Clinical Frailty Score for Hospital Outcome for Patients Aged ≥ 75 Following Emergency Department Resuscitation Room Admission: A Retrospective Monocenter Study

Clinical Frailty Score for Hospital Outcome for Patients Aged ≥ 75 Following Emergency Department Resuscitation Room Admission: A Retrospective Monocenter Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical Frailty Score for Hospital Outcome for Patients Aged ≥ 75 Following Emergency Department Resuscitation Room Admission: A Retrospective Monocenter Study Fabien Coisy, Mathilde Jallade, Florian Regal, Camille Moser, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7460546/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2026 Read the published version in Internal and Emergency Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Introduction: Elderly patients (≥ 75 years) often require resuscitation room (RR) care in the emergency department (ED), yet decisions regarding intensive care unit (ICU) admission remain complex. Assessment of quality of life and frailty is necessary to determine the level of care required for elderly patients. The Clinical Frailty Scale (CFS) is a validated tool for assessing frailty and predicting mortality, but its role in ICU triage remains unclear. The aim of this study was to compare the CFS of patients admitted to the ICU with those admitted to the general inpatient unit (GIU) after receiving initial intensive care. Methods: This was a retrospective, single-centre study including patients aged ≥ 75 years admitted to the ED RR from November 1, 2023, to March 31, 2024. The primary outcome was the comparison of CFS between ICU and GIU admissions. Secondary outcomes included predictive performance of CFS for ICU admission and in-hospital mortality. Results: Of the 392 patients enrolled, 170 (43%) were admitted to the ICU and 222 (57%) to the GIU. The median CFS was 3 [ 2 – 4 ] in ICU-admitted patients and 4 [ 3 – 5 ] in GIU-admitted patients (p < 0.001). In-hospital mortality rate was 35/222 (16%) in GIU-admitted group and 30/180 (18%) in ICU-admitted group (p = 0.72). CFS predictive value for ICU admission had an area under curve of 0.68 (95% confidence interval (95%CI): 0.63–0.73) and for in-hospital mortality of 0.62 (95%CI: 0.55–0.69). Conclusions: CFS differed between ICU and GIU groups, suggesting its potential role in orientation after RR admission. Although CFS’s predictive value remains limited, in this study it was better than the Charlson Index or SAPS 2 for predicting ICU admission. Further prospective studies should evaluate its integration with other clinical parameters to optimize decision making in elderly ED patients. clinical frailty score emergency department intensive care resuscitation room Figures Figure 1 Figure 2 INTRODUCTION Emergency departments (EDs) are the first point of care for patients with acute illnesses [ 1 ]. For these patients, admission to the resuscitation room (RR) is recommended to closely initiate intensive care [ 2 ]. Care provided in the RR ranges from close monitoring to invasive resuscitation therapy. Elderly patients over 75 years of age represent a growing proportion of ED and subsequent RR admissions [ 3 , 4 ]. This aging and comorbid population forces emergency and critical care physicians to question their admission to the intensive care unit (ICU)[ 5 , 6 ]. This decision raises the question of the expected benefit at the end of care, taking into account frailty, comorbidities, initial pathology and its severity, as well as the patient's own wishes [ 7 ]. Assessment of quality of life and frailty is necessary to determine the level of care required for elderly patients. A simple, standardized tool is needed in emergency medicine. The Clinical Frailty Scale (CFS) has been validated as a screening tool for frailty in geriatric patients [ 8 ]. It is described as a predictive marker of mortality one month after ICU admission in patients over 80 years of age, independent of other geriatric scores [ 9 , 10 ]. This tool defines 9 levels, ranging from a person in very good physical condition (CFS 1) to the need for maximal end-of-life care (CFS 9). For each point increase in CFS, there is an 11% higher risk of death at 30 days [ 11 ]. The CFS is quick to administer: data are collected from the patient's medical record, clinical examination, and interviews with the patient or family. It is also reproducible, whether administered by a physician or a nurse [ 12 ]. Few studies have examined the hospital outcome of patients over 75 years old admitted to the RR according to their CFS. We hypothesized that patients admitted to the ICU after RR would have lower CFS than those admitted to the general inpatient unit (GIU). The primary objective of our study was to compare the CFS of patients admitted to ICU after RR initial admission with the CFS of patients admitted to GIU after RR initial admission. METHODS Legal statement This was an observational, retrospective, single-center study conducted in the Emergency Department of the University Hospital of Nimes, France. We studied all consecutive patients aged 75 and older admitted to the RR from November 1, 2023, to March 31, 2024. This study was approved by the Ethics Committee of the University Hospital of Nimes (IRB CHU Nîmes 24.03.07). Being a retrospective observational study, information consent was waived. A non-opposition letter was sent to patients or their relatives. The authors did not receive any information about the opposition. The authors had access to information that could identify individual participants during data collection, which was not collected. This study is reported following the strengthening of reporting of observational studies in epidemiology guidelines [ 13 ]. Objectives The primary objective of the study was to compare CFS between patients admitted to the ICU and those admitted to the GIU after an initial admission in the RR. The primary outcome was CFS. The secondary objectives were to analyze the predictive performance of the CFS, the Charlson index [ 13 ], and the Simplified Acute Physiology Score II (SAPS II) [ 14 ] for predicting ICU admission and in-hospital death. Finally, we aimed to describe patient outcomes after hospitalization. Secondary outcomes included Charlson index, SAPS II score, in-hospital mortality, documentation of treatment withholding or withdrawal (TWW) decision in the ED or hospital [ 15 ], analysis of discharge destination (home, rehabilitation care, or institutionalization), and analysis of the implementation of a care plan if discharged home. Population Inclusion criteria were patients aged 75 or older who were admitted to the ED and required admission to the RR, regardless of their mode of admission, and who were subsequently admitted to the hospital. Exclusion criteria were patients whose decision to be admitted to the ICU was for end-of-life care (including multi-organ donation) and medical records with missing relevant data (e.g., unreported lifestyle, missing biological data, or unreported vital signs). Data collection Data were collected retrospectively by reviewing electronic medical records (Hôpital Manager, Softway Medical, Fuveau, France), prescription software, and biological data linked to patients. Data were collected retrospectively from patients' electronic ED files by individual chart review by a single investigator using a predefined questionnaire. In case of doubt, a second investigator reviewed the chart. For quality assessment, files with abnormal values during statistical analysis were reviewed and corrections were made if there was an error. If data were outliers in the chart, they were excluded. For each patient, we estimated the CFS using the Rockwood scale, based on the medical history and lifestyle recorded in the medical records [ 8 ]. We also recorded the patients' vital signs on admission to the RR and the reason for admission to the RR, categorized into five causes: respiratory, neurological, cardiac, trauma, or other (including metabolic or hemorrhagic causes). The Charlson index was calculated based on the medical history noted in the records. We collected therapeutic interventions initiated during the RR stay: need for ventilatory support (non-invasive ventilation or high-flow oxygen therapy), vasopressors, transfusions, antibiotics, and volume of fluid administration. The hospitalization site was divided into two groups: GIU, corresponding to medical or surgical wards, or ICU, including general and organ-specific ICUs. In addition, the SAPS II corresponding to the RR stay was calculated based on all its criteria [ 14 ]. All TWW decisions in the ED or on the ward were collected. Discharge destination and initiation or modification of a home care plan, based on hospital records and discharge letters were also collected. Finally, we documented in-hospital mortality if it was recorded in the hospital software. Sample size calculation Assuming a mean CFS of 3 ± 2 for patients admitted to the ICU after RR and a mean CFS of 4 ± 4 for patients not