Impact of dynamic parameter of trends in vital signs on the prediction of serious events in hospitalized patients -a retrospective observational study

Aim

Although early detection of patients' deterioration may improve outcomes, most of the detection criteria use on-the-spot values of vital signs. We investigated whether adding trend values over time enhanced the ability to predict adverse events among hospitalized patients.

Methods

Patients who experienced adverse events, such as unexpected cardiac arrest or unplanned ICU admission were enrolled in this retrospective study. The association between the events and the combination of vital signs was evaluated at the time of the worst vital signs 0-8 hours before events (near the event) and at 24-48 hours before events (baseline). Multivariable logistic analysis was performed, and the area under the receiver operating characteristic curve (AUC) was used to assess the prediction power for adverse events among various combinations of vital sign parameters.

Results

Among 24,509 in-patients, 54 patients experienced adverse events(cases) and 3,116 control patients eligible for data analysis were included. At the timepoint near the event, systolic blood pressure (SBP) was lower, heart rate (HR) and respiratory rate (RR) were higher in the case group, and this tendency was also observed at baseline. The AUC for event occurrence with reference to SBP, HR, and RR was lower when evaluated at baseline than at the timepoint near the event (0.85 [95%CI: 0.79-0.92] vs. 0.93 [0.88-0.97]). When the trend in RR was added to the formula constructed of baseline values of SBP, HR, and RR, the AUC increased to 0.92 [0.87-0.97].

Conclusion

Trends in RR may enhance the accuracy of predicting adverse events in hospitalized patients.

Early predictors of clinical deterioration in intermediate-high risk pulmonary embolism: clinical needs, research imperatives, and pathways forward

A subset of intermediate-high risk pulmonary embolism (PE) patients will suffer clinical deterioration in the early hours following the acute event. Current evidence-based guidelines for the management of acute PE have provided limited direction for identification of which intermediate-high risk PE patients will go on to develop haemodynamic decompensation. Furthermore, a paucity of data further hampers guideline recommendations regarding the optimal approach and duration of intensive monitoring, best methods to assess the early response to anticoagulation, and the ideal window for reperfusion therapy, if decompensation threatens. The aim of the present article is to identify the current unmet needs related to the early identification of intermediate-high risk PE patients at higher risