BACKGROUND: Prediction-based strategies for physiologic deterioration offer the potential for earlier clinical interventions that improve patient outcomes. Current strategies are limited because they operate on inconsistent definitions of deterioration, attempt to dichotomize a dynamic and progressive phenomenon, and offer poor performance.
OBJECTIVE: Can a deep learning deterioration prediction model (Deep Learning Enhanced Triage and Emergency Response for Inpatient Optimization [DETERIO]) based on a consensus definition of deterioration (the Adult Inpatient Decompensation Event [AIDE] criteria) and that approaches deterioration as a state \"value-estimation\" problem outperform a commercially available deterioration score?
UNASSIGNED: The derivation cohort contained retrospective patient data collected from both inpatient services (inpatient) and emergency departments (EDs) of two hospitals within the University of California San Diego Health System. There were 330,729 total patients; 71,735 were inpatient and 258,994 were ED. Of these data, 20% were randomly sampled as a retrospective \"testing set.\"
UNASSIGNED: The validation cohort contained temporal patient data. There were 65,898 total patients; 13,750 were inpatient and 52,148 were ED.
UNASSIGNED: DETERIO was developed and validated on these data, using the AIDE criteria to generate a composite score. DETERIO\'s architecture builds upon previous work. DETERIO\'s prediction performance up to 12 hours before T0 was compared against Epic Deterioration Index (EDI).
RESULTS: In the retrospective testing set, DETERIO\'s area under the receiver operating characteristic curve (AUC) was 0.797 and 0.874 for inpatient and ED subsets, respectively. In the temporal validation cohort, the corresponding AUC were 0.775 and 0.856, respectively. DETERIO outperformed EDI in the inpatient validation cohort (AUC, 0.775 vs. 0.721; p < 0.01) while maintaining superior sensitivity and a comparable rate of false alarms (sensitivity, 45.50% vs. 30.00%; positive predictive value, 20.50% vs. 16.11%).
CONCLUSIONS: DETERIO demonstrates promise in the viability of a state value-estimation approach for predicting adult physiologic deterioration. It may outperform EDI while offering additional clinical utility in triage and clinician interaction with prediction confidence and explanations. Additional studies are needed to assess generalizability and real-world clinical impact.

BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: Data revealed five main themes: identification of deterioration, deciding to escalate care, responsiveness of peers/team, communication during rapid responses, and perception of effectiveness.
CONCLUSIONS: 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.

Accurate measurement of vital signs are important at skilled nursing facilities (SNF). Recent technological advancements now enable automated vital sign measurements. This overcomes the limitations of traditional manual vital sign measurement, which is time-consuming and error-prone. We present a novel case where continuous vital sign measurement was used to detect meaningful vital sign changes that led to early detection of a COVID-19 outbreak at a SNF. Residents were continuously monitored for changes to baseline respiratory rate and heart rate and with a Probability of Change (POC). Variations in baseline respiratory rate and heart rate occurred in 66% and 42%, respectively, of COVID-19 positive individuals; 83% of participants had statistically significant variations in either vital sign. Clinical investigations are typically triggered by vital signs outside normal ranges. We present a novel methodology to detect subtle vital sign changes that can lead to earlier diagnosis, treatment, and recovery from infections, like COVID-19.

OBJECTIVE: To develop a scale to assess difficulties that nurses experience when collaborating with physicians in responding to clinical deterioration during night shifts and identify factors associated with scoring using the developed scale.
METHODS: A web-based questionnaire with a draft scale, the Nighttime Collaboration Difficulties between Nurses and Physicians for Nurses (NCDNP-N), was distributed to nurses working night shifts in acute-care hospitals across Japan. Data were collected between July and October 2023. Of 435 responses, 405 were examined for the NCDNP-N\'s psychometric validation, including structural validity, criterion-related validity, and reliability assessments. Multiple linear regression analysis was performed for 385 responses excluded by listwise methods to identify factors associated with NCDNP-N scores.
RESULTS: The NCDNP-N has 10 items and three domains: Domain 1, dissatisfaction with physicians\' actions; Domain 2, burden of working with night-shift physicians; and Domain 3, barriers to reporting during night shifts. Estimated reliability coefficients exceeded the recommended values. Multiple regression analyses demonstrated that more years of experience in the current ward and frequency of calling the covering physician at night were markedly associated with higher scores, whereas more nursing experience was associated with lower scores.
CONCLUSIONS: We developed the NCDNP-N and confirmed its validity and reliability. The study results suggest that the responsibilities and competence of nurses working night shifts and communication with the night-covering physician are associated with difficulties in nighttime collaboration. The NCDNP-N may help identify challenges in clinical settings as well as can be utilized in the evaluation study for improving nighttime collaboration.

