Closed intensive care units and sepsis patient outcomes: a secondary analysis of data from a multicenter prospective observational study in South Korea

Kyeongman Jeon; Jin Hyoung Kim; Kyung Chan Kim; Heung Bum Lee; Hongyeul Lee; Song I Lee; Jin-Won Huh; Won Gun Kwack; Youjin Chang; Yun-Seong Kang; Won Yeon Lee; Je Hyeong Kim; on behalf of on behalf of the MOSAICS II Study Group of Korean Society of Critical Care Medicine

doi:10.4266/acc.004128

Articles

Page Path: HOME > Acute Crit Care > Volume 40(2); 2025 > Article

Original Article Pulmonary Closed intensive care units and sepsis patient outcomes: a secondary analysis of data from a multicenter prospective observational study in South Korea: Acute and Critical Care 2025;40(2):209-220.
DOI: https://doi.org/10.4266/acc.004128
Published online: May 22, 2025

Kyeongman Jeon^1,*

, Jin Hyoung Kim^2,*

, Kyung Chan Kim³

, Heung Bum Lee⁴

, Hongyeul Lee⁵

, Song I Lee⁶

, Jin-Won Huh⁷

, Won Gun Kwack⁸

, Youjin Chang⁹

, Yun-Seong Kang¹⁰

Won Yeon Lee¹¹

, Je Hyeong Kim¹²

, on behalf of on behalf of the MOSAICS II Study Group of Korean Society of Critical Care Medicine

¹Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

²Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea

³Department of Internal Medicine, Daegu Catholic University Hospital, Daegu, Korea

⁴Department of Internal Medicine, Research Center for Pulmonary Disorders, Jeonbuk National University Medical School and Hospital, Jeonju, Korea

⁵Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Inje University Busan Paik Hospital, Inje University College of Medicine, Busan, Korea

⁶Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Korea

⁷Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

⁸Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea

⁹Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea

¹⁰Division of Pulmonology and Critical Care Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea

¹¹Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea

¹²Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Korea

Corresponding author: Je Hyeong Kim Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan 15355, Korea Tel: +82-31-412-5950 Fax: +82-31-413-5950 E-mail: chepraxis@korea.ac.kr

*These authors contributed equally to this work.

• Received: October 28, 2024 • Revised: February 8, 2025 • Accepted: February 26, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

734 Views
39 Download

prev next

Full Article

Download PDF

Abstract
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
KEY MESSAGES
NOTES
REFERENCES

Abstract

Background
Sepsis is a leading cause of intensive care unit (ICU) admission. However, few studies have evaluated how the ICU model affects the outcomes of patients with sepsis.
Methods
This post hoc analysis of data from the Management of Severe Sepsis in Asia’s Intensive Care Units II study included 537 patients with sepsis admitted to 27 ICUs in Korea. The outcome measures of interest were compared between the closed ICU group, patients admitted under the full responsibility of an intensivist as the primary attending physician, and the open ICU group. The association between a closed ICU and ICU mortality was evaluated using a logistic regression analysis.
Results
Altogether, 363 and 174 enrolled patients were treated in open and closed ICUs, respectively. Compliance with the sepsis bundles did not differ between the two groups; however, the closed ICU group had a higher rate of renal replacement therapy and shorter duration of ventilator support. The closed ICU group also had a lower ICU mortality rate than the open ICU group (24.7% vs. 33.1%). In a logistic regression analysis, management in the closed ICU was significantly associated with a decreased ICU mortality rate even after adjusting for potential confounding factors (adjusted odds ratio, 0.576; 95% CI, 0.342–0.970), and that association was observed for up to 90 days.
Conclusions
Sepsis management in closed ICUs was significantly associated with improved ICU survival and decreased length of ICU stay, even though the compliance rates for the sepsis bundles did not differ between open and closed ICUs.
Key Words: intensive care unit; organization and administration; patient care bundles; sepsis; treatment outcome

INTRODUCTION

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection [1], and it carries a high risk of death [2]. Recent advances in early identification and hemodynamic resuscitation have improved the management of sepsis patients in emergency departments (EDs) and hospital wards [3,4]. However, the clinical outcomes of critically ill sepsis patients depend on timely application of therapeutic interventions in an appropriate setting. Therefore, sepsis patients often need to be admitted to an intensive care unit (ICU). A previous multicenter cohort study on sepsis in Korea demonstrated that one-third of all sepsis patients who visit an ED require ICU admission [4].

Although the positive effects that prompt admission to the ICU have on the outcome of critically ill patients with sepsis has previously been described [5,6], few studies have explored how the ICU organizational model (open/closed) affects mortality among patients with sepsis. A prospective cohort study involving Asian ICUs reported no difference in hospital mortality between patients treated in open and closed ICU models [7]. Nationwide observational studies on sepsis in Japan showed contradictory findings about an association between the closed ICU model and the clinical outcomes of sepsis patients admitted to the ICU [8,9]. However, it is difficult to generalize conclusions from one country to other countries, even when all the countries under consideration are in Asia [7].

In Korea, a special payment system was implemented in August 2015 for hospitals that employ trained ICU intensivists. Recently, a population-based retrospective cohort study using national health insurance claims data reported how intensivist coverage affects the survival of critically ill patients [10]. However, no information is available to indicate whether closed ICUs perform better than open ICUs in terms of the clinical outcomes of sepsis patients admitted to ICUs in Korea. Therefore, using national data from a multinational, prospective point prevalence study that assessed the epidemiology, management, and outcomes of patients with sepsis in Asian ICUs [11], we evaluated the association between management in closed ICUs and the survival of patients with sepsis.

MATERIALS AND METHODS

Study Design and Setting

This post hoc analysis used data from the Management of Severe Sepsis in Asia’s Intensive Care Units II (MOSAICS II) study [11], which collected data on the management of patients with sepsis who were admitted to ICUs in Asia. In Korea, 27 adult ICUs (19 medical ICUs and 8 mixed-patient ICUs) from 27 hospitals participated in the MOSAICS II study. In this study, we evaluated the association between the ICU model and the outcomes of patients with sepsis by using the Korean database from the MOSAICS II study.

A closed ICU was defined as an ICU in which patient care is directed by an intensivist and the ICU team. Only ICU team doctors can write orders in a closed ICU. In an open ICU, on the other hand, patient care is managed by a variety of attending physicians, and intensivists are available for consultation. An intensivist is a physician who has passed the intensive care certification examinations, completed training in an accredited intensive care fellowship, or is recognized by their institution as an intensivist because they treat each patient as totality, not just a single organ system.

