External validation of the clinical prediction score for predicting 28-day mortality of older sepsis patients in emergency department

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Existing mortality prediction scoring systems have demonstrated limited accuracy in this population. To address this limitation, the Ramathibodi Older Sepsis Score (ROSS) was developed. However, external validation is necessary to assess its efficacy in predicting 28-day mortality. Objective: This study aimed to validate the Ramathibodi Older Sepsis Score (ROSS) for predicting 28-day mortality in older sepsis patients in the emergency department (ED). Methods: Data for the development cohort were retrospectively collected from August to December 2018, while data for the validation cohort were collected from January to June 2022. Seven prespecified prognostic factors for 28-day mortality were used to calculate a predictive score. Results: A total of 500 older sepsis patients were included in the validation cohort, and 599 patients were included in the development cohort. The predictive ability of the ROSS model in the validation cohort (Area under receiver operating characteristic curve; AuROC: 0.69, 95% CI: 0.61–0.77) decreased compared to the development cohort (AuROC: 0.87, 95% CI: 0.82–0.92); P<0.01. This performance was compared with other scoring models: SIRS (AuROC: 0.50, 95% CI: 0.42–0.58; P < 0.01), qSOFA (AuROC: 0.70, 95% CI: 0.64–0.77; P = 0.75), NEWS (AuROC: 0.68, 95% CI: 0.60–0.76; P = 0.81), and REWS (AuROC: 0.66, 95% CI: 0.57–0.75; P = 0.51) Conclusion: The external validation of the ROSS demonstrated moderate performance in predicting 28-day mortality in older sepsis patients, with AuROC values similar to qSOFA and NEWS. validation mortality older sepsis emergency department Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction As the global population ages, the World Health Organization (WHO) predicts that the proportion of individuals aged 60 and above will grow by 3% annually, nearly doubling from 12–22% by 2050 ( 1 ) . In Thailand, the older population is expected to increase 2.5 times by 2050 ( 2 ) . Sepsis remains a significant cause of morbidity and mortality in older adults, who experience higher incidence rate and worse outcomes due to age-related physiological changes and multiple comorbidities. Among patients over 60, diagnosis is particularly challenging due to atypical symptoms frequently influenced by age-specific factors. In the emergency department (ED), these atypical presentations may obscure or delay timely diagnosis and intervention for sepsis ( 3 ) . Early severity prediction and prompt intervention for high-risk patients are essential for improving outcomes in the ED, where clinical tools must be both simple and efficient. Established clinical prediction scores for sepsis such as Systemic Inflammatory Response Syndrome (SIRS) ( 4 , 5 ) , Sequential Organ Failure Assessment (SOFA) ( 6 ) , quick SOFA (qSOFA) ( 7 ) , National Early Warning Score (NEWS2) ( 8 ) , have demonstrated poor prognostic performance in older patients, with area under the receiver operating characteristic (AuROC) values ranging from 0.50 to 0.60 ( 9 , 10 ) . These tools incorporate initial physiological parameters at triage—such as blood pressure, respiratory rate, oxygen saturation, consciousness, temperature, and heart rate—but show limited accuracy in the elderly population. The Mortality in ED Sepsis (MEDS) ( 11 ) , and Sepsis Patient Evaluation in the ED (SPEED) ( 12 ) offer valuable assessments of mortality risk and inform treatment decisions in general sepsis populations, achieving an AuROC range of 0.78 to 0.81 ( 11 , 12 ) . However, these scores include laboratory values, such as complete blood count, markers of acidosis, lactate levels, and respiratory infection indicators, which may not be readily available in the initial phase of ED assessment. Additionally, these scores lack specific modifications for older adults, who have unique physiological responses and risk factors. Zelis N. et al proposed the Rapid Emergency Medicine Score for Upstream Prediction (RISE UP) ( 13 ) which incorporates age-specific factors tailored for older patients with general conditions in the ED and demonstrates good prognostic performance achieving an AuROC range of 0.83 to 0.84. However, its dependence on blood chemistry results [e.g., serum albumin, blood urea nitrogen (BUN), lactate dehydrogenase (LDH), bilirubin may delay timely application in the ED. Our previous study introduced the Ramathibodi Older Sepsis Score (ROSS) ( 14 ) , a clinical prediction tool designed for the initial assessment of older sepsis patients (≥ 60 years) in the ED, the ROSS incorporates criteria including: Altered consciousness, Blood lactate ≥ 4 mmol/L, Cancer (comorbidity), Dyspnea (respiratory rate ≥ 24 breaths per minute), Desaturation (Oxygen saturation ≤ 93%), Dependent functional status, and Heart rate, providing an accessible tool at the point of care for predicting 28-day mortality. The present study aimed to validate the ROSS clinical prediction score for predicting 28-day mortality in older sepsis patients in the ED, using data from the initial development study conducted at Ramathibodi Hospital in 2018, as well as additional validation data collected in 2022. Materials and Methods Study design and Setting This study, designed to validate a clinical prediction rule, was a single-center, retrospective cohort study involving ED patients at Ramathibodi Hospital, a tertiary university hospital in Bangkok, Thailand. Data for the development cohort from our previous study (14) were retrospectively collected between August and December 2018, with approval from the Human Research Ethics Committee of the Faculty of Medicine, Ramathibodi Hospital, Mahidol University (Study Code: COA. MURA2021/434). Data for the validation cohort were collected from January to June 2022, with study approval granted by the same ethics committee (Study Code: COA. MURA2023/141). Participants In both the validation and development cohort, all patients aged 60 years and older who presented to the ED with suspected sepsis during the specified periods were included. Suspected sepsis (4, 5, 9, 15, 16) was defined as the presence of fever or signs of infection in a patient managed according to the Ramathibodi sepsis protocol. Sepsis diagnosis was confirmed based on criteria from the International Classification of Diseases, Tenth Revision (ICD-10), or through positive blood or body fluid cultures. Exclusion criteria included patients who had received treatment at an outpatient unit or another hospital prior to transfer to the ED, those who had undergone cardiopulmonary resuscitation earlier in the same visit, and patients with incomplete data in the electronic medical record (EMR) database. Predictive variables The ROSS score, ranging from 0 to 15, assigns points based on seven key predictors: Altered consciousness (defined as a state of not being alert, characterized by impairment in at least one of the following: verbal response, response to pain, or unresponsiveness), Blood lactate ≥4 mmol/L, Cancer (comorbidity), Dyspnea (respiratory rate ≥24 breaths per minute), Desaturation (Oxygen saturation ≤93%), Dependent functional status (defined as at least one dependency to perform activities of daily living (ADL), and Heart rate ( Table 1 ). Patients with a total score of 0 to 5 are categorized as low-risk for 28-day mortality, while those with a score of 6 to 15 are classified as high-risk for 28-day mortality. Outcome The primary endpoint of this study was 28-day all-cause mortality. Secondary outcomes included the application of the sepsis treatment bundle in the ED, ED disposition (intensive care unit admission, general ward admission, discharge, referral, or death), and length of hospital stay (LOS). Sample size The sample size was calculated based on our develop cohort study (14) , selecting the requirement for each variable in the ROSS score, on AuROC = 0.87 with 8 parameters (Altered consciousness, Blood lactate ≥4 mmol/L, Cancer (comorbidity), Dyspnea (respiratory rate ≥24 breaths per minute), Desaturation (Oxygen saturation ≤93%), Dependent functional status, and Heart rate≤ 49 or ≥120 beat per minute ), assumptions included an acceptable difference of 0.05 between apparent and adjusted R-squared, with a margin of error of 0.05 for the intercept estimation. The Events per Predictor Parameter (EPP) was estimated assuming a 28-day mortality prevalence of 0.07, using Stata version 16 (StataCorp LLC, College Station, TX, USA). The final required sample size was 482, with an anticipated 34 events. Statistical Analysis Descriptive statistics were calculated for all clinical characteristics and relevant variables. Continuous variables are presented as means and standard deviations (SD) for normally distributed data or as medians for non-parametric tests and were analyzed using either the independent t-test or the Mann–Whitney U test, as appropriate. Categorical data are presented as percentages and were analyzed using the chi-square test or Fisher’s exact test. Statistical significance was defined as a two-tailed p-value of <0.05. Validation data were compared with development data by examining areas under the receiver operating characteristic (ROC) curves. The predictive ability of the scoring system was visually assessed for both datasets using probability or risk curves. Hosmer–Lemeshow goodness-of-fit statistics and a calibration plot were used to evaluate the agreement between observed and predicted score values. All statistical analyses were conducted using STATA 16.0 (StataCorp LP, College Station, TX, USA). Result Baseline characteristics of studied cases A total of 605 older patients with suspected sepsis admitted to the ED were included in the development cohort, while 726 older patients with suspected sepsis were included in validation cohort. Six patients were excluded from development cohort, and 226 patients were excluded from validation