BACKGROUND: Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation.
METHODS: Nurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment.
RESULTS: Fifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management.
CONCLUSIONS: Prediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking.

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.

Clinical deterioration (CD) is the physiological decompensation that incurs care escalation, protracted hospital stays, or even death. The early warning score (EWS) calculates the occurrence of CD based on five vital signs. However, there are limited reports regarding EWS monitoring in smart home settings. This study aims to design a CD detection system for health monitoring at home (HM@H) that automatically identifies unstable vital signs and alarms the medical emergency team. We conduct a requirement analysis by interviewing experts. We use unified modeling language (UML) diagrams to define the behavioral and structural aspects of HM@H. We developed a prototype using a SQL-based database and Python to calculate the EWS in the front end. A team of five experts assessed the accuracy and validity of the designed system. The requirement analysis for four main users yielded 30 data elements and 10 functions. Three main components of HM@H are the graphical user interface (GUI), the application programming interface (API), and the server. Results show the possibility of using unobtrusive sensors to collect smart home residents\' vital signs and calculate their EWS scores in real-time. However, further implementation with real data, for frail elderly and hospital-discharged patients is required.

OBJECTIVE: We aimed to validate and, if performance was unsatisfactory, update the previously published prognostic model to predict clinical deterioration in patients hospitalized for COVID-19, using data following vaccine availability.
METHODS: Using electronic health records of patients ≥18 years, with laboratory-confirmed COVID-19, from a large care-delivery network in Massachusetts, USA, from March 2020 to November 2021, we tested the performance of the previously developed prediction model and updated the prediction model by incorporating data after availability of COVID-19 vaccines. We randomly divided data into development (70%) and validation (30%) cohorts. We built a model predicting worsening in a published severity scale in 24 h by LASSO regression and evaluated performance by c-statistic and Brier score.
RESULTS: Our study cohort consisted of 8185 patients (Development: 5730 patients [mean age: 62; 44% female] and Validation: 2455 patients [mean age: 62; 45% female]). The previously published model had suboptimal performance using data after November 2020 (N = 4973, c-statistic = 0.60. Brier score = 0.11). After retraining with the new data, the updated model included 38 predictors including 18 changing biomarkers. Patients hospitalized after Jun 1st, 2021 (when COVID-19 vaccines became widely available in Massachusetts) were younger and had fewer comorbidities than those hospitalized before. The c-statistic and Brier score were 0.77 and 0.13 in the development cohort, and 0.73 and 0.14 in the validation cohort.
CONCLUSIONS: The characteristics of patients hospitalized for COVID-19 differed substantially over time. We developed a new dynamic model for rapid progression with satisfactory performance in the validation set.

