Evaluation of Emergency Department Length of Stay and 30-Day Mortality in Critically Ill Patients

Evaluation of Emergency Department Length of Stay and 30-Day Mortality in Critically Ill Patients | 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 Evaluation of Emergency Department Length of Stay and 30-Day Mortality in Critically Ill Patients Rezan KARAALİ, Onur SALI, İbrahim KORKMAZ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7917990/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 Purpose Emergency department length of stay (EDLOS) is considered a potential quality indicator in the management of critically ill patients. However, the relationship between EDLOS and mortality remains controversial, particularly in patients requiring intensive care unit (ICU) admission. This study aimed to evaluate whether prolonged EDLOS is associated with increased 30-day mortality among patients with ICU indications and to identify other independent predictors of mortality and prolonged EDLOS. Methods In this retrospective observational study, 2,916 patients with ICU admission indications were included. Patients were divided into two groups based on EDLOS < 8 hours and ≥ 8 hours. Multivariate logistic regression was used to assess independent predictors of mortality and prolonged EDLOS. Results The overall 30-day mortality rate was 39.8%. Although univariate analysis showed higher mortality in patients with EDLOS ≥ 8 hours, multivariate analysis revealed that EDLOS was not an independent predictor of mortality. Age, Charlson Comorbidity Index (CCI), and sepsis, pneumonia, renal-metabolic disorders were significantly associated with increased mortality. In addition, prolonged EDLOS was independently associated with nighttime ED presentation, advanced age, and higher CCI scores. Conclusions Prolonged EDLOS was not independently associated with mortality among patients requiring ICU admission. Mortality appeared to be more strongly related to patient-specific clinical factors. Early identification and prioritization of high-risk patients, particularly those with advanced age and high comorbidity burden, are essential to optimize emergency care outcomes. Critical Care Emergency Department Length of Stay Mortality Intensive Care Unit Admission 1. INTRODUCTION The emergency department (ED) is a hospital unit where patients in need of urgent medical care are assessed, stabilized, and managed. Following initial interventions in the ED, patients are admitted to an appropriate inpatient unit or intensive care unit (ICU). Timely admission of critically ill patients to the ICU is particularly important to initiate appropriate treatment and ensure close monitoring of the patient [ 1 , 2 ]. In recent years, global population growth and increasing population density in urban centers have led to overcrowding in hospitals (3, 4). Patients requiring inpatient care often face difficulties in being admitted due to a lack of available beds in hospital wards and intensive care units. During the interval before transfer to the ICU, these patients are monitored and treated in the emergency department. ED length of stay (EDLOS), defined as the time interval from when a patient arrives at the ED until the patient leaves the ED [ 3 ]. The length of hospital stay prior to admission to the ICU is an independent predictor of ICU outcomes [ 1 , 4 – 6 ]. Even though guidelines for ICU admission and targets (such as < 4-h wait or < 6-h wait for ICU transfer) have been established, delays to ICU admission can occur [ 7 , 8 ]. Patients who cannot be transferred to an intensive care unit or inpatient ward bed contribute to overcrowding of the emergency department. In addition, the existing crowding in the emergency department, the constant arrival of new patients, and the inherently dynamic and cyclical nature of ED operations may lead to interruptions in the monitoring of critically ill patients. In overcrowded emergency departments (ED), doctors and nurses may not be able to provide timely care to critically ill patients [ 3 , 4 ]. Therefore, there is a potential advantage in transferring critically ill patients immediately after stabilization from the ED to the ICU, which is a highly specialized and skilled setting for critical care. Prolonged EDLOS is associated with inadequate ED organization, delayed care, and poor adherence to clinical guidelines [ 7 – 9 ]. EDLOS has also been used as a proxy for ED overcrowding and boarding, which are potential threats to patient safety [ 1 , 3 ]. In critically ill patients, prolonged EDLOS is associated with adverse outcomes, including an increased risk of mortality [ 3 , 10 ]. This study aimed to evaluate patients in the ED who were indicated for ICU admission. We planned to investigate the EDLOS, and its impact on mortality. As a secondary endpoint, we aimed to identify the factors influencing EDLOS and mortality. 2. MATERIALS & METHODS The study was planned retrospectively. Our hospital’s emergency department (ED) is a tertiary care facility, with an average daily admission of 1500 to 1800 patients. The ED includes a 10-bed red area for the monitoring and treatment of critically ill patients, and a 42-bed yellow area for the evaluation and treatment of stable patients. Our intensive care units consist of a 38-bed tertiary anesthesia ICU, a 38-bed tertiary general ICU, a 35-bed secondary internal medicine-surgical ICU, and a 38-bed secondary ICU for neurology, cardiology, and pulmonology. Patients admitted to our hospital’s emergency department and who were indicated for ICU admission between 01.01.2023–30.06.2024 were included in the study. Only patients aged 18 and above, with sufficient data available in their medical records, were included in the study. Trauma patients, pregnant patients, those brought to the emergency department with cardiopulmonary arrest, and patients with insufficient data in their medical records were excluded from the study. Patients transferred from other hospitals were also excluded. Since our hospital does not have an angiography unit, patients with ST-elevation myocardial infarction, non-ST-elevation myocardial infarction requiring emergency angiography, and those requiring angiography were excluded from the study. Patients requiring urgent surgical intervention were excluded. Patients who were transferred to another hospital due to lack of capacity in our facility were also excluded. The patients' age and gender were recorded. The diagnoses made in the emergency department were grouped as follows: decompensated heart failure (DHF), metabolic disorders-fluid-electrolyte imbalance-renal failure, gastrointestinal bleeding (GI bleeding), chronic obstructive pulmonary disease (COPD), pneumonia, cerebrovascular diseases (CVD), sepsis, drug-substance intoxications, and other conditions (e.g., pancreatitis, anemia, oncological diseases, etc.). The time of ED admission for the patients was categorized into four-time intervals (00:00–05:59, 06:00–11:59, 12:00–17:59, 18:00–23:59). The EDLOS was calculated as the time between the patient's admission to the ED and the transfer to the ICU. Based on the information obtained from patient records, comorbidities were identified, and the Charlson Comorbidity Index (CCI) was calculated [ 11 ]. Patients were divided into two groups based on all causes of 30-day mortality status. The data obtained were compared between the deceased and surviving patients. According to the Emergency Department Regulation for Inpatient Treatment Institutions published by the Ministry of Health of the Republic of Turkey in 2022, patients with an indication for hospitalization in the ED should not be kept in the ED for more than 8 hours, and it is stated that patients should not be followed-up in the ED for more than 8 hours. Based on this regulation, patients who were monitored in the ED for 8 hours or longer were considered as patients who had been waiting for an extended period [ 12 ]. The parameters examined in the study were compared between patients who had been waiting for a long time and those who had been waiting for a short time. 2.1. Statistical Analysis Statistical analyses were performed using R (version 2024.12.0 + 467, R Foundation for Statistical Computing, Vienna, Austria). The normality of continuous variables was assessed using the Shapiro-Wilk test. Variables that followed a normal distribution were presented as mean ± standard deviation (mean ± SD), while variables that did not follow a normal distribution were presented as median \[Q1;Q3]. Categorical variables were expressed as counts (percentages, %). For comparisons between two independent groups, independent two-sample t-tests were used for continuous variables with normal distribution, and the Mann-Whitney U test was used for those without normal distribution. Chi-square tests were applied for categorical variables, and Fisher’s Exact Test was used when the number of observations was insufficient. For multiple group comparisons, one-way ANOVA was used for normally distributed variables, and the Kruskal-Wallis test was used for non-normally distributed variables. Post-hoc analyses and Bonferroni correction were applied for significant differences. Logistic regression analysis was conducted to identify factors affecting mortality and prolonged length of stay. The explanatory power of the model was assessed using McFadden, Cox & Snell, and Nagelkerke R². The overall significance of the model was tested using the Likelihood Ratio Test. A two-tailed p value of < 0.05 was considered statistically significant for all statistical analyses. 3. RESULTS A total of 2916 patients were included in the study. Of these patients, 53.1% were male (n = 1547). The median age of the patients was 74.0 years (IQR: 64.0–82.0). CCI had a median value of 6.00 (IQR: 4.00–7.00). When examining the time intervals of patients' arrivals at the emergency department, the highest number of admissions occurred between 12:00–17:59 (33.1%, n = 966). Regarding the diagnoses upon admission, the most common diagnoses were DHF (22.0%, n = 642), pneumonia (20.2%, n = 589), and metabolic disorders-fluid-electrolyte imbalance-renal failure (19.5%, n = 568). The median hospital length of stay was 6.00 days (IQR: 3.00–15.0), and the median EDLOS was 286 minutes (IQR: 172–507 minutes). Of the patients included in the study, 27.0% (n = 788) had an EDLOS ≥ 8 hours. Mortality occurred in 39.8% of the patients (Table 1 ). Table 1 Distribution of patients’ demographic characteristics, emergency department admission time intervals, diagnoses, emergency department length of stay (EDLOS), and mortality rates. Parameters n = 2916 Gender Male 1547 (53.1%) Female 1369 (46.9%) Length of in ICU/day 6.00 [3.00;15.0] ED admission time/h 00–06 419 (14.4%) 06–12 754 (25.9%) 12–18 966 (33.1%) 18–00 777 (26.6%) Age 74.0 [64.0;82.0] Diagnosis Metabolic-electrolyte disturbances, kidney failure 568 (19.5%) Other 109 (3.74%) GI bleeding 70 (2.40%) Drug-substance intake 79 (2.71%) DCHF 642 (22.0%) COPD 291 (9.98%) Pneumonia 589 (20.2%) CVD 467 (16.0%) Sepsis 101 (3.46%) CCI 6.00 [4.00;7.00] Mortality Alive 1756 (60.2%) Decased 1160 (39.8%) EDLOS/min 286 [172;507] Short stay (< 8 h) 2128 (73.0%) Long stay (≥ 8 h) 788 (27.0%) ICU:Intensive care unit, ED:emergency department, GI:Gastrointestinal, DCHF:decompansated congestive heart failure, COPD: chronic obstructive pulmonary disease, CVD:cerebro vascular disease, CCI:charlson comorbidity index, EDLOS:emergency department length of stay. Deceased patients were older compared to survivors, with a median age of 77 years (IQR: 69.0–84.0), and this difference was statistically significant (p < 0.001). The CCI was higher in patients who died (7.00 [5.00–8.00] vs. 5.00 [3.00–6.00], p < 0.001). A statistically significant difference was found between deceased and surviving patients regarding their disease