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![Presenting complaint algorithm; chest pain. ECG: Electrocardiography; VAS: Visual Analog Scale. Definitions of the terms used in the figure, e.g. 'ECG changes', 'chest pain of cardiac origin','functional dyspnoea' and 'risk patients' are found in the triage manual [7].](profile/Kai-Lange/publication/223971716/figure/fig3/AS:195362687066122@1423589502505/Presenting-complaint-algorithm-chest-pain-ECG-Electrocardiography-VAS-Visual-Analog.png)
Presenting complaint algorithm; chest pain. ECG: Electrocardiography; VAS: Visual Analog Scale. Definitions of the terms used in the figure, e.g. 'ECG changes', 'chest pain of cardiac origin','functional dyspnoea' and 'risk patients' are found in the triage manual [7].
Source publication
Assessment and treatment of the acutely ill patient have improved by introducing systematic assessment and accelerated protocols for specific patient groups. Triage systems are widely used, but few studies have investigated the ability of the triage systems in predicting outcome in the unselected acute population. The aim of this study was to quant...
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Variability in rapid response system (RRS) characteristics based on the admitted wards is unknown. We aimed to compare differences in the clinical characteristics of RRS activation between patients admitted to medical versus surgical services. We reviewed patients admitted to the hospital who were detected by the RRS from October 2012 to February 2...
Citations
... Approximately 30% of the visitors to EDs are above 65 years old with medical consequences of advanced age, such as the fragility syndrome, multiple comorbidities, and a higher risk of life-threatening conditions [3,4]. This overcrowding in EDs results in an increase in burden on the medical staff, need for resources, boarding time, and time to treatment [5,6]. A crucial organizational need in EDs is the proper allocation of resources to provide medical services to patients on time, adjusted to the urgency of their condition. ...
... The in-hospital medical triage is aimed at the prioritization of patients to determine the time during which physicians should assess them. It facilitates the flow of patients within the ED [1,3,5,6]. The emergency room triage is usually performed by a trained nurse or paramedic who assesses patients' signs and symptoms as well as vital signs. ...
... The emergency room triage is usually performed by a trained nurse or paramedic who assesses patients' signs and symptoms as well as vital signs. Additionally, the in-hospital triage aims to determine the amount and type of resources required for patients as well as allocation of these resources to provide care in time to patients according to their severity [1,3,6]. Experiments were conducted to assess the effect of replacing nurse-performed triage systems with AI-run triage systems [7,8], or by the patients themselves, but results have not been clear [9]. ...
The Emergency Department (ED) is a facility meant to treat patients in need of medical assistance. The choice of triage system hugely impactsed the organization of any given ED and it is important to analyze them for their effectiveness. The goal of this review is to briefly describe selected triage systems in an attempt to find the perfect one. Papers published in PubMed from 1990 to 2022 were reviewed. The following terms were used for comparison: “ED” and “triage system”. The papers contained data on the design and function of the triage system, its validation, and its performance. After studies comparing the distinct means of patient selection were reviewed, they were meant to be classified as either flawed or non-ideal. The validity of all the comparable segregation systems was similar. A possible solution would be to search for a new, measurable parameter for a more accurate risk estimation, which could be a game changer in terms of triage assessment. The dynamic development of artificial intelligence (AI) technologies has recently been observed. The authors of this study believe that the future segregation system should be a combination of the experience and intuition of trained healthcare professionals and modern technology (artificial intelligence).
... Vital signs, including respiration rate, heartbeat rate, blood pressure, body temperature, etc., are a crucial set of indicators for evaluating one's physical state 1 . Abnormal respiration and heartbeat rates are often early indicators of various illnesses or health problems such as lung disease, cardiovascular disorders, and epileptic seizures [2][3][4] . Real-time monitoring of respiration and heartbeat can facilitate early intervention, help individuals adjust their lifestyle, and improve diet and exercise habits, which are important for the health of modern humans and are receiving increasing attention. ...
Real-time vital sign monitoring is gaining immense significance not only in the medical field but also in personal health management. Facing the needs of different application scenarios of the smart and healthy city in the future, the low-cost, large-scale, scalable, and distributed vital sign monitoring system is of great significance. In this work, a seamlessly integrated contact and contactless vital sign monitoring system, which can simultaneously implement respiration and heartbeat monitoring, is proposed. In contact vital sign monitoring, the chest wall movement due to respiration and heartbeat is translated into changes in the optical output intensity of a fiber Bragg grating (FBG). The FBG is also an important part of radar signal generation for contactless vital sign monitoring, in which the chest wall movement is translated into phase changes of the radar de-chirped signal. By analyzing the intensity of the FBG output and phase of the radar de-chirped signal, real-time respiration and heartbeat monitoring are realized. In addition, due to the distributed structure of the system and its good integration with the wavelength-division multiplexing optical network, it can be massively scaled by employing more wavelengths. A proof-of-concept experiment is carried out. Contact and contactless respiration and heartbeat monitoring of three people are simultaneously realized. During a monitoring time of 60 s, the maximum absolute measurement errors of respiration and heartbeat rates are 1.6 respirations per minute and 2.3 beats per minute, respectively. The measurement error does not have an obvious change even when the monitoring time is decreased to 5 s.
... 44 Changes in vital signs have been shown to precede a serious adverse event by several hours, with studies demonstrating that the presence of abnormal vital signs during ED triage is strongly predictive of in-hospital mortality and ICU admission. 44,45 The inclusion of age and vital signs in SERP+ and SERP further reinforces the significance of these variables in mortality prediction. Additionally, SERP+-7d and SERP+-30d selected cancer history as a variable for mortality prediction. ...
Objective
The Score for Emergency Risk Prediction (SERP) is a novel mortality risk prediction score which leverages machine learning in supporting triage decisions. In its derivation study, SERP-2d, SERP-7d and SERP-30d demonstrated good predictive performance for 2-day, 7-day and 30-day mortality. However, the dataset used had significant class imbalance. This study aimed to determine if addressing class imbalance can improve SERP's performance, ultimately improving triage accuracy.
Methods
The Singapore General Hospital (SGH) emergency department (ED) dataset was used, which contains 1,833,908 ED records between 2008 and 2020. Records between 2008 and 2017 were randomly split into a training set (80%) and validation set (20%). The 2019 and 2020 records were used as test sets. To address class imbalance, we used random oversampling and random undersampling in the AutoScore-Imbalance framework to develop SERP+-2d, SERP+-7d, and SERP+-30d scores. The performance of SERP+, SERP, and the commonly used triage risk scores was compared.
Results
The developed SERP+ scores had five to six variables. The AUC of SERP+ scores (0.874 to 0.905) was higher than that of the corresponding SERP scores (0.859 to 0.894) on both test sets. This superior performance was statistically significant for SERP+-7d (2019: Z = −5.843, p < 0.001, 2020: Z = −4.548, p < 0.001) and SERP+-30d (2019: Z = −3.063, p = 0.002, 2020: Z = −3.256, p = 0.001). SERP+ outperformed SERP marginally on sensitivity, specificity, balanced accuracy, and positive predictive value measures. Negative predictive value was the same for SERP+ and SERP. Additionally, SERP+ showed better performance compared to the commonly used triage risk scores.
Conclusions
Accounting for class imbalance during training improved score performance for SERP+. Better stratification of even a small number of patients can be meaningful in the context of the ED triage. Our findings reiterate the potential of machine learning-based scores like SERP+ in supporting accurate, data-driven triage decisions at the ED.
... Barford et al. examined ED colour-coded triage categories for presenting complaints and vital signs to assess the need for ICU care. 12 However, they also used an established ED triage system that was not explicitly designed for intensive care. Given the diversity and variability of ICU patients, Blackwell et al. concluded that existing models are not ideal for day-to-day practice. ...
... Moreover, this study revealed that those patients who had a respiratory rate less than 12 breaths per minute had 2.7 times higher hazard of death as compared with those who had a respiratory rate of 12-20 breaths per minute. This is supported by the studies done in Denmark and Sweden [37,38]. The possible reason for this might be attributed to the decreasing respiratory rate, which is an indicator of brain dysfunction from many causes, like structural or non-structural processes affecting the central nervous system, which leads to death [37,38]. ...
... This is supported by the studies done in Denmark and Sweden [37,38]. The possible reason for this might be attributed to the decreasing respiratory rate, which is an indicator of brain dysfunction from many causes, like structural or non-structural processes affecting the central nervous system, which leads to death [37,38]. Therefore, measuring the respiratory rate is important for early identification of high-risk patients because the respiratory rate is a superior indicator to other physiologic parameters. ...
Introduction
Mechanical ventilation is the most common intervention for patients with respiratory failure in the intensive care unit. There is limited data from African countries, including Ethiopia on time to death and its predictors among patients on mechanical ventilators. Therefore, this study aimed to assess time to death and its predictors among adult patients on mechanical ventilation admitted in comprehensive specialized hospitals in West Amhara, Ethiopia.
Methods
An institutional-based retrospective follow-up study was conducted from January 1, 2020, to December 31, 2022. A simple random sampling was used to select a total of 391 patients’ charts. Data were collected using data the extraction tool, entered into Epi-data version 4.6.0, and exported to STATA version 14 for analysis. Kaplan–Meier failure curve and the log-rank test were fitted to explore the survival difference among groups. The Cox regression model was fitted, and variables with a p-value < 0.25 in the bivariable Cox regression were candidates for the multivariable analysis. In the multivariable Cox proportional hazard regression, an adjusted hazard ratio with 95% confidence intervals were reported to declare the strength of association between mortality and predictors when a p value is < 0.05.
Results
A total of 391 mechanically ventilated patients were followed for 4098 days at risk. The overall mortality of patients on mechanical ventilation admitted to the intensive care units was 62.2%, with a median time to death of 16 days (95% CI: 11, 22). Those patients who underwent tracheostomy procedure (AHR = 0.40, 95% CI: 0.20, 0.80), received cardio-pulmonary resuscitation (AHR = 8.78, 95% CI: 5.38, 14.35), being hypotensive (AHR = 2.96, 95% CI: 1.11, 7.87), and had a respiratory rate less than 12 (AHR = 2.74, 95% CI: 1.48, 5.07) were statistically significant predictors of time to death among mechanically ventilated patients.
Conclusion
The mortality rate of patients on mechanical ventilation was found to be high and the time to death was short. Being cardiopulmonary resuscitated, hypotensive, and had lower respiratory rate were significant predictors of time to death, whereas patients who underwent tracheostomy was negatively associated with time to death. Tracheostomy is needed for patients who received longer mechanical ventilation, and healthcare providers should give a special attention for patients who are cardiopulmonary resuscitated, hypotensive, and have lower respiratory rate.
... Vital signs were categorized as normal or abnormal. The normal ranges for vital signs were systolic blood pressure, 100-150 mmHg; diastolic blood pressure, 60-90 mmHg; pulse rate, 50-100 bpm; respiratory rate, 12-20 breaths/min; body temperature, 36-37.5 • C; SpO2, 95%-100% [43][44][45]. The visit time was categorized in three groups based on shift changes for the nurses: 07:00 to 14:59, 15:00 to 22:59, and 23:00 to 06:59. ...
Background and aims:
This study developed a clinical support system based on federated learning to predict the need for a revised Korea Triage Acuity Scale (KTAS) to facilitate triage.
Methods:
This was a retrospective study that used data from 11,952,887 patients in the Korean National Emergency Department Information System (NEDIS) from 2016 to 2018 for model development. Separate cohorts were created based on the emergency medical center level in the NEDIS: regional emergency medical center (REMC), local emergency medical center (LEMC), and local emergency medical institution (LEMI). External and temporal validation used data from emergency department (ED) of the study site from 2019 to 2021. Patient features obtained during the triage process and the initial KTAS scores were used to develop the prediction model. Federated learning was used to rectify the disparity in data quality between EDs. The patient's demographic information, vital signs in triage, mental status, arrival information, and initial KTAS were included in the input feature.
Results:
3,626,154 patients' visits were included in the regional emergency medical center cohort; 8,278,081 patients' visits were included in the local emergency medical center cohort; and 48,652 patients' visits were included in the local emergency medical institution cohort. The study site cohort, which is used for external and temporal validation, included 135,780 patients visits. Among the patients in the REMC and study site cohorts, KTAS level 3 patients accounted for the highest proportion at 42.4% and 45.1%, respectively, whereas in the LEMC and LEMI cohorts, KTAS level 4 patients accounted for the highest proportion. The area under the receiver operating characteristic curve for the prediction model was 0.786, 0.750, and 0.770 in the external and temporal validation. Patients with revised KTAS scores had a higher admission rate and ED mortality rate than those with unaltered KTAS scores.
Conclusions:
This novel system might accurately predict the likelihood of KTAS acuity revision and support clinician-based triage.
... In-hospital patient outcome prediction is a major research area at the intersection of machine learning and medicine [Barfod et al., 2012, Taylor et al., 2016, Brajer et al., 2020, Naemi et al., 2021, Soffer et al., 2021, Wiesenfeld et al., 2022]. An important application of such models is 'early' risk prediction -for example, using risk scores for triage [Raita et al., 2019, Klug et al., 2020. ...
... There is a large and growing literature at the intersection of machine learning on predicting patient in-hospital risk [Barfod et al., 2012, Taylor et al., 2016, Brajer et al., 2020, Naemi et al., 2021, Soffer et al., 2021, Wiesenfeld et al., 2022, deterioration [Brekke et al., 2019, Escobar et al., 2020, Gerry et al., 2020, and post-discharge re-admission [Jamei et al., 2017, Mahmoudi et al., 2020. A large fraction of this literature focuses on feature selection Brekke et al. ...
We consider a patient risk models which has access to patient features such as vital signs, lab values, and prior history but does not have access to a patient's diagnosis. For example, this occurs in a model deployed at intake time for triage purposes. We show that such `all-cause' risk models have good generalization across diagnoses but have a predictable failure mode. When the same lab/vital/history profiles can result from diagnoses with different risk profiles (e.g. E.coli vs. MRSA) the risk estimate is a probability weighted average of these two profiles. This leads to an under-estimation of risk for rare but highly risky diagnoses. We propose a fix for this problem by explicitly modeling the uncertainty in risk prediction coming from uncertainty in patient diagnoses. This gives practitioners an interpretable way to understand patient risk beyond a single risk number.
... La supervivencia fue mayor en los pacientes ingresados a la UCI, concluyendo que puede existir una ventana de oportunidad crítica que se pierde si el acceso a la UCI es inoportuno 113 . En consonancia con lo anterior, las guías actuales de la Sociedad Americana de Cuidado Crítico recomiendan que los pacientes ingresados en la UCI cumplan criterios específicos en su condición grave y en la necesidad de soportes avanzados 114 . En general, para las decisiones se ha demostrado que un servicio de clasificación de la UCI dirigido por un intensivista tiene un impacto favorable en los tiempos de espera de admisión, duración y alta en la UCI durante las operaciones normales o en situaciones de emergencia 114,115 . ...
... En consonancia con lo anterior, las guías actuales de la Sociedad Americana de Cuidado Crítico recomiendan que los pacientes ingresados en la UCI cumplan criterios específicos en su condición grave y en la necesidad de soportes avanzados 114 . En general, para las decisiones se ha demostrado que un servicio de clasificación de la UCI dirigido por un intensivista tiene un impacto favorable en los tiempos de espera de admisión, duración y alta en la UCI durante las operaciones normales o en situaciones de emergencia 114,115 . ...
