Recognising clinical instability in hospital patients before cardiac arrest or unplanned admission to intensive care. A pilot study in a tertiary-care hospital

PeriOperative Quality Initiative (POQI) international consensus statement on perioperative arterial pressure management

Arterial pressure monitoring and management are mainstays of haemodynamic therapy in patients having surgery. This article presents updated consensus statements and recommendations on perioperative arterial pressure management developed during the 11th POQI PeriOperative Quality Initiative (POQI) consensus conference held in London, UK, on June 4-6, 2023, which included a diverse group of international experts. Based on a modified Delphi approach, we recommend keeping intraoperative mean arterial pressure ≥60 mm Hg in at-risk patients. We further recommend increasing mean arterial pressure targets when venous or compartment pressures are elevated and treating hypotension based on presumed underlying causes. When intraoperative hypertension is treated, we recommend doing so carefully to avoid hypotension. Clinicians should consider continuous intraoperative arterial pressure monitoring as it can help reduce the severity and duration of hypotension compared to intermittent arterial pressure monitoring. Postoperative hypotension is often unrecognised and might be more important than intraoperative hypotension because it is often prolonged and untreated. Future research should focus on identifying patient-specific and organ-specific hypotension harm thresholds and optimal treatment strategies for intraoperative hypotension including choice of vasopressors. Research is also needed to guide monitoring and management strategies for recognising, preventing, and treating postoperative hypotension.

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.

RAPID-ED: A predictive model for risk assessment of patient's early in-hospital deterioration from emergency department

Introduction

The objective of this multi-center retrospective cohort study was to devise a predictive tool known as RAPID-ED. This model identifies non-traumatic adult patients at significant risk for cardiac arrest within 48 hours post-admission from the emergency department.

Methods

Data from 224,413 patients admitted through the emergency department (2016-2020) was analyzed, incorporating vital signs, lab tests, and administered therapies. A multivariable regression model was devised to anticipate early cardiac arrest. The efficacy of the RAPID-ED model was evaluated against traditional scoring systems like National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS) and its predictive ability was gauged via the area under the receiver operating characteristic curve (AUC) in both hold-out validation set and external validation set.

Results

RAPID-ED outperformed traditional models in predicting cardiac arrest with an AUC of 0.819 in the hold-out validation set and 0.807 in the external validation set. In this critical care update, RAPID-ED offers an innovative approach to assessing patient risk, aiding emergency physicians in post-discharge care decisions from the emergency department. High-risk score patients (≥13) may benefit from early ICU admission for intensive monitoring.

Conclusion

As we progress with advancements in critical care, tools like RAPID-ED will prove instrumental in refining care strategies for critically ill patients, fostering an improved prognosis and potentially mitigating mortality rates.

Antecedents to and outcomes for in-hospital cardiac arrests in Australian hospitals with mature medical emergency teams: A multicentre prospective observational study

BACKGROUND

The epidemiology and predictability of in-hospital cardiac arrests (IHCAs) in hospitals with established medical emergency teams (METs) is underinvestigated.

OBJECTIVES

We categorised IHCAs into three categories: "possible suboptimal end-of-life planning" (possible SELP), "potentially predictable", or "sudden and unexpected" using age, Charlson Comorbidity Index, place of residence, functional independence, along with documented vital signs, K+ and HCO3 in the period prior to the IHCA. We also described the differences in characteristics and outcomes amongst these three categories of IHCAs.

METHODS

This was a prospective observational study (1st July 2017 to 9th August 2018) of adult (18 years) IHCA patients in wards of seven Australian hospitals with well-established METs.

RESULTS

Amongst 152 IHCA patients, 145 had complete data. The number (%) classified as possible SELP, potentially predictable, and sudden and unexpected IHCA was 50 (34.5%), 52 (35.8%), and 43 (29.7%), respectively. Amongst the 52 potentially predictable patients, six (11.5%) had missing vital signs in the preceding 6 hr, 18 (34.6%) breached MET criteria in the prior 24 hr but received no MET call, and 6 (11.5%) had a MET call but remained on the ward. Abnormal K+ and HCO3 was present in 15 of 51 (29.5%) and 13 of 51 (25.5%) of such patients, respectively. The 43 sudden and unexpected IHCA patients were mostly (97.6%) functionally independent and had the lowest median Charlson Comorbidity Index. In-hospital mortality for IHCAs classified as possible SELP, potentially predictable, and sudden and unexpected was 76.0%, 61.5%, and 44.2%, respectively (p = 0.007). Only four of 12 (33.3%) possible SELP survivors had a good functional outcome.

CONCLUSIONS

In seven Australian hospitals with mature METs, only one-third of IHCAs were sudden and unexpected. Improving end-of-life care in elderly comorbid patients and enhancing the response to objective signs of deterioration may further reduce IHCAs in the Australian context.

Deep Learning-Based Early Warning Score for Predicting Clinical Deterioration in General Ward Cancer Patients

BACKGROUND

Cancer patients who are admitted to hospitals are at high risk of short-term deterioration due to treatment-related or cancer-specific complications. A rapid response system (RRS) is initiated when patients who are deteriorating or at risk of deteriorating are identified. This study was conducted to develop a deep learning-based early warning score (EWS) for cancer patients (Can-EWS) using delta values in vital signs.

METHODS

A retrospective cohort study was conducted on all oncology patients who were admitted to the general ward between 2016 and 2020. The data were divided into a training set (January 2016-December 2019) and a held-out test set (January 2020-December 2020). The primary outcome was clinical deterioration, defined as the composite of in-hospital cardiac arrest (IHCA) and unexpected intensive care unit (ICU) transfer.

RESULTS

During the study period, 19,739 cancer patients were admitted to the general wards and eligible for this study. Clinical deterioration occurred in 894 cases. IHCA and unexpected ICU transfer prevalence was 1.77 per 1000 admissions and 43.45 per 1000 admissions, respectively. We developed two models: Can-EWS V1, which used input vectors of the original five input variables, and Can-EWS V2, which used input vectors of 10 variables (including an additional five delta variables). The cross-validation performance of the clinical deterioration for Can-EWS V2 (AUROC, 0.946; 95% confidence interval [CI], 0.943-0.948) was higher than that for MEWS of 5 (AUROC, 0.589; 95% CI, 0.587-0.560; p < 0.001) and Can-EWS V1 (AUROC, 0.927; 95% CI, 0.924-0.931). As a virtual prognostic study, additional validation was performed on held-out test data. The AUROC and 95% CI were 0.588 (95% CI, 0.588-0.589), 0.890 (95% CI, 0.888-0.891), and 0.898 (95% CI, 0.897-0.899), for MEWS of 5, Can-EWS V1, and the deployed model Can-EWS V2, respectively. Can-EWS V2 outperformed other approaches for specificities, positive predictive values, negative predictive values, and the number of false alarms per day at the same sensitivity level on the held-out test data.

CONCLUSIONS

We have developed and validated a deep learning-based EWS for cancer patients using the original values and differences between consecutive measurements of basic vital signs. The Can-EWS has acceptable discriminatory power and sensitivity, with extremely decreased false alarms compared with MEWS.

