A Dynamic Prognostic Model for Identifying Vulnerable COVID-19 Patients at High Risk of Rapid Deterioration.

PURPOSE: 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. CONCLUSION: 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.

Accuracy of routine laboratory tests to predict mortality and deterioration to severe or critical COVID-19 in people with SARS-CoV-2.

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. OBJECTIVES: 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. SEARCH 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. SELECTION CRITERIA: 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. DATA COLLECTION AND ANALYSIS: 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. MAIN 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). AUTHORS' 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.

Oncological pediatric early warning score: a dedicated tool to predict patient's clinical deterioration and need for pediatric intensive care treatment.

Pediatric oncohematological patients frequently require PICU admission during their clinical history. The O-PEWS is a specific score developed to predict the need for PICU admission of oncohematological children. This study aimed at i) describing the trend of the O-PEWS in a cohort of patients hospitalized in the Pediatric Oncohematology ward and transferred to the PICU of Padua University Hospital, measured at different time-points in the 24 hours before PICU admission and to evaluate its association with mortality and presence of organ failure; ii) investigating the association between the recorded O-PEWS, and PIM3, number of organ failure and the need for ventilation, dialysis and inotropes.This retrospective single-center study enrolled oncohematological children admitted to the PICU between 2017 and 2021. The O-PEWS, ranging between 0 and 15, was calculated on the available medical records and the TIPNet-Network database at 24 (T-24), 12 (T-12), 6 (T-6) and 0 (T0) hours before PICU admission.RESULTS: 101 PICU admissions, related to 80 children, were registered. During the 24 hours prior to PICU admission, the O-PEWS progressively increased in all the patients. At T-24 the median O-PEWS was 3 (IQR 1-5), increasing to a median value of 6 (IQR 4-8) at T0. The O-PEWS was positively associated with mortality, organ failure and the need for ventilation at all the analyzed time-points and with the need for dialysis at T-6.The O-PEWS appears as a useful tool for predicting early clinical deterioration in oncohematological patients and for anticipating the initiation of life-support treatments.

Prioritising deteriorating patients using time-to-event analysis: prediction model development and internal-external validation.

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. CONCLUSION: 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.

Paediatric out-of-hospital clinical deterioration: a mixed-methods scoping review protocol.

INTRODUCTION: 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 AND ANALYSIS: 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. ETHICS AND DISSEMINATION: Ethical approval is not required. The findings will be disseminated through publication in a peer-reviewed journal and presentation at conferences and/or seminars.

Incorporating informatively collected laboratory data from EHR in clinical prediction models.

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. CONCLUSION: 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.

A systematic review and meta-analysis, investigating dose and time of fluvoxamine treatment efficacy for COVID-19 clinical deterioration, death, and Long-COVID complications.

There have been 774,075,242 cases of COVID-19 and 7,012,986 deaths worldwide as of January 2024. In the early stages of the pandemic, there was an urgent need to reduce the severity of the disease and prevent the need for hospitalization to avoid stress on healthcare systems worldwide. The repurposing of drugs to prevent clinical deterioration of COVID-19 patients was trialed in many studies using many different drugs. Fluvoxamine (an SSRI and sigma-1 receptor agonist) was initially identified to potentially provide beneficial effects in COVID-19-infected patients, preventing clinical deterioration and the need for hospitalization. Fourteen clinical studies have been carried out to date, with seven of those being randomized placebo-controlled studies. This systematic review and meta-analysis covers the literature from the outbreak of SARS-CoV-2 in late 2019 until January 2024. Search terms related to fluvoxamine, such as its trade names and chemical names, along with words related to COVID-19, such as SARS-CoV-2 and coronavirus, were used in literature databases including PubMed, Google Scholar, Scopus, and the ClinicalTrials.gov database from NIH, to identify the trials used in the subsequent analysis. Clinical deterioration and death data were extracted from these studies where available and used in the meta-analysis. A total of 7153 patients were studied across 14 studies (both open-label and double-blind placebo-controlled). 681 out of 3553 (19.17%) in the standard care group and 255 out of 3600 (7.08%) in the fluvoxamine-treated group experienced clinical deterioration. The estimated average log odds ratio was 1.087 (95% CI 0.200 to 1.973), which differed significantly from zero (z = 2.402, p = 0.016). The seven placebo-controlled studies resulted in a log odds ratio of 0.359 (95% CI 0.1111 to 0.5294), which differed significantly from zero (z = 3.103, p = 0.002). The results of this study identified fluvoxamine as effective in preventing clinical deterioration, and subgrouping analysis suggests that earlier treatment with a dose of 200 mg or above provides the best outcomes. We hope the outcomes of this study can help design future studies into respiratory viral infections and potentially improve clinical outcomes.

