The ongoing coronavirus disease 2019 (COVID-19) pandemic presents serious public health threats. Omicron, the current most prevalent strain of COVID-19, has a low fatality rate and very high transmissibility, so the number of patients with mild symptoms of COVID-19 is rapidly increasing. This change of pandemic challenges medical systems worldwide in many aspects, including sharp increases in demands for hospital infrastructure, critical shortages in medical equipment, and medical staff. Predicting deterioration in mild patients could alleviate these problems. A novel scoring system was proposed for predicting the deterioration of patients whose condition may worsen rapidly and those who all still mild or asymptomatic. Retrospective cohorts of 954 and 2,035 patients that quarantined in the Residential Treatment Center were assembled for derivation and external validation of mild COVID-19, respectively. Deterioration was defined as transfer to a local hospital due to worsening condition of the patients during the 2-week isolation period. A total of 15 variables: sex, age, seven pre-existing conditions (diabetes, hypertension, cardiovascular disease, respiratory disease, liver disease, kidney disease, and organ transplant), and five vital signs (systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), body temperature, and oxygen saturation (SpO2)) were collected. A scoring system was developed using seven variables (age, pulse rate, SpO2, SBP, DBP, temperature, and hypertension) with significant differences between the transfer and not transfer groups in logistic regression. The proposed system was compared with existing scoring systems that assess the severity of patient conditions. The performance of the proposed scoring system to predict deterioration in patients with mild COVID-19 showed an area under the receiver operating characteristic (AUC) of 0.868. This is a statistically significant improvement compared to the performance of the previous patient condition assessment scoring systems. During external validation, the proposed system showed the best and most robust predictive performance (AUC = 0.768; accuracy = 0.899). In conclusion, we proposed a novel scoring system for predicting patients with mild COVID-19 who will experience deterioration which could predict the deterioration of the patient\'s condition early with high predictive performance. Furthermore, because the scoring system does not require special calculations, it can be easily measured to predict the deterioration of a patients\' condition. This system can be used as effective tool for early detection of deterioration in mild COVID-19 patients.

OBJECTIVE: This study explores the impact of simulation-based training on nursing students\' readiness to respond to patient deterioration in clinical practice.
BACKGROUND: With limited in-hospital training opportunities, nursing educators seek alternative strategies to teach clinical skills. Simulation-based training offers a controlled environment for skill development and refinement.
METHODS: A narrative research design was used to examine the experiences of senior undergraduate nursing students (n = 12) at a private university in the Western United States.
METHODS: Data were collected through semi-structured interviews conducted via Zoom.
RESULTS: The study identified three key themes: the application of simulation experiences to real-world settings, the aspects of simulation training valued by students and the role of simulation in emotional regulation and coping. The findings highlight the importance of simulation training in preparing nursing students for clinical emergencies.
CONCLUSIONS: Simulation training enhances clinical judgment and emotional resilience in nursing students, equipping them to handle emergent patient care. Integrating simulation into nursing curricula prepares students for clinical roles and nurse educators can enhance this by creating realistic clinical challenges in simulations.

BACKGROUND: Early warning score systems are widely used for identifying patients who are at the highest risk of deterioration to assist clinical decision-making. This could facilitate early intervention and consequently improve patient outcomes; for example, the National Early Warning Score (NEWS) system, which is recommended by the Royal College of Physicians in the United Kingdom, uses predefined alerting thresholds to assign scores to patients based on their vital signs. However, there is limited evidence of the reliability of such scores across patient cohorts in the United Arab Emirates.
OBJECTIVE: Our aim in this study was to propose a data-driven model that accurately predicts in-hospital deterioration in an inpatient cohort in the United Arab Emirates.
METHODS: We conducted a retrospective cohort study using a real-world data set that consisted of 16,901 unique patients associated with 26,073 inpatient emergency encounters and 951,591 observation sets collected between April 2015 and August 2021 at a large multispecialty hospital in Abu Dhabi, United Arab Emirates. The observation sets included routine measurements of heart rate, respiratory rate, systolic blood pressure, level of consciousness, temperature, and oxygen saturation, as well as whether the patient was receiving supplementary oxygen. We divided the data set of 16,901 unique patients into training, validation, and test sets consisting of 11,830 (70%; 18,319/26,073, 70.26% emergency encounters), 3397 (20.1%; 5206/26,073, 19.97% emergency encounters), and 1674 (9.9%; 2548/26,073, 9.77% emergency encounters) patients, respectively. We defined an adverse event as the occurrence of admission to the intensive care unit, mortality, or both if the patient was admitted to the intensive care unit first. On the basis of 7 routine vital signs measurements, we assessed the performance of the NEWS system in detecting deterioration within 24 hours using the area under the receiver operating characteristic curve (AUROC). We also developed and evaluated several machine learning models, including logistic regression, a gradient-boosting model, and a feed-forward neural network.
RESULTS: In a holdout test set of 2548 encounters with 95,755 observation sets, the NEWS system achieved an overall AUROC value of 0.682 (95% CI 0.673-0.690). In comparison, the best-performing machine learning models, which were the gradient-boosting model and the neural network, achieved AUROC values of 0.778 (95% CI 0.770-0.785) and 0.756 (95% CI 0.749-0.764), respectively. Our interpretability results highlight the importance of temperature and respiratory rate in predicting patient deterioration.
CONCLUSIONS: Although traditional early warning score systems are the dominant form of deterioration prediction models in clinical practice today, we strongly recommend the development and use of cohort-specific machine learning models as an alternative. This is especially important in external patient cohorts that were unseen during model development.

BACKGROUND: Early recognition and response to clinical deterioration reduce the frequency of in-hospital cardiac arrests, mortality, and unplanned intensive care unit (ICU) admissions. This study aimed to investigate the impact of the Prioritising Responses Of Nurses To deteriorating patient Observations (PRONTO) intervention on hospital costs and patient length of stay (LOS).
METHODS: The PRONTO cluster randomised control trial was conducted to improve nurses\' responses to patients with abnormal vital signs. Hospital data were collected pre-intervention (T0) at 6 months (T1) and 12 months (T2) post-intervention. The economic evaluation involved a cost-consequence analysis from the hospital\'s perspective. Generalised estimating equations were used to estimate the parameters for regression models of the difference in costs and LOS between study groups and time points.
RESULTS: Hospital admission data for 6065 patients (intervention group, 3102; control group, 2963) were collected from four hospitals for T0, T1 and T2. The intervention cost was 69.61 A$ per admitted patient, including the additional intervention training for nurses and associated labour costs. The results showed cost savings and a shorter LOS in the intervention group between T0 - T1 and T0 - T2 (cost differences T0 - T1: -364 (95% CI -3,782; 3049) A$ and T0 - T2: -1,710 (95% CI -5,162; 1,742) A$; and LOS differences T0 - T1: -1.10 (95% CI -2.44; 0.24) days and T0 & T2: -2.18 (95% CI -3.53; -0.82) days).
CONCLUSIONS: The results of the economic analysis demonstrated that the PRONTO intervention improved nurses\' responses to patients with abnormal vital signs and significantly reduced hospital LOS by two days at 12 months in the intervention group compared to baseline. From the hospital\'s perspective, savings from reduced hospitalisations offset the costs of implementing PRONTO.

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UNASSIGNED: Although early detection of patients\' deterioration may improve outcomes, most of the detection criteria use on-the-spot values of vital signs. We investigated whether adding trend values over time enhanced the ability to predict adverse events among hospitalized patients.
UNASSIGNED: Patients who experienced adverse events, such as unexpected cardiac arrest or unplanned ICU admission were enrolled in this retrospective study. The association between the events and the combination of vital signs was evaluated at the time of the worst vital signs 0-8 hours before events (near the event) and at 24-48 hours before events (baseline). Multivariable logistic analysis was performed, and the area under the receiver operating characteristic curve (AUC) was used to assess the prediction power for adverse events among various combinations of vital sign parameters.
UNASSIGNED: Among 24,509 in-patients, 54 patients experienced adverse events(cases) and 3,116 control patients eligible for data analysis were included. At the timepoint near the event, systolic blood pressure (SBP) was lower, heart rate (HR) and respiratory rate (RR) were higher in the case group, and this tendency was also observed at baseline. The AUC for event occurrence with reference to SBP, HR, and RR was lower when evaluated at baseline than at the timepoint near the event (0.85 [95%CI: 0.79-0.92] vs. 0.93 [0.88-0.97]). When the trend in RR was added to the formula constructed of baseline values of SBP, HR, and RR, the AUC increased to 0.92 [0.87-0.97].
UNASSIGNED: Trends in RR may enhance the accuracy of predicting adverse events in hospitalized patients.

BACKGROUND: In low and middle-income countries like Kenya, critical care facilities are limited, meaning acutely ill patients are managed in the general wards. Nurses in these wards are expected to detect and respond to patient deterioration to prevent cardiac arrest or death. This study examined nurses\' vital signs documentation practices during clinical deterioration and explored factors influencing their ability to detect and respond to deterioration.
METHODS: This convergent parallel mixed methods study was conducted in the general medical and surgical wards of three hospitals in Kenya\'s coastal region. Quantitative data on the extent to which the nurses monitored and documented the vital signs 24 h before a cardiac arrest (death) occurred was retrieved from patients\' medical records. In-depth, semi-structured interviews were conducted with twenty-four purposefully drawn registered nurses working in the three hospitals\' adult medical and surgical wards.
RESULTS: This study reviewed 405 patient records and found most of the documentation of the vital signs was done in the nursing notes and not the vital signs observation chart. During the 24 h prior to death, respiratory rate was documented the least in only 1.2% of the records. Only a very small percentage of patients had any vital event documented for all six-time points, i.e. four hourly. Thematic analysis of the interview data identified five broad themes related to detecting and responding promptly to deterioration. These were insufficient monitoring of vital signs linked to limited availability of equipment and supplies, staffing conditions and workload, lack of training and guidelines, and communication and teamwork constraints among healthcare workers.
CONCLUSIONS: The study showed that nurses did not consistently monitor and record vital signs in the general wards. They also worked in suboptimal ward environments that do not support their ability to promptly detect and respond to clinical deterioration. The findings illustrate the importance of implementation of standardised systems for patient assessment and alert mechanisms for deterioration response. Furthermore, creating a supportive work environment is imperative in empowering nurses to identify and respond to patient deterioration. Addressing these issues is not only beneficial for the nurses but, more importantly, for the well-being of the patients they serve.

BACKGROUND: Few studies have used standardized nursing records with Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) to identify predictors of clinical deterioration.
OBJECTIVE: This study aims to standardize the nursing documentation records of patients with COVID-19 using SNOMED CT and identify predictive factors of clinical deterioration in patients with COVID-19 via standardized nursing records.
METHODS: In this study, 57,558 nursing statements from 226 patients with COVID-19 were analyzed. Among these, 45,852 statements were from 207 patients in the stable (control) group and 11,706 from 19 patients in the exacerbated (case) group who were transferred to the intensive care unit within 7 days. The data were collected between December 2019 and June 2022. These nursing statements were standardized using the SNOMED CT International Edition released on November 30, 2022. The 260 unique nursing statements that accounted for the top 90% of 57,558 statements were selected as the mapping source and mapped into SNOMED CT concepts based on their meaning by 2 experts with more than 5 years of SNOMED CT mapping experience. To identify the main features of nursing statements associated with the exacerbation of patient condition, random forest algorithms were used, and optimal hyperparameters were selected for nursing problems or outcomes and nursing procedure-related statements. Additionally, logistic regression analysis was conducted to identify features that determine clinical deterioration in patients with COVID-19.
RESULTS: All nursing statements were semantically mapped to SNOMED CT concepts for \"clinical finding,\" \"situation with explicit context,\" and \"procedure\" hierarchies. The interrater reliability of the mapping results was 87.7%. The most important features calculated by random forest were \"oxygen saturation below reference range,\" \"dyspnea,\" \"tachypnea,\" and \"cough\" in \"clinical finding,\" and \"oxygen therapy,\" \"pulse oximetry monitoring,\" \"temperature taking,\" \"notification of physician,\" and \"education about isolation for infection control\" in \"procedure.\" Among these, \"dyspnea\" and \"inadequate food diet\" in \"clinical finding\" increased clinical deterioration risk (dyspnea: odds ratio [OR] 5.99, 95% CI 2.25-20.29; inadequate food diet: OR 10.0, 95% CI 2.71-40.84), and \"oxygen therapy\" and \"notification of physician\" in \"procedure\" also increased the risk of clinical deterioration in patients with COVID-19 (oxygen therapy: OR 1.89, 95% CI 1.25-3.05; notification of physician: OR 1.72, 95% CI 1.02-2.97).
CONCLUSIONS: The study used SNOMED CT to express and standardize nursing statements. Further, it revealed the importance of standardized nursing records as predictive variables for clinical deterioration in patients.

BACKGROUND: Trauma care in Nunavik, Quebec, is highly challenging. Geographic distances and delays in transport can translate into precarious patient transfers to tertiary trauma care centres. The objective of this study was to identify predictors of clinical deterioration during transport and eventual intensive care unit (ICU) admission for trauma patients transferred from Nunavik to a tertiary trauma care centre.
METHODS: This is a retrospective cohort study using the Montreal General Hospital (MGH) trauma registry. All adult trauma patients transferred from Nunavik and admitted to the MGH from 2010 to 2019 were included. Main outcomes of interest were hemodynamic and neurologic deterioration during transport and ICU admission.
RESULTS: In total, 704 patients were transferred from Nunavik and admitted to the MGH during the study period. The median age was 33 (interquartile range [IQR] 23-47) years and the median Injury Severity Score was 10 (IQR 5-17). On multiple regression analysis, transport time from site of injury to the MGH (odds ratio [OR] 1.04, 95% confidence interval [CI] 1.01-1.06), thoracic injuries (OR 1.75, 95% CI 1.03-2.99), and head and neck injuries (OR 3.76, 95% CI 2.10-6.76) predicted clinical deterioration during transfer. Injury Severity Score (OR 1.04, 95% CI 1.01-1.08), abnormal local Glasgow Coma Scale score (OR 2.57, 95% CI 1.34-4.95), clinical deterioration during transfer (OR 4.22, 95% CI 1.99-8.93), traumatic brain injury (OR 2.44, 95% CI 1.05-5.68), and transfusion requirement at the MGH (OR 4.63, 95% CI 2.35-9.09) were independent predictors of ICU admission.
CONCLUSIONS: Our study identified several predictors of clinical deterioration during transfer and eventual ICU admission for trauma patients transferred from Nunavik. These factors could be used to refine triage criteria in Nunavik for more timely evacuation and higher level care during transport.

OBJECTIVE: Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent directly to front-line providers would reduce escalations.
METHODS: Single-center prospective pragmatic nonrandomized clustered clinical trial.
METHODS: Academic tertiary care medical center.
METHODS: Adult patients admitted to four medical-surgical units. Assignment to intervention or control arms was determined by initial unit admission.
METHODS: Real-time alerts stratified according to predicted likelihood of deterioration sent either to the primary team or directly to the rapid response team (RRT). Clinical care and interventions were at the providers\' discretion. For the control units, alerts were generated but not sent, and standard RRT activation criteria were used.
RESULTS: The primary outcome was the rate of escalation per 1000 patient bed days. Secondary outcomes included the frequency of orders for fluids, medications, and diagnostic tests, and combined in-hospital and 30-day mortality. Propensity score modeling with stabilized inverse probability of treatment weight (IPTW) was used to account for differences between groups. Data from 2740 patients enrolled between July 2019 and March 2020 were analyzed (1488 intervention, 1252 control). Average age was 66.3 years and 1428 participants (52%) were female. The rate of escalation was 12.3 vs. 11.3 per 1000 patient bed days (difference, 1.0; 95% CI, -2.8 to 4.7) and IPTW adjusted incidence rate ratio 1.43 (95% CI, 1.16-1.78; p < 0.001). Patients in the intervention group were more likely to receive cardiovascular medication orders (16.1% vs. 11.3%; 4.7%; 95% CI, 2.1-7.4%) and IPTW adjusted relative risk (RR) (1.74; 95% CI, 1.39-2.18; p < 0.001). Combined in-hospital and 30-day-mortality was lower in the intervention group (7% vs. 9.3%; -2.4%; 95% CI, -4.5% to -0.2%) and IPTW adjusted RR (0.76; 95% CI, 0.58-0.99; p = 0.045).
CONCLUSIONS: Real-time machine learning alerts do not reduce the rate of escalation but may reduce mortality.