Development and validation of the early warning system scores ontology

BACKGROUND

Clinical early warning scoring systems, have improved patient outcomes in a range of specializations and global contexts. These systems are used to predict patient deterioration. A multitude of patient-level physiological decompensation data has been made available through the widespread integration of early warning scoring systems within EHRs across national and international health care organizations. These data can be used to promote secondary research. The diversity of early warning scoring systems and various EHR systems is one barrier to secondary analysis of early warning score data. Given that early warning score parameters are varied, this makes it difficult to query across providers and EHR systems. Moreover, mapping and merging the parameters is challenging. We develop and validate the Early Warning System Scores Ontology (EWSSO), representing three commonly used early warning scores: the National Early Warning Score (NEWS), the six-item modified Early Warning Score (MEWS), and the quick Sequential Organ Failure Assessment (qSOFA) to overcome these problems.

METHODS

We apply the Software Development Lifecycle Framework-conceived by Winston Boyce in 1970-to model the activities involved in organizing, producing, and evaluating the EWSSO. We also follow OBO Foundry Principles and the principles of best practice for domain ontology design, terms, definitions, and classifications to meet BFO requirements for ontology building.

RESULTS

We developed twenty-nine new classes, reused four classes and four object properties to create the EWSSO. When we queried the data our ontology-based process could differentiate between necessary and unnecessary features for score calculation 100% of the time. Further, our process applied the proper temperature conversions for the early warning score calculator 100% of the time.

CONCLUSIONS

Using synthetic datasets, we demonstrate the EWSSO can be used to generate and query health system data on vital signs and provide input to calculate the NEWS, six-item MEWS, and qSOFA. Future work includes extending the EWSSO by introducing additional early warning scores for adult and pediatric patient populations and creating patient profiles that contain clinical, demographic, and outcomes data regarding the patient.

Effect of Implementing a Commercial Electronic Early Warning System on Outcomes of Hospitalized Patients

Despite the widespread adoption of early warning systems (EWSs), it is uncertain if their implementation improves patient outcomes. The authors report a pre-post quasi-experimental evaluation of a commercially available EWS on patient outcomes at a 700-bed academic medical center. The EWS risk scores were visible in the electronic medical record by bedside clinicians. The EWS risk scores were also monitored remotely 24/7 by critical care trained nurses who actively contacted bedside nurses when a patient's risk levels increased. The primary outcome was inpatient mortality. Secondary outcomes were rapid response team calls and activation of cardiopulmonary arrest (code-4) response teams. The study team conducted a regression discontinuity analysis adjusting for age, gender, insurance, severity of illness, risk of mortality, and hospital occupancy at admission. The analysis included 53,229 hospitalizations. Adjusted analysis showed no significant change in inpatient mortality, rapid response team call, or code-4 activations after implementing the EWS. This study confirms the continued uncertainty in the effectiveness of EWSs and the need for further rigorous examinations of EWSs.

Association between time of day for rapid response team activation and mortality

PURPOSE

To evaluate the frequency of rapid response team (RRT) calls by time of day and their association with in-hospital mortality.

MATERIALS AND METHODS

This was a retrospective cohort study of all RRT calls at a tertiary teaching hospital in Porto Alegre, Brazil. Patients were categorized according to the time of initial RRT activation. Activations were classified as daytime (7:00-18:59) or nighttime (19:00-6:59). The primary outcome was in-hospital mortality rate. The secondary outcome was ICU admission within 48 h of RRT assessment.

RESULTS

During the study period, 4522 patients were included in the final analysis. Cardiovascular and respiratory changes were more common causes of nighttime activation, whereas neurological and laboratory changes were more common during the daytime. The in-hospital mortality rate was 23.9% (1081/4522). Nighttime RRT calls were not associated with worse outcomes than daytime calls. However, a decrease in the number of calls was observed during nursing handover periods (7:00, 13:00 and 19:00). Two time periods were associated with increased adjusted odds for mortality: 12:00-13:00 (adjusted OR 2.277; 95% CI 1.392-3.725) and 19:00-20:00 (adjusted OR 1.873; CI 1.873; 95% 1.099-3.190).

CONCLUSION

We found that nighttime RRT calls were not associated with worse outcomes than daytime RRT calls. However, a decrease in the number of calls and higher mortality was observed during nursing handover periods.

Implementing a Rapid Response Team: A Quality Improvement Project in a Low- to Middle-Income Country

BACKGROUND

The benefits of rapid response teams (RRTs) have been controversial with few studies conducted in low- to middle-income countries.

OBJECTIVE

The aim of this study was to investigate the effectiveness of implementing an RRT on 4 patient outcomes.

METHODS

We conducted a quality improvement pre-and-post design using the Plan-Do-Study-Act model in a tertiary hospital in a low- to middle-income country. We collected data before and after implementing the RRT in 4 phases and over 4 years.

RESULTS

Survival to discharge after cardiac arrest was 25.0% per 1000 discharges in 2016 and increased to 50% in 2019, a 50% increase. The rate of activations per 1000 discharges was 20.45% for the code team in 2016 and 33.6% for the RRT team in 2019. Thirty-one patients who arrested were transferred to a critical care unit before implementing the RRT, and 33% of such patients were transferred after. The time it took the code team to arrive at the bedside was 3.1 minutes in 2016 and decreased to 1.7 minutes for the RRT team to arrive in 2019, a 46% decrease.

DISCUSSION AND CLINICAL IMPLICATIONS

Implementing an RTT led by nurses in a low- to middle-income country increased the survival rate of patients who had a cardiac arrest by 50%. The role of nurses in improving patient outcomes and saving lives is substantial and empowers nurses to call for assistance to save patient lives who show early signs of a cardiac arrest. Hospital administrators should continue to use strategies to improve nurses' timely response to the clinical deterioration of patients and to continue to collect data to assess the effect of the RRT over time.

Dosage and clinical outcomes of medical emergency team and conventional referral mediated unplanned intensive care admissions

Background: Unplanned intensive care unit (ICU) admission occurs via activation of medical emergency team (MET) and conventional ICU referral (CIR), i.e., ICU consultation. We aimed to compare the dosage, association with unplanned ICU admissions and hospital mortality between MET and CIR systems. Methods: We performed a retrospective, single centre observational study on unplanned ICU admissions from hospital wards between July 2017 and June 2018. We evaluated the dosage (expressed per 1000 admissions) and association of CIR and MET system with unplanned ICU admission using Chi-square test. The relationship (unadjusted and adjusted to Australia and New Zealand risk of death (ANZROD) and lead time) between unplanned ICU admission pathway (MET vs CIR) and hospital mortality was tested by binary logistic regression analysis [Odds ratio (OR) with 95% confidence interval (CI)]. Results: Out of 38,628 patients hospitalised, 679 had unplanned ICU admission (2%) with an ICU admission rate of 18 per 1000 ward admissions. There were 2153 MET and 453 CIR activations, producing a dosage of 56 and 12 per 1000 admissions, respectively. Higher unplanned ICU admission was significantly associated with CIR compared to MET activation (324/453 (71.5%) vs 355/2153 (16.5%) p < 0.001). On binary logistic regression, MET system was significantly associated with higher hospital mortality on unadjusted analysis (OR 1.65 (95% CI: 1.09-2.48) p = 0.02) but not after adjustment with ANZROD and lead time (OR 1.15 (95% CI: 0.71-1.86), p = 0.58). Conclusions: Compared to CIR, MET system had higher dosage but lower frequency of unplanned ICU admissions and lacked independent association with hospital mortality.