cohort due to contraindications for outpatient treatment prior to ED transfer, receipt of cardiopulmonary resuscitation during the same visit, or missing data. This resulted in a final eligible sample of 1,099 patients, with 599 in the development cohort and 500 in the validation cohort. The mortality rates for the development and validation cohorts were 7.12% and 8.80%, respectively ( Figure 1 ). Table 2 . provides a comparison of baseline characteristics, including vital signs, ESI triage levels, infection sources, and laboratory values, between the development and validation cohorts. In the development cohort, there was a higher proportion of males and patients with comorbidities, such as neuromuscular and pulmonary diseases, as well as systolic blood pressure. In the validation cohort, higher proportions were noted for comorbidities including congestive heart failure, airway disease, oncology-related conditions, transplantation, ischemic heart disease, immunocompromised status, dependent status, and bloodstream infection sources. Additionally, several ROSS predictor components—namely dependent status, respiratory rate, level of consciousness, and initial venous lactate—differed between the two cohorts. The outcomes of length of stay and ED disposition (death, discharge, admission to intensive care, admission to the general ward, and referral out) differed between the two cohorts. The area under the ROC curve (AuROC), reflecting the predictive ability of the ROSS model, was 0.87 (95% CI: 0.82-0.92) in the development cohort and 0.69 (95% CI: 0.61-0.77) in the validation cohort; P <0.01 ( Figure 2 ). Calibration plots for the ROSS model in both cohorts are presented in Figures 3A and 3B , demonstrating an acceptable alignment between predicted probabilities and observed 28-day mortality rates. This alignment is evident by the proximity of observed events, represented by circles, to the predictive line. Using a cutoff score of 6 or higher, the ROSS model effectively stratifies older sepsis patients into low-risk and high-risk mortality groups. In the validation cohort, the ROSS model demonstrated predictive capability for 28-day mortality in older patients with suspected sepsis, with a discriminative ability of AuROC: 0.69 (95% CI: 0.61-0.77). This performance was compared to other scoring models: SIRS (AuROC: 0.50, 95% CI: 0.42-0.58; P < 0.01), qSOFA (AuROC: 0.70, 95% CI: 0.64-0.77; P = 0.75), NEWS (AuROC: 0.68, 95% CI: 0.60-0.76; P = 0.81), and REWS (AuROC: 0.66, 95% CI: 0.57-0.75; P = 0.51) as shown in Figure 4 . Sensitivity, specificity, positive likelihood ratio, and the AuROC for predicting 28-day mortality for each ROSS score from the validation cohort are reported in Supplementary Table S1 . In our previous study based on the development cohort, we established a cutoff of ROSS ≥6 to differentiate between low- and high-risk 28-day mortality groups, with mortality rates of 2.73% and 23.77%, respectively (14) . In development cohort, a cutoff of ROSS ≥6 demonstrated a sensitivity of 69.1% (95% CI: 52.9%-82.4%) and a specificity of 83.3% (95% CI: 79.9%-86.3%). In the validation cohort, the sensitivity was 56.8% (95% CI: 41.0%-71.7%), and the specificity was 70% (95% CI: 65.5%-74.1%). Additionally, the validation cohort yielded a positive predictive value of 15.4% (95% CI: 10.2%-21.9%) and a negative predictive value of 94.4% (95% CI: 91.4%-96.6%), as presented in Supplementary Table S2 . Discussion Sepsis scoring systems in the ED are essential for the early identification of high-risk patients to enhance survival outcomes. These tools are commonly utilized to assess sepsis severity and predict mortality risk. However, most sepsis scores were developed based on mixed-age populations and lack adjustments for age-specific physiological changes, particularly in older adults (3) . This limitation may lead to under-estimation, where high-risk older patients are not adequately identified, or overestimation, resulting in unnecessary monitoring and interventions. The SPEED score (AuROC 0.81) shows good performance for mortality prognosis in the general sepsis population but includes laboratory parameters that may limit its immediate use in the ED (12) . Similarly, the RISE UP score (AuROC 0.84), designed for older patients, demonstrates good performance but also relies on laboratory data, which may hinder its timely application in clinical decision-making. Both models highlight the challenge of balancing predictive accuracy with practicality in time-sensitive settings (13) . This study represents the third in a series of investigations on older sepsis patients in the ED at our institution. It focuses on the external validation of the Ramathibodi Older Sepsis Score (ROSS), with simple parameter specifically assessing its temporal validity using data from different periods, validation data from 2022 and developmental data from 2018. In this validation study, the mortality rate of older sepsis patient in ED was higher than in the development cohort (8.80% vs. 7.12%) (14) . This validation cohort could also show the performance of the score with a different prevalence in 28-day mortality. The predictive performance of the ROSS model was satisfactory, with an area under the receiver operating characteristic (AuROC) curve of 0.87 in the development cohort and 0.69 in the validation cohort. Although the performance in the validation set was lower, the model still effectively distinguished between survivors and non-survivors. Compared to other established scoring systems, the ROSS demonstrated superior accuracy relative to SIRS (AuROC 0.50, 95% CI: 0.42–0.58), REWS (AuROC 0.66, 95% CI: 0.57–0.75), and NEWS (AuROC 0.68, 95% CI: 0.60–0.76), but was marginally less accurate than qSOFA (AuROC 0.70, 95% CI: 0.64–0.77). The performance of ROSS shows similar as qSOFA and NEWS in this validation cohort. qSOFA is a simple tool for predicting mortality in the ED, consisting of three components: systolic blood pressure 22 breaths per minute, and altered consciousness. Its predictive performance for mortality has been reported with an AuROC ranging from 0.63 to 0.70 in general sepsis population (17-19) , an AuROC ranging from 0.56-0.60 in older sepsis population (9, 13) . NEWS2 includes six parameters: respiratory rate, oxygen saturation (adjusted for the general population or individuals with obstructive lung disease), systolic blood pressure, pulse rate, consciousness, and temperature. Each parameter is scored from 0 to 3, with a total score range of 0 to 18, providing a comprehensive and detailed assessment of sepsis severity. Analysis of sensitivity, specificity, AuROC, and 28-day mortality from the validation cohort is presented in Supplementary Table S1 . As the threshold score increases, sensitivity decreases, meaning the model becomes less effective at identifying all patients at risk of 28-day mortality. In contrast, specificity increases, indicating the model is more accurate at identifying patients who are not at risk. At a threshold of ≥1, sensitivity is very high (97.73%), but specificity is low (7.68%), resulting in a large number of false positives. At a threshold of ≥8, specificity improves significantly (88.82%), but sensitivity drops to 31.82%, potentially missing many high-risk patients. At a threshold of ≥9, the likelihood ratio positive (LR+) reaches 3.45, suggesting moderate predictive value. However, beyond ≥10, LR+ becomes less reliable due to small sample sizes. The AuROC values range from 0.50 to 0.64, indicating poor to moderate predictive performance. At a threshold of ≥12, mortality is reported as 0%, likely due to an insufficient sample size of high-risk patients. Supplementary Table S2 ; The development cohort we selected a cutoff of ROSS ≥6 to differentiate between low- and high-risk 28-day mortality groups. ROSS demonstrated high sensitivity and a strong negative predictive value, with specificity in the development set at 83.3% (95% CI: 79.9%-86.3%), but decreased to 70% (95% CI: 65.5%-74.1%) in the validation set. The negative predictive value also declined slightly from 97.3% (95% CI: 95.4%-98.5%) in the development cohort to 94.4% (95% CI: 91.4%-96.6%) in the validation cohort. The lower performance of the ROSS in the validation cohort compared to the development cohort can be attributed to several key factors. These include differences in population characteristics, such as patient comorbidities and severity of sepsis, which may have affected the model’s ability to accurately predict outcomes. The higher mortality rate in the validation cohort also likely contributed to the reduced discriminative ability of the score, as models tend to perform less effectively when the prevalence of the outcome is high. Additionally, potential changes in clinical practices, diagnostic protocols, and model calibration between the development and validation periods may have further impacted the model’s predictive accuracy. The small sample size for higher threshold scores also limits the reliability of performance metrics. These factors highlight the need for ongoing recalibration and validation of predictive models to ensure their accuracy and applicability across different patient populations and clinical settings. This study emphasizes the need for sepsis scoring systems tailored to specific patient groups, such as older adults, to enhance clinical prognosis and decision-making in the ED. In this validation cohort, the ROSS demonstrated a decrease in performance compared to the development cohort but still showed moderate performance, similar to qSOFA and NEWS2. For simplicity, we recommend using qSOFA, ROSS, or NEWS2 for predicting mortality. The performance of these scores may vary across different populations and settings, underscoring the importance of continuous validation and recalibration to maintain accuracy. Future research should focus on refining these tools by incorporating dynamic and population-specific factors to ensure that sepsis scoring systems remain practical, efficient, and