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Background: Some patients experience early (within 48 h) clinical deterioration and medical emergency team (MET) review following intensive care unit (ICU) discharge. Few studies have explored early MET review, despite it being a recognised quality and safety indicator. Aims: To evaluate the (i) proportion of patients discharged from ICU receiving MET review and timing of reviews; (ii) characteristics of patients who received early MET review and (iii) predictors of early MET review and associations with clinical outcomes. Methods: This is a retrospective observational study of ICU discharges over 2 years in a tertiary hospital and involves descriptive and inferential statistics, including logistic regression analysis. Results: Of 3712 patients, 312 (8.4%) had an early MET review. Patients with cardiothoracic, cardiovascular, gastrointestinal and general surgical diagnoses, higher illness severity or who received invasive ventilation had a higher risk of early MET review. On multivariable analysis, early MET review was associated with an increased risk of ICU re‐admission (odds ratio (OR) 6.76, 95% confidence interval (CI) 5.01–9.13, P < 0.001), in‐hospital mortality (OR 3.62, 95% CI 2.19–5.99, P < 0.001) and discharge to a nursing home (OR 2.49, 95% CI 1.25–4.97, P = 0.01). Length of stay was longer in patients requiring early post‐ICU MET review compared to those who did not (median 16 days vs. 10 days, P < 0.001). Conclusions: One in 12 patients received post‐ICU early MET review. This was more likely in patients who were invasively ventilated, had higher illness severity and had certain admission diagnoses. Such patients were at risk for worse outcomes. There is a need to identify reversible factors contributing to such increased risk. [ABSTRACT FROM AUTHOR]

Background: Internationally, there is an increasing trend in using Rapid Response Systems (RRS) to stabilize in‐patient deterioration. Despite a growing evidence base, there remains limited understanding of the processes in place to aid the early recognition and response to deteriorating children in hospitals across Europe. Aim: To describe the processes in place for early recognition and response to in‐patient deterioration in children in European hospitals. Study Design: A cross‐sectional opportunistic multi‐centre European study, of hospitals with paediatric in‐patients, using a descriptive self‐reported, web‐based survey, was conducted between September 2021 and March 2022. The sampling method used chain referral through members of European and national societies, led by country leads. The survey instrument was an adaptation to the survey of Recognition and Response Systems in Australia. The study received ethics approval. Descriptive analysis and Chi‐squared tests were performed to compare results in European regions. Results: A total of 185 questionnaires from 21 European countries were received. The majority of respondents (n = 153, 83%) reported having written policies, protocols, or guidelines, regarding the measurement of physiological observations. Over half (n = 120, 65%) reported that their hospital uses a Paediatric Early Warning System (PEWS) and 75 (41%) reported having a Rapid Response Team (RRT). Approximately one‐third (38%) reported that their hospital collects specific data about the effectiveness of their RRS, while 100 (54%) reported providing regular training and education to support it. European regional differences existed in PEWS utilization (North = 98%, Centre = 25%, South = 44%, p <.001) and process evaluation (North = 49%, Centre = 6%, South = 36%, p <.001). Conclusions: RRS practices in European hospitals are heterogeneous. Differences in the uptake of PEWS and RRS process evaluation emerged across Europe. Relevance to Clinical Practice: It is important to scope practices for the safe monitoring and management of deteriorating children in hospital across Europe. To reduce variance in practice, a consensus statement endorsed by paediatric and intensive care societies could provide guidance and resources to support PEWS implementation and for the operational governance required for continuous quality improvement. [ABSTRACT FROM AUTHOR]

Response teams, via the rapid response system (RRS), are activated when a patient's condition deteriorates; respiratory, neurological, or cardiac diseases are precursors to unexpected in-hospital cardiac arrest or death. In December 2019, our hospital introduced the RRS to reduce serious adverse events. Herein, we report a case where RRS was initiated twice for a patient who developed septic shock due to cervical necrotizing fasciitis and non-occlusive mesenteric ischemia (NOMI) after the septic shock. In August 2021, a male in his 70 s developed facial swelling, difficulty opening his mouth, and difficulty with oral intake. We observed necrotic skin on the face, soft tissue swelling in the neck, and hypotension. The RRS was initiated, and the patient was immediately admitted to the intensive care unit (ICU) where he was diagnosed with septic shock and necrotizing fasciitis of the neck. The patient underwent surgical necrotic lesion debridement and incisional drainage under general anesthesia. After weaning from shock, owing to his hypotensive and hemorrhagic status, the RRS was requested again. The patient was re-admitted to the ICU and diagnosed with NOMI. His general condition improved, and he was discharged on day 47. [ABSTRACT FROM AUTHOR]

Background: Rapid response systems (RRSs) are used in hospitals to identify and treat deteriorating patients. However, RRS implementation and outcomes in orthopedic and surgical patients remain controversial. We aimed to investigate whether the RRS affects mortality and complications after orthopedic surgery. Methods: The National Health Insurance Service of South Korea provided the data for this population-based cohort study. Individuals who were admitted to the hospital that implemented RRS were categorized into the RRS group and those admitted to a hospital that did not implement the RRS were categorized into the non-RRS group. In-hospital mortality and postoperative complications were the endpoints. Results: A total of 931,774 adult patients were included. Among them, 93,293 patients underwent orthopedic surgery in a hospital that implemented RRS and were assigned to the RRS group, whereas 838,481 patients were assigned to the non-RRS group. In multivariable logistic regression analysis, the RRS group was not associated with in-hospital mortality after orthopedic surgery compared with the non-RRS group (odds ratio [OR] 0.93, 95% confidence interval [CI] 0.80, 1.08; P = 0.350). However, the RRS group was associated with a 14% lower postoperative complication rate after orthopedic surgery than the non-RRS group (OR 0.86, 95% CI 0.84, 0.86; P < 0.001). Conclusions: The RRS was not associated with in-hospital mortality following orthopedic surgery in South Korea. However, RRS deployment was related to a decreased risk of postoperative complications in patients undergoing orthopedic surgery. [ABSTRACT FROM AUTHOR]

Abstract Background Rapid response systems (RRSs) are used in hospitals to identify and treat deteriorating patients. However, RRS implementation and outcomes in orthopedic and surgical patients remain controversial. We aimed to investigate whether the RRS affects mortality and complications after orthopedic surgery. Methods The National Health Insurance Service of South Korea provided the data for this population-based cohort study. Individuals who were admitted to the hospital that implemented RRS were categorized into the RRS group and those admitted to a hospital that did not implement the RRS were categorized into the non-RRS group. In-hospital mortality and postoperative complications were the endpoints. Results A total of 931,774 adult patients were included. Among them, 93,293 patients underwent orthopedic surgery in a hospital that implemented RRS and were assigned to the RRS group, whereas 838,481 patients were assigned to the non-RRS group. In multivariable logistic regression analysis, the RRS group was not associated with in-hospital mortality after orthopedic surgery compared with the non-RRS group (odds ratio [OR] 0.93, 95% confidence interval [CI] 0.80, 1.08; P = 0.350). However, the RRS group was associated with a 14% lower postoperative complication rate after orthopedic surgery than the non-RRS group (OR 0.86, 95% CI 0.84, 0.86; P

Background: This study aimed to determine whether implementing a rapid response system (RRS) is associated with improved short‐term outcomes in critically ill patients with haematological malignancies. Methods: Our monocentric pre‐ versus post‐intervention study was conducted between January 2012 and April 2020. RRS was activated at early signs of haemodynamic or respiratory failure. The primary outcome was the reduction in Sequential Organ Failure Assessment (SOFA) score on Day 3 after intensive care unit (ICU) admission. Secondary outcomes included time to ICU admission and mortality. Results: A total of 209 patients with a median age of 59 years were enrolled (108 in the pre‐intervention period and 101 in the post‐intervention period). 22% of them had received an allogeneic transplant. The post‐intervention period was associated with a shorter time to ICU admission (195 vs. 390 min, p <.001), a more frequent favourable trend in SOFA score (57% vs. 42%, adjusted odds ratio, 2.02, 95% confidence interval, 1.09 to 3.76), no significant changes in ICU (22% vs. 26%, p =.48) and 1‐year (62% vs. 58%, p =.62) mortality rates. Conclusion: Detection of early organ failure and activation of an RRS was associated with faster ICU admission and lower SOFA scores on Day 3 of admission in critically ill patients with haematological malignancies. [ABSTRACT FROM AUTHOR]

Aim Design Methods Results Conclusion Implications for the Profession and/or Patient Care Impact What problem did the study address? What were the main findings? Where and on whom will the research have an impact? Reporting Method Patient or Public Contribution This study observed changes in respiratory rate measurement (RRM) and identified barriers and challenges in clinical practice that influence healthcare worker behaviour, aiming to improve RRM in a hospital setting.An observational study was conducted.We observed and analysed changes in the behaviour of healthcare workers at a hospital where multi‐nudges were introduced to improve RRM.We checked respiration rate using electronic data and discovered that the original measurement rates were low. Measurement rates rapidly increased after posters were added. Barriers such as time constraints and measurement equipment were also noted.RRM was found to be effective in promoting behavioural economics in medical settings. The results show that incorporating behavioural science principles into medical interventions has the potential to positively change behaviour.By increasing nurses' awareness of respiratory rate measurement and addressing barriers to it, the measurement rate of respiratory rate can also increase, leading to more accurate patient evaluations and triage.The proportion of respiratory rate measurements leading to rapid response system (RRS) calls was low.The study observed that a multi‐nudge approach effectively changes the behaviour of ward nurses, resulting in enhanced quality of medical care.This research can serve as a valuable reference for leaders promoting healthcare quality projects, by offering a method to encourage behavioural change.This study complied with the EQUATOR guidelines and its reporting adheres to the STROBE checklist.No patient or public contribution. [ABSTRACT FROM AUTHOR]

Aim(s): To explore the published research related to nurses' documentation and use of vital signs in recognising and responding to deteriorating patients. Design: Scoping review of international, peer‐reviewed research studies. Data Sources: Cumulative Index to Nursing and Allied Health Literature Complete, Medline Complete, American Psychological Association PsycInfo and Excerpta Medica were searched on 25 July 2023. Reporting Method: Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for scoping reviews. Results: Of 3880 potentially eligible publications, 32 were included. There were 26 studies of nurses' vital sign documentation: 21 adults and five paediatric. The most and least frequently documented vital signs were blood pressure and respiratory rate respectively. Seven studies focused on vital signs and rapid response activation or afferent limb failure. Five studies of vital signs used to trigger the rapid response system showed heart rate was the most frequent and respiratory rate and conscious state were the least frequent. Heart rate was least likely and oxygen saturation was most likely to be associated with afferent limb failure (n = 4 studies). Conclusion: Despite high reliance on using vital signs to recognise clinical deterioration and activate a response to deteriorating patients in hospital settings, nurses' documentation of vital signs and use of vital signs to activate rapid response systems is poorly understood. There were 21studies of nurses' vital sign documentation in adult patients and five studies related to children. Implications for the profession and/or patient care: A deeper understanding of nurses' decisions to assess (or not assess) specific vital signs, analysis of the value or importance nurses place (or not) on specific vital sign parameters is warranted. The influence of patient characteristics (such as age) or the clinical practice setting, and the impact of nurses' workflows of vital sign assessment warrants further investigation. Patient or Public Contribution: No Patient or Public Contribution. [ABSTRACT FROM AUTHOR]

Background: Rapid Response Systems are hospital-wide patient-focused systems aiming to improve recognition of acute deterioration in patients and trigger a rapid response aimed at preventing potentially avoidable adverse events such as cardiac arrest and death. In 1994, the Royal Children’s Hospital in Brisbane, Australia, was one of the first institutions to adopt a paediatric rapid response system (RRS). The purpose of this study was to investigate the impacts of both introduction of a paediatric RRS and increasing RRS activations (MET dose) on hospital mortality. Methods: Prospectively collected data from institutional databases at a specialist paediatric hospital was used to determine hospital mortality rate pre- and post- implementation of the RRS. An interrupted time series model using segmented regression was utilised to assess the pre-intervention trend, as well as immediate and sustained effects of RRS implementation on hospital mortality. Univariate linear regression examined potential effects of MET dose on mortality. Results: Hospital mortality rate did not change significantly over 15 years before RRS implementation. In the first year after implementation, mortality rate fell significantly (−1.4; 95 %CI −2.27 to −0.52; p = 0.0027). For each year that passed after the intervention, there was no significant change in hospital mortality rate (Estimate: −0.08; 95 %CI −0.17 to 0.02; p = 0.11). Univariate linear regression modelling showed that with every unit increase in MET Dose, hospital mortality rate decreased by −0.13 (95 % CI: −0.27 to 0; p = 0.05). Conclusions: Utilising data from one of the earliest and longest duration single-centre cohort of paediatric MET events, this study reaffirms the association between implementation of a paediatric RRS and decreased hospital mortality. The study also provides novel evidence of the impact of MET dose on patient outcome in the paediatric population. It is recommended that factors influencing the benefit of rapid response systems in paediatric populations are further identified so that this life saving initiative can be optimised.

Introduction: Early Warning Score (EWS) protocols are based on intermittent vital sign measurements, and aim to detect clinical deterioration in a timely manner. Despite its predictive value, its effectiveness remains suboptimal. An important limitation appears to be poor compliance with the EWS protocol and its variation between general wards. The current research does not yet provide an understanding of EWS compliance and variation in different nursing wards. Aim: To explore the variation in nurses' compliance with the EWS protocol among patients with and without complications and between different nursing wards. Methods: In a retrospective single-center cohort study, all patient files from three nursing wards of a tertiary teaching hospital in the Netherlands were reviewed over a 1-month period. Compliance was divided into three categories:1) calculation accuracy, 2) monitoring frequency end 3) clinical response. Results: The cohort of 210 patients contained 5864 measurements, of which 4125 (70.6 %) included EWS. Significant differences in the measured vital signs within incomplete measurements were found among nursing wards. Compliance to monitoring frequency was higher within EWSs of 0–1 (78.4 %) than within EWSs of ≥2 (26.1 %). The proportion of correct follow-up was significantly higher in patients with complications, as was the correct clinical response to an EWS of ≥3 (84.8 % vs. 55.0; p = .011). Conclusion: Our results suggest suboptimal compliance with the EWS protocol, with large variations between patients with and without complications and between different general care wards. Nurses tended to be more compliant with the EWS protocol for patients with complications.

RATIONALE: Clinical deterioration of patients hospitalized outside the ICU is a source of potentially reversible morbidity and mortality. To address this, some acute care hospitals have implemented systems aimed at detecting and responding to such patients. OBJECTIVES: To provide evidence-based recommendations for hospital clinicians and administrators to optimize recognition and response to clinical deterioration in non-ICU patients. PANEL DESIGN: The 25-member panel included representatives from medicine, nursing, respiratory therapy, pharmacy, patient/family partners, and clinicianmethodologists with expertise in developing evidence-based Clinical Practice Guidelines. METHODS: We generated actionable questions using the Population, Intervention, Control, and Outcomes (PICO) format and performed a systematic review of the literature to identify and synthesize the best available evidence. We used the Grading of Recommendations Assessment, Development, and Evaluation Approach to determine certainty in the evidence and to formulate recommendations and good practice statements (GPSs). RESULTS: The panel issued 10 statements on recognizing and responding to non-ICU patients with critical illness. Healthcare personnel and institutions should ensure that all vital sign acquisition is timely and accurate (GPS). We make no recommendation on the use of continuous vital sign monitoring among unselected patients. We suggest focused education for bedside clinicians in signs of clinical deterioration, and we also suggest that patient/family/care partners' concerns be included in decisions to obtain additional opinions and help (both conditional recommendations). We recommend hospital-wide deployment of a rapid response team or medical emergency team (RRT/MET) with explicit activation criteria (strong recommendation). We make no recommendation about RRT/MET professional composition or inclusion of palliative care members on the responding team but suggest that the skill set of responders should include eliciting patients' goals of care (conditional recommendation). Finally, quality improvement processes should be part of a rapid response system. CONCLUSIONS: The panel provided guidance to inform clinicians and administrators on effective processes to improve the care of patients at-risk for developing critical illness outside the ICU. [ABSTRACT FROM AUTHOR]