admitted to the ICU, the expected effect size was d = 0.32, with an assumed admission ratio of 0.33 (1 admission to the ICU to 3 admissions to the GIU). Planning to compare medians using a U Mann-Whitney test with an alpha risk of 0.05 and a power (1-beta) of 80%, we needed to include a total of 334 patients (83 ICU and 251 GIU). Assuming a risk of missing data and RR death of 50%, we needed to screen at least 501 files. To be pragmatic, we decided to continue enrollment until the end of the current month. Statistical analysis Qualitative variables are presented as absolute values and percentages. Quantitative variables are presented as median and first and third quartiles (Q1-Q3). Qualitative variables were compared using the chi-squared test or Fisher's exact test when the theoretical number was less than five. Quantitative variables were compared using the Student's t-test after checking the normality of the distribution, or the U-Mann-Whitney test if normality was not reached. To determine independent factors associated with the type of hospital admission, a multivariate analysis was conducted, using a generalized linear model. The model included all parameters with a p-value < 0.2 in the bivariate analysis. No further adjustment was made. Final variables included were: age, sex, reason for RR admission (respiratory failure, neurologic failure, circulatory failure, other), pulsed oxygen saturation, CFS, Charlson’s index, treatment initiated in RR (ventilatory support, antibiotics, vasopressors) and TWW decision. The variance inflation factor was verified to be less than 5 for each variable included. Adjusted odds ratio were calculated, alongside with their 95% confidence interval (95%CI). Receiver operating characteristic (ROC) curves were plotted for CFS, SAPS II, and Charlson index in predicting ICU admission and in-hospital mortality. The area under the curve (AUC), sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) were calculated for each curve. The optimal threshold was determined for curves using the Youden index. The 95%CI were calculated using a bootstrap method with n = 1 000. AUC was interpreted as excellent if ≥ 0.9, good if between 0.8 and 0.9, fair if between 0.7 and 0.8, poor if between 0.6 and 0.7, and failed if between 0.5 and 0.6 [ 16 ]. The AUC of the ROC curves were compared in pairs using the DeLong method with Bonferonni's correction. The significance level was set at 5%. Data were analyzed using R software, version 4.4.0 (R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria) with the pROC package. RESULTS From November 1st, 2023 to March 31, 2024, 575 patients aged 75 or older were consecutively admitted to the RR, and 392 (68%) were finally included for analysis: 170 (43%) were admitted to the ICU and 222 (57%) to the GIU. The flow chart of the study is shown in Supplemental material, Figure S1 . The median age of the population was 83 (78–87) years and 212 (54%) were male. Charlson's index was 6 (5–8) in the GIU group and 5 (4–6) in the ICU group (p < 0.01). Table 1 summarizes patient characteristics with univariate and multivariate analysis. Table 1 Bivariate and univariate analysis of the total population. Bivariate analysis Multivariate analysis Overall population (n = 392) General inpatient unit (n = 222) Intensive care unit (n = 170) p-value aOR [95% CI] p-value Demographics Age in years, median (Q1-Q3) 83 (78–87) 84 (79–89) 81 (77–84) < 0.001 0.95 [0.91–0.99] < 0.01 Sex, men, n (%) 212 (54) 111 (50) 101 (59) 0.08 1.74 [1.06–2.89] < 0.05 Reason for resuscitation room admission, n (%) Respiratory failure, n (%) 167 (43) 109 (49) 58 (35) < 0.01 0.70 [0.24–2.02] 0.50 Neurologic failure, n (%) 56 (14) 25 (11) 31 (18) 0.07 2.02 [0.69–6.06] 0.20 Circulatory failure, n (%) 55 (14) 11 (5) 44 (26) < 0.01 6.97 [2.26–22.8] < 0.001 Traumatology, n (%) 25 (6) 15 (7) 10 (6) 0.89 - Other, n (%) 89 (23) 62 (28) 27 (16) < 0.01 0.77 [0.27–2.22] 0.60 Admission parameters SBP in mmHg, median (Q1-Q3) 132 (111–155) 133 (112–156) 131 (111–155) 0.57 - - DBP in mmHg, median (Q1-Q3) 74 (60–86) 73 (59–85) 74 (60–88) 0.59 - - MBP in mmHg, median (Q1-Q3) 93 (79–108) 93 (78–108) 93 (80–107) 0.99 - - HR in bpm, median (Q1-Q3) 89 (70–102) 85 (71–102) 90 (70–102) 0.83 - - Pulsed oxygen saturation in %, median (Q1-Q3) 93 (91–99) 92 (90–99) 94 (92–98) < 0.05 1.01 [0.98–1.05] 0.5 Clinical frailty score, median (Q1-Q3) 3 (2–5) 4 (3–5) 3 (2–4) < 0.001 0.74 [0.62–0.89] 0.001 Charlson’s index, median (Q1-Q3) 6 (5–7) 6 (5–8) 5 (4–6) < 0.01 0.87 [0.76–0.98] < 0.05 SAPS II, median (Q1-Q3) 26 (20–33) 27 (21–33) 24 (19–31) 0.36 - - Treatment initiated in resuscitation room Ventilatory support, yes, n (%) 86 (22) 40 (18) 46 (27) 0.04 4.65 [2.31–9.65] < 0.001 Antibiotics, yes, n (%) 144 (37) 98 (44) 46 (27) < 0.01 0.63 [0.36–1.10] 0.11 Vasopressors, yes, n (%) 10 (3) 1 (1) 9 (5) < 0.01 11.1 [1.67–224] < 0.05 Red blood cell transfusion, yes, n (%) 22 (6) 16 (7) 6 (4) 0.19 - - Fluid filing volume in mL, median (Q1-Q3) 1 197 (500–1500) 1 186 (500-1 500) 1 212 (500-1 500) 0.91 - - TWW decision in ED, yes, n (%) 64 (16) 53 (24) 11 (6) < 0.01 0.21 [0.09–0.48] < 0.001 CFS: clinical frailty score, SAPS II: simplified acute physiologic score II, SBP: systolic blood pressure, DBP: diastolic blood pressure, MBP: mean blood pressure, HR: heart rate, TWW: treatment withholding or treatment withdrawal, ED: emergency department In the overall population, CFS was 3 (2–5). In patients admitted to the GIU, the median CFS was 4 (3–5) and 3 (2–4) in the group admitted to the ICU (p < 0.001). Figure 1 shows the proportion of patients with a given CFS according to ICU or GIU admission. ROC curves for predicting type of admission and in-hospital mortality based on CFS; Charlson’s index and SAPS II are shown in Fig. 2 A and Fig. 2 B, respectively. Table 2 summarizes the characteristics of the different curves. CFS had an AUC of 0.68 (95%CI: 0.63–0.73) for predicting admission type and SAPS II had an AUC of 0.55 (95%CI: 0.50–0.61; adjusted p-value 0.99). Table 2 Characteristics of different scores to predict intensive care unit admission and in-hospital death. AUC ROC [95%CI] Se Sp PPV NPV Youden’s index Score’s value Adjusted p-value compared to CFS Adjusted p-value compared to SAPS II Admission’s type CFS 0.68 [0.63–0.73] 0.88 0.34 0.57 0.88 0.25 5 - - Charlson’s index 0.65 [0.60–0.70] 0.60 0.64 0.57 0.88 0.24 6 > 0.94 0.03 SAPS II 0.55 [0.50–0.61] 0.77 0.33 0.49 0.96 0.10 21 < 0.01 - In-Hospital Death CFS 0.62 [0.55–0.69] 0.65 0.53 0.53 0.90 0.18 4 - - Charlson’s index 0.5 [0.50–0.64] 0.63 0.48 0.50 0.95 0.11 6 0.70 0.14 SAPS II 0.66 [0.58–0.72] 0.68 0.56 0.57 0.88 0.24 27 > 0.99 - CFS: clinical frailty score, SAPS II: simplified acute physiologic score II, AUC: aera under curve, ROC: receiver operating characteristics, Se: sensitivity, Sp: Specificity, PPV: predictive positive value, NPV: negative predictive value. Patient outcomes are shown in Table 3 . A total of 35 (16%) and 30 (18%) patients died during hospitalization in the GIU and in the ICU, respectively (p = 0.29). Table 3 Patients’ outcome depending on admission unit. General inpatient unit n = 222 Intensive care unit n = 170 p-value TWW decision during hospitalization, n (%) 50 (23) 30 (18) 0.29 In-hospital death, n(%) 35 (16) 30 (18) 0.72 Hospital discharge Home, n (%) 141 (64) 110 (65) 0.59 Rehabilitation, n (%) 29 (13) 18 (11) 0.61 Institutionalization, n (%) 12 (5) 4 (2) 0.2 Hospital transfer, n (%) 5 (2) 8 (5) 0.25 Home care plan modification, n(%) 37 (17) 14 (8) 0.04 TWW: treatment withholding or treatment withdrawal DISCUSSION This retrospective study of patients hospitalized after initial RR admission showed a higher CFS for patients admitted to the GIU compared to patients hospitalized in the ICU (4 [ 3 – 5 ] vs. 3 [ 2 – 4 ]). Seventy (41%) of the ICU patients had a CFS of 2 or less, indicating a non-frail population. CFS had a low AUC for predicting hospital sector. For mortality prediction, CFS and SAPS II had low and not different AUCs. In a prospective multicenter European study, Jung et al. pointed out that a standardized geriatric assessment cannot be performed in an acute care setting [ 16 ]. Therefore, the CFS seems to be the gold standard of geriatric assessment, allowing to take into account the patient's general condition outside of an acute illness [ 17 ]. The results show that the CFS decision threshold for ICU admission in this hospital was 5. The CFS used for ICU admission varies from country to country in Europe [ 18 – 20 ]. This reflects differences in health care systems, care policies and available resources. In Belgium, scientific societies recommend ICU admission for patients older than 75 years with a CFS score less than 5 [ 9 ]. In Switzerland, the Academy of Medical Sciences sets higher thresholds, with a CFS of 7 for those over 65 and 6 for those over 85, to consider exclusion from ICU admission [ 19 ]. The variation in thresholds raises the question of standardization of practice. Currently, there is no universal consensus on frailty-based ICU