accurate in identifying high-risk patients, particularly in the ED. Limitations Several limitations must be acknowledged. First, our study was retrospective and conducted at a single university hospital, which may limit the generalizability of the results. Second, the exclusion of 226 patients from the validation cohort due to incomplete data or critical events, such as received treatment at OPD or other hospital before transfer to ED, may have introduced selection or misclassification bias, as these excluded cases might have exhibited differing outcomes or characteristics. Third, the lower performance of ROSS may have resulted in a large number of false positives, potentially missing many high-risk patients, along with a small sample size at higher threshold scores. Further validation and recalibration of the prediction model are needed before it can be applied in clinical practice. Conclusion The external validation of the Ramathibodi Older Sepsis Score (ROSS) demonstrated moderate performance in predicting 28-day mortality in older sepsis patients in the ED, with satisfactory AuROC values comparable to those of qSOFA and NEWS. However, further validation and recalibration, particularly for high-risk patients and at higher threshold scores, are needed to clarify and enhance the performance of the ROSS score. Declarations Ethics approval and consent to participate This study was approved by the Committee on Human Rights Related to Research Involving Human Subjects, Faculty of Medicine Ramathibodi Hospital, Mahidol University (COA. MURA2023/141). Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding No funding was obtained for this study Authors’ contributions Pitsucha Sanguanwita: Conceptualization, study design, supervision of data collection, data analysis, interpretation and discussion of results, and manuscript preparation. Nichaphat Nilkosita: Data collection, data analysis, interpretation of results, and manuscript preparation.Chaiyaporn Yuksena: Interpretation and discussion of the results, and manuscript preparation.Piraya Vichiensantha: Data collection, and contribution to manuscript preparation. Phatcha Termkijwanicha: Manuscript preparation and corresponding author. All authors read and approved the final manuscript. Acknowledgments None Declaration of Interest Statements The authors declare no conflicts of interest in the conduct of this research. Declaration of generative AI in scientific writing The authors confirm that no generative artificial intelligence (AI) or AI assistance tools were used in the writing or preparation of this manuscript. 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Archives of Academic Emergency Medicine. 2024;12(1):e56. alliance.org gs. WHAT IS SEPSIS? – DEFINITION OF SEPSIS 2021 [Available from: https://www.global-sepsis-alliance.org/sepsis. Lopansri BK, Miller Iii RR, Burke JP, Levy M, Opal S, Rothman RE, et al. Physician agreement on the diagnosis of sepsis in the intensive care unit: estimation of concordance and analysis of underlying factors in a multicenter cohort. J Intensive Care. 2019;7:13. Churpek MM, Snyder A, Han X, Sokol S, Pettit N, Howell MD, Edelson DP. Quick Sepsis-related Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and Early Warning Scores for Detecting Clinical Deterioration in Infected Patients outside the Intensive Care Unit. Am J Respir Crit Care Med. 2017;195(7):906-11. Brink A, Alsma J, Verdonschot R, Rood PPM, Zietse R, Lingsma HF, Schuit SCE. Predicting mortality in patients with suspected sepsis at the Emergency Department; A retrospective cohort study comparing qSOFA, SIRS and National Early Warning Score. PLoS One. 2019;14(1):e0211133. Abdullah S, Sorensen RH, Nielsen FE. Prognostic Accuracy of SOFA, qSOFA, and SIRS for Mortality Among Emergency Department Patients with Infections. Infect Drug Resist. 2021;14:2763-75. Tables Table 1: Seven predictors of ROSS with score assignment . Ramathibodi Older Sepsis Score (ROSS) Score Alteration of consciousness 1 Oxygen saturation ≤ 93% 2 Heart rate (bpm) 50 – 119 ≤ 49 ≥ 120 0 5 2 Lactate (mmol/l) ≥ 4 1 Respiratory rate ≥ 24 (/minute) 1 Malignancy as comorbid disease 3 Dependent status 2 ROSS: Ramathibodi Older Sepsis Score; bpm: beat per minute. Table 2: Baseline characteristics of older sepsis patient in emergency department by development (n= 599) and validation (n= 500) Characteristic All (n = 1,099) Development (n = 599) Validation ( n= 500 ) P value Age, year, mean (±SD) 77.63 (9.17) 77.13 (9.42) 78.22 (8.84) 0.05 Very Elderly (≥80), n (%) 493 (44.86) 259 (43.24) 234 (46.80) 0.25 Male, n (%) 559 (50.86) 338 (56.43) 221 (44.20) < 0.01 Comorbidities, n (%) Systemic hypertension 777 (70.70) 406 (67.78) 371 (74.20) 0.02 Diabetes mellitus 449 (40.86) 229 (38.23) 220 (44.00) 0.06 Congestive Heart failure 81 (7.37) 32 (5.34) 49 (9.80) < 0.01 Chronic kidney disease 286 (26.02) 144 (24.04) 142 (28.40) 0.11 Airway disease 96 (8.74) 35 (5.84) 61 (12.20) < 0.01 Oncologic 323 (29.39) 157 (26.21) 166 (33.20) 0.01 Transplant 8 (0.73) 1 (0.17) 7 (1.40) 0.03 Liver cirrhosis 78 (7.10) 41 (6.84) 37 (7.40) 0.73 Ischemic heart disease 255 (23.20) 118 (19.70) 137 (27.40) < 0.01 Neuromuscular disease 214 (19.47) 186 (31.05) 28 (5.60) < 0.01 Pulmonary 201 (18.29) 125 (20.87) 76 (15.20) 0.02 Immunocompromised 123 (11.19) 11 (1.84) 112 (22.40) < 0.01 Status, n (%) Independent 548 (49.86) 351 (58.60) 197 (39.40) < 0.01 Partially dependent 265 (24.11) 132 (22.04) 133 (26.60) Totally dependent 286 (26.02) 116 (19.37) 170 (34.00) Source of infection, n (%) Respiratory system 518 (47.13) 291 (48.58) 227 (45.40) 0.30 Gastrointestinal system 116 (10.56) 66 (11.02) 50 (10.00) 0.62 Hepatobiliary system 46 (4.19) 21 (3.51) 25 (5.00) 0.23 Urinary system 334 (30.39) 181 (30.22) 153 (30.60) 0.90 Skin joint infection 49 (4.46) 23 (3.84) 26 (5.20) 0.31 CNS 22 (2.00) 13 (2.17) 9 (1.80) 0.83 Blood stream 60 (5.46) 21 (3.51) 39 (7.80) < 0.01 Other 11 (1.00) 4 (0.67) 7 (1.40) 0.24 ESI triage, n (%) ESI 1 154 (14.01) 5 (0.83) 149 (29.80) < 0.01 ESI 2 689 (62.69) 421 (70.28) 268 (53.60) ESI 3 242 (22.02) 160 (26.71) 82 (16.40) ESI 4 10 (0.91) 10 (1.67) 0 (0.00) ESI 5 4 (0.36) 3 (0.50) 1 (0.20) Triage initial assessment, mean (±SD) Systolic blood pressure 131.62 (32.30) 134.87 (33.23) 127.72 (30.73) < 0.01 Mean arterial pressure 90.26 (19.86) 92.40 (19.98) 87.70 (19.42) < 0.01 Diastolic blood pressure 69.63 (15.44) 71.25 (15.25) 67.69 (15.45) < 0.01 Heart rate 101.62 (23.12) 101.24 (22.11) 102.07 (24.29) 0.55 Temperature 38.10 (1.04) 38.12 (1.02) 38.07 (1.06) 0.47 Respiratory rate 24.95 (5.30) 24.37 (5.12) 25.63 (5.43) < 0.01 Oxygen saturation % 94.54 (5.58) 94.59 (5.37) 94.54 (5.82) 0.88 Conscious, mean (±SD) Alert 815 (74.16) 448 (74.79) 367 (73.40) < 0.01 Response to verbal 172 (15.65) 94 (15.69) 78 (15.60) Response to pain 83 (7.55) 52 (8.68) 31 (6.20) Unresponsive 29 (2.64) 5 (0.83) 24 (4.80) Initial lactate, mmol/L (median, IQR) 2.20 [1.60-3.20] 2.00 [1.40-2.90] 2.50 [1.80-3.60] < 0.01 WBC (×10 3 ) (median, IQR) 10.690 [7.20-1.45] 10.30 [6.80-13.90] 11.285 [7.925-15.170] < 0.01 Platelet count (×10 3 ) (median, IQR) 211.0 [157.0-291.0] 202.0 [150.0-270.0] 226.5 [165.0-313.5] < 0.01 Creatinine, mmol/l (median, IQR) 1.01 [0.70-1.54] 0.94 [0.66-1.46] 1.08 [0.75-1.635] < 0.01 Length of stay, day (median, IQR) 4 [1-10] 3 [1-8] 5[2-12] < 0.01 ED Disposition, n (%) Death 12 (1.09) 10 (11.63) 2 (0.20) < 0.01 Discharge 532 (48.41) 6 (6.98) 526 (51.92) ICU 170 (15.47) 22 (25.58) 148 (14.61) Ward 349 (31.76) 46 (53.49) 303 (29.91) Refer 36 (3.76) 2 (2.33) 34 (3.36) CNS: Central Nervous System, ESI: The Emergency Severity Index, ED: Emergency Department, ICU: Intensive Care Unit Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 18 Nov, 2025 Read the published version in BMC Emergency Medicine → Version 1 posted Editorial decision: Revision requested 12 May, 2025 Reviews received at journal 11 May, 2025 Reviewers agreed at journal 09 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviews received at journal 09 Apr, 2025 Reviews received at journal 02 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers invited by journal 30 Mar, 2025 Editor assigned by journal 22 Mar, 2025 Submission checks completed at journal 22 Mar, 2025 First submitted to journal 21 Mar, 2025 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. 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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-6279299","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441211567,"identity":"64689ef2-8e72-4769-b472-33b60fb2a81c","order_by":0,"name":"Pitsucha Sanguanwit","email":"","orcid":"","institution":"Ramathibodi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Pitsucha","middleName":"","lastName":"Sanguanwit","suffix":""},{"id":441211568,"identity":"f94025ee-6c2c-48cc-8dce-55baf704ff38","order_by":1,"name":"Nichaphat Nilkosit","email":"","orcid":"","institution":"Ramathibodi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nichaphat","middleName":"","lastName":"Nilkosit","suffix":""},{"id":441211569,"identity":"a6ff0ce5-05d0-485a-8020-3f9395396c36","order_by":2,"name":"Chaiyaporn Yuksen","email":"","orcid":"","institution":"Ramathibodi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chaiyaporn","middleName":"","lastName":"Yuksen","suffix":""},{"id":441211570,"identity":"5e9bf99b-5f98-46d3-88fb-18738b18f577","order_by":3,"name":"Piraya Vichiensanth","email":"","orcid":"","institution":"Ramathibodi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Piraya","middleName":"","lastName":"Vichiensanth","suffix":""},{"id":441211571,"identity":"1bbaab9c-4862-47dd-9be6-b06e74a26945","order_by":4,"name":"Phatcha Termkijwanich","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYBAC+xlAgrEBzGZ8wMBwgIfhADOEa4BDi8ENoAaQFh4GBmYDiBaICRLEaGGTAGphIKzldvPzBz932DHYs/c+q7pRc0eG73hjA8OPGoY6c1x+mXPMsLH3TDIDD89xs9s5x57xSJ452MDYc4xBwrIBhy0SCYbNjG3MDDwSaWy3c9gO8xjcSGxg4G0AOuwALi3pH4Fa6hl45J+xFef8g2hh/ItXSw7IlsNAW9jYmHPbIFqY8duSUzizt+04D8+ZNGbp3L7DYL8cljkmIbkBt8M2fPjZVi3H3n6M8XPOt8P2fMebDz58U2PDj8sWGOBB4QEVS+BXPwpGwSgYBaMALwAADRNfIIeCKLQAAAAASUVORK5CYII=","orcid":"","institution":"Ramathibodi Hospital","correspondingAuthor":true,"prefix":"","firstName":"Phatcha","middleName":"","lastName":"Termkijwanich","suffix":""}],"badges":[],"createdAt":"2025-03-21 16:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6279299/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6279299/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12873-025-01382-x","type":"published","date":"2025-11-18T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81026492,"identity":"3d7bfd20-48ed-4eb2-9951-030999b15250","added_by":"auto","created_at":"2025-04-21 10:39:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":177753,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow diagram\u003cbr\u003e\nOPD: outpatients’ unit, ED: emergency department, CPR: cardiopulmonary resuscitation.