Acute and Critical Care

Original Articles

Rapid response system
Impact of the National Early Warning Score-based sepsis response system on hospital-onset sepsis in a tertiary hospital in South Korea: Dong-gon Hyun, Sohyeon Lee, Sunhui Choi, Jeongsuk Son, So-Hee Park, Sang-Bum Hong, Chae-Man Lim; Acute Crit Care. 2025;40(2):186-196. Published online May 20, 2025; DOI: https://doi.org/10.4266/acc.000625

8,624 View
110 Download
1 Web of Science
1 Crossref

Background
The effectiveness of electronic medical record-based alert systems, response protocols for sepsis diagnosis, and treatment in hospitalized patients remains unclear. This study aimed to determine whether the introduction of an electronic medical record-based sepsis response protocol (SRP) along with a 24/7 operating rapid response system affects the prognosis for patients with hospital-onset sepsis.
Methods
In August 2022, a SRP based on the National Early Warning Score was implemented in the electronic medical record system at Asan Medical Center. We retrospectively analyzed patients screened by the detection system for 1 year after the SRP implementation. Patients of the first 6 months (preliminary group) and those of the second 6 months (SRP group) were matched 1:1 based on propensity scores. The primary outcome was 30-day mortality.
Results
Of the 608 hospitalized patients screened by the system, 176 were assigned to each group after 1:1 propensity score matching. Patients in the SRP group were significantly more likely to receive blood cultures (58.5%) compared with the preliminary group (45.5%) (P=0.019). The SRP group showed a lower 30-day mortality risk (hazard ratio, 0.56; 95% CI, 0.36–0.86; P=0.017) compared to the preliminary group. A restricted cubic spline curve showed that SRP survival benefit began to manifest after the first 4 months (P=0.036).
Conclusions
Alongside an existing rapid response system, the National Early Warning Score-based SRP in the electronic medical record reduced mortality for hospital-onset sepsis within 1 year.

Citations

Citations to this article as recorded by

Characteristics and management of mechanically ventilated patients in South Korea compared with other high-income Asian countries and regions
Kyung Hun Nam, Kyeongman Jeon, Suk-Kyung Hong, Ah Young Leem, Jee Hwan Ahn, Hang Jea Jang, Ki Sup Byun, So Hee Park, Sojung Park, Yoon Mi Shin, Jisoo Park, Sung Wook Kang, Jin Hyoung Kim, Jinkyeong Park, Deokkyu Kim, Bo young Lee, Woo Hyun Cho, Kwangha Le
Acute and Critical Care.2025; 40(3): 413. CrossRef

Rapid response system
Resident and nurse attitudes toward a rapid response team in a tertiary hospital in South Korea: Sung Yoon Lim, Ho Geol Woo, Jong Sun Park, Young-Jae Cho, Jae Ho Lee, Yeon Joo Lee; Acute Crit Care. 2025;40(1):29-37. Published online February 12, 2025; DOI: https://doi.org/10.4266/acc.004272

6,452 View
226 Download
1 Web of Science
1 Crossref

Abstract PDF Supplementary Material

Background
Residents and nurses who activate rapid response teams (RRTs) are well positioned to offer insights on its effectiveness. Here, we assess such evaluation of RRTs and identify barriers to activation in a 1,400-bed teaching hospital.
Methods
We conducted a 24-item Likert-scale survey from January to May 2017 among residents and ward nurses with RRT experience. Factor analysis was used to identify the barriers.
Results
This study comprised 305 nurses and 53 residents, most of whom were satisfied with their RRT experiences. Factor analysis showed that lack of awareness of activation criteria was a major barrier, with only 21.4% and 22.2% participants, respectively, confident about their knowledge of activation protocols. Of the survey respondents, 85.7% reported first contacting the doctor before activating the RRT. Despite the protocol, 66.7% first discussed the decision with other staff, and 71.5% called the RRT when the patient’s condition worsened despite management.
Conclusions
Nurses and residents value RRTs but face barriers in initiation, primarily due to a lack of confidence in applying the activation criteria. Many prefer to consult a doctor or manage the patient before calling the RRT.

Citations

Citations to this article as recorded by

The effect of nurses' perceptions and satisfaction with hospital rapid response teams on burnout related to emergency situations in Korea: a cross-sectional study
Bumin Kim, Nahyun Kim
Journal of Korean Biological Nursing Science.2025; 27(2): 234. CrossRef

Epidemiology
Eleven years of experience in operating a pediatric rapid response system at a children’s hospital in South Korea: Yong Hyuk Jeon, Bongjin Lee, You Sun Kim, Won Jin Jang, June Dong Park; Acute Crit Care. 2023;38(4):498-506. Published online November 29, 2023; DOI: https://doi.org/10.4266/acc.2023.01354

4,171 View
112 Download
1 Crossref

Abstract PDF Supplementary Material

Background
Various rapid response systems have been developed to detect clinical deterioration in patients. Few studies have evaluated single-parameter systems in children compared to scoring systems. Therefore, in this study we evaluated a single-parameter system called the acute response system (ARS).
Methods
This retrospective study was performed at a tertiary children’s hospital. Patients under 18 years old admitted from January 2012 to August 2023 were enrolled. ARS parameters such as systolic blood pressure, heart rate, respiratory rate, oxygen saturation, and whether the ARS was activated were collected. We divided patients into two groups according to activation status and then compared the occurrence of critical events (cardiopulmonary resuscitation or unexpected intensive care unit admission). We evaluated the ability of ARS to predict critical events and calculated compliance. We also analyzed the correlation between each parameter that activates ARS and critical events.
Results
The critical events prediction performance of ARS has a specificity of 98.5%, a sensitivity of 24.0%, a negative predictive value of 99.6%, and a positive predictive value of 8.1%. The compliance rate was 15.6%. Statistically significant increases in the risk of critical events were observed for all abnormal criteria except low heart rate. There was no significant difference in the incidence of critical events.
Conclusions
ARS, a single parameter system, had good specificity and negative predictive value for predicting critical events; however, sensitivity and positive predictive value were not good, and medical staff compliance was poor.

Citations

Citations to this article as recorded by

Impact of an independent pediatric rapid response team on pediatric patient safety
Jin Hwan Lyu, Soo Keun Lee, Min Joo Kang, Sumin Lee, Mireu Park, Soo Yeon Kim, Kyung Won Kim, Jong Deok Kim
Archives of Pediatric Critical Care.2024; 2(2): 91. CrossRef

Rapid response system
Analysis of avoidable cardiopulmonary resuscitation incidents with a part-time rapid response system in place: Jun Yeun Cho, Dong Seon Lee, Yun Young Choi, Jong Sun Park, Young-Jae Cho, Ho Il Yoon, Jae Ho Lee, Choon-Taek Lee, Yeon Joo Lee; Acute Crit Care. 2021;36(2):109-117. Published online April 16, 2021; DOI: https://doi.org/10.4266/acc.2020.01095

