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.

BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
METHODS: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.

BACKGROUND: Code Blue activations in patients who are not for resuscitation (NFR) may be regarded as non-beneficial and may cause harm to patients, relatives and hospital staff.
OBJECTIVE: To estimate the prevalence of non-beneficial Code Blue calls in a metropolitan teaching hospital and identify modifiable factors that could be utilised to reduce these events.
METHODS: The study consisted of two parts: (i) a retrospective analysis of all Code Blue activations over a 12-month period using prospectively collected data. Non-beneficial activations were defined as calls made in patients with a NFR order in either the current or any previous hospital admissions and (ii) an anonymous voluntary survey of staff who were present at a Code Blue activation.
RESULTS: There were 186 Code Blue activations over the study period, with 48 (25.8%) defined as non-beneficial. Such patients had more comorbidities, previous hospitalisations and greater levels of frailty. Most non-beneficial calls occurred on general wards and more than three-quarters of patients had been reviewed by a consultant prior to the call. The survey determined that despite ward staff having a considerable degree of resuscitation experience, there were deficiencies in understanding of Code Blue criteria, the resuscitation status of patients under their care and the interpretation of goals of care.
CONCLUSIONS: Over a quarter of Code Blue calls were deemed non-beneficial. Improving the visibility of NFR status and staff understanding of patient goals of care are needed, along with timely, proactive documentation of NFR status by experienced clinicians.

Immune checkpoint inhibitors (ICIs) have significantly improved the clinical outcome in multiple types of advanced or metastatic malignancies and are prescribed increasingly. However, immune-related adverse events (irAEs) occur frequently. Here, we present a patient with multifocal motor neuropathy and melanoma, with worsening of muscle weakness upon ICI therapy and concomitant use of steroids for the treatment of hepatitis, which was considered an irAE. Upon treatment with highly dosed immunoglobulins and steroid tapering, the patients\' muscular symptoms improved while hepatitis resolved. This case highlights the importance of careful evaluation of patients with multifocal motor neuropathy treated with ICIs, highlights the risks of treatment with steroids in multifocal motor neuropathy patients and suggests an alternative treatment of irAEs with intravenous immunoglobulins.
[Box: see text].

BACKGROUND: Electronic Health Records (EHR) are widely used to develop clinical prediction models (CPMs). However, one of the challenges is that there is often a degree of informative missing data. For example, laboratory measures are typically taken when a clinician is concerned that there is a need. When data are the so-called Not Missing at Random (NMAR), analytic strategies based on other missingness mechanisms are inappropriate. In this work, we seek to compare the impact of different strategies for handling missing data on CPMs performance.
METHODS: We considered a predictive model for rapid inpatient deterioration as an exemplar implementation. This model incorporated twelve laboratory measures with varying levels of missingness. Five labs had missingness rate levels around 50%, and the other seven had missingness levels around 90%. We included them based on the belief that their missingness status can be highly informational for the prediction. In our study, we explicitly compared the various missing data strategies: mean imputation, normal-value imputation, conditional imputation, categorical encoding, and missingness embeddings. Some of these were also combined with the last observation carried forward (LOCF). We implemented logistic LASSO regression, multilayer perceptron (MLP), and long short-term memory (LSTM) models as the downstream classifiers. We compared the AUROC of testing data and used bootstrapping to construct 95% confidence intervals.
RESULTS: We had 105,198 inpatient encounters, with 4.7% having experienced the deterioration outcome of interest. LSTM models generally outperformed other cross-sectional models, where embedding approaches and categorical encoding yielded the best results. For the cross-sectional models, normal-value imputation with LOCF generated the best results.
CONCLUSIONS: Strategies that accounted for the possibility of NMAR missing data yielded better model performance than those did not. The embedding method had an advantage as it did not require prior clinical knowledge. Using LOCF could enhance the performance of cross-sectional models but have countereffects in LSTM models.

BACKGROUND: Systematic adoption of early warning systems in healthcare settings is dependent on the optimal and reliable application by the user. Psychosocial issues and hospital culture influence clinicians\' patient safety behaviours.
OBJECTIVE: (i) To examine the sociocultural factors that influence nurses\' EWS compliance behaviours, using a theory driven behavioural model and (ii) to propose a conceptual model of sociocultural factors for EWS compliance behaviour.
METHODS: A cross-sectional survey.
METHODS: Nurses employed in public hospitals across Queensland, Australia.
METHODS: Using convenience and snowball sampling techniques eligible nurses accessed a dedicated web site and survey containing closed and open-ended questions. 291 nurses from 60 hospitals completed the survey.
METHODS: Quantitative data were analysed using ANOVA or t-tests to test differences in means. A series of path models based on the theory were conducted to develop a new model. Directed or theory driven content analysis informed qualitative data analysis.
RESULTS: Nurses report high levels of previous compliance behaviour and strong intentions to continue complying in the future (M=4.7; SD 0.48). Individual compliance attitudes (β 0.29, p<.05), perceived value of escalation (β 0.24, p<.05) and perceived ease or difficulty complying with documentation (β -0.31, p<.05) were statistically significant, predicting 24% of variation in compliance behaviour. Positive personal charting beliefs (β 0.14, p<.05) and subjective norms both explain higher behavioural intent indirectly through personal attitudes. High ratings of peer charting beliefs indirectly explain attitudes through subjective norms (β 0.20, p<.05). Perceptions of control over one\'s clinical actions (β -0.24, p<.05) and early warning system training (β -0.17, p<.05) directly contributed to fewer difficulties complying with documentation requirements. Prior difficulties when escalating care (β -0.31, p<.05) directly influenced the perceived value of escalating.
CONCLUSIONS: The developed theory-based conceptual model identified sociocultural variables that inform compliance behaviour (documenting and escalation protocols). The model highlights areas of clinical judgement, education, interprofessional trust, workplace norms and cultural factors that directly or indirectly influence nurses\' intention to comply with EWS protocols. Extending our understanding of the sociocultural and system wide factors that hamper nurses\' use of EWSs and professional accountability has the potential to improve the compliance behaviour of staff and subsequently enhance the safety climate attitudes of hospitals.
CONCLUSIONS: A newly developed model reports nurse\'s personal attitudes, peer influence, perceived difficulties encountered documenting and escalation beliefs all predict early warning system compliance behaviour.