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6279299/v1/17778c50ee5aafa3cce3ec93.png"},{"id":81025247,"identity":"07ade0a3-8cd8-4e1b-a5c4-b3d63fbb12a2","added_by":"auto","created_at":"2025-04-21 10:31:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203499,"visible":true,"origin":"","legend":"\u003cp\u003ecompare ROC curve of ROSS score between validation and development cohort.\u003c/p\u003e\n\u003cp\u003eROC: receiver operating characteristic, ROSS: Ramathibodi older sepsis score\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6279299/v1/32e385d182ad02b28ba77422.png"},{"id":81025244,"identity":"f97904f7-8822-4a22-8184-8b895a694a29","added_by":"auto","created_at":"2025-04-21 10:31:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":463264,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots of the ROSS score in the development and validation sets.\u003cbr\u003e\nThe dash line at the cutoff point of 6 categorized older patients with sepsis into low-risk and high-risk group of 28-day mortality\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROSS: Ramathibodi older sepsis score.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6279299/v1/9b24298913a7f1ac31cd3e79.png"},{"id":81025260,"identity":"c3ad20e0-4d93-4090-b3a6-61b43a9d5142","added_by":"auto","created_at":"2025-04-21 10:31:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":992645,"visible":true,"origin":"","legend":"\u003cp\u003eArea under the ROC curves of different models from the validation cohort (N=500) in predicting 28-day mortality.\u003c/p\u003e\n\u003cp\u003eROC: receiver operating characteristic; ROSS: Ramathibodi older sepsis score; SIRS: Systemic Inflammatory Response Syndrome; qSOFA: quick Sequential Organ Failure Assessment; NEWS: National early warning score; REWS: Ramathibodi early warning score.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6279299/v1/d2f1927f3587f8893d05c17d.png"},{"id":96649942,"identity":"c86878cb-08fc-4e09-b57c-152cdd6b67bf","added_by":"auto","created_at":"2025-11-24 16:00:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1910465,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6279299/v1/5189c5f3-4ccf-4a6f-904c-09771511c4ad.pdf"},{"id":81027235,"identity":"c716cefc-f8c4-4422-9d85-0ab78941270a","added_by":"auto","created_at":"2025-04-21 10:47:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18589,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6279299/v1/11b3cbe79b7eb549c15104fb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eExternal validation of the clinical prediction score for predicting 28-day mortality of older sepsis patients in emergency department\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs the global population ages, the World Health Organization (WHO) predicts that the proportion of individuals aged 60 and above will grow by 3% annually, nearly doubling from 12\u0026ndash;22% by 2050\u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e. In Thailand, the older population is expected to increase 2.5 times by 2050\u003csup\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/sup\u003e. Sepsis remains a significant cause of morbidity and mortality in older adults, who experience higher incidence rate and worse outcomes due to age-related physiological changes and multiple comorbidities. Among patients over 60, diagnosis is particularly challenging due to atypical symptoms frequently influenced by age-specific factors. In the emergency department (ED), these atypical presentations may obscure or delay timely diagnosis and intervention for sepsis\u003csup\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEarly severity prediction and prompt intervention for high-risk patients are essential for improving outcomes in the ED, where clinical tools must be both simple and efficient. Established clinical prediction scores for sepsis such as Systemic Inflammatory Response Syndrome (SIRS)\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/sup\u003e, Sequential Organ Failure Assessment (SOFA)\u003csup\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/sup\u003e, quick SOFA (qSOFA)\u003csup\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e, National Early Warning Score (NEWS2)\u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/sup\u003e, have demonstrated poor prognostic performance in older patients, with area under the receiver operating characteristic (AuROC) values ranging from 0.50 to 0.60 \u003csup\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/sup\u003e. These tools incorporate initial physiological parameters at triage\u0026mdash;such as blood pressure, respiratory rate, oxygen saturation, consciousness, temperature, and heart rate\u0026mdash;but show limited accuracy in the elderly population.\u003c/p\u003e \u003cp\u003eThe Mortality in ED Sepsis (MEDS)\u003csup\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e, and Sepsis Patient Evaluation in the ED (SPEED)\u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e offer valuable assessments of mortality risk and inform treatment decisions in general sepsis populations, achieving an AuROC range of 0.78 to 0.81\u003csup\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e. However, these scores include laboratory values, such as complete blood count, markers of acidosis, lactate levels, and respiratory infection indicators, which may not be readily available in the initial phase of ED assessment. Additionally, these scores lack specific modifications for older adults, who have unique physiological responses and risk factors. Zelis N. et al proposed\u003c/p\u003e \u003cp\u003ethe Rapid Emergency Medicine Score for Upstream Prediction (RISE UP)\u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e which incorporates age-specific factors tailored for older patients with general conditions in the ED and demonstrates good prognostic performance achieving an AuROC range of 0.83 to 0.84. However, its dependence on blood chemistry results [e.g., serum albumin, blood urea nitrogen (BUN), lactate dehydrogenase (LDH), bilirubin may delay timely application in the ED.\u003c/p\u003e \u003cp\u003eOur previous study introduced the Ramathibodi Older Sepsis Score (ROSS)\u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e, a clinical prediction tool designed for the initial assessment of older sepsis patients (\u0026ge;\u0026thinsp;60 years) in the ED, the ROSS incorporates criteria including: Altered consciousness, Blood lactate\u0026thinsp;\u0026ge;\u0026thinsp;4 mmol/L, Cancer (comorbidity), Dyspnea (respiratory rate\u0026thinsp;\u0026ge;\u0026thinsp;24 breaths per minute), Desaturation (Oxygen saturation\u0026thinsp;\u0026le;\u0026thinsp;93%), Dependent functional status, and Heart rate, providing an accessible tool at the point of care for predicting 28-day mortality.\u003c/p\u003e \u003cp\u003eThe present study aimed to validate the ROSS clinical prediction score for predicting 28-day mortality in older sepsis patients in the ED, using data from the initial development study conducted at Ramathibodi Hospital in 2018, as well as additional validation data collected in 2022.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study, designed to validate a clinical prediction rule, was a single-center, retrospective cohort study involving ED patients at Ramathibodi Hospital, a tertiary university hospital in Bangkok, Thailand. Data for the development cohort from our previous study\u003csup\u003e(14)\u003c/sup\u003e were retrospectively collected between August and December 2018, with approval from the Human Research Ethics Committee of the Faculty of Medicine, Ramathibodi Hospital, Mahidol University (Study Code: COA. MURA2021/434). Data for the validation cohort were collected from January to June 2022, with study approval granted by the same ethics committee (Study Code: COA. MURA2023/141).