RATIONALE: Clinical deterioration of patients hospitalized outside the ICU is a source of potentially reversible morbidity and mortality. To address this, some acute care facilities have implemented systems aimed at detecting and responding to such patients. OBJECTIVES: To provide evidence-based recommendations for hospital clinicians and administrators to optimize recognition and response to clinical deterioration in non-ICU patients. PANEL DESIGN: The 25-member panel included representatives from medicine, nursing, respiratory therapy, pharmacy, patient/family partners, and clinicianmethodologists with expertise in developing evidence-based clinical practice guidelines. METHODS: We generated actionable questions using the Population, Intervention, Control, and Outcomes format and performed a systematic review of the literature to identify and synthesize the best available evidence. We used the Grading of Recommendations Assessment, Development, and Evaluation approach to determine certainty in the evidence and to formulate recommendations and good practice statements (GPSs). RESULTS: The panel issued 10 statements on recognizing and responding to non-ICU patients with critical illness. Healthcare personnel and institutions should ensure that all vital sign acquisition is timely and accurate (GPS). We make no recommendation on the use of continuous vital sign monitoring among "unselected" patients due to the absence of data regarding the benefit and the potential harms of false positive alarms, the risk of alarm fatigue, and cost. We suggest focused education for bedside clinicians in signs of clinical deterioration, and we also suggest that patient/family/care partners' concerns be included in decisions to obtain additional opinions and help (both conditional recommendations). We recommend hospital-wide deployment of a rapid response team or medical emergency team (RRT/MET) with explicit activation criteria (strong recommendation). We make no recommendation about RRT/MET professional composition or inclusion of palliative care members on the responding team but suggest that the skill set of responders should include eliciting patients' goals of care (conditional recommendation). Finally, quality improvement processes should be part of a rapid response system (GPS). CONCLUSIONS: The panel provided guidance to inform clinicians and administrators on effective processes to improve the care of patients at-risk for developing critical illness outside the ICU. [ABSTRACT FROM AUTHOR]

This study investigated the accuracy of a machine learning algorithm for predicting mortality in patients receiving rapid response system (RRS) activation. This retrospective cohort study used data from the In-Hospital Emergency Registry in Japan, which collects nationwide data on patients receiving RRS activation. The missing values in the dataset were replaced using multiple imputations (mode imputation, BayseRidge sklearn. linear model, and K-nearest neighbor model), and the enrolled patients were randomly assigned to the training and test cohorts. We established prediction models for 30-day mortality using the following four types of machine learning classifiers: Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting, random forest, and neural network. Fifty-two variables (patient characteristics, details of RRS activation, reasons for RRS initiation, and hospital capacity) were used to construct the prediction algorithm. The primary outcome was the accuracy of the prediction model for 30-day mortality. Overall, the data from 4,997 patients across 34 hospitals were analyzed. The machine learning algorithms using LightGBM demonstrated the highest predictive value for 30-day mortality (area under the receiver operating characteristic curve, 0.860 [95 % confidence interval, 0.825–0.895]). The SHapley Additive exPlanations summary plot indicated that hospital capacity, site of incidence, code status, and abnormal vital signs within 24 h were important variables in the prediction model for 30-day mortality.

Introduction: Proactive surveillance by a critical care outreach team (CCOT) can promote early recognition of deterioration in hospitalized patients but is uncommon in pediatric rapid response systems (RRSs). After our children’s hospital introduced a CCOT in 2019, we aimed to characterize early implementation outcomes. We hypothesized that CCOT rounding would identify additional children at risk for deterioration. Methods: The CCOT, staffed by a dedicated critical care nurse (RN), respiratory therapist, and attending, conducts daily in-person rounds with charge RNs on medical-surgical units, to screen RRS-identified high-risk patients for deterioration. In this prospective study, observers tracked rounds discussion content, participation, and identification of new high-risk patients. We compared ‘identified-patient-discussions’ (IPD) about RRS-identified patients, and ‘new-patient-discussions’ (NPD) about new patients with Fisher’s exact test. For new patients, we performed thematic analysis of clinical data to identify deterioration related themes. Results: During 348 unit-rounds over 20 days, we observed 383 discussions − 35 (9%) were NPD. Frequent topics were screening for clinical concerns (374/383, 98%), active clinical concerns (147/383, 39%), and watcher activation (66/383, 17%). Most discussions only included standard participants (353/383, 92%). Compared to IPD, NPD more often addressed active concerns (74.3% vs 34.8%, p

Chutipong Kwantong,1 Yuda Sutherasan,2 Detajin Junhasavasdikul,2 Tananchai Petnak,2 Pongdhep Theerawit3 1Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; 2Division of Pulmonary and Pulmonary Critical Care Medicine, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; 3Division of Critical Care Medicine, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, ThailandCorrespondence: Yuda Sutherasan, Division of Pulmonary and Pulmonary Critical Care Medicine, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, 270 Rama VI Road, Ratchathewi, Bangkok, 10400, Thailand, Tel +6622011619, Fax +6622011629 Ext 2, Email Sutherasan_yuda@yahoo.comPurpose: The Ramathibodi Rapid Response System (RRRS), implemented in March 2017, aims to identify and respond to patients with deteriorating conditions outside the ICU. It employs the Ramathibodi early warning score and clinical signs to monitor all admitted patients, providing expert physician monitoring and early treatment for stabilization and appropriate care triage. This study assesses the RRRS’s effectiveness in reducing in-hospital mortality and CPR events outside the ICU.Patients and Methods: We conducted a retrospective observational study from March 2014 to February 2020 in a tertiary care hospital’s general wards. We included adult patients experiencing unplanned ICU admission, sudden cardiac arrest, or unexpected death. The study compared in-hospital mortality and CPR incidence outside the ICU between pre- and post-RRRS implementation groups. The associations between RRRS implementation and in-hospital mortality and the incidence of CPR outside the ICU were assessed using multiple logistic regression analyses.Results: We evaluated 17,741 admissions, with 9168 before RRRS implementation (1 March 2014 to 28 February 2017) and 8573 after RRRS implementation (1 March 2017 to 29 February 2020). The implementation of RRRS was associated with a significant reduction in in-hospital mortality, which decreased from 30.0% to 20.8% (odds ratio, 0.62; 95% confidence interval [CI], 0.57 to 0.66; P< 0.0001). Even after adjusting for age, sex, and comorbidities, the reduction in in-hospital mortality remained significant (adjusted odds ratio, 0.58; 95% CI, 0.54 to 0.63; P< 0.0001). The incidence of CPR outside the ICU also decreased from 1.8% to 1.1% (adjusted odds ratio, 0.6; 95% CI, 0.46 to 0.77; P< 0.0001). Additionally, the rate of ICU transfer increased from 85.4% to 92.1% (risk difference, 6.7; 95% CI, 7.6 to 5.8; P< 0.0001) after implementing the RRRS.Conclusion: Implementing the RRRS is associated with a reduction in in-hospital mortality and the incidence of CPR outside the ICU.Keywords: rapid response system, rapid response team, deteriorating patient, in-hospital mortality, cardiopulmonary resuscitation, intensive care unit, implementation protocol

Abstract Background Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice. Methods This prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea. All adult patients admitted to general wards during the 3-month study period were included. The primary outcome was predictive accuracy for the occurrence of IHCA or UIT within 24 h of the alarm being triggered. Area under the receiver operating characteristic curve (AUROC) values were used to compare the DeepCARS™ with the modified early warning score (MEWS), national early warning Score (NEWS), and single-parameter track-and-trigger systems. Results Among 55,083 patients, the incidence rates of IHCA and UIT were 0.90 and 6.44 per 1,000 admissions, respectively. In terms of the composite outcome, the AUROC for the DeepCARS™ was superior to those for the MEWS and NEWS (0.869 vs. 0.756/0.767). At the same sensitivity level of the cutoff values, the mean alarm counts per day per 1,000 beds were significantly reduced for the DeepCARS™, and the rate of appropriate alarms was higher when using the DeepCARS™ than when using conventional systems. Conclusion The DeepCARS™ predicts IHCA and UIT more accurately and efficiently than conventional methods. Thus, the DeepCARS™ may be an effective screening tool for detecting clinical deterioration in real-world clinical practice. Trial registration This study was registered at ClinicalTrials.gov ( NCT04951973 ) on June 30, 2021.

