Impact of ICU Admission Thresholds on Outcomes in Rapid Response System Activations: A Multicenter Study in Japan

2024 · doi:10.21203/rs.3.rs-4485450/v1
preprint OA: closed CC-BY-4.0
📄 Open PDF Full text JSON View at publisher
⚙ AI-generated summary by claude@2026-07, 2026-07-16 ⓘ

This multicenter study found that a higher standardized ICU admission ratio for rapid response system activations was associated with improved patient outcomes, including reduced mortality and better neurological function at 30 days.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

⚙ AI-generated deep summary by claude@2026-07, 2026-07-16 · read from full text ⓘ

This multicenter retrospective observational study used a Japanese in-hospital emergency registry to analyze 8,794 patients (2018–2022) who triggered Rapid Response System (RRS) activations across 35 institutions, focusing on variability in ICU admission thresholds and its association with 30-day outcomes (death and Cerebral Performance Category [CPC] ≥ 3). For each institution, the authors computed the ICU admission rate and a standardized ICU admission ratio (SIAR), comparing actual to predicted ICU admissions based on patient severity, and assessed associations with multivariable generalized estimating equations (GEE); the preprint explicitly notes limitations as it is not peer reviewed. They found substantial between-institution variability in ICU admission rates and SIAR, and higher SIAR (i.e., higher-than-predicted ICU admission) was associated with lower odds of CPC ≥ 3 or death at 30 days (GEE odds ratio 0.78, P=0.015), while the association with death alone was not statistically significant. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Abstract Background The variability in ICU admission rates for patients activated by the Rapid Response System (RRS) is substantial and differs significantly across institutions. This study explores the disparities in ICU admission thresholds and their impact on patient outcomes. Methods A multicenter retrospective observational study was conducted using a Japanese in-hospital emergency registry, focusing on patients for whom the RRS was activated from 2018 to 2022. We calculated the ICU admission rate (ratio of ICU admissions to RRS activations) and the Standardized ICU Admission Ratio (SIAR: ratio of actual to predicted ICU admissions) for each institution (N = 35). The relationship between SIAR and patient outcomes, specifically death or Cerebral Performance Category (CPC) at 30 days, was analyzed using multivariable analysis with the Generalized Estimating Equation (GEE) model. Results The study included 8,794 patients, with 26.9% admitted to the ICU. The median ICU admission rate was 0.33 (1st quantile: 0.21, 3rd quantile: 0.47), and the median SIAR was 0.98 (1st quantile: 0.75, 3rd quantile: 1.17). Univariable analysis indicated that a higher SIAR significantly correlated with a lower incidence of CPC ≥ 3 or death at 30 days (P = 0.037) and showed a trend towards lower mortality at 30 days (P = 0.059). The GEE model revealed that the odds ratio of SIAR for death at 30 days was 0.89 (95% CI = 0.72 to 1.09; P = 0.30), and for CPC ≥ 3 or death at 30 days was 0.78 (95% CI = 0.64 to 0.95; P = 0.015). Conclusions This study demonstrates a significant association between higher SIAR and improved patient outcomes, suggesting that lower ICU admission thresholds during RRS activations may enhance patient prognosis.
Full text 128,389 characters · extracted from preprint-html · click to expand
Impact of ICU Admission Thresholds on Outcomes in Rapid Response System Activations: A Multicenter Study in Japan | 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 Impact of ICU Admission Thresholds on Outcomes in Rapid Response System Activations: A Multicenter Study in Japan Shohei Ono, Shigehiko Uchino, Miho Tokito, Taishi Saito, Yusuke Sasabuchi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4485450/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The variability in ICU admission rates for patients activated by the Rapid Response System (RRS) is substantial and differs significantly across institutions. This study explores the disparities in ICU admission thresholds and their impact on patient outcomes. Methods A multicenter retrospective observational study was conducted using a Japanese in-hospital emergency registry, focusing on patients for whom the RRS was activated from 2018 to 2022. We calculated the ICU admission rate (ratio of ICU admissions to RRS activations) and the Standardized ICU Admission Ratio (SIAR: ratio of actual to predicted ICU admissions) for each institution (N = 35). The relationship between SIAR and patient outcomes, specifically death or Cerebral Performance Category (CPC) at 30 days, was analyzed using multivariable analysis with the Generalized Estimating Equation (GEE) model. Results The study included 8,794 patients, with 26.9% admitted to the ICU. The median ICU admission rate was 0.33 (1st quantile: 0.21, 3rd quantile: 0.47), and the median SIAR was 0.98 (1st quantile: 0.75, 3rd quantile: 1.17). Univariable analysis indicated that a higher SIAR significantly correlated with a lower incidence of CPC ≥ 3 or death at 30 days (P = 0.037) and showed a trend towards lower mortality at 30 days (P = 0.059). The GEE model revealed that the odds ratio of SIAR for death at 30 days was 0.89 (95% CI = 0.72 to 1.09; P = 0.30), and for CPC ≥ 3 or death at 30 days was 0.78 (95% CI = 0.64 to 0.95; P = 0.015). Conclusions This study demonstrates a significant association between higher SIAR and improved patient outcomes, suggesting that lower ICU admission thresholds during RRS activations may enhance patient prognosis. Rapid response system ICU admission General ward Standardized ICU Admission Ratio ICU admission thresholds In-Hospital Emergency Registry in Japan ICU admission variability Figures Figure 1 Figure 2 Figure 3 Take-home massage This study of 8,794 patients with Rapid Response System activations shows significant variability in ICU admission rates across institutions. Higher Standardized ICU Admission Ratio (SIAR) is linked to better outcomes, suggesting that lower ICU thresholds can improve prognosis. Background The Rapid Response System (RRS) is designed to mitigate unexpected in-hospital cardiac arrests by promptly identifying and managing physiologically unstable patients [ 1 – 4 ]. Despite the lack of statistically significant results from the sole multicenter randomized controlled trial conducted across 23 Australian centers [ 5 ], systematic reviews of observational studies indicate that the implementation of RRS is linked with reduced rates of in-hospital cardiac arrests and overall mortality [ 6 ]. Globally, the utilization of RRS is on the rise [ 7 – 9 ]. In Japan, the initiative to introduce RRS began in 2008 under the auspices of the Japanese Coalition for Patient Safety [ 10 ], and since 2018, the Japan Council for Quality Health Care has been assessing the introduction of RRS teams as a core hospital function [ 11 ]. When the Rapid Response System (RRS) is triggered for a ward patient, the RRS team must decide whether to escalate care to the ICU or continue management on the ward. While various exploratory studies have identified factors influencing ICU admissions post-RRS activation [ 12 – 14 ], the external validity of these indicators remains unverified, and no standardized criteria for ICU admission have been universally adopted. Jones et al. reported that decisions for ICU admission were influenced by the staffing levels and experience on the ward, the availability of ICU beds, and the feasibility of administering specific treatments in both the ward and high-dependency settings [ 15 ]. They also highlighted that individual hospitals frequently established their own detailed criteria for ICU admissions, with ratios ranging from 10 to 25% of all RRS activations [ 16 ]. Nonetheless, the actual variability in ICU admission ratios across different institutions can be broader [ 12 , 17 – 21 ]. Prior studies have not conclusively determined whether this variability in ICU admission rates is attributed to differences in admission thresholds or to variations in patient severity [ 12 – 14 , 17 – 20 ]. Our study explored the extent to which ICU admission thresholds varied across institutions and assessed their impact on patient outcomes. We refined the ICU admission ratio by accounting for the predicted probability of ICU admission based on patient severity. Additionally, we examined the relationship between the standardized ICU admission ratio and key outcomes such as in-hospital mortality and the Cerebral Performance Category (CPC). Methods Design This study was a retrospective observational analysis utilizing the In-Hospital Emergency Registry in Japan (IHER-J), managed by the In-Hospital Emergency Committee in Japan (IHEC-J) [ 22 ]. The registry compiles data related to the Rapid Response System (RRS) to facilitate clinical research and enhance RRS efficacy. It includes comprehensive data on participating institutions, patient demographics, RRS activations, interventions performed, and patient outcomes. Ethical approval was granted by the Ethics Committee of Jichi Medical University Saitama Medical Center (approval number: S23-088), and the requirement for informed consent was waived due to the retrospective nature of the study. Our reporting adheres to the STROBE guidelines for observational studies in epidemiology. [ 23 ]. Patients and data collection The study population consisted of patients recorded in the IHER-J between 2018 and 2022. Exclusion criteria were following: patients younger than 18 years, non-inpatients, patients who died or were transferred to another hospital immediately following an RRS activation, or those from institutions that registered fewer than ten patients. Data collected included demographic details (age, sex), Cerebral Performance Category (CPC) ≥ 3 at hospital admission, initial diagnoses, patient condition and vital signs at the time of the RRS activations, the initiator of the RRS activations, the reason and diagnosis associated with the RRS activations, interventions administered during the RRS event, mortality, and CPC ≥ 3 at 30 days post-RRS activations, as well as any do-not-attempt-resuscitation (DNAR) orders issued post-RRS activations. The primary outcomes for this study were death and CPC ≥ 3 at 30 days post-RRS activations. Age and vital signs were categorized for detailed analysis. Statistics Categorical data were presented as counts and percentages, and numerical data as medians with interquartile ranges. We utilized logistic regression to predict the probability of ICU admission, employing ICU admission as the dependent variable. The predictive accuracy of the model was assessed using the Area Under the Receiver Operating Curve (AUROC). The ICU admission rate was determined by dividing the total number of ICU admissions at each institution by the number of RRS activations, and the Standardized ICU Admission Ratio (SIAR) was calculated by dividing the total number of actual ICU admissions by the predicted number of ICU admissions. Scatter plots displaying the relationship between SIAR and primary outcomes were analyzed using Spearman's correlation coefficients. To address missing data, multiple imputation was