An economic evaluation of the Prioritising Responses Of Nurses To deteriorating patient Observations (PRONTO) clinical trial.

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). METHOD: 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). CONCLUSION: 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.

Performance of 21 Early Warning System scores in predicting in-hospital deterioration among undifferentiated admitted patients managed by ambulance services.

BACKGROUND: The optimal Early Warning System (EWS) scores for identifying patients at risk of clinical deterioration among those transported by ambulance services remain uncertain. This retrospective study compared the performance of 21 EWS scores to predict clinical deterioration using vital signs (VS) measured in the prehospital or emergency department (ED) setting. METHODS: Adult patients transported to a single ED by ambulances and subsequently admitted to the hospital between 1 January 2019 and 18 April 2019 were eligible for inclusion. The primary outcome was 30-day mortality; secondary outcomes included 3-day mortality, admission to intensive care or coronary care units, length of hospital stay and emergency call activations. The discriminative ability of the EWS scores was assessed using the area under the receiver operating characteristic curve (AUROC). Subanalyses compared the performance of EWS scores between surgical and medical patient types. RESULTS: Of 1414 patients, 995 (70.4%) (53.1% male, mean age 68.7±17.5 years) were included. In the ED setting, 30-day mortality was best predicted by VitalPAC EWS (AUROC 0.71, 95% CI (0.65 to 0.77)) and National Early Warning Score (0.709 (0.65 to 0.77)). All EWS scores calculated in the prehospital setting had AUROC <0.70. Rapid Emergency Medicine Score (0.83 (0.73 to 0.92)) and New Zealand EWS (0.88 (0.81 to 0.95)) best predicted 3-day mortality in the prehospital and ED settings, respectively. EWS scores calculated using either prehospital or ED VS were more effective in predicting 3-day mortality in surgical patients, whereas 30-day mortality was best predicted in medical patients. Among the EWS scores that achieved AUROC ≥0.70, no statistically significant differences were detected in their discriminatory abilities to identify patients at risk of clinical deterioration. CONCLUSIONS: EWS scores better predict 3-day as opposed to 30-day mortality and are more accurate when estimated using VS measured in the ED. The discriminatory performance of EWS scores in identifying patients at higher risk of clinical deterioration may vary by patient type.

Beyond Unplanned ICU Transfers: Linking a Revised Definition of Deterioration to Patient Outcomes.

OBJECTIVES: To develop an electronic descriptor of clinical deterioration for hospitalized patients that predicts short-term mortality and identifies patient deterioration earlier than current standard definitions. DESIGN: A retrospective study using exploratory record review, quantitative analysis, and regression analyses. SETTING: Twelve-hospital community-academic health system. PATIENTS: All adult patients with an acute hospital encounter between January 1, 2018, and December 31, 2022. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Clinical trigger events were selected and used to create a revised electronic definition of deterioration, encompassing signals of respiratory failure, bleeding, and hypotension occurring in proximity to ICU transfer. Patients meeting the revised definition were 12.5 times more likely to die within 7 days (adjusted odds ratio 12.5; 95% CI, 8.9-17.4) and had a 95.3% longer length of stay (95% CI, 88.6-102.3%) compared with those who were transferred to the ICU or died regardless of meeting the revised definition. Among the 1812 patients who met the revised definition of deterioration before ICU transfer (52.4%), the median detection time was 157.0 min earlier (interquartile range 64.0-363.5 min). CONCLUSIONS: The revised definition of deterioration establishes an electronic descriptor of clinical deterioration that is strongly associated with short-term mortality and length of stay and identifies deterioration over 2.5 hours earlier than ICU transfer. Incorporating the revised definition of deterioration into the training and validation of early warning system algorithms may enhance their timeliness and clinical accuracy.

Early Warning Systems for Critical Illness Outside the Intensive Care Unit.

Early warning systems (EWSs) are designed and deployed to create a rapid assessment and response for patients with clinical deterioration outside the intensive care unit (ICU). These models incorporate patient-level data such as vital signs and laboratory values to detect or prevent adverse clinical events, such as vital signs and laboratories to allow detection and prevention of adverse clinical events such as cardiac arrest, intensive care transfer, or sepsis. The applicability, development, clinical utility, and general perception of EWS in clinical practice vary widely. Here, we review the field as it has grown from early vital sign-based scoring systems to contemporary multidimensional algorithms and predictive technologies for clinical decompensation outside the ICU.