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn both the validation and development cohort, all patients aged 60 years and older who presented to the ED with suspected sepsis during the specified periods were included. Suspected sepsis\u003csup\u003e(4, 5, 9, 15, 16)\u003c/sup\u003e was defined as the presence of fever or signs of infection in a patient managed according to the Ramathibodi sepsis protocol. Sepsis diagnosis was confirmed based on criteria from the International Classification of Diseases, Tenth Revision (ICD-10), or through positive blood or body fluid cultures. Exclusion criteria included patients who had received treatment at an outpatient unit or another hospital prior to transfer to the ED, those who had undergone cardiopulmonary resuscitation earlier in the same visit, and patients with incomplete data in the electronic medical record (EMR) database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive variables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ROSS score, ranging from 0 to 15, assigns points based on seven key predictors: Altered consciousness (defined as a state of not being alert, characterized by impairment in at least one of the following: verbal response, response to pain, or unresponsiveness), Blood lactate \u0026ge;4 mmol/L, Cancer (comorbidity), Dyspnea (respiratory rate \u0026ge;24 breaths per minute), Desaturation (Oxygen saturation \u0026le;93%), Dependent functional status (defined as at least one dependency to perform activities of daily living (ADL), and Heart rate (\u003cstrong\u003eTable 1\u003c/strong\u003e). Patients with a total score of 0 to 5 are categorized as low-risk for 28-day mortality, while those with a score of 6 to 15 are classified as high-risk for 28-day mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary endpoint of this study was 28-day all-cause mortality. Secondary outcomes included the application of the sepsis treatment bundle in the ED, ED disposition (intensive care unit admission, general ward admission, discharge, referral, or death), and length of hospital stay (LOS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample size was calculated based on our develop cohort study \u003csup\u003e(14)\u003c/sup\u003e, selecting the requirement for each variable in the ROSS score, on AuROC = 0.87 with 8 parameters (Altered consciousness, Blood lactate \u0026ge;4 mmol/L, Cancer (comorbidity), Dyspnea (respiratory rate \u0026ge;24 breaths per minute), Desaturation (Oxygen saturation \u0026le;93%), Dependent functional status, and Heart rate\u0026le; 49 or \u0026ge;120 beat per minute \u0026nbsp;), assumptions included an acceptable difference of 0.05 between apparent and adjusted R-squared, with a margin of error of 0.05 for the intercept estimation. The Events per Predictor Parameter (EPP) was estimated assuming a 28-day mortality prevalence of 0.07, using Stata version 16 (StataCorp LLC, College Station, TX, USA). The final required sample size was 482, with an anticipated 34 events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were calculated for all clinical characteristics and relevant variables. Continuous variables are presented as means and standard deviations (SD) for normally distributed data or as medians for non-parametric tests and were analyzed using either the independent t-test or the Mann\u0026ndash;Whitney U test, as appropriate. Categorical data are presented as percentages and were analyzed using the chi-square test or Fisher\u0026rsquo;s exact test. Statistical significance was defined as a two-tailed p-value of \u0026lt;0.05. Validation data were compared with development data by examining areas under the receiver operating characteristic (ROC) curves. The predictive ability of the scoring system was visually assessed for both datasets using probability or risk curves. Hosmer\u0026ndash;Lemeshow goodness-of-fit statistics and a calibration plot were used to evaluate the agreement between observed and predicted score values. All statistical analyses were conducted using STATA 16.0 (StataCorp LP, College Station, TX, USA).\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of studied cases\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 605 older patients with suspected sepsis admitted to the ED were included in the development cohort, while 726 older patients with suspected sepsis were included in validation cohort. Six patients were excluded from development cohort, and 226 patients were excluded from validation cohort due to contraindications for outpatient treatment prior to ED transfer, receipt of cardiopulmonary resuscitation during the same visit, or missing data. This resulted in a final eligible sample of 1,099 patients, with 599 in the development cohort and 500 in the validation cohort. The mortality rates for the development and validation cohorts were 7.12% and 8.80%, respectively (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. provides a comparison of baseline characteristics, including vital signs, ESI triage levels, infection sources, and laboratory values, between the development and validation cohorts. In the development cohort, there was a higher proportion of males and patients with comorbidities, such as neuromuscular and pulmonary diseases, as well as systolic blood pressure. In the validation cohort, higher proportions were noted for comorbidities including congestive heart failure, airway disease, oncology-related conditions, transplantation, ischemic heart disease, immunocompromised status, dependent status, and bloodstream infection sources. Additionally, several ROSS predictor components\u0026mdash;namely dependent status, respiratory rate, level of consciousness, and initial venous lactate\u0026mdash;differed between the two cohorts. The outcomes of length of stay and ED disposition (death, discharge, admission to intensive care, admission to the general ward, and referral out) differed between the two cohorts.\u003c/p\u003e\n\u003cp\u003eThe area under the ROC curve (AuROC), reflecting the predictive ability of the ROSS model, was 0.87 (95% CI: 0.82-0.92) in the development cohort and 0.69 (95% CI: 0.61-0.77) in the validation cohort; P \u0026lt;0.01 (\u003cstrong\u003eFigure 2\u003c/strong\u003e). Calibration plots for the ROSS model in both cohorts are presented in \u003cstrong\u003eFigures 3A\u003c/strong\u003e and \u003cstrong\u003e3B\u003c/strong\u003e, demonstrating an acceptable alignment between predicted probabilities and observed 28-day mortality rates. This alignment is evident by the proximity of observed events, represented by circles, to the predictive line. Using a cutoff score of 6 or higher, the ROSS model effectively stratifies older sepsis patients into low-risk and high-risk mortality groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the validation cohort, the ROSS model demonstrated predictive capability for 28-day mortality in older patients with suspected sepsis, with a discriminative ability of AuROC: 0.69 (95% CI: 0.61-0.77). This performance was compared to other scoring models: SIRS (AuROC: 0.50, 95% CI: 0.42-0.58; P \u0026lt; 0.01), qSOFA (AuROC: 0.70, 95% CI: 0.64-0.77; P = 0.75), NEWS (AuROC: 0.68, 95% CI: 0.60-0.76; P = 0.81), and REWS (AuROC: 0.66, 95% CI: 0.57-0.75; P = 0.51) as shown in \u003cstrong\u003eFigure 4\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eSensitivity, specificity, positive likelihood ratio, and the AuROC for predicting 28-day mortality for each ROSS score from the validation cohort are reported in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e. In our previous study based on the development cohort, we established a cutoff of ROSS \u0026ge;6 to differentiate between low- and high-risk 28-day mortality groups,\u0026nbsp;with mortality rates of 2.73% and 23.77%, respectively \u003csup\u003e(14)\u003c/sup\u003e. In development cohort, a cutoff of ROSS \u0026ge;6 demonstrated a sensitivity of 69.1% (95% CI: 52.9%-82.4%) and a specificity of 83.3% (95% CI: 79.9%-86.3%). In the validation cohort, the sensitivity was 56.8% (95% CI: 41.0%-71.7%), and the specificity was 70% (95% CI: 65.5%-74.1%). Additionally, the validation cohort yielded a positive predictive value of 15.4% (95% CI: 10.2%-21.9%) and a negative predictive value of 94.4% (95% CI: 91.4%-96.6%), as presented in \u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSepsis scoring systems in the ED are essential for the early identification of high-risk patients to enhance survival outcomes. These tools are commonly utilized to assess sepsis severity and predict mortality risk. However, most sepsis scores were developed based on mixed-age populations and lack adjustments for age-specific physiological changes, particularly in older adults\u003csup\u003e(3)\u003c/sup\u003e. This limitation may lead to under-estimation, where high-risk older patients are not adequately identified, or overestimation, resulting in unnecessary monitoring and interventions. The SPEED score (AuROC 0.81) shows good performance for mortality prognosis in\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethe general sepsis population but includes laboratory parameters that may limit its immediate use in the ED\u003csup\u003e(12)\u003c/sup\u003e. Similarly, the RISE UP score (AuROC 0.84), designed for older patients, demonstrates good performance but also relies on laboratory data, which may hinder its timely application in clinical decision-making. Both models highlight the challenge of balancing predictive accuracy with practicality in time-sensitive settings \u003csup\u003e(13)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis study represents the third in a series of investigations on older sepsis patients in the ED at our institution. It focuses on the external validation of the Ramathibodi Older Sepsis Score (ROSS), with simple parameter specifically assessing its temporal validity using data from different periods, validation data from 2022 and developmental data from 2018. In this validation study, the mortality rate of older sepsis patient in ED was higher than in the development cohort (8.80% vs. 7.12%)\u003csup\u003e(14)\u003c/sup\u003e. This validation cohort could also show the performance of the score with a different prevalence in 28-day mortality. The predictive performance of the ROSS model was satisfactory, with an area under the receiver operating characteristic (AuROC) curve of 0.87 in the development cohort and 0.69 in the validation cohort. Although the performance in the validation set was lower, the model still effectively distinguished between survivors and non-survivors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to other established scoring systems, the ROSS demonstrated superior accuracy relative to SIRS (AuROC 0.50, 95% CI: 0.42\u0026ndash;0.58), REWS (AuROC 0.66, 95% CI: 0.57\u0026ndash;0.75), and NEWS (AuROC 0.68, 95% CI: 0.60\u0026ndash;0.76), but was marginally less accurate than qSOFA (AuROC 0.70, 95% CI: 0.64\u0026ndash;0.77). The performance of ROSS shows similar as qSOFA and NEWS in this validation cohort. qSOFA is a simple tool for predicting mortality in the ED, consisting of three components: systolic blood pressure \u0026lt;100 mmHg, respiratory rate \u0026gt;22 breaths per minute, and altered consciousness. Its predictive performance for mortality has been reported with an AuROC ranging from 0.63 to 0.70 in general sepsis population \u003csup\u003e(17-19)\u003c/sup\u003e, an AuROC ranging from 0.56-0.60 in older sepsis population \u003csup\u003e(9, 13)\u003c/sup\u003e. NEWS2 includes six parameters: respiratory rate, oxygen saturation (adjusted for the general population or individuals with obstructive lung disease), systolic blood pressure, pulse rate, consciousness, and temperature. Each parameter is scored from 0 to 3, with a total score range of 0 to 18, providing a comprehensive and detailed assessment of sepsis severity.