7,977 View
174 Download
2 Web of Science
3 Crossref

Abstract PDF Supplementary Material

Background
Although a rapid response system (RRS) can reduce the incidence of cardiopulmonary resuscitation (CPR) in general wards, avoidable CPR cases still occur. This study aimed to investigate the incidence and causes of avoidable CPR.
Methods
We retrospectively reviewed the medical records of all adult patients who received CPR between April 2013 and March 2016 (35 months) at a tertiary teaching hospital where a part-time RRS was introduced in October 2012. Four experts reviewed all of the CPR cases and determined whether each event was avoidable.
Results
A total of 192 CPR cases were identified, and the incidence of CPR was 0.190 per 1,000 patient admissions. Of these, 56 (29.2%) were considered potentially avoidable, with the most common cause being doctor error (n=32, 57.1%), followed by delayed do-not-resuscitate (DNR) placement (n=12, 21.4%) and procedural complications (n=5, 8.9%). The percentage of avoidable CPR was significantly lower in the RRS operating time group than in the RRS non-operating time group (20.7% vs. 35.5%; P=0.026). Among 44 avoidable CPR events (excluding cases related to DNR issues), the rapid response team intervened in only three cases (6.8%), and most of the avoidable CPR cases (65.9%) occurred during the non-operating time.
Conclusions
A significant number of avoidable CPR events occurred with a well-functioning, part-time RRS in place. However, RRS operation does appear to lower the occurrence of avoidable CPR. Thus, it is necessary to extend RRS operation time and modify RRS activation criteria. Moreover, policy and cultural changes are needed prior to implementing a full-time RRS.

Citations

Citations to this article as recorded by

The effect of nurses' perceptions and satisfaction with hospital rapid response teams on burnout related to emergency situations in Korea: a cross-sectional study
Bumin Kim, Nahyun Kim
Journal of Korean Biological Nursing Science.2025; 27(2): 234. CrossRef
Changes in the incidence of cardiopulmonary resuscitation before and after implementation of the Life-Sustaining Treatment Decisions Act
Hyunjae Im, Hyun Woo Choe, Seung-Young Oh, Ho Geol Ryu, Hannah Lee
Acute and Critical Care.2022; 37(2): 237. CrossRef
A Review of the Commercially Available ECG Detection and Transmission Systems—The Fuzzy Logic Approach in the Prevention of Sudden Cardiac Arrest
Michał Lewandowski
Micromachines.2021; 12(12): 1489. CrossRef

Rapid response system
Effectiveness of a daytime rapid response system in hospitalized surgical ward patients: Eunjin Yang, Hannah Lee, Sang-Min Lee, Sulhee Kim, Ho Geol Ryu, Hyun Joo Lee, Jinwoo Lee, Seung-Young Oh; Acute Crit Care. 2020;35(2):77-86. Published online May 13, 2020; DOI: https://doi.org/10.4266/acc.2019.00661

10,241 View
237 Download
8 Web of Science
13 Crossref

Abstract PDF Supplementary Material

Background
Clinical deteriorations during hospitalization are often preventable with a rapid response system (RRS). We aimed to investigate the effectiveness of a daytime RRS for surgical hospitalized patients.
Methods
A retrospective cohort study was conducted in 20 general surgical wards at a 1,779-bed University hospital from August 2013 to July 2017 (August 2013 to July 2015, pre-RRS-period; August 2015 to July 2017, post-RRS-period). The primary outcome was incidence of cardiopulmonary arrest (CPA) when the RRS was operating. The secondary outcomes were the incidence of total and preventable cardiopulmonary arrest, in-hospital mortality, the percentage of “do not resuscitate” orders, and the survival of discharged CPA patients.
Results
The relative risk (RR) of CPA per 1,000 admissions during RRS operational hours (weekdays from 7 AM to 7 PM) in the post-RRS-period compared to the pre-RRS-period was 0.53 (95% confidence interval [CI], 0.25 to 1.13; P=0.099) and the RR of total CPA regardless of RRS operating hours was 0.76 (95% CI, 0.46 to 1.28; P=0.301). The preventable CPA after RRS implementation was significantly lower than that before RRS implementation (RR, 0.31; 95% CI, 0.11 to 0.88; P=0.028). There were no statistical differences in in-hospital mortality and the survival rate of patients with in-hospital cardiac arrest. Do-not-resuscitate decisions significantly increased during after RRS implementation periods compared to pre-RRS periods (RR, 1.91; 95% CI, 1.40 to 2.59; P<0.001).
Conclusions
The day-time implementation of the RRS did not significantly reduce the rate of CPA whereas the system effectively reduced the rate of preventable CPA during periods when the system was operating.

Citations

Citations to this article as recorded by

The effect of nurses' perceptions and satisfaction with hospital rapid response teams on burnout related to emergency situations in Korea: a cross-sectional study
Bumin Kim, Nahyun Kim
Journal of Korean Biological Nursing Science.2025; 27(2): 234. CrossRef
Education, Implementation, and Teams: 2025 International Liaison Committee on Resuscitation Consensus on Science With Treatment Recommendations
Robert Greif, Adam Cheng, Cristian Abelairas-Gómez, Katherine S. Allan, Jan Breckwoldt, Andrea Cortegiani, Aaron J. Donoghue, Kathryn J. Eastwood, Barbara Farquharson, Ming-Ju Hsieh, Tracy Kidd, Ying-Chih Ko, Kasper G. Lauridsen, Yiqun Lin, Andrew S. Lock
Resuscitation.2025; 215: 110807. CrossRef
Education, Implementation, and Teams: 2025 International Liaison Committee on Resuscitation Consensus on Science With Treatment Recommendations
Robert Greif, Adam Cheng, Cristian Abelairas-Gómez, Katherine S. Allan, Jan Breckwoldt, Andrea Cortegiani, Aaron J. Donoghue, Kathryn J. Eastwood, Barbara Farquharson, Ming-Ju Hsieh, Tracy Kidd, Ying-Chih Ko, Kasper G. Lauridsen, Yiqun Lin, Andrew S. Lock
Circulation.2025;[Epub] CrossRef
Médecine périopératoire et situations particulières
Julien Amour, Paul Balendraud, Jacques Boddaert, Pierre Bouzat, Laure Champ-Rigot, Olivier Clovet, Judith Cohen-Bittan, Isabelle Constant, Charles Court, Sabine Drevet, Christophe Hulet, Eric Jeziorski, Olivier Joannes-Boyau, Hélène Kovacsik, Morgan Le Gu
Anesthésie & Réanimation.2025;[Epub] CrossRef
Development and Validation of a Machine Learning Algorithm Using Clinical Pages to Predict Imminent Clinical Deterioration
Bryan D. Steitz, Allison B. McCoy, Thomas J. Reese, Siru Liu, Liza Weavind, Kipp Shipley, Elise Russo, Adam Wright
Journal of General Internal Medicine.2024; 39(1): 27. CrossRef
Effects of a Rapid Response Team on Patient Outcomes: A Systematic Review
Qiuxia Zhang, Khuan Lee, Zawiah Mansor, Iskasymar Ismail, Yi Guo, Qiao Xiao, Poh Ying Lim
Heart & Lung.2024; 63: 51. CrossRef
Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration
Supreeth P. Shashikumar, Joshua Pei Le, Nathan Yung, James Ford, Karandeep Singh, Atul Malhotra, Shamim Nemati, Gabriel Wardi
Critical Care Explorations.2024; 6(9): e1151. CrossRef
Clinical significance of acute care surgery system as a part of hospital medical emergency team for hospitalized patients
Kyoung Won Yoon, Kyoungjin Choi, Keesang Yoo, Eunmi Gil, Chi-Min Park
Annals of Surgical Treatment and Research.2023; 104(1): 43. CrossRef
The associations between rapid response systems and their components with patient outcomes: A scoping review
Rebecca J. Piasecki, Cheryl R. Dennison Himmelfarb, Kelly T. Gleason, Rachel M. Justice, Elizabeth A. Hunt
International Journal of Nursing Studies Advances.2023; 5: 100134. CrossRef
Changes in the incidence of cardiopulmonary resuscitation before and after implementation of the Life-Sustaining Treatment Decisions Act
Hyunjae Im, Hyun Woo Choe, Seung-Young Oh, Ho Geol Ryu, Hannah Lee
Acute and Critical Care.2022; 37(2): 237. CrossRef
Estructura y función de los equipos de respuesta rápida para la atención de adultos en contextos hospitalarios de alta complejidad: Revisión sistemática de alcance
Juliana Vanessa Rincón-López, Diego Larrotta-Castillo, Kelly Estrada-Orozco, Hernando Gaitán-Duarte
Revista Colombiana de Obstetricia y Ginecología.2021; 72(2): 171. CrossRef
Characteristics and Prognosis of Hospitalized Patients at High Risk of Deterioration Identified by the Rapid Response System: a Multicenter Cohort Study
Sang Hyuk Kim, Ji Young Hong, Youlim Kim
Journal of Korean Medical Science.2021;[Epub] CrossRef
Effects of a Rapid Response Team on the Clinical Outcomes of Cardiopulmonary Resuscitation of Patients Hospitalized in General Wards
Mi-Jung Yoon, Jin-Hee Park
Journal of Korean Academy of Fundamentals of Nursing.2021; 28(4): 491. CrossRef