diagnoses. Post-hoc analyses revealed significant differences between different diagnostic groups: acute kidney failure versus GI bleeding (p.adj = 0.001), acute kidney failure versus drug-substance intake (p.adj < 0.001), drug-substance intake versus other diagnoses (p.adj < 0.001), acute kidney failure versus DHF (p.adj < 0.001), and drug-substance intake versus DHF (p.adj = 0.023). The mortality rate was 38.0% in patients with a short length of stay, while it was 44.7% in those with a prolonged stay, and the difference was statistically significant (p = 0.001). The length of stay in the ICU was significantly longer in patients who died (8.00 [3.00–19.0] vs. 6.00 [3.00–12.0], p < 0.001) (Table 2 ). According to the results of multivariable logistic regression analysis, predictors of mortality were patient age (OR: 1.02, 95% CI: 1.01–1.92, p < 0.001) and the CCI (OR: 2.79, 95% CI: 2.52–3.10, p < 0.001). When GI bleeding was used as the reference diagnosis, patients with acute kidney failure and metabolic disorders had a significantly higher risk of mortality compared to the reference group (OR: 2.23, 95% CI: 1.18–4.23, p = 0.01). Patients classified under the "other" diagnosis group also had a higher mortality risk (OR: 2.73, 95% CI: 1.29–5.78, p = 0.01). Pneumonia (OR: 2.45, 95% CI: 1.30–4.64, p = 0.01) and sepsis (OR: 2.47, 95% CI: 1.16–5.24, p = 0.02) were also associated with significantly higher mortality risk compared to the reference group. The length of stay in the intensive care unit (ICU) (OR: 1.01, 95% CI: 1.00–1.01, p = 0.00) was identified as another predictor of mortality. However, emergency department length of stay (EDLOS) was not an independent predictor of mortality (Table 3 ). Table 2 Comparison of the parameters between deceased and surviving patients. Alive Decased p.overall Parameters n = 1756 n = 1160 Gender 0.539 Male 923 (59.7%) 624 (40.3%) Female 833 (60.8%) 536 (39.2%) Length of in ICU/day 6.00 [3.00;12.0] 8.00 [3.00;19.0] <0.001 ED admission time/h 0.070 00–06 268 (64.0%) 151 (36.0%) 06–12 470 (62.3%) 284 (37.7%) 12–18 555 (57.5%) 411 (42.5%) 18–00 463 (59.6%) 314 (40.4%) Age 71.0 [60.0;79.0] 77.0 [69.0;84.0] <0.001 Diagnosis <0.001 Metabolic-electrolyte disturbances, renal failure 271 (47.7%) 297 (52.3%) Other 65 (59.6%) 44 (40.4%) GI bleeding 52 (74.3%) 18 (25.7%) Drug-substance intake 71 (89.9%) 8 (10.1%) COPD 457 (71.2%) 185 (28.8%) DCHF 205 (70.4%) 86 (29.6%) Pneumonia 283 (48.0%) 306 (52.0%) CVD 306 (65.5%) 161 (34.5%) Sepsis 46 (45.5%) 55 (54.5%) CCI 5.00 [3.00;6.00] 7.00 [5.00;8.00] <0.001 EDLOS/min 285 [176;478] 288 [163;574] 0.516 Short stay (< 8 h) 1320 (62.0%) 808 (38.0%) 0.001 Long stay (≥ 8 h) 436 (55.3%) 352 (44.7%) ICU:Intensive care unit, ED:emergency department, GI:Gastrointestinal, DCHF:decompansated congestive heart failure, COPD: chronic obstructive pulmonary disease, CVD:cerebro vascular disease, CCI:charlson comorbidity index, EDLOS:emergency department length of stay. Table 3 Multivariate logistic regression analysis for factors predicting 30 days mortality. Diagnosis OR %95 Cl z p Metabolic-electrolyte disturbances, renal failure Other GI bleeding Drug-substance intake DCHF COPD Pneumonia SVD Sepsis 2.23 2.73 ref 1.00 1.03 1.27 2.45 1.44 2.47 1.18–4.23 1.29–5.78 ref 0,36 − 2,79 0,54 − 1,95 0,65–2,49 1,30 − 4,64 0,75 − 2,76 1,16 − 5,24 2.47 2.61 ref -0.01 0.09 0.70 2,76 1.11 2,35 0.01 0,01 ref 0,99 0,93 0,48 0,01 0,27 0,02 Length of in ICU/day 1,01 1–1,01 3,02 0,00 CCI 2,79 2,52 − 3,1 19,45 0,00 Age 1,02 1,01–1,92 13,26 0,00 EDLOS/min 1 1–1 0,78 0,43 Nagelkerke R 2 = 0.330742, Likelihood.ratio.test p.value: 2.7965e-167 ICU:Intensive care unit, ED:emergency department, GI:Gastrointestinal, DCHF:decompansated congestive heart failure, COPD: chronic obstructive pulmonary disease, CVD:cerebro vascular disease, CCI:charlson comorbidity index, EDLOS:emergency department length of stay. In the comparison between patients with EDLOS ≥ 8 hours and those with < 8 hours, the time intervals of patients' ED admission showed a significant difference (p < 0.001). Post-hoc analysis revealed that the 00:00–06:00 time interval was significantly different from all other time intervals (Bonferroni corrected p.adj < 0.001). Patients with a longer ED stay were significantly older (75.0 [65.0;83.0], p < 0.001). Although a significant relationship was found between patients' diagnoses upon admission and their ED stay duration, no significant differences were found between any diagnostic groups after Bonferroni correction (p.adj > 0.05) (Table 4 ). A multivariable regression analysis was performed to determine the factors affecting patients' EDLOS. According to this analysis, the time interval of ED admission was strongly associated with prolonged stay (p < 0.001). Compared to the reference category (patients admitted between 00:00–05:59), patients admitted between 06:00–11:59 had a 75% lower likelihood of a prolonged EDLOS (OR = 0.25, p < 0.001), patients admitted between 12:00–17:59 had a 79% lower likelihood (OR = 0.21, p < 0.001), and patients admitted between 18:00–23:59 had a 51% lower likelihood (OR = 0.49, p 0.05) (Table 5 ). Table 4 Comparison of the parameters evaluated in the study according to the EDLOS < 8 hours vs. EDLOS ≥ 8 hours, univariable analysis results. EDLOS < 8 h EDLOS ≥ 8 h p.overall n = 2128 n = 788 Gender 0.767 Male 1133 (73.2%) 414 (26.8%) Female 995 (72.7%) 374 (27.3%) ED admission time/h <0.001 00–06 215 (51.3%) 204 (48.7%) 06–12 598 (79.3%) 156 (20.7%) 12–18 795 (82.3%) 171 (17.7%) 18–00 520 (66.9%) 257 (33.1%) Age 73.0 [63.0;82.0] 75.0 [65.0;83.0] <0.001 Diagnosis <0.001 Metabolic-electrolyte disturbances, renal failure 422 (74.3%) 146 (25.7%) Other 83 (76.1%) 26 (23.9%) GI bleeding 53 (75.7%) 17 (24.3%) Drug-substance intake 67 (84.8%) 12 (15.2%) DCHF 454 (70.7%) 188 (29.3%) COPD 205 (70.4%) 86 (29.6%) Pneumonia 402 (68.3%) 187 (31.7%) CVD 374 (80.1%) 93 (19.9%) Sepsis 68 (67.3%) 33 (32.7%) CCI 6.00 [4.00;7.00] 6.00 [4.00;7.00] <0.001 ICU:Intensive care unit, ED:emergency department, GI:Gastrointestinal, DCHF:decompansated congestive heart failure, COPD: chronic obstructive pulmonary disease, CVD:cerebro vascular disease, CCI:charlson comorbidity index, EDLOS:emergency department length of stay. Table 5 Multivariate logistic regression analyses for prolonged EDLOS OR %95 Cl z p ED admission time 00–06 ref ref 06–12 0,25 0,19 − 0,33 -10,17 0 12–18 0,21 0,16 − 0,27 -11,89 0 18–00 0,49 0,38 − 0,63 -5,66 0 CCI 1,06 0,98 − 1,16 1,46 0,15 Age 1,01 1–1,02 1,18 0,24 Nagelkerke R 2 = 0.0937980, Likelihood.ratio.test p.value: 5.1079e-41 CCI:charlson comorbidity index. 4. DISCUSSION In our study, the overall mortality rate was 39.8%. We observed that patients who remained in the emergency department (ED) for more than 8 hours had a higher rate of mortality, and this difference was statistically significant. However, multivariable regression analysis revealed that EDLOS was not an independent predictor of mortality. Consistent with our findings, Anttila et al., [ 6 ] also reported that prolonged EDLOS had no effect on mortality. Nevertheless, the mortality rates in their study were lower than those observed in our cohort. We believe this discrepancy may be attributed to the methodological differences between the studies, as Anttila et al., [ 6 ] evaluated EDLOS as 180-minutes and mortality within 90-days. Additionally, their study included patients referred from other hospitals. In contrast, our study excluded transferred patients from other institutions. Patients referred from other hospitals are often clinically stabilized prior to transfer and admitted directly to the ICU once a bed is secured. Since these patients do not experience the typical ED waiting process, we excluded them from our study to provide a more accurate evaluation of EDLOS and its association with clinical outcomes. These factors likely contributed to the lower mortality rate reported by Anttila et al., [ 6 ]. Several studies have indicated that an EDLOS of up to six hours is not associated with increased mortality [13, 14. In fact, the DUTCH study reported a negative correlation between an ED stay longer than 3.7 hours and hospital mortality [ 15 ]. In their study investigating the association between EDLOS and mortality, Asheim et al., [ 2 ] reported a mean EDLOS of 2.9 hours and a 30-day mortality rate of 3.4%. However, that study also included patients who were discharged from the emergency department in the analysis. While the absence of an association between EDLOS and mortality is consistent with our results, the lower mortality rate reported in that study is likely due to the inclusion of discharged patients, who generally have a lower risk of death. In contrast to our results, some studies have reported a significant association between prolonged EDLOS and increased mortality [ 3 , 9 , 10 ]. We believe that these discrepancies may be explained by differences in sample sizes and methodological approaches of these studies. Jones et al., [ 10 ] analyzed data from over five million patients, categorizing ED waiting times into two-hour intervals and comparing mortality rates among these groups. They reported that mortality increased by 10% in patients with EDLOS of 8–12 hours and emphasized that patients should be transferred from the emergency department within 6 hours [ 10 ]. Similarly, Lee et al., [ 10 ] evaluated EDLOS as six-hours and assessed mortality based on in-hospital death. In contrast, our study considered an EDLOS of eight hours and used 30-day mortality. Aletreby et al., [ 9 ] conducted their study in a hospital where a “four-hour rule” is enforced, requiring ICU-eligible patients to be transferred to the ICU within four hours. We believe that their identification of EDLOS as an independent predictor of mortality may be influenced by the institutional policies and clinical practices specific to that setting. Although national regulations in our country set the maximum ED waiting time at 8 hours [ 12 ], our study found a shorter duration. We believe that EDLOS was not identified as a significant factor influencing mortality in our cohort because patients in our emergency department were generally transferred earlier than expected. Another factor associated with mortality in our study was the diagnosis established at the emergency department admission. Using GI bleeding as the reference category, we found that patients diagnosed with pneumonia, sepsis, renal-metabolic disorders, and categorized in the “other” diagnosis group had significantly higher mortality rates. Similar findings have been reported in previous studies a significant association between sepsis and mortality. [ 16 – 19 ]. Sepsis is a complex condition involving multiple organ systems and requiring a multidisciplinary approach. Early and appropriate management has been shown to improve outcomes. Clinical guidelines, including the Surviving Sepsis Campaign, emphasize that early goal-directed therapy can reduce mortality in septic patients [ 20 ]. While certain diagnoses were associated with increased mortality, the absence of a significant association between EDLOS and mortality indicates that emergency department care was delivered in an effective and appropriate manner. Supporting this, we also found no significant association between patients’ diagnoses and EDLOS, a result consistent with the findings of Asheim et al., [ 2 ]. However, when stratified by diagnosis, patients with sepsis, pneumonia, CHF, and COPD tended to have longer ED stays. This observation aligns with prior studies reporting that sepsis patients often stay in the ED for extended periods [ 10 , 22 ]. Teklie et al., [ 1 ] also noted that critically ill patients, particularly those requiring ventilator support or presenting with septic shock, had prolonged EDLOS. Similarly, Carter et al., [ 22 ] reported longer EDLOS in patients diagnosed with sepsis