admission criteria. Finally, CFS should be part of systematic interrogation when receiving critically ill elderly in the ED, to guide further in-hospital orientation. Interestingly, men were more likely to be admitted to the ICU than to the GIU. This could be due to more severe presentation in men or a sex bias. In a vignette-based clinical case, Vromant et al. found that women were more at risk of ceiling of care in the ED than men [ 21 ]. Further studies should explore the reasons for this issue, given that women receive less care during admission to the ICU [ 22 ]. We found no difference in in-hospital mortality across hospital ward groups. Guidet et al. found similar results when comparing mortality between systematic ICU admission of elderly patients and standard practice [ 6 ]. A prospective multicenter study focusing on elderly patients admitted to the ICU found that patients with CFS ≥ 5 had higher mortality than those with lower scores [ 11 ]. A meta-analysis also confirmed that higher CFS scores correlate with an increased risk of mortality, even after adjustment for other risk factors such as age and disease severity [ 23 ]. Regarding the decision for TWW, there were no differences during hospitalization, whereas in the ED, significantly more patients in the GIU group had a TWW decision. This highlights the role of the ED physician in the TWW decision. The recommendations of the French Society of Emergency Medicine specify that decisions must be made after a comprehensive evaluation of the patient [ 24 ]. TWW and frailty assessment are two complementary concepts in the management of elderly patients [ 9 ]. While TWW focuses on the non-initiation, non-optimization, or even discontinuation of one or more therapeutics with an emphasis on the patient's comfort and quality of life, frailty assessment allows for a comprehensive understanding of the elderly patient's health status [ 25 ]. Both aspects should be taught to ED residents and develop throughout their medical practice, with a focus on the fact that more than 30% of patients with TWW decision in the ED are still alive at day-30 [ 26 ]. Hospitalization represents a turning point for the elderly, which can lead to an iatrogenic increase in dependency [ 27 ]. After hospitalization, many patients find it difficult to return to their previous level of autonomy. In the present study, among patients discharged alive from the hospital, more than 50% in each group were able to return home directly. Patients admitted to the GIU were more likely to have home care plan modification, which can reflect a more global approach of patients [ 28 ]. We did not measure readmission rates by CFS, nor did we observe variations in CFS before and after hospitalization. The results highlight the importance of adapting the ED and ICU to the specific needs of the elderly. The introduction of geriatric EDs and ICUs may represent an evolution in the continuum of care for this vulnerable population [ 29 , 30 ]. By adapting spaces and medical protocols to geriatrics, these services could play a key role in reducing mortality and readmission rates, while improving transitions between acute care, rehabilitation, and long-term care [ 30 ]. Limitations This study has several limitations that should be discussed. First, its retrospective nature exposes it to selection and information bias, with some missing data, particularly regarding patients' medical history and clinical decisions influencing ICU versus GIU admission. In addition, this was a single-center study conducted in a university hospital, which limits the generalizability of the results to other institutions with different admission policies and resource availability. Furthermore, assessment of frailty using the CFS relies in part on clinical judgment, which may vary according to clinician experience and expertise. Although we attempted to minimize inter-rater variability by having a single investigator perform the assessment, observer bias cannot be completely excluded. A prospective study with multiple raters and cross-validation may improve the reliability of this measure. In addition, our study does not consider certain factors that may influence patient triage, such as patient and family preferences, ICU bed availability, or multidisciplinary team decisions in the emergency setting. These factors play a critical role in ICU admission decisions and may provide further context for our findings. Finally, although we compared the predictive performance of the CFS, Charlson Index, and SAPS II for ICU admission and in-hospital mortality, the CFS had a low AUC, suggesting that other unmeasured factors likely contribute to these outcomes. A more comprehensive risk stratification approach that incorporates frailty, acuity, and patient-centered preferences may improve decision-making regarding patient admission and management. CONCLUSION CFS differed between ICU and GIU groups, suggesting its potential role in orientation after RR admission. Although CFS’s predictive value remains limited, in this study it was better than the Charlson Index or SAPS 2 for predicting ICU admission. Further prospective studies should evaluate its integration with other clinical parameters to optimize decision making in patients aged ≥ 75 in the ED. Declarations Statement of Ethics The local ethics committee of Nimes’ university hospital approved the study (CHU Nimes IRB 24.03.07) and waived the consent in accordance with French law (Law no. 2012 − 300 of 5 March 2012 on research involving the human person). Patients or their relative received a non-opposition letter, describing the aims of this study. Clinical trial number Not applicable Conflict of Interest Statement The authors have no conflicts of interest to declare. Funding Sources None Author Contributions Conceptualization: FC, MJ, FR & XB; Methodology: FC, MJ, FR & XB; Formal analysis: FC; Acknowledgement Authors acknowledge Chloé Silighini for her advice concerning the study protocol and Sylvain Benenati for his help in initial data collection. Data Availability Statement The data that support the findings of this study are available from the corresponding author, FC, upon reasonable request. References Graham CA (2009) Critical care in emergency medicine. 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Arch Gerontol Geriatr 84:103893 Supplementary Files Conflictofinterestdisclosureform.pdf Supplementalmaterial.docx Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2026 Read the published version in Internal and Emergency Medicine → Version 1 posted Reviewers agreed at journal 06 Oct, 2025 Reviewers invited by journal 03 Oct, 2025 Editor assigned by journal 29 Aug, 2025 First submitted to journal 28 Aug, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7460546","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524111152,"identity":"b1c5a625-3911-4b88-9f1d-927c82c4029d","order_by":0,"name":"Fabien Coisy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBAC9gYehgNgFjMQfwBiNnYCWngOIGlhnAHSwkyEFjhg5oFZh1cL+9mDBz7uYJDnZ2d/Jm3za5s8HzMD44ePOXi08OQlHJx5hsFwZjOPmXRu323DNmYGZsmZ23BrsWfIMTjM28aQYHCYh006t+c2I1ALGzMvHi08/G8MDv8FawE6zLLntj1hLRJAWxjBWhjMpBl+3E4kQsu7hIO9bRIgvxhb9jbcTm5jZmzG6xce/tzDH3622cjz8x9/eOPHn9u289ubD374iEcLFEiACBYJoAuBgLGBoHoYYP7A8IdoxaNgFIyCUTCCAAAz40rJBign1gAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2812-3534","institution":"Nimes University Hospital Centre Division of Anaesthesiology Intensive Care Pain Medicine and Emergencies: Centre Hospitalier Universitaire de Nimes Pole anesthesie reanimation douleur urgences","correspondingAuthor":true,"prefix":"","firstName":"Fabien","middleName":"","lastName":"Coisy","suffix":""},{"id":524111153,"identity":"f69a3100-d641-4d93-8017-3d5bf233bf86","order_by":1,"name":"Mathilde Jallade","email":"","orcid":"","institution":"Nimes University Hospital Centre Division of Anaesthesiology Intensive Care Pain Medicine and Emergencies: Centre Hospitalier Universitaire de Nimes Pole anesthesie reanimation douleur urgences","correspondingAuthor":false,"prefix":"","firstName":"Mathilde","middleName":"","lastName":"Jallade","suffix":""},{"id":524111154,"identity":"739eac85-89dd-4587-9908-242ac191ad22","order_by":2,"name":"Florian Regal","email":"","orcid":"","institution":"Nimes University Hospital Centre Division of Anaesthesiology Intensive Care Pain Medicine and Emergencies: Centre Hospitalier Universitaire de Nimes Pole anesthesie reanimation douleur urgences","correspondingAuthor":false,"prefix":"","firstName":"Florian","middleName":"","lastName":"Regal","suffix":""},{"id":524111155,"identity":"5c4a4a1b-a313-4727-b0f6-85b0e784830f","order_by":3,"name":"Camille Moser","email":"","orcid":"","institution":"Nimes University Hospital Centre Division of Anaesthesiology Intensive Care Pain Medicine and Emergencies: Centre Hospitalier Universitaire de Nimes Pole anesthesie reanimation douleur urgences","correspondingAuthor":false,"prefix":"","firstName":"Camille","middleName":"","lastName":"Moser","suffix":""},{"id":524111156,"identity":"19d0fb63-33fd-4b59-806a-190d015ba539","order_by":4,"name":"Céline