The Institutional Review Boards of each participating hospital reviewed and approved this study (No. 2018AS0248, Korea University Ansan Hospital), and the requirement for obtaining informed consent was waived because it was non-interventional and observational. Additionally, patient information was anonymized and de-identified prior to analysis. This study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology guidelines for observational cohort studies [12].

Participants

All patients admitted to the participating ICUs on 4 days that represented the four different seasons of 2019 (January 9, April 3, July 3, and October 9) were screened for eligibility. We included all adult patients (≥18 years) who were diagnosed with sepsis, admitted to a participating ICU in Korea for sepsis, and still in the ICU from 00:00 to 23:59 on one of the study days (Figure 1). We defined sepsis as an infection with a Sequential Organ Failure Assessment (SOFA) score of ≥2 from baseline [1]. The baseline SOFA score was assumed to be zero in patients not known to have preexisting organ dysfunction [11]. Patients were divided into the following two groups: closed ICU (treated in closed ICUs) and open ICU (treated in open ICUs).

Data Collection

The MOSAICS II study used password-protected online case report forms. The details of data collection are described in the original study [11]. For this study, data compiled by the Korean investigators were obtained from the MOSACIS II database.

A questionnaire on the participating centers’ characteristics (e.g., hospital and ICU type, open or closed ICU model, university affiliation status, presence of accredited training program, and nurse-to-patient ratio) and microbiology-processing capabilities was completed by ICU representatives. Baseline characteristics (demographics, comorbidities, and details of admission) and parameters upon ICU admission (vital signs, laboratory findings, severity of illness using the Acute Physiology and Chronic Health Evaluation (APACHE) II score, site of infection, and microbiology performed to determine the true pathogen) were assessed.

The timing of the sepsis bundles and surgical source control were measured referencing Time 0, which was determined as follows: (1) time of triage in the ED for those presenting with sepsis to the ED, (2) time of clinical documentation of patient deterioration in general wards or other non-ED areas for those who developed sepsis after hospital admission, and (3) time of ICU admission when the first two time points for a patient could not be determined from the clinical documentation. The following sepsis bundle elements were evaluated based on the Surviving Sepsis Campaign’s 2018 update: antibiotic administration, blood cultures, lactate level measurement, fluid administration, and vasopressor initiation [13]. However, timings more than 24 hours from Time 0 were excluded. Finally, data about life-sustaining treatments provided during the ICU stay were also collected.

All patients were followed until hospital discharge, death in the ICU or hospital, or up to 90 days after enrollment, whichever was earliest. However, the main outcome measure was ICU mortality. The secondary outcome measures for this study were compliance with the sepsis bundle elements and length of ICU stay.

Statistical Analyses

Given that most of the data did not follow a normal distribution, all results and tables are presented as median and interquartile range (IQR) or number (percentage). Baseline patient characteristics and outcome measures of interest were compared between the closed and open ICU groups using the Mann-Whitney U-test for continuous variables and the chi-square or Fisher’s exact test for categorical variables. The association between the closed ICU model and ICU mortality was tested using multiple logistic regression analyses. The following three models were constructed: the first was adjusted for age and sex, the second model included severity of organ dysfunction at ICU admission, and the third model was additionally adjusted for all variables with a P-value <0.25 in the univariate analyses [14] and a priori variables that were clinically relevant [15]. To reduce the risk of multicollinearity, only one variable from closely correlated variables was a candidate for inclusion in the final model. The results are presented as odds ratios (ORs) with 95% CIs. A risk-adjusted 90-day survival curve was plotted from the proportional hazards model using the mean of covariates method [16]. A two-tailed P-value <0.05 was considered to be statistically significant for all analyses. Data were analyzed using IBM SPSS version 27.0 (IBM Corp.).

RESULTS

Overall, 537 critically ill sepsis patients were admitted to participating ICUs from an ED (n=348, 64.8%), general wards (n=153, 28.5%), and other routes (n=36, 6.7%). Among them, 300 patients (55.9%) met the clinical criteria for septic shock. The baseline characteristics at ICU admission are summarized in Table 1. The median age was 73 years (IQR, 62–81 years), and 63.5% of the patients were male. Diabetes (33.1%), cardiovascular diseases (26.1%), and chronic neurologic diseases (25.7%) were the most frequent comorbidities. The respiratory system (64.4%) was the most common site of infection, followed by the urinary system (17.5%) and gastrointestinal system (13.6%). The median SOFA score was 8 (IQR, 6–10), and the median APACHE II score was 21 (16–27). Among the participating ICUs, 9 (33.3%) operated on the closed model, and 18 (66.7%) operated on the open model.

Altogether, 363 and 174 patients were treated in the open and closed ICUs, respectively. The baseline characteristics of the two groups are compared in Table 1. The closed ICU group was younger and had lower proportions of chronic lung (12.6% vs. 22.9%, P=0.005) and chronic neurological (19.0% vs. 28.9%, P=0.015) diseases, but it had a higher proportion of hematologic malignancies (11.5% vs. 4.7%, P=0.006). The sites of infection were similar between the groups. Regarding disease severity, the median SOFA score was higher in the closed ICU group than the open ICU group (9 vs. 7, P<0.001), but the median APACHE II scores were comparable between the groups. A nurse-to-patient ratio of 1:2 was noted for 72.4% of the patients admitted to the closed ICUs, which was a higher proportion than in the open ICU group at 34.2% (P<0.001).

The completion rates for the 1-hour and 3-hour sepsis bundles were compared between the two groups, and no significant differences were found (Table 2). Comparisons of life-sustaining treatments during the ICU stay and clinical outcomes are presented in Table 3. Treatments during the ICU stay did not differ significantly between the groups, except for renal replacement therapy, which was provided at a higher rate in the closed ICU group (P=0.001). ICU mortality was lower in the closed ICU group than in the open ICU group (24.7% vs. 33.1%, P=0.049), but hospital mortality did not differ significantly between the groups (37.5% vs. 44.0%, P=0.167), with 13 patients (8 in the closed ICU group and 5 in the open ICU group) who were alive upon discharge from the relevant ICU stay but still hospitalized after 90 days excluded from the analysis. Additionally, the median length of the ICU stay was shorter in the closed ICU group (13 days) than the open ICU group (18 days) (P=0.006).