The Effect of Continuous Versus Periodic Vital Sign Monitoring on Disease Severity of Patients with an Unplanned ICU Transfer

Continuous vital sign monitoring (CM) may detect ward patient's deterioration earlier than periodic monitoring. This could result in timely ICU transfers or in a transfer delay due to misperceived higher level of care on the ward. The primary objective of this study was to compare patient's disease severity upon unplanned ICU transfer, before and after CM implementation. We included a one-year period before and after CM implementation between August 1, 2017 - July 31, 2019. Before implementation, surgical and internal medicine patients' vital signs were periodically monitored, compared to continuous monitoring with wireless linkage to hospital systems after implementation. In both periods the same early warning score (EWS) protocol was in place. Primary outcome was disease severity scores upon ICU transfer. Secondary outcomes were ICU and hospital length of stay, incidence of mechanical ventilation and ICU mortality. In the two one-year periods 93 and 59 unplanned ICU transfer episodes were included, respectively. Median SOFA (3 (2-6) vs 4 (2-7), p = .574), APACHE II (17 (14-20) vs 16 (14-21), p = .824) and APACHE IV (59 (46-67) vs 50 (36-65), p = .187) were comparable between both periods, as were the median ICU LOS (3.0 (1.7-5.8) vs 3.1 (1.6-6.1), p.962), hospital LOS (23.6 (11.5-38.0) vs 19 (13.9-39.2), p = .880), incidence of mechanical ventilation (28 (47%) vs 22 (54%), p.490), and ICU mortality (11 (13%) vs 10 (19%), p.420). This study shows no difference in disease severity upon unplanned ICU transfer after CM implementation for patients who have deteriorated on the ward.

A wearable patch based remote early warning score (REWS) in major abdominal cancer surgery patients

INTRODUCTION

The shift toward remote patient monitoring methods to detect clinical deterioration requires testing of wearable devices in real-life clinical settings. This study aimed to develop a remote early warning scoring (REWS) system based on continuous measurements using a wearable device, and compare its diagnostic performance for the detection of deterioration to the diagnostic performance of the conventional modified early warning score (MEWS).

MATERIALS AND METHODS

The study population of this prospective, single center trial consisted of patients who underwent major abdominal cancer surgery and were monitored using routine in-hospital spotcheck measurements of the vital parameters. Heart and respiratory rates were measured continuously using a wireless accelerometer patch (HealthDot). The prediction by MEWS of deterioration toward a complication graded Clavien-Dindo of 2 or higher was compared to the REWS derived from continuous measurements by the wearable patch.

MAIN RESULTS

A total of 103 patients and 1909 spot-check measurements were included in the analysis. Postoperative deterioration was observed in 29 patients. For both EWS systems, the sensitivity (MEWS: 0.20 95% CI: [0.13-0.29], REWS: 0.20 95% CI: [0.13-0.29]) and specificity (MEWS: 0.96 95% CI: [0.95-0.97], REWS: 0.96 95% CI: [0.95-0.97]) were assessed.

CONCLUSIONS

The diagnostic value of the REWS method, based on continuous measurements of the heart and respiratory rates, is comparable to that of the MEWS in patients following major abdominal cancer surgery. The wearable patch could detect the same amount of deteriorations, without requiring manual spot check measurements.

Clinical impact of vital sign abnormalities in patients admitted with acute exacerbation of chronic obstructive pulmonary disease: an observational study using continuous wireless monitoring

Early detection of abnormal vital signs is critical for timely management of acute hospitalised patients and continuous monitoring may improve this. We aimed to assess the association between preceding vital sign abnormalities and serious adverse events (SAE) in patients hospitalised with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Two hundred patients' vital signs were wirelessly and continuously monitored with peripheral oxygen saturation, heart rate, and respiratory rate during the first 4 days after admission for AECOPD. Non-invasive blood pressure was also measured every 30-60 min. The primary outcome was occurrence of SAE according to international definitions within 30 days and physiological data were analysed for preceding vital sign abnormalities. Data were presented as the mean cumulative duration of vital sign abnormalities per 24 h and analysed using Wilcoxon rank sum test. SAE during ongoing continuous monitoring occurred in 50 patients (25%). Patients suffering SAE during the monitoring period had on average 455 min (SD 413) per 24 h of any preceding vital sign abnormality versus 292 min (SD 246) in patients without SAE, p = 0.08, mean difference 163 min [95% CI 61-265]. Mean duration of bradypnea (respiratory rate < 11 min^-1) was 48 min (SD 173) compared with 30 min (SD 84) in patients without SAE, p = 0.01. In conclusion, the duration of physiological abnormalities was substantial in patients with AECOPD. There were no statistically significant differences between patients with and without SAE in the overall duration of preceding physiological abnormalities.Study registration: http://ClinicalTrials.gov (NCT03660501). Date of registration: Sept 6 2018.

Effect of a Wireless Vital Sign Monitoring System on the Rapid Response System in the General Ward

While wireless vital sign monitoring is expected to reduce the vital sign measurement time (thus reducing the nursing workload), its impact on the rapid response system is unclear. This study compared the time from vital sign measurement to recording and rapid response system activation between wireless and conventional vital sign monitoring in the general ward, to investigate the impact of wireless vital sign monitoring system on the rapid response system. The study divided 249 patients (age > 18 years; female: 47, male: 202) admitted to the general ward into non-wireless (n = 101) and wireless (n = 148) groups. Intervals from vital sign measurement to recording and from vital sign measurement to rapid response system activation were recorded. Effects of wireless system implementation for vital sign measurement on the nursing workload were surveyed in 30 nurses. The interval from vital sign measurement to recording was significantly shorter in the wireless group than in the non-wireless group (4.3 ± 2.9 vs. 44.7 ± 14.4 min, P < 0.001). The interval from vital sign measurement to rapid response system activation was also significantly lesser in the wireless group than in the non-wireless group (27.5 ± 12.9 vs. 41.8 ± 19.6 min, P = 0.029). The nursing workload related to vital sign measurement significantly decreased from 3 ± 0.87 to 2.4 ± 9.7 (P = 0.021) with wireless system implementation. Wireless vital sign monitoring significantly reduced the time to rapid response system activation by shortening the time required to measure the vital signs. It also significantly reduced the nursing workload.

A pilot study to investigate real-time digital alerting from wearable sensors in surgical patients

Background

Continuous vital sign monitoring may identify changes sooner than current standard monitoring.

Objective

To investigate if the use of real-time digital alerts sent to healthcare staff can improve the time taken to identify unwell patients and those with sepsis.

Design

A prospective cohort study design.

Setting

West Middlesex University Hospital, UK.

Participants

Fifty acutely unwell surgical patients admitted to hospital.