Occelli","email":"","orcid":"","institution":"CHU Nice: Centre Hospitalier Universitaire de Nice","correspondingAuthor":false,"prefix":"","firstName":"Céline","middleName":"","lastName":"Occelli","suffix":""},{"id":524111157,"identity":"8b6dda5c-1204-4ec7-83d4-98e8d1ee78e1","order_by":5,"name":"Xavier Bobbia","email":"","orcid":"","institution":"CHU de Montpellier: Centre Hospitalier Universitaire de Montpellier","correspondingAuthor":false,"prefix":"","firstName":"Xavier","middleName":"","lastName":"Bobbia","suffix":""},{"id":524111158,"identity":"7f404245-efbc-416b-aab7-ecac6e07d9fb","order_by":6,"name":"Romain Genre Grandpierre","email":"","orcid":"","institution":"Nimes University Hospital Centre Division of Anaesthesiology Intensive Care Pain Medicine and Emergencies: Centre Hospitalier Universitaire de Nimes Pole anesthesie reanimation douleur urgences","correspondingAuthor":false,"prefix":"","firstName":"Romain","middleName":"Genre","lastName":"Grandpierre","suffix":""}],"badges":[],"createdAt":"2025-08-26 08:25:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7460546/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7460546/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11739-026-04263-8","type":"published","date":"2026-01-20T15:57:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93804501,"identity":"4965600e-3ccf-4650-834a-dc97bd969433","added_by":"auto","created_at":"2025-10-17 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1","display":"","copyAsset":false,"role":"figure","size":17091,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of patients discharged in ward or in intensive care unit, based upon clinical frailty score. CFS: clinical frailty score\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7460546/v1/7089276fb00f90d7e6fbbade.png"},{"id":93804498,"identity":"3cc990d3-7fcf-456a-a051-0f7b83436439","added_by":"auto","created_at":"2025-10-17 17:49:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27996,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristics of clinical frailty score, Charlson’s index and simplified acute physiologic score II curves for A. intensive care unit admission B. in hospital mortality.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7460546/v1/08af34bc49c8834392f20564.png"},{"id":101151778,"identity":"e29435e2-e908-47e5-b5b1-ddb02c80c723","added_by":"auto","created_at":"2026-01-26 16:05:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1114585,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7460546/v1/bc03a948-a791-4709-8fdd-a06c1140f18f.pdf"},{"id":93804500,"identity":"d2c8608b-ec65-44a5-984f-663e1decb72e","added_by":"auto","created_at":"2025-10-17 17:49:46","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":58462,"visible":true,"origin":"","legend":"","description":"","filename":"Conflictofinterestdisclosureform.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7460546/v1/1ad07bac27524ac71adf59a4.pdf"},{"id":93804504,"identity":"42f4d2a4-2c02-4d99-9a7a-15e66299a0f0","added_by":"auto","created_at":"2025-10-17 17:49:46","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4824308,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7460546/v1/2d43597d86f5cf7351860ad7.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eClinical Frailty Score for Hospital Outcome for Patients Aged ≥ 75 Following Emergency Department Resuscitation Room Admission: A Retrospective Monocenter Study\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eEmergency departments (EDs) are the first point of care for patients with acute illnesses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. For these patients, admission to the resuscitation room (RR) is recommended to closely initiate intensive care [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Care provided in the RR ranges from close monitoring to invasive resuscitation therapy. Elderly patients over 75 years of age represent a growing proportion of ED and subsequent RR admissions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This aging and comorbid population forces emergency and critical care physicians to question their admission to the intensive care unit (ICU)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This decision raises the question of the expected benefit at the end of care, taking into account frailty, comorbidities, initial pathology and its severity, as well as the patient's own wishes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Assessment of quality of life and frailty is necessary to determine the level of care required for elderly patients. A simple, standardized tool is needed in emergency medicine.\u003c/p\u003e\u003cp\u003eThe Clinical Frailty Scale (CFS) has been validated as a screening tool for frailty in geriatric patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is described as a predictive marker of mortality one month after ICU admission in patients over 80 years of age, independent of other geriatric scores [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This tool defines 9 levels, ranging from a person in very good physical condition (CFS 1) to the need for maximal end-of-life care (CFS 9). For each point increase in CFS, there is an 11% higher risk of death at 30 days [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The CFS is quick to administer: data are collected from the patient's medical record, clinical examination, and interviews with the patient or family. It is also reproducible, whether administered by a physician or a nurse [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFew studies have examined the hospital outcome of patients over 75 years old admitted to the RR according to their CFS. We hypothesized that patients admitted to the ICU after RR would have lower CFS than those admitted to the general inpatient unit (GIU). The primary objective of our study was to compare the CFS of patients admitted to ICU after RR initial admission with the CFS of patients admitted to GIU after RR initial admission.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eLegal statement\u003c/h2\u003e\u003cp\u003eThis was an observational, retrospective, single-center study conducted in the Emergency Department of the University Hospital of Nimes, France. We studied all consecutive patients aged 75 and older admitted to the RR from November 1, 2023, to March 31, 2024. This study was approved by the Ethics Committee of the University Hospital of Nimes (IRB CHU N\u0026icirc;mes 24.03.07). Being a retrospective observational study, information consent was waived. A non-opposition letter was sent to patients or their relatives. The authors did not receive any information about the opposition. The authors had access to information that could identify individual participants during data collection, which was not collected. This study is reported following the strengthening of reporting of observational studies in epidemiology guidelines [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eObjectives\u003c/h3\u003e\n\u003cp\u003eThe primary objective of the study was to compare CFS between patients admitted to the ICU and those admitted to the GIU after an initial admission in the RR. The primary outcome was CFS. The secondary objectives were to analyze the predictive performance of the CFS, the Charlson index [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and the Simplified Acute Physiology Score II (SAPS II) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] for predicting ICU admission and in-hospital death. Finally, we aimed to describe patient outcomes after hospitalization. Secondary outcomes included Charlson index, SAPS II score, in-hospital mortality, documentation of treatment withholding or withdrawal (TWW) decision in the ED or hospital [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], analysis of discharge destination (home, rehabilitation care, or institutionalization), and analysis of the implementation of a care plan if discharged home.\u003c/p\u003e\n\u003ch3\u003ePopulation\u003c/h3\u003e\n\u003cp\u003eInclusion criteria were patients aged 75 or older who were admitted to the ED and required admission to the RR, regardless of their mode of admission, and who were subsequently admitted to the hospital. Exclusion criteria were patients whose decision to be admitted to the ICU was for end-of-life care (including multi-organ donation) and medical records with missing relevant data (e.g., unreported lifestyle, missing biological data, or unreported vital signs).\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData were collected retrospectively by reviewing electronic medical records (H\u0026ocirc;pital Manager, Softway Medical, Fuveau, France), prescription software, and biological data linked to patients. Data were collected retrospectively from patients' electronic ED files by individual chart review by a single investigator using a predefined questionnaire. In case of doubt, a second investigator reviewed the chart. For quality assessment, files with abnormal values during statistical analysis were reviewed and corrections were made if there was an error. If data were outliers in the chart, they were excluded.