and exitus status in the ED (p\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eWhen comparing BE categories regarding patients taken directly to surgery, no statistically significant difference was identified between the groups (p\u0026thinsp;=\u0026thinsp;0.336). The highest surgery rates occurred in the mild-to-moderate acidosis group (4.3%) and the normal BE group (3.2%). In contrast, no patients in the severe acidosis and severe alkalosis groups were taken directly to surgery. However, the power of the chi-square test may be limited by the numerical imbalance between the groups.\u003c/p\u003e \u003cp\u003eA statistically significant relationship was identified between BE categories and admission to the ICU (p\u0026thinsp;=\u0026thinsp;0.015). In the severe acidosis group, 88.9% of patients were admitted to intensive care, compared to 80.9% in the mild-to-moderate acidosis group. The highest transfer rates to intensive care were observed in the alkalosis groups, with 95.4% in the mild-to-moderate alkalosis group and 95.9% in the severe alkalosis group.\u003c/p\u003e \u003cp\u003eThe predictive power of the BE value for 30-day mortality was assessed using ROC analysis. The analysis yielded an AUC value of 0.688, which was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a moderate level of discriminative performance. \u0026ldquo; discharged, consequently leading to exitus.\u003c/p\u003e \u003cp\u003eIn comparing the predictive values of BE and lactate levels for 30-day mortality using ROC analysis, the AUC for BE was 0.688, while the AUC for lactate was 0.654. Both AUC values were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with BE demonstrating slightly greater discriminative power than lactate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe optimal cut-off value for BE was determined to be \u0026minus;\u0026thinsp;0.5 mEq/L, with a sensitivity of 91.15% for patients below this threshold. At this point, the specificity was 40.53%. In contrast, a threshold value of \u0026gt;\u0026thinsp;17 mmol/L for lactate resulted in a sensitivity of 62.11% and a specificity of 65.04%. According to the Youden index, BE (J\u0026thinsp;=\u0026thinsp;0.315) exhibits higher diagnostic performance compared to lactate (J\u0026thinsp;=\u0026thinsp;0.2716). The analysis results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\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\u003eComparison of the predictive power of base excess and lactate levels for 30-day mortality.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBase Excess\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.641\u0026ndash;0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.609\u0026ndash;0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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\u003eCut-off Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le; \u0026minus;\u0026thinsp;0.5 mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;17 mg/dL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYouden Index (J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2716\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\u003eThe prognostic values of BE and lactate levels on patient outcomes (discharge or exitus) were evaluated using ROC analysis. The AUC was 0.686 (95% CI: 0.641\u0026ndash;0.728) for BE and 0.643 (95% CI: 0.597\u0026ndash;0.687) for lactate. Both AUC values were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), although the higher AUC for BE suggests it has a stronger discriminative ability than lactate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A summary of the analysis results can be found in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\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\u003eComparison of the predictive power of base excess and lactate levels for clinical outcomes (discharge/ exitus).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBase Excess\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.641\u0026ndash;0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.597\u0026ndash;0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimal Cut-off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.5 mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;17 mg/dL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.02%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYouden Index (J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2599\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\u003eThe prognostic values of BE and lactate levels on outcome (discharge/exitus) status were evaluated using ROC analysis. The AUC was 0.686 (95% CI: 0.641\u0026ndash;0.728) for BE and 0.643 (95% CI: 0.597\u0026ndash;0.687) for lactate. Both AUC values were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the AUC for BE was higher than that for lactate, indicating BE\u0026rsquo;s stronger discriminative ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Analysis results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe optimal threshold values were established as \u0026le;\u0026thinsp;1.5 mEq/L for BE and \u0026gt;\u0026thinsp;17 mg/dL for lactate. At these thresholds, BE demonstrated a sensitivity of 61.86% and a specificity of 70.51%. In contrast, lactate exhibited a sensitivity of 61.02% and a specificity of 64.98%. Based on the Youden index, BE (J\u0026thinsp;=\u0026thinsp;0.324) exhibited superior diagnostic performance compared to lactate (J\u0026thinsp;=\u0026thinsp;0.2599).\u003c/p\u003e \u003cp\u003eThe relationship between exitus time and both negative and positive BE values was evaluated using Spearman correlation analysis. In the BE-negative group, the correlation coefficient was (r = -0.053), which was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.556). Similarly, in the BE-positive group, the correlation coefficient was (r = -0.096), and this relationship also lacked statistical significance (p\u0026thinsp;=\u0026thinsp;0.084).\u003c/p\u003e \u003cp\u003eThe relationship between BE value and ICU stay length was evaluated using Spearman correlation analysis. For patients with negative BE (BE\u0026thinsp;\u0026lt;\u0026thinsp;0; n\u0026thinsp;=\u0026thinsp;128), a statistically significant but very weak positive correlation was observed between BE value and ICU stay duration (r\u0026thinsp;=\u0026thinsp;0.193, p\u0026thinsp;=\u0026thinsp;0.029). This finding suggests that as the severity of acidosis diminishes, the length of ICU stay increases. Conversely, in patients with positive BE (BE\u0026thinsp;\u0026gt;\u0026thinsp;0; n\u0026thinsp;=\u0026thinsp;325), the correlation was weak and not statistically significant (r\u0026thinsp;=\u0026thinsp;0.022, p\u0026thinsp;=\u0026thinsp;0.697).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the relationship between BE, a blood gas parameter, and prognosis in non-traumatic hemorrhagic strokes. Our findings indicate that BE is crucial in predicting patient prognosis in such cases. Specifically, we identified a significant relationship between negative BE values and poor clinical outcomes, including high mortality, neurological deficits, and severe complications. In instances where BE was negative, the recovery process tended to be prolonged, and patients experienced extended stays in the ICU.\u003c/p\u003e \u003cp\u003eRecent studies indicate that in stress conditions, such as ischemic and hemorrhagic strokes, the brain utilizes lactate produced from anaerobic metabolism as an energy source [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the effect of elevated lactate levels on prognosis remains uncertain. While numerous studies suggest an association between higher lactate levels and favorable outcomes, others report potential adverse effects [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a study investigating the relationship between venous blood lactate levels and prognosis, a lactate value exceeding 2 mmol/L upon admission was classified as hyperlactatemia. This condition was associated with increased 3-month mortality and poor prognosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While a limited number of clinical studies have highlighted the association between SAH and elevated lactate levels, [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. our study found that BE demonstrated superior diagnostic performance compared to lactate levels. The discriminative power of BE was notably higher than that of lactate. This difference is attributed to the numerous parameters influencing lactate levels, as well as the potential for an increase in lactate alone to lead to an elevation in BE.\u003c/p\u003e \u003cp\u003eYoun-Jung Kim and colleagues identified hypoxemia, hypercapnic acidosis, and lactic acidosis as primary abnormalities in blood gas analysis. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this study, the patients\u0026rsquo; mean pH was 7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08, with pCO₂ at 44.13\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70 mmHg and pO₂ at 60.04\u0026thinsp;\u0026plusmn;\u0026thinsp;34.27 mmHg. The mean lactate level was 21.05\u0026thinsp;\u0026plusmn;\u0026thinsp;15.42 mg/dL. The mean pO₂ indicates that the patient group was generally hypoxemic, suggesting significant tissue hypoperfusion. Some patients also exhibited severe acidosis (minimum: \u0026minus;\u0026thinsp;23.1) and severe alkalosis (maximum: +39). This wide range of blood gas parameters demonstrates a high degree of metabolic and respiratory heterogeneity and reflects a critical patient profile. This study is the first to analyze the BE value specifically in hemorrhagic strokes. Previously, there has been limited data on how acid-base imbalances affect the central nervous system. Blood and cerebrospinal gas evaluations have mainly focused on patients with SAH.\u003c/p\u003e \u003cp\u003eCerebrovascular acid-base balance is known to influence cerebral blood flow, and conditions such as COPD, pulmonary disease, and diabetic ketoacidosis can impact both cerebral blood flow and cerebrospinal fluid pH. An examination of data from the past 75 years reveals diverse descriptions of the relationships among cerebrospinal fluid, pCO₂, HCO₃⁻, and pH. These factors significantly modify the dynamics of acid-base compensation and the cerebrovascular responses involving arterial\u0026ndash;extracellular fluid kinetics and extracellular fluid\u0026ndash;intracellular fluid kinetics. Variations in arterial [HCO₃⁻], pCO₂, and pH, caused by respiratory acidosis or respiratory alkalosis, influence the cerebrovascular acid-base balance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHypocapnic alkalosis, resulting from hyperventilation and its subsequent effects on cerebrospinal fluid (CSF), is commonly observed in patients experiencing acute cerebrovascular events. Although the precise pathophysiological mechanisms remain unclear, a study by Langer et al. investigated patients with SAH and found that SAH correlates with a reduced strong ion difference in CSF and elevated lactate levels. This localized acidifying effect is counterbalanced by hypocapnia in the CSF, maintaining normal CSF pH values and leading to pronounced arterial hypocapnic alkalosis.