Intervention

Patients wore a lightweight wearable sensor measuring heart rate (HR), respiratory rate (RR) and temperature every 2 min whilst standard intermittent ward monitoring of vital signs was performed by nurses. Digital alerts were sent to healthcare staff from the sensor to a smartphone device. All alerts were reviewed for recruited patients to identify the exact time on the sensor in which deterioration occurred. The time to acknowledgement was then reviewed for each action and an average time to acknowledgement calculated.

Results

There were 50 patients recruited in the pilot study, of which there were vital sign alerts in 18 patients (36%). The total number of vital sign alerts generated in these 18 patients was 51. Of these 51 alerts, there were 7 alerts for high HR (13.7%), 33 for RR (64.7%) and 11 for temperature (21.6%). Out of the 27 acknowledged alerts, there were 2 alerts for HR, 17 for RR and 8 for temperature. The average time to staff acknowledgement of the notification for all alerts was 154 min (2.6 h).

There were some patients which had shown signs of deterioration in the cohort. The frequency of routine observation monitoring was increased in 2 cases, 3 patients were referred to a senior clinician and 2 patients were initiated on the sepsis pathway.

Conclusion

This study demonstrates the evaluation of digital alerts to nurses in real time. Although not all alerts were acknowledged, deterioration on the ward observations was detected and actions were taken accordingly. Patients were started on the sepsis pathway and escalation to senior clinicians occurred. Further research is required to review why only some alerts were acknowledged and the effects of digital alerting on patient outcomes.

Trial registration

ClinicalTrials.gov, NCT04638738

Artificial intelligence and clinical deterioration

PURPOSE OF REVIEW

To provide an overview of the systems being used to identify and predict clinical deterioration in hospitalised patients, with focus on the current and future role of artificial intelligence (AI).

RECENT FINDINGS

There are five leading AI driven systems in this field: the Advanced Alert Monitor (AAM), the electronic Cardiac Arrest Risk Triage (eCART) score, Hospital wide Alert Via Electronic Noticeboard, the Mayo Clinic Early Warning Score, and the Rothman Index (RI). Each uses Electronic Patient Record (EPR) data and machine learning to predict adverse events. Less mature but relevant evolutions are occurring in the fields of Natural Language Processing, Time and Motion Studies, AI Sepsis and COVID-19 algorithms.

SUMMARY

Research-based AI-driven systems to predict clinical deterioration are increasingly being developed, but few are being implemented into clinical workflows. Escobar et al. (AAM) provide the current gold standard for robust model development and implementation methodology. Multiple technologies show promise, however, the pathway to meaningfully affect patient outcomes remains challenging.

Implementing a Rapid Response System in a tertiary-care hospital. A cost-effectiveness study

PURPOSE

The occurrence of adverse events (AE) in hospitalized patients substancially increases the risk of disability or death, having a major negative clinical and economic impact on public health. For early identification of patients at risk and to establish preventive measures, different healthcare systems have implemented rapid response systems (RRS). The aim of this study was to carry out a cost-effectiveness analysis of implementing a RRS in a tertiary-care hospital.

METHODS

We included all the patients admitted to Hospital Clínic de Barcelona from 1 to 2016 to 31 December 2016. The cost-effectiveness analysis was summarized as the incremental cost-effectiveness ratio (incremental cost divided by the incremental effectiveness of the two alternatives, RRS versus non-RRS). The effectiveness of the RRS, defined as improvements in health outcomes (AE, cardiopulmonary arrest and mortality), was obtained from the literature and applied to the included patient cohort. A budget impact analysis on the implementation of the RRS from a hospital perspective was performed over a 5-year time horizon.

RESULTS

42,409 patients were included, and 448 (1.05%) had severe AE requiring ICU admission. The cost-effectiveness analysis showed an incremental cost (savings) of EUR - 1,471,101 of RRS versus the non-RRS. The budgetary impact showed a cost reduction of EUR 896,762.00 in the first year and EUR 1,588,579.00 from the second to the fifth year.

CONCLUSIONS

The present analysis shows the RRS as a dominant, less costly and more effective structure compared to the non-RRS.

Non-contact physiological monitoring of post-operative patients in the intensive care unit

Prolonged non-contact camera-based monitoring in critically ill patients presents unique challenges, but may facilitate safe recovery. A study was designed to evaluate the feasibility of introducing a non-contact video camera monitoring system into an acute clinical setting. We assessed the accuracy and robustness of the video camera-derived estimates of the vital signs against the electronically-recorded reference values in both day and night environments. We demonstrated non-contact monitoring of heart rate and respiratory rate for extended periods of time in 15 post-operative patients. Across day and night, heart rate was estimated for up to 53.2% (103.0 h) of the total valid camera data with a mean absolute error (MAE) of 2.5 beats/min in comparison to two reference sensors. We obtained respiratory rate estimates for 63.1% (119.8 h) of the total valid camera data with a MAE of 2.4 breaths/min against the reference value computed from the chest impedance pneumogram. Non-contact estimates detected relevant changes in the vital-sign values between routine clinical observations. Pivotal respiratory events in a post-operative patient could be identified from the analysis of video-derived respiratory information. Continuous vital-sign monitoring supported by non-contact video camera estimates could be used to track early signs of physiological deterioration during post-operative care.

Automated alert and activation of medical emergency team using early warning score

Background

Timely recognition of warning signs from deteriorating patients and proper treatment are important in improving patient safety. In comparison to the traditional medical emergency team (MET) activation triggered by phone calls, automated activation of MET may minimize activation delays. However, limited data are available on the effects of automated activation systems on the time from derangement to MET activation and on clinical outcomes. The objective of this study was to determine the impact of an automated alert and activation system for MET on clinical outcomes in unselected hospitalized patients.

Methods

This is an observational study using prospectively collected data from consecutive patients managed by the MET at a university-affiliated, tertiary hospital from March 2013 to December 2019. The automated alert system automatically calculates the Modified Early Warning Score (MEWS) and subsequently activates MET when the MEWS score is 7 or higher, which was implemented since August 2016. The outcome measures of interest including hospital mortality in patients with MEWS of 7 or higher were compared between pre-implementation and post-implementation groups of the automated alert and activation system in the primary analysis. The association between the implementation of the system and hospital mortality was evaluated with logistic regression analysis.

Results

Of the 7678 patients who were managed by MET during the study period, 639 patients during the pre-implementation period and 957 patients during the post-implementation period were included in the primary analysis. MET calls due to abnormal physiological variables were more common during the pre-implementation period, while MET calls due to medical staff’s worries or concern about the patient’s condition were more common during the post-implementation period. The median time from deterioration to MET activation was significantly shortened in the post-implementation period compared to the pre-implementation period (34 min vs. 60 min, P < 0.001). In addition, unplanned ICU admission rates (41.2% vs. 71.8%, P < 0.001) was reduced during the post-implementation period. Hospital mortality was decreased after implementation of the automated alert system (27.2% vs. 38.5%, P < 0.001). The implementation of the automated alert and activation system was associated with decreased risk of death in the multivariable analysis (adjusted OR 0.73, 95% CI 0.56–0.90).

Conclusions

After implementing an automated alert and activation system, the time from deterioration to MET activation was shortened and clinical outcomes were improved in hospitalized patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40560-021-00588-y.

