Rapid response system in Japanese outpatient departments based on online registry: Multicentre observational study

Machine learning algorithm for predicting 30-day mortality in patients receiving rapid response system activation: A retrospective nationwide cohort study

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.

Nurse-led medical emergency response reduces code blue team activations in non-hospitalized patients

Objective

We describe the creation of a two-tier emergency response system with a nurse-led first responder program titled "MET-RN" (Medical Emergency Team-Registered Nurse) created for ambulatory settings supported by a critical care code blue team for escalation of care. This observational study evaluated the clinical characteristics and effects of a MET-RN program on the code blue response.

Methods

A retrospective review of the MET-RN response data was assessed from January 2016 to June 2021. Data collected included time of call, call location, patient comorbidities, triage category (minor, urgent, or emergent), activation trigger, interventions performed, duration of the event, and patient disposition. In instances where the patient was admitted to the hospital, the discharge diagnosis and emergency department (ED) triage score were collected. Differences were tested using analysis of variance (ANOVA) F-tests, with Tukey post-hoc testing where applicable.

Results

MET-RN responded to 6,564 encounters from January 2016 to June 2021. The most frequent trigger call was dizziness/lightheadedness, with a prevalence of 12.0%. 33.9% of the patients seen by MET-RN were transported to the ED for further evaluation. Establishing a MET-RN system led to an estimated median of 58.3% reduction in utilization of the code blue team per quarter.

Conclusion

The creation of MET-RN first responder system enabled the ambulatory areas to receive minor, urgent, and emergent patient care support, leading to a decrease in utilization of the code blue team for the hospital. A two-tiered response system resulted in an improved allocation of hospital resources and kept critical care teams in high-acuity areas while maintaining patient safety.

Rapid Response Team Activations in Oncologic Ambulatory Sites: Characteristics, Interventions, and Outcomes

PURPOSE

Patients with cancer are vulnerable to clinical deterioration. Rapid response teams (RRTs) identify and manage patients with acute changes in clinical status. Although RRTs have been well studied in the hospital setting, there are limited data on patients who require support in the ambulatory or outpatient oncologic settings. Describe baseline characteristics, reasons for activations, interventions, and outcomes of ambulatory oncologic patients receiving RRT activation in a tertiary cancer center.

METHODS

We conducted a retrospective review of adult (age ≥ 18 years) patients requiring RRT activation at multiple ambulatory sites between July 2020 and June 2021. Demographic and clinical data captured include age, sex, race, ethnicity, do not resuscitate status, vital signs, receipt of active cancer treatment within 30 days, and cancer type. Using Kaplan-Meier survival analysis and multivariable Cox proportion hazard ratio regression models, outcomes of 90-day mortality and hospitalization were assessed.

RESULTS

There were 322 RRT activations among 427,734 visits to 10 ambulatory sites (0.75 RRTs/1,000 visits). The most frequent reasons were syncope (25.2%), fall (24.5%), and adverse reaction to cancer therapy or intravenous contrast (16.5%). One hundred thirty-seven (42.5%) required transfer to an emergency department, of which 81 (59.1%) required hospital admission. At 90 days, 51 (15.8%) had died, with 44 (86.3%) receiving comfort measures. Kaplan-Meier survival analysis and multivariable Cox proportional hazard ratio regression showed that heart rate > 100 at RRT presentation and hospitalization after a RRT event were significantly associated with 90-day mortality.

CONCLUSION

Although uncommon, patients with cancer undergoing care at ambulatory sites can suffer acute clinical deterioration needing RRT review. The rates of hospitalization and mortality among such patients are high, suggesting the need for improved end-of-life care.

Emergency and rapid response systems: a bibliometric analysis

Background

The emergency rapid response system (RRS) can reduce the mortality of hospitalized patients, and its core is the activation criteria and the rapid response team (RRT). This study adopted a bibliometric method to analyze the research status of RRSs for hospitalized patients.

Methods

The Science Citation Index Expanded (SCI-E) database was searched using the keywords "emergency" and "rapid response system", and the search results were analyzed using CiteSpace software. The retrieved data included the annual distribution of studies and literature citations; the source country of the literature; the distribution of institutions and authors of the literature; the cooperation between countries, institutions, and authors; the distribution of journals that published the literature, and the use of keywords in the literature.

Results

A total of 1,320 research papers were found, with a total of 29,920 citations. The number of papers and their citations increased yearly. The top 5 countries in terms of number of publications were the United States, Australia, China, the United Kingdom, and Canada. The top 5 countries in terms of centrality were the United States, the United Kingdom, Argentina, the Czech Republic, and Switzerland. The research institutions were mainly located in developed countries, such as the United States and Australia. There was relatively little collaboration between researchers. The journals that published the literature mainly specialized in critical care medicine and emergency medicine. The keyword analysis revealed that most studies focused on medical emergency teams (METs) and mortality.

Conclusions

There were few studies related to the emergency RRS for hospitalized patients. The majority of studies were from developed countries and mainly focused on the impact of team building and the effect of the RRS on mortality.