performed, creating 20 datasets with 50 iterations each, utilizing the predictive mean matching (PMM) method. We constructed a Generalized Estimating Equations (GEE) model to assess the influence of institution-related variations on the relationship between SIAR and the primary outcomes. Sensitivity analyses were conducted to ensure the robustness of the results, which included excluding patients with pre-existing DNAR orders before the RRS activations and those with missing data. Statistical significance was determined using a two-tailed test with a P-value threshold of 0.05. All statistical analyses were conducted using R software (version 4.3.2). The manuscript underwent a review and revision process by the coauthors, followed by English language proofreading with ChatGPT®. Results Figure 1 shows the patient selection process. Out of 12,674 patients assessed for eligibility, 8,794 were included in the main analysis. The demographics of patients in Table 1 shows that about 60% were male and 80% were older than 60 years. The predominant primary diagnoses at admission included noncardiac conditions (38.8%), noncardiac surgery (26.1%), and cancer (14.1%). Prior to the RRS activations, 15% of patients had a do-not-attempt-resuscitation (DNAR) order, and 50% were receiving oxygen. Table 1 Patient characteristics Main analysis (n = 8,794) Exclude DNAR (n = 7,430) Exclude missing data (n = 4,147) Sex (women) 3568 (40.6) 3045 (41.0) 1709 (41.2) Age (yrs) -39 442 (5.0) 435 (5.9) 176 (4.2) 40–59 1172 (13.3) 1099 (14.8) 499 (12.0) 60–79 4025 (45.8) 3505 (47.2) 1893 (45.6) 80- 3155 (35.9) 2391 (32.2) 1579 (38.1) CPC ≥ 3 at hospital admission 1435 (16.3) 1074 (14.5) 679 (16.4) Primary diagnosis of admission Cardiac 744 (8.5) 615 (8.3) 304 (7.3) Noncardiac 3410 (38.8) 2675 (36.0) 1744 (42.1) Cardiac surgery 413 (4.7) 399 (5.4) 152 (3.7) Noncardiac surgery 2294 (26.1) 2034 (27.4) 939 (22.6) Trauma 535 (6.1) 493 (6.6) 319 (7.7) Cancer 1238 (14.1) 1062 (14.3) 567 (13.7) Infection 610 (6.9) 512 (6.9) 299 (7.2) Others 449 (5.1) 413 (5.6) 169 (4.1) Patient status at RRS activation Days from hospital admission 8.00 [3.00, 23.00] 8.00 [3.00, 22.00] 9.00 [3.00, 23.00] ICU discharge within 72hr 505 (5.7) 471 (6.3) 209 (5.0) Sedation within 24hr 476 (5.4) 454 (6.1) 193 (4.7) Surgery within 1w 1184 (13.5) 1145 (15.4) 493 (11.9) Acute surgery 271 (3.1) 259 (3.5) 124 (3.0) DNAR before RRS 1364 (15.5) 0 (0.0) 767 (18.5) CPA before RRS arrival 228 (2.6) 216 (2.9) 19 (0.5) Oxygen supply before RRS activation 4476 (50.9) 3579 (48.2) 2313 (55.8) We present categorical data as counts (%) and numerical data as medians (interquartile ranges). Abbreviations: CPA, Cardiopulmonary Arrest; CPC, Cerebral Performance Category; DNAR, Do Not Attempt Resuscitation; RRS, Rapid Response System. Table 2 shows information related to the RRS activation. The most common vital sign abnormalities recorded were tachycardia (45.2%) and tachypnea (69.1%), with desaturation (29.4%), altered mental status (23.1%), and hypotension (21.7%) being prevalent reasons for the RRS activation. The most frequent diagnosis at the time of RRS activation was respiratory-related (30%). The most common interventions included blood tests (39%), oxygen administration (32.2%), and ICU admissions (26.9%). Notably, there were missing values in nine measurements, primarily in vital signs, which were addressed using the multiple imputation method as shown in Supplementary Fig. 1. Table 2 Conditions, interventions, and outcomes of study patients Main analysis (n = 8,794) Exclude DNAR (n = 7,430) Exclude missing data (n = 4,147) Heart rate (/min) -59 759 (8.6) 665 (9.0) 214 (5.2) 60–99 4062 (46.2) 3418 (46.0) 2110 (50.9) 100- 3973 (45.2) 3347 (45.0) 1823 (44.0) Systolic blood pressure (mmHg) -89 2168 (24.7) 1900 (25.6) 844 (20.4) 90–179 6273 (71.3) 5204 (70.0) 3183 (76.8) 180- 353 (4.0) 326 (4.4) 120 (2.9) Respiratory rate (/min) -9 317 (3.6) 294 (4.0) 38 (0.9) 10–19 2402 (27.3) 2040 (27.5) 1113 (26.8) 20–29 4061 (46.2) 3394 (45.7) 2081 (50.2) 30- 2014 (22.9) 1702 (22.9) 915 (22.1) SpO2 (%) -84 1229 (14.0) 1071 (14.4) 275 (6.6) 85–89 718 (8.2) 570 (7.7) 285 (6.9) 90–94 1903 (21.6) 1545 (20.8) 1019 (24.6) 95- 4944 (56.2) 4244 (57.1) 2568 (61.9) Temperature (℃) -35 61 (0.7) 44 (0.6) 23 (0.6) 35-37.5 5899 (67.1) 4970 (66.9) 2756 (66.5) 37.5–38.5 1787 (20.3) 1525 (20.5) 870 (21.0) 38.5- 1047 (11.9) 891 (12.0) 498 (12.0) Consciousness Alert 4006 (45.6) 3510 (47.2) 2165 (52.2) Voice 2710 (30.8) 2244 (30.2) 1271 (30.6) Painful 918 (10.4) 698 (9.4) 420 (10.1) Unresponsive 1160 (13.2) 978 (13.2) 291 (7.0) Person activated RRS Nurse 7029 (79.9) 5810 (78.2) 3483 (84.0) Doctor 1707 (19.4) 1570 (21.1) 638 (15.4) Others 58 (0.7) 50 (0.7) 26 (0.6) Reason for RRS activation Desaturation 2585 (29.4) 2222 (29.9) 974 (23.5) Tachypnea 1379 (15.7) 1199 (16.1) 752 (18.1) Bradypnea 228 (2.6) 205 (2.8) 54 (1.3) Dyspnea 857 (9.7) 732 (9.9) 378 (9.1) Hypotension 1906 (21.7) 1716 (23.1) 794 (19.1) Bradycardia 319 (3.6) 290 (3.9) 73 (1.8) Tachycardia 1046 (11.9) 913 (12.3) 467 (11.3) Chest pain 136 (1.5) 126 (1.7) 42 (1.0) Altered mental status 2030 (23.1) 1827 (24.6) 648 (15.6) Seizure 221 (2.5) 200 (2.7) 63 (1.5) Agitation 62 (0.7) 60 (0.8) 15 (0.4) Nurse concern 1736 (19.7) 1481 (19.9) 977 (23.6) Others 1465 (16.7) 1192 (16.0) 919 (22.2) Diagnosis for RRS activation Respiratory 2640 (30.0) 2143 (28.8) 1268 (30.6) Cardiogenic 1319 (15.0) 1098 (14.8) 605 (14.6) Hypovolemic 787 (8.9) 711 (9.6) 298 (7.2) Distributive 1660 (18.9) 1429 (19.2) 1026 (24.7) Neurogenic 355 (4.0) 275 (3.7) 160 (3.9) Metabolic 286 (3.3) 245 (3.3) 125 (3.0) Unknown 989 (11.2) 811 (10.9) 487 (11.7) Others 1765 (20.1) 1514 (20.4) 726 (17.5) Intervention by RRS Oxygen supply 2836 (32.2) 2415 (32.5) 969 (23.4) Airway suction 1385 (15.7) 1135 (15.3) 443 (10.7) NPPV 295 (3.4) 245 (3.3) 110 (2.7) Manual ventilation 939 (10.7) 861 (11.6) 213 (5.1) Intubation 826 (9.4) 797 (10.7) 193 (4.7) Cardiopulmonary resuscitation 194 (2.2) 186 (2.5) 16 (0.4) Defibrillation 73 (0.8) 65 (0.9) 9 (0.2) Transfusion 264 (3.0) 248 (3.3) 67 (1.6) Fluid bolus 1614 (18.4) 1452 (19.5) 521 (12.6) Blood test 3429 (39.0) 3081 (41.5) 1199 (28.9) Drug administration 2234 (25.4) 2009 (27.0) 687 (16.6) ICU admission 2366 (26.9) 2253 (30.3) 850 (20.5) Outcome Dead at 30 days 1934 (22.0) 1320 (17.8) 881 (21.2) CPC ≥ 3 at 30 days 3536 (40.2) 2635 (35.5) 1614 (38.9) DNAR post-RRS activation 482 (5.5) 481 (6.5) 196 (4.7) We present categorical data as counts (%) and numerical data as medians (interquartile ranges) (*). Abbreviations: NPPV, Noninvasive Positive Pressure Ventilation; CPC, Cerebral Performance Category; DNAR, Do Not Attempt Resuscitation. This dataset was analyzed using logistic regression, with ICU admission as the dependent variable (details in Supplementary Table 1). We incorporated all variables from Tables 1 and 2 as predictors, excluding the dependent variable itself, to compute the probability of ICU admission for each patient. The logistic model's Area Under the Receiver Operating Characteristic Curve (AUROC) was 0.8424, indicating good predictive power. We subsequently calculated the ICU admission rate (ratio of ICU admissions to RRS activations) and the SIAR for each institution, which are shown in Fig. 2 . The median ICU admission rate was 0.33 (interquartile range: 0.21 to 0.47) and the median SIAR was 0.98 (interquartile range: 0.75 to 1.17). Figure 3 shows scatterplots of the SIAR against the 30-day mortality rate for each institution (3A) and the rate of CPC ≥ 3 or death at 30 days (3B). There was a significant correlation between SIAR and the incidence of CPC ≥ 3 or death at 30 days (P = 0.037), and a trend toward a correlation with death at 30 days (P = 0.059). We further analyzed the relationship between the SIAR and patient outcomes using GEE models, adjusted for institution effects, as shown in Table 3 . Two models were constructed: one evaluating death at 30 days, and the other assessing CPC ≥ 3 or death at 30 days as the outcomes. The odds ratio for SIAR impacting death at 30 days was 0.89 (95% confidence interval (CI), 0.72 to 1.09, P = 0.30), while the odds ratio for CPC ≥ 3 or death at 30 days was 0.78 (95% CI, 0.64 to 0.95, P = 0.015). The odds ratios, confidence intervals, and P-values for additional explanatory variables in the GEE models are presented in Supplementary Table 2. Table 3 Odds ratio of standardized ICU admission ratio by GEE model. Dead at 30 days CPC ≥ 3 or dead at 30 days Odds ratio [95%CI] P-value Odds ratio [95%CI] P-value Standardized ICU admission ratio Main analysis (n = 8,794) 0.89 [0.72, 1.09] 0.30 0.78 [0.64, 0.95] 0.015 Exclude DNAR (n = 7,430) 0.83 [0.64, 1.06] 0.14 0.68 [0.54, 0.85] 0.001 Exclude missing data (n = 4,147) 0.96 [0.68, 1.34] 0.80 0.90 [0.67, 1.21] 0.50 GEE, Generalized Estimating Equations; DNAR, Do Not Attempt Resuscitation; CPC, Cerebral Performance Category. To confirm the robustness of our findings, we conducted two sensitivity analyses: one excluding patients who had DNAR orders prior to the RRS activations (n = 7,430), and another excluding patients with missing data (n = 4,147). Logistic regression results predicting ICU admission are presented in Supplementary Table 1, and outcomes of GEE models in Tables 3 , Supplementary Tables 3 and 4. Across all analyses, SIAR consistently demonstrated a reduction in both 30-day mortality and CPC ≥ 3 or death, although not all findings were statistically significant. Discussion In this multicenter observational study, we explored the variability in ICU admission rates during RRS activations and assessed their correlation with patient outcomes. Our analysis revealed that ICU admissions per institution varied significantly, with rates ranging from 0.07 to 0.85, and the SIAR—a measure of ICU admissions adjusted by ICU probability—ranged from 0.36 to 1.83. A positive correlation between higher SIAR and improved patient outcomes was apparent from scatter plot observations. Furthermore, in the GEE model, adjusted for both covariates and intra-institutional correlations, a statistically significant reduction in the incidence of Cerebral Performance Category (CPC) ≥ 3 or death at 30 days was observed with higher SIAR, while the reduction in death alone at 30 days was not statistically significant. These findings were consistently robust across two sensitivity analyses. We found that ICU admission rates in this study were notably more varied compared to previous research [ 12 , 16 – 20 ]. Jones et al. documented that ICU admission rates through RRS activations fluctuated between 10–25% [ 16 ],, yet other observational studies have observed even broader ranges, reaching up to 57% [ 12 , 17 – 21 ]. In Japan, where only 1% of hospital beds are designated as ICU beds [ 24 ], critically ill patients are frequently managed in general wards [ 24 – 26 ], contributing to the extensive variability observed in our study. Earlier studies have demonstrated that ICU admissions enhance the prognosis of critically ill patients [ 25 – 30 ]. Nonetheless, resource constraints necessitate a selective approach to ICU admissions. According to the ICU Admission, Discharge, and Triage Guidelines [ 31 ] resource allocation should be prioritized based on patient conditions and bed availability (grade 2D). Factors such as staffing levels and their experience, the availability of ICU