Univariate comparisons of the baseline characteristics and treatments between the survivors and non-survivors of the ICU stays are presented in Table 4. No significant differences in age, sex, site of infection, admission source, or nurse-to-patient ratio were found between the groups. However, ICU mortality was associated with illness severity, organ failure, and life-sustaining treatments during the ICU stay. The median APACHE II scores of the non-survivors were significantly higher than those of the survivors (P<0.001). Organ support using mechanical ventilation and renal replacement therapy was also greater in the non-survivors than the survivors (P<0.001 and P<0.001, respectively). The compliance rate for the 3-hour sepsis bundle was higher in the survivors than in the non-survivors (50.0% vs. 36.8%, P=0.005). The proportion of patients treated in a closed ICU was higher in the survivor group (35.0% vs. 26.4%, P=0.049).

The results of the multivariate analyses with multiple logistic regression models are shown in Table 5. The closed ICU model was associated with crude ICU mortality (OR, 0.665; 95% CI, 0.442–1.000; P=0.050). After adjusting for the a priori variables of age and sex (model 1), and severity of organ dysfunction at ICU admission (model 2), the association remained significant. The final logistic regression model (model 3) considered the a priori parameters of age, sex, severity of organ dysfunction at ICU admission; a priori variables that were clinically relevant; and other variables with P<0.25 in the univariate analysis (Table 4). In model 3, the closed ICU model was still significantly associated with a decreased ICU mortality rate (adjusted OR, 0.576; 95% CI, 0.342–0.970; P=0.038). The association between the closed ICU model and mortality was observed for up to 90 days (Figure 2). The other factors that were independently associated with ICU mortality were solid malignant tumor comorbidity (adjusted OR, 1.726; 95% CI, 1.024–2.910; P=0.041), the need for mechanical ventilation (adjusted OR, 3.460; 95% CI, 1.770–6.736; P<0.001) or renal replacement therapy (adjusted OR, 1.955; 95% CI, 1.210–3.157; P=0.006), and a do-not-resuscitate order (adjusted OR, 5.052; 95% CI, 3.182–8.024; P<0.001).

DISCUSSION

Using national data from a multinational, prospective point prevalence study assessing the epidemiology, management, and outcomes of sepsis patients admitted to ICUs in Asia, we evaluated the association between management in a closed ICU and the survival of patients with sepsis. The results of our observational study indicate that patient management in a closed ICU was significantly associated with improved ICU survival and decreased length of ICU stay, even though the rates of compliance with the 1-hour and 3-hour sepsis bundles did not differ between the open and closed ICU groups.

A trained intensivist can provide high-quality intensive care, possibly leading to improved outcomes [17]. Observational studies have shown that when physician staffing in ICUs includes an intensivist who is directly involved in patient care, the outcomes of critically ill patients are better than when an intensivist is involved only as a consultant [18-20]. Nonetheless, a recent cross-sectional study of ICU organizational practices in the United States found that approximately one-third of ICUs did not have an intensivist available regularly [21], despite recommendations from the American College of Critical Care Medicine [17]. This incomplete adoption of ICU staffing protocols linked to superior outcomes for patients might be caused by institutional barriers [22]. Additionally, the reported effects of ICU staffing on patient outcomes remain contradictory [23].

In a 2013 multinational survey of Asian ICUs [24], approximately 20% of the surveyed ICUs had no board-certified intensivist, although variations in physician staffing were considerable among the Asian ICUs. Additionally, two recent population-based cohort studies conducted in Korea [10] and Japan [25], where the National Health Insurance Service has a differential management payment system according to ICU staffing, reported that ICUs with intensivist coverage were associated with improved hospital mortality. Although those results suggest the benefits of high-intensity staffing for the outcomes of general ICU patients in Asia, a previous cohort study involving Asian ICUs did not confirm an association between intensivist physician staffing and coverage and a reduction in the in-hospital mortality rate in patients with sepsis [7].

The ICU models (open or closed) evaluated here use different organizational assumptions to inform ICU staffing (high- or low-intensity staffing) [18]. In a closed ICU, admitted patients are under the full responsibility of intensivists as the primary attending physicians. In contrast, in an open ICU, patient care is managed by another attending physician, and intensivists are available for consultation. A third model, the mandatory critical care consultation ICU model, is a high-intensity staffing model that is not altogether closed [18]. Previous studies on the effects of ICU staffing on patient outcome could not evaluate the effects of the different ICU models [26], but a recent meta-analysis found that the mortality rate was higher in the open ICU group than the closed ICU group [27].

Although 216 out of 335 (65%) of Asian ICUs surveyed in 2013 operated according to the closed model [24], Japan (54%) and Korea (56%) had a lower proportion of closed ICUs than China (87%) and Saudi Arabia (89%). Only a few previous studies evaluated how the closed ICU model affected the clinical outcomes of sepsis patients. A prospective cohort study investigating the outcomes of patients with sepsis in Asian ICUs reported that hospital mortality did not differ between the open and closed ICU groups [7]. Additionally, the rate of compliance with the entire resuscitation bundle did not differ between the ICU models. Recently, a nationwide observational study on sepsis conducted in Japan found significantly lower hospital mortality in the closed ICU group than the open ICU group [8]. However, that study compared the outcomes of patients with sepsis in the two ICU models based on a definition of sepsis that was subjectively reported by each institution. Additionally, it did not evaluate the details of differences in the quality of care for the initial management of sepsis. A subsequent analysis using the database from another multicenter study of sepsis in Japan showed that hospital mortality was similar between the closed and open ICU groups, even after adjusting for rates of sepsis bundle compliance, which were higher in the closed ICU group, and the different organizational structures [9]. In our study, the closed ICU model was found to be independently associated with decreased ICU mortality in a multivariate analysis that adjusted for potential confounding factors. Furthermore, a nurse-to-patient ratio of 1:2 was achieved more frequently in the closed ICUs than the open ICUs. Additionally, the closed ICU group had a higher rate of renal replacement therapy and shorter duration of ventilator support. These differences might be partly responsible for the significant association observed between management in a closed ICU and the survival of patients with sepsis, even though the rate of compliance with the 1-hour and 3-hour sepsis bundles, which were mostly performed before ICU admission, did not differ between the two ICU models.

The strengths of our study include the fact that it is the first analysis of the association between ICU models and the clinical outcomes of patients with sepsis in Korea, where daytime intensivist physician staffing has been expanded [10]. Moreover, we used prospective data and clear definitions of ICU models and staffing. Finally, missing data were minimal. These strengths increase the study’s representativeness.