Background: Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice. Methods: This prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea. All adult patients admitted to general wards during the 3-month study period were included. The primary outcome was predictive accuracy for the occurrence of IHCA or UIT within 24 h of the alarm being triggered. Area under the receiver operating characteristic curve (AUROC) values were used to compare the DeepCARS™ with the modified early warning score (MEWS), national early warning Score (NEWS), and single-parameter track-and-trigger systems. Results: Among 55,083 patients, the incidence rates of IHCA and UIT were 0.90 and 6.44 per 1,000 admissions, respectively. In terms of the composite outcome, the AUROC for the DeepCARS™ was superior to those for the MEWS and NEWS (0.869 vs. 0.756/0.767). At the same sensitivity level of the cutoff values, the mean alarm counts per day per 1,000 beds were significantly reduced for the DeepCARS™, and the rate of appropriate alarms was higher when using the DeepCARS™ than when using conventional systems. Conclusion: The DeepCARS™ predicts IHCA and UIT more accurately and efficiently than conventional methods. Thus, the DeepCARS™ may be an effective screening tool for detecting clinical deterioration in real-world clinical practice. Trial registration This study was registered at ClinicalTrials.gov (NCT04951973) on June 30, 2021. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to develop a new warning score for cancer patients who are at risk of getting worse in the hospital. Cancer patients can have serious problems due to their treatment or cancer itself. A quick response system is used to find and treat these patients. This study used a deep-learning method to create a warning score based on the changes in vital signs. The warning score, called Can-EWS, was better than the existing score, called MEWS, in predicting and preventing bad outcomes. Can-EWS also had fewer false alarms than MEWS. This study could help improve the safety and care of cancer patients in the hospital. Background: Cancer patients who are admitted to hospitals are at high risk of short-term deterioration due to treatment-related or cancer-specific complications. A rapid response system (RRS) is initiated when patients who are deteriorating or at risk of deteriorating are identified. This study was conducted to develop a deep learning-based early warning score (EWS) for cancer patients (Can-EWS) using delta values in vital signs. Methods: A retrospective cohort study was conducted on all oncology patients who were admitted to the general ward between 2016 and 2020. The data were divided into a training set (January 2016–December 2019) and a held-out test set (January 2020–December 2020). The primary outcome was clinical deterioration, defined as the composite of in-hospital cardiac arrest (IHCA) and unexpected intensive care unit (ICU) transfer. Results: During the study period, 19,739 cancer patients were admitted to the general wards and eligible for this study. Clinical deterioration occurred in 894 cases. IHCA and unexpected ICU transfer prevalence was 1.77 per 1000 admissions and 43.45 per 1000 admissions, respectively. We developed two models: Can-EWS V1, which used input vectors of the original five input variables, and Can-EWS V2, which used input vectors of 10 variables (including an additional five delta variables). The cross-validation performance of the clinical deterioration for Can-EWS V2 (AUROC, 0.946; 95% confidence interval [CI], 0.943–0.948) was higher than that for MEWS of 5 (AUROC, 0.589; 95% CI, 0.587–0.560; p < 0.001) and Can-EWS V1 (AUROC, 0.927; 95% CI, 0.924–0.931). As a virtual prognostic study, additional validation was performed on held-out test data. The AUROC and 95% CI were 0.588 (95% CI, 0.588–0.589), 0.890 (95% CI, 0.888–0.891), and 0.898 (95% CI, 0.897–0.899), for MEWS of 5, Can-EWS V1, and the deployed model Can-EWS V2, respectively. Can-EWS V2 outperformed other approaches for specificities, positive predictive values, negative predictive values, and the number of false alarms per day at the same sensitivity level on the held-out test data. Conclusions: We have developed and validated a deep learning-based EWS for cancer patients using the original values and differences between consecutive measurements of basic vital signs. The Can-EWS has acceptable discriminatory power and sensitivity, with extremely decreased false alarms compared with MEWS. [ABSTRACT FROM AUTHOR]

Aim: A medical emergency team (MET) stand‐down decision is the decision to end a MET response and hand responsibility for the patient back to ward staff for ongoing management. Little research has explored this decision. This study aimed to obtain expert consensus on the essential elements required to make optimal MET call stand‐down decisions and the communication required before MET departure. Design: A Delphi design was utilised. Methods: An expert panel of 10 members were recruited based on their expert knowledge and recent clinical MET responder experience in acute hospital settings. Participants were emailed a consent form and an electronic interactive PDF for each survey. Two rounds were conducted with no attrition between rounds. The CREDES guidance on conducting and reporting Delphi studies was used to report this study. Results: Consensus by an expert panel of 10 MET responders generated essential elements of MET stand‐down decisions. Essential elements comprised of two steps: (1) the stand‐down decision that was influenced by both the patient situation and the ward/organisational context; and (2) the communication required before actioning stand‐down. Communication after the decision required both verbal discussions and written documentation to hand over patient responsibility. Specific patient information, a management plan and an escalation plan were considered essential. Conclusion: The Delphi surveys reached consensus on the actions and communication required to stand down a MET call. Passing responsibility back to ward staff after a MET call requires both patient and ward safety assessments, and a clearly articulated patient plan for ward staff. Observation of MET call stand‐down decision‐making is required to validate the essential elements. Implication for the Profession and Patient/or Patient Care: In specifying the essential elements, this study offers clinical and MET staff a process to support the handing over of clinical responsibility from the MET to the ward staff, and clarification of management plans in order to reduce repeat MET calls and improve patient outcomes. Impact: Minimal research has been focussed on the decision to hand responsibility back to ward staff so the MET may leave the ward with safety plan in place. This study provided expert consensus to optimise MET stand‐down decision‐making and the ultimate decision to end a MET call. Communication of agreed patient treatment and escalation plans is recommended before leaving the ward. This study can be used as a checklist for MET responder staff making these decisions and ward staff responsible for post‐MET call care. The aim being to reduce the likelihood of potentially preventable repeat deterioration in the MET patient population. Reporting Method: The CREDES guidance on conducting and reporting Delphi studies. Patient or Public Contribution: None. [ABSTRACT FROM AUTHOR]

Aim(s): To explore vital sign assessment (both complete and incomplete sets of vital signs), and escalation of care per policy and nursing interventions in response to clinical deterioration. Design: This cohort study is a secondary analysis of data from the Prioritising Responses of Nurses To deteriorating patient Observations cluster randomised controlled trial of a facilitation intervention on nurses' vital sign measurement and escalation of care for deteriorating patients. Methods: The study was conducted in 36 wards at four metropolitan hospitals in Victoria, Australia. Medical records of all included patients from the study wards during three randomly selected 24‐h periods within the same week were audited at three time points: pre‐intervention (June 2016), and at 6 (December 2016) and 12 months (June 1017) post‐intervention. Descriptive statistics were used to summarise the study data, and relationships between variables were examined using chi‐square test. Results: A total of 10,383 audits were conducted. At least one vital sign measurement was documented every 8 h in 91.6% of audits, and a complete set of vital signs was documented every 8 h in 83.1% of audits. There were pre‐Medical Emergency Team, Medical Emergency Team or Cardiac Arrest Team triggers in 25.8% of audits. When triggers were present, a rapid response system call occurred in 26.8% of audits. There were 1350 documented nursing interventions in audits with pre‐Medical Emergency Team (n = 2403) or Medical Emergency Team triggers (n = 273). One or more nursing interventions were documented in 29.5% of audits with pre‐Medical Emergency Team triggers and 63.7% of audits with Medical Emergency Team triggers. Conclusion: When rapid response system triggers were documented, there were gaps in escalation of care per policy; however, nurses undertook a range of interventions within their scope of practice in response to clinical deterioration. Relevance to Clinical Practice: Medical and surgical ward nurses in acute care wards frequently engage in vital sign assessment. Interventions by medical and nurgical nurses may occur prior to, or in parallel with calling the rapid response system. Nursing interventions are a key but under‐recognised element of the organisational response to deteriorating patients. Implications for the profession and/or patient care: Nurses engage in a range of nursing interventions to manage deteriorating patients, (aside from rapid response system activation) that are not well understood, nor well described in the literature to date. Impact: This study addresses the gap in the literature regarding nurses' management of deteriorating patients within their scope of practice (aside from RRS activation) in real world settings.When rapid response system triggers were documented, there were gaps in escalation of care per policy; however, nurses undertook a range of interventions within their scope of practice in response to clinical deterioration.The results of this research are relevant to nurses working on medical and surgical wards. Reporting Method: The trial was reported according to the Consolidated Standards of Reporting Trials extension for Cluster Trials recommendations, and this paper is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology Statement. Patient or Public Contribution: No Patient or Public Contribution. [ABSTRACT FROM AUTHOR]

Aim: This cross-sectional study aimed to assess the readiness of international hospitals to implement consensus-based quality metrics for rapid response systems (RRS) and evaluate the feasibility of collecting these metrics. Methods: A digital survey was developed and distributed to hospital administrators and clinicians worldwide. The survey captured data on the recommended quality metrics for RRS and collected information on hospital characteristics. Statistical analysis included descriptive evaluations and comparisons by country and hospital type. Results: A total of 109 hospitals from 11 countries participated in the survey. Most hospitals had some form of RRS in place, with multiple parameter track and trigger systems being commonly used. The survey revealed variations in the adoption of quality metrics among hospitals. Metrics related to patient-activated rapid response and organizational culture were collected less frequently. Geographical differences were observed, with hospitals in Australia and New Zealand demonstrating higher adoption of core quality metrics. Urban hospitals reported a lower number of recorded metrics compared to metropolitan and rural hospitals. Conclusion: The study highlights the feasibility of collecting consensus-based quality metrics for RRS in international hospitals. However, variations in data collection and adoption of specific metrics suggest potential barriers and the need for further exploration. Standardized quality metrics are crucial for effective RRS functioning and continuous improvement in patient care. Collaborative initiatives and further research are needed to overcome barriers, enhance data collection capabilities, and facilitate knowledge sharing among healthcare providers to improve the quality and safety of RRS implementation globally.