\u003c/p\u003e\u003cp\u003eFor each patient, we estimated the CFS using the Rockwood scale, based on the medical history and lifestyle recorded in the medical records [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. We also recorded the patients' vital signs on admission to the RR and the reason for admission to the RR, categorized into five causes: respiratory, neurological, cardiac, trauma, or other (including metabolic or hemorrhagic causes). The Charlson index was calculated based on the medical history noted in the records. We collected therapeutic interventions initiated during the RR stay: need for ventilatory support (non-invasive ventilation or high-flow oxygen therapy), vasopressors, transfusions, antibiotics, and volume of fluid administration. The hospitalization site was divided into two groups: GIU, corresponding to medical or surgical wards, or ICU, including general and organ-specific ICUs. In addition, the SAPS II corresponding to the RR stay was calculated based on all its criteria [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. All TWW decisions in the ED or on the ward were collected. Discharge destination and initiation or modification of a home care plan, based on hospital records and discharge letters were also collected. Finally, we documented in-hospital mortality if it was recorded in the hospital software.\u003c/p\u003e\n\u003ch3\u003eSample size calculation\u003c/h3\u003e\n\u003cp\u003eAssuming a mean CFS of 3\u0026thinsp;\u0026plusmn;\u0026thinsp;2 for patients admitted to the ICU after RR and a mean CFS of 4\u0026thinsp;\u0026plusmn;\u0026thinsp;4 for patients not admitted to the ICU, the expected effect size was d\u0026thinsp;=\u0026thinsp;0.32, with an assumed admission ratio of 0.33 (1 admission to the ICU to 3 admissions to the GIU). Planning to compare medians using a U Mann-Whitney test with an alpha risk of 0.05 and a power (1-beta) of 80%, we needed to include a total of 334 patients (83 ICU and 251 GIU). Assuming a risk of missing data and RR death of 50%, we needed to screen at least 501 files. To be pragmatic, we decided to continue enrollment until the end of the current month.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eQualitative variables are presented as absolute values and percentages. Quantitative variables are presented as median and first and third quartiles (Q1-Q3). Qualitative variables were compared using the chi-squared test or Fisher's exact test when the theoretical number was less than five. Quantitative variables were compared using the Student's t-test after checking the normality of the distribution, or the U-Mann-Whitney test if normality was not reached.\u003c/p\u003e\u003cp\u003eTo determine independent factors associated with the type of hospital admission, a multivariate analysis was conducted, using a generalized linear model. The model included all parameters with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.2 in the bivariate analysis. No further adjustment was made. Final variables included were: age, sex, reason for RR admission (respiratory failure, neurologic failure, circulatory failure, other), pulsed oxygen saturation, CFS, Charlson\u0026rsquo;s index, treatment initiated in RR (ventilatory support, antibiotics, vasopressors) and TWW decision. The variance inflation factor was verified to be less than 5 for each variable included. Adjusted odds ratio were calculated, alongside with their 95% confidence interval (95%CI).\u003c/p\u003e\u003cp\u003eReceiver operating characteristic (ROC) curves were plotted for CFS, SAPS II, and Charlson index in predicting ICU admission and in-hospital mortality. The area under the curve (AUC), sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) were calculated for each curve. The optimal threshold was determined for curves using the Youden index. The 95%CI were calculated using a bootstrap method with n\u0026thinsp;=\u0026thinsp;1 000. AUC was interpreted as excellent if\u0026thinsp;\u0026ge;\u0026thinsp;0.9, good if between 0.8 and 0.9, fair if between 0.7 and 0.8, poor if between 0.6 and 0.7, and failed if between 0.5 and 0.6 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The AUC of the ROC curves were compared in pairs using the DeLong method with Bonferonni's correction.\u003c/p\u003e\u003cp\u003eThe significance level was set at 5%. Data were analyzed using R software, version 4.4.0 (R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria) with the pROC package.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eFrom November 1st, 2023 to March 31, 2024, 575 patients aged 75 or older were consecutively admitted to the RR, and 392 (68%) were finally included for analysis: 170 (43%) were admitted to the ICU and 222 (57%) to the GIU. The flow chart of the study is shown in Supplemental material, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The median age of the population was 83 (78\u0026ndash;87) years and 212 (54%) were male. Charlson's index was 6 (5\u0026ndash;8) in the GIU group and 5 (4\u0026ndash;6) in the ICU group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes patient characteristics with univariate and multivariate analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBivariate and univariate analysis of the total population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eBivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall population\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;392)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGeneral inpatient unit\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;222)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntensive care unit\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eaOR [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge in years, median (Q1-Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (78\u0026ndash;87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (79\u0026ndash;89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81 (77\u0026ndash;84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95 [0.91\u0026ndash;0.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, men, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e212 (54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101 (59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.74 [1.06\u0026ndash;2.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReason for resuscitation room admission, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory failure, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.70 [0.24\u0026ndash;2.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeurologic failure, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.02 [0.69\u0026ndash;6.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCirculatory failure, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.97 [2.26\u0026ndash;22.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraumatology, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.77 [0.27\u0026ndash;2.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdmission parameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP in mmHg, median (Q1-Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (111\u0026ndash;155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133 (112\u0026ndash;156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131 (111\u0026ndash;155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP in mmHg, median (Q1-Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (60\u0026ndash;86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73 (59\u0026ndash;85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74 (60\u0026ndash;88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBP in mmHg, median (Q1-Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (79\u0026ndash;108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (78\u0026ndash;108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93 (80\u0026ndash;107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR in bpm, median (Q1-Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (70\u0026ndash;102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (71\u0026ndash;102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90 (70\u0026ndash;102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulsed oxygen saturation in %, median (Q1-Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (91\u0026ndash;99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 (90\u0026ndash;99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94 (92\u0026ndash;98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.01 [0.98\u0026ndash;1.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical frailty score, median (Q1-Q3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (2\u0026ndash;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (3\u0026ndash;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (2\u0026ndash;4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.74 [0.62\u0026ndash;0.