Patient Deterioration in Australian Regional and Rural Hospitals: Is the Queensland Adult Deterioration Detection System the Criterion Standard?

OBJECTIVE

This study compares the efficiency of six early warning systems (EWSs) to determine whether the EWS used in most public hospitals in Queensland, Australia, The Queensland Adult Deterioration Detection System (Q-ADDS), is best suited for use in small regional and rural hospitals.

METHOD

In this retrospective case-control study, patients who experienced an in-hospital severe adverse event (index patients) for a 3.5-year period were demographically and diagnostically matched with patients who had uneventful hospital stays (control patients). The EWS efficiency was based on the area under the receiver operator characteristic curve (AUROC) and the number of false and true alerts generated by each EWS.

RESULT

The incidence of severe adverse events was 1.2% of in-hospital patients, and 2500 sets of vital signs were collected from 159 index and 172 control patients. The EWSs were only able to identify approximately half of the index patients. The AUROC was 0.666 to 0.801 and the EWS generated 2.4 to 7.6 false alerts to every true alert per 1000 admissions. The National Early Warning Score had the best ratio of false to true alerts (2.4:1) but was only able to identify 40.8% of deteriorating patients. The Q-ADDS identified 46.5% of the deteriorating patients and had a false to true alert ratio of 3.2:1. When compared with the National Early Warning Score, systems with higher AUROCs (0.744 and 0.801) also had higher proportion of false alerts. None of the alternative EWSs seem to provide marked benefits over Q-ADDS.

CONCLUSIONS

At present, there is insufficient evidence to replace Q-ADDS with an alternative EWS. Because the EWSs were only able to identify half of the deteriorating patients, EWSs should be used in conjunction with good clinical judgment.

Pediatric rapid response system innovations

Rapid Response Systems (RRSs) are an organizational approach to support the timely recognition and treatment of decompensating patients and are used in many pediatric hospitals. These systems are comprised of afferent and efferent Limbs, as well as oversight arms. When incorporated into an RRS, standardized care algorithms can be helpful in identifying deteriorating patients and improving behaviors of the multidisciplinary team. The aim of this paper is to provide an overview of pediatric RRS and provide an example in which standardized care algorithms developed for the efferent limb of a pediatric RRS were associated with improvement in early escalation of care.PLAIN LANGUAGE SUMMARYThe Rapid Response System (RRS) is used in hospitals to recognize and care for hospitalized patients that are decompensating outside of an Intensive Care Unit. RRSs are made up of two main response components. The afferent limb focuses on the recognition and calls for help; the efferent limb focuses on correcting the deteriorating patient's physiology. Much energy has been put into afferent limb development to identify worsening patients before they progress to full cardiac or respiratory arrest. Standardization of efferent limb care algorithms can assist in developing and maintaining a shared mental model of care to improve communication and function of the multidisciplinary team.

Do patients suffering an out-of-hospital cardiac arrest present to the ambulance service with symptoms in the preceding 48 h?

AIMS

In-hospital cardiac arrests are often preceded by a period of physiological deterioration that has often gone unnoticed. We proposed that the same might be true for out-of-hospital cardiac arrests (OHCAs) where ambulance crews leave patients at home who then subsequently go on to suffer a cardiac arrest.

METHODS AND RESULTS

We identified all OHCA over a 12-month period that had been seen and assessed by an ambulance crew within the 48 h preceding their cardiac arrest. We retrospectively calculated the patient's NEWS2 score at the time of their initial assessment as a marker of their physiological status and need for hospital admission. Of 1960 OHCA patients, 184 (9.4%) had been assessed by ambulance crews within the preceding 48 h. Excluding those who had been taken to hospital (and then discharged), declined hospital conveyance or were on end-of-life care pathways, 79 (56% of total) were left at home through crew discretion. Thirty-four out of 79 (43%) patients not conveyed had either a NEWS score of 3 in a single parameter or a score of ≥5, which in hospital would mandate an urgent medical review. The most overlooked observation was respiratory rate.

CONCLUSIONS

In total, 1.7% of all OHCA had been assessed in the previous 48 h and inappropriately left at home by ambulance crews. This represents a missed opportunity to avert cardiac arrest. NEWS scoring has the potential to improve pre-hospital triage of these patients and avoid missing the deteriorating patient.

Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS)

OBJECTIVE

We aimed to develop a model for accurate prediction of general care inpatient deterioration.

MATERIALS AND METHODS

Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call.

RESULTS

Training, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score.

DISCUSSION

Low alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering.

CONCLUSIONS

MC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.

The problem of postoperative respiratory depression

WHAT IS KNOWN AND OBJECTIVE

Postsurgical recovery is influenced by multiple pre-, intra- and perioperative pharmacotherapeutic interventions, including the administration of medications that can induce respiratory depression postoperatively. We present a succinct overview of the topic, including the nature and magnitude of the problem, contributing factors, current limited options, and potential novel therapeutic approach.

COMMENT

Pre-, intra- and perioperative medications are commonly administered for anxiety, anaesthesia, muscle relaxation and pain relief among other reasons. Several of the medications alone or in joint-action can be additive or synergistic producing respiratory depression. Given the large number of surgical procedures that are performed each year, even a small percentage of postoperative respiratory complications translates into a large number of affected patients.

WHAT IS NEW AND CONCLUSION

Due to the large number of surgeries performed each year, and the variety of medications used before, during, and after surgery, the occurrence of postoperative respiratory depression is surprisingly common. It is a significant medical problem and burden on hospital resources. There is a need for new strategies to prevent and treat the acute and collateral problems associated with postoperative respiratory depression.

Improving the prioritization of children at the emergency department: Updating the Manchester Triage System using vital signs

Background

Vital signs are used in emergency care settings in the first assessment of children to identify those that need immediate attention. We aimed to develop and validate vital sign based Manchester Triage System (MTS) discriminators to improve triage of children at the emergency department.

Methods and findings

The TrIAGE project is a prospective observational study based on electronic health record data from five European EDs (Netherlands (n = 2), United Kingdom, Austria, and Portugal). In the current study, we included 117,438 consecutive children <16 years presenting to the ED during the study period (2012–2015). We derived new discriminators based on heart rate, respiratory rate, and/or capillary refill time for specific subgroups of MTS flowcharts. Moreover, we determined the optimal cut-off value for each vital sign. The main outcome measure was a previously developed 3-category reference standard (high, intermediate, low urgency) for the required urgency of care, based on mortality at the ED, immediate lifesaving interventions, disposition and resource use. We determined six new discriminators for children <1 year and ≥1 year: “Very abnormal respiratory rate”, “Abnormal heart rate”, and “Abnormal respiratory rate”, with optimal cut-offs, and specific subgroups of flowcharts. Application of the modified MTS reclassified 744 patients (2.5%). Sensitivity increased from 0.66 (95%CI 0.60–0.72) to 0.71 (0.66–0.75) for high urgency patients and from 0.67 (0.54–0.76) to 0.70 (0.58–0.80) for high and intermediate urgency patients. Specificity decreased from 0.90 (0.86–0.93) to 0.89 (0.85–0.92) for high and 0.66 (0.52–0.78) to 0.63 (0.50–0.75) for high and intermediate urgency patients. These differences were statistically significant. Overall performance improved (R² 0.199 versus 0.204).

Conclusions

Six new discriminators based on vital signs lead to a small but relevant increase in performance and should be implemented in the MTS.

Quels sont le fonctionnement, les caractéristiques, les effets et les modalités d’implantation des équipes d’intervention rapide ? Une revue de la littérature

Introduction&#160;: Hospitalized patients are at risk of unrecognized clinical deterioration that may lead to adverse events.Context&#160;: Rapid Response Teams (RRTs) exist around the world as a strategy to improve patient safety.Objective&#160;: To explore how RRTs work, their characteristics, impacts, and methods of implementation.Design&#160;: Literature review.Method&#160;: Consultation of the databases CINAHL, MEDLINE, PUBMED, COCHRANE library, SCOPUS, and PROQUEST Dissertations and Theses. Keywords&#160;: &#8220;health care team&#8221; and &#8220;rapid response team&#8221;.Results&#160;: 121 articles were included. The collected data were divided into five categories&#160;: 1) composition and operation of RRTs, 2) benefits and limitations of RRTs, 3) perceptions of RRTs by health care teams, organizations, and patients, 4) implementation strategies, and 5) facilitators and barriers to implementation.Discussion&#160;: Although there are many articles related to RRTs, it appears that&#160;: 1) few studies analyze the difference in outcomes in hospitalized patients related to the composition of RRTs, 2) few studies describe how RRTs should work, 3) more studies are needed on the impacts of RRTs on hospitalized patients, 4) organizations&#8217; and patients&#8217; perceptions of RRTs are not well studied, and 5) more studies are needed on the best way to implement an RRT.Conclusion&#160;: The results show that there is a lack of studies on the difference in outcomes in hospitalized patients related to the composition of RRTs, on how RRTs should work, on the impacts of RRTs on hospitalized patients, on organizations&#8217; and patients&#8217; perceptions of RRTs, and on the factors that influence the success or failure of the implementation of an RRT.

Reduction of in-hospital cardiac arrest with sequential deployment of rapid response team and medical emergency team to the emergency department and acute care wards

Purpose

This study aimed to determine if sequential deployment of a nurse-led Rapid Response Team (RRT) and an intensivist-led Medical Emergency Team (MET) for critically ill patients in the Emergency Department (ED) and acute care wards improved hospital-wide cardiac arrest rates.

Methods

In this single-center, retrospective observational cohort study, we compared the cardiac arrest rates per 1000 patient-days during two time periods. Our hospital instituted a nurse-led RRT in 2012 and added an intensivist-led MET in 2014. We compared the cardiac arrest rates during the nurse-led RRT period and the combined RRT-MET period. With the sequential approach, nurse-led RRT evaluated and managed rapid response calls in acute care wards and if required escalated care and co-managed with an intensivist-led MET. We specifically compared the rates of pulseless electrical activity (PEA) in the two periods. We also looked at the cardiac arrest rates in the ED as RRT-MET co-managed patients with the ED team.

Results

Hospital-wide cardiac arrests decreased from 2.2 events per 1000 patient-days in the nurse-led RRT period to 0.8 events per 1000 patient-days in the combined RRT and MET period (p-value = 0.001). Hospital-wide PEA arrests and shockable rhythms both decreased significantly. PEA rhythms significantly decreased in acute care wards and the ED.

Conclusion

Implementing an intensivist-led MET-RRT significantly decreased the overall cardiac arrest rate relative to the rate under a nurse-led RRT model. Additional MET capabilities and early initiation of advanced, time-sensitive therapies likely had the most impact.

Safety in Pediatric Hospice and Palliative Care: A Qualitative Study

Introduction

Patient safety is extensively studied in both adults and pediatric medicine; however, knowledge is limited regarding particular safety events in pediatric hospice and palliative care (HPC). Additionally, pediatric HPC lacks a unified definition of safe care. This qualitative study sought to explore caregiver views regarding safe care in pediatric HPC.

Methods

This is a secondary analysis of qualitative data from a multisite study utilizing semistructured interview data to evaluate parental perspectives of quality in pediatric home-based HPC programs across 3 different pediatric tertiary care hospitals. Eligible participants included parents and caregivers of children who were enrolled in a pediatric home-based hospice and palliative care program (HBHPC) from 2012 to 2016. The analysis was done using grounded theory methodology.

Results

Forty-three parents participated in 39 interviews across all 3 sites; 19 families were bereaved. Responses to the prompt regarding safe care produced 8 unique domains encompassing parental definitions of safe care in pediatric HPC.

Discussion

Parents of children in HPC programs describe "safe care" in novel ways, some of which echo Maslow's hierarchy of needs. The use of traditional hospital safety measures for patients receiving HPC could undermine the patient's goals or dignity, ultimately leading to harm to the patient.

Concluding summary

Patients' and families' unique goals and values must be considered when defining safety for children in this population. Future studies should continue to explore family perspectives of safety in the hospital and ambulatory settings and seek to identify measurable indicators in safety which are truly patient- and family-centered.

The Modified Early Warning Score as a Predictive Tool During Unplanned Surgical Intensive Care Unit Admission

Background: The Modified Early Warning Score (MEWS) has been proposed to warn healthcare providers of potentially serious adverse events. We evaluated this scoring system during unplanned escalation of care in hospitalized surgical patients during a 1-year period.

Methods: Following institutional review board approval, all consecutive, unplanned surgical admissions into the surgical intensive care unit (SICU) during 2016 were entered into this study. MEWS and patient demographics during bedside evaluation for SICU admission were extracted from electronic medical records. Logistic regression was used to analyze the association of MEWS with the incidence of future mortality. P values were set at <0.01 for statistical significance.

Results: In this series of 263 consecutive patients, the incidence of mortality following unplanned escalation of care was 29.3% (confidence interval [CI] 24.1% to 35.0%), ranging from 22% to 57%, with all positive MEWS values. The association of MEWS with future mortality was not statistically significant (P=0.0107). A misclassification rate of 0.29 (CI 0.24 to 0.35) was observed with this association.

Conclusion: MEWS provided no clinical benefit as an early warning system, as mortality was elevated throughout the MEWS scale in this clinical setting. The high misclassification rate indicates MEWS does not provide discriminatory support for patients at risk for mortality.

Clinical impact of multi-parameter continuous non-invasive monitoring in hospital wards: a systematic review and meta-analysis

OBJECTIVE

Delayed response to clinical deterioration as a result of intermittent vital sign monitoring is a cause of preventable morbidity and mortality. This review focuses on the clinical impact of multi-parameter continuous non-invasive monitoring of vital signs (CoNiM) in non-intensive care unit patients.

DESIGN

Systematic review and meta-analysis of primary studies. Embase, MEDLINE, HMIC, PsycINFO and Cochrane were searched from April 1964 to 18 June 2019 with no language restriction.

SETTING

The search was limited to hospitalised, non-intensive care unit adult patients who had two or more vital signs continuously monitored.

