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Under-triage is a major threat when admitting patients at the Intermediate Care Unit (IMCU). This study aims to identify risk factors and predict early deterioration of IMCU admissions, to reduce the risk of under-triage. This retrospective cohort study included all admissions to the mixed-surgical stand-alone IMCU of a tertiary referral hospital (2001–2015). Variables included were age, sex, admission indication, admitting specialty, re-admission, and nursing interventions. Early clinical deterioration was defined as ICU transfer or death ≤24 h of admission. Multinomial and logistic regression analyses were performed to identify risk factors and obtain predictions, for several frequently encountered subgroups. A total of 9103 admissions were included, of which 350 (3.8%) early deteriorated. Patients admitted for hemodynamic and respiratory instability had a high risk of early deterioration (OR 16.3 (CI 4.5-59.1)), probability 47.1%. Patients admitted with respiratory insufficiency and active diuresis or complicated sepsis had a high probability of early deterioration (≥29% and ≥26% respectively). The model had an optimism-corrected c-statistic of 0.79 (IQR 0.78-0.80). Patients with combined hemodynamic and respiratory instability should not be admitted to the IMCU. Patients with respiratory insufficiency and active diuresis, or complicated sepsis require close monitoring. •The presented nomogram can be used to assess the probability of early clinical deterioration•Patients with hemodynamic and respiratory instability should be admitted at the ICU•IMCU Patients with respiratory insufficiency and active diuresis, or complicated sepsis require close monitoring

Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs-based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results. This study aimed to identify, summarize, and evaluate the available research, current state of utility, and challenges with machine learning-based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings. PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to \"vital signs,\" \"clinical deterioration,\" and \"machine learning.\" Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines. We identified 24 peer-reviewed studies from 417 articles for inclusion; 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, intensive care units, emergency departments, step-down units, medical assessment units, postanesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods, and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97. In studies that compared performance, reported results suggest that machine learning-based early warning systems can achieve greater accuracy than aggregate-weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.

Monitoring vital signs in hospitalized patients is crucial for evaluating their clinical condition. While early warning scores like the modified early warning score (MEWS) are typically calculated 3 to 4 times daily through spot checks, they might not promptly identify early deterioration. Leveraging technologies that provide continuous monitoring of vital signs, combined with an early warning system, has the potential to identify clinical deterioration sooner. This approach empowers health care providers to intervene promptly and effectively. This study aimed to assess the impact of a Remote Patient Monitoring System (RPMS) with an automated early warning system (R-EWS) on patient safety in noncritical care at a tertiary hospital. R-EWS performance was compared with a simulated Modified Early Warning System (S-MEWS) and a simulated threshold-based alert system (S-Threshold). Patient outcomes, including intensive care unit (ICU) transfers due to deterioration and discharges for nondeteriorating cases, were analyzed in Ramaiah Memorial Hospital's general wards with RPMS. Sensitivity, specificity, chi-square test for alert frequency distribution equality, and the average time from the first alert to ICU transfer in the last 24 hours was determined. Alert and patient distribution by tiers and vitals in R-EWS groups were examined. Analyzing 905 patients, including 38 with deteriorations, R-EWS, S-Threshold, and S-MEWS generated more alerts for deteriorating cases. R-EWS showed high sensitivity (97.37%) and low specificity (23.41%), S-Threshold had perfect sensitivity (100%) but low specificity (0.46%), and S-MEWS demonstrated moderate sensitivity (47.37%) and high specificity (81.31%). The average time from initial alert to clinical deterioration was at least 18 hours for RPMS and S-Threshold in deteriorating participants. R-EWS had increased alert frequency and a higher proportion of critical alerts for deteriorating cases. This study underscores R-EWS role in early deterioration detection, emphasizing timely interventions for improved patient outcomes. Continuous monitoring enhances patient safety and optimizes care quality.

Background: Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates. Objective: This review describes published studies on the development, validation, and implementation of tools for predicting patient deterioration in general wards in hospitals. Methods: An electronic database search of peer reviewed journal papers from 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration, defined by unplanned transfer to the intensive care unit, cardiac arrest, or death. Studies conducted solely in intensive care units, emergency departments, or single diagnosis patient groups were excluded. Results: A total of 46 publications were eligible for inclusion. These publications were heterogeneous in design, setting, and outcome measures. Most studies were retrospective studies using cohort data to develop, validate, or statistically evaluate prediction tools. The tools consisted of early warning, screening, or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time data, deal with complexities of longitudinal data, and warn of deterioration risk earlier. Only a few studies detailed the results of the implementation of deterioration warning tools. Conclusions: Despite relative progress in the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvements in patient outcomes. Further work is needed to realize the potential of automated predictions and update dynamic risk estimates as part of an operational early warning system for inpatient deterioration.