\u003c/p\u003e\n\u003cp\u003eAnalysis of sensitivity, specificity, AuROC, and 28-day mortality from the validation cohort is presented in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e. As the threshold score increases, sensitivity decreases, meaning the model becomes less effective at identifying all patients at risk of 28-day mortality. In contrast, specificity increases, indicating the model is more accurate at identifying patients who are not at risk. At a threshold of \u0026ge;1, sensitivity is very high (97.73%), but specificity is low (7.68%), resulting in a large number of false positives. At a threshold of \u0026ge;8, specificity improves significantly (88.82%), but sensitivity drops to 31.82%, potentially missing many high-risk patients. At a threshold of \u0026ge;9, the likelihood ratio positive (LR+) reaches 3.45, suggesting moderate predictive value. However, beyond \u0026ge;10, LR+ becomes less reliable due to small sample sizes. The AuROC values range from 0.50 to 0.64, indicating poor to moderate predictive performance. At a threshold of \u0026ge;12, mortality is reported as 0%, likely due to an insufficient sample size of high-risk patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e; The development cohort we selected a cutoff of ROSS \u0026ge;6 to differentiate between low- and high-risk 28-day mortality groups. ROSS demonstrated high sensitivity and a strong negative predictive value, with specificity in the development set at 83.3% (95% CI: 79.9%-86.3%), but decreased to 70% (95% CI: 65.5%-74.1%) in the validation set. The negative predictive value also declined slightly from 97.3% (95% CI: 95.4%-98.5%) in the development cohort to 94.4% (95% CI: 91.4%-96.6%) in the validation cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe lower performance of the ROSS in the validation cohort compared to the development cohort can be attributed to several key factors. These include differences in population characteristics, such as patient comorbidities and severity of sepsis, which may have affected the model\u0026rsquo;s ability to accurately predict outcomes. The higher mortality rate in the validation cohort also likely contributed to the reduced discriminative ability of the score, as models tend to perform less effectively when the prevalence of the outcome is high. Additionally, potential changes in clinical practices, diagnostic protocols, and model calibration between the development and validation periods may have further impacted the model\u0026rsquo;s predictive accuracy. The small sample size for higher threshold scores also limits the reliability of performance metrics. These factors highlight the need for ongoing recalibration and validation of predictive models to ensure their accuracy and applicability across different patient populations and clinical settings.\u003c/p\u003e\n\u003cp\u003eThis study emphasizes the need for sepsis scoring systems tailored to specific patient groups, such as older adults, to enhance clinical prognosis and decision-making in the ED. In this validation cohort, the ROSS demonstrated a decrease in performance compared to the development cohort but still showed moderate performance, similar to qSOFA and NEWS2. For simplicity, we recommend using qSOFA, ROSS, or NEWS2 for predicting mortality. The performance of these scores may vary across different populations and settings, underscoring the importance of continuous validation and recalibration to maintain accuracy. Future research should focus on refining these tools by incorporating dynamic and population-specific factors to ensure that sepsis scoring systems remain practical, efficient, and accurate in identifying high-risk patients, particularly in the ED.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations must be acknowledged. First, our study was retrospective and conducted at a single university hospital, which may limit the generalizability of the results. Second, the exclusion of 226 patients from the validation cohort due to incomplete data or critical events, such as received treatment at OPD or other hospital before transfer to ED, may have introduced selection or misclassification bias, as these excluded cases might have exhibited differing outcomes or characteristics. Third, the lower performance of ROSS may have resulted in a large number of false positives, potentially missing many high-risk patients, along with a small sample size at higher threshold scores. Further validation and recalibration of the prediction model are needed before it can be applied in clinical practice.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe external validation of the Ramathibodi Older Sepsis Score (ROSS) demonstrated moderate performance in predicting 28-day mortality in older sepsis patients in the ED, with satisfactory AuROC values comparable to those of qSOFA and NEWS. However, further validation and recalibration, particularly for high-risk patients and at higher threshold scores, are needed to clarify and enhance the performance of the ROSS score.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Committee on Human Rights Related to Research Involving Human Subjects, Faculty of Medicine Ramathibodi Hospital, Mahidol University (COA. MURA2023/141).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was obtained for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePitsucha Sanguanwita: Conceptualization, study design, supervision of data collection, data analysis, interpretation and discussion of results, and manuscript preparation. Nichaphat Nilkosita: Data collection, data analysis, interpretation of results, and manuscript preparation.Chaiyaporn Yuksena: Interpretation and discussion of the results, and manuscript preparation.Piraya Vichiensantha: Data collection, and contribution to manuscript preparation. Phatcha Termkijwanicha: Manuscript preparation and corresponding author. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest in the conduct of this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI in scientific writing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that no generative artificial intelligence (AI) or AI assistance tools were used in the writing or preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOrganization WH. Ageing and health: WHO; 2022 [Available from: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health.\u003c/li\u003e\n\u003cli\u003eBureau PR. World population data sheet 2022: PRB; 2022 [Available from: https://www.prb.org/wp-content/uploads/2022/09/2022-World-Population-Data-Sheet-Booklet.pdf.\u003c/li\u003e\n\u003cli\u003eRowe TA, McKoy JM. Sepsis in Older Adults. Infect Dis Clin North Am. 2017;31(4):731-42.\u003c/li\u003e\n\u003cli\u003eBone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM, Knaus WA, et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest. 1992;101(6):1644-55.\u003c/li\u003e\n\u003cli\u003eLevy MM, Fink MP, Marshall JC, Abraham E, Angus D, Cook D, et al. 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Crit Care Med. 2003;31(4):1250-6.\u003c/li\u003e\n\u003cli\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801-10.\u003c/li\u003e\n\u003cli\u003eSeymour CW, Liu VX, Iwashyna TJ, Brunkhorst FM, Rea TD, Scherag A, et al. Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Jama. 2016;315(8):762-74.\u003c/li\u003e\n\u003cli\u003eUsman OA, Usman AA, Ward MA. Comparison of SIRS, qSOFA, and NEWS for the early identification of sepsis in the Emergency Department. Am J Emerg Med. 2019;37(8):1490-7.\u003c/li\u003e\n\u003cli\u003eBoonmee P, Ruangsomboon O, Limsuwat C, Chakorn T. Predictors of Mortality in Elderly and Very Elderly Emergency Patients with Sepsis: A Retrospective Study. West J Emerg Med. 2020;21(6):210-8.\u003c/li\u003e\n\u003cli\u003ede Groot B, Stolwijk F, Warmerdam M, Lucke JA, Singh GK, Abbas M, et al. The most commonly used disease severity scores are inappropriate for risk stratification of older emergency department sepsis patients: an observational multi-centre study. Scand J Trauma Resusc Emerg Med. 2017;25(1):91.\u003c/li\u003e\n\u003cli\u003eShapiro NI, Wolfe RE, Moore RB, Smith E, Burdick E, Bates DW. Mortality in Emergency Department Sepsis (MEDS) score: a prospectively derived and validated clinical prediction rule. Crit Care Med. 2003;31(3):670-5.\u003c/li\u003e\n\u003cli\u003eBewersdorf JP, Hautmann O, Kofink D, Abdul Khalil A, Zainal Abidin I, Loch A. The SPEED (sepsis patient evaluation in the emergency department) score: a risk stratification and outcome prediction tool. European Journal of Emergency Medicine. 2017;24(3):170-5.\u003c/li\u003e\n\u003cli\u003eZelis N, Buijs J, de Leeuw PW, van Kuijk SMJ, Stassen PM. A new simplified model for predicting 30-day mortality in older medical emergency department patients: The rise up score. European Journal of Internal Medicine. 2020;77:36-43.\u003c/li\u003e\n\u003cli\u003eSanguanwit P, Yuksen C, Khorana J, Sutham K, Phootothum Y, Damdin S. Development of a Clinical Score for Predicting 28-Day Mortality in Geriatric Sepsis Patients; a Cohort study. Archives of Academic Emergency Medicine. 2024;12(1):e56.\u003c/li\u003e\n\u003cli\u003ealliance.org gs. WHAT IS SEPSIS? \u0026ndash; DEFINITION OF SEPSIS 2021 [Available from: https://www.global-sepsis-alliance.org/sepsis.\u003c/li\u003e\n\u003cli\u003eLopansri BK, Miller Iii RR, Burke JP, Levy M, Opal S, Rothman RE, et al. Physician agreement on the diagnosis of sepsis in the intensive care unit: estimation of concordance and analysis of underlying factors in a multicenter cohort. J Intensive Care. 2019;7:13.