Rapid response system
Utilization of a rapid response team and associated outcomes in patients with malignancy: Jongmin Lee, Woo Ho Ban, Sei Won Kim, Eun Young Kim, Mi Ra Han, Seok Chan Kim; Acute Crit Care. 2020;35(1):16-23. Published online February 29, 2020; DOI: https://doi.org/10.4266/acc.2019.00675

8,400 View
157 Download
3 Web of Science
6 Crossref

Abstract PDF

Background
Recent advances in diagnosis and treatment have improved long-term outcomes in cancer patients. As a result, the requirement for a rapid response team (RRT) for cancer patients is also increasing. This study aimed to analyze utilization of an RRT and the associations between related factors and mortality in a population of cancer patients. Methods: This retrospective cohort study included hospitalized patients at a single academic medical center in Seoul, Korea, who required RRT activation during a 6-year period from June 2013 to December 2018. Results: Overall, 164 of the 457 patients who met the above criteria were cancer patients, and they had a significantly higher Charlson comorbidity score than the non-cancer patients (5.0 vs. 7.0, P<0.001). A significantly larger proportion of cancer patients required intensive care unit transfer (51.8% vs. 41.0%, P=0.032). Cancer patients also had significantly higher in-hospital mortality compared with other patients (39.6% vs. 10.9%, P<0.001). Furthermore, presence of cancer was independently associated with in-hospital mortality (adjusted odds ratio [OR], 2.09; 95% confidence interval [CI], 1.11 to 3.93). Among cancer patients, higher Acute Physiology and Chronic Health Evaluation (APACHE) II at the time of RRT activation was significantly associated with in-hospital mortality regardless of malignancy (adjusted OR, 1.08; 95% CI, 1.01 to 1.15). Conclusions: Cancer patients requiring RRT activation have significantly higher rates of inhospital mortality than patients not using RRT. Higher severity score at the time of RRT activation in patients with malignancy was significantly associated with in-hospital mortality.

Citations

Citations to this article as recorded by

Intensivmedizinisches Kontinuum in der Versorgung von Krebskranken
Catherina Lück
InFo Hämatologie + Onkologie.2023; 26(5): 10. CrossRef
Characteristics and outcomes of patients screened by rapid response team who transferred to the intensive care unit
Song-I. Lee, Jeong Suk Koh, Yoon Joo Kim, Da Hyun Kang, Jeong Eun Lee
BMC Emergency Medicine.2022;[Epub] CrossRef
Characteristics and Prognosis of Hospitalized Patients at High Risk of Deterioration Identified by the Rapid Response System: a Multicenter Cohort Study
Sang Hyuk Kim, Ji Young Hong, Youlim Kim
Journal of Korean Medical Science.2021;[Epub] CrossRef
Novel Adaptive T-Cell Oncological Treatments Lead to New Challenges for Medical Emergency Teams: A 2-Year Experience From a Tertiary-Care Hospital in Switzerland
Anna Sarah Messmer, Yok-Ai Que, Christoph Schankin, Yara Banz, Ulrike Bacher, Urban Novak, Thomas Pabst
Critical Care Explorations.2021; 3(10): e0552. CrossRef
Outcomes of second-tier rapid response activations in a tertiary referral hospital: A prospective observational study
Ken Junyang Goh, Hui Zhong Chai, Lit Soo Ng, Joanna Phone Ko, Deshawn Chong Xuan Tan, Hui Li Tan, Constance Wei-Shan Teo, Ghee Chee Phua, Qiao Li Tan
Annals of the Academy of Medicine, Singapore.2021; 50(11): 838. CrossRef
Effectiveness of Rapid Response Team on In-hospital Mortality in Patients with Hematologic Malignancy
So-Jung Park, Sang-Bum Hong, Chae-Man Lim, Youn-Suck Koh, Jin-Won Huh
Quality Improvement in Health Care.2021; 27(2): 18. CrossRef

Rapid response system
Effect of a rapid response system on code rates and in-hospital mortality in medical wards: Hong Yeul Lee, Jinwoo Lee, Sang-Min Lee, Sulhee Kim, Eunjin Yang, Hyun Joo Lee, Hannah Lee, Ho Geol Ryu, Seung-Young Oh, Eun Jin Ha, Sang-Bae Ko, Jaeyoung Cho; Acute Crit Care. 2019;34(4):246-254. Published online November 29, 2019; DOI: https://doi.org/10.4266/acc.2019.00668

8,901 View
223 Download
10 Web of Science
10 Crossref

Abstract PDF

Background
To determine the effects of implementing a rapid response system (RRS) on code rates and in-hospital mortality in medical wards.
Methods
This retrospective study included adult patients admitted to medical wards at Seoul National University Hospital between July 12, 2016 and March 12, 2018; the sample comprised 4,224 patients admitted 10 months before RRS implementation and 4,168 patients admitted 10 months following RRS implementation. Our RRS only worked during the daytime (7 AM to 7 PM) on weekdays. We compared code rates and in-hospital mortality rates between the preintervention and postintervention groups.
Results
There were 62.3 RRS activations per 1,000 admissions. The most common reasons for RRS activation were tachypnea or hypopnea (44%), hypoxia (31%), and tachycardia or bradycardia (21%). Code rates from medical wards during RRS operating times significantly decreased from 3.55 to 0.96 per 1,000 admissions (adjusted odds ratio [aOR], 0.29; 95% confidence interval [CI], 0.10 to 0.87; P=0.028) after RRS implementation. However, code rates from medical wards during RRS nonoperating times did not differ between the preintervention and postintervention groups (2.60 vs. 3.12 per 1,000 admissions; aOR, 1.23; 95% CI, 0.55 to 2.76; P=0.614). In-hospital mortality significantly decreased from 56.3 to 42.7 per 1,000 admissions after RRS implementation (aOR, 0.79; 95% CI, 0.64 to 0.97; P=0.024).
Conclusions
Implementation of an RRS was associated with significant reductions in code rates during RRS operating times and in-hospital mortality in medical wards.