Continuous monitoring of vital signs by using wearable wireless devices may allow for timely detection of clinical deterioration in patients in general wards in comparison to detection by standard intermittent vital signs measurements. A large number of studies on many different wearable devices have been reported in recent years, but a systematic review is not yet available to date. The aim of this study was to provide a systematic review for health care professionals regarding the current evidence about the validation, feasibility, clinical outcomes, and costs of wearable wireless devices for continuous monitoring of vital signs. A systematic and comprehensive search was performed using PubMed/MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials from January 2009 to September 2019 for studies that evaluated wearable wireless devices for continuous monitoring of vital signs in adults. Outcomes were structured by validation, feasibility, clinical outcomes, and costs. Risk of bias was determined by using the Mixed Methods Appraisal Tool, quality assessment of diagnostic accuracy studies 2nd edition, or quality of health economic studies tool. In this review, 27 studies evaluating 13 different wearable wireless devices were included. These studies predominantly evaluated the validation or the feasibility outcomes of these devices. Only a few studies reported the clinical outcomes with these devices and they did not report a significantly better clinical outcome than the standard tools used for measuring vital signs. Cost outcomes were not reported in any study. The quality of the included studies was predominantly rated as low or moderate. Wearable wireless continuous monitoring devices are mostly still in the clinical validation and feasibility testing phases. To date, there are no high quality large well-controlled studies of wearable wireless devices available that show a significant clinical benefit or cost-effectiveness. Such studies are needed to help health care professionals and administrators in their decision making regarding implementation of these devices on a large scale in clinical practice or in-home monitoring.

It is unclear which Early Warning System (EWS) score best predicts in-hospital deterioration of patients when applied in the Emergency Department (ED) or prehospital setting. This systematic review (SR) and meta-analysis assessed the predictive abilities of five commonly used EWS scores (National Early Warning Score (NEWS) and its updated version NEWS2, Modified Early Warning Score (MEWS), Rapid Acute Physiological Score (RAPS), and Cardiac Arrest Risk Triage (CART)). Outcomes of interest included admission to intensive care unit (ICU), and 3-to-30-day mortality following hospital admission. Using DerSimonian and Laird random-effects models, pooled estimates were calculated according to the EWS score cut-off points, outcomes, and study setting. Risk of bias was evaluated using the Newcastle-Ottawa scale. Meta-regressions investigated between-study heterogeneity. Funnel plots tested for publication bias. The SR is registered in PROSPERO (CRD42020191254). Overall, 11,565 articles were identified, of which 20 were included. In the ED setting, MEWS, and NEWS at cut-off points of 3, 4, or 6 had similar pooled diagnostic odds ratios (DOR) to predict 30-day mortality, ranging from 4.05 (95% Confidence Interval (CI) 2.35-6.99) to 6.48 (95% CI 1.83-22.89), p = 0.757. MEWS at a cut-off point ≥3 had a similar DOR when predicting ICU admission (5.54 (95% CI 2.02-15.21)). MEWS ≥5 and NEWS ≥7 had DORs of 3.05 (95% CI 2.00-4.65) and 4.74 (95% CI 4.08-5.50), respectively, when predicting 30-day mortality in patients presenting with sepsis in the ED. In the prehospital setting, the EWS scores significantly predicted 3-day mortality but failed to predict 30-day mortality. EWS scores' predictability of clinical deterioration is improved when the score is applied to patients treated in the hospital setting. However, the high thresholds used and the failure of the scores to predict 30-day mortality make them less suited for use in the prehospital setting.

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.