\u003c/li\u003e\n\u003cli\u003eChurpek MM, Snyder A, Han X, Sokol S, Pettit N, Howell MD, Edelson DP. Quick Sepsis-related Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and Early Warning Scores for Detecting Clinical Deterioration in Infected Patients outside the Intensive Care Unit. Am J Respir Crit Care Med. 2017;195(7):906-11.\u003c/li\u003e\n\u003cli\u003eBrink A, Alsma J, Verdonschot R, Rood PPM, Zietse R, Lingsma HF, Schuit SCE. Predicting mortality in patients with suspected sepsis at the Emergency Department; A retrospective cohort study comparing qSOFA, SIRS and National Early Warning Score. PLoS One. 2019;14(1):e0211133.\u003c/li\u003e\n\u003cli\u003eAbdullah S, Sorensen RH, Nielsen FE. Prognostic Accuracy of SOFA, qSOFA, and SIRS for Mortality Among Emergency Department Patients with Infections. Infect Drug Resist. 2021;14:2763-75.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eSeven predictors of ROSS with score assignment\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRamathibodi Older Sepsis Score (ROSS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eAlteration of consciousness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eOxygen saturation \u0026le; 93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHeart rate (bpm)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 50 \u0026ndash; 119\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026le; 49\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge; 120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eLactate (mmol/l) \u0026ge; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eRespiratory rate \u0026ge; 24 (/minute)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eMalignancy as comorbid disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eDependent status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eROSS: Ramathibodi Older Sepsis Score; bpm: beat per minute.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003eBaseline characteristics of older sepsis patient in emergency department by development (n= 599) and validation (n= 500)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 1,099)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevelopment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 599)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003en=\u0026nbsp;500\u0026nbsp;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eAge, year, mean (\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e77.63 (9.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e77.13 (9.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e78.22 (8.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eVery Elderly (\u0026ge;80), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e493 (44.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e259 (43.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e234 (46.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e559 (50.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e338 (56.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e221 (44.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 610px;\"\u003e\n \u003cp\u003eComorbidities, n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eSystemic hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e777 (70.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e406 (67.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e371 (74.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e449 (40.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e229 (38.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e220 (44.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eCongestive Heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e81 (7.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e32 (5.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e49 (9.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e286 (26.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e144 (24.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e142 (28.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eAirway disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e96 (8.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e35 (5.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e61 (12.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eOncologic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e323 (29.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e157 (26.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e166 (33.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eTransplant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e8 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e7 (1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eLiver cirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e78 (7.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e41 (6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e37 (7.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eIschemic heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e255 (23.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e118 (19.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e137 (27.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eNeuromuscular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e214 (19.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e186 (31.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e28 (5.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003ePulmonary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e201 (18.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e125 (20.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e76 (15.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eImmunocompromised\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e123 (11.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e11 (1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e112 (22.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 610px;\"\u003e\n \u003cp\u003eStatus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e548 (49.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e351 (58.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e197 (39.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003ePartially dependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e265 (24.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e132 (22.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e133 (26.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eTotally dependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e286 (26.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e116 (19.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e170 (34.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 610px;\"\u003e\n \u003cp\u003eSource of infection, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eRespiratory system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e518 (47.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e291 (48.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e227 (45.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eGastrointestinal system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e116 (10.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e66 (11.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e50 (10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eHepatobiliary system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e46 (4.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e21 (3.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e25 (5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003eUrinary system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e334 (30.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e181 (30.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e153 (30.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eSkin joint infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e49 (4.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e23 (3.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e26 (5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eCNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e22 (2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e13 (2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e9 (1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eBlood stream\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e60 (5.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e21 (3.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e39 (7.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e11 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4 (0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e7 (1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 610px;\"\u003e\n \u003cp\u003eESI triage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eESI 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e154 (14.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e5 (0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e149 (29.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eESI 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e689 (62.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e421 (70.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e268 (53.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eESI 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e242 (22.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e160 (26.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e82 (16.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eESI 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e10 (0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e10 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eESI 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 610px;\"\u003e\n \u003cp\u003eTriage initial assessment, mean (\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e131.62 (32.