Aim: Rapid response systems (RRS) are present in many acute hospitals in western nations but are not widely adopted in Asia. The influence of healthcare culture and the effect of implementing an RRS over time are infrequently reported. We describe the introduction a RRS into a Singaporean hospital and the barriers encountered. The efferent limb activation rates, cardiac arrest rates and unplanned intensive care unit (ICU) admissions are trended over eleven years. Methods: We conducted a retrospective observational study using prospectively collected data derived from administrative and Medical Emergency Team (MET) databases. Results: The RRS used a MET with a single parameter track and trigger and physician led efferent limb. Barriers encountered included clinical leadership buy-in, assembling and equipping the efferent team, maintaining a non-punitive mindset, improving accessibility to MET and communicating the impact of the MET. Over an 11-year period with 488,252 hospital admissions, MET activation rates increased from 1.6/1000 admissions (2009) to 14.1/1000 admissions (2019). Code blue activations and unplanned ICU admission rates decreased from 2.9 to 1.7 and from 8.8 to 2.0/1000 admissions, respectively over the 11 years. There were associations between increasing MET activation rate and reduction in code blue activations (p = 0.013) and unplanned medical ICU admission rates (p = 0.001). Conclusion: Implementing, sustaining and continued improvement of an RRS in Singapore is possible despite challenges encountered. With increasing activation rates over a decade, there were reductions in cardiac arrest rates and unplanned medical ICU admissions.

Aim: To explore general ward nurses' attitudes and perceptions towards recognising and responding to clinical deterioration in a hospital with automated rapid response system activation. Background: There is growing interest in deploying automated clinical deterioration notification systems to reduce delayed or failed recognition and response to clinical deterioration of ward patients. However, little is known about its impact on ward nurses' perspectives and work patterns. Design: A mixed‐methods study. Methods: Online survey of 168 registered nurses and individual interviews with 10 registered nurses in one acute hospital in Singapore. The study adhered to the STROBE checklist for cross‐sectional studies and the COREQ guidelines for qualitative studies. Results: Many nurses (38.1%) rarely performed patient assessments or observations other than vital signs assessment to assess for early signs of clinical deterioration. About 30% were worried about being criticised for calling the primary team doctors. Four themes emerged from the qualitative analysis: automated rapid response system activation as a safety net, being more cautious with vital signs monitoring, the NEWS2 alone is inadequate, and ward nurses as the 'middleman' between the intensive care unit outreach nurse and primary team doctors. Conclusions: Although nurses value the automated rapid response system activation as a safety net to minimise delays in accessing urgent critical care resources, it does not address the sociocultural barriers inherent in escalation of care. Although the automated system led nurses to be more cautious with vital signs monitoring, it does not encourage them to perform comprehensive patient assessments to detect early signs of deterioration. Relevance to clinical practice: Nurse education on assessing for clinical deterioration should focus on the use of broader patient assessment skills other than vital signs. Sociocultural barriers to escalation of care remain a key issue that needs to be addressed by hospital management. No patient or public contribution: No patients, service users, care‐givers or members of the public were involved in the study. [ABSTRACT FROM AUTHOR]

In-hospital clinical deterioration is a major worldwide healthcare burden in the intensive care units (ICUs), as it requires rapid intervention. Rapid response systems (RRSs) are widely used in many hospitals for the early detection of clinical deterioration to prevent cardiac arrest. Recently, with the increasing use of deep learning (DL) and electronic health records (EHR), many DL models have been developed for the intensive care domain, such as prediction of cardiac arrest, sepsis, or transferring to ICU. However, most existing methods do not explicitly learn the structure of multivariate time-series data, and this leads to high false-alarm rates and low sensitivity. In this research, we propose a novel DL-based framework that interpolates high-dimensional sequential data. Our approach combines two graph neural networks with an attention mechanism to learn the complex dependencies among multivariate time series. The experiments were conducted on two datasets: a private clinical dataset collected from Chonnam National University Hospital (CNUH) and a public dataset from the University of Virginia (UV). The experimental results show the potential performance of our model compared to some other related research.

Abstract Background Effective team leadership and good activation criteria can effectively initiate rapid response system (RRS) to reduce hospital mortality and improve quality of life. The first reaction time of nurses plays an important role in the rescue process. To construct a nurse-led (nurse-led RRS) and activation criteria and then to conduct a pragmatic evaluation of the nurse-led RRS. Methods We used literature review and the Delphi method to construct a nurse-led RRS and activation criteria based on the theory of “rapid response system planning.” Then, we conducted a quasi-experimental study to verify the nurse-led RRS. The control group patients were admitted from August to October 2020 and performed traditional rescue procedures. The intervention group patients were admitted from August to October 2021 and implemented nurse-led RRS. The primary outcome was success rate of rescue. Setting Emergency department, Gansu Province, China. Results The nurse-led RRS and activation criteria include 4 level 1 indicators, 14 level 2 indicators, and 88 level 3 indicators. There were 203 patients who met the inclusion criteria to verify the nurse-led RRS. The results showed that success rate of rescue in intervention group (86.55%) was significantly higher than that in control group (66.5%), the rate of cardiac arrest in intervention group (33.61%) was significantly lower than that in control group (72.62%), the effective rescue time of intervention group (46.98 ± 12.01 min) was shorter than that of control group (58.67 ± 13.73 min), and the difference was statistically significant (P 0.05). Conclusions The nurse-led RRS and activation criteria can improve the success rate of rescue, reduce the rate of cardiac arrest, shorten the effective time of rescue, effectively improve the rescue efficiency of patients.

Failure to recognize the deterioration of hospital patients has led to the implementation of a system known as the Rapid Response System. The aim was to explore nurses’ use of the afferent limb of the Rapid Response System to recognize and respond to deteriorating patients. Data were collected via video recordings with observations of 20 registered nurses (RNs) from general wards performing scenarios in a simulation laboratory with focus group interviews. Data were analyzed using systematic text condensation. COREQ were followed. In the first scenario, nurses did not apply ABCDE or ISBAR, and the use of NEWS was insufficient. Completing an education program led to evident improvement in the use of the tools during the second scenario. Nurses initially viewed their new competency as useful, but it was not sustained a year later. Customized education programs and fidelity-scale simulations are suited but not sufficient to change clinical competency without management anchoring. [ABSTRACT FROM AUTHOR]

While timely activation and collaborative teamwork of Rapid Response Teams (RRTs) are crucial to promote a culture of safety and reduce preventable adverse events, these do not always occur. Understanding nurses' perceptions of and experiences with RRTs is important to inform education and policy that improve nurse performance, RRT effectiveness, and patient outcomes. The aim of this study was to explore nurse perceptions of detecting patient deterioration, deciding to initiate RRTs, and experience during and at conclusion of RRTs. A qualitative descriptive study using semi-structured focus group interviews was conducted with 24 nurses in a Chicago area hospital. Interviews were audio-recorded, transcribed verbatim, and coded independently by investigators. Thematic analysis identified and organized patterns of meaning across participants. Several strategies supported trustworthiness. Data revealed five main themes: identification of deterioration, deciding to escalate care, responsiveness of peers/team, communication during rapid responses, and perception of effectiveness. Findings provide insight into developing a work environment supportive of nurse performance and interprofessional collaboration to improve RRT effectiveness. Nurses described challenges in identification of subtle changes in patient deterioration. Delayed RRT activation was primarily related to negative attitudes of responders and stigma. RRT interventions were often considered a temporary fix leading to subsequent RRTs, especially when patients needing a higher level of care were not transferred. Implications include the need for ongoing RRT monitoring and education on several areas such as patient hand-off, RRT activation, nurse empowerment, interprofessional communication, role delineation, and code status discussions. • Timely activation and teamwork, crucial to RRT effectiveness, do not always occur. • Focus group interviews explored nurse experiences with RRTs. • Using RRTs as band-aid approach to address patient deterioration was frustrating. • Nurses can struggle to "paint a clinical picture" of deterioration. • Bedside report played a critical role in identifying subtle patient changes. [ABSTRACT FROM AUTHOR]