and acute kidney injury. Taken together, these findings suggest that patients with complex medical conditions requiring extensive diagnostic evaluation and multiple consultations are more likely to experience longer ED stays. However, prolonged EDLOS in such patients does not appear to be an independent predictor of mortality. Instead, mortality is more closely associated to the severity and nature of the presenting condition itself. Our finding that ICU length of stay was significantly associated with mortality further supports this interpretation. CCI emerged as a strong and independent predictor of mortality. The multivariable regression model revealed that each one-point increase in CCI was associated with an approximately 2.8-fold increase in the risk of death. CCI is a widely used and validated tool for estimating mortality risk based on patients’ burden of chronic comorbid conditions. Additionally, the median CCI score among deceased patients in our study was 7, indicating that most of patients included in our cohort had a high level of comorbidity. This finding aligns with the original study by Charlson et al., [ 11 ] which reported a 1-year mortality rate of 85% among patients with CCI scores of 5 or higher. In this context, our patient population can be considered at high risk for mortality, and our results further reinforce the prognostic value of CCI in emergency settings. The observed association between advanced age and mortality in our study also supports this conclusion. As age increases, the prevalence of comorbid conditions tends to rise, and such patients typically require complex, multidisciplinary care [ 3 , 10 , 18 ]. Elderly patients are at increased risk for hospital readmission and adverse outcomes following discharge [ 23 ]. Moreover, they often present with complex care needs that may not be adequately addressed in the ED setting [ 19 ]. We believe that these age-related vulnerabilities contribute to the higher mortality observed among older adults. In addition, elderly patients often require more extensive diagnostic workups, multiple specialty consultations, and longer stabilization periods all of which can prolong their ED stay. In our study, the positive association observed between older age and prolonged EDLOS supports this interpretation. Furthermore, patients of advanced age and/or those with multiple comorbidities may experience longer ED stays due to the clinical complexity of their conditions and, in some cases, difficulties in communicating their symptoms effectively. Our regression analysis confirmed that both higher age and elevated CCI scores were independently associated with prolonged EDLOS. These findings are consistent with prior studies reporting that older patients tend to stay longer in the ED compared to younger individuals [ 4 , 6 , 10 ]. Taken together, these findings highlight the importance of early identification and prioritization of patients who are either elderly or have multiple comorbidities in the emergency department. Prompt triage and timely allocation of appropriate clinical resources for these high-risk individuals are essential not only to optimize patient care but also to improve overall ED efficiency. From this perspective, our findings also suggest that the clinical management of patients in our emergency department aligns with recommended standards. Among the 2,916 patients included in our study, 73% were transferred to the intensive care unit within 8 hours. Furthermore, the average EDLOS for patients is shorter than the maximum duration specified in national regulations (286 min). Previous studies have reported varying lengths of EDLOS, likely reflecting differences in institutional policies, patient populations, and study designs. Asheim et al., [ 2 ] reported a mean EDLOS of 2.9 hours; however, their analysis included patients who were discharged directly from the ED, which may have contributed to the lower EDLOS, and overall mortality rate observed in their study. Similarly, Chrilly et al., [ 5 ] found a median EDLOS of 386.5 minutes for patients admitted to the ICU, and an even longer median stay of 505 minutes for those transferred to general hospital wards. Teklie et al., [ 1 ] reported an average EDLOS of 13.5 hours among critically ill patients. Their study differs from ours in several key aspects: it included a smaller sample size (n = 102), as well as patients with cardiopulmonary arrest, trauma-related conditions, and inter-hospital transfers. In contrast, our study excluded patients transferred from external facilities to ensure a more homogeneous cohort and to better assess EDLOS in patients admitted directly through our emergency department. These methodological differences likely account for the variation in EDLOS and associated outcomes observed across studies. When we compared patients with EDLOS ≥ 8 hours to those with EDLOS < 8 hours, we found statistically significant differences in time of ED presentation, age, CCI, and diagnostic categories. Multivariate logistic regression analysis identified time of presentation as an independent predictor of prolonged EDLOS. In line with our findings, Lee et al., [ 3 ] reported that patients with an EDLOS of 6 hours or more were more likely to present during nighttime hours and were generally older with higher CCI scores. Their analysis also identified nighttime ED presentation and a CCI score > 1 as independent predictors of prolonged EDLOS. This may be explained by reduced staffing levels, limited access to diagnostic services, delays in consultations, and higher inpatient bed occupancy during nighttime hours all of which can contribute to longer ED stays for patients presenting during off-hours. These observations highlight the need for organizational improvements in emergency department workflow during nighttime hours to reduce avoidable delays in the management of critically ill patients. 4.1. Limitations The retrospective and single-center design may limit the generalizability of the findings. There is a degree of diagnostic heterogeneity. It is unclear from the available data whether this is caused by more diagnostic or therapeutic interventions in ED, or a delay in access and transfer to ICU over the study period. Although EDLOS was not associated with mortality in multivariable analysis, unmeasured factors such as ED overcrowding, staffing variability, or delays in diagnostics were not captured. We did not include post-discharge outcomes or quality of care metrics in the ICU, which may also influence 30-day mortality. 5. CONCLUSION In this study, we investigated the impact of EDLOS on 30-day mortality among critically ill patients with indications for ICU admission. Multivariable logistic regression demonstrated that EDLOS was not an independent predictor of mortality. Conversely, age and CCI were identified as strong and independent predictors of mortality. Additionally, certain presenting diagnoses, such as sepsis, pneumonia, and renal-metabolic disorders, were associated with significantly higher mortality. Furthermore, nighttime ED presentations were associated to longer ED stays, possibly due to delays in diagnostic processes, limited consultant availability, and high inpatient bed occupancy during late hours. Taken together, our findings underscore the importance of early identification and prioritization of patients with advanced age, high comorbidity burden and diagnosis in the emergency department. However, EDLOS did not emerge as an independent predictor of mortality. This finding suggests that contemporary emergency departments have evolved into high-acuity care units and that emergency physicians are increasingly equipped to stabilize and manage critically ill patients awaiting intensive care beds. Future prospective multicenter studies are warranted to validate these findings and to explore strategies for improving the care of critically ill patients in the emergency department. Declarations Author Contributions: conceptualization Karaali R, Salı O, data curation Salı O, Korkmaz İ, formal analysis: Karaali R, Korkmaz İ, ınvestigation: Karaali R, Salı O, methodology: Karaali R, Salı O, resources: Karaali R, Salı O, software: Korkmaz I, Salı O, supervision: Karaali R, writing – original draft: Karaali R, Korkmaz İ, writing – review and editing: Karaali R. Conflict of interest This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No conflict of interest. Ethical approval: The study began after receiving approval from the Non-Interventional Research Ethics Committee of Buca Seyfi Demirsoy Training and Research Hospital on 24.04.2024, with reference number 2024/266. Consent to Participate declaration: Due to the retrospective nature of the study, consent to participate was not required. There was no Funding Clinical trial number: not applicable. This study was conducted in accordance with the ethical principles of human rights stated in the World Medical Association Declaration of Helsinki. Human Ethics and Consent to Participate declarations: not applicable References Teklie H, Engida H, Melaku B, Workina A (2021) Factors contributing to delay intensive care unit admission of critically ill patients from the adult emergency Department in Tikur Anbessa Specialized Hospital. BMC Emerg Med 21:123. https://doi.org/10.1186/s12873-021-00518-z. Asheim A, Nilsen SM, Carlsen F, et al (2019) The effect of emergency department delays on 30-day mortality in Central Norway. Eur J Emerg Med 26:446-452. https://doi.org/10.1097/MEJ.0000000000000609. Lee KS, Min HS, Moon JY, et al (2022) Patient and hospital characteristics predict prolonged emergency department length of stay and in-hospital mortality: a nationwide analysis in Korea. BMC Emerg Med 22:183. https://doi.org/10.1186/s12873-022-00745-y. Huang Q, Thind A, Dreyer JF, Zaric GS (2010) The impact of delays to admission from the emergency department on inpatient outcomes. BMC Emerg Med 10:16. https://doi.org/10.1186/1471-227X-10-16. Crilly J, Sweeny A, O'Dwyer J, Richards B, Green D, Marshall AP (2021) Identifying 'at-risk' critically ill patients who present to the emergency department and require intensive care unit admission: A retrospective observational cohort study. Aust Crit Care 34:195-203. https://doi.org/10.1016/j.aucc.2020.07.007. Aitavaara-Anttila M, Liisanantti JH, Raatiniemi L, Ohtonen P, Ala-Kokko T (2019) Factors related to delayed intensive care unit admission from emergency department-A retrospective cohort study. Acta Anaesthesiol Scand 63:939-946. https://doi.org/10.1111/aas.13355. National Health Service Department of Health. Reforming emergency care:first steps to a new approach. 2001. Avaliable at, http://webarchive. nationalarchives.gov.uk/þ/http://www.dh.gov.uk/en/ Publications and statistics/ Publications/ Publications Policy And Guidance/DH_ 4008702 [Accessed 29 March 2019]. Nates JL, Nunnally M, Kleinpell R, Blosser S, Goldner J, Birriel B, et al (2016) ICU admission, discharge, and triage guidelines: a framework to enhance clinical operations, development of institutional policies, and further research. Crit Care Med 44:1553e602. https://doi.org/ 10.1097/CCM.0000000000001856. Aletreby WT, Brindley PG, Balshi AN, et al (2021) Delayed intensive care unit admission from the emergency department: impact on patient outcomes. A retrospective study. Retardo na transferência do pronto-socorro para a unidade de terapia intensiva: impacto nos desfechos do paciente. Um estudo retrospectivo. Rev Bras Ter Intensiva 33:125-137. https://doi.org/10.5935/0103-507X.20210014. Jones S, Moulton C, Swift S, et al (2022) Association between delays to patient admission from the emergency department and all-cause 30-day mortality. Emerg Med J. 39:168-173. https://doi.org/10.1136/emermed-2021-211572. Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373-383. https://doi.org/10.1016/0021-9681(87)90171-8. Yatakli Saglik Tesislerinde Acil Servis Hizmetlerinin Uygulama Usul Ve Esaslari Hakkinda Teblig. 