beds, and the feasibility of implementing treatments on the ward [ 15 ], all seem to have contributed to the substantial diversity in ICU admission rates observed. We discovered that a higher SIAR may have correlated with improved patient outcomes. This finding is reasonable, considering the challenging decisions regarding ICU admissions for critically ill patients in the context of limited resources. RRS is designed to promptly identify physiologically unstable patients, allowing for timely interventions [ 1 – 4 ]. Continued observation of these patients in general wards may lead to adverse outcomes. Additionally, research by Wunsch et al. demonstrated a strong inverse relationship between the availability of ICU beds and hospital mortality rates across various countries (r = -0.82), using data from eight nations [ 32 ], providing indirect support for our findings. Although it remains unclear whether the determinants of SIAR are primarily influenced by bed availability or physician preference, the evidence suggests a potential need to enhance ICU capacity, particularly in places like Japan where ICU availability is notably limited [ 24 ]. Our study contains several strengths. It involved a large cohort of patients and institutions compared to prior research on RRS activations, facilitating precise calculations of the SIAR and an objective assessment of heterogeneity across institutions. To our knowledge, the In-Hospital Emergency Registry in Japan is the only registry that collects such detailed information on RRS activations. The observed variability among institutions enhances the comprehensiveness of our report. Furthermore, no other studies have explored the relationship between the diversity of ICU admissions and patient outcomes. Optimizing RRS interventions could lead to improved hospital-wide outcomes. Our findings indicate that SIAR might play a role in enhancing patient outcomes through RRS. However, there are also limitations to our study. The results exhibited some inconsistencies; for example, while SIAR did not show a statistically significant link with mortality at 30 days, it did demonstrate a significant reduction in the occurrence of CPC ≥ 3 or death at 30 days. These discrepancies could be attributed to the smaller sample sizes at some institutions, despite the overall large sample size. Although the trends suggest that higher SIAR may lessen poor prognoses, these findings did not reach statistical significance and might do so with a larger sample size. Additionally, our study was challenged by numerous missing data points, predominantly in vital signs. We addressed these gaps using multiple imputation, a technique that not only helps mitigate selection bias but also supports the conduct of Generalized Estimating Equations analysis, which necessitates a larger dataset. Conclusions Our research demonstrated that a higher Standardized ICU Admission Ratio correlates with improved patient outcomes. Lowering the ICU admission thresholds at each institution during Rapid Response System activations may enhance patient prognosis. Abbreviations ICU : Intensive care unit RRS : Rapid response system SIAR : Standardized ICU Admission Ratio. CPC : Cerebral Performance Category GEE : Generalized Estimating Equation. IHER-J : In-Hospital Emergency Registry in Japan IHEC-J : In-Hospital Emergency Committee in Japan DNAR : Do Not Attempt Resuscitation AUROC : Area Under the Receiver Operating Curve PMM : Predictive mean matching CI : Confidence interval Cardiopulmonary Arrest : CPA NPPV : Noninvasive Positive Pressure Ventilation Declarations Ethics approval and consent to participate: Ethical approval was granted by the Ethics Committee of Jichi Medical University Saitama Medical Center (approval number: S23-088), and the requirement for informed consent was waived due to the retrospective nature of the study. Consent for publication: Not applicable Availability of data and materials: The datasets generated and/or analyzed during the current study are not available since the dataset was obtained from In-Hospital Emergency Committee in Japan through a formal request/approval process as above but is available from the corresponding author on reasonable request. Competing Interests: The authors declare no conflicts of interest. Funding: Not applicable. Authors' contributions: SO is responsible for the analysis and the first draft of the manuscript. YS contributed as an advisor of statistical analysis. MS, MT, TS and SU contributed to the subsequent drafts. All authors read and approved the final manuscript. Acknowledgement: Not applicable. References Jones DA, DeVita MA, Bellomo R: Rapid-response teams . N Engl J Med 2011, 365 (2):139-146. Neale G, Woloshynowych M, Vincent C: Exploring the causes of adverse events in NHS hospital practice . J R Soc Med 2001, 94 (7):322-330. Buist MD, Jarmolowski E, Burton PR, Bernard SA, Waxman BP, Anderson J: Recognising clinical instability in hospital patients before cardiac arrest or unplanned admission to intensive care. A pilot study in a tertiary-care hospital . Med J Aust 1999, 171 (1):22-25. Franklin C, Mathew J: Developing strategies to prevent inhospital cardiac arrest: analyzing responses of physicians and nurses in the hours before the event . Crit Care Med 1994, 22 (2):244-247. Hillman K, Chen J, Cretikos M, Bellomo R, Brown D, Doig G, Finfer S, Flabouris A: Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial . Lancet 2005, 365 (9477):2091-2097. Maharaj R, Raffaele I, Wendon J: Rapid response systems: a systematic review and meta-analysis . Crit Care 2015, 19 (1):254. Lee BY, Hong SB: Rapid response systems in Korea . Acute Crit Care 2019, 34 (2):108-116. IHI Launches National Campaign to Save 100,000 Lives in U.S. Hospitals [https://www.psqh.com/janfeb05/100k.html] Australian Commission on Safety and Quality in Health Care. Safety and Quality Improvement Guide [https://www.safetyandquality.gov.au/sites/default/files/migrated/Standard9_Oct_2012_WEB.pdf] Japanese Coalition for Patient Safety ( JCPS ) [https://kyodokodo.jp/] Japan Council for Quality Health Care [https://jcqhc.or.jp/] Lee SI, Koh JS, Kim YJ, Kang DH, Lee JE: Characteristics and outcomes of patients screened by rapid response team who transferred to the intensive care unit . BMC Emerg Med 2022, 22 (1):18. Le Guen MP, Tobin AE, Reid D: Intensive care unit admission in patients following rapid response team activation: call factors, patient characteristics and hospital outcomes . Anaesth Intensive Care 2015, 43 (2):211-215. Orosz J, Bailey M, Udy A, Pilcher D, Bellomo R, Jones D: Unplanned ICU Admission From Hospital Wards After Rapid Response Team Review in Australia and New Zealand . Crit Care Med 2020, 48 (7):e550-e556. Jones D, DeVita M, Warrillow S: Ten clinical indicators suggesting the need for ICU admission after Rapid Response Team review . Intensive Care Med 2016, 42 (2):261-263. Jones D: The epidemiology of adult Rapid Response Team patients in Australia . Anaesth Intensive Care 2014, 42 (2):213-219. Casamento AJ, Dunlop C, Jones DA, Duke G: Improving the documentation of medical emergency team reviews . Crit Care Resusc 2008, 10 (1):29. Litvak E, Pronovost PJ: Rethinking rapid response teams . Jama 2010, 304 (12):1375-1376. Salamonson Y, Kariyawasam A, van Heere B, O'Connor C: The evolutionary process of Medical Emergency Team (MET) implementation: reduction in unanticipated ICU transfers . Resuscitation 2001, 49 (2):135-141. Wunderink RG, Diederich ER, Caramez MP, Donnelly HK, Norwood SD, Kho A, Reed KD: Rapid response team-triggered procalcitonin measurement predicts infectious intensive care unit transfers* . Crit Care Med 2012, 40 (7):2090-2095. Morris DS, Schweickert W, Holena D, zel R, Sims C, Pascual JL, Sarani B: Differences in outcomes between ICU attending and senior resident physician led medical emergency team responses . Resuscitation 2012, 83 (12):1434-1437. Naito T, Fujiwara S, Kawasaki T, Sento Y, Nakada TA, Arai M, Atagi K, Fujitani S: First report based on the online registry of a Japanese multicenter rapid response system: a descriptive study of 35 institutions in Japan . Acute Med Surg 2020, 7 (1):e454. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies . Ann Intern Med 2007, 147 (8):573-577. Sirio CA, Tajimi K, Taenaka N, Ujike Y, Okamoto K, Katsuya H: A cross-cultural comparison of critical care delivery: Japan and the United States . Chest 2002, 121 (2):539-548. Oami T, Imaeda T, Nakada TA, Abe T, Takahashi N, Yamao Y, Nakagawa S, Ogura H, Shime N, Umemura Y et al : Mortality analysis among sepsis patients in and out of intensive care units using the Japanese nationwide medical claims database: a study by the Japan Sepsis Alliance study group . J Intensive Care 2023, 11 (1):2. Iwashita Y, Yamashita K, Ikai H, Sanui M, Imai H, Imanaka Y: Epidemiology of mechanically ventilated patients treated in ICU and non-ICU settings in Japan: a retrospective database study . Crit Care 2018, 22 (1):329. Hersch M, Sonnenblick M, Karlic A, Einav S, Sprung CL, Izbicki G: Mechanical ventilation of patients hospitalized in medical wards vs the intensive care unit--an observational, comparative study . J Crit Care 2007, 22 (1):13-17. Ohbe H, Sasabuchi Y, Yamana H, Matsui H, Yasunaga H: Intensive care unit versus high-dependency care unit for mechanically ventilated patients with pneumonia: a nationwide comparative effectiveness study . Lancet Reg Health West Pac 2021, 13 :100185. Lieberman D, Nachshon L, Miloslavsky O, Dvorkin V, Shimoni A, Zelinger J, Friger M, Lieberman D: Elderly patients undergoing mechanical ventilation in and out of intensive care units: a comparative, prospective study of 579 ventilations . Crit Care 2010, 14 (2):R48. Basoulis D, Liatis S, Skouloudi M, Makrilakis K, Daikos GL, Sfikakis PP: Survival predictors after intubation in medical wards: A prospective study in 151 patients . PLoS One 2020, 15 (6):e0234181. Nates JL, Nunnally M, Kleinpell R, Blosser S, Goldner J, Birriel B, Fowler CS, Byrum D, Miles WS, Bailey H, Sprung CL: ICU Admission, Discharge, and Triage Guidelines: A Framework to Enhance Clinical Operations, Development of Institutional Policies, and Further Research . Crit Care Med 2016, 44 (8):1553-1602. Wunsch H, Angus DC, Harrison DA, Collange O, Fowler R, Hoste EA, de Keizer NF, Kersten A, Linde-Zwirble WT, Sandiumenge A, Rowan KM: Variation in critical care services across North America and Western Europe . Crit Care Med 2008, 36 (10):2787-2793, e2781-2789. Additional Declarations No competing interests reported. Supplementary Files supplementarytablefigure0520.docx Supplementary Figure 1. Bar Chart of Missing Values. Abbreviations: RRS, Rapid Response System; DNAR, Do Not Attempt Resuscitation. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4485450","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311880305,"identity":"c66067a6-c89f-49a2-9cae-4c19a15b0aaa","order_by":0,"name":"Shohei Ono","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3RMQrCMBSA4ReCcVG6pgh6hTqJZ3HxAtk7aMkUF90VRK+gi3NLIC4FV8FBJ2fHDh3ME3WrcRTMP72UfKQhAD7fL0bfQwrAYxyo/JKwoSU5fiEO8g4JUTg5SDBpXi+3MoGAQW3TX40HwcSSIt5VEq7rve5CaQgVsGO424u5JpJM81P1MZqxVlOmEJ0lEiOkJZSoatJBUtofiwyesjRi7SIREmD0SeRIbFyka0k4U7oRKqKO3KRia0n26S7tg2G8KJN2wKg58VEiVgedXYr4w/WfNfA5KAf9WKXO/a/IDZKvN/t8Pt//dAcnsVPBqcZRTAAAAABJRU5ErkJggg==","orcid":"","institution":"Jichi Medical University Saitama Medical Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shohei","middleName":"","lastName":"Ono","suffix":""},{"id":311880306,"identity":"745267d6-07cd-4da2-a407-25b6505d8ea1","order_by":1,"name":"Shigehiko Uchino","email":"","orcid":"","institution":"Jichi Medical University Saitama Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shigehiko","middleName":"","lastName":"Uchino","suffix":""},{"id":311880307,"identity":"b08a9987-3bbc-486c-bc0c-2e99bee69847","order_by":2,"name":"Miho Tokito","email":"","orcid":"","institution":"Jichi Medical University Saitama Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Miho","middleName":"","lastName":"Tokito","suffix":""},{"id":311880308,"identity":"a723176b-e1bc-456a-9560-36cf11f9a6ee","order_by":3,"name":"Taishi Saito","email":"","orcid":"","institution":"Jichi Medical University Saitama Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Taishi","middleName":"","lastName":"Saito","suffix":""},{"id":311880309,"identity":"1ce865a8-77b6-4542-8ed2-6f7bf0da27ce","order_by":4,"name":"Yusuke Sasabuchi","email":"","orcid":"","institution":"Jichi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Sasabuchi","suffix":""},{"id":311880310,"identity":"aa06af7d-b57a-4826-84c0-6fc77e8b5e8e","order_by":5,"name":"Masamitsu Sanui","email":"","orcid":"","institution":"Jichi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Masamitsu","middleName":"","lastName":"Sanui","suffix":""}],"badges":[],"createdAt":"2024-05-27 13:42:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4485450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4485450/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58172841,"identity":"3bc4fac3-1b2c-401c-b9b3-b636c57f44f8","added_by":"auto","created_at":"2024-06-12 03:55:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113125,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Patient Selection. RRS, Rapid Response System; DNAR, Do Not Attempt Resuscitation.\u003c/p\u003e","description":"","filename":"fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4485450/v1/ce95ec63afa584f62996539c.png"},{"id":58172843,"identity":"ba2109e4-3193-4ef4-9c38-6572dc565fea","added_by":"auto","created_at":"2024-06-12 03:55:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80431,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart and Boxplot of ICU Admissions by Facility in Descending Order. Panel A: ICU Admission Rate. Panel B: Standardized ICU Admission Ratio.\u003c/p\u003e","description":"","filename":"fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4485450/v1/0304e3ad03531eca3aa1b0b7.png"},{"id":58172842,"identity":"3fcd9708-08d5-4004-a944-a26b99205a21","added_by":"auto","created_at":"2024-06-12 03:55:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129945,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of Patient Outcomes and Standardized ICU Admission Ratio (SIAR). Panel A: 30-Day Mortality Rate. Panel B: Rate of CPC ≥ 3 or Death within 30 Days. Spearman's correlation coefficients are presented.\u003c/p\u003e","description":"","filename":"fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4485450/v1/dac91e4e260ef88060199f41.png"},{"id":62975739,"identity":"6e566319-a39c-4b74-a50f-8c07803eab60","added_by":"auto","created_at":"2024-08-21 15:57:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1803514,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4485450/v1/326e2009-c2f7-4a19-9280-b8cd2ada5567.pdf"},{"id":58172844,"identity":"69e54311-2b32-47cf-bdd9-59c36f1da2d1","added_by":"auto","created_at":"2024-06-12 03:55:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":285187,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 1. Bar Chart of Missing Values. Abbreviations: RRS, Rapid Response System; DNAR, Do Not Attempt Resuscitation.\u003c/p\u003e","description":"","filename":"supplementarytablefigure0520.docx","url":"https://assets-eu.researchsquare.com/files/rs-4485450/v1/7364ff6f95447bc69cb6b805.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of ICU Admission Thresholds on Outcomes in Rapid Response System Activations: A Multicenter Study in Japan","fulltext":[{"header":"Take-home massage","content":"\u003cp\u003eThis study of 8,794 patients with Rapid Response System activations shows significant variability in ICU admission rates across institutions. Higher Standardized ICU Admission Ratio (SIAR) is linked to better outcomes, suggesting that lower ICU thresholds can improve prognosis.\u003c/p\u003e\n"},{"header":"Background","content":"\u003cp\u003eThe Rapid Response System (RRS) is designed to mitigate unexpected in-hospital cardiac arrests by promptly identifying and managing physiologically unstable patients [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite the lack of statistically significant results from the sole multicenter randomized controlled trial conducted across 23 Australian centers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], systematic reviews of observational studies indicate that the implementation of RRS is linked with reduced rates of in-hospital cardiac arrests and overall mortality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Globally, the utilization of RRS is on the rise [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In Japan, the initiative to introduce RRS began in 2008 under the auspices of the Japanese Coalition for Patient Safety [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and since 2018, the Japan Council for Quality Health Care has been assessing the introduction of RRS teams as a core hospital function [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen the Rapid Response System (RRS) is triggered for a ward patient, the RRS team must decide whether to escalate care to the ICU or continue management on the ward. While various exploratory studies have identified factors influencing ICU admissions post-RRS activation [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], the external validity of these indicators remains unverified, and no standardized criteria for ICU admission have been universally adopted. Jones et al. reported that decisions for ICU admission were influenced by the staffing levels and experience on the ward, the availability of ICU beds, and the feasibility of administering specific treatments in both the ward and high-dependency settings [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. They also highlighted that individual hospitals frequently established their own detailed criteria for ICU admissions, with ratios ranging from 10 to 25% of all RRS activations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Nonetheless, the actual variability in ICU admission ratios across different institutions can be broader [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Prior studies have not conclusively determined whether this variability in ICU admission rates is attributed to differences in admission thresholds or to variations in patient severity [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study explored the extent to which ICU admission thresholds varied across institutions and assessed their impact on patient outcomes. We refined the ICU admission ratio by accounting for the predicted probability of ICU admission based on patient severity. Additionally, we examined the relationship between the standardized ICU admission ratio and key outcomes such as in-hospital mortality and the Cerebral Performance Category (CPC).\u003c/p\u003e "},{"header":"Methods","content":"\n\u003ch3\u003eDesign\u003c/h3\u003e\n\u003cp\u003eThis study was a retrospective observational analysis utilizing the In-Hospital Emergency Registry in Japan (IHER-J), managed by the In-Hospital Emergency Committee in Japan (IHEC-J) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The registry compiles data related to the Rapid Response System (RRS) to facilitate clinical research and enhance RRS efficacy. It includes comprehensive data on participating institutions, patient demographics, RRS activations, interventions performed, and patient outcomes. Ethical approval was granted by the Ethics Committee of Jichi Medical University Saitama Medical Center (approval number: S23-088), and the requirement for informed consent was waived due to the retrospective nature of the study. Our reporting adheres to the STROBE guidelines for observational studies in epidemiology. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and data collection\u003c/h2\u003e \u003cp\u003eThe study population consisted of patients recorded in the IHER-J between 2018 and 2022. Exclusion criteria were following: patients younger than 18 years, non-inpatients, patients who died or were transferred to another hospital immediately following an RRS activation, or those from institutions that registered fewer than ten patients. Data collected included demographic details (age, sex), Cerebral Performance Category (CPC)\u0026thinsp;\u0026ge;\u0026thinsp;3 at hospital admission, initial diagnoses, patient condition and vital signs at the time of the RRS activations, the initiator of the RRS activations, the reason and diagnosis associated with the RRS activations, interventions administered during the RRS event, mortality, and CPC\u0026thinsp;\u0026ge;\u0026thinsp;3 at 30 days post-RRS activations, as well as any do-not-attempt-resuscitation (DNAR) orders issued post-RRS activations. The primary outcomes for this study were death and CPC\u0026thinsp;\u0026ge;\u0026thinsp;3 at 30 days post-RRS activations. Age and vital signs were categorized for detailed analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eCategorical data were presented as counts and percentages, and numerical data as medians with interquartile ranges. We utilized logistic regression to predict the probability of ICU admission, employing ICU admission as the dependent variable. The predictive accuracy of the model was assessed using the Area Under the Receiver Operating Curve (AUROC). The ICU admission rate was determined by dividing the total number of ICU admissions at each institution by the number of RRS activations, and the Standardized ICU Admission Ratio (SIAR) was calculated by dividing the total number of actual ICU admissions by the predicted number of ICU admissions. Scatter plots displaying the relationship between SIAR and primary outcomes were analyzed using Spearman's correlation coefficients. To address missing data, multiple imputation was performed, creating 20 datasets with 50 iterations each, utilizing the predictive mean matching (PMM) method. We constructed a Generalized Estimating Equations (GEE) model to assess the influence of institution-related variations on the relationship between SIAR and the primary outcomes.\u003c/p\u003e \u003cp\u003eSensitivity analyses were conducted to ensure the robustness of the results, which included excluding patients with pre-existing DNAR orders before the RRS activations and those with missing data. Statistical significance was determined using a two-tailed test with a P-value threshold of 0.05. All statistical analyses were conducted using R software (version 4.3.2). The manuscript underwent a review and revision process by the coauthors, followed by English language proofreading with ChatGPT\u0026reg;.