Citations

Citations to this article as recorded by

Nurses' decisions to recognize and respond to deterioration not meeting rapid response system activation criteria: A qualitative descriptive study
Gabrielle Burdeu, Bodil Rasmussen, Grainne Lowe, Julie Considine
Applied Nursing Research.2026; 87: 152040. CrossRef
Education, Implementation, and Teams: 2025 International Liaison Committee on Resuscitation Consensus on Science With Treatment Recommendations
Robert Greif, Adam Cheng, Cristian Abelairas-Gómez, Katherine S. Allan, Jan Breckwoldt, Andrea Cortegiani, Aaron J. Donoghue, Kathryn J. Eastwood, Barbara Farquharson, Ming-Ju Hsieh, Tracy Kidd, Ying-Chih Ko, Kasper G. Lauridsen, Yiqun Lin, Andrew S. Lock
Resuscitation.2025; 215: 110807. CrossRef
Education, Implementation, and Teams: 2025 International Liaison Committee on Resuscitation Consensus on Science With Treatment Recommendations
Robert Greif, Adam Cheng, Cristian Abelairas-Gómez, Katherine S. Allan, Jan Breckwoldt, Andrea Cortegiani, Aaron J. Donoghue, Kathryn J. Eastwood, Barbara Farquharson, Ming-Ju Hsieh, Tracy Kidd, Ying-Chih Ko, Kasper G. Lauridsen, Yiqun Lin, Andrew S. Lock
Circulation.2025;[Epub] CrossRef
The role of emergency medical services in the management of in-hospital emergencies: Causes and outcomes of emergency calls – A descriptive retrospective register-based study
Henna Myrskykari, Timo Iirola, Hilla Nordquist
Australasian Emergency Care.2024; 27(1): 42. CrossRef
Effects of a Rapid Response Team on Patient Outcomes: A Systematic Review
Qiuxia Zhang, Khuan Lee, Zawiah Mansor, Iskasymar Ismail, Yi Guo, Qiao Xiao, Poh Ying Lim
Heart & Lung.2024; 63: 51. CrossRef
Society of Critical Care Medicine Guidelines on Recognizing and Responding to Clinical Deterioration Outside the ICU: 2023
Kimia Honarmand, Randy S. Wax, Daleen Penoyer, Geoffery Lighthall, Valerie Danesh, Bram Rochwerg, Michael L. Cheatham, Daniel P. Davis, Michael DeVita, James Downar, Dana Edelson, Alison Fox-Robichaud, Shigeki Fujitani, Raeann M. Fuller, Helen Haskell, Ma
Critical Care Medicine.2024; 52(2): 314. CrossRef
Rapid Response Systems
Bradford D. Winters
Critical Care Clinics.2024; 40(3): 583. CrossRef
Improving sepsis recognition and management
Merrilee I Cox, Hillary Voss
Current Problems in Pediatric and Adolescent Health Care.2021; 51(4): 101001. CrossRef
A Somogy Megyei Kaposi Mór Oktató Kórház által bevezetett gyors reagálású rendszer hatása a kórházi mortalitásra
János Fogas, Rita Koroseczné Pavlin, Krisztina Szabó, Eszter Héra, Imre Repa, Mariann Moizs
Orvosi Hetilap.2021; 162(20): 782. CrossRef
Evidence revealed the effects of rapid response system
Jae Hwa Cho
Acute and Critical Care.2019; 34(4): 282. CrossRef

Review Articles

Rapid response system
Rapid response systems in Korea: Bo Young Lee, Sang-Bum Hong; Acute Crit Care. 2019;34(2):108-116. Published online May 31, 2019; DOI: https://doi.org/10.4266/acc.2019.00535

17,660 View
456 Download
32 Web of Science
33 Crossref

Abstract PDF

The inpatient treatment process is becoming more and more complicated with advanced treatments, aging of the patient population, and multiple comorbidities. During the process, patients often experience unexpected deterioration, about half of which might be preventable. Early identification of patient deterioration and the proper response are priorities in most healthcare facilities. A rapid response system (RRS) is a safety net to identify antecedents of these adverse events and to respond in a timely manner. The RRS has become an essential part of the medical system worldwide, supported by all major quality improvement organizations. An RRS consists of a trigger system and response team and needs constant assessment and process improvement. Although the effectiveness and cost-benefit of RRS remain controversial, according to previous studies, it may be beneficial by decreasing in-hospital cardiac arrest and mortality. Since the first implementation of RRS in Korea in 2008, it has been developed in over 15 medical centers and continues to expand. Recent accreditation standards and an RRS pilot program by the Korean government will promote the proliferation of RRSs in Korea.