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCharlson\u0026rsquo;s index, median (Q1-Q3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (5\u0026ndash;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (5\u0026ndash;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (4\u0026ndash;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87 [0.76\u0026ndash;0.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSAPS II, median (Q1-Q3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (20\u0026ndash;33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (21\u0026ndash;33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (19\u0026ndash;31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment initiated in resuscitation room\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVentilatory support, yes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86 (22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.65 [2.31\u0026ndash;9.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntibiotics, yes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.63 [0.36\u0026ndash;1.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVasopressors, yes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.1 [1.67\u0026ndash;224]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell transfusion, yes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFluid filing volume in mL, median (Q1-Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 197 (500\u0026ndash;1500)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 186 (500-1 500)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 212 (500-1 500)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTWW decision in ED, yes, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21 [0.09\u0026ndash;0.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eCFS: clinical frailty score, SAPS II: simplified acute physiologic score II, SBP: systolic blood pressure, DBP: diastolic blood pressure, MBP: mean blood pressure, HR: heart rate, TWW: treatment withholding or treatment withdrawal, ED: emergency department\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the overall population, CFS was 3 (2\u0026ndash;5). In patients admitted to the GIU, the median CFS was 4 (3\u0026ndash;5) and 3 (2\u0026ndash;4) in the group admitted to the ICU (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the proportion of patients with a given CFS according to ICU or GIU admission.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eROC curves for predicting type of admission and in-hospital mortality based on CFS; Charlson\u0026rsquo;s index and SAPS II are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, respectively. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the characteristics of the different curves. CFS had an AUC of 0.68 (95%CI: 0.63\u0026ndash;0.73) for predicting admission type and SAPS II had an AUC of 0.55 (95%CI: 0.50\u0026ndash;0.61; adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). For in-hospital mortality, CFS had an AUC of 0.62 (95%CI: 0.55\u0026ndash;0.70) and SAPS II an AUC of 0.66 (95%CI: 0.59\u0026ndash;0.73; adjusted p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.99).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of different scores to predict intensive care unit admission and in-hospital death.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC ROC [95%CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSe\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNPV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYouden\u0026rsquo;s index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eScore\u0026rsquo;s value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAdjusted p-value compared to CFS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAdjusted p-value compared to SAPS II\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eAdmission\u0026rsquo;s type\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.68 [0.63\u0026ndash;0.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharlson\u0026rsquo;s index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.65 [0.60\u0026ndash;0.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAPS II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55 [0.50\u0026ndash;0.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIn-Hospital Death\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62 [0.55\u0026ndash;0.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharlson\u0026rsquo;s index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5 [0.50\u0026ndash;0.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAPS II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.66 [0.58\u0026ndash;0.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eCFS: clinical frailty score, SAPS II: simplified acute physiologic score II, AUC: aera under curve, ROC: receiver operating characteristics, Se: sensitivity, Sp: Specificity, PPV: predictive positive value, NPV: negative predictive value.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePatient outcomes are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A total of 35 (16%) and 30 (18%) patients died during hospitalization in the GIU and in the ICU, respectively (p\u0026thinsp;=\u0026thinsp;0.29).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatients\u0026rsquo; outcome depending on admission unit.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral inpatient unit\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;222\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntensive care unit\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;170\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTWW decision during hospitalization, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIn-hospital death, n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHospital discharge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141 (64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRehabilitation, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstitutionalization, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital transfer, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHome care plan modification, n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eTWW: treatment withholding or treatment withdrawal\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis retrospective study of patients hospitalized after initial RR admission showed a higher CFS for patients admitted to the GIU compared to patients hospitalized in the ICU (4 [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] vs. 3 [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]). Seventy (41%) of the ICU patients had a CFS of 2 or less, indicating a non-frail population. CFS had a low AUC for predicting hospital sector. For mortality prediction, CFS and SAPS II had low and not different AUCs.\u003c/p\u003e\u003cp\u003eIn a prospective multicenter European study, Jung et al. pointed out that a standardized geriatric assessment cannot be performed in an acute care setting [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, the CFS seems to be the gold standard of geriatric assessment, allowing to take into account the patient's general condition outside of an acute illness [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The results show that the CFS decision threshold for ICU admission in this hospital was 5. The CFS used for ICU admission varies from country to country in Europe [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This reflects differences in health care systems, care policies and available resources. In Belgium, scientific societies recommend ICU admission for patients older than 75 years with a CFS score less than 5 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In Switzerland, the Academy of Medical Sciences sets higher thresholds, with a CFS of 7 for those over 65 and 6 for those over 85, to consider exclusion from ICU admission [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The variation in thresholds raises the question of standardization of practice. Currently, there is no universal consensus on frailty-based ICU admission criteria. Finally, CFS should be part of systematic interrogation when receiving critically ill elderly in the ED, to guide further in-hospital orientation.