13 Eylül 2022. Sayi : 31952. https://www.resmigazete.gov.tr/eskiler/2022/09/20220913-5.htm Al-Qahtani S, Alsultan A, Haddad S, Alsaawi A, Alshehri M, Alsolamy S, et al (2017) The association of duration of boarding in the emergency room and themoutcome of patients admitted to the intensive care unit. BMC Emerg Med 17:34. https://doi.org/10.1186/s12873-017-0143-4. Agustin M, Price LL, Andoh-Duku A, LaCamera P (2017) Impact of delayed admission to the intensive care unit from the emergency department upon sepsis outcomes and sepsis protocol compliance. Crit Care Res Pract 9616545. https://doi.org/10.1155/2017/9616545 Groenland CN, Termorshuizen F, Rietdijk WJ, van den Brule J, Dongelmans DA, de Jonge E, et al (2019) Emergency department to ICU time is associated with hospital mortality: a registry analysis of 14,788 patients from six university hospitals in The Netherlands. Crit Care Med. 47:1564-71. https://doi.org/10.1097/CCM.0000000000003957. Lin S, Ge S, He W, Zeng M (2021) Association of delayed time in the emergency department with the clinical outcomes for critically ill patients. QJM 114:311-317. https://doi.org/10.1093/qjmed/hcaa192. Santos FRQ, Machado MN, Lobo SMA (2020) Adverse outcomes of delayed intensive care unit. Resultados adversos de admissões tardias à unidade de terapia intensiva a partir do pronto-socorro. Rev Bras Ter Intensiva 32:92-98. https://doi.org/10.5935/0103-507x.20200014. Chong CP, Haywood C, Barker A, Lim WK (2013) Is Emergency Department length of stay associated with inpatient mortality? Australas J Ageing 32:122-124. https://doi.org/10.1111/j.1741-6612.2012.00651.x. Cardoso LT, Grion CM, Matsuo T, et al (2011) Impact of delayed admission to intensive care units on mortality of critically ill patients: a cohort study. Crit Care 15:R28. https://doi.org/10.1186/cc9975. Evans L, Rhodes A, Alhazzani W, et al (2021) Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med 47:1181-1247. https://doi.org/10.1007/s00134-021-06506-y García-Gigorro R, de la Cruz Vigo F, Andrés-Esteban EM, et al (2017) Impact on patient outcome of emergency department length of stay prior to ICU admission. Med Intensiva 41:201-208. https://doi.org/10.1016/j.medin.2016.05.008. Carter AW, Pilcher D, Bailey M, Cameron P, Duke GJ, Cooper J (2010) Is ED length of stay before ICU admission related to patient mortality?. Emerg Med Australas. 22:145-150. https://doi.org/10.1111/j.1742-6723.2010.01272.x. George G, Jell C, Todd BS (2006) Effect of population ageing on emergency department speed and efficiency: a historical perspective from a district general hospital in the UK. Emerg Med J 23:379-383. https://doi.org/10.1136/emj.2005.029793. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7917990","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541939505,"identity":"67b1006b-587a-4683-9342-3072a124e5f1","order_by":0,"name":"Rezan KARAALİ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYPCCBBDBeCChQoKxAciSIFYLw4GEM3AtBkRqYWxjIKzFvL394ecChjR58/bDDw48nGchu+EA88HbPAx/8nFpkTlzIFl6BkOO4ZwzaQYHErdJGG84wJZszcNgYNmAQ4uERMIBaR6GCsYZDAlgLYkbDvCYAUUMcLpMQv5h82+gFvsZ/M8/HEicA9LC/w2/FglmNqCCnMQZEjlAWxrAtrDh18KTxmbNY5CWPEPiTcGBhGMSxjMPsxlbzjEwxq2F/fjj2zwVybYz+NM3PvxRUyfbd7z54Y03FXIEIgZFmhlDZBSMglEwCkYBqQAAvh1QwlKOX7oAAAAASUVORK5CYII=","orcid":"","institution":"Izmir Democracy University","correspondingAuthor":true,"prefix":"","firstName":"Rezan","middleName":"","lastName":"KARAALİ","suffix":""},{"id":541939506,"identity":"6a0b896b-6e62-411a-839c-6c5ba53024d0","order_by":1,"name":"Onur SALI","email":"","orcid":"","institution":"Dr. Suat Seren Göğüs Hastalıkları Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Onur","middleName":"","lastName":"SALI","suffix":""},{"id":541939507,"identity":"ad0764c6-952f-4425-8d08-611cf2e10bcc","order_by":2,"name":"İbrahim KORKMAZ","email":"","orcid":"","institution":"İzmir City Hospital","correspondingAuthor":false,"prefix":"","firstName":"İbrahim","middleName":"","lastName":"KORKMAZ","suffix":""}],"badges":[],"createdAt":"2025-10-22 08:13:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7917990/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7917990/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95508974,"identity":"bf029213-251f-46dc-8861-9bfde3bcd095","added_by":"auto","created_at":"2025-11-10 07:02:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":60084,"visible":true,"origin":"","legend":"","description":"","filename":"EDLOSmaintext.docx","url":"https://assets-eu.researchsquare.com/files/rs-7917990/v1/09a03aebb7b3750885e172a0.docx"},{"id":95508973,"identity":"e94be3dc-0de4-485e-984a-bbbdb2f97cb5","added_by":"auto","created_at":"2025-11-10 07:02:43","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5247,"visible":true,"origin":"","legend":"","description":"","filename":"8fd92f87ce274867a475551cecf11809.json","url":"https://assets-eu.researchsquare.com/files/rs-7917990/v1/19eaca86754048a53b51a77d.json"},{"id":95528085,"identity":"e0855d5a-8630-479d-a6f6-64f24df06fe7","added_by":"auto","created_at":"2025-11-10 10:15:33","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106047,"visible":true,"origin":"","legend":"","description":"","filename":"8fd92f87ce274867a475551cecf118091enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7917990/v1/08b94254618cb255d6854a24.xml"},{"id":95508975,"identity":"86d5a295-5356-4663-935e-12b965a93b2c","added_by":"auto","created_at":"2025-11-10 07:02:43","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104107,"visible":true,"origin":"","legend":"","description":"","filename":"8fd92f87ce274867a475551cecf118091structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7917990/v1/8d899930c37b4eb2146ef480.xml"},{"id":95508976,"identity":"27785559-b504-40ba-acf8-1f4ed4180191","added_by":"auto","created_at":"2025-11-10 07:02:43","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110775,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7917990/v1/22fa6e9fca92214e191c6d50.html"},{"id":98797601,"identity":"900a7ee6-6537-46ca-816b-6fe01d2b6917","added_by":"auto","created_at":"2025-12-22 13:34:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":759510,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7917990/v1/8c533c7d-c206-4473-8e79-299ea4aba68f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Emergency Department Length of Stay and 30-Day Mortality in Critically Ill Patients","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe emergency department (ED) is a hospital unit where patients in need of urgent medical care are assessed, stabilized, and managed. Following initial interventions in the ED, patients are admitted to an appropriate inpatient unit or intensive care unit (ICU). Timely admission of critically ill patients to the ICU is particularly important to initiate appropriate treatment and ensure close monitoring of the patient [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In recent years, global population growth and increasing population density in urban centers have led to overcrowding in hospitals (3, 4). Patients requiring inpatient care often face difficulties in being admitted due to a lack of available beds in hospital wards and intensive care units. During the interval before transfer to the ICU, these patients are monitored and treated in the emergency department. ED length of stay (EDLOS), defined as the time interval from when a patient arrives at the ED until the patient leaves the ED [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The length of hospital stay prior to admission to the ICU is an independent predictor of ICU outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Even though guidelines for ICU admission and targets (such as \u0026lt;\u0026thinsp;4-h wait or \u0026lt;\u0026thinsp;6-h wait for ICU transfer) have been established, delays to ICU admission can occur [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Patients who cannot be transferred to an intensive care unit or inpatient ward bed contribute to overcrowding of the emergency department. In addition, the existing crowding in the emergency department, the constant arrival of new patients, and the inherently dynamic and cyclical nature of ED operations may lead to interruptions in the monitoring of critically ill patients. In overcrowded emergency departments (ED), doctors and nurses may not be able to provide timely care to critically ill patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, there is a potential advantage in transferring critically ill patients immediately after stabilization from the ED to the ICU, which is a highly specialized and skilled setting for critical care. Prolonged EDLOS is associated with inadequate ED organization, delayed care, and poor adherence to clinical guidelines [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. EDLOS has also been used as a proxy for ED overcrowding and boarding, which are potential threats to patient safety [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In critically ill patients, prolonged EDLOS is associated with adverse outcomes, including an increased risk of mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study aimed to evaluate patients in the ED who were indicated for ICU admission. We planned to investigate the EDLOS, and its impact on mortality. As a secondary endpoint, we aimed to identify the factors influencing EDLOS and mortality.\u003c/p\u003e"},{"header":"2. MATERIALS \u0026 METHODS","content":"\u003cp\u003eThe study was planned retrospectively. Our hospital\u0026rsquo;s emergency department (ED) is a tertiary care facility, with an average daily admission of 1500 to 1800 patients. The ED includes a 10-bed red area for the monitoring and treatment of critically ill patients, and a 42-bed yellow area for the evaluation and treatment of stable patients. Our intensive care units consist of a 38-bed tertiary anesthesia ICU, a 38-bed tertiary general ICU, a 35-bed secondary internal medicine-surgical ICU, and a 38-bed secondary ICU for neurology, cardiology, and pulmonology.\u003c/p\u003e\u003cp\u003ePatients admitted to our hospital\u0026rsquo;s emergency department and who were indicated for ICU admission between 01.01.2023\u0026ndash;30.06.2024 were included in the study. Only patients aged 18 and above, with sufficient data available in their medical records, were included in the study. Trauma patients, pregnant patients, those brought to the emergency department with cardiopulmonary arrest, and patients with insufficient data in their medical records were excluded from the study. Patients transferred from other hospitals were also excluded. Since our hospital does not have an angiography unit, patients with ST-elevation myocardial infarction, non-ST-elevation myocardial infarction requiring emergency angiography, and those requiring angiography were excluded from the study. Patients requiring urgent surgical intervention were excluded. Patients who were transferred to another hospital due to lack of capacity in our facility were also excluded.\u003c/p\u003e\u003cp\u003eThe patients' age and gender were recorded. The diagnoses made in the emergency department were grouped as follows: decompensated heart failure (DHF), metabolic disorders-fluid-electrolyte imbalance-renal failure, gastrointestinal bleeding (GI bleeding), chronic obstructive pulmonary disease (COPD), pneumonia, cerebrovascular diseases (CVD), sepsis, drug-substance intoxications, and other conditions (e.g., pancreatitis, anemia, oncological diseases, etc.). The time of ED admission for the patients was categorized into four-time intervals (00:00\u0026ndash;05:59, 06:00\u0026ndash;11:59, 12:00\u0026ndash;17:59, 18:00\u0026ndash;23:59). The EDLOS was calculated as the time between the patient's admission to the ED and the transfer to the ICU. Based on the information obtained from patient records, comorbidities were identified, and the Charlson Comorbidity Index (CCI) was calculated [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Patients were divided into two groups based on all causes of 30-day mortality status. The data obtained were compared between the deceased and surviving patients.