To fully appreciate our results, however, potential limitations must be acknowledged. First, because this study was observational, selection bias might have influenced the significance of its findings. In addition, baseline characteristics and treatments received differed between the two groups. However, those differences were addressed by performing an adjusted multivariate analysis. Nonetheless, the potential for bias due to an unmeasured confounding factor remains. Second, we performed a sub-analysis of a multinational, prospective, four-day point prevalence study. A prevalence study shows only a snapshot of an overall situation in a limited number of patients. We are unable to confirm that the patients present in each ICU on each study day were representative of the population of patients with sepsis generally admitted to those ICUs. Nevertheless, this study addressed the limitations of previous observational studies [8,9] by, for example, using predefined ICU model definitions. Therefore, the apparent differences in mortality identified from our data after adjusting for multiple potentially confounding factors could be used in a future study to further explore the independent influences of the ICU model on the outcomes of sepsis.

In conclusion, this nationwide post hoc analysis of patients with sepsis in Korea has demonstrated that patient management in a closed ICU was significantly associated with improved ICU survival outcomes and decreased length of ICU stay. The superiority of a closed ICU model might be related to the high quality of care provided during the ICU stay.

KEY MESSAGES

▪ Few studies have tested how intensive care unit (ICU) models affect the outcomes of patients with sepsis.

▪ Compliance with the sepsis bundles did not differ between the closed and open ICUs.

▪ The closed ICU group had a lower ICU mortality rate than the open ICU group.

NOTES

CONFLICT OF INTEREST

Kyeongman Jeon is an editorial board member of the journal but was not involved in the peer reviewer selection, evaluation, or decision process of this article. No other potential conflicts of interest relevant to this article were reported.

FUNDING

This work was supported by a Samsung Medical Center grant (OTA2202901).

ACKNOWLEDGMENTS

The authors thank the MOSAICS II Korean Study Group of the Korean Society of Critical Care Medicine: Kyung Chan Kim (Department of Internal Medicine, Daegu Catholic University Hospital, Daegu); Hyo Jin Han, Seung Yong Park, and Heung Bum Lee (Department of Internal Medicine, Research Center for Pulmonary Disorders, Jeonbuk National University Medical School and Hospital, Jeonju); Jin Hyoung Kim and Jong Joon Ahn (Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan); Beong Ki Kim and Je Hyeong Kim (Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Ansan); Kyeongman Jeon (Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul); Hongyeul Lee (Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Inje University Busan Paik Hospital, Inje University College of Medicine, Busan); Song I Lee and Jae Young Moon (Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon); Jin-Won Huh (Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul); Won Gun Kwack (Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Kyung Hee University, Seoul); Youjin Chang (Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Inje University College of Medicine Sanggye Paik Hospital, Seoul); Yun-Seong Kang (Division of Pulmonology and Critical Care Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang); Won Yeon Lee (Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju); Yoon Mi Shin (Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju); Jongmin Lee (Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul); Young Jae Cho (Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Bundang); In Byung Kim (Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Myongji Hospital, Goyang); Young Seok Lee (Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul); Tai Sun Park (Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri); Yong Jun Choi and Jae Hwa Cho (Division of Pulmonology and Critical Care Medicine, Department of Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul); Ho Cheol Kim (Division of Pulmonology and Critical Care Medicine, Department of Medicine, Gyeongsang National University Changwon Hospital, Gyeongsang National University College of Medicine, Changwon); Sunghoon Park (Department of Pulmonary, Allergy and Critical Care Medicine, Hallym University Sacred Heart Hospital, Anyang); Jinwoo Lee and Sang-Min Lee (Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul); Sojung Park (Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mokdong Hospital, College of Medicine, Ewha Womans University, Seoul); Yun Su Sim (Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Hallym University Kangnam Sacred Heart Hospital, Seoul); Shin Young Kim (St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon); Do Wan Kim (Department of Thoracic and Cardiovascular Surgery, Chonnam National University Hospital, Chonnam National University College of Medicine, Gwangju); Tae Yun Park (Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, Seoul National University Boramae Hospital, Seoul National University College of Medicine, Seoul); Su Hwan Lee (Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul), Korea.

AUTHOR CONTRIBUTIONS

Conceptualization: JK, JHK (Jin Hyoung Kim), JHK (Je Hyeong Kim). Data curation: JK, JHK (Jin Hyoung Kim), JHK (Je Hyeong Kim). Formal analysis: JK, JHK (Jin Hyoung Kim), JHK (Je Hyeong Kim). Funding acquisition: JK. Investigation: KJ, JHK, KCK, HBL, HL, SIL, JWH, WGK, YC, YSK, WYL, JHK. Methodology: JK, JHK (Jin Hyoung Kim), JHK (Je Hyeong Kim). Project administration: JHK (Je Hyeong Kim). Software: KJ, JHK, KCK, HBL, HL, SIL, JWH, WGK, YC, YSK, WYL, JHK. Supervision: JHK (Je Hyeong Kim). Validation: JHK (Je Hyeong Kim). Visualization: JK. Writing - original draft: JK . Writing - review & editing: JK, JHK (Jin Hyoung Kim), JHK (Je Hyeong Kim). All authors read and agreed to the published version of the manuscript.

Figure 1.

Scheme of group distribution. ICU: intensive care unit; MOSAICS II: Management of Severe Sepsis in Asia’s Intensive Care Units II [11].

Figure 2.

Adjusted 90-day survival curves plotted from the proportional hazards model comparing intensive care unit (ICU) models in patients with sepsis admitted to ICUs.

Table 1.

Baseline characteristics of patients with sepsis admitted to intensive care units

Characteristic	All (n=537)	Open ICU (n=363)	Closed ICU (n=174)	P-value
Age (yr)	73 (62–81)	75 (64–81)	70 (59–79)	0.003
Male sex	341 (63.5)	233 (64.2)	108 (62.1)	0.633
Comorbidity^a)
Diabetes mellitus	178 (33.1)	120 (33.1)	58 (33.3)	>0.999
Cardiovascular disease	140 (26.1)	104 (28.7)	36 (20.7)	0.058
Chronic lung disease	105 (19.6)	83 (22.9)	22 (12.6)	0.005
Chronic kidney disease	83 (15.5)	51 (14.0)	32 (18.4)	0.203
Chronic liver disease	37 (6.9)	26 (7.2)	11 (6.3)	0.856
Chronic neurological disease	138 (25.7)	105 (28.9)	33 (19.0)	0.015
Solid malignant tumor	108 (20.1)	70 (19.3)	38 (21.8)	0.492
Hematological malignancy	37 (6.9)	17 (4.7)	20 (11.5)	0.006
Immunosuppression	20 (3.7)	13 (3.6)	7 (4.0)	0.810
Connective tissue disease	13 (2.4)	10 (2.8)	3 (1.7)	0.562
Peptic ulcer disease	9 (1.7)	6 (1.7)	3 (1.7)	>0.999
Site of infection^a)
Respiratory	346 (64.4)	239 (65.8)	10 (61.5)	0.337
Urinary tract	94 (17.5)	61 (16.8)	33 (19.0)	0.546
Abdominal	73 (13.6)	44 (12.1)	29 (16.7)	0.178
Neurological	9 (1.7)	5 (1.4)	4 (2.3)	0.480
Bones or joints	8 (1.5)	6 (1.7)	2 (1.1)	>0.999
Skin or cutaneous sites	23 (4.3)	15 (4.1)	8 (4.6)	0.822
Intravascular catheter	17 (3.2)	10 (2.8)	7 (4.0)	0.438
Severity
APACHE II score	21 (16–27)	20 (15–27)	21 (17–29)	0.129
SOFA score	8 (6–10)	7 (5–10)	9 (7–11)	<0.001
Admission source				0.024
Emergency department	348 (64.8)	244 (67.2)	104 (59.8)
General wards	153 (28.5)	101 (27.8)	52 (29.9)
Other^b)	36 (6.7)	18 (5.0)	18 (10.3)
Nurse-to-patient ratio				<0.001
1 Nurse: 2 patients	250 (46.6)	124 (34.2)	126 (72.4)
1 Nurse: 3 or more patients	287 (53.4)	239 (65.8)	48 (27.6)

Values are presented as median (interquartile range) or number (%).

ICU: intensive care unknit; APACHE: Acute Physiology and Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment.

^a)Cases are duplicated;

^b)Other category includes admissions from other ICUs (n=21), operating rooms (n=8), and interhospital transfer (n=6). The remaining data are not defined (n=2).

Table 2.

Completion of the sepsis bundle elements

Variable	All (n=537)	Open ICU (n=363)	Closed ICU (n=174)	P-value
Completion of elements within 1 hour
Lactate measurement	334 (62.2)	224 (61.7)	110 (63.2)	0.735
Blood cultures	234 (43.6)	164 (45.2)	70 (40.2)	0.279
Antibiotics	163 (30.4)	110 (30.3)	53 (30.5)	0.971
Full bundle	98 (18.2)	73 (20.1)	25 (14.4)	0.107
Completion of elements within 3 hours
Lactate measurement	397 (73.9)	263 (72.5)	134 (77.0)	0.260
Blood cultures	355 (66.1)	239 (65.8)	116 (66.7)	0.850
Antibiotics	351 (65.4)	237 (65.3)	114 (65.5)	0.959
Full bundle	247 (46.0)	170 (46.8)	77 (44.3)	0.575

Values are presented as number (%).

ICU: intensive care unit.

Table 3.

Treatments received by the study participants during their intensive care unit stay and their clinical outcomes

Treatments and outcome	All (n=537)	Open ICU (n=363)	Closed ICU (n=174)	P-value
Life-sustaining treatments during ICU stay^a)
Vasopressors/inotropes	300 (55.9)	196 (54.0)	104 (59.8)	0.228
Renal replacement therapy	181 (33.7)	105 (28.9)	76 (43.7)	0.001
Nonsurgical source control	93 (17.3)	60 (16.5)	33 (19.0)	0.542
Surgical source control	34 (6.3)	27 (7.4)	7 (4.0)	0.184
Respiratory support^a)
Mechanical ventilation	408 (76.0)	281 (77.4)	127 (73.0)	0.281
Duration of MV (day)^b)	12 (7–22)	14 (8–25)	11 (5–18)	0.002
Among survivors in ICU	12 (7–23)	14 (8–26)	11 (5–17)	0.005
Among non-survivors in ICU	12 (8–21)	13 (8–24)	12 (6–19)	0.149
Nonsurgical ventilation	12 (2.2)	10 (2.8)	2 (1.1)	0.354
High-flow nasal oxygen	148 (27.6)	100 (27.5)	48 (27.6)	>0.999
Clinical outcome
In-ICU mortality	163 (30.4)	120 (33.1)	43 (24.7)	0.049
In-hospital mortality^c)	208 (41.9)	148 (44.0)	60 (37.5)	0.167
90-Day mortality	208 (41.9)	148 (40.8)	60 (34.5)	0.162
LOS in ICU (day)	16 (7–30)	18 (7–33)	13 (6–24)	0.006
Among survivors	15 (6–28)	17 (7–34)	12 (6–23)	0.017
Among non-survivors	18 (10–32)	19 (10–33)	17 (8–28)	0.304
LOS in hospital (day)	28 (17–52)	29 (18–53)	26 (14–52)	0.534
Do-not-resuscitate order	131 (24.4)	96 (26.4)	35 (20.1)	0.110

Values are presented as number (%) or median (interquartile range).

ICU: intensive care unit; MV: mechanical ventilation; LOS: length of stay.

^a)Cases are duplicated;

^b)Data were obtained from 408 patients receiving MV support;

^c)13 Patients (8 in closed ICU group vs. 5 in open ICU group) who were alive upon discharge from the relevant ICU stay but still hospitalized after 90 days were excluded from this analysis.

Table 4.

Univariate comparisons of baseline characteristics and treatments between the survivors and non-survivors at the time of discharge from the intensive care units

Characteristics	ICU survivor (n=374)	ICU non-survivor (n=163)	P-value
Age (yr)	74 (63–81)	72 (62–80)	0.328
Male sex	232 (62.0)	109 (66.9)	0.284
Comorbidity^a)
Diabetes mellitus	125 (33.4)	53 (32.5)	0.837
Cardiovascular disease	92 (24.6)	48 (29.4)	0.239
Chronic lung disease	70 (18.7)	35 (21.5)	0.459
Chronic kidney disease	58 (15.5)	25 (15.3)	0.960
Chronic liver disease	24 (6.4)	13 (8.0)	0.512
Chronic neurological disease	103 (27.5)	35 (21.5)	0.139
Solid malignant tumor	66 (17.6)	42 (25.8)	0.031
Hematological malignancy	19 (5.1)	18 (11.0)	0.012
Immunosuppression	11 (2.9)	9 (5.5)	0.147
Site of infection^a)
Respiratory	233 (62.3)	113 (69.3)	0.118
Urinary tract	72 (19.3)	22 (13.5)	0.107
Abdominal	57 (15.2)	16 (9.8)	0.092
Severity
APACHE II score	20 (15–17)	23 (16–30)	0.018
SOFA score	8 (6–10)	8 (6–11)	0.145
Admission source			0.074
Emergency department	256 (68.4)	92 (56.4)
General wards	91 (24.3)	62 (38.0)
Other^b)	27 (7.2)	9 (5.5)
Nurse-to-patient ratio			0.983
1 Nurse: 2 patients	174 (46.5)	76 (46.6)
1 Nurse: 3 or more patients	200 (53.5)	87 (53.4)
Completion of elements within 1 hour
Lactate measurement	239 (63.9)	95 (58.3)	0.217
Blood cultures	170 (45.5)	64 (39.3)	0.183
Antibiotics	130 (34.8)	33 (20.2)	0.001
Full bundle	75 (20.1)	23 (14.1)	0.101
Completion of elements within 3 hour
Lactate measurement	263 (72.5)	134 (77.0)	0.260
Blood cultures	263 (70.3)	92 (56.4)	0.002
Antibiotics	263 (70.3)	88 (54.0)	<0.001
Full bundle	187 (50.0)	60 (36.8)	0.005
Closed ICU	131 (35.0)	43 (26.4)	0.049
Life-sustaining treatments during ICU stay
Vasopressors/inotropes	299 (79.9)	146 (89.6)	0.006
Renal replacement therapy	107 (28.6)	74 (45.4)	<0.001
Mechanical ventilation	259 (69.3)	149 (91.4)	<0.001
Do-not-resuscitate order	51 (13.6)	80 (49.1)	<0.001

Values are presented as median (interquartile range) or number (%).

ICU: intensive care unknit; APACHE: Acute Physiology and Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment.

^a)Cases are duplicated;

^b)Other category includes admissions from other ICUs (n=21), operating rooms (n=8), or inter-hospital transfers (n=6). The remaining data are not defined (n=2).

Table 5.

Associations between the closed model and intensive care unit mortality after adjusting for potential confounding factors

Closed ICU	Variables in the equation
Closed ICU	Coefficient	SE	P-value	OR	95% CI
Crude state	−0.408	0.208	0.050	0.665	0.442–1.000
Adjusted state^a)
Model 1	−0.422	0.210	0.045	0.656	0.434–0.990
Model 2	−0.520	0.216	0.016	0.594	0.389–0.908
Model 3	−0.552	0.266	0.038	0.576	0.342–0.970

ICU: intensive care unit; SE: standard error; OR: odds ratio.

^a)Model 1 was adjusted for age and sex. Model 2 was additionally adjusted for the severity of organ dysfunction at ICU admission. Model 3 additionally included cardiovascular disease, chronic neurological disease, solid malignant tumor, hematological malignancy, and immunosuppression as comorbidities; sites of infection; admission source; nurse-to-patient ratio; compliance with the 1-hour and 3-hour sepsis bundles; the need for vasopressors/inotropes, renal replacement therapy, or mechanical ventilation; and a do-not-resuscitate order.

REFERENCES

1. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016;315:801-10.Article PubMed PMC
2. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet 2020;395:200-11.Article PubMed PMC
3. Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med 2021;47:1181-247.Article PMC
4. Jeon K, Na SJ, Oh DK, Park S, Choi EY, Kim SC, et al. Characteristics, management and clinical outcomes of patients with sepsis: a multicenter cohort study in Korea. Acute Crit Care 2019;34:179-91.Article PubMed PMC PDF
5. Mohr NM, Wessman BT, Bassin B, Elie-Turenne MC, Ellender T, Emlet LL, et al. Boarding of critically ill patients in the emergency department. Crit Care Med 2020;48:1180-7.Article PubMed PMC
6. Shibata J, Osawa I, Fukuchi K, Goto T. The association between time from emergency department visit to ICU admission and mortality in patients with sepsis. Crit Care Explor 2023;5:e0915. Article PubMed PMC
7. Phua J, Koh Y, Du B, Tang YQ, Divatia JV, Tan CC, et al. Management of severe sepsis in patients admitted to Asian intensive care units: prospective cohort study. BMJ 2011;342:d3245.Article PubMed PMC
8. Ogura T, Nakamura Y, Takahashi K, Nishida K, Kobashi D, Matsui S. Treatment of patients with sepsis in a closed intensive care unit is associated with improved survival: a nationwide observational study in Japan. J Intensive Care 2018;6:57.Article PubMed PMC PDF
9. Nagata I, Abe T, Ogura H, Kushimoto S, Fujishima S, Gando S, et al. Intensive care unit model and in-hospital mortality among patients with severe sepsis and septic shock: a secondary analysis of a multicenter prospective observational study. Medicine (Baltimore) 2021;100:e26132. Article PubMed PMC
10. Oh TK, Song IA. Trained intensivist coverage and survival outcomes in critically ill patients: a nationwide cohort study in South Korea. Ann Intensive Care 2023;13:4.Article PubMed PMC PDF
11. Li A, Ling L, Qin H, Arabi YM, Myatra SN, Egi M, et al. Epidemiology, management, and outcomes of sepsis in ICUs among countries of differing national wealth across Asia. Am J Respir Crit Care Med 2022;206:1107-16.Article PubMed
12. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med 2007;147:573-7.Article PubMed PDF
13. Levy MM, Evans LE, Rhodes A. The Surviving Sepsis Campaign Bundle: 2018 update. Crit Care Med 2018;46:997-1000.Article PubMed
14. Mickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol 1989;129:125-37.Article PubMed
15. Sun GW, Shook TL, Kay GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 1996;49:907-16.Article PubMed
16. Nieto FJ, Coresh J. Adjusting survival curves for confounders: a review and a new method. Am J Epidemiol 1996;143:1059-68.Article PubMed
17. Weled BJ, Adzhigirey LA, Hodgman TM, Brilli RJ, Spevetz A, Kline AM, et al. Critical care delivery: the importance of process of care and ICU structure to improved outcomes: an update from the American College of Critical Care Medicine Task Force on Models of Critical Care. Crit Care Med 2015;43:1520-5.Article PubMed
18. Pronovost PJ, Angus DC, Dorman T, Robinson KA, Dremsizov TT, Young TL. Physician staffing patterns and clinical outcomes in critically ill patients: a systematic review. JAMA 2002;288:2151-62.Article PubMed
19. Wilcox ME, Chong CA, Niven DJ, Rubenfeld GD, Rowan KM, Wunsch H, et al. Do intensivist staffing patterns influence hospital mortality following ICU admission?: a systematic review and meta-analyses. Crit Care Med 2013;41:2253-74.Article PubMed
20. Vahedian-Azimi A, Rahimibashar F, Ashtari S, Guest PC, Sahebkar A. Comparison of the clinical features in open and closed format intensive care units: a systematic review and meta-analysis. Anaesth Crit Care Pain Med 2021;40:100950.Article PubMed
21. Kohn R, Madden V, Kahn JM, Asch DA, Barnato AE, Halpern SD, et al. Diffusion of evidence-based intensive care unit organizational practices: a state-wide analysis. Ann Am Thorac Soc 2017;14:254-61.Article PubMed PMC
22. Kahn JM, Matthews FA, Angus DC, Barnato AE, Rubenfeld GD. Barriers to implementing the Leapfrog Group recommendations for intensivist physician staffing: a survey of intensive care unit directors. J Crit Care 2007;22:97-103.Article PubMed
23. Costa DK, Wallace DJ, Kahn JM. The association between daytime intensivist physician staffing and mortality in the context of other ICU organizational practices: a multicenter cohort study. Crit Care Med 2015;43:2275-82.Article PubMed PMC
24. Arabi YM, Phua J, Koh Y, Du B, Faruq MO, Nishimura M, et al. Structure, organization, and delivery of critical care in Asian ICUs. Crit Care Med 2016;44:e940-8.Article PubMed
25. Ikumi S, Shiga T, Ueda T, Takaya E, Iwasaki Y, Kaiho Y, et al. Intensive care unit mortality and cost-effectiveness associated with intensivist staffing: a Japanese nationwide observational study. J Intensive Care 2023;11:60.Article PubMed PMC PDF
26. Proper JL, Wacker DA, Shaker S, Heisdorffer J, Shaker RM, Shiue LT, et al. Patient outcomes and unit composition with transition to a high-intensity ICU staffing model: a before-and-after study. Crit Care Explor 2023;5:e0864. Article PubMed PMC
27. Yang Q, Du JL, Shao F. Mortality rate and other clinical features observed in open vs closed format intensive care units: a systematic review and meta-analysis. Medicine (Baltimore) 2019;98:e16261. Article PubMed PMC