PARTICIPANTS

All primary studies that evaluated the clinical impact of using multi-parameter CoNiM in adult hospital wards outside of the intensive care unit.

MAIN OUTCOME MEASURES

Clinical impact of multi-parameter CoNiM.

RESULTS

This systematic review identified 14 relevant studies from 3846 search results. Five studies were classified as Group A - associations found between measured vital signs and clinical parameters. Nine studies were classified as Group B - comparison between clinical outcomes of patients with and without multi-parameter CoNiM. Vital signs data from CoNiM were found to associate with type of presenting complaint, level of renal function and incidence of major clinical events. CoNiM also assisted in diagnosis by differentiating between patients with acute heart failure, stroke and sepsis (with sub-clustering of septic patients). In the meta-analysis, patients on multi-parameter CoNiM had a 39% decrease in risk of mortality (risk ratio [RR] 0.61; 95% confidence interval [95% CI] -0.39-0.95) when compared to patients with regular intermittent monitoring. There was a trend of reduced intensive care unit transfer (RR 0.86; 95% CI -0.67-1.11) and reduced rapid response team activation (RR 0.61; 95% CI 0.26-1.43). A trend towards reduced hospital length of stay was also found using weighted mean difference (WMD -3.32 days; 95% CI -8.82-2.19 days).

CONCLUSION

There is evidence of clinical benefit in implementing CoNiM in non-intensive care unit patients. This review supports the use of multi-parameter CoNiM outside of intensive care unit with further large-scale RCTs required to further affirm clinical impact.

Patterns in continuous pulse oximetry data prior to pulseless electrical activity arrest in the general care setting

The study objective was to understand if features derived from continuous pulse oximetry data can provide advanced warning of pulseless electrical activity arrest in the general care inpatient setting. Retrospective analysis of SpO2 and pulse rate data derived from continuous pulse oximetry was performed for pulseless electrical activity (n = 38) and control (n = 42) patient cohorts. Measures of central tendency and variation over time intervals ranging from 1 min to 1 h were used for inter- and intra-group comparisons. Logistic regression was applied to understand ability of features to predict pulseless electrical activity in future time intervals. Overall, the pulseless electrical activity arrest group tended to have lower mean SpO2 and higher mean pulse rate values than the control group. SpO2 and pulse rate variability was higher in the pulseless electrical activity arrest cohort. Changes in variability were observed beginning several hours prior to the rescue event. Up to 20 min before rescue events, pulse rate features were significantly different from feature values for the preceding 30-min interval (> 10% difference in mean, > 46% difference in range). Similar results were found for SpO2 features 10 min before the event (> 4% difference in mean, > 60% difference in range). There is a significant difference in SpO2 and pulse rate features derived from continuous pulse oximetry between pulseless electrical activity and control groups. Integration of automated feature calculation and clinician notification into clinical monitoring and information systems may increase patient safety by supporting early detection of such events.

Nurses' judgments of patient risk of deterioration at change-of-shift handoff: Agreement between nurses and comparison with early warning scores

BACKGROUND

Nurses begin forming judgments regarding patients' clinical stability during change-of-shift handoffs.

OBJECTIVES

To examine the agreement between incoming and outgoing nurses' judgments of deterioration risk following handoff and compare these judgments to commonly used early warning scores (MEWS, NEWS, ViEWS).

METHODS

Following handoffs on three medical/surgical units, nurses completed the Patient Acuity Rating. Nurse ratings were compared with computed early warning scores based on clinical data. In follow-up interviews, nurses were invited to describe their experiences of using the rating scale.

RESULTS

Sixty-two nurses carried out 444 handoffs for 158 patients. While the agreement between incoming and outgoing nurses was fair, correlations with early warning scores were low. Nurses struggled with predicting risk and used their impressions of differential risk across all the patients to whom they had been assigned to arrive at their ratings.

CONCLUSION

Nurses shared information that influenced their clinical judgments at handoff; not all of these cues may necessarily be captured in early warning scores.

Nurses' just-in-time training for clinical deterioration: Development, implementation and evaluation

BACKGROUND

'Just-in-time training' is an innovative approach to nursing education. It has demonstrated positive outcomes in other industries, such as manufacturing and aviation, but it has limited published application in the acute-care setting.

OBJECTIVES

We aimed to implement and evaluate a nursing 'just-in-time training' program for the recognition and response to patient deterioration.

DESIGN

To promote consistency, one Clinical Deterioration Educator provided education to nursing staff in both recognising the need for escalation and providing subsequent care for the deteriorating ward patient. Nurses' perception of the 'just-in-time training' program was determined using electronic questionnaire responses. Medical Emergency Team call prevalence and outcome data was compared before and after the program implementation for further evaluation.

SETTING

The 'just-in-time training' program was implemented in a 508-bed acute metropolitan private hospital over a 12-month period. Education was provided in general medical and surgical wards, not specialty areas.

PARTICIPANTS

Nurses received the just-in-time training based on their patients' perceived risk of deterioration, therefore, participants are not randomised.

METHODS

A quantitative research study investigated nurses' self-perceived confidence after receiving just-in-time training. Medical Emergency Team call frequency data was also examined to identify trends.

RESULTS

The 'just-in-time training' program consisted of 534 bedside nursing encounters over 12 months. During the study, the need for the educator to recommend that nurses escalate care reduced in prevalence from 20% to 5.5%. Questionnaire responses demonstrated a self-perceived confidence following intervention of 4.32/5.0. Medical Emergency Team call prevalence, per 1000 patient bed days, increased from 13.6 pre-intervention to 15.4 post-intervention.

CONCLUSIONS

Just-in-time training' can be effectively implemented to educate ward nursing staff in recognising and responding to the deteriorating patient. The program is well received by nursing staff and leads to high self-perceived confidence to recognise and appropriately care for a deteriorating patient.

Examining the Occurrence of Adverse Events within 72 hours of Discharge from the Intensive Care Unit

Adverse events have negative consequences for patients, including increased risk of death or permanent disability. Reports describe suboptimal patient care on hospital wards and reasons for readmission to the intensive care unit (ICU) but limited data exists on the occurrence of adverse events, their characteristics and outcomes in patients recently discharged from the ICU to the ward. This prospective observational study describes the incidence and outcomes of adverse events within 72 hours of discharge from an Australian ICU over 12 weeks in 2006. Patients were excluded if they were admitted to ICU after booked surgery or uncomplicated drug overdose, were discharged from ICU to the high dependency unit or had a ‘do-not-resuscitate’ order. Clinical antecedents and preventability were determined for each event. Seventeen (10%) of the 167 discharges that met the inclusion criteria were associated with an adverse event, with nine (52%) judged as probably preventable. Seven adverse events occurred from discharges between 1700 and 0700 hours and seven were on weekends. The most common adverse events were related to fluid management (47%). Outcomes included three ICU readmissions, two high dependency unit admissions and two required one-to-one ward nursing. Two adverse events resulted in temporary disability, seven resulted in prolonged hospital stays and two were associated with death. Delay in taking action for abnormal physiological signs and infrequent charting were evident. Whilst the adverse event rate compared favourably with other reports, 64% of the events were considered preventable. A review of support systems and processes is recommended to better target transition from the ICU.