\u003c/p\u003e\u003cp\u003eAccording to the Emergency Department Regulation for Inpatient Treatment Institutions published by the Ministry of Health of the Republic of Turkey in 2022, patients with an indication for hospitalization in the ED should not be kept in the ED for more than 8 hours, and it is stated that patients should not be followed-up in the ED for more than 8 hours. Based on this regulation, patients who were monitored in the ED for 8 hours or longer were considered as patients who had been waiting for an extended period [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The parameters examined in the study were compared between patients who had been waiting for a long time and those who had been waiting for a short time.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Statistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R (version 2024.12.0\u0026thinsp;+\u0026thinsp;467, R Foundation for Statistical Computing, Vienna, Austria). The normality of continuous variables was assessed using the Shapiro-Wilk test. Variables that followed a normal distribution were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), while variables that did not follow a normal distribution were presented as median \\[Q1;Q3]. Categorical variables were expressed as counts (percentages, %). For comparisons between two independent groups, independent two-sample t-tests were used for continuous variables with normal distribution, and the Mann-Whitney U test was used for those without normal distribution. Chi-square tests were applied for categorical variables, and Fisher\u0026rsquo;s Exact Test was used when the number of observations was insufficient. For multiple group comparisons, one-way ANOVA was used for normally distributed variables, and the Kruskal-Wallis test was used for non-normally distributed variables. Post-hoc analyses and Bonferroni correction were applied for significant differences. Logistic regression analysis was conducted to identify factors affecting mortality and prolonged length of stay. The explanatory power of the model was assessed using McFadden, Cox \u0026amp; Snell, and Nagelkerke R\u0026sup2;. The overall significance of the model was tested using the Likelihood Ratio Test. A two-tailed p value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant for all statistical analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eA total of 2916 patients were included in the study. Of these patients, 53.1% were male (n\u0026thinsp;=\u0026thinsp;1547). The median age of the patients was 74.0 years (IQR: 64.0\u0026ndash;82.0). CCI had a median value of 6.00 (IQR: 4.00\u0026ndash;7.00). When examining the time intervals of patients' arrivals at the emergency department, the highest number of admissions occurred between 12:00\u0026ndash;17:59 (33.1%, n\u0026thinsp;=\u0026thinsp;966). Regarding the diagnoses upon admission, the most common diagnoses were DHF (22.0%, n\u0026thinsp;=\u0026thinsp;642), pneumonia (20.2%, n\u0026thinsp;=\u0026thinsp;589), and metabolic disorders-fluid-electrolyte imbalance-renal failure (19.5%, n\u0026thinsp;=\u0026thinsp;568). The median hospital length of stay was 6.00 days (IQR: 3.00\u0026ndash;15.0), and the median EDLOS was 286 minutes (IQR: 172\u0026ndash;507 minutes). Of the patients included in the study, 27.0% (n\u0026thinsp;=\u0026thinsp;788) had an EDLOS\u0026thinsp;\u0026ge;\u0026thinsp;8 hours. Mortality occurred in 39.8% of the patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of patients\u0026rsquo; demographic characteristics, emergency department admission time intervals, diagnoses, emergency department length of stay (EDLOS), and mortality rates.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\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\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;2916\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1547 (53.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1369 (46.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength of in ICU/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.00 [3.00;15.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED admission time/h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e00\u0026ndash;06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e419 (14.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e06\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e754 (25.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u0026ndash;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e966 (33.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e777 (26.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.0 [64.0;82.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolic-electrolyte disturbances,\u003c/p\u003e\u003cp\u003ekidney failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e568 (19.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109 (3.74%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGI bleeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (2.40%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug-substance intake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (2.71%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDCHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e642 (22.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e291 (9.98%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePneumonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e589 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e467 (16.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101 (3.46%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.00 [4.00;7.00]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1756 (60.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1160 (39.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDLOS/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e286 [172;507]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShort stay (\u0026lt;\u0026thinsp;8 h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2128 (73.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong stay (\u0026ge;\u0026thinsp;8 h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e788 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eICU:Intensive care unit, ED:emergency department, GI:Gastrointestinal, DCHF:decompansated congestive heart failure, COPD: chronic obstructive pulmonary disease, CVD:cerebro vascular disease, CCI:charlson comorbidity index, EDLOS:emergency department length of stay.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDeceased patients were older compared to survivors, with a median age of 77 years (IQR: 69.0\u0026ndash;84.0), and this difference was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The CCI was higher in patients who died (7.00 [5.00\u0026ndash;8.00] vs. 5.00 [3.00\u0026ndash;6.00], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A statistically significant difference was found between deceased and surviving patients regarding their disease diagnoses. Post-hoc analyses revealed significant differences between different diagnostic groups: acute kidney failure versus GI bleeding (p.adj\u0026thinsp;=\u0026thinsp;0.001), acute kidney failure versus drug-substance intake (p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.001), drug-substance intake versus other diagnoses (p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.001), acute kidney failure versus DHF (p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and drug-substance intake versus DHF (p.adj\u0026thinsp;=\u0026thinsp;0.023). The mortality rate was 38.0% in patients with a short length of stay, while it was 44.7% in those with a prolonged stay, and the difference was statistically significant (p\u0026thinsp;=\u0026thinsp;0.001). The length of stay in the ICU was significantly longer in patients who died (8.00 [3.00\u0026ndash;19.0] vs. 6.00 [3.00\u0026ndash;12.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). According to the results of multivariable logistic regression analysis, predictors of mortality were patient age (OR: 1.02, 95% CI: 1.01\u0026ndash;1.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the CCI (OR: 2.79, 95% CI: 2.52\u0026ndash;3.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When GI bleeding was used as the reference diagnosis, patients with acute kidney failure and metabolic disorders had a significantly higher risk of mortality compared to the reference group (OR: 2.23, 95% CI: 1.18\u0026ndash;4.23, p\u0026thinsp;=\u0026thinsp;0.01). Patients classified under the \"other\" diagnosis group also had a higher mortality risk (OR: 2.73, 95% CI: 1.29\u0026ndash;5.78, p\u0026thinsp;=\u0026thinsp;0.01). Pneumonia (OR: 2.45, 95% CI: 1.30\u0026ndash;4.64, p\u0026thinsp;=\u0026thinsp;0.01) and sepsis (OR: 2.47, 95% CI: 1.16\u0026ndash;5.24, p\u0026thinsp;=\u0026thinsp;0.02) were also associated with significantly higher mortality risk compared to the reference group. The length of stay in the intensive care unit (ICU) (OR: 1.01, 95% CI: 1.00\u0026ndash;1.01, p\u0026thinsp;=\u0026thinsp;0.00) was identified as another predictor of mortality. However, emergency department length of stay (EDLOS) was not an independent predictor of mortality (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of the parameters between deceased and surviving patients.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDecased\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep.overall\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;1756\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;1160\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e923 (59.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e624 (40.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e833 (60.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e536 (39.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength of in ICU/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.00 [3.00;12.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e8.00 [3.00;19.0]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED admission time/h\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e00\u0026ndash;06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e268 (64.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e151 (36.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e06\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e470 (62.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e284 (37.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u0026ndash;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e555 (57.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e411 (42.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e463 (59.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e314 (40.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.0 [60.0;79.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e77.0 [69.0;84.0]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis\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=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolic-electrolyte disturbances, renal failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e271 (47.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e297 (52.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (59.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (40.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGI bleeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (74.