BACKGROUND: In pre-hospital settings, identifying a deteriorating child can be challenging, especially considering that the proportion of paediatric patients with acute illnesses is lower compared with adults. This challenge is exacerbated in pre-hospital settings, where information might be scarce. Physiological alterations indicating changes in a patient\'s condition can be detected hours preceding a cardiac arrest. Therefore, maintaining continuous monitoring of the patient\'s clinical condition is crucial to detecting any physiological changes promptly, facilitating early identification of critical illness. This scoping review aims to assess the extent, range and nature of published research related to recognising paediatric out-of-hospital clinical deterioration by pre-hospital staff.
METHODS: This scoping review is registered with the Open Science Framework. The review will follow the Joanna Briggs Institute\'s (JBI) methodology for scoping reviews. A systematic search of relevant databases (MEDLINE, EMBASE, Web of Science, CINAHL and Scopus) will be conducted. In this scoping review, all types of study designs including quantitative and qualitative studies will be considered. The inclusion is limited to English-language studies published between January 1990 and March 2024. Two independent reviewers (AG and SS) will conduct a thorough screening of titles and abstracts against the pre-defined inclusion criteria for the review. For the selected citations, the full texts will undergo detailed assessment by the two reviewers, ensuring alignment with the inclusion criteria. A quality assessment of the included studies will be done using the Mixed Methods Appraisal Tool. The findings will be presented using diagrams or tables, supplemented by narrative summaries following the JBI guidelines.
BACKGROUND: Ethical approval is not required. The findings will be disseminated through publication in a peer-reviewed journal and presentation at conferences and/or seminars.

BACKGROUND: Binary classification models are frequently used to predict clinical deterioration, however they ignore information on the timing of events. An alternative is to apply time-to-event models, augmenting clinical workflows by ranking patients by predicted risks. This study examines how and why time-to-event modelling of vital signs data can help prioritise deterioration assessments using lift curves, and develops a prediction model to stratify acute care inpatients by risk of clinical deterioration.
METHODS: We developed and validated a Cox regression for time to in-hospital mortality. The model used time-varying covariates to estimate the risk of clinical deterioration. Adult inpatient medical records from 5 Australian hospitals between 1 January 2019 and 31 December 2020 were used for model development and validation. Model discrimination and calibration were assessed using internal-external cross validation. A discrete-time logistic regression model predicting death within 24 h with the same covariates was used as a comparator to the Cox regression model to estimate differences in predictive performance between the binary and time-to-event outcome modelling approaches.
RESULTS: Our data contained 150,342 admissions and 1016 deaths. Model discrimination was higher for Cox regression than for discrete-time logistic regression, with cross-validated AUCs of 0.96 and 0.93, respectively, for mortality predictions within 24 h, declining to 0.93 and 0.88, respectively, for mortality predictions within 1 week. Calibration plots showed that calibration varied by hospital, but this can be mitigated by ranking patients by predicted risks.
CONCLUSIONS: Time-varying covariate Cox models can be powerful tools for triaging patients, which may lead to more efficient and effective care in time-poor environments when the times between observations are highly variable.

The aim of this study was to describe the implementation of a novel 50-bed continuous remote monitoring service for high-risk acute inpatients treated in non-critical wards, known as Health in a Virtual Environment (HIVE). We report the initial results, presenting the number and type of patients connected to the service, and assess key outcomes from this cohort. This was a prospective, observational study of characteristics and outcomes of patients connected to the HIVE continuous monitoring service at a major tertiary hospital and a smaller public hospital in Western Australia between January 2021 and June 2023. In the first two and a half years following implementation, 7541 patients were connected to HIVE for a total of 331,118 h. Overall, these patients had a median length of stay of 5 days (IQR 2, 10), 11.0% (n = 833) had an intensive care unit admission, 22.4% (n = 1691) had an all-cause emergency readmission within 28 days from hospital discharge, and 2.2% (n = 167) died in hospital. Conclusions: Our initial results show promise, demonstrating that this innovative approach to inpatient care can be successfully implemented to monitor high-risk patients in medical and surgical wards. Future studies will investigate the effectiveness of the program by comparing patients receiving HIVE supported care to comparable patients receiving routine care.