Identifying patients with COVID-19 disease who will deteriorate can be useful to assess whether they should receive intensive care, or whether they can be treated in a less intensive way or through outpatient care. In clinical care, routine laboratory markers, such as C-reactive protein, are used to assess a person\'s health status.
To assess the accuracy of routine blood-based laboratory tests to predict mortality and deterioration to severe or critical (from mild or moderate) COVID-19 in people with SARS-CoV-2.
On 25 August 2022, we searched the Cochrane COVID-19 Study Register, encompassing searches of various databases such as MEDLINE via PubMed, CENTRAL, Embase, medRxiv, and ClinicalTrials.gov. We did not apply any language restrictions.
We included studies of all designs that produced estimates of prognostic accuracy in participants who presented to outpatient services, or were admitted to general hospital wards with confirmed SARS-CoV-2 infection, and studies that were based on serum banks of samples from people. All routine blood-based laboratory tests performed during the first encounter were included. We included any reference standard used to define deterioration to severe or critical disease that was provided by the authors.
Two review authors independently extracted data from each included study, and independently assessed the methodological quality using the Quality Assessment of Prognostic Accuracy Studies tool. As studies reported different thresholds for the same test, we used the Hierarchical Summary Receiver Operator Curve model for meta-analyses to estimate summary curves in SAS 9.4. We estimated the sensitivity at points on the SROC curves that corresponded to the median and interquartile range boundaries of specificities in the included studies. Direct and indirect comparisons were exclusively conducted for biomarkers with an estimated sensitivity and 95% CI of ≥ 50% at a specificity of ≥ 50%. The relative diagnostic odds ratio was calculated as a summary of the relative accuracy of these biomarkers.
We identified a total of 64 studies, including 71,170 participants, of which 8169 participants died, and 4031 participants deteriorated to severe/critical condition. The studies assessed 53 different laboratory tests. For some tests, both increases and decreases relative to the normal range were included. There was important heterogeneity between tests and their cut-off values. None of the included studies had a low risk of bias or low concern for applicability for all domains. None of the tests included in this review demonstrated high sensitivity or specificity, or both. The five tests with summary sensitivity and specificity above 50% were: C-reactive protein increase, neutrophil-to-lymphocyte ratio increase, lymphocyte count decrease, d-dimer increase, and lactate dehydrogenase increase. Inflammation For mortality, summary sensitivity of a C-reactive protein increase was 76% (95% CI 73% to 79%) at median specificity, 59% (low-certainty evidence). For deterioration, summary sensitivity was 78% (95% CI 67% to 86%) at median specificity, 72% (very low-certainty evidence). For the combined outcome of mortality or deterioration, or both, summary sensitivity was 70% (95% CI 49% to 85%) at median specificity, 60% (very low-certainty evidence). For mortality, summary sensitivity of an increase in neutrophil-to-lymphocyte ratio was 69% (95% CI 66% to 72%) at median specificity, 63% (very low-certainty evidence). For deterioration, summary sensitivity was 75% (95% CI 59% to 87%) at median specificity, 71% (very low-certainty evidence). For mortality, summary sensitivity of a decrease in lymphocyte count was 67% (95% CI 56% to 77%) at median specificity, 61% (very low-certainty evidence). For deterioration, summary sensitivity of a decrease in lymphocyte count was 69% (95% CI 60% to 76%) at median specificity, 67% (very low-certainty evidence). For the combined outcome, summary sensitivity was 83% (95% CI 67% to 92%) at median specificity, 29% (very low-certainty evidence). For mortality, summary sensitivity of a lactate dehydrogenase increase was 82% (95% CI 66% to 91%) at median specificity, 60% (very low-certainty evidence). For deterioration, summary sensitivity of a lactate dehydrogenase increase was 79% (95% CI 76% to 82%) at median specificity, 66% (low-certainty evidence). For the combined outcome, summary sensitivity was 69% (95% CI 51% to 82%) at median specificity, 62% (very low-certainty evidence). Hypercoagulability For mortality, summary sensitivity of a d-dimer increase was 70% (95% CI 64% to 76%) at median specificity of 56% (very low-certainty evidence). For deterioration, summary sensitivity was 65% (95% CI 56% to 74%) at median specificity of 63% (very low-certainty evidence). For the combined outcome, summary sensitivity was 65% (95% CI 52% to 76%) at median specificity of 54% (very low-certainty evidence). To predict mortality, neutrophil-to-lymphocyte ratio increase had higher accuracy compared to d-dimer increase (RDOR (diagnostic Odds Ratio) 2.05, 95% CI 1.30 to 3.24), C-reactive protein increase (RDOR 2.64, 95% CI 2.09 to 3.33), and lymphocyte count decrease (RDOR 2.63, 95% CI 1.55 to 4.46). D-dimer increase had higher accuracy compared to lymphocyte count decrease (RDOR 1.49, 95% CI 1.23 to 1.80), C-reactive protein increase (RDOR 1.31, 95% CI 1.03 to 1.65), and lactate dehydrogenase increase (RDOR 1.42, 95% CI 1.05 to 1.90). Additionally, lactate dehydrogenase increase had higher accuracy compared to lymphocyte count decrease (RDOR 1.30, 95% CI 1.13 to 1.49). To predict deterioration to severe disease, C-reactive protein increase had higher accuracy compared to d-dimer increase (RDOR 1.76, 95% CI 1.25 to 2.50). The neutrophil-to-lymphocyte ratio increase had higher accuracy compared to d-dimer increase (RDOR 2.77, 95% CI 1.58 to 4.84). Lastly, lymphocyte count decrease had higher accuracy compared to d-dimer increase (RDOR 2.10, 95% CI 1.44 to 3.07) and lactate dehydrogenase increase (RDOR 2.22, 95% CI 1.52 to 3.26).
Laboratory tests, associated with hypercoagulability and hyperinflammatory response, were better at predicting severe disease and mortality in patients with SARS-CoV-2 compared to other laboratory tests. However, to safely rule out severe disease, tests should have high sensitivity (> 90%), and none of the identified laboratory tests met this criterion. In clinical practice, a more comprehensive assessment of a patient\'s health status is usually required by, for example, incorporating these laboratory tests into clinical prediction rules together with clinical symptoms, radiological findings, and patient\'s characteristics.