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the patient selection process. Out of 12,674 patients assessed for eligibility, 8,794 were included in the main analysis. The demographics of patients in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that about 60% were male and 80% were older than 60 years. The predominant primary diagnoses at admission included noncardiac conditions (38.8%), noncardiac surgery (26.1%), and cancer (14.1%). Prior to the RRS activations, 15% of patients had a do-not-attempt-resuscitation (DNAR) order, and 50% were receiving oxygen.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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\u003eMain analysis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;8,794)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclude DNAR\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7,430)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExclude missing data\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4,147)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (women)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3568 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3045 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1709 (41.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (yrs)\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\u003e-39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e442 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e435 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e176 (4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1172 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1099 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e499 (12.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4025 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3505 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1893 (45.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3155 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2391 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1579 (38.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPC\u0026thinsp;\u0026ge;\u0026thinsp;3 at hospital admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1435 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1074 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e679 (16.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary diagnosis of admission\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\u003eCardiac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e744 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e615 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e304 (7.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoncardiac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3410 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2675 (36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1744 (42.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e413 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e399 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152 (3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoncardiac surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2294 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2034 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e939 (22.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e535 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e493 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e319 (7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1238 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1062 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e567 (13.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e610 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e512 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e299 (7.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e449 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e413 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e169 (4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient status at RRS activation\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\u003eDays from hospital admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.00 [3.00, 23.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.00 [3.00, 22.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.00 [3.00, 23.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU discharge within 72hr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e505 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e471 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e209 (5.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedation within 24hr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e476 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e454 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193 (4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery within 1w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1184 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1145 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e493 (11.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e271 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e259 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNAR before RRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1364 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e767 (18.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPA before RRS arrival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e228 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen supply before RRS activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4476 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3579 (48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2313 (55.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eWe present categorical data as counts (%) and numerical data as medians (interquartile ranges). Abbreviations: CPA, Cardiopulmonary Arrest; CPC, Cerebral Performance Category; DNAR, Do Not Attempt Resuscitation; RRS, Rapid Response System.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows information related to the RRS activation. The most common vital sign abnormalities recorded were tachycardia (45.2%) and tachypnea (69.1%), with desaturation (29.4%), altered mental status (23.1%), and hypotension (21.7%) being prevalent reasons for the RRS activation. The most frequent diagnosis at the time of RRS activation was respiratory-related (30%). The most common interventions included blood tests (39%), oxygen administration (32.2%), and ICU admissions (26.9%). Notably, there were missing values in nine measurements, primarily in vital signs, which were addressed using the multiple imputation method as shown in Supplementary Fig.\u0026nbsp;1.\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\u003eConditions, interventions, and outcomes of study patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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\u003eMain analysis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;8,794)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclude DNAR\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7,430)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExclude missing data\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4,147)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (/min)\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\u003e-59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e759 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e665 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e214 (5.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4062 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3418 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2110 (50.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3973 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3347 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1823 (44.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\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\u003e-89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2168 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1900 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e844 (20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u0026ndash;179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6273 (71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5204 (70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3183 (76.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e180-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e353 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e326 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120 (2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate (/min)\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\u003e-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e317 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (0.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2402 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2040 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1113 (26.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4061 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3394 (45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2081 (50.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1702 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e915 (22.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO2 (%)\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\u003e-84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1229 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1071 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e275 (6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85\u0026ndash;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e718 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e570 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e285 (6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90\u0026ndash;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1903 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1545 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1019 (24.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4944 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4244 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2568 (61.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (℃)\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\u003e-35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35-37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5899 (67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4970 (66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2756 (66.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37.5\u0026ndash;38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1787 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1525 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e870 (21.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38.5-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1047 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e891 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e498 (12.