Citations

Citations to this article as recorded by

Temporal variational autoencoder model for in-hospital clinical emergency prediction
Trong-Nghia Nguyen, Soo-Hyung Kim, Bo-Gun Kho, Nhu-Tai Do, Ngumimi-Karen Iyortsuun, Guee-Sang Lee, Hyung-Jeong Yang
Biomedical Signal Processing and Control.2025; 100: 106975. CrossRef
Association of rapid response system with clinical outcomes after surgery under general anesthesia
In-Ae Song, Tak Kyu Oh
Journal of Clinical Anesthesia.2025; 102: 111749. CrossRef
Resident and nurse attitudes toward a rapid response team in a tertiary hospital in South Korea
Sung Yoon Lim, Ho Geol Woo, Jong Sun Park, Young-Jae Cho, Jae Ho Lee, Yeon Joo Lee
Acute and Critical Care.2025; 40(1): 29. CrossRef
Impact of implementing a patient and carer escalation system for clinical deterioration in general wards: A systematic review
Jing Min Teo, Gayathiri Balamurugan, Evangeline Loh
Intensive and Critical Care Nursing.2025; 90: 104063. CrossRef
Acute Care for Elders Risk Score: A Practical Machine Learning–Based Tool for Screening High-Risk Older Inpatients
Sunghwan Ji, Geon Young Jang, Ji Yeon Baek, Hee-Won Jung, Eunju Lee, Jae Yong Yu, Yura Lee, Il-Young Jang
Journal of the American Medical Directors Association.2025; 26(10): 105818. CrossRef
Actual Circumstances of In-hospital Stroke Set-up Using Rapid Response System for Stroke in the Endovascular Thrombectomy Era
Yasuhiro TAKAHASHI, Takeshi MIKAMI, Kei MIYATA, Ryohei SAITO, Ayumu YAMAOKA, Yusuke KIMURA, Katsuya KOMATSU, Sangnyon KIM, Rei ENATSU, Yukinori AKIYAMA, Akiyoshi HASHIMOTO, Nobuhiro MIKUNI
Neurologia medico-chirurgica.2025; 65(10): 427. CrossRef
Impact of the Standardized Intensive Care Unit Admission Ratio on Outcomes in Rapid Response System Activations: A Retrospective Multicenter Study in Japan
Shohei Ono, Shigehiko Uchino, Miho Tokito, Taishi Saito, Yusuke Sasabuchi, Masamitsu Sanui
Anesthesiology.2025; 143(5): 1255. CrossRef
Temporal trends in the causes of in-hospital cardiac arrest: a retrospective observational cohort study on the limitations of the current alert system at a tertiary hospital in Korea
Joong Min Lee, Min Soo Cho, Myung-Jin Cha, Jun Kim, Kee-Joon Choi, Ah-Ram Kim, Min-Seok Kim, JinMi Lee, Yujung Shin, Un Jung Go, Hae Yeon Oh, Sang-Bum Hong, Gi-Byoung Nam
BMJ Open.2025; 15(12): e107692. CrossRef
Impact of rapid response system in mortality and complications post-orthopedic surgery: a retrospective cohort study
Hey-ran Choi, In-Ae Song, Tak Kyu Oh
Perioperative Medicine.2024;[Epub] CrossRef
Characteristics and outcomes of patients screened by the rapid response team and transferred to intensive care unit in South Korea
Yunha Nam, Byung Ju Kang, Sang-Bum Hong, Kyeongman Jeon, Dong-Hyun Lee, Jung Soo Kim, Jisoo Park, Sang-Min Lee, Song I Lee
Scientific Reports.2024;[Epub] CrossRef
Association between the timing of ICU admission and mortality in patients with hospital-onset sepsis: a nationwide prospective cohort study
Yoon Hae Ahn, Jinwoo Lee, Dong Kyu Oh, Su Yeon Lee, Mi Hyeon Park, Haein Lee, Chae-Man Lim, Sang-Min Lee, Hong Yeul Lee, Chae-Man Lim, Sang-Bum Hong, Dong Kyu Oh, Gee Young Suh, Kyeongman Jeon, Ryoung-Eun Ko, Young-Jae Cho, Yeon Joo Lee, Sung Yoon Lim, Su
Journal of Intensive Care.2023;[Epub] CrossRef
Findings from a decade of experience following implementation of a Rapid Response System into an Asian hospital
Augustine Tee, Bryan Peide Choo, Roshni Sadashiv Gokhale, Xiqin Wang, Mashithah Mansor, Hong Choon Oh, Daryl Jones
Resuscitation Plus.2023; 16: 100461. CrossRef
Time de resposta rápida nas unidades de terapia intensiva e internação de um hospital de grande porte de Belo Horizonte/MG: um estudo observacional
Karina De Freitas Santos, Bruna Ferreira Cícero Lima
Revista Brasileira de Pesquisa em Saúde/Brazilian Journal of Health Research.2023; 24(3): 30. CrossRef
Development of a comprehensive model for the role of the rapid response team nurse
Youn-Hui Won, Jiyeon Kang
Intensive and Critical Care Nursing.2022; 68: 103136. CrossRef
Characteristics and outcomes of patients screened by rapid response team who transferred to the intensive care unit
Song-I. Lee, Jeong Suk Koh, Yoon Joo Kim, Da Hyun Kang, Jeong Eun Lee
BMC Emergency Medicine.2022;[Epub] CrossRef
Mortality of patients with hospital-onset sepsis in hospitals with all-day and non-all-day rapid response teams: a prospective nationwide multicenter cohort study
Dong-gon Hyun, Su Yeon Lee, Jee Hwan Ahn, Jin Won Huh, Sang-Bum Hong, Younsuck Koh, Chae-Man Lim, Dong Kyu Oh, Gee Young Suh, Kyeongman Jeon, Ryoung-Eun Ko, Young-Jae Cho, Yeon Joo Lee, Sung Yoon Lim, Sunghoon Park, Jeongwon Heo, Jae-myeong Lee, Kyung Cha
Critical Care.2022;[Epub] CrossRef
Korean nurses’ perception and performance on communication with physicians in clinical deterioration
Bo-Gyeong Jin, Kyoungrim Kang, Hyun-Jin Cho
Medicine.2022; 101(38): e30570. CrossRef
Clinical Nurses’ Intention to Use Defibrillators in South Korea: A Path Analysis
Dongchoon Uhm, Gye-Hyun Jung
Healthcare.2022; 11(1): 61. CrossRef
Rapid Response System Should Be Enhanced at Non-general Ward Locations: a Retrospective Multicenter Cohort Study in Korea
Byung Ju Kang, Sang-Bum Hong, Kyeongman Jeon, Sang-Min Lee, Dong Hyun Lee, Jae Young Moon, Yeon Joo Lee, Jung Soo Kim, Jisoo Park, Jong-Joon Ahn
Journal of Korean Medical Science.2021;[Epub] CrossRef
Impact of hospitalization duration before medical emergency team activation: A retrospective cohort study
Jinmi Lee, Yujung Shin, Eunjoo Choi, Sunhui Choi, Jeongsuk Son, Youn Kyung Jung, Sang-Bum Hong, Tai-Heng Chen
PLOS ONE.2021; 16(2): e0247066. CrossRef
A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards
Yeon Joo Lee, Kyung-Jae Cho, Oyeon Kwon, Hyunho Park, Yeha Lee, Joon-Myoung Kwon, Jinsik Park, Jung Soo Kim, Man-Jong Lee, Ah Jin Kim, Ryoung-Eun Ko, Kyeongman Jeon, You Hwan Jo
Resuscitation.2021; 163: 78. CrossRef
Characteristics and Prognosis of Hospitalized Patients at High Risk of Deterioration Identified by the Rapid Response System: a Multicenter Cohort Study
Sang Hyuk Kim, Ji Young Hong, Youlim Kim
Journal of Korean Medical Science.2021;[Epub] CrossRef
Predicting severe outcomes using national early warning score (NEWS) in patients identified by a rapid response system: a retrospective cohort study
Sang Hyuk Kim, Hye Suk Choi, Eun Suk Jin, Hayoung Choi, Hyun Lee, Sang-Hwa Lee, Chang Youl Lee, Myung Goo Lee, Youlim Kim
Scientific Reports.2021;[Epub] CrossRef
A physician-led medical emergency team increases the rate of medical interventions: A multicenter study in Korea
Su Yeon Lee, Jee Hwan Ahn, Byung Ju Kang, Kyeongman Jeon, Sang-Min Lee, Dong Hyun Lee, Yeon Joo Lee, Jung Soo Kim, Jisoo Park, Jae Young Moon, Sang-Bum Hong, Amit Bahl
PLOS ONE.2021; 16(10): e0258221. CrossRef
Incorporating a real-time automatic alerting system based on electronic medical records could improve rapid response systems: a retrospective cohort study
Seung-Hun You, Sun-Young Jung, Hyun Joo Lee, Sulhee Kim, Eunjin Yang
Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine.2021;[Epub] CrossRef
Effects of a Rapid Response Team on the Clinical Outcomes of Cardiopulmonary Resuscitation of Patients Hospitalized in General Wards
Mi-Jung Yoon, Jin-Hee Park
Journal of Korean Academy of Fundamentals of Nursing.2021; 28(4): 491. CrossRef
Early Warning Score and Cancer Patients at End-of-Life
Jae-woo Lee, Ye-Seul Kim, Yonghwan Kim, Hyo-Sun Yoo, Hee-Taik Kang
Korean Journal of Clinical Geriatrics.2021; 22(2): 67. CrossRef
Effectiveness of a daytime rapid response system in hospitalized surgical ward patients
Eunjin Yang, Hannah Lee, Sang-Min Lee, Sulhee Kim, Ho Geol Ryu, Hyun Joo Lee, Jinwoo Lee, Seung-Young Oh
Acute and Critical Care.2020; 35(2): 77. CrossRef
Current State and Strategy for Establishing a Digitally Innovative Hospital: Memorial Review Article for Opening of Yongin Severance Hospital
Soo-Jeong Kim, Ji Woong Roh, Sungwon Kim, Jin Young Park, Donghoon Choi
Yonsei Medical Journal.2020; 61(8): 647. CrossRef
Bleeding management after implementation of the Hemorrhage Code (Code H) at the Hospital Israelita Albert Einstein, São Paulo, Brazil
Michele Jaures, Neila Maria Marques Negrini Pigatti, Roseny dos Reis Rodrigues, Fernanda Paulino Fernandes, João Carlos de Campos Guerra
Einstein (São Paulo).2020;[Epub] CrossRef
Management of post-cardiac arrest syndrome
Youngjoon Kang
Acute and Critical Care.2019; 34(3): 173. CrossRef
Effect of a rapid response system on code rates and in-hospital mortality in medical wards
Hong Yeul Lee, Jinwoo Lee, Sang-Min Lee, Sulhee Kim, Eunjin Yang, Hyun Joo Lee, Hannah Lee, Ho Geol Ryu, Seung-Young Oh, Eun Jin Ha, Sang-Bae Ko, Jaeyoung Cho
Acute and Critical Care.2019; 34(4): 246. CrossRef
Evidence revealed the effects of rapid response system
Jae Hwa Cho
Acute and Critical Care.2019; 34(4): 282. CrossRef