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug-substance intake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (89.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e457 (71.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185 (28.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDCHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e205 (70.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePneumonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e283 (48.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e306 (52.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e306 (65.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e161 (34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (45.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (54.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.00 [3.00;6.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e7.00 [5.00;8.00]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDLOS/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e285 [176;478]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e288 [163;574]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShort stay (\u0026lt;\u0026thinsp;8 h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1320 (62.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e808 (38.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong stay (\u0026ge;\u0026thinsp;8 h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e436 (55.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e352 (44.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eICU:Intensive care unit, ED:emergency department, GI:Gastrointestinal, DCHF:decompansated congestive heart failure, COPD: chronic obstructive pulmonary disease, CVD:cerebro vascular disease, CCI:charlson comorbidity index, EDLOS:emergency department length of stay.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression analysis for factors predicting 30 days mortality.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%95 Cl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolic-electrolyte disturbances, renal failure\u003c/p\u003e\u003cp\u003eOther\u003c/p\u003e\u003cp\u003eGI bleeding\u003c/p\u003e\u003cp\u003eDrug-substance intake\u003c/p\u003e\u003cp\u003eDCHF\u003c/p\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003cp\u003ePneumonia\u003c/p\u003e\u003cp\u003eSVD\u003c/p\u003e\u003cp\u003eSepsis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003cp\u003e2.73\u003c/p\u003e\u003cp\u003eref\u003c/p\u003e\u003cp\u003e1.00\u003c/p\u003e\u003cp\u003e1.03\u003c/p\u003e\u003cp\u003e1.27\u003c/p\u003e\u003cp\u003e2.45\u003c/p\u003e\u003cp\u003e1.44\u003c/p\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1.18\u0026ndash;4.23\u003c/p\u003e\u003cp\u003e1.29\u0026ndash;5.78\u003c/p\u003e\u003cp\u003eref\u003c/p\u003e\u003cp\u003e0,36\u0026thinsp;\u0026minus;\u0026thinsp;2,79\u003c/p\u003e\u003cp\u003e0,54 \u0026minus;\u0026thinsp;1,95\u003c/p\u003e\u003cp\u003e0,65\u0026ndash;2,49\u003c/p\u003e\u003cp\u003e1,30\u0026thinsp;\u0026minus;\u0026thinsp;4,64\u003c/p\u003e\u003cp\u003e0,75\u0026thinsp;\u0026minus;\u0026thinsp;2,76\u003c/p\u003e\u003cp\u003e1,16\u0026thinsp;\u0026minus;\u0026thinsp;5,24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003cp\u003e2.61\u003c/p\u003e\u003cp\u003eref\u003c/p\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003cp\u003e0.09\u003c/p\u003e\u003cp\u003e0.70\u003c/p\u003e\u003cp\u003e2,76\u003c/p\u003e\u003cp\u003e1.11\u003c/p\u003e\u003cp\u003e2,35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0,01\u003c/b\u003e\u003c/p\u003e\u003cp\u003eref\u003c/p\u003e\u003cp\u003e0,99\u003c/p\u003e\u003cp\u003e0,93\u003c/p\u003e\u003cp\u003e0,48\u003c/p\u003e\u003cp\u003e\u003cb\u003e0,01\u003c/b\u003e\u003c/p\u003e\u003cp\u003e0,27\u003c/p\u003e\u003cp\u003e\u003cb\u003e0,02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength of in ICU/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1\u0026ndash;1,01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3,02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2,52\u0026thinsp;\u0026minus;\u0026thinsp;3,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19,45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1,01\u0026ndash;1,92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13,26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDLOS/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eNagelkerke R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.330742, Likelihood.ratio.test p.value: 2.7965e-167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eICU:Intensive care unit, ED:emergency department, GI:Gastrointestinal, DCHF:decompansated congestive heart failure, COPD: chronic obstructive pulmonary disease, CVD:cerebro vascular disease, CCI:charlson comorbidity index, EDLOS:emergency department length of stay.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the comparison between patients with EDLOS\u0026thinsp;\u0026ge;\u0026thinsp;8 hours and those with \u0026lt;\u0026thinsp;8 hours, the time intervals of patients' ED admission showed a significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Post-hoc analysis revealed that the 00:00\u0026ndash;06:00 time interval was significantly different from all other time intervals (Bonferroni corrected p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with a longer ED stay were significantly older (75.0 [65.0;83.0], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although a significant relationship was found between patients' diagnoses upon admission and their ED stay duration, no significant differences were found between any diagnostic groups after Bonferroni correction (p.adj\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A multivariable regression analysis was performed to determine the factors affecting patients' EDLOS. According to this analysis, the time interval of ED admission was strongly associated with prolonged stay (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared to the reference category (patients admitted between 00:00\u0026ndash;05:59), patients admitted between 06:00\u0026ndash;11:59 had a 75% lower likelihood of a prolonged EDLOS (OR\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), patients admitted between 12:00\u0026ndash;17:59 had a 79% lower likelihood (OR\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and patients admitted between 18:00\u0026ndash;23:59 had a 51% lower likelihood (OR\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age and the CCI were not significantly associated with prolonged EDLOS (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of the parameters evaluated in the study according to the EDLOS\u0026thinsp;\u0026lt;\u0026thinsp;8 hours vs. EDLOS\u0026thinsp;\u0026ge;\u0026thinsp;8 hours, univariable analysis results.\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEDLOS\u0026thinsp;\u0026lt;\u0026thinsp;8 h\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEDLOS\u0026thinsp;\u0026ge;\u0026thinsp;8 h\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep.overall\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;2128\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;788\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1133 (73.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e414 (26.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e995 (72.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e374 (27.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eED admission time/h\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\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e00\u0026ndash;06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e215 (51.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e204 (48.7%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e06\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e598 (79.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e156 (20.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u0026ndash;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e795 (82.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e171 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e520 (66.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e257 (33.1%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.0 [63.0;82.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e75.0 [65.0;83.0]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis\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\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolic-electrolyte disturbances, renal failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e422 (74.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (76.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (23.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGI bleeding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (75.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrug-substance intake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (84.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDCHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e454 (70.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e188 (29.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e205 (70.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePneumonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e402 (68.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e187 (31.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e374 (80.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (19.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (67.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (32.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.00 [4.00;7.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e6.00 [4.00;7.00]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eICU:Intensive care unit, ED:emergency department, GI:Gastrointestinal, DCHF:decompansated congestive heart failure, COPD: chronic obstructive pulmonary disease, CVD:cerebro vascular disease, CCI:charlson comorbidity index, EDLOS:emergency department length of stay.