Figure & Data

References

Citations

Citations to this article as recorded by

PubReader
ePub Link

Cite

CITE: export Copy Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

Closed intensive care units and sepsis patient outcomes: a secondary analysis of data from a multicenter prospective observational study in South Korea

Acute Crit Care. 2025;40(2):209-220. Published online May 22, 2025

DOI: https://doi.org/10.4266/acc.004128

XML Download

Figure

Closed intensive care units and sepsis patient outcomes: a secondary analysis of data from a multicenter prospective observational study in South Korea

Figure 1. Scheme of group distribution. ICU: intensive care unit; MOSAICS II: Management of Severe Sepsis in Asia’s Intensive Care Units II [11].

Figure 2. Adjusted 90-day survival curves plotted from the proportional hazards model comparing intensive care unit (ICU) models in patients with sepsis admitted to ICUs.

Figure 1.

Figure 2.

Closed intensive care units and sepsis patient outcomes: a secondary analysis of data from a multicenter prospective observational study in South Korea

Characteristic	All (n=537)	Open ICU (n=363)	Closed ICU (n=174)	P-value
Age (yr)	73 (62–81)	75 (64–81)	70 (59–79)	0.003
Male sex	341 (63.5)	233 (64.2)	108 (62.1)	0.633
Comorbidity^a)
Diabetes mellitus	178 (33.1)	120 (33.1)	58 (33.3)	>0.999
Cardiovascular disease	140 (26.1)	104 (28.7)	36 (20.7)	0.058
Chronic lung disease	105 (19.6)	83 (22.9)	22 (12.6)	0.005
Chronic kidney disease	83 (15.5)	51 (14.0)	32 (18.4)	0.203
Chronic liver disease	37 (6.9)	26 (7.2)	11 (6.3)	0.856
Chronic neurological disease	138 (25.7)	105 (28.9)	33 (19.0)	0.015
Solid malignant tumor	108 (20.1)	70 (19.3)	38 (21.8)	0.492
Hematological malignancy	37 (6.9)	17 (4.7)	20 (11.5)	0.006
Immunosuppression	20 (3.7)	13 (3.6)	7 (4.0)	0.810
Connective tissue disease	13 (2.4)	10 (2.8)	3 (1.7)	0.562
Peptic ulcer disease	9 (1.7)	6 (1.7)	3 (1.7)	>0.999
Site of infection^a)
Respiratory	346 (64.4)	239 (65.8)	10 (61.5)	0.337
Urinary tract	94 (17.5)	61 (16.8)	33 (19.0)	0.546
Abdominal	73 (13.6)	44 (12.1)	29 (16.7)	0.178
Neurological	9 (1.7)	5 (1.4)	4 (2.3)	0.480
Bones or joints	8 (1.5)	6 (1.7)	2 (1.1)	>0.999
Skin or cutaneous sites	23 (4.3)	15 (4.1)	8 (4.6)	0.822
Intravascular catheter	17 (3.2)	10 (2.8)	7 (4.0)	0.438
Severity
APACHE II score	21 (16–27)	20 (15–27)	21 (17–29)	0.129
SOFA score	8 (6–10)	7 (5–10)	9 (7–11)	<0.001
Admission source				0.024
Emergency department	348 (64.8)	244 (67.2)	104 (59.8)
General wards	153 (28.5)	101 (27.8)	52 (29.9)
Other^b)	36 (6.7)	18 (5.0)	18 (10.3)
Nurse-to-patient ratio				<0.001
1 Nurse: 2 patients	250 (46.6)	124 (34.2)	126 (72.4)
1 Nurse: 3 or more patients	287 (53.4)	239 (65.8)	48 (27.6)

Variable	All (n=537)	Open ICU (n=363)	Closed ICU (n=174)	P-value
Completion of elements within 1 hour
Lactate measurement	334 (62.2)	224 (61.7)	110 (63.2)	0.735
Blood cultures	234 (43.6)	164 (45.2)	70 (40.2)	0.279
Antibiotics	163 (30.4)	110 (30.3)	53 (30.5)	0.971
Full bundle	98 (18.2)	73 (20.1)	25 (14.4)	0.107
Completion of elements within 3 hours
Lactate measurement	397 (73.9)	263 (72.5)	134 (77.0)	0.260
Blood cultures	355 (66.1)	239 (65.8)	116 (66.7)	0.850
Antibiotics	351 (65.4)	237 (65.3)	114 (65.5)	0.959
Full bundle	247 (46.0)	170 (46.8)	77 (44.3)	0.575