A model-based cost-utility analysis of an automated notification system for deteriorating patients on general wards.

BACKGROUND: Delayed response to clinical deterioration of hospital inpatients is common. Deployment of an electronic automated advisory vital signs monitoring and notification system to signal clinical deterioration is associated with significant improvements in clinical outcomes but there is no evidence on the cost-effectiveness compared with routine monitoring, in the National Health Service (NHS) in the United Kingdom (UK). METHODS: A decision analytic model was developed to estimate the cost-effectiveness of an electronic automated advisory notification system versus standard care, in adults admitted to a district general hospital. Analyses considered: (1) the cost-effectiveness of the technology based on secondary analysis of patient level data of 3787 inpatients in a before-and-after study; and (2) the cost-utility (cost per quality-adjusted life-year (QALY)) over a lifetime horizon, extrapolated using published data. Analysis was conducted from the perspective of the NHS. Uncertainty in the model was assessed using a range of sensitivity analyses. RESULTS: The study population had a mean age of 68 years, 48% male, with a median inpatient stay of 6 days. Expected life expectancy at discharge was assumed to be 17.74 years. (1) Cost-effectiveness analysis: The automated notification system was more effective (-0.027 reduction in mean events per patient) and provided a cost saving of -£12.17 (-182.07 to 154.80) per patient admission. (2) Cost-utility analysis: Over a lifetime horizon the automated notification system was dominant, demonstrating a positive incremental QALY gain (0.0287 QALYs, equivalent to ~10 days of perfect health) and a cost saving of £55.35. At a threshold of £20,000 per QALY, the probability of automated monitoring being cost-effective in the NHS was 81%. Increased use of cableless sensors may reduce cost-savings, however, the intervention remains cost-effective at 100% usage (ICER: £3,107/QALY). Stratified cost-effectiveness analysis by age, National Early Warning Score (NEWS) on admission, and primary diagnosis indicated the automated notification system was cost-effective for most strategies and that use representative of the patient population studied was the most cost-saving strategy. CONCLUSION: Automated notification system for adult patients admitted to general wards appears to be a cost-effective use in the NHS; adopting this technology could be good use of scarce resources with significance for patient safety.

Risk Factors for Deterioration Events in the Pediatric Acute Care Setting.

OBJECTIVES: Rapid response system (RRS) activations resulting in emergency transfers (ETs) and codes outside the ICU are associated with increased mortality and length of stay. We aimed to evaluate the patient and care team characteristics of RRS activations resulting in ETs and codes outside the ICU (together classified as "deterioration events") versus those that did not result in a deterioration event. METHODS: For each RRS activation at our institution from 2019 to 2021, data were gathered on patient demographics and medical diagnoses, care team and treatment factors, and ICU transfer. Descriptive statistics, bivariate analyses, and multivariable logistic regression using a backward elimination model selection method were performed to assess potential risk factors for deterioration events. RESULTS: Over the 3-year period, 1765 RRS activations were identified. Fifty-three (3%) activations were deemed acute care codes, 64 (4%) were noncode ETs, 921 (52%) resulted in nonemergent transfers to an ICU, and 727 (41%) patients remained in an acute care unit. In a multivariable model, any complex chronic condition (adjusted odds ratio, 6.26; 95% confidence interval, 2.83-16.60) and hematology/oncology service (adjusted odds ratio, 2.19; 95% confidence interval, 1.28-3.74) were independent risk factors for a deterioration event. CONCLUSIONS: Patients with medical complexity and patients on the hematology/oncology service had a higher risk of deterioration events than other patients with RRS activations. Further analyzing how our hospital evaluates and treats these specific patient populations is critical as we develop targeted interventions to reduce deterioration events.

Cardiac herniation post left upper lobectomy and thymectomy: a case report.