BACKGROUND: Implementing evidence that changes practice in emergency departments (EDs) is notoriously difficult due to well-established barriers including high levels of uncertainty arising from undifferentiated nature of ED patients, resource shortages, workload unpredictability, high staff turnover, and a constantly changing environment. We developed and implemented a behaviour-change informed strategy to mitigate these barriers for a clinical trial to implement the evidence-based emergency nursing framework HIRAID® (History including Infection risk, Red flags, Assessment, Interventions, Diagnostics, communication, and reassessment) to reduce clinical variation, and increase safety and quality of emergency nursing care.
OBJECTIVE: To evaluate the behaviour-change-informed HIRAID® implementation strategy on reach, effectiveness, adoption, quality (dose, fidelity) and maintenance (sustainability).
METHODS: An effectiveness-implementation hybrid design including a step-wedge cluster randomised control trial (SW-cRCT) was used to implement HIRAID® with 1300 + emergency nurses across 29 Australian rural, regional, and metropolitan EDs. Evaluation of our behaviour-change informed strategy was informed by the RE-AIM Scoring Instrument and measured using data from (i) a post HIRAID® implementation emergency nurse survey, (ii) HIRAID® Instructor surveys, and (iii) twelve-week and 6-month documentation audits. Quantitative data were analysed using descriptive statistics to determine the level of each component of RE-AIM achieved. Qualitative data were analysed using content analysis and used to understand the \'how\' and \'why\' of quantitative results.
RESULTS: HIRAID® was implemented in all 29 EDs, with 145 nurses undertaking instructor training and 1123 (82%) completing all four components of provider training at 12 weeks post-implementation. Modifications to the behaviour-change informed strategy were minimal. The strategy was largely used as intended with 100% dose and very high fidelity. We achieved extremely high individual sustainability (95% use of HIRAID® documentation templates) at 6 months and 100% setting sustainability at 3 years.
CONCLUSIONS: The behaviour-change informed strategy for the emergency nursing framework HIRAID® in rural, regional, and metropolitan Australia was highly successful with extremely high reach and adoption, dose, fidelity, individual and setting sustainability across substantially variable clinical contexts.
BACKGROUND: ANZCTR, ACTRN12621001456842 . Registered 25 October 2021.