Epidemiology of in-hospital cardiac arrest in Abu Dhabi, United Arab Emirates, 2013-2015

Objective

Estimate the incidence and outcomes of in-hospital cardiac arrest (IHCA) in a tertiary-care hospital in Abu Dhabi emirate, United Arab Emirates (UAE).

Methods

Retrospective data from 685 inpatients who experienced an IHCA at a hospital in Abu Dhabi (UAE) between 1 January 2013 and 31 December 2015 were analysed. Sociodemographic variables were age and gender, and IHCA event variables were shift, day, event location, initial cardiac rhythm and the total number of IHCA events. Outcome variables were the return of spontaneous circulation (ROSC) and survival to discharge (StD).

Results

The incidence of IHCA was 11.7 (95% CI 10.8 to 12.6) per 1000 hospital admissions. Non-shockable rhythms were 91.1% of the cardiac rhythms at presentation. The majority of IHCA cases occurred in the intensive care unit (46.1%) and on weekdays (74.6%). More than a third (38.3%) of patients who experienced an IHCA achieved ROSC and 7.7% StD. Both ROSC and StD were significantly higher in patients who were younger and presenting with a shockable rhythm (all p’s≤0.05). Survival outcomes were not significantly different between dayshifts and nightshifts or weekdays and weekends.

Conclusions

The incidence of IHCA was higher and its outcomes were lower compared with other high-income/developed countries. Survival outcomes were better for patients who were younger and had a shockable rhythm, and similar between time of day and days of the week. These findings may help to inform health managers about the magnitude and quality of IHCA care in the UAE.

Factors influencing the activation of the rapid response system for clinically deteriorating patients by frontline ward clinicians: a systematic review

Purpose

To synthesize factors influencing the activation of the rapid response system (RRS) and reasons for suboptimal RRS activation by ward nurses and junior physicians.

Data sources

Nine electronic databases were searched for articles published between January 1995 and January 2016 in addition to a hand-search of reference lists and relevant journals.

Study selection

Published primary studies conducted in adult general ward settings and involved the experiences and views of ward nurses and/or junior physicians in RRS activation were included.

Data extraction

Data on design, methods and key findings were extracted and collated.

Results of data synthesis

Thirty studies were included for the review. The process to RRS activation was influenced by the perceptions and clinical experiences of ward nurses and physicians, and facilitated by tools and technologies, including the sensitivity and specificity of the activation criteria, and monitoring technology. However, the task of enacting the RRS activations was challenged by seeking further justification, deliberating over reactions from the rapid response team and the impact of workload and staffing. Finally, adherence to the traditional model of escalation of care, support from colleagues and hospital leaders, and staff training were organizational factors that influence RRS activation.

Conclusion

This review suggests that the factors influencing RRS activation originated from a combination of socio-cultural, organizational and technical aspects. Institutions that strive for improvements in the existing RRS or are considering to adopt the RRS should consider the complex interactions between people and the elements of technologies, tasks, environment and organization in healthcare settings.

Risk stratification tools in emergency general surgery

The use of risk stratification tools (RST) aids in clinical triage, decision making and quality assessment in a wide variety of medical fields. Although emergency general surgery (EGS) is characterized by a comorbid, physiologically acute patient population with disparately high rates of perioperative morbidity and mortality, few RST have been explicitly examined in this setting. We examined the available RST with the intent of identifying a tool that comprehensively reflects an EGS patients perioperative risk for death or complication. The ideal tool would combine individualized assessment with relative ease of use. Trauma Scoring Systems, Critical Care Scoring Systems, Surgical Scoring Systems and Track and Trigger Models are reviewed here, with the conclusion that Emergency Surgery Acuity Score and the American College of Surgeons National Surgical Quality Improvement Programme Universal Surgical Risk Calculator are the most applicable and appropriate for EGS.

Evaluating the effect of delayed activation of rapid response teams on patient outcomes: a systematic review protocol

BACKGROUND

Rapid response teams have been widely adopted across the world. Although evidence for their efficacy is not clear, they remain a popular means to detect and react to patient deterioration. This may in part be due to there being no standardized approach to their usage or implementation. A key component of their ability to be effective is the speed of response.

OBJECTIVE

The objective of this review is to evaluate the effect of delayed response by rapid response teams on hospital mortality (primary), cardiac arrest, and intensive care transfer rates (secondary).

METHODS

This review will include randomized and non-randomized studies which examined the effect of delayed response times by rapid response teams on patient mortality, cardiac arrest, and intensive care unit admission rates. This review will include studies of adult patients who have experienced a rapid response team consultation. The search strategy will utilize a combination of keywords and MeSH terms. MEDLINE and Embase will be searched, as well as examining gray literature. Two reviewers will independently screen retrieved citations to determine if they meet inclusion criteria. Studies will be selected that provide information about the impact of response time on patient outcomes. Comparisons will be made between consults that arrive in a timely manner and consults that are delayed. Quality assessment of randomized studies will be conducted in accordance with guidelines from the Cochrane Handbook for Systematic Reviews of Interventions. Quality assessment of non-randomized studies will be based on the Risk of Bias in Non-randomized Studies-of Interventions (ROBINS-I) assessment tool. Results of the review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

DISCUSSION

This systematic review will identify and synthesize evidence around the impact of delayed response by rapid response teams on patient mortality, cardiac arrest, and intensive care transfer rates.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO Registration: CRD42017071842 .

Predicting Unplanned Transfers to the Intensive Care Unit: A Machine Learning Approach Leveraging Diverse Clinical Elements

Background

Early warning scores aid in the detection of pediatric clinical deteriorations but include limited data inputs, rarely include data trends over time, and have limited validation.

Objective

Machine learning methods that make use of large numbers of predictor variables are now commonplace. This work examines how different types of predictor variables derived from the electronic health record affect the performance of predicting unplanned transfers to the intensive care unit (ICU) at three large children’s hospitals.

Methods

We trained separate models with data from three different institutions from 2011 through 2013 and evaluated models with 2014 data. Cases consisted of patients who transferred from the floor to the ICU and met one or more of 5 different priori defined criteria for suspected unplanned transfers. Controls were patients who were never transferred to the ICU. Predictor variables for the models were derived from vitals, labs, acuity scores, and nursing assessments. Classification models consisted of L1 and L2 regularized logistic regression and neural network models. We evaluated model performance over prediction horizons ranging from 1 to 16 hours.

Results

Across the three institutions, the c-statistic values for our best models were 0.892 (95% CI 0.875-0.904), 0.902 (95% CI 0.880-0.923), and 0.899 (95% CI 0.879-0.919) for the task of identifying unplanned ICU transfer 6 hours before its occurrence and achieved 0.871 (95% CI 0.855-0.888), 0.872 (95% CI 0.850-0.895), and 0.850 (95% CI 0.825-0.875) for a prediction horizon of 16 hours. For our first model at 80% sensitivity, this resulted in a specificity of 80.5% (95% CI 77.4-83.7) and a positive predictive value of 5.2% (95% CI 4.5-6.2).

Conclusions

Feature-rich models with many predictor variables allow for patient deterioration to be predicted accurately, even up to 16 hours in advance.