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Background: Clinical prediction models have the potential to improve the quality of care and enhance patient safety outcomes. A Computer-aided Risk Scoring system (CARSS) was previously developed to predict in-hospital mortality following emergency admissions based on routinely collected blood tests and vitals. We aimed to externally validate the CARSS model., Methods: In this retrospective external validation study, we considered all adult (≥18 years) emergency medical admissions discharged between 11/11/2020 and 11/11/2022 from The Rotherham Foundation Trust (TRFT), UK. We assessed the predictive performance of the CARSS model based on its discriminative (c-statistic) and calibration characteristics (calibration slope and calibration plots)., Results: Out of 32,774 admissions, 20,422 (62.3 %) admissions were included. The TRFT sample had similar demographic characteristics to the development sample but had higher mortality (6.1 % versus 5.7 %). The CARSS model demonstrated good discrimination (c-statistic 0.87 [95 % CI 0.86-0.88]) and good calibration to the TRFT dataset (slope = 1.03 [95 % CI 0.98-1.08] intercept = 0 [95 % CI -0.06-0.07]) after re-calibrating for differences in baseline mortality (intercept = 0.96 [95 % CI 0.90-1.03] before re-calibration)., Conclusion: In summary, the CARSS model is externally validated after correcting the baseline risk of death between development and validation datasets. External validation of the CARSS model showed that it under-predicted in-hospital mortality. Re-calibration of this model showed adequate performance in the TRFT dataset., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Aims: International early warning scores (EWS) including the additive National Early Warning Score (NEWS) and logistic EWS currently utilise physiological snapshots to predict clinical deterioration. We hypothesised that a dynamic score including vital sign trajectory would improve discriminatory power.Methods: Multicentre retrospective analysis of electronic health record data from postoperative patients admitted to cardiac surgical wards in four UK hospitals. Least absolute shrinkage and selection operator-type regression (LASSO) was used to develop a dynamic model (DyniEWS) to predict a composite adverse event of cardiac arrest, unplanned intensive care re-admission or in-hospital death within 24 h.Results: A total of 13,319 postoperative adult cardiac patients contributed 442,461 observations of which 4234 (0.96%) adverse events in 24 h were recorded. The new dynamic model (AUC = 0.80 [95% CI 0.78-0.83], AUPRC = 0.12 [0.10-0.14]) outperforms both an updated snapshot logistic model (AUC = 0.76 [0.73-0.79], AUPRC = 0.08 [0.60-0.10]) and the additive National Early Warning Score (AUC = 0.73 [0.70-0.76], AUPRC = 0.05 [0.02-0.08]). Controlling for the false alarm rates to be at current levels using NEWS cut-offs of 5 and 7, DyniEWS delivers a 7% improvement in balanced accuracy and increased sensitivities from 41% to 54% at NEWS 5 and 18% to -30% at NEWS 7.Conclusions: Using an advanced statistical approach, we created a model that can detect dynamic changes in risk of unplanned readmission to intensive care, cardiac arrest or in-hospital mortality and can be used in real time to risk-prioritise clinical workload. [ABSTRACT FROM AUTHOR]

Background: The assessment of acute-illness severity in adult non-pregnant patients in the United Kingdom is based on early warning score (EWS) values that determine the urgency and nature of the response to patient deterioration. This study aimed to describe, and identify variations in, the expected clinical response outlined in 'deteriorating patient' policies/guidelines in acute NHS hospitals.Methods: A copy of the local 'deteriorating patient' policy/guideline was requested from 152 hospitals. Each was analysed against pre-determined areas of interest, e.g., minimum expected vital sign observations frequency, expected response and expected staff response times.Results: In the 55 responding hospitals (36.2%), the documented structure and process of the response to deterioration varied considerably. All hospitals used a 12-hourly minimum vital signs measurement frequency. Thereafter, for a low-risk patient, the minimum frequency varied from '6-12 hourly' to 'hourly'. Frequencies were higher for higher risk categories. Expected escalation responses were highly individualised between hospitals. Other than repeat observations, only nine (16.4%) documents described specific clinical actions for ward staff to consider/perform whilst awaiting responding personnel. Maximum permitted response times for medium-risk and high-risk patients varied widely, even when based on the same EWS. Only 33/55 documents (60%) gave clear instructions regarding who to contact 'out of hours'.Conclusions: The 'deteriorating patient' policies of the hospitals studied varied in their contents and often omitted precise instructions for staff. We recommend that individual hospitals review these documents, and that research and/or consensus are used to develop a national algorithm regarding the response to patient deterioration. [ABSTRACT FROM AUTHOR]

Evidence suggests that the identification and response to the deteriorating patient continues to be an ongoing concern, despite the widespread use of track and trigger score (TTS) systems. This article discusses the variations in the parameters included in the different TTS systems in use across the NHS and their sensitivity. Clinical guidelines and physiological theory are used to appraise the parameters allocated in the National Early Warning Score (NEWS 1 and 2), highlighting potential limitations of the tool. The findings lead to the conclusion that registered nurses should not rely solely on NEWS, but should use it to support their clinical judgement. [ABSTRACT FROM AUTHOR]

Objective: Routine administrative data have been used to show that patients admitted to hospitals over the weekend appear to have a higher mortality compared to weekday admissions. Such data do not take the severity of sickness of a patient on admission into account. Our aim was to incorporate a standardized vital signs physiological-based measure of sickness known as the National Early Warning Score to investigate if weekend admissions are: sicker as measured by their index National Early Warning Score; have an increased mortality; and experience longer delays in the recording of their index National Early Warning Score. Methods: We extracted details of all adult emergency medical admissions during 2014 from hospital databases and linked these with electronic National Early Warning Score data in four acute hospitals. We analysed 47,117 emergency admissions after excluding 1657 records, where National Early Warning Score was missing or the first (index) National Early Warning Score was recorded outside ±24 h of the admission time. Results: Emergency medical admissions at the weekend had higher index National Early Warning Score (weekend: 2.53 vs. weekday: 2.30, p<0.001) with a higher mortality (weekend: 706/11,332 6.23% vs. weekday: 2039/35,785 5.70%; odds ratio=1.10, 95% CI 1.01 to 1.20, p=0.04) which was no longer seen after adjusting for the index National Early Warning Score (odds ratio=0.99, 95% CI 0.90 to 1.09, p=0.87). Index National Early Warning Score was recorded sooner (-0.45 h, 95% CI -0.52 to -0.38, p<0.001) for weekend admissions. Conclusions: Emergency medical admissions at the weekend with electronic National Early Warning Score recorded within 24 h are sicker, have earlier clinical assessments, and after adjusting for the severity of their sickness, do not appear to have a higher mortality compared to weekday admissions. A larger definitive study to confirm these findings is needed. [ABSTRACT FROM AUTHOR]

Background: NHS England has mandated use of the National Early Warning Score (NEWS), more recently NEWS2, in acute settings, and suggested its use in primary care. However, there is reluctance from GPs to adopt NEWS/NEWS2., Aim: To assess whether NEWS calculated at the point of GP referral into hospital is associated with outcomes in secondary care., Design and Setting: An observational study using routinely collected data from primary and secondary care., Method: NEWS values were prospectively collected for 13 047 GP referrals into acute care between July 2017 and December 2018. NEWS values were examined and multivariate linear and logistic regression used to assess associations with process measures and clinical outcomes., Results: Higher NEWS values were associated with faster conveyance for patients travelling by ambulance, for example, median 94 minutes (interquartile range [IQR] 69-139) for NEWS ≥7; median 132 minutes, (IQR 84-236) for NEWS = 0 to 2); faster time from hospital arrival to medical review (54 minutes [IQR 25-114] for NEWS ≥7; 78 minutes [IQR 34-158] for NEWS = 0 to 2); as well as increased length of stay (5 days [IQR 2-11] versus 1 day [IQR 0-5]); intensive care unit admissions (2.0% versus 0.5%); sepsis diagnosis (11.7% versus 2.5%); and mortality, for example, 30-day mortality 12.0% versus 4.1% for NEWS ≥7 versus NEWS = 0 to 2, respectively. On average, for patients referred without a NEWS value (NEWS = NR), most clinical outcomes were comparable with patients with NEWS = 3 to 4, but ambulance conveyance time and time to medical review were comparable with patients with NEWS = 0 to 2., Conclusion: This study has demonstrated that higher NEWS values calculated at GP referral into hospital are associated with a faster medical review and poorer clinical outcomes., (©The Authors.)

Background: The National Early Warning Score (NEWS/NEWS 2) has been adopted across the National Health Service (NHS) in the U.K. as a method of escalating care for deteriorating patients. Intensive Care Unit (ICU) resources are limited and in high demand, with patient discharge a focal point for managing resources effectively. There are currently no universally accepted methods for assessing discharge of patients from an ICU, which can cause premature discharges and put patients at risk of subsequent deterioration, readmission to ICU or death., Methods: We tested the ability of the NEWS to discriminate patients within 24h of admission to an ICU in a U.S. hospital during 2001-2012, by their end discharge location: home; hospital ward; nursing facility; hospice and death. The NEWS performance was compared across five different ICU specialties, using the area under the receiver operating characteristic (AUROC) curve and a large vital signs database (n=2,723,055) collected from 28,523 critical care admissions., Results: The NEWS AUROC (95% CI) at 24h following admission: all patients 0.727 (0.709-0.745); Coronary Care Unit (CCU) 0.829 (0.821-0.837); Cardiac Surgery Recovery Unit (CSRU) 0.844 (0.838-0.850); Medical Intensive Care Unit (MICU) 0.778 (0.767-0.791); Surgical Intensive Care Unit (SICU) 0.775 (0.762-0.788); Trauma Surgical Intensive Care Unit (TSICU) 0.765 (0.751-0.773)., Conclusions: The NEWS has reasonable discrimination for any ICU patient's discharge location. The NEWS has greater ability to discriminate patients in the Coronary Care Unit (CCU) and Cardiac Surgery Recovery Unit (CSRU) compared to other ICU specialties. The NEWS has the real potential to be applied within a universal discharge planning tool for ICU, improving patient safety at the point of discharge.

This article discusses the evidence in relation to preventable deaths and a reported culture of suboptimal care. It warns of the dangers of over-relying on track and trigger systems (TTS) in place of clinical judgement. The article explores cultural and behavioural factors, the effects of short staffing and inappropriate skill mix, which all increase the risk of human error. It emphasises a key message that registered nurses must reflect on the need to change their individual and team approaches to the recognition and assessment of the deteriorating patient.

Objectives: There are no established mortality risk equations specifically for emergency medical patients who are admitted to a general hospital ward. Such risk equations may be useful in supporting the clinical decision-making process. We aim to develop and externally validate a computer-aided risk of mortality (CARM) score by combining the first electronically recorded vital signs and blood test results for emergency medical admissions., Design: Logistic regression model development and external validation study., Setting: Two acute hospitals (Northern Lincolnshire and Goole NHS Foundation Trust Hospital (NH)-model development data; York Hospital (YH)-external validation data)., Participants: Adult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic National Early Warning Score(s) and blood test results recorded on admission., Results: The risk of in-hospital mortality following emergency medical admission was 5.7% (NH: 1766/30 996) and 6.5% (YH: 1703/26 247). The C-statistic for the CARM score in NH was 0.87 (95% CI 0.86 to 0.88) and was similar in an external hospital setting YH (0.86, 95% CI 0.85 to 0.87) and the calibration slope included 1 (0.97, 95% CI 0.94 to 1.00)., Conclusions: We have developed a novel, externally validated CARM score with good performance characteristics for estimating the risk of in-hospital mortality following an emergency medical admission using the patient's first, electronically recorded, vital signs and blood test results. Since the CARM score places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Hospital-acquired acute kidney injury (H-AKI) is a common cause of avoidable morbidity and mortality. Therefore , in the current study, we investigated whether vital signs data from patients, as defined by a National Early Warning Score (NEWS), can predict H-AKI following emergency admission to hospital. We analysed all emergency admissions (n=33,608) to York Hospital with NEWS data over a 24-month period. Here, we report the area under the curve (AUC) for logistic regression models that used the index NEWS (model A0), plus age and sex (A1), plus subcomponents of NEWS (A2) and two-way interactions (A3), and similarly for maximum NEWS (models B0,B1,B2,B3). Of the total emergency admissions, 4.05% (1,361/33,608) had H-AKI. Models using the index NEWS had lower AUCs (0.59-0.68) than models using the maximum NEWS AUCs (0.75-0.77). The maximum NEWS model (B3) was more sensitive than the index NEWS model (A0) (67.60% vs 19.84%) but identified twice as many cases as being at risk of H-AKI (9581 vs 4099) at a NEWS of 5. Based on these results, we suggest that the index NEWS is a poor predictor of H-AKI. The maximum NEWS is a better predictor but appears to be unfeasible because it is only knowable in retrospect and is associated with a substantial increase in workload, albeit with improved sensitivity., (© Royal College of Physicians 2018. All rights reserved.)