Background: Effective team leadership and good activation criteria can effectively initiate rapid response system (RRS) to reduce hospital mortality and improve quality of life. The first reaction time of nurses plays an important role in the rescue process. To construct a nurse-led (nurse-led RRS) and activation criteria and then to conduct a pragmatic evaluation of the nurse-led RRS. Methods: We used literature review and the Delphi method to construct a nurse-led RRS and activation criteria based on the theory of "rapid response system planning." Then, we conducted a quasi-experimental study to verify the nurse-led RRS. The control group patients were admitted from August to October 2020 and performed traditional rescue procedures. The intervention group patients were admitted from August to October 2021 and implemented nurse-led RRS. The primary outcome was success rate of rescue. Setting: Emergency department, Gansu Province, China. Results: The nurse-led RRS and activation criteria include 4 level 1 indicators, 14 level 2 indicators, and 88 level 3 indicators. There were 203 patients who met the inclusion criteria to verify the nurse-led RRS. The results showed that success rate of rescue in intervention group (86.55%) was significantly higher than that in control group (66.5%), the rate of cardiac arrest in intervention group (33.61%) was significantly lower than that in control group (72.62%), the effective rescue time of intervention group (46.98 ± 12.01 min) was shorter than that of control group (58.67 ± 13.73 min), and the difference was statistically significant (P < 0.05). The rate of unplanned ICU admissions in intervention group (42.85%) was lower than that in control group (44.04%), but the difference was not statistically significant (P > 0.05). Conclusions: The nurse-led RRS and activation criteria can improve the success rate of rescue, reduce the rate of cardiac arrest, shorten the effective time of rescue, effectively improve the rescue efficiency of patients. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a prediction model to estimate the risk of Medical Emergency Team (MET) review, within 48 hours of an emergency admission, using information routinely available at the time of hospital admission.Design: Development and validation of a multivariable risk model using prospectively collected data. Transparent Reporting of a multivariable model for Individual Prognosis Or Diagnosis recommendations were followed to develop and report the prediction model.Setting: A 560-bed teaching hospital, with a 22-bed ICU and 24-hour Emergency Department in Melbourne, Australia.Patients: A total of 45,170 emergency admissions of 30,064 adult patients (≥18 yr), with an inpatient length of stay greater than 24 hours, admitted under acute medical or surgical hospital services between 2015 and 2017.Measurements and Main Results: The outcome was MET review within 48 hours of emergency admission. Thirty candidate variables were selected from a routinely collected hospital dataset based on their availability to clinicians at the time of admission. The final model included nine variables: age; comorbid alcohol-related behavioral diagnosis; history of heart failure, chronic obstructive pulmonary disease (COPD), or renal disease; admitted from residential care; Charlson Comorbidity Index score 1 or 2, or 3+; at least one planned and one emergency admission in the last year; and admission diagnosis and one interaction (past history of COPD × admission diagnosis). The discrimination of the model was comparable in the training (C-statistics 0.82; 95% CI, 0.81-0.83) and the validation set (0.81; 0.80-0.83). Calibration was reasonable for training and validation sets.Conclusions: Using only nine predictor variables available to clinicians at the time of admission, the MET-risk model can predict the risk of MET review during the first 48 hours of an emergency admission. Model utility in improving patient outcomes requires further investigation. [ABSTRACT FROM AUTHOR]

While wireless vital sign monitoring is expected to reduce the vital sign measurement time (thus reducing the nursing workload), its impact on the rapid response system is unclear. This study compared the time from vital sign measurement to recording and rapid response system activation between wireless and conventional vital sign monitoring in the general ward, to investigate the impact of wireless vital sign monitoring system on the rapid response system. The study divided 249 patients (age > 18 years; female: 47, male: 202) admitted to the general ward into non-wireless (n = 101) and wireless (n = 148) groups. Intervals from vital sign measurement to recording and from vital sign measurement to rapid response system activation were recorded. Effects of wireless system implementation for vital sign measurement on the nursing workload were surveyed in 30 nurses. The interval from vital sign measurement to recording was significantly shorter in the wireless group than in the non-wireless group (4.3 ± 2.9 vs. 44.7 ± 14.4 min, P < 0.001). The interval from vital sign measurement to rapid response system activation was also significantly lesser in the wireless group than in the non-wireless group (27.5 ± 12.9 vs. 41.8 ± 19.6 min, P = 0.029). The nursing workload related to vital sign measurement significantly decreased from 3 ± 0.87 to 2.4 ± 9.7 (P = 0.021) with wireless system implementation. Wireless vital sign monitoring significantly reduced the time to rapid response system activation by shortening the time required to measure the vital signs. It also significantly reduced the nursing workload. [ABSTRACT FROM AUTHOR]

Background: Intensive care outreach nurses (ICONs) can reduce deterioration and death of patients in hospitals. Purpose: Evaluate outcomes associated with implementation of the ICON role across 4 UAE hospitals. Methods: Trend analyses and χ2 tests were used to measure changes before ICON program, during ICON year 1, ICON year 2, when the service coverage extended 24/7, and until the end of 2019. Results: From year 1 to year 2, failures to escalate decreased from a rate of 14.8 to 5.6 episodes per 1000 admissions for all sites combined (P <.001). The cardiac arrest rate went from 4.04 to 1.42 per 1000 admissions in year 2 and continued downward to 0.72 per 1000 (P <.001). Transfer from ward or readmission to intensive care unit/high dependency unit varied by site, although there was a statistically significant trend for all hospitals combined. Conclusion: The ICON role contributed to fewer failure to escalate incidents and lower cardiac arrest rates. [ABSTRACT FROM AUTHOR]

Purpose: The occurrence of adverse events (AE) in hospitalized patients substancially increases the risk of disability or death, having a major negative clinical and economic impact on public health. For early identification of patients at risk and to establish preventive measures, different healthcare systems have implemented rapid response systems (RRS). The aim of this study was to carry out a cost-effectiveness analysis of implementing a RRS in a tertiary-care hospital. Methods: We included all the patients admitted to Hospital Clínic de Barcelona from 1 to 2016 to 31 December 2016. The cost-effectiveness analysis was summarized as the incremental cost-effectiveness ratio (incremental cost divided by the incremental effectiveness of the two alternatives, RRS versus non-RRS). The effectiveness of the RRS, defined as improvements in health outcomes (AE, cardiopulmonary arrest and mortality), was obtained from the literature and applied to the included patient cohort. A budget impact analysis on the implementation of the RRS from a hospital perspective was performed over a 5-year time horizon. Results: 42,409 patients were included, and 448 (1.05%) had severe AE requiring ICU admission. The cost-effectiveness analysis showed an incremental cost (savings) of EUR − 1,471,101 of RRS versus the non-RRS. The budgetary impact showed a cost reduction of EUR 896,762.00 in the first year and EUR 1,588,579.00 from the second to the fifth year. Conclusions: The present analysis shows the RRS as a dominant, less costly and more effective structure compared to the non-RRS. [ABSTRACT FROM AUTHOR]