\u003c/p\u003e \u003cp\u003eConducted under intensive care with 45 participants, the study assessed CSF and arterial pH, pCO₂, lactate, and strong ion difference. In SAH patients, a lower strong ion difference (23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3 vs. 26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 mmol/L) and decreased pCO₂ (40\u0026thinsp;\u0026plusmn;\u0026thinsp;4 vs. 46\u0026thinsp;\u0026plusmn;\u0026thinsp;3 mm Hg) were noted compared to the control group. No significant difference was found between the groups concerning weak non-carbonic acids and pH (7.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 vs. 7.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02). The study also reported that a low strong ion difference in CSF was linked to high lactate levels (3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 vs. 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 mmol/L) and that the strong ion difference in CSF correlated with arterial pH (r\u0026thinsp;=\u0026thinsp;0.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a study by Lehman et al. on patients with SAH, the authors investigated changes in fluid, electrolyte, and acid-base balance. Hypernatremia and hyponatremia are commonly observed in SAH patients and are linked to poor outcomes; thus, managing fluid balance is crucial for these patients\u0026rsquo; electrolyte and acid-base stability. The double-blind study included 36 SAH patients and compared normal saline, Voluven, and colloids (Ringer\u0026rsquo;s Fundin and Tetraspan). Samples were collected at 24 and 48 hours post-administration. The saline group alone showed increases in serum sodium, chloride, osmolality, and a positive fluid balance (\u0026gt;\u0026thinsp;1500 mL). Additionally, in the saline group, 12 patients exhibited a BE value of \u0026lt; \u0026minus;\u0026thinsp;2, compared to two patients in the other groups. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our study found that patients with severe acidosis had lower 30-day survival rates than those with BE values in the alkalotic range. Notably, a higher mortality rate was associated with low (acidotic) BE values, while a shift toward alkalosis correlated with significantly improved survival rates. This suggests that severe metabolic acidosis at admission negatively affects prognosis. Furthermore, when BE values surpassed the normal range, indicating alkalosis, survival rates increased significantly, whereas patients with normal BE values had more balanced survival rates. These findings suggest the BE level, especially when it moves into negative values, is a key predictor of 30-day mortality and serves as an early clinical warning.\u003c/p\u003e \u003cp\u003ePatients with severe acidosis were transferred to the ICU at a higher rate. Conversely, as the severity of acidosis decreased, the length of stay in the ICU increased, which may be associated with a prolonged time to death. However, when compared with the time to death, the BE value alone was not a significant marker. These findings suggest that the BE value influences the severity of the clinical condition and the need for intensive care.\u003c/p\u003e \u003cp\u003eOur study demonstrates that BE, especially in the context of acidosis, provides a limited yet significant contribution to intensive care prognosis. Furthermore, the study highlights that BE serves as an important physiological marker reflected in the clinical presentation and suggests that disturbances in BE can affect the immediate prognosis of patients.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eOur study has several limitations. First, its retrospective design may have introduced systematic errors during both clinical management and data collection. Additionally, as data were collected from a single center, larger-scale, multi-center studies could enhance the generalizability of our findings. Despite these limitations, our results offer valuable insights into the prognostic value of BE in non-traumatic hemorrhagic strokes. Another limitation is that BE was examined as a standalone parameter. When combined with other clinical indicators, BE may provide more robust prognostic information. For instance, analyzing BE alongside the GCS, lactate levels, and other blood gas parameters could offer a more accurate prediction of patient response to treatment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe data from this study suggest that the BE value, especially when measured at initial admission, may serve as a clinically significant biomarker for predicting 30-day survival. CKD and hypertension (HT) were identified as factors that negatively influence the clinical course following a stroke. The study determined that the discriminative power of BE for predicting 30-day mortality slightly exceeds that of lactate. Disturbances in BE potentially affect patient prognosis. Specifically, a shift in BE toward alkalosis was associated with a significant increase in survival rates. This finding indicates that severe metabolic acidosis at the time of admission adversely affects prognosis.