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsciousness\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\u003eAlert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4006 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3510 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2165 (52.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2710 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2244 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1271 (30.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePainful\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e918 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e698 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e420 (10.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnresponsive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1160 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e978 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e291 (7.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson activated RRS\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\u003eNurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7029 (79.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5810 (78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3483 (84.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1707 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1570 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e638 (15.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReason for RRS activation\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\u003eDesaturation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2585 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2222 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e974 (23.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTachypnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1379 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1199 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e752 (18.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBradypnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e228 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e205 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e857 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e732 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e378 (9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypotension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1906 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1716 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e794 (19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBradycardia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e319 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e290 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTachycardia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1046 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e913 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e467 (11.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAltered mental status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2030 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1827 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e648 (15.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeizure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e221 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1736 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1481 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e977 (23.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1465 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1192 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e919 (22.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis for RRS activation\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\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2640 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2143 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1268 (30.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1319 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1098 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e605 (14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypovolemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e787 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e711 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e298 (7.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistributive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1660 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1429 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1026 (24.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e355 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e275 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160 (3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e286 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e989 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e811 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e487 (11.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1765 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1514 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e726 (17.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention by RRS\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\u003eOxygen supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2836 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2415 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e969 (23.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAirway suction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1385 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1135 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e443 (10.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e295 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110 (2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManual ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e939 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e861 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e213 (5.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntubation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e826 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e797 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193 (4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiopulmonary resuscitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransfusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e248 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluid bolus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1614 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1452 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e521 (12.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3429 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3081 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1199 (28.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug administration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2234 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2009 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e687 (16.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2366 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2253 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e850 (20.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\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\u003eDead at 30 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1934 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1320 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e881 (21.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPC\u0026thinsp;\u0026ge;\u0026thinsp;3 at 30 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3536 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2635 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1614 (38.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNAR post-RRS activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e482 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e481 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e196 (4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eWe present categorical data as counts (%) and numerical data as medians (interquartile ranges) (*). Abbreviations: NPPV, Noninvasive Positive Pressure Ventilation; CPC, Cerebral Performance Category; DNAR, Do Not Attempt Resuscitation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis dataset was analyzed using logistic regression, with ICU admission as the dependent variable (details in Supplementary Table\u0026nbsp;1). We incorporated all variables from Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e as predictors, excluding the dependent variable itself, to compute the probability of ICU admission for each patient. The logistic model's Area Under the Receiver Operating Characteristic Curve (AUROC) was 0.8424, indicating good predictive power. We subsequently calculated the ICU admission rate (ratio of ICU admissions to RRS activations) and the SIAR for each institution, which are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The median ICU admission rate was 0.33 (interquartile range: 0.21 to 0.47) and the median SIAR was 0.98 (interquartile range: 0.75 to 1.17).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows scatterplots of the SIAR against the 30-day mortality rate for each institution (3A) and the rate of CPC\u0026thinsp;\u0026ge;\u0026thinsp;3 or death at 30 days (3B). There was a significant correlation between SIAR and the incidence of CPC\u0026thinsp;\u0026ge;\u0026thinsp;3 or death at 30 days (P\u0026thinsp;=\u0026thinsp;0.037), and a trend toward a correlation with death at 30 days (P\u0026thinsp;=\u0026thinsp;0.059).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further analyzed the relationship between the SIAR and patient outcomes using GEE models, adjusted for institution effects, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Two models were constructed: one evaluating death at 30 days, and the other assessing CPC\u0026thinsp;\u0026ge;\u0026thinsp;3 or death at 30 days as the outcomes. The odds ratio for SIAR impacting death at 30 days was 0.89 (95% confidence interval (CI), 0.72 to 1.09, P\u0026thinsp;=\u0026thinsp;0.30), while the odds ratio for CPC\u0026thinsp;\u0026ge;\u0026thinsp;3 or death at 30 days was 0.78 (95% CI, 0.64 to 0.95, P\u0026thinsp;=\u0026thinsp;0.015). The odds ratios, confidence intervals, and P-values for additional explanatory variables in the GEE models are presented in Supplementary Table\u0026nbsp;2.\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\u003eOdds ratio of standardized ICU admission ratio by GEE model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDead at 30 days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCPC\u0026thinsp;\u0026ge;\u0026thinsp;3 or dead at 30 days\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds ratio [95%CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds ratio [95%CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardized ICU admission ratio\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 \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain analysis (n\u0026thinsp;=\u0026thinsp;8,794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 [0.72, 1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78 [0.64, 0.