Background: Rapid Response Systems are hospital-wide patient-focused systems aiming to improve recognition of acute deterioration in patients and trigger a rapid response aimed at preventing potentially avoidable adverse events such as cardiac arrest and death. In 1994, the Royal Children's Hospital in Brisbane, Australia, was one of the first institutions to adopt a paediatric rapid response system (RRS). The purpose of this study was to investigate the impacts of both introduction of a paediatric RRS and increasing RRS activations (MET dose) on hospital mortality., Methods: Prospectively collected data from institutional databases at a specialist paediatric hospital was used to determine hospital mortality rate pre- and post- implementation of the RRS. An interrupted time series model using segmented regression was utilised to assess the pre-intervention trend, as well as immediate and sustained effects of RRS implementation on hospital mortality. Univariate linear regression examined potential effects of MET dose on mortality., Results: Hospital mortality rate did not change significantly over 15 years before RRS implementation. In the first year after implementation, mortality rate fell significantly (-1.4; 95 %CI -2.27 to -0.52; p = 0.0027). For each year that passed after the intervention, there was no significant change in hospital mortality rate (Estimate: -0.08; 95 %CI -0.17 to 0.02; p = 0.11). Univariate linear regression modelling showed that with every unit increase in MET Dose, hospital mortality rate decreased by -0.13 (95 % CI: -0.27 to 0; p = 0.05)., Conclusions: Utilising data from one of the earliest and longest duration single-centre cohort of paediatric MET events, this study reaffirms the association between implementation of a paediatric RRS and decreased hospital mortality. The study also provides novel evidence of the impact of MET dose on patient outcome in the paediatric population. It is recommended that factors influencing the benefit of rapid response systems in paediatric populations are further identified so that this life saving initiative can be optimised., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Objective: Hospitals use rapid response systems (RRSs) to identify and treat patients whose conditions rapidly worsen after admission. However, no study has examined the effects of RRS deployment on the clinical outcomes of patients undergoing cranial neurosurgery. Thus, the authors investigated whether use of RRS affects clinical outcomes in these patients., Methods: This nationwide, retrospective, population-based cohort study included adult patients who underwent cranial neurosurgery in South Korea between January 1, 2019, and December 31, 2021. The authors classified patients admitted to hospitals that operated RRS into the RRS group and those admitted to hospitals that did not operate RRS into the non-RRS group., Results: Overall, 73,600 hospitalized patients who underwent cranial neurosurgery were included in this study. These patients were divided into 2 groups: 38,544 (52.4%) were included in the RRS group and 35,066 (47.6%) in the non-RRS group. After propensity score matching, 40,058 patients (20,029 in each group) remained. The in-hospital mortality rate was 10.1% (2022/20,029) for the non-RRS group and 8.9% (1792/20,029) for the RRS group. In the logistic regression analysis, the in-hospital mortality rate of the RRS group was 12% (OR 0.88, 95% CI 0.82-0.94, p < 0.001) lower than that of the non-RRS group. The 1-year all-cause mortality rate was 26.5% (5300/20,029) in the non-RRS group and 24.6% (4921/20,029) in the RRS group. In the Cox regression analysis, the 1-year all-cause mortality rate of the RRS group was 9% (HR 0.91, 95% CI 0.88-0.95; p < 0.001) lower than that of the non-RRS group., Conclusions: This population-based cohort study revealed that implementing RRS was associated with enhanced short- and long-term survival outcomes in patients who underwent cranial neurosurgery. The authors' findings indicate that the introduction of RRS can enhance patient survival rates after cranial neurosurgery.

Objective: This study aimed to quantify the workload involved in patient monitoring by vital signs and early warning scores (EWS), and the time spent by a rapid response team locally known as the Patient-at-Risk (PaR) team in responding to deteriorating patients. Methods: The workload involved in the measurement and the documentation of vital signs and EWS was quantified by time and motion study using electronic stopwatch application in 167 complete sets of vital signs observations taken by nursing staff on general hospital wards at Taranaki Base Hospital, New Plymouth, New Zealand. The workload involved in responding to deteriorating patients was measured by the PaR team in real-time and recorded in an electronic logbook specifically designed for this purpose. Dependent variables were studied using analysis of variance (ANOVA), post hoc Tukey, Kruskal Wallis test, Mann-Whitney test and correlation tests. Results: The mean time to measure and record a complete set of vital signs including interruptions was 4:18 (95% CI: 4:07–4:28) minutes. After excluding interruptions, the mean time taken to measure and record a set of vital signs was 3:24 (95% CI: 3:15–3:33) minutes. We found no statistical difference between the observer, location of the patient, staff characteristics or experience and patient characteristics. PaR nurses' mean time to provide rapid response was 47:36 (95% CI: 44:57–50:15) minutes. Significantly more time was spent on patients having severe degrees of deterioration (higher EWS) < 0.001. No statistical difference was observed between ward specialty, and nursing shifts. Conclusions: Patient monitoring and response to deterioration consumed considerable time. Time spent in monitoring was not affected by independent and random factors studied; however, time spent on the response was greater when patients had higher degrees of deterioration.

Aims and objectives: This study investigated clinical staff perceptions of learning about current monitoring practices and the planned introduction of an electronic system for patient monitoring. The aim of this research was to evaluate the perceptions of clinical staff (nurses and doctors) about the perceived strengths and weaknesses of the current state of the rapid response system (RRS) and how those strengths and weakness would be affected by introducing an electronic RRS. Methods: This research applied a descriptive study methodology. Two detailed sessions on demonstration on the electronic RRS for measuring and recording vital signs and the electronic Early Warning System (EWS) were followed by two structured surveys administered through an online portal (SurveyMonkey) for nurses and doctors working at Taranaki District Health Board. The study was planned and conducted between October 2020 and May 2021 at Taranaki Base Hospital, New Plymouth, New Zealand. Results: We found that the perceptions of clinical staff were a combination of key practice issues with current manual monitoring, expectations of improved visibility of vital sign charts, better communication between staff and thus improved patient care with the introduction of an electronic system. A majority (24, 60%) of nurses reported that, when called to assess deteriorating patients, the responders arrive at bedside within 5–30 min and an additional 11 (27%) said the responders arrive within 5 min. That is a collective 87% responder arrival within 30 min Conclusion: Staff believe that an electronic RRS could improve communication, speed up decision making and have a positive impact on patient outcomes.

Early recognition of clinical deterioration plays a pivotal role in a Rapid Response System (RRS), and it has been a crucial step in reducing inpatient morbidity and mortality. Traditional Early Warning Scores (EWS) and Deep Early Warning Scores (DEWS) for identifying patients at risk are still limited because of the challenges of imbalanced multivariate temporal data. Typical issues leading to their limitations are low sensitivity, high late alarm rate, and lack of interpretability; this hinders the system's deployment in clinical settings. This study develops an EWS based on Temporal Variational Autoencoder (TVAE) and a window interval learning framework, which uses the latent space features generated from the input multivariate temporal features, to learn the temporal dependence distribution between the target labels (clinical deterioration probability). Implementing the target information in the Fully Connected Network (FCN) with a loss function assists in addressing the imbalance problem and improving the performance of the time series classification task. The proposed method is validated on an in-house clinical dataset collected from Chonnam National University Hospitals (CNUH) and a public dataset from the University of Virginia (UV). Extensive trials with diverse validation methods and study cases demonstrate that our system outperforms existing methods, showcasing remarkable performance across usual criteria for classification models, typically as the Area Under The Receiver Operating Characteristic Curve (AUROC) and the Area Under The Precision-Recall Curve (AUPRC). TVAE exhibits a notable decrease in late alarm issues across two datasets, presenting stable performance even with a limited number of data samples. [Display omitted] • Predicting a patient's deterioration is an urgent problem for healthcare system. • Medical data characteristics pose challenges for deep learning applications. • Proposed approach boosts early recognition, enhancing patient care. • Extensive trials confirm superior performance over other methods. • Ongoing integration into hospital systems for practical clinical application and enhanced care. [ABSTRACT FROM AUTHOR]

Background: The increased complexity of the nursing care needs of patients and acuity in general wards present nursing care challenges for nurses. Self-led nurses are attentive, taking responsibility for activating the rapid response service when a patient is starting to deteriorate. Objectives: The purpose of this article is to describe nurses’ experiences practising mindfulness during self-leadership in delivering a rapid response system (RRS) in a private hospital in Gauteng. Method: A qualitative, exploratory, descriptive and contextual design was followed. Homogenous purposive sampling was used and a total of eight focus groups were conducted. Focus groups durations were between 45 min and 60 min. The data analysis was carried out through open coding on Atlas.ti using the noticing things, collecting things and thinking about things (NCT) approach. An independent coder met with the researcher during a consensus meeting and finalised the analysis. Results: The findings indicated an underlying theme of nurses being mindful in their self-leadership through the development of self-motivation and self-direction in the RRS. Three categories with six subcategories emerged from the data analysis, namely self-motivation in an RRS by the team, self-direction through role-modelling to peers and training. Conclusion: Nurses practised mindfulness during self-leadership, utilising behavioural and natural reward approaches and constructive thought patterns. These findings could assist management with developing training programmes for nurses.