Rapid response system
Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning: Youngnam Lee, Joon-myoung Kwon, Yeha Lee, Hyunho Park, Hugh Cho, Jinsik Park; Acute Crit Care. 2018;33(3):117-120. Published online August 31, 2018; DOI: https://doi.org/10.4266/acc.2018.00290

17,474 View
585 Download
23 Web of Science
26 Crossref

Abstract PDF

With the wider adoption of electronic health records, the rapid response team initially believed that mortalities could be significantly reduced but due to low accuracy and false alarms, the healthcare system is currently fraught with many challenges. Rule-based methods (e.g., Modified Early Warning Score) and machine learning (e.g., random forest) were proposed as a solution but not effective. In this article, we introduce the DeepEWS (Deep learning based Early Warning Score), which is based on a novel deep learning algorithm. Relative to the standard of care and current solutions in the marketplace, there is high accuracy, and in the clinical setting even when we consider the number of alarms, the accuracy levels are superior.

Citations

Citations to this article as recorded by

Artificial Intelligence in Medical Diagnostics: Foundations, Clinical Applications, and Future Directions
Dorota Bartusik-Aebisher, Daniel Roshan Justin Raj, David Aebisher
Applied Sciences.2026; 16(2): 728. CrossRef
Internal and External Validation of a Deep Learning-Based Early Warning System of Cardiac Arrest with Variable-Length and Irregularly Measured Time Series Data
Jyun-Yi Wang, Su-Yin Hsu, Jen-Tang Sun, Chia-Hsin Ko, Chien-Hua Huang, Chu-Lin Tsai, Li-Chen Fu
Journal of Healthcare Informatics Research.2025;[Epub] CrossRef
Artificial intelligence in resuscitation: a scoping review
Drieda Zace, Federico Semeraro, Sebastian Schnaubelt, Jonathan Montomoli, Giuseppe Ristagno, Nino Fijačko, Lorenzo Gamberini, Elena G. Bignami, Robert Greif, Koenraad G. Monsieurs, Andrea Scapigliati
Resuscitation Plus.2025; 24: 100973. CrossRef
How Deep is Your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation
Linglong Qian, Hugh Logan Ellis, Tao Wang, Jun Wang, Robin Mitra, Richard Dobson, Zina Ibrahim
IEEE Journal of Biomedical and Health Informatics.2025; 29(9): 6814. CrossRef
Adaptive Deep Learning for Multimodal Cardiac Risk Prediction: A Feature Fused Multichannel Approach
Krishna Priya Remamany, Anju S Pillai, Ahmed Al Shahri
Annals of Emerging Technologies in Computing.2025; 9(5): 99. CrossRef
External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods: A Single-Center Study
Kyung-Jae Cho, Kwan Hyung Kim, Jaewoo Choi, Dongjoon Yoo, Jeongmin Kim
Critical Care Medicine.2024; 52(3): e110. CrossRef
New Innovations to Address Sudden Cardiac Arrest
Christine P Shen, Sanjeev P Bhavnani, John D Rogers
US Cardiology Review.2024;[Epub] CrossRef
Development of deep learning algorithm for detecting dyskalemia based on electrocardiogram
Jung Nam An, Minje Park, Sunghoon Joo, Mineok Chang, Do Hyoung Kim, Dong Geum Shin, Yeongyeon Na, Jwa-Kyung Kim, Hyung-Seok Lee, Young Rim Song, Yeha Lee, Sung Gyun Kim
Scientific Reports.2024;[Epub] CrossRef
Evaluation of optimal scene time interval for out-of-hospital cardiac arrest using a deep neural network
Seung Jae Shin, Hee Sun Bae, Hyung Jun Moon, Gi Woon Kim, Young Soon Cho, Dong Wook Lee, Dong Kil Jeong, Hyun Joon Kim, Hyun Jung Lee
The American Journal of Emergency Medicine.2023; 63: 29. CrossRef
Short-term load forecasting based on CEEMDAN and Transformer
Peng Ran, Kun Dong, Xu Liu, Jing Wang
Electric Power Systems Research.2023; 214: 108885. CrossRef
Artificial Intelligence in Resuscitation: A Scoping Review
Dmitriy Viderman, Yerkin Abdildin, Kamila Batkuldinova, Rafael Badenes, Federico Bilotta
Journal of Clinical Medicine.2023; 12(6): 2254. CrossRef
Prediction of Out-of-Hospital Cardiac Arrest Survival Outcomes Using a Hybrid Agnostic Explanation TabNet Model
Hung Viet Nguyen, Haewon Byeon
Mathematics.2023; 11(9): 2030. CrossRef
Enhancing breast cancer diagnosis with deep learning and evolutionary algorithms: A comparison of approaches using different thermographic imaging treatments
Alberto Nogales, Fernando Pérez-Lara, Álvaro J. García-Tejedor
Multimedia Tools and Applications.2023; 83(14): 42955. CrossRef
Machine learning pre-hospital real-time cardiac arrest outcome prediction (PReCAP) using time-adaptive cohort model based on the Pan-Asian Resuscitation Outcome Study
Hansol Chang, Ji Woong Kim, Weon Jung, Sejin Heo, Se Uk Lee, Taerim Kim, Sung Yeon Hwang, Sang Do Shin, Won Chul Cha, Marcus Ong
Scientific Reports.2023;[Epub] CrossRef
Attempting cardiac arrest prediction using artificial intelligence on vital signs from Electronic Health Records
Bassel Soudan, Fetna F. Dandachi, Ali Bou Nassif
Smart Health.2022; 25: 100294. CrossRef
BERT Learns From Electroencephalograms About Parkinson’s Disease: Transformer-Based Models for Aid Diagnosis
Alberto Nogales, Alvaro J. Garcia-Tejedor, Ana M. Maitin, Antonio Perez-Morales, Maria Dolores Del Castillo, Juan Pablo Romero
IEEE Access.2022; 10: 101672. CrossRef
Artificial intelligence decision points in an emergency department
Hansol Chang, Won Chul Cha
Clinical and Experimental Emergency Medicine.2022; 9(3): 165. CrossRef
Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database
Arom Choi, Min Joung Kim, Ji Min Sung, Sunhee Kim, Jayoung Lee, Heejung Hyun, Hyeon Chang Kim, Ji Hoon Kim, Hyuk-Jae Chang
Journal of Cardiovascular Development and Disease.2022; 9(12): 430. CrossRef
Short-Term Load Forecasting Based on Ceemdan and Transformer
Peng Ran, Kun Dong, Xu Liu, Jing Wang
SSRN Electronic Journal .2022;[Epub] CrossRef
A survey of deep learning models in medical therapeutic areas
Alberto Nogales, Álvaro J. García-Tejedor, Diana Monge, Juan Serrano Vara, Cristina Antón
Artificial Intelligence in Medicine.2021; 112: 102020. CrossRef
A predictive framework in healthcare: Case study on cardiac arrest prediction
Samaneh Layeghian Javan, Mohammad Mehdi Sepehri
Artificial Intelligence in Medicine.2021; 117: 102099. CrossRef
Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database (Preprint)
Arom Choi, Min Joung Kim, Ji Min Sung, Sunhee Kim, Jayong Lee, Heejung Hyun, Ji Hoon Kim, Hyuk-Jae Chang
JMIR Medical Informatics.2021;[Epub] CrossRef
Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach
Kuan-Han Wu, Fu-Jen Cheng, Hsiang-Ling Tai, Jui-Cheng Wang, Yii-Ting Huang, Chih-Min Su, Yun-Nan Chang
PeerJ.2021; 9: e11988. CrossRef
Cardioinformatics: the nexus of bioinformatics and precision cardiology
Bohdan B Khomtchouk, Diem-Trang Tran, Kasra A Vand, Matthew Might, Or Gozani, Themistocles L Assimes
Briefings in Bioinformatics.2020; 21(6): 2031. CrossRef
Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study
Junetae Kim, Yu Rang Park, Jeong Hoon Lee, Jae-Ho Lee, Young-Hak Kim, Jin Won Huh
JMIR Medical Informatics.2020; 8(3): e16349. CrossRef
Rapid response systems in Korea
Bo Young Lee, Sang-Bum Hong
Acute and Critical Care.2019; 34(2): 108. CrossRef