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExclude DNAR (n\u0026thinsp;=\u0026thinsp;7,430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83 [0.64, 1.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68 [0.54, 0.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExclude missing data (n\u0026thinsp;=\u0026thinsp;4,147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 [0.68, 1.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90 [0.67, 1.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eGEE, Generalized Estimating Equations; DNAR, Do Not Attempt Resuscitation; CPC, Cerebral Performance Category.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo confirm the robustness of our findings, we conducted two sensitivity analyses: one excluding patients who had DNAR orders prior to the RRS activations (n\u0026thinsp;=\u0026thinsp;7,430), and another excluding patients with missing data (n\u0026thinsp;=\u0026thinsp;4,147). Logistic regression results predicting ICU admission are presented in Supplementary Table\u0026nbsp;1, and outcomes of GEE models in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Tables\u0026nbsp;3 and 4. Across all analyses, SIAR consistently demonstrated a reduction in both 30-day mortality and CPC\u0026thinsp;\u0026ge;\u0026thinsp;3 or death, although not all findings were statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this multicenter observational study, we explored the variability in ICU admission rates during RRS activations and assessed their correlation with patient outcomes. Our analysis revealed that ICU admissions per institution varied significantly, with rates ranging from 0.07 to 0.85, and the SIAR\u0026mdash;a measure of ICU admissions adjusted by ICU probability\u0026mdash;ranged from 0.36 to 1.83. A positive correlation between higher SIAR and improved patient outcomes was apparent from scatter plot observations. Furthermore, in the GEE model, adjusted for both covariates and intra-institutional correlations, a statistically significant reduction in the incidence of Cerebral Performance Category (CPC)\u0026thinsp;\u0026ge;\u0026thinsp;3 or death at 30 days was observed with higher SIAR, while the reduction in death alone at 30 days was not statistically significant. These findings were consistently robust across two sensitivity analyses.\u003c/p\u003e \u003cp\u003eWe found that ICU admission rates in this study were notably more varied compared to previous research [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Jones et al. documented that ICU admission rates through RRS activations fluctuated between 10\u0026ndash;25% [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e],, yet other observational studies have observed even broader ranges, reaching up to 57% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In Japan, where only 1% of hospital beds are designated as ICU beds [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], critically ill patients are frequently managed in general wards [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], contributing to the extensive variability observed in our study. Earlier studies have demonstrated that ICU admissions enhance the prognosis of critically ill patients [\u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Nonetheless, resource constraints necessitate a selective approach to ICU admissions. According to the ICU Admission, Discharge, and Triage Guidelines [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] resource allocation should be prioritized based on patient conditions and bed availability (grade 2D). Factors such as staffing levels and their experience, the availability of ICU beds, and the feasibility of implementing treatments on the ward [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], all seem to have contributed to the substantial diversity in ICU admission rates observed.\u003c/p\u003e \u003cp\u003eWe discovered that a higher SIAR may have correlated with improved patient outcomes. This finding is reasonable, considering the challenging decisions regarding ICU admissions for critically ill patients in the context of limited resources. RRS is designed to promptly identify physiologically unstable patients, allowing for timely interventions [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Continued observation of these patients in general wards may lead to adverse outcomes. Additionally, research by Wunsch et al. demonstrated a strong inverse relationship between the availability of ICU beds and hospital mortality rates across various countries (r = -0.82), using data from eight nations [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], providing indirect support for our findings. Although it remains unclear whether the determinants of SIAR are primarily influenced by bed availability or physician preference, the evidence suggests a potential need to enhance ICU capacity, particularly in places like Japan where ICU availability is notably limited [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study contains several strengths. It involved a large cohort of patients and institutions compared to prior research on RRS activations, facilitating precise calculations of the SIAR and an objective assessment of heterogeneity across institutions. To our knowledge, the In-Hospital Emergency Registry in Japan is the only registry that collects such detailed information on RRS activations. The observed variability among institutions enhances the comprehensiveness of our report. Furthermore, no other studies have explored the relationship between the diversity of ICU admissions and patient outcomes. Optimizing RRS interventions could lead to improved hospital-wide outcomes. Our findings indicate that SIAR might play a role in enhancing patient outcomes through RRS. However, there are also limitations to our study. The results exhibited some inconsistencies; for example, while SIAR did not show a statistically significant link with mortality at 30 days, it did demonstrate a significant reduction in the occurrence of CPC\u0026thinsp;\u0026ge;\u0026thinsp;3 or death at 30 days. These discrepancies could be attributed to the smaller sample sizes at some institutions, despite the overall large sample size. Although the trends suggest that higher SIAR may lessen poor prognoses, these findings did not reach statistical significance and might do so with a larger sample size. Additionally, our study was challenged by numerous missing data points, predominantly in vital signs. We addressed these gaps using multiple imputation, a technique that not only helps mitigate selection bias but also supports the conduct of Generalized Estimating Equations analysis, which necessitates a larger dataset.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur research demonstrated that a higher Standardized ICU Admission Ratio correlates with improved patient outcomes. Lowering the ICU admission thresholds at each institution during Rapid Response System activations may enhance patient prognosis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eICU\u003c/em\u003e\u003c/strong\u003e: Intensive care unit\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRRS\u003c/em\u003e\u003c/strong\u003e: Rapid response system\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSIAR\u003c/em\u003e\u003c/strong\u003e: Standardized ICU Admission Ratio.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCPC\u003c/em\u003e\u003c/strong\u003e: Cerebral Performance Category\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGEE\u003c/em\u003e\u003c/strong\u003e: Generalized Estimating Equation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIHER-J\u003c/em\u003e\u003c/strong\u003e: In-Hospital Emergency Registry in Japan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIHEC-J\u003c/em\u003e\u003c/strong\u003e: In-Hospital Emergency Committee in Japan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDNAR\u003c/em\u003e\u003c/strong\u003e: Do Not Attempt Resuscitation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAUROC\u003c/em\u003e\u003c/strong\u003e: Area Under the Receiver Operating Curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePMM\u003c/em\u003e\u003c/strong\u003e: Predictive mean matching\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/strong\u003e: Confidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCardiopulmonary Arrest\u003c/em\u003e\u003c/strong\u003e: CPA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNPPV\u003c/em\u003e\u003c/strong\u003e: Noninvasive Positive Pressure Ventilation\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: Ethical approval was granted by the Ethics Committee of Jichi Medical University Saitama Medical Center (approval number: S23-088), and the requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The datasets generated and/or analyzed during the current study are not available since the dataset was obtained from In-Hospital Emergency Committee in Japan through a formal request/approval process as above but is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting Interests: The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding: Not applicable.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions: SO is responsible for the analysis and the first draft of the manuscript. YS contributed as an advisor of statistical analysis. MS, MT, TS and SU contributed to the subsequent drafts. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgement: Not applicable.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJones DA, DeVita MA, Bellomo R: \u003cstrong\u003eRapid-response teams\u003c/strong\u003e. \u003cem\u003eN Engl J Med \u003c/em\u003e2011, \u003cstrong\u003e365\u003c/strong\u003e(2):139-146.\u003c/li\u003e\n\u003cli\u003eNeale G, Woloshynowych M, Vincent C: \u003cstrong\u003eExploring the causes of adverse events in NHS hospital practice\u003c/strong\u003e. \u003cem\u003eJ R Soc Med \u003c/em\u003e2001, \u003cstrong\u003e94\u003c/strong\u003e(7):322-330.\u003c/li\u003e\n\u003cli\u003eBuist MD, Jarmolowski E, Burton PR, Bernard SA, Waxman BP, Anderson J: \u003cstrong\u003eRecognising clinical instability in hospital patients before cardiac arrest or unplanned admission to intensive care. A pilot study in a tertiary-care hospital\u003c/strong\u003e. \u003cem\u003eMed J Aust \u003c/em\u003e1999, \u003cstrong\u003e171\u003c/strong\u003e(1):22-25.\u003c/li\u003e\n\u003cli\u003eFranklin C, Mathew J: \u003cstrong\u003eDeveloping strategies to prevent inhospital cardiac arrest: analyzing responses of physicians and nurses in the hours before the event\u003c/strong\u003e. \u003cem\u003eCrit Care Med \u003c/em\u003e1994, \u003cstrong\u003e22\u003c/strong\u003e(2):244-247.\u003c/li\u003e\n\u003cli\u003eHillman K, Chen J, Cretikos M, Bellomo R, Brown D, Doig G, Finfer S, Flabouris A: \u003cstrong\u003eIntroduction of the medical emergency team (MET) system: a cluster-randomised controlled trial\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2005, \u003cstrong\u003e365\u003c/strong\u003e(9477):2091-2097.\u003c/li\u003e\n\u003cli\u003eMaharaj R, Raffaele I, Wendon J: \u003cstrong\u003eRapid response systems: a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eCrit Care \u003c/em\u003e2015, \u003cstrong\u003e19\u003c/strong\u003e(1):254.