BACKGROUND: Cardiac herniation occurs when there is a residual pericardial defect post thoracic surgery and is recognised as a rare but fatal complication. It confers a high mortality and requires immediate surgical correction upon recognition. We present a case of cardiac herniation occurring post thymectomy and left upper lobectomy. CASE PRESENTATION: Initial presentation: A 48-year-old male, hypertensive smoker presented with progressive breathlessness and was found to have a left upper zone mass confirmed on CT biopsy as carcinoid of unclear origin. PET-CT revealed avidity in a left anterior mediastinal area, left upper lobe (LUL) lung mass, mediastinal lymph nodes, and a right thymic satellite nodule. Intraoperatively: Access via left thoracotomy and sternotomy. The LUL tumour involved the left thymic lobe (LTL), left superior pulmonary vein (LSPV), left phrenic nerve and intervening mediastinal fat and pericardium, which were resected en-masse. The satellite nodule in the right thymic lobe (RTL) was adjacent to the junction between the left innominate vein and superior vena cava (SVC). The pericardium was resected from the SVC to the left atrial appendage. Clinical deterioration: Initially the patient was doing well clinically on day 1, however there was sudden bradycardia, hypotension, clamminess, and oligoanuria, with raised central venous pressures and troponins. ECG: no capture in leads V1-2, but positive deflections seen on posterior leads. Echo: no acoustic windows, but good windows seen posteriorly. CXR: left mediastinal shift. Redo operation: After initial resuscitation and stabilisation on the intensive care unit, on day 2 a redo-sternotomy revealed cardiac herniation into the left thoracic cavity with the left ventricular apex pointing towards the spine, and inferior caval kinking. After reduction and repair of the pericardial defect with a fenestrated GoreTex patch, the patient recovered well with complete resolution of the ECG and CXR. CONCLUSION: Cardiac herniation can even occur following sub-pneumonectomy lung resections and should be considered as a differential when faced with a sudden clinical deterioration, warranting early surgical correction.

British societies guideline on the management of emergencies in implantable left ventricular assist device recipients in transplant centres.

An implantable left ventricular assist device (LVAD) is indicated as a bridge to transplantation or recovery in the United Kingdom (UK). The mechanism of action of the LVAD results in a unique state of haemodynamic stability with diminished arterial pulsatility. The clinical assessment of an LVAD recipient can be challenging because non-invasive blood pressure, pulse and oxygen saturation measurements may be hard to obtain. As a result of this unusual situation and complex interplay between the device and the native circulation, resuscitation of LVAD recipients requires bespoke guidelines. Through collaboration with key UK stakeholders, we assessed the current evidence base and developed guidelines for the recognition of clinical deterioration, inadequate circulation and time-critical interventions. Such guidelines, intended for use in transplant centres, are designed to be deployed by those providing immediate care of LVAD patients under conditions of precipitous clinical deterioration. In summary, the Joint British Societies and Transplant Centres LVAD Working Group present the UK guideline on management of emergencies in implantable LVAD recipients for use in advanced heart failure centres. These recommendations have been made with a UK resuscitation focus but are widely applicable to professionals regularly managing patients with implantable LVADs.

Using the NEWS2 and ABCDE assessment to identify early signs of clinical deterioration.

Nurses may encounter deteriorating patients in their clinical practice, so they require an understanding of the early physiological signs of deterioration and a structured approach to patient assessment. This enables appropriate management and a timely response to the most life-threatening issues identified, such as a compromised airway. This article describes how nurses can use early warning scores and a structured patient assessment, using the ABCDE (airway, breathing, circulation, disability, exposure) framework, to identify early signs of deterioration and facilitate the timely escalation of patient care where necessary.

Effectiveness of an Artificial Intelligence-Enabled Intervention for Detecting Clinical Deterioration.

Importance: Inpatient clinical deterioration is associated with substantial morbidity and mortality but may be easily missed by clinicians. Early warning scores have been developed to alert clinicians to patients at high risk of clinical deterioration, but there is limited evidence for their effectiveness. Objective: To evaluate the effectiveness of an artificial intelligence deterioration model-enabled intervention to reduce the risk of escalations in care among hospitalized patients using a study design that facilitates stronger causal inference. Design, Setting, and Participants: This cohort study used a regression discontinuity design that controlled for confounding and was based on Epic Deterioration Index (EDI; Epic Systems Corporation) prediction model scores. Compared with other observational research, the regression discontinuity design facilitates causal analysis. Hospitalized adults were included from 4 general internal medicine units in 1 academic hospital from January 17, 2021, through November 16, 2022. Exposure: An artificial intelligence deterioration model-enabled intervention, consisting of alerts based on an EDI score threshold with an associated collaborative workflow among nurses and physicians. Main Outcomes and Measures: The primary outcome was escalations in care, including rapid response team activation, transfer to the intensive care unit, or cardiopulmonary arrest during hospitalization. Results: During the study, 9938 patients were admitted to 1 of the 4 units, with 963 patients (median [IQR] age, 76.1 [64.2-86.2] years; 498 males [52.3%]) included within the primary regression discontinuity analysis. The median (IQR) Elixhauser Comorbidity Index score in the primary analysis cohort was 10 (0-24). The intervention was associated with a -10.4-percentage point (95% CI, -20.1 to -0.8 percentage points; P = .03) absolute risk reduction in the primary outcome for patients at the EDI score threshold. There was no evidence of a discontinuity in measured confounders at the EDI score threshold. Conclusions and Relevance: Using a regression discontinuity design, this cohort study found that the implementation of an artificial intelligence deterioration model-enabled intervention was associated with a significantly decreased risk of escalations in care among inpatients. These results provide evidence for the effectiveness of this intervention and support its further expansion and testing in other care settings.