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression analyses for prolonged EDLOS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%95 Cl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eED admission time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e00\u0026ndash;06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e06\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,19\u0026thinsp;\u0026minus;\u0026thinsp;0,33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10,17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u0026ndash;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,16\u0026thinsp;\u0026minus;\u0026thinsp;0,27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-11,89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,38\u0026thinsp;\u0026minus;\u0026thinsp;0,63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5,66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,98\u0026thinsp;\u0026minus;\u0026thinsp;1,16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026ndash;1,02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNagelkerke R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0937980, Likelihood.ratio.test p.value: 5.1079e-41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eCCI:charlson comorbidity index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eIn our study, the overall mortality rate was 39.8%. We observed that patients who remained in the emergency department (ED) for more than 8 hours had a higher rate of mortality, and this difference was statistically significant. However, multivariable regression analysis revealed that EDLOS was not an independent predictor of mortality. Consistent with our findings, Anttila et al., [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] also reported that prolonged EDLOS had no effect on mortality. Nevertheless, the mortality rates in their study were lower than those observed in our cohort. We believe this discrepancy may be attributed to the methodological differences between the studies, as Anttila et al., [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] evaluated EDLOS as 180-minutes and mortality within 90-days. Additionally, their study included patients referred from other hospitals. In contrast, our study excluded transferred patients from other institutions. Patients referred from other hospitals are often clinically stabilized prior to transfer and admitted directly to the ICU once a bed is secured. Since these patients do not experience the typical ED waiting process, we excluded them from our study to provide a more accurate evaluation of EDLOS and its association with clinical outcomes. These factors likely contributed to the lower mortality rate reported by Anttila et al., [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Several studies have indicated that an EDLOS of up to six hours is not associated with increased mortality [13, 14. In fact, the DUTCH study reported a negative correlation between an ED stay longer than 3.7 hours and hospital mortality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In their study investigating the association between EDLOS and mortality, Asheim et al., [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] reported a mean EDLOS of 2.9 hours and a 30-day mortality rate of 3.4%. However, that study also included patients who were discharged from the emergency department in the analysis. While the absence of an association between EDLOS and mortality is consistent with our results, the lower mortality rate reported in that study is likely due to the inclusion of discharged patients, who generally have a lower risk of death.\u003c/p\u003e\u003cp\u003eIn contrast to our results, some studies have reported a significant association between prolonged EDLOS and increased mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. We believe that these discrepancies may be explained by differences in sample sizes and methodological approaches of these studies. Jones et al., [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] analyzed data from over five million patients, categorizing ED waiting times into two-hour intervals and comparing mortality rates among these groups. They reported that mortality increased by 10% in patients with EDLOS of 8\u0026ndash;12 hours and emphasized that patients should be transferred from the emergency department within 6 hours [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, Lee et al., [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] evaluated EDLOS as six-hours and assessed mortality based on in-hospital death. In contrast, our study considered an EDLOS of eight hours and used 30-day mortality. Aletreby et al., [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] conducted their study in a hospital where a \u0026ldquo;four-hour rule\u0026rdquo; is enforced, requiring ICU-eligible patients to be transferred to the ICU within four hours. We believe that their identification of EDLOS as an independent predictor of mortality may be influenced by the institutional policies and clinical practices specific to that setting. Although national regulations in our country set the maximum ED waiting time at 8 hours [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], our study found a shorter duration. We believe that EDLOS was not identified as a significant factor influencing mortality in our cohort because patients in our emergency department were generally transferred earlier than expected.\u003c/p\u003e\u003cp\u003eAnother factor associated with mortality in our study was the diagnosis established at the emergency department admission. Using GI bleeding as the reference category, we found that patients diagnosed with pneumonia, sepsis, renal-metabolic disorders, and categorized in the \u0026ldquo;other\u0026rdquo; diagnosis group had significantly higher mortality rates. Similar findings have been reported in previous studies a significant association between sepsis and mortality. [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Sepsis is a complex condition involving multiple organ systems and requiring a multidisciplinary approach. Early and appropriate management has been shown to improve outcomes. Clinical guidelines, including the Surviving Sepsis Campaign, emphasize that early goal-directed therapy can reduce mortality in septic patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While certain diagnoses were associated with increased mortality, the absence of a significant association between EDLOS and mortality indicates that emergency department care was delivered in an effective and appropriate manner. Supporting this, we also found no significant association between patients\u0026rsquo; diagnoses and EDLOS, a result consistent with the findings of Asheim et al., [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, when stratified by diagnosis, patients with sepsis, pneumonia, CHF, and COPD tended to have longer ED stays. This observation aligns with prior studies reporting that sepsis patients often stay in the ED for extended periods [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Teklie et al., [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] also noted that critically ill patients, particularly those requiring ventilator support or presenting with septic shock, had prolonged EDLOS. Similarly, Carter et al., [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] reported longer EDLOS in patients diagnosed with sepsis and acute kidney injury. Taken together, these findings suggest that patients with complex medical conditions requiring extensive diagnostic evaluation and multiple consultations are more likely to experience longer ED stays. However, prolonged EDLOS in such patients does not appear to be an independent predictor of mortality. Instead, mortality is more closely associated to the severity and nature of the presenting condition itself. Our finding that ICU length of stay was significantly associated with mortality further supports this interpretation.\u003c/p\u003e\u003cp\u003eCCI emerged as a strong and independent predictor of mortality. The multivariable regression model revealed that each one-point increase in CCI was associated with an approximately 2.8-fold increase in the risk of death. CCI is a widely used and validated tool for estimating mortality risk based on patients\u0026rsquo; burden of chronic comorbid conditions. Additionally, the median CCI score among deceased patients in our study was 7, indicating that most of patients included in our cohort had a high level of comorbidity. This finding aligns with the original study by Charlson et al., [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] which reported a 1-year mortality rate of 85% among patients with CCI scores of 5 or higher. In this context, our patient population can be considered at high risk for mortality, and our results further reinforce the prognostic value of CCI in emergency settings. The observed association between advanced age and mortality in our study also supports this conclusion. As age increases, the prevalence of comorbid conditions tends to rise, and such patients typically require complex, multidisciplinary care [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Elderly patients are at increased risk for hospital readmission and adverse outcomes following discharge [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, they often present with complex care needs that may not be adequately addressed in the ED setting [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We believe that these age-related vulnerabilities contribute to the higher mortality observed among older adults. In addition, elderly patients often require more extensive diagnostic workups, multiple specialty consultations, and longer stabilization periods all of which can prolong their ED stay. In our study, the positive association observed between older age and prolonged EDLOS supports this interpretation. Furthermore, patients of advanced age and/or those with multiple comorbidities may experience longer ED stays due to the clinical complexity of their conditions and, in some cases, difficulties in communicating their symptoms effectively. Our regression analysis confirmed that both higher age and elevated CCI scores were independently associated with prolonged EDLOS. These findings are consistent with prior studies reporting that older patients tend to stay longer in the ED compared to younger individuals [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Taken together, these findings highlight the importance of early identification and prioritization of patients who are either elderly or have multiple comorbidities in the emergency department. Prompt triage and timely allocation of appropriate clinical resources for these high-risk individuals are essential not only to optimize patient care but also to improve overall ED efficiency. From this perspective, our findings also suggest that the clinical management of patients in our emergency department aligns with recommended standards. Among the 2,916 patients included in our study, 73% were transferred to the intensive care unit within 8 hours. Furthermore, the average EDLOS for patients is shorter than the maximum duration specified in national regulations (286 min). Previous studies have reported varying lengths of EDLOS, likely reflecting differences in institutional policies, patient populations, and study designs. Asheim et al., [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] reported a mean EDLOS of 2.9 hours; however, their analysis included patients who were discharged directly from the ED, which may have contributed to the lower EDLOS, and overall mortality rate observed in their study. Similarly, Chrilly et al., [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] found a median EDLOS of 386.5 minutes for patients admitted to the ICU, and an even longer median stay of 505 minutes for those transferred to general hospital wards. Teklie et al., [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] reported an average EDLOS of 13.5 hours among critically ill patients. Their study differs from ours in several key aspects: it included a smaller sample size (n\u0026thinsp;=\u0026thinsp;102), as well as patients with cardiopulmonary arrest, trauma-related conditions, and inter-hospital transfers. In contrast, our study excluded patients transferred from external facilities to ensure a more homogeneous cohort and to better assess EDLOS in patients admitted directly through our emergency department. These methodological differences likely account for the variation in EDLOS and associated outcomes observed across studies.\u003c/p\u003e\u003cp\u003eWhen we compared patients with EDLOS\u0026thinsp;\u0026ge;\u0026thinsp;8 hours to those with EDLOS\u0026thinsp;\u0026lt;\u0026thinsp;8 hours, we found statistically significant differences in time of ED presentation, age, CCI, and diagnostic categories. Multivariate logistic regression analysis identified time of presentation as an independent predictor of prolonged EDLOS. In line with our findings, Lee et al., [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] reported that patients with an EDLOS of 6 hours or more were more likely to present during nighttime hours and were generally older with higher CCI scores. Their analysis also identified nighttime ED presentation and a CCI score\u0026thinsp;\u0026gt;\u0026thinsp;1 as independent predictors of prolonged EDLOS. This may be explained by reduced staffing levels, limited access to diagnostic services, delays in consultations, and higher inpatient bed occupancy during nighttime hours all of which can contribute to longer ED stays for patients presenting during off-hours. These observations highlight the need for organizational improvements in emergency department workflow during nighttime hours to reduce avoidable delays in the management of critically ill patients.