\u003c/p\u003e\u003cp\u003eInterestingly, men were more likely to be admitted to the ICU than to the GIU. This could be due to more severe presentation in men or a sex bias. In a vignette-based clinical case, Vromant et al. found that women were more at risk of ceiling of care in the ED than men [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Further studies should explore the reasons for this issue, given that women receive less care during admission to the ICU [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe found no difference in in-hospital mortality across hospital ward groups. Guidet et al. found similar results when comparing mortality between systematic ICU admission of elderly patients and standard practice [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A prospective multicenter study focusing on elderly patients admitted to the ICU found that patients with CFS\u0026thinsp;\u0026ge;\u0026thinsp;5 had higher mortality than those with lower scores [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A meta-analysis also confirmed that higher CFS scores correlate with an increased risk of mortality, even after adjustment for other risk factors such as age and disease severity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegarding the decision for TWW, there were no differences during hospitalization, whereas in the ED, significantly more patients in the GIU group had a TWW decision. This highlights the role of the ED physician in the TWW decision. The recommendations of the French Society of Emergency Medicine specify that decisions must be made after a comprehensive evaluation of the patient [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. TWW and frailty assessment are two complementary concepts in the management of elderly patients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While TWW focuses on the non-initiation, non-optimization, or even discontinuation of one or more therapeutics with an emphasis on the patient's comfort and quality of life, frailty assessment allows for a comprehensive understanding of the elderly patient's health status [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Both aspects should be taught to ED residents and develop throughout their medical practice, with a focus on the fact that more than 30% of patients with TWW decision in the ED are still alive at day-30 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHospitalization represents a turning point for the elderly, which can lead to an iatrogenic increase in dependency [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. After hospitalization, many patients find it difficult to return to their previous level of autonomy. In the present study, among patients discharged alive from the hospital, more than 50% in each group were able to return home directly. Patients admitted to the GIU were more likely to have home care plan modification, which can reflect a more global approach of patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. We did not measure readmission rates by CFS, nor did we observe variations in CFS before and after hospitalization. The results highlight the importance of adapting the ED and ICU to the specific needs of the elderly. The introduction of geriatric EDs and ICUs may represent an evolution in the continuum of care for this vulnerable population [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. By adapting spaces and medical protocols to geriatrics, these services could play a key role in reducing mortality and readmission rates, while improving transitions between acute care, rehabilitation, and long-term care [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations that should be discussed. First, its retrospective nature exposes it to selection and information bias, with some missing data, particularly regarding patients' medical history and clinical decisions influencing ICU versus GIU admission. In addition, this was a single-center study conducted in a university hospital, which limits the generalizability of the results to other institutions with different admission policies and resource availability. Furthermore, assessment of frailty using the CFS relies in part on clinical judgment, which may vary according to clinician experience and expertise. Although we attempted to minimize inter-rater variability by having a single investigator perform the assessment, observer bias cannot be completely excluded. A prospective study with multiple raters and cross-validation may improve the reliability of this measure. In addition, our study does not consider certain factors that may influence patient triage, such as patient and family preferences, ICU bed availability, or multidisciplinary team decisions in the emergency setting. These factors play a critical role in ICU admission decisions and may provide further context for our findings. Finally, although we compared the predictive performance of the CFS, Charlson Index, and SAPS II for ICU admission and in-hospital mortality, the CFS had a low AUC, suggesting that other unmeasured factors likely contribute to these outcomes. A more comprehensive risk stratification approach that incorporates frailty, acuity, and patient-centered preferences may improve decision-making regarding patient admission and management.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eCFS differed between ICU and GIU groups, suggesting its potential role in orientation after RR admission. Although CFS\u0026rsquo;s predictive value remains limited, in this study it was better than the Charlson Index or SAPS 2 for predicting ICU admission. Further prospective studies should evaluate its integration with other clinical parameters to optimize decision making in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;75 in the ED.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003eStatement of Ethics\u003c/h2\u003e\u003cp\u003e The local ethics committee of Nimes\u0026rsquo; university hospital approved the study (CHU Nimes IRB 24.03.07) and waived the consent in accordance with French law (Law no. 2012\u0026thinsp;\u0026minus;\u0026thinsp;300 of 5 March 2012 on research involving the human person). Patients or their relative received a non-opposition letter, describing the aims of this study.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Sources\u003c/h2\u003e\u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eConceptualization: FC, MJ, FR \u0026amp; XB; Methodology: FC, MJ, FR \u0026amp; XB; Formal analysis: FC;\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAuthors acknowledge Chlo\u0026eacute; Silighini for her advice concerning the study protocol and Sylvain Benenati for his help in initial data collection.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, FC, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGraham CA (2009) Critical care in emergency medicine. Eur J Emerg Med 16:295\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDziegielewski J, Schulte FC, Jung C, Wolff G, Hannappel O, K\u0026uuml;mpers P et al (2023) Resuscitation room management of patients with non-traumatic critical illness in the emergency department (OBSERvE-DUS-study). BMC Emerg Med 23:43\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMochmann HC, Arntz HR, Dincklage FV, Rauch U, Schultheiss HP, Bobbert P (2014) Old age and chronic disease: is the emergency medical system the appropriate provider for the elderly? Eur J Emerg Med 21:105\u0026ndash;111\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarbiellini Amidei C, Macci\u0026ograve; S, Cantarutti A, Gessoni F, Bardin A, Zanier L et al (2021) Hospitalizations and emergency department visits trends among elderly individuals in proximity to death: a retrospective population-based study. Sci Rep 11:21472\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClaret PG, Bobbia X, Olive S, Demattei C, Yan J, Cohendy R et al (2016) The impact of emergency department segmentation and nursing staffing increase on inpatient mortality and management times. BMC Health Serv Res 16:279\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuidet B, Leblanc G, Simon T, Woimant M, Quenot JP, Ganansia O et al (2017) Effect of Systematic Intensive Care Unit Triage on Long-term Mortality Among Critically Ill Elderly Patients in France. JAMA 318:1450\u0026ndash;1459\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eF\u0026oslash;rde R, Aasland OG, Steen PA (2002) Medical end-of-life decisions in Norway. Resuscitation 55:235\u0026ndash;240\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I et al (2005) A global clinical measure of fitness and frailty in elderly people. CMAJ 173:489\u0026ndash;495\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuidet B, de Lange DW, Boumendil A, Leaver S, Watson X, Boulanger C et al (2020) The contribution of frailty, cognition, activity of daily life and comorbidities on outcome in acutely admitted patients over 80 years in European ICUs: the VIP2 study. Intensive Care Med 46:57\u0026ndash;69\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrummel NE, Bell SP, Girard TD, Pandharipande PP, Jackson JC, Morandi A et al (2017) Frailty and Subsequent Disability and Mortality among Patients with Critical Illness. Am J Respir Crit Care Med 196:9\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFronczek J, Polok K, de Lange DW, Jung C, Beil M, Rhodes A et al (2021) Relationship between the Clinical Frailty Scale and short-term mortality in patients\u0026thinsp;\u0026ge;\u0026thinsp;80 years old acutely admitted to the ICU: a prospective cohort study. Crit Care 