Original Articles

Cardiology/Pediatric
Effectiveness of Bradycardia as a Single Parameter in the Pediatric Acute Response System: Yu Hyeon Choi, Hyeon Seung Lee, Bong Jin Lee, Dong In Suh, June Dong Park; Korean J Crit Care Med. 2014;29(4):297-303. Published online November 30, 2014; DOI: https://doi.org/10.4266/kjccm.2014.29.4.297

6,757 View
57 Download
4 Crossref

Abstract PDF

BACKGROUND
Various tools for the acute response system (ARS) predict and prevent acute deterioration in pediatric patients. However, detailed criteria have not been clarified. Thus we evaluated the effectiveness of bradycardia as a single parameter in pediatric ARS.
METHODS
This retrospective study included patients who had visited a tertiary care children's hospital from January 2012 to June 2013, in whom ARS was activated because of bradycardia. Patient's medical records were reviewed for clinical characteristics, cardiologic evaluations, and reversible causes that affect heart rate.
RESULTS
Of 271 cases, 261 (96%) had ARS activation by bradycardia alone with favorable outcomes. Evaluations and interventions were performed in 165 (64.5%) and 13 cases (6.6%) respectively. All patients in whom ARS was activated owing to bradycardia and another criteria underwent evaluation, unlike those with bradycardia alone (100.0% vs. 63.2%, p = 0.016). Electrocardiograms were evaluated in 233 (86%) cases: arrhythmias were due to borderline QT prolongation and atrioventricular block (1st and 2nd-degree) in 25 cases (9.2%). Bradycardia-related causes were reversible in 202 patients (74.5%). Specific causes were different in departments at admission. Patients admitted to the hemato-oncology department required ARS activation during the night (69.3%, p = 0.03), those to the endocrinology department required ARS activation because of medication (72.4%, p < 0.001), and those to the gastroenterology department had low body mass indexes (32%, p = 0.01).
CONCLUSIONS
Using bradycardia alone in pediatric ARS is not useful, because of its low specificity and poor predictive ability for deterioration. However, bradycardia can be applied to ARS concurrently with other parameters.

Citations

Citations to this article as recorded by

Impact of an independent pediatric rapid response team on pediatric patient safety
Jin Hwan Lyu, Soo Keun Lee, Min Joo Kang, Sumin Lee, Mireu Park, Soo Yeon Kim, Kyung Won Kim, Jong Deok Kim
Archives of Pediatric Critical Care.2024; 2(2): 91. CrossRef
Effect of Diurnal Variation of Heart Rate and Respiratory Rate on Activation of Rapid Response System and Clinical Outcome in Hospitalized Children
Lia Kim, Kyoung Sung Yun, June Dong Park, Bongjin Lee
Children.2023; 10(1): 167. CrossRef
Eleven years of experience in operating a pediatric rapid response system at a children’s hospital in South Korea
Yong Hyuk Jeon, Bongjin Lee, You Sun Kim, Won Jin Jang, June Dong Park
Acute and Critical Care.2023; 38(4): 498. CrossRef
Pediatric triage modifications based on vital signs: a nationwide study
Bongjin Lee, June Dong Park, Young Ho Kwak, Do Kyun Kim
Clinical and Experimental Emergency Medicine.2022; 9(3): 224. CrossRef

Quality Improvement
Successful Implementation of a Rapid Response System in the Department of Internal Medicine: Yeon Joo Lee, Jin Joo Park, Yeonyee E Yoon, Jin Won Kim, Jong Sun Park, Taeyun Kim, Jae Hyuk Lee, Jung Won Suh, You Hwan Jo, Sangheon Park, Kyuseok Kim, Young Jae Cho; Korean J Crit Care Med. 2014;29(2):77-82. Published online May 31, 2014; DOI: https://doi.org/10.4266/kjccm.2014.29.2.77