\u003c/p\u003e \u003cp\u003eAs the BE value decreases, indicating increased acidosis, clinical deterioration becomes more apparent. This suggests that the BE value may hold prognostic significance. Utilizing the BE value in clinical practice could serve as a guide, particularly for early risk stratification and decision-making in intensive care units.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate:\u003c/h2\u003e \u003cp\u003eOur research is retrospective and began after receiving approval from the Adana City Hospital Clinical Research Ethics Committee of our hospital. The approval, dated November 9, 2023, corresponds to meeting number 139 and ethics approval number 2929. Informed consent to participate was obtained from all of the participants in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eWritten informed consent for publication was obtained from all of the patients for this study.\u003c/p\u003e \u003c/p\u003e \u003cdiv class=\"Heading\"\u003eDeclarations\u003c/div\u003e \u003cp\u003e \u003cb\u003eof Generative AI and AI-assisted technologies in the\u003c/b\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003ewriting process\u003c/h2\u003e \u003cp\u003eDuring the preparation of this work the authors used ChatGPT for languadge editing. But the authors checked and corrected the manuscript after ChatGPT.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNone to declare\u003c/p\u003e \u003cp\u003eOur research is retrospective and began after receiving approval from the Clinical Research Ethics Committee of our hospital. The approval, dated November 9, 2023, corresponds to meeting number 139 and ethics approval number 2929. The study adheres to the Declaration of Helsinki and Good Clinical Practice guidelines.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFA, AA, SY conceived of the presented idea. UA, AO, MY, TS, AK, UI, MO developed the methods and performed the patient data. AA, AO and SY verified the analytical methods and made the statistical analyse. AA and SY supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eAll authors declare that they have no conflict of interest.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDeclaration of Helsinki and Good Clinical Practice\u003c/b\u003e: The study adheres to the Declaration of Helsinki and Good Clinical Practice guidelines.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData Avaibility\u003c/b\u003e: All the data will be available from the corresponding author upon reasonable request.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll the data will be available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlerhand S, Lay C. Spontaneous Intracerebral Hemorrhage. Emerg Med Clin North Am. 2017;35(4):825\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrazzelli M, Sandercock PA, Chappell FM, Celani MG, Righetti E, Arestis N, et al. Magnetic resonance imaging versus computed tomography for detection of acute vascular lesions in patients presenting with stroke symptoms. Cochrane Database Syst Rev. 2009;4:7424.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBradac GB, Bergui M, Ferrio MF, Fontanella M, Stura G. False-negative angiograms in subarachnoid haemorrhage due to intracranial aneurysms. Neuroradiology. 1997;39(11):772\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolcun JPG, Gernsback JE, Richardson AM, Jagid JR, Flow. Liver, Flow: A Retrospective Analysis of the Interplay of Liver Disease and Coagulopathy in Chronic Subdural Hematoma. World Neurosurg. 2017;102:246\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahyouni R, Goshtasbi K, Mahmoodi A, Tran DK, Chen JW. Chronic Subdural Hematoma: A Historical and Clinical Perspective. World Neurosurg. 2017;108:948\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBloom BM, Grundlingh J, Bestwick JP, Harris T. The role of venous blood gas in the emergency department: a systematic review and meta-analysis. Eur J Emerg Med. 2014;21(2):81\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKompanje EJ. De Glascow coma schaal [The Glasgow coma scale]. Tijdschr Ziekenverpl. 1982;35(4):107\u0026thinsp;\u0026ndash;\u0026thinsp;13. Dutch. PMID: 6917585.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeo MH, Choa M, You JS, Lee HS, Hong JH, Park YS, et al. Hypoalbuminemia, Low Base Excess Values, and Tachypnea Predict 28-Day Mortality in Severe Sepsis and Septic Shock Patients in the Emergency Department. Yonsei Med J. 2016;57(6):1361\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerthet C, Lei H, Thevenet J, Gruetter R, Magistretti PJ, Hirt L. Neuroprotective role of lactate after cerebral ischemia. J Cereb Blood Flow Metab. 2009;29(11):1780\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWyss MT, Jolivet R, Buck A, Magistretti PJ, Weber B. In vivo evidence for lactate as a neuronal energy source. J Neurosci. 2011;31(20):7477\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOddo M, Levine JM, Frangos S, Maloney-Wilensky E, Carrera E, Daniel RT, et al. Brain lactate metabolism in humans with subarachnoid hemorrhage. Stroke. 