\u003c/li\u003e\n\u003cli\u003eLee BY, Hong SB: \u003cstrong\u003eRapid response systems in Korea\u003c/strong\u003e. \u003cem\u003eAcute Crit Care \u003c/em\u003e2019, \u003cstrong\u003e34\u003c/strong\u003e(2):108-116.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIHI Launches National Campaign to Save 100,000 Lives in U.S. Hospitals \u003c/strong\u003e[https://www.psqh.com/janfeb05/100k.html]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eAustralian Commission on Safety and Quality in Health Care. Safety and Quality Improvement Guide \u003c/strong\u003e[https://www.safetyandquality.gov.au/sites/default/files/migrated/Standard9_Oct_2012_WEB.pdf]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eJapanese Coalition for Patient Safety ( JCPS ) \u003c/strong\u003e[https://kyodokodo.jp/]\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eJapan Council for Quality Health Care \u003c/strong\u003e[https://jcqhc.or.jp/]\u003c/li\u003e\n\u003cli\u003eLee SI, Koh JS, Kim YJ, Kang DH, Lee JE: \u003cstrong\u003eCharacteristics and outcomes of patients screened by rapid response team who transferred to the intensive care unit\u003c/strong\u003e. \u003cem\u003eBMC Emerg Med \u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(1):18.\u003c/li\u003e\n\u003cli\u003eLe Guen MP, Tobin AE, Reid D: \u003cstrong\u003eIntensive care unit admission in patients following rapid response team activation: call factors, patient characteristics and hospital outcomes\u003c/strong\u003e. \u003cem\u003eAnaesth Intensive Care \u003c/em\u003e2015, \u003cstrong\u003e43\u003c/strong\u003e(2):211-215.\u003c/li\u003e\n\u003cli\u003eOrosz J, Bailey M, Udy A, Pilcher D, Bellomo R, Jones D: \u003cstrong\u003eUnplanned ICU Admission From Hospital Wards After Rapid Response Team Review in Australia and New Zealand\u003c/strong\u003e. \u003cem\u003eCrit Care Med \u003c/em\u003e2020, \u003cstrong\u003e48\u003c/strong\u003e(7):e550-e556.\u003c/li\u003e\n\u003cli\u003eJones D, DeVita M, Warrillow S: \u003cstrong\u003eTen clinical indicators suggesting the need for ICU admission after Rapid Response Team review\u003c/strong\u003e. \u003cem\u003eIntensive Care Med \u003c/em\u003e2016, \u003cstrong\u003e42\u003c/strong\u003e(2):261-263.\u003c/li\u003e\n\u003cli\u003eJones D: \u003cstrong\u003eThe epidemiology of adult Rapid Response Team patients in Australia\u003c/strong\u003e. \u003cem\u003eAnaesth Intensive Care \u003c/em\u003e2014, \u003cstrong\u003e42\u003c/strong\u003e(2):213-219.\u003c/li\u003e\n\u003cli\u003eCasamento AJ, Dunlop C, Jones DA, Duke G: \u003cstrong\u003eImproving the documentation of medical emergency team reviews\u003c/strong\u003e. \u003cem\u003eCrit Care Resusc \u003c/em\u003e2008, \u003cstrong\u003e10\u003c/strong\u003e(1):29.\u003c/li\u003e\n\u003cli\u003eLitvak E, Pronovost PJ: \u003cstrong\u003eRethinking rapid response teams\u003c/strong\u003e. \u003cem\u003eJama \u003c/em\u003e2010, \u003cstrong\u003e304\u003c/strong\u003e(12):1375-1376.\u003c/li\u003e\n\u003cli\u003eSalamonson Y, Kariyawasam A, van Heere B, O\u0026apos;Connor C: \u003cstrong\u003eThe evolutionary process of Medical Emergency Team (MET) implementation: reduction in unanticipated ICU transfers\u003c/strong\u003e. \u003cem\u003eResuscitation \u003c/em\u003e2001, \u003cstrong\u003e49\u003c/strong\u003e(2):135-141.\u003c/li\u003e\n\u003cli\u003eWunderink RG, Diederich ER, Caramez MP, Donnelly HK, Norwood SD, Kho A, Reed KD: \u003cstrong\u003eRapid response team-triggered procalcitonin measurement predicts infectious intensive care unit transfers*\u003c/strong\u003e. \u003cem\u003eCrit Care Med \u003c/em\u003e2012, \u003cstrong\u003e40\u003c/strong\u003e(7):2090-2095.\u003c/li\u003e\n\u003cli\u003eMorris DS, Schweickert W, Holena D, zel R, Sims C, Pascual JL, Sarani B: \u003cstrong\u003eDifferences in outcomes between ICU attending and senior resident physician led medical emergency team responses\u003c/strong\u003e. \u003cem\u003eResuscitation \u003c/em\u003e2012, \u003cstrong\u003e83\u003c/strong\u003e(12):1434-1437.\u003c/li\u003e\n\u003cli\u003eNaito T, Fujiwara S, Kawasaki T, Sento Y, Nakada TA, Arai M, Atagi K, Fujitani S: \u003cstrong\u003eFirst report based on the online registry of a Japanese multicenter rapid response system: a descriptive study of 35 institutions in Japan\u003c/strong\u003e. \u003cem\u003eAcute Med Surg \u003c/em\u003e2020, \u003cstrong\u003e7\u003c/strong\u003e(1):e454.\u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP: \u003cstrong\u003eThe Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies\u003c/strong\u003e. \u003cem\u003eAnn Intern Med \u003c/em\u003e2007, \u003cstrong\u003e147\u003c/strong\u003e(8):573-577.\u003c/li\u003e\n\u003cli\u003eSirio CA, Tajimi K, Taenaka N, Ujike Y, Okamoto K, Katsuya H: \u003cstrong\u003eA cross-cultural comparison of critical care delivery: Japan and the United States\u003c/strong\u003e. \u003cem\u003eChest \u003c/em\u003e2002, \u003cstrong\u003e121\u003c/strong\u003e(2):539-548.\u003c/li\u003e\n\u003cli\u003eOami T, Imaeda T, Nakada TA, Abe T, Takahashi N, Yamao Y, Nakagawa S, Ogura H, Shime N, Umemura Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMortality analysis among sepsis patients in and out of intensive care units using the Japanese nationwide medical claims database: a study by the Japan Sepsis Alliance study group\u003c/strong\u003e. \u003cem\u003eJ Intensive Care \u003c/em\u003e2023, \u003cstrong\u003e11\u003c/strong\u003e(1):2.\u003c/li\u003e\n\u003cli\u003eIwashita Y, Yamashita K, Ikai H, Sanui M, Imai H, Imanaka Y: \u003cstrong\u003eEpidemiology of mechanically ventilated patients treated in ICU and non-ICU settings in Japan: a retrospective database study\u003c/strong\u003e. \u003cem\u003eCrit Care \u003c/em\u003e2018, \u003cstrong\u003e22\u003c/strong\u003e(1):329.\u003c/li\u003e\n\u003cli\u003eHersch M, Sonnenblick M, Karlic A, Einav S, Sprung CL, Izbicki G: \u003cstrong\u003eMechanical ventilation of patients hospitalized in medical wards vs the intensive care unit--an observational, comparative study\u003c/strong\u003e. \u003cem\u003eJ Crit Care \u003c/em\u003e2007, \u003cstrong\u003e22\u003c/strong\u003e(1):13-17.\u003c/li\u003e\n\u003cli\u003eOhbe H, Sasabuchi Y, Yamana H, Matsui H, Yasunaga H: \u003cstrong\u003eIntensive care unit versus high-dependency care unit for mechanically ventilated patients with pneumonia: a nationwide comparative effectiveness study\u003c/strong\u003e. \u003cem\u003eLancet Reg Health West Pac \u003c/em\u003e2021, \u003cstrong\u003e13\u003c/strong\u003e:100185.\u003c/li\u003e\n\u003cli\u003eLieberman D, Nachshon L, Miloslavsky O, Dvorkin V, Shimoni A, Zelinger J, Friger M, Lieberman D: \u003cstrong\u003eElderly patients undergoing mechanical ventilation in and out of intensive care units: a comparative, prospective study of 579 ventilations\u003c/strong\u003e. \u003cem\u003eCrit Care \u003c/em\u003e2010, \u003cstrong\u003e14\u003c/strong\u003e(2):R48.\u003c/li\u003e\n\u003cli\u003eBasoulis D, Liatis S, Skouloudi M, Makrilakis K, Daikos GL, Sfikakis PP: \u003cstrong\u003eSurvival predictors after intubation in medical wards: A prospective study in 151 patients\u003c/strong\u003e. \u003cem\u003ePLoS One \u003c/em\u003e2020, \u003cstrong\u003e15\u003c/strong\u003e(6):e0234181.\u003c/li\u003e\n\u003cli\u003eNates JL, Nunnally M, Kleinpell R, Blosser S, Goldner J, Birriel B, Fowler CS, Byrum D, Miles WS, Bailey H, Sprung CL: \u003cstrong\u003eICU Admission, Discharge, and Triage Guidelines: A Framework to Enhance Clinical Operations, Development of Institutional Policies, and Further Research\u003c/strong\u003e. \u003cem\u003eCrit Care Med \u003c/em\u003e2016, \u003cstrong\u003e44\u003c/strong\u003e(8):1553-1602.\u003c/li\u003e\n\u003cli\u003eWunsch H, Angus DC, Harrison DA, Collange O, Fowler R, Hoste EA, de Keizer NF, Kersten A, Linde-Zwirble WT, Sandiumenge A, Rowan KM: \u003cstrong\u003eVariation in critical care services across North America and Western Europe\u003c/strong\u003e. \u003cem\u003eCrit Care Med \u003c/em\u003e2008, \u003cstrong\u003e36\u003c/strong\u003e(10):2787-2793, e2781-2789.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rapid response system, ICU admission, General ward, Standardized ICU Admission Ratio, ICU admission thresholds, In-Hospital Emergency Registry in Japan, ICU admission variability","lastPublishedDoi":"10.21203/rs.3.rs-4485450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4485450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe variability in ICU admission rates for patients activated by the Rapid Response System (RRS) is substantial and differs significantly across institutions. This study explores the disparities in ICU admission thresholds and their impact on patient outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA multicenter retrospective observational study was conducted using a Japanese in-hospital emergency registry, focusing on patients for whom the RRS was activated from 2018 to 2022. We calculated the ICU admission rate (ratio of ICU admissions to RRS activations) and the Standardized ICU Admission Ratio (SIAR: ratio of actual to predicted ICU admissions) for each institution (N\u0026thinsp;=\u0026thinsp;35). The relationship between SIAR and patient outcomes, specifically death or Cerebral Performance Category (CPC) at 30 days, was analyzed using multivariable analysis with the Generalized Estimating Equation (GEE) model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 8,794 patients, with 26.9% admitted to the ICU. The median ICU admission rate was 0.33 (1st quantile: 0.21, 3rd quantile: 0.47), and the median SIAR was 0.98 (1st quantile: 0.75, 3rd quantile: 1.17). Univariable analysis indicated that a higher SIAR significantly correlated with a lower incidence of CPC\u0026thinsp;\u0026ge;\u0026thinsp;3 or death at 30 days (P\u0026thinsp;=\u0026thinsp;0.037) and showed a trend towards lower mortality at 30 days (P\u0026thinsp;=\u0026thinsp;0.059). The GEE model revealed that the odds ratio of SIAR for death at 30 days was 0.89 (95% CI\u0026thinsp;=\u0026thinsp;0.72 to 1.09; P\u0026thinsp;=\u0026thinsp;0.30), and for CPC\u0026thinsp;\u0026ge;\u0026thinsp;3 or death at 30 days was 0.78 (95% CI\u0026thinsp;=\u0026thinsp;0.64 to 0.95; P\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study demonstrates a significant association between higher SIAR and improved patient outcomes, suggesting that lower ICU admission thresholds during RRS activations may enhance patient prognosis.\u003c/p\u003e","manuscriptTitle":"Impact of ICU Admission Thresholds on Outcomes in Rapid Response System Activations: A Multicenter Study in Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 03:55:12","doi":"10.21203/rs.3.rs-4485450/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff099d66-00a6-413b-bc99-b21201850be0","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-21T15:49:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-12 03:55:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4485450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4485450","identity":"rs-4485450","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: preprint-html ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2024) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-05-24T02:00:01.246996+00:00
License: CC-BY-4.0