... La supervivencia fue mayor en los pacientes ingresados a la UCI, concluyendo que puede existir una ventana de oportunidad crítica que se pierde si el acceso a la UCI es inoportuno 113 . En consonancia con lo anterior, las guías actuales de la Sociedad Americana de Cuidado Crítico recomiendan que los pacientes ingresados en la UCI cumplan criterios específicos en su condición grave y en la necesidad de soportes avanzados 114 . En general, para las decisiones se ha demostrado que un servicio de clasificación de la UCI dirigido por un intensivista tiene un impacto favorable en los tiempos de espera de admisión, duración y alta en la UCI durante las operaciones normales o en situaciones de emergencia 114,115 . ...
... En consonancia con lo anterior, las guías actuales de la Sociedad Americana de Cuidado Crítico recomiendan que los pacientes ingresados en la UCI cumplan criterios específicos en su condición grave y en la necesidad de soportes avanzados 114 . En general, para las decisiones se ha demostrado que un servicio de clasificación de la UCI dirigido por un intensivista tiene un impacto favorable en los tiempos de espera de admisión, duración y alta en la UCI durante las operaciones normales o en situaciones de emergencia 114,115 . ...
t It is important to standardize the admission processes to the Intensive Care Units (ICU), and the practices established by consensus determine quality interventions that can enhance specific contexts. ICU are finite, high-cost services that require policies to ensure rational use and to provide quality care to patients. In response to the variability of ICU admission criteria in the country, the Colombian Association of Critical Medicine and Intensive Care (AMCI) convened a multidisciplinary team of experts in critical medicine to establish a scientific statement using the formal consensus methodology, mainly by the Delphi method, about the recommendations and practices that allow to homogenize the criteria for admission to ICU in Colombia. As part of the mission of the AMCI, it is intended to have a positive impact on the different levels of the health system, providers, administrators, insurers and government and that in the end it will be reflected in benefits for critically ill or at-risk patients. The consensus invites all ICU in the country to select their own criteria taking into account the list of recommendations it contains; it is clarified that the contents are generated in a scientific, academic and non-commercial context. Each health institution must be a guarantor, through the care coordination of the ICUs, of the responsible use of these criteria both for the safe and quality care of patients and to use them for the different commercial relationships established with the administrators of the regimens of health. This document has a national scope and its content is expected to be updated in no more than 4 years.
... Early recognition of clinical deterioration followed by appropriate and timely treatment reduces in-hospital mortality, unplanned intensive care unit (ICU) admissions, treatment costs and unexpected cardiac arrests [1][2][3][4]. Patients who experience such adverse events often exhibit abnormal physiological signs prior to the event [5,6]. As many as 25 % of Emergency Department (ED) patients have at least one or more abnormal vital signs and between 1.5 % and 23 % experience clinical deterioration meeting hospital rapid response team (RRT) activation criteria [7]. ...
Aim:
To determine the impact implementation of Emergency Department Clinical Emergency Response System (EDCERS) on inpatient deterioration events and identify contributing causal factors.
Methods:
EDCERS was implemented in an Australian regional hospital, integrating a single parameter track and trigger criteria for escalation of care, and emergency, specialty and critical care clinician response to patient deterioration. In this controlled pre-post study, electronic medical records of patients who experienced a deterioration event (rapid response call, cardiac arrest or unplanned intensive care admission) on the ward within 72 h of admission from the emergency department (ED) were reviewed. Causal factors contributing to the deteriorating event were assessed using a validated human factors framework.
Results:
Implementation of EDCERS reduced the number of inpatient deterioration events within 72 h of emergency admission with failure or delayed response to ED patient deterioration as a causal factor. There was no change in the overall rate of inpatient deterioration events.
Conclusion:
This study supports wider implementation of rapid response systems in the ED to improve management of deteriorating patients. Tailored implementation strategies should be used to achieve successful and sustainable uptake of ED rapid response systems and improve outcomes in deteriorating patients.