2012;43(5):1418\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrouns R, Sheorajpanday R, Wauters A, De Surgeloose D, Mari\u0026euml;n P, De Deyn PP. Evaluation of lactate as a marker of metabolic stress and cause of secondary damage in acute ischemic stroke or TIA. Clin Chim Acta. 2008;397(1\u0026ndash;2):27\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerthet C, Castillo X, Magistretti PJ, Hirt L. New evidence of neuroprotection by lactate after transient focal cerebral ischaemia: extended benefit after intracerebroventricular injection and efficacy of intravenous administration. Cerebrovasc Dis. 2012;34(5\u0026ndash;6):329\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDienel GA. Brain lactate metabolism: the discoveries and the controversies. J Cereb Blood Flow Metab. 2012;32(7):1107\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJo S, Jeong T, Lee JB, Jin YH, Yoon J, Jun YK, et al. Initial hyperlactatemia in the ED is associated with poor outcome in patients with ischemic stroke. Am J Emerg Med. 2012;30(3):449\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParsons MW, Li T, Barber PA, Yang Q, Darby DG, Desmond PM, et al. Combined (1)H MR spectroscopy and diffusion-weighted MRI improves the prediction of stroke outcome. Neurology. 2000;55(4):498\u0026ndash;505.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim YJ, Lee YJ, Ryoo SM, Sohn CH, Ahn S, Seo DW, et al. Role of blood gas analysis during cardiopulmonary resuscitation in out-of-hospital cardiac arrest patients. Med (Baltim). 2016;95(25):3960.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaldwell HG, Carr J, Minhas JS, Swenson ER, Ainslie PN. Acid-base balance and cerebrovascular regulation. J Physiol. 2021;599(24):5337\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanger T, Zadek F, Carbonara M, Caccioppola A, Brusatori S, Zoerle T, et al. Cerebrospinal Fluid and Arterial Acid-Base Equilibrium of Spontaneously Breathing Patients with Aneurismal Subarachnoid Hemorrhage. Neurocrit Care. 2022;37(1):102\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehmann L, Bendel S, Uehlinger DE, Takala J, Schafer M, Reinert M, et al. Randomized, double-blind trial of the effect of fluid composition on electrolyte, acid-base, and fluid homeostasis in patients early after subarachnoid hemorrhage. Neurocrit Care. 2013;18(1):5\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Non-traumatic Hemorrhage, base excess, arterial blood gas, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-8410165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8410165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe base excess (BE) parameter reflects the body\u0026rsquo;s acid-base imbalance and compensatory capacity. It can predict the prognosis of various clinical conditions in the emergency department (ED). This study aims to evaluate the prognostic value of BE in patients admitted to the ED with non-traumatic hemorrhage.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study involved 453 adult patients with non-traumatic hemorrhage who presented to our ED. We recorded data on demographics, gender, comorbidities, Glasgow Coma Scale (GCS) scores, history of antiaggregant and anticoagulant use, type of hemorrhage, arterial blood gas (ABG) values, 30-day mortality rates, length of hospitalization, and surgical requirements.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean age of the study group was 64.99\u0026thinsp;\u0026plusmn;\u0026thinsp;15.18 years, with a range of 20 to 99 years. Among patients who survived for 30 days, the mean BE level was +\u0026thinsp;2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47 mEq/L. In contrast, the mean BE level was +\u0026thinsp;0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;4.97 mEq/L among those who did not survive beyond 30 days. There was a statistically significant relationship between BE categories and 30-day survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, BE level was significantly associated with both discharge and death outcomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ROC analysis for BE and 30-day mortality yielded an area under the curve (AUC) of 0.688 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For the relationship between lactate level and 30-day mortality, the ROC analysis showed an AUC of 0.654.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study demonstrated that admission BE levels could be a significant predictor of 30-day mortality in patients diagnosed with non-traumatic hemorrhage, compared to lactate levels.\u003c/p\u003e","manuscriptTitle":"The Prognosis Prediction in Non-Traumatic Hemorrhagic Stroke with Base Excess Levels in the Emergency Department","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 08:42:57","doi":"10.21203/rs.3.rs-8410165/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":"afce7839-3fc6-4003-9a43-3c626d53e7ea","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T04:40:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 08:42:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8410165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8410165","identity":"rs-8410165","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}