8,399 View
116 Download
7 Crossref

Abstract PDF

Background
A rapid response system (RRS) aims to prevent unexpected patient death due to clinical errors and is becoming an essential part of intensive care. We examined the activity and outcomes of RRS for patients admitted to our institution’s department of internal medicine.
Methods
We retrospectively reviewed patients detected by the RRS and admitted to the medical intensive care unit (MICU) from October 2012 through August 2013. We studied the overall activity of the RRS and compared patient outcomes between those admitted via the RRS and those admitted conventionally.
Results
A total of 4,849 alert lists were generated from 2,505 medical service patients. The RRS was activated in 58 patients: A (Admit to ICU), B (Borderline intervention), C (Consultation), and D (Do not resuscitate) in 26 (44.8%), 21 (36.2%), 4 (6.9%), and 7 (12.1%) patients, respectively. Low oxygen saturation was the most common criterion for RRS activation. MICU admission via the RRS resulted in a shorter ICU stay than that via conventional admission (6.2 vs. 9.9 days, p = 0.018).
Conclusions
An RRS can be successfully implemented in medical services. ICU admission via the RRS resulted in a shorter ICU stay than that via conventional admission. Further study is required to determine long-term outcomes.

Citations

Citations to this article as recorded by

Temporal variational autoencoder model for in-hospital clinical emergency prediction
Trong-Nghia Nguyen, Soo-Hyung Kim, Bo-Gun Kho, Nhu-Tai Do, Ngumimi-Karen Iyortsuun, Guee-Sang Lee, Hyung-Jeong Yang
Biomedical Signal Processing and Control.2025; 100: 106975. CrossRef
Effectiveness of Rapid Response Team implementation in a tertiary hospital in Egypt: an interventional study
Rania Hosny, Rasha Saad Hussein, Wafaa Mohamed Hussein, Sally Adel Hakim, Ihab Shehad Habil
BMJ Open Quality.2024; 13(3): e002540. CrossRef
Characteristics and Prognosis of Hospitalized Patients at High Risk of Deterioration Identified by the Rapid Response System: a Multicenter Cohort Study
Sang Hyuk Kim, Ji Young Hong, Youlim Kim
Journal of Korean Medical Science.2021;[Epub] CrossRef
Effects of a Rapid Response Team on the Clinical Outcomes of Cardiopulmonary Resuscitation of Patients Hospitalized in General Wards
Mi-Jung Yoon, Jin-Hee Park
Journal of Korean Academy of Fundamentals of Nursing.2021; 28(4): 491. CrossRef
Rapid response systems in Korea
Bo Young Lee, Sang-Bum Hong
Acute and Critical Care.2019; 34(2): 108. CrossRef
Effect of a rapid response system on code rates and in-hospital mortality in medical wards
Hong Yeul Lee, Jinwoo Lee, Sang-Min Lee, Sulhee Kim, Eunjin Yang, Hyun Joo Lee, Hannah Lee, Ho Geol Ryu, Seung-Young Oh, Eun Jin Ha, Sang-Bae Ko, Jaeyoung Cho
Acute and Critical Care.2019; 34(4): 246. CrossRef
Differences in the Clinical Characteristics of Rapid Response System Activation in Patients Admitted to Medical or Surgical Services
Yeon Joo Lee, Dong Seon Lee, Hyunju Min, Yun Young Choi, Eun Young Lee, Inae Song, Yeonyee E. Yoon, Jin Won Kim, Jong Sun Park, Young-Jae Cho, Jae Hyuk Lee, Jung-Won Suh, You Hwan, Kyuseok Kim, Sangheon Park
Journal of Korean Medical Science.2017; 32(4): 688. CrossRef

Usefulness of Screening Criteria System Used by Medical Alert Team in a General Hospital: Hyejin Joo, So Hee Park, Sang Bum Hong, Chae Man Lim, Younsuck Koh, Young Seok Lee, Jin Won Huh; Korean J Crit Care Med. 2012;27(3):151-156.; DOI: https://doi.org/10.4266/kjccm.2012.27.3.151

4,404 View
67 Download
4 Crossref

Abstract PDF

BACKGROUND
Rapid response team (RRT) is becoming an essential part of patient safety by the early recognition and management of patients on general hospital wards. In this study, we analyzed the usefulness of screening criteria of RRT used at Asan Medical Center.
METHODS
On a retrospective basis, we reviewed the records of 675 cases in 543 patients that were managed by RRT (called medical alert team in the Asan Medical Center), from July 2011 to December 2011. The medical alert team was acted by requests of attending doctors or nurses or the medical alert system (MAS) criteria composed of abnormal vital sign, neurology, laboratory data and increasing oxygen demand. We investigated the patterns of MAS criteria for targeting the patients who were managed by the medical alert team.
RESULTS
Respiratory distress (RR > 25/min) was the most common item for identifying patients whose condition had worsened. The criteria consist with respiratory distress and abnormal blood pressure (mean BP < 60 mmHg or systolic BP < 90 mmHg) found 70.0% of patients with deteriorated conditions. Vital sign (RR > 25/min, mean BP < 60 mmHg or systolic BP < 90 mmHg, pulse rate, PR > 130/min or < 50/min) and oxygen demand found 79.2% of them. Vital signs, arterial blood gas analysis (ABGA) with lactate level (pH, pO2, pCO2, and lactate) and O2 demand found 98.6% of patient conditions had worsened.
CONCLUSIONS
Vital signs, especially RR > 25/min is useful criteria for detecting patients whose conditions have deteriorated. The addition of ABGA data with lactate levels leads to a more powerful screening tool.

Citations

Citations to this article as recorded by

Influence of the Rapid Response Team Activation via Screening by Nurses on Unplanned Intensive Care Unit Admissions
Ye-Ji Huh, Seongmi Moon, Eun Kyeung Song, Minyoung Kim
Korean Journal of Adult Nursing.2020; 32(5): 539. CrossRef
Early Experience of Medical Alert System in a Rural Training Hospital: a Pilot Study
Maru Kim
The Korean Journal of Critical Care Medicine.2017; 32(1): 47. CrossRef
Temporal patterns of change in vital signs and Cardiac Arrest Risk Triage scores over the 48 hours preceding fatal in‐hospital cardiac arrest
HyunSoo Oh, KangIm Lee, WhaSook Seo
Journal of Advanced Nursing.2016; 72(5): 1122. CrossRef
A combination of early warning score and lactate to predict intensive care unit transfer of inpatients with severe sepsis/septic shock
Jung-Wan Yoo, Ju Ry Lee, Youn Kyung Jung, Sun Hui Choi, Jeong Suk Son, Byung Ju Kang, Tai Sun Park, Jin-Won Huh, Chae-Man Lim, Younsuck Koh, Sang Bum Hong
The Korean Journal of Internal Medicine.2015; 30(4): 471. CrossRef

First
Prev
Page of 1
Next
Last

Search