Aim: Activating a rapid response system (RRS) at general wards requires memorizing trigger criteria, identifying deterioration, and timely notification of abnormalities. We aimed to assess the effect of decision support (DS)-linked RRS activation on management and outcomes.Methods: We retrospectively analyzed general ward RRS activation cases from 2013 to 2017 and the incidence of cardiopulmonary resuscitations (CPR) from 2013 to 2020. A DS-alerting mechanism was added to the conventional RRS activation process in 2017, with an alert window appearing whenever the system automatically detected any verified abnormal vital sign entry, alerting the nurse to take further action. Logistic and linear regression analyses were used to compare outcomes.Results: We analyzed 27,747 activations and 64,592 DS alerts. RRS activations increased from 3.5 to 30.3 per 1,000 patient-days (P < 0.001) after DS implementation. The first DS activations occurred earlier than conventional ones (-2.9 days, 95% confidence interval = -3.6 to -2.1 days). After adjustment with inverse probability of treatment weighting, main (conventional vs DS-linked activations after implementation) and sensitivity analyses showed that DS activation cases had a lower risk of CPR and in-hospital mortality. Cases with more DS alerts before RRS activation had a higher risk of CPR (P trend = 0.017) and in-hospital mortality (P trend < 0.001). The incidence of CPR at the general ward decreased.Conclusion: Implementing a DS mechanism with an automated screening of verified abnormal vital signs linked to RRS activations at general wards was associated with improved practice and timeliness of hospital-wide RRS activations and reduced in-hospital resuscitations and mortality. [ABSTRACT FROM AUTHOR]

We performed FMEA on the existing RRS with the help of routine users of the RRS who acted as subject matter experts and evaluated the failures for their criticality using the Risk Priority Number approach based on their experience of the RRS. The FMEA found 35 potential failure modes and 101 failure mode effects across 13 process steps of the RRS. The afferent limb of RRS was found to be more prone to these failures (62, 61.4%) than the efferent limb of the RRS (39, 38.6%). Modification of calling criteria (12, 11.9%) and calculation of New Zealand Early Warning Scores (NZEWS) calculation (11, 10.9%) steps were found to potentially give rise to the highest number of these failures. Causes of these failures include human error and related factors (35, 34.7%), staff workload/staffing levels (30, 29.7%) and limitations due to paper-based charts and organisational factors (n = 30, 29.7%). The demonstrated electronic system was found to potentially eliminate or reduce the likelihood of 71 (70.2%) failures. The failures not eliminated by the electronic RRS require targeted corrective measures including scenario-based training and education, and revised calling criteria to include triggers for hypothermia and high systolic blood pressure.

Introduction: Early Warning Score (EWS) protocols are based on intermittent vital sign measurements, and aim to detect clinical deterioration in a timely manner. Despite its predictive value, its effectiveness remains suboptimal. An important limitation appears to be poor compliance with the EWS protocol and its variation between general wards. The current research does not yet provide an understanding of EWS compliance and variation in different nursing wards., Aim: To explore the variation in nurses' compliance with the EWS protocol among patients with and without complications and between different nursing wards., Methods: In a retrospective single-center cohort study, all patient files from three nursing wards of a tertiary teaching hospital in the Netherlands were reviewed over a 1-month period. Compliance was divided into three categories:1) calculation accuracy, 2) monitoring frequency end 3) clinical response., Results: The cohort of 210 patients contained 5864 measurements, of which 4125 (70.6 %) included EWS. Significant differences in the measured vital signs within incomplete measurements were found among nursing wards. Compliance to monitoring frequency was higher within EWSs of 0-1 (78.4 %) than within EWSs of ≥2 (26.1 %). The proportion of correct follow-up was significantly higher in patients with complications, as was the correct clinical response to an EWS of ≥3 (84.8 % vs. 55.0; p = .011)., Conclusion: Our results suggest suboptimal compliance with the EWS protocol, with large variations between patients with and without complications and between different general care wards. Nurses tended to be more compliant with the EWS protocol for patients with complications., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background and Objectives: For effective function of the rapid response system (RRS), prompt identification of patients at a high risk of cardiac arrest and RRS activation without hesitation are important. This study aimed to identify clinical factors that increase the risk of intensive care unit (ICU) transfer and cardiac arrest to identify patients who are likely to develop serious conditions requiring ICU management and appropriate RRS activation in Japan. Materials and Methods: We performed a single-center, case control study among patients requiring a rapid response team (RRT) call from 2017 to 2020. We extracted the demographic data, vital parameters, blood oxygen saturation (SpO2 ) and the fraction of inspired oxygen (FiO2 ) from the medical records at the time of RRT call. The patients were divided into two groups to identify clinical signs that correlated with the progression of clinical deterioration. Patient characteristics in the two groups were compared using statistical tests based on the distribution. Receiver operating characteristic (ROC) curve analysis was used to identify the appropriate cut-off values of vital parameters or FiO2 that showed a significant difference between-group. Multivariate logistic regression analysis was used to identify patient factors that were predictive of RRS necessity. Results: We analyzed the data of 65 patients who met our hospital’s RRT call criteria. Among the clinical signs in RRT call criteria, respiratory rate (RR) (p < 0.01) and the needed FiO2 were significantly increased (p < 0.01) in patients with severe disease course. ROC curve analysis revealed RR and needed FiO2 cut-off values of 25.5 breaths/min and 30%. The odds ratio for the progression of clinical deterioration was 40.5 times higher with the combination of RR ≥ 26 breaths/min and needed FiO2 ≥ 30%. Conclusions: The combined use of RR ≥ 26 breaths/min and needed FiO2 ≥ 30% might be valid for identifying patients requiring intensive care management. [ABSTRACT FROM AUTHOR]

This article describes the staged restructure of the rapid response program into a dedicated 24/7 proactive rapid response system in a quaternary academic medical center in the southern United States. Rapid response nurses (RRNs) completed clinical leadership training on artificial intelligence, electronic risk stratification alerts, expert nurse rounding, emergency response, teamwork, closed-loop communication, and outcome measurement. The program goal was to reduce preventable deaths and resuscitation events outside the intensive care unit (ICU). Program outcomes between 2017 and 2019 included a 65% decrease in cardiac arrests outside the ICU, a 27% decrease of cardiac arrests inside the ICU, a 4.7% decrease in patients admitted to the ICU from inpatient beds, and a 27% reduction in the risk-adjusted mortality index for patients with expert proactive rounding encounters. Hospital peer group ranking on the Hospital Survey of Patient Safety improved in the areas of events reported, actions promoting patient safety, and continuous improvement suggesting a positive cultural shift. Implementation of a dedicated 24/7 RRN model of care integrating proactive rounding, technology, and ART can improve outcomes for patients and staff. [ABSTRACT FROM AUTHOR]

Introduction: Even with preoperative screening and medical preparation, elderly hip fracture surgery (HFS) patients may have serious events including cardiopulmonary arrest (CPA) and death during the perioperative period. The rapid response system (RRS) that responds early to changes in the condition of patients is helping to improve the survival rate. To date, there have been no studies on the effectiveness of rapid response system in HFS patients.Methods: In October 2012, our institution, a tertiary referral hospital, implemented an RRS. The system activated in heart rate <40/min or >140/min, systolic blood pressure <80mmHg, respiratory rate <10/min or >30/min, oxygen saturation <90%, sudden change in the level of consciousness, and seizure. We conducted before-and-after study using electronic medical records of patients older than 60 years, who underwent surgery during before implemented period (May 2003 to September 2012) and during after implemented period (October 2013 to December 2018). 1,483 pre-RRS patients and 1,315 post-RRS patients were enrolled in this study. We aimed to evaluate the effect of implementing the RRS on the management of patients undergoing HFS. We analysis 1) interval between the detection of abnormal vital sign and notification to attending physician; 2) interval between the detection of abnormal vital sign and adequate intervention; 3) incidence of CPA, 4) admission to intensive care unit 5) unexpected death; 6) duration of hospital stay; and 7) survival rate.Results: The interval between the detection of abnormal vital sign and notification to attending physician decreased from 23.9(±28.1) minutes to 11.4(±11.02) minutes (p<0.001). The interval between the detection of abnormal vital sign and intervention by the attending physician decreased from 67.3(±40.3) minutes to 15.8(±10.9) minutes (p<0.001). There were no significant differences in unexpected admission to intensive care unit (32/50 versus 20/88, p = 0.213) and in-hospital death (15 versus 16, p = 0.605) between the two groups. The duration of hospitalization decreased from 24.9 days to 15.4 days (p < 0.001). The 5-year survival rate was 57% in the pre-RRS group and 72% in the post-RRS group (hazard ratio = 0.73; 95% confidence interval = 0.61-0.87, p < 0.001).Conclusions: Implementation of RRS rendered early notification and prompt intervention of deteriorating patients undergoing HFS and reduced the duration of hospital stay. [ABSTRACT FROM AUTHOR]