Vital signs, i.e. respiratory rate, oxygen saturation, pulse, blood pressure and temperature, are regarded as an essential part of monitoring hospitalized patients. Changes in vital signs prior to clinical deterioration are well documented and early detection of preventable outcomes is key to timely intervention. Despite their role in clinical practice, how to best monitor and interpret them is still unclear. To evaluate the ability of vital sign trends to predict clinical deterioration in patients hospitalized with acute illness. PubMed, Embase, Cochrane Library and CINAHL were searched in December 2017. Studies examining intermittently monitored vital sign trends in acutely ill adult patients on hospital wards and in emergency departments. Outcomes representing clinical deterioration were of interest. Performed separately by two authors using a preformed extraction sheet. Of 7,366 references screened, only two were eligible for inclusion. Both were retrospective cohort studies without controls. One examined the accuracy of different vital sign trend models using discrete-time survival analysis in 269,999 admissions. One included 44,531 medical admissions examining trend in Vitalpac Early Warning Score weighted vital signs. They stated that vital sign trends increased detection of clinical deterioration. Critical appraisal was performed using evaluation tools. The studies had moderate risk of bias, and a low certainty of evidence. Additionally, four studies examining trends in early warning scores, otherwise eligible for inclusion, were evaluated. This review illustrates a lack of research in intermittently monitored vital sign trends. The included studies, although heterogeneous and imprecise, indicates an added value of trend analysis. This highlights the need for well-controlled trials to thoroughly assess the research question.

AbstractObjectiveTo provide an overview and critical appraisal of early warning scores for adult hospital patients.DesignSystematic review.Data sourcesMedline, CINAHL, PsycInfo, and Embase until June 2019.Eligibility criteria for study selectionStudies describing the development or external validation of an early warning score for adult hospital inpatients.Results13 171 references were screened and 95 articles were included in the review. 11 studies were development only, 23 were development and external validation, and 61 were external validation only. Most early warning scores were developed for use in the United States (n=13/34, 38%) and the United Kingdom (n=10/34, 29%). Death was the most frequent prediction outcome for development studies (n=10/23, 44%) and validation studies (n=66/84, 79%), with different time horizons (the most frequent was 24 hours). The most common predictors were respiratory rate (n=30/34, 88%), heart rate (n=28/34, 83%), oxygen saturation, temperature, and systolic blood pressure (all n=24/34, 71%). Age (n=13/34, 38%) and sex (n=3/34, 9%) were less frequently included. Key details of the analysis populations were often not reported in development studies (n=12/29, 41%) or validation studies (n=33/84, 39%). Small sample sizes and insufficient numbers of event patients were common in model development and external validation studies. Missing data were often discarded, with just one study using multiple imputation. Only nine of the early warning scores that were developed were presented in sufficient detail to allow individualised risk prediction. Internal validation was carried out in 19 studies, but recommended approaches such as bootstrapping or cross validation were rarely used (n=4/19, 22%). Model performance was frequently assessed using discrimination (development n=18/22, 82%; validation n=69/84, 82%), while calibration was seldom assessed (validation n=13/84, 15%). All included studies were rated at high risk of bias.ConclusionsEarly warning scores are widely used prediction models that are often mandated in daily clinical practice to identify early clinical deterioration in hospital patients. However, many early warning scores in clinical use were found to have methodological weaknesses. Early warning scores might not perform as well as expected and therefore they could have a detrimental effect on patient care. Future work should focus on following recommended approaches for developing and evaluating early warning scores, and investigating the impact and safety of using these scores in clinical practice.Systematic review registrationPROSPERO CRD42017053324.

This year is the 10th anniversary since the launch of the National Early Warning Score (NEWS) by the Royal College of Physicians in 2012. This review reflects on the journey, from the nascent concept of a standardised system to detect acute illness severity and clinical deterioration through to the adoption of NEWS2 by the NHS and, ultimately, its incorporation into quality indicators of acute care provision. The impact of NEWS/NEWS2 on the transformation of provision and configuration and training of acute care teams in hospitals is reviewed. User feedback has been key in iterating guidance on the use of NEWS/NEWS2 and key elements of this are discussed. The ultimate aim of NEWS was to improve patient outcomes with acute illness or deterioration and the impact on outcomes is now becoming apparent but, paradoxically, an effective response can eliminate the link between the score and the ultimate outcome. This review concludes with a reflection on what the next 10 years may bring, particularly with the digital transformation of healthcare and its potential impact on scoring systems, as well as the necessary permeation of NEWS2 beyond the acute hospital setting into emergency response triage in primary and community care settings. Ten years on, via NEWS/NEWS2, the NHS is the first healthcare system globally with a ‘common language’ of illness severity and a standardised early warning system for acute clinical illness and deterioration, a system that is now being replicated in many other areas of the world.