25:231\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbraham P, Courvoisier DS, Annweiler C, Lenoir C, Millien T, Dalmaz F et al (2019) Validation of the clinical frailty score (CFS) in French language. BMC Geriatr 19:322\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40:373\u0026ndash;383\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLe Gall JR, Lemeshow S, Saulnier F (1993) A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 270:2957\u0026ndash;2963\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDouplat M, Fraticelli L, Claustre C, Peiretti A, Serre P, Bischoff M et al (2020) Management of decision of withholding and withdrawing life-sustaining treatments in French EDs. Scand J Trauma Resusc Emerg Med 28:52\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJung C, Flaatten H, Fj\u0026oslash;lner J, Bruno RR, Wernly B, Artigas A et al (2021) The impact of frailty on survival in elderly intensive care patients with COVID-19: the COVIP study. Crit Care 25:149\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJung C, Guidet B, Flaatten H, VIP study group (2023) Frailty in intensive care medicine must be measured, interpreted and taken into account! Intensive Care Med 49:87\u0026ndash;90\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouthillier M-\u0026Egrave;, Lorange M, Laflamme MC, Fortin J-S (2021) Prioritization for Access to Intensive Care (Adults) in an Extreme Pandemic Context. Quebec: Directorate General of University, Medical, Nursing and Pharmaceutical Affairs (DGAUMIP); Available on \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://publications.msss.gouv.qc.ca/msss/fichiers/directives-covid/archives/dgaumip-007-rev1-a1.pdf\u003c/span\u003e\u003cspan address=\"https://publications.msss.gouv.qc.ca/msss/fichiers/directives-covid/archives/dgaumip-007-rev1-a1.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSwiss Medical Weekly (2021) Intensive care triage under exceptional resource scarcity. Swiss Med Wkly 151:30077\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHautain C (2022) Clinical Frailty Score: Consensus from Scientific Societies for Admissions to Critical Care and Intensive Care Units. Med Catastrophe Mass Emergencies 6:96\u0026ndash;98\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVromant A, Alam\u0026eacute; K, Cassard C, Bloom B, Mir\u0026oacute; O, Freund Y (2024) Effect of patient gender on the decision of ceiling of care: a European study of emergency physicians\u0026rsquo; treatment decisions in simulated cases. Eur J Emerg Med 31:423\u0026ndash;428\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eModra LJ, Higgins AM, Abeygunawardana VS, Vithanage RN, Bailey MJ, Bellomo R (2022) Sex Differences in Treatment of Adult Intensive Care Patients: A Systematic Review and Meta-Analysis. Crit Care Med 50:913\u0026ndash;923\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBruno RR, Wernly B, Bagshaw SM, van den Boogaard M, Darvall JN, De Geer L et al (2023) The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data. Ann Intensive Care 13:37\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReignier J, Feral-Pierssens AL, Boulain T, Carpentier F, Le Borgne P, Del Nista D et al (2019) Withholding and withdrawing life-support in adults in emergency care: joint position paper from the French Intensive Care Society and French Society of Emergency Medicine. Ann Intensive Care 9:105\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStolz E, Gro\u0026szlig;sch\u0026auml;dl F, Mayerl H, R\u0026aacute;sky \u0026Eacute;, Freidl W Determinants of acceptance of end-of-life interventions: a comparison between withdrawing life-prolonging treatment and euthanasia in Austria. BMC Med Ethics. 1; 16:81\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoisy F, Desbrosses C, Markarian T, Grau-Mercier L, Lavielle S, Tikvesa D et al (2025) Thirty-day survival rate of patients having a treatment withholding or treatment withdrawal decision in the emergency department: A retrospective monocentric study. Geriatr Gerontol Int 25:528\u0026ndash;534\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeguin P, Arnouat M, Launey Y (2019) The elderly patient in intensive care. Anesth Rea 5:510\u0026ndash;520\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsplund K, Gustafson Y, Jacobsson C, Bucht G, Wahlin A, Peterson J et al (2000) Geriatric-based versus general wards for older acute medical patients: a randomized comparison of outcomes and use of resources. J Am Geriatr Soc 48:1381\u0026ndash;1388\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNaouri D, Pelletier-Fleury N, Lapidus N, Yordanov Y (2022) The effect of direct admission to acute geriatric units compared to admission after an emergency department visit on length of stay, postacute care transfers and ED return visits. BMC Geriatr 22:555\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVincent A, Jomard N, Haesebaert J, Comte B, Gilbert T, Schott AM (2019) Referral pathway of patients aged 75 years and older after a telephone triage by the French emergency medical communication center (SAMU). Arch Gerontol Geriatr 84:103893\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"clinical frailty score, emergency department, intensive care, resuscitation room","lastPublishedDoi":"10.21203/rs.3.rs-7460546/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7460546/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e\u003cp\u003eElderly patients (\u0026ge;\u0026thinsp;75 years) often require resuscitation room (RR) care in the emergency department (ED), yet decisions regarding intensive care unit (ICU) admission remain complex. Assessment of quality of life and frailty is necessary to determine the level of care required for elderly patients. The Clinical Frailty Scale (CFS) is a validated tool for assessing frailty and predicting mortality, but its role in ICU triage remains unclear. The aim of this study was to compare the CFS of patients admitted to the ICU with those admitted to the general inpatient unit (GIU) after receiving initial intensive care.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eThis was a retrospective, single-centre study including patients aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years admitted to the ED RR from November 1, 2023, to March 31, 2024. The primary outcome was the comparison of CFS between ICU and GIU admissions. Secondary outcomes included predictive performance of CFS for ICU admission and in-hospital mortality.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eOf the 392 patients enrolled, 170 (43%) were admitted to the ICU and 222 (57%) to the GIU. The median CFS was 3 [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] in ICU-admitted patients and 4 [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] in GIU-admitted patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In-hospital mortality rate was 35/222 (16%) in GIU-admitted group and 30/180 (18%) in ICU-admitted group (p\u0026thinsp;=\u0026thinsp;0.72). CFS predictive value for ICU admission had an area under curve of 0.68 (95% confidence interval (95%CI): 0.63\u0026ndash;0.73) and for in-hospital mortality of 0.62 (95%CI: 0.55\u0026ndash;0.69).\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eCFS differed between ICU and GIU groups, suggesting its potential role in orientation after RR admission. Although CFS\u0026rsquo;s predictive value remains limited, in this study it was better than the Charlson Index or SAPS 2 for predicting ICU admission. Further prospective studies should evaluate its integration with other clinical parameters to optimize decision making in elderly ED patients.\u003c/p\u003e","manuscriptTitle":"Clinical Frailty Score for Hospital Outcome for Patients Aged ≥ 75 Following Emergency Department Resuscitation Room Admission: A Retrospective Monocenter Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 17:49:41","doi":"10.21203/rs.3.rs-7460546/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-10-06T12:47:31+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-03T07:19:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-29T06:36:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Internal and Emergency Medicine","date":"2025-08-28T04:36:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9f6b6e7b-bb5f-415e-b595-1a178a002364","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:01:39+00:00","versionOfRecord":{"articleIdentity":"rs-7460546","link":"https://doi.org/10.1007/s11739-026-04263-8","journal":{"identity":"internal-and-emergency-medicine","isVorOnly":false,"title":"Internal and Emergency Medicine"},"publishedOn":"2026-01-20 15:57:39","publishedOnDateReadable":"January 20th, 2026"},"versionCreatedAt":"2025-10-17 17:49:41","video":"","vorDoi":"10.1007/s11739-026-04263-8","vorDoiUrl":"https://doi.org/10.1007/s11739-026-04263-8","workflowStages":[]},"version":"v1","identity":"rs-7460546","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7460546","identity":"rs-7460546","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}