Treatments and outcome	All (n=537)	Open ICU (n=363)	Closed ICU (n=174)	P-value
Life-sustaining treatments during ICU stay^a)
Vasopressors/inotropes	300 (55.9)	196 (54.0)	104 (59.8)	0.228
Renal replacement therapy	181 (33.7)	105 (28.9)	76 (43.7)	0.001
Nonsurgical source control	93 (17.3)	60 (16.5)	33 (19.0)	0.542
Surgical source control	34 (6.3)	27 (7.4)	7 (4.0)	0.184
Respiratory support^a)
Mechanical ventilation	408 (76.0)	281 (77.4)	127 (73.0)	0.281
Duration of MV (day)^b)	12 (7–22)	14 (8–25)	11 (5–18)	0.002
Among survivors in ICU	12 (7–23)	14 (8–26)	11 (5–17)	0.005
Among non-survivors in ICU	12 (8–21)	13 (8–24)	12 (6–19)	0.149
Nonsurgical ventilation	12 (2.2)	10 (2.8)	2 (1.1)	0.354
High-flow nasal oxygen	148 (27.6)	100 (27.5)	48 (27.6)	>0.999
Clinical outcome
In-ICU mortality	163 (30.4)	120 (33.1)	43 (24.7)	0.049
In-hospital mortality^c)	208 (41.9)	148 (44.0)	60 (37.5)	0.167
90-Day mortality	208 (41.9)	148 (40.8)	60 (34.5)	0.162
LOS in ICU (day)	16 (7–30)	18 (7–33)	13 (6–24)	0.006
Among survivors	15 (6–28)	17 (7–34)	12 (6–23)	0.017
Among non-survivors	18 (10–32)	19 (10–33)	17 (8–28)	0.304
LOS in hospital (day)	28 (17–52)	29 (18–53)	26 (14–52)	0.534
Do-not-resuscitate order	131 (24.4)	96 (26.4)	35 (20.1)	0.110

Characteristics	ICU survivor (n=374)	ICU non-survivor (n=163)	P-value
Age (yr)	74 (63–81)	72 (62–80)	0.328
Male sex	232 (62.0)	109 (66.9)	0.284
Comorbidity^a)
Diabetes mellitus	125 (33.4)	53 (32.5)	0.837
Cardiovascular disease	92 (24.6)	48 (29.4)	0.239
Chronic lung disease	70 (18.7)	35 (21.5)	0.459
Chronic kidney disease	58 (15.5)	25 (15.3)	0.960
Chronic liver disease	24 (6.4)	13 (8.0)	0.512
Chronic neurological disease	103 (27.5)	35 (21.5)	0.139
Solid malignant tumor	66 (17.6)	42 (25.8)	0.031
Hematological malignancy	19 (5.1)	18 (11.0)	0.012
Immunosuppression	11 (2.9)	9 (5.5)	0.147
Site of infection^a)
Respiratory	233 (62.3)	113 (69.3)	0.118
Urinary tract	72 (19.3)	22 (13.5)	0.107
Abdominal	57 (15.2)	16 (9.8)	0.092
Severity
APACHE II score	20 (15–17)	23 (16–30)	0.018
SOFA score	8 (6–10)	8 (6–11)	0.145
Admission source			0.074
Emergency department	256 (68.4)	92 (56.4)
General wards	91 (24.3)	62 (38.0)
Other^b)	27 (7.2)	9 (5.5)
Nurse-to-patient ratio			0.983
1 Nurse: 2 patients	174 (46.5)	76 (46.6)
1 Nurse: 3 or more patients	200 (53.5)	87 (53.4)
Completion of elements within 1 hour
Lactate measurement	239 (63.9)	95 (58.3)	0.217
Blood cultures	170 (45.5)	64 (39.3)	0.183
Antibiotics	130 (34.8)	33 (20.2)	0.001
Full bundle	75 (20.1)	23 (14.1)	0.101
Completion of elements within 3 hour
Lactate measurement	263 (72.5)	134 (77.0)	0.260
Blood cultures	263 (70.3)	92 (56.4)	0.002
Antibiotics	263 (70.3)	88 (54.0)	<0.001
Full bundle	187 (50.0)	60 (36.8)	0.005
Closed ICU	131 (35.0)	43 (26.4)	0.049
Life-sustaining treatments during ICU stay
Vasopressors/inotropes	299 (79.9)	146 (89.6)	0.006
Renal replacement therapy	107 (28.6)	74 (45.4)	<0.001
Mechanical ventilation	259 (69.3)	149 (91.4)	<0.001
Do-not-resuscitate order	51 (13.6)	80 (49.1)	<0.001

Closed ICU	Variables in the equation
Closed ICU	Coefficient	SE	P-value	OR	95% CI
Crude state	−0.408	0.208	0.050	0.665	0.442–1.000
Adjusted state^a)
Model 1	−0.422	0.210	0.045	0.656	0.434–0.990
Model 2	−0.520	0.216	0.016	0.594	0.389–0.908
Model 3	−0.552	0.266	0.038	0.576	0.342–0.970

Table 1. Baseline characteristics of patients with sepsis admitted to intensive care units

Values are presented as median (interquartile range) or number (%).

ICU: intensive care unknit; APACHE: Acute Physiology and Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment.

Cases are duplicated;

Other category includes admissions from other ICUs (n=21), operating rooms (n=8), and interhospital transfer (n=6). The remaining data are not defined (n=2).

Table 2. Completion of the sepsis bundle elements

Values are presented as number (%).

ICU: intensive care unit.

Table 3. Treatments received by the study participants during their intensive care unit stay and their clinical outcomes

Values are presented as number (%) or median (interquartile range).

ICU: intensive care unit; MV: mechanical ventilation; LOS: length of stay.

Cases are duplicated;

Data were obtained from 408 patients receiving MV support;

13 Patients (8 in closed ICU group vs. 5 in open ICU group) who were alive upon discharge from the relevant ICU stay but still hospitalized after 90 days were excluded from this analysis.

Table 4. Univariate comparisons of baseline characteristics and treatments between the survivors and non-survivors at the time of discharge from the intensive care units

Values are presented as median (interquartile range) or number (%).

ICU: intensive care unknit; APACHE: Acute Physiology and Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment.

Cases are duplicated;

Other category includes admissions from other ICUs (n=21), operating rooms (n=8), or inter-hospital transfers (n=6). The remaining data are not defined (n=2).

Table 5. Associations between the closed model and intensive care unit mortality after adjusting for potential confounding factors

ICU: intensive care unit; SE: standard error; OR: odds ratio.

Model 1 was adjusted for age and sex. Model 2 was additionally adjusted for the severity of organ dysfunction at ICU admission. Model 3 additionally included cardiovascular disease, chronic neurological disease, solid malignant tumor, hematological malignancy, and immunosuppression as comorbidities; sites of infection; admission source; nurse-to-patient ratio; compliance with the 1-hour and 3-hour sepsis bundles; the need for vasopressors/inotropes, renal replacement therapy, or mechanical ventilation; and a do-not-resuscitate order.