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e134.87 (33.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e127.72 (30.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eMean arterial pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e90.26 (19.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e92.40 (19.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e87.70 (19.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e69.63 (15.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e71.25 (15.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e67.69 (15.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eHeart rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e101.62 (23.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e101.24 (22.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e102.07 (24.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e38.10 (1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e38.12 (1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e38.07 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eRespiratory rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e24.95 (5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e24.37 (5.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e25.63 (5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eOxygen saturation\u0026nbsp;%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e94.54 (5.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e94.59 (5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e94.54 (5.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 610px;\"\u003e\n \u003cp\u003eConscious, mean (\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eAlert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e815 (74.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e448 (74.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e367 (73.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eResponse to verbal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e172 (15.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e94 (15.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e78 (15.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eResponse to pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e83 (7.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e52 (8.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e31 (6.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eUnresponsive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e29 (2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e5 (0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e24 (4.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eInitial lactate, mmol/L\u003cbr\u003e\u0026nbsp;(median, IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2.20 [1.60-3.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2.00 [1.40-2.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e2.50 [1.80-3.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eWBC (\u0026times;10\u003csup\u003e3\u003c/sup\u003e)\u003cbr\u003e\u0026nbsp;(median, IQR)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e10.690\u003cbr\u003e\u0026nbsp;[7.20-1.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e10.30\u003cbr\u003e\u0026nbsp;[6.80-13.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e11.285\u003cbr\u003e\u0026nbsp;[7.925-15.170]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003ePlatelet count (\u0026times;10\u003csup\u003e3\u003c/sup\u003e) (median, IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e211.0\u003cbr\u003e\u0026nbsp;[157.0-291.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e202.0\u003cbr\u003e\u0026nbsp;[150.0-270.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e226.5\u003cbr\u003e\u0026nbsp;[165.0-313.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eCreatinine, mmol/l\u003cbr\u003e\u0026nbsp;(median, IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1.01 [0.70-1.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.94 [0.66-1.46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.08 [0.75-1.635]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eLength of stay, day\u003c/p\u003e\n \u003cp\u003e(median, IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4 [1-10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3 [1-8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e5[2-12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 610px;\"\u003e\n \u003cp\u003eED Disposition, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e12 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e10 (11.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e2 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eDischarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e532 (48.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e6 (6.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e526 (51.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e170 (15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e22 (25.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e148 (14.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eWard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e349 (31.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e46 (53.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e303 (29.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003eRefer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e36 (3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2 (2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e34 (3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;CNS: Central Nervous System, ESI: The Emergency Severity Index, ED: Emergency Department, ICU: Intensive Care Unit \u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"validation, mortality, older, sepsis, emergency department","lastPublishedDoi":"10.21203/rs.3.rs-6279299/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6279299/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSepsis in older patients is associated with a high mortality rate, presenting a considerable clinical challenge. Existing mortality prediction scoring systems have demonstrated limited accuracy in this population. To address this limitation, the Ramathibodi Older Sepsis Score (ROSS) was developed. However, external validation is necessary to assess its efficacy in predicting 28-day mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aimed to validate the Ramathibodi Older Sepsis Score (ROSS) for predicting 28-day mortality in older sepsis patients in the emergency department (ED).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data for the development cohort were retrospectively collected from August to December 2018, while data for the validation cohort were collected from January to June 2022. Seven prespecified prognostic factors for 28-day mortality were used to calculate a predictive score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 500 older sepsis patients were included in the validation cohort, and 599 patients were included in the development cohort. The predictive ability of the ROSS model in the validation cohort (Area under receiver operating characteristic curve; AuROC: 0.69, 95% CI: 0.61–0.77) decreased compared to the development cohort (AuROC: 0.87, 95% CI: 0.82–0.92); P\u0026lt;0.01. This performance was compared with other scoring models: SIRS (AuROC: 0.50, 95% CI: 0.42–0.58; P \u0026lt; 0.01), qSOFA (AuROC: 0.70, 95% CI: 0.64–0.77; P = 0.75), NEWS (AuROC: 0.68, 95% CI: 0.60–0.76; P = 0.81), and REWS (AuROC: 0.66, 95% CI: 0.57–0.75; P = 0.51)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The external validation of the ROSS demonstrated moderate performance in predicting 28-day mortality in older sepsis patients, with AuROC values similar to qSOFA and NEWS.\u003c/p\u003e","manuscriptTitle":"External validation of the clinical prediction score for predicting 28-day mortality of older sepsis patients in emergency department","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 10:31:20","doi":"10.21203/rs.3.rs-6279299/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-12T04:23:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-11T18:47:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233339391429801032520425630798055661476","date":"2025-05-09T06:20:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34744723068381278677543774801030159204","date":"2025-05-06T19:43:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121880236722903237337488882856310123442","date":"2025-04-10T13:44:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-09T17:50:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T18:30:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86638910500398393555865659958500903738","date":"2025-03-31T21:29:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256444865825705741840064361946216628964","date":"2025-03-30T20:49:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-30T17:19:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-22T13:24:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-22T13:22:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2025-03-21T16:33:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32ff3c30-e5dc-46b9-8ac7-e67bf4e150c6","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T15:59:24+00:00","versionOfRecord":{"articleIdentity":"rs-6279299","link":"https://doi.org/10.1186/s12873-025-01382-x","journal":{"identity":"bmc-emergency-medicine","isVorOnly":false,"title":"BMC Emergency Medicine"},"publishedOn":"2025-11-18 15:56:52","publishedOnDateReadable":"November 18th, 2025"},"versionCreatedAt":"2025-04-21 10:31:20","video":"","vorDoi":"10.1186/s12873-025-01382-x","vorDoiUrl":"https://doi.org/10.1186/s12873-025-01382-x","workflowStages":[]},"version":"v1","identity":"rs-6279299","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6279299","identity":"rs-6279299","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}