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Limitations\u003c/h2\u003e\u003cp\u003eThe retrospective and single-center design may limit the generalizability of the findings. There is a degree of diagnostic heterogeneity. It is unclear from the available data whether this is caused by more diagnostic or therapeutic interventions in ED, or a delay in access and transfer to ICU over the study period. Although EDLOS was not associated with mortality in multivariable analysis, unmeasured factors such as ED overcrowding, staffing variability, or delays in diagnostics were not captured. We did not include post-discharge outcomes or quality of care metrics in the ICU, which may also influence 30-day mortality.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eIn this study, we investigated the impact of EDLOS on 30-day mortality among critically ill patients with indications for ICU admission. Multivariable logistic regression demonstrated that EDLOS was not an independent predictor of mortality. Conversely, age and CCI were identified as strong and independent predictors of mortality. Additionally, certain presenting diagnoses, such as sepsis, pneumonia, and renal-metabolic disorders, were associated with significantly higher mortality. Furthermore, nighttime ED presentations were associated to longer ED stays, possibly due to delays in diagnostic processes, limited consultant availability, and high inpatient bed occupancy during late hours. Taken together, our findings underscore the importance of early identification and prioritization of patients with advanced age, high comorbidity burden and diagnosis in the emergency department. However, EDLOS did not emerge as an independent predictor of mortality. This finding suggests that contemporary emergency departments have evolved into high-acuity care units and that emergency physicians are increasingly equipped to stabilize and manage critically ill patients awaiting intensive care beds. Future prospective multicenter studies are warranted to validate these findings and to explore strategies for improving the care of critically ill patients in the emergency department.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u0026nbsp; conceptualization Karaali R, Salı O, data curation Salı O, Korkmaz İ, formal analysis: Karaali R, Korkmaz İ, ınvestigation: Karaali R, Salı O, methodology: Karaali R, Salı O, resources: Karaali R, Salı O, software: Korkmaz I, Salı O, supervision: Karaali R, writing \u0026ndash; original draft: Karaali R, Korkmaz İ, writing \u0026ndash; review and editing: Karaali R.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e The study began after receiving approval from the Non-Interventional Research Ethics Committee of Buca Seyfi Demirsoy Training and Research Hospital on 24.04.2024, with reference number 2024/266.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e Due to the retrospective nature of the study, consent to participate was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThere was no Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles of human rights stated in the World Medical Association Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations:\u003c/strong\u003e not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eTeklie H, Engida H, Melaku B, Workina A (2021) Factors contributing to delay intensive care unit admission of critically ill patients from the adult emergency Department in Tikur Anbessa Specialized Hospital. BMC Emerg Med 21:123. https://doi.org/10.1186/s12873-021-00518-z.\u003c/li\u003e\n \u003cli\u003eAsheim A, Nilsen SM, Carlsen F, et al (2019) The effect of emergency department delays on 30-day mortality in Central Norway. Eur J Emerg Med 26:446-452. https://doi.org/10.1097/MEJ.0000000000000609.\u003c/li\u003e\n \u003cli\u003eLee KS, Min HS, Moon JY, et al (2022) Patient and hospital characteristics predict prolonged emergency department length of stay and in-hospital mortality: a nationwide analysis in Korea. BMC Emerg Med 22:183. https://doi.org/10.1186/s12873-022-00745-y.\u003c/li\u003e\n \u003cli\u003eHuang Q, Thind A, Dreyer JF, Zaric GS (2010) The impact of delays to admission from the emergency department on inpatient outcomes. BMC Emerg Med 10:16. https://doi.org/10.1186/1471-227X-10-16.\u003c/li\u003e\n \u003cli\u003eCrilly J, Sweeny A, O\u0026apos;Dwyer J, Richards B, Green D, Marshall AP (2021) Identifying \u0026apos;at-risk\u0026apos; critically ill patients who present to the emergency department and require intensive care unit admission: A retrospective observational cohort study. Aust Crit Care 34:195-203. https://doi.org/10.1016/j.aucc.2020.07.007.\u003c/li\u003e\n \u003cli\u003eAitavaara-Anttila M, Liisanantti JH, Raatiniemi L, Ohtonen P, Ala-Kokko T (2019) Factors related to delayed intensive care unit admission from emergency department-A retrospective cohort study. Acta Anaesthesiol Scand 63:939-946. https://doi.org/10.1111/aas.13355.\u003c/li\u003e\n \u003cli\u003eNational Health Service Department of Health. Reforming emergency care:first steps to a new approach. 2001. Avaliable at, http://webarchive. nationalarchives.gov.uk/\u0026thorn;/http://www.dh.gov.uk/en/ Publications and statistics/ Publications/ Publications Policy And Guidance/DH_ 4008702 [Accessed 29 March 2019].\u003c/li\u003e\n \u003cli\u003eNates JL, Nunnally M, Kleinpell R, Blosser S, Goldner J, Birriel B, et al (2016) ICU admission, discharge, and triage guidelines: a framework to enhance clinical operations, development of institutional policies, and further research. Crit Care Med 44:1553e602. https://doi.org/ 10.1097/CCM.0000000000001856.\u003c/li\u003e\n \u003cli\u003eAletreby WT, Brindley PG, Balshi AN, et al (2021) Delayed intensive care unit admission from the emergency department: impact on patient outcomes. A retrospective study. Retardo na transfer\u0026ecirc;ncia do pronto-socorro para a unidade de terapia intensiva: impacto nos desfechos do paciente. Um estudo retrospectivo. Rev Bras Ter Intensiva 33:125-137. https://doi.org/10.5935/0103-507X.20210014.\u003c/li\u003e\n \u003cli\u003eJones S, Moulton C, Swift S, et al (2022) Association between delays to patient admission from the emergency department and all-cause 30-day mortality. Emerg Med J. 39:168-173. https://doi.org/10.1136/emermed-2021-211572.\u003c/li\u003e\n \u003cli\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373-383. https://doi.org/10.1016/0021-9681(87)90171-8.\u003c/li\u003e\n \u003cli\u003eYatakli Saglik Tesislerinde Acil Servis Hizmetlerinin Uygulama Usul Ve Esaslari Hakkinda Teblig. 13 Eyl\u0026uuml;l 2022. 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Crit Care Med. 47:1564-71. https://doi.org/10.1097/CCM.0000000000003957.\u003c/li\u003e\n \u003cli\u003eLin S, Ge S, He W, Zeng M (2021) Association of delayed time in the emergency department with the clinical outcomes for critically ill patients. QJM 114:311-317. https://doi.org/10.1093/qjmed/hcaa192.\u003c/li\u003e\n \u003cli\u003eSantos FRQ, Machado MN, Lobo SMA (2020) Adverse outcomes of delayed intensive care unit. Resultados adversos de admiss\u0026otilde;es tardias \u0026agrave; unidade de terapia intensiva a partir do pronto-socorro. Rev Bras Ter Intensiva 32:92-98. https://doi.org/10.5935/0103-507x.20200014.\u003c/li\u003e\n \u003cli\u003eChong CP, Haywood C, Barker A, Lim WK (2013) Is Emergency Department length of stay associated with inpatient mortality? Australas J Ageing 32:122-124. https://doi.org/10.1111/j.1741-6612.2012.00651.x.\u003c/li\u003e\n \u003cli\u003eCardoso LT, Grion CM, Matsuo T, et al (2011) Impact of delayed admission to intensive care units on mortality of critically ill patients: a cohort study. Crit Care 15:R28. https://doi.org/10.1186/cc9975.\u003c/li\u003e\n \u003cli\u003eEvans L, Rhodes A, Alhazzani W, et al (2021) Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med 47:1181-1247. https://doi.org/10.1007/s00134-021-06506-y\u003c/li\u003e\n \u003cli\u003eGarc\u0026iacute;a-Gigorro R, de la Cruz Vigo F, Andr\u0026eacute;s-Esteban EM, et al (2017) Impact on patient outcome of emergency department length of stay prior to ICU admission. Med Intensiva 41:201-208. https://doi.org/10.1016/j.medin.2016.05.008.\u003c/li\u003e\n \u003cli\u003eCarter AW, Pilcher D, Bailey M, Cameron P, Duke GJ, Cooper J (2010) Is ED length of stay before ICU admission related to patient mortality?. Emerg Med Australas. 22:145-150. https://doi.org/10.1111/j.1742-6723.2010.01272.x.\u003c/li\u003e\n \u003cli\u003eGeorge G, Jell C, Todd BS (2006) Effect of population ageing on emergency department speed and efficiency: a historical perspective from a district general hospital in the UK. Emerg Med J 23:379-383. https://doi.org/10.1136/emj.2005.029793.\u003c/li\u003e\n\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":"Critical Care, Emergency Department Length of Stay, Mortality, Intensive Care Unit Admission","lastPublishedDoi":"10.21203/rs.3.rs-7917990/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7917990/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eEmergency department length of stay (EDLOS) is considered a potential quality indicator in the management of critically ill patients. However, the relationship between EDLOS and mortality remains controversial, particularly in patients requiring intensive care unit (ICU) admission. This study aimed to evaluate whether prolonged EDLOS is associated with increased 30-day mortality among patients with ICU indications and to identify other independent predictors of mortality and prolonged EDLOS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this retrospective observational study, 2,916 patients with ICU admission indications were included. Patients were divided into two groups based on EDLOS\u0026thinsp;\u0026lt;\u0026thinsp;8 hours and \u0026ge;\u0026thinsp;8 hours. Multivariate logistic regression was used to assess independent predictors of mortality and prolonged EDLOS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe overall 30-day mortality rate was 39.8%. Although univariate analysis showed higher mortality in patients with EDLOS\u0026thinsp;\u0026ge;\u0026thinsp;8 hours, multivariate analysis revealed that EDLOS was not an independent predictor of mortality. Age, Charlson Comorbidity Index (CCI), and sepsis, pneumonia, renal-metabolic disorders were significantly associated with increased mortality. In addition, prolonged EDLOS was independently associated with nighttime ED presentation, advanced age, and higher CCI scores.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eProlonged EDLOS was not independently associated with mortality among patients requiring ICU admission. Mortality appeared to be more strongly related to patient-specific clinical factors. Early identification and prioritization of high-risk patients, particularly those with advanced age and high comorbidity burden, are essential to optimize emergency care outcomes.\u003c/p\u003e","manuscriptTitle":"Evaluation of Emergency Department Length of Stay and 30-Day Mortality in Critically Ill Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 07:02:38","doi":"10.21203/rs.3.rs-7917990/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":"2cf7bba7-9176-4c4d-96d3-bde892fa02a1","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T04:54:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 07:02:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7917990","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7917990","identity":"rs-7917990","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}