Predictors of early neurological deterioration in patients with intracerebral hemorrhage: a systematic review and meta-analysis.

BACKGROUND: Early neurological deterioration, a common complication in patients with intracerebral hemorrhage, is associated with poor outcomes. Despite the fact that the prevalence and predictors of early neurological impairment are widely addressed, few studies have consolidated these findings. This study aimed to systematically investigate the prevalence and predictors of early neurological deterioration. METHODS: The PubMed, Embase, Cochrane Library, CIHNAL, and Web of Science databases were systematically searched for relevant studies from the inception to December 2023. The data were extracted using a predefined worksheet. Quality assessment was conducted using the Newcastle-Ottawa Scale. Two reviewers independently performed the study selection, data extraction, and quality appraisal. The pooled effect size and 95% confidence intervals were calculated using the STATA 17.0 software package. RESULTS: In total, 32 studies and 5,014 patients were included in this meta-analysis. The prevalence of early neurological deterioration was 23% (95% CI 21-26%, p < 0.01). The initial NIHSS score (OR = 1.24, 95% CI 1.17, 1.30, p < 0.01), hematoma volume (OR = 1.07, 95% CI 1.06, 1.09, p < 0.01), intraventricular hemorrhage (OR = 3.50, 95% CI 1.64, 7.47, p < 0.01), intraventricular extension (OR = 3.95, 95% CI 1.96, 7.99, p < 0.01), hematoma expansion (OR = 9.77, 95% CI 4.43, 17.40, p < 0.01), and computed tomographic angiography spot sign (OR = 5.77, 95% CI 1.53, 20.23, p = 0.01) were predictors of early neurological deterioration. The funnel plot and Egger's test revealed significant publication bias (p < 0.001). CONCLUSIONS: This meta-analysis revealed a pooled prevalence of early neurological deterioration of 23% in patients with intracerebral hemorrhage. The initial NIHSS score, hematoma volume, intraventricular hemorrhage, intraventricular expansion, hematoma expansion, and spot sign enhanced the probability of early neurological deterioration. These findings provide healthcare providers with an evidence-based basis for detecting and managing early neurological deterioration in patients with intracerebral hemorrhage.

Importance of specific vital signs in nurses' recognition and response to deteriorating patients: A scoping review.

AIM(S): To explore the published research related to nurses' documentation and use of vital signs in recognising and responding to deteriorating patients. DESIGN: Scoping review of international, peer-reviewed research studies. DATA SOURCES: Cumulative Index to Nursing and Allied Health Literature Complete, Medline Complete, American Psychological Association PsycInfo and Excerpta Medica were searched on 25 July 2023. REPORTING METHOD: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. RESULTS: Of 3880 potentially eligible publications, 32 were included. There were 26 studies of nurses' vital sign documentation: 21 adults and five paediatric. The most and least frequently documented vital signs were blood pressure and respiratory rate respectively. Seven studies focused on vital signs and rapid response activation or afferent limb failure. Five studies of vital signs used to trigger the rapid response system showed heart rate was the most frequent and respiratory rate and conscious state were the least frequent. Heart rate was least likely and oxygen saturation was most likely to be associated with afferent limb failure (n = 4 studies). CONCLUSION: Despite high reliance on using vital signs to recognise clinical deterioration and activate a response to deteriorating patients in hospital settings, nurses' documentation of vital signs and use of vital signs to activate rapid response systems is poorly understood. There were 21studies of nurses' vital sign documentation in adult patients and five studies related to children. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: A deeper understanding of nurses' decisions to assess (or not assess) specific vital signs, analysis of the value or importance nurses place (or not) on specific vital sign parameters is warranted. The influence of patient characteristics (such as age) or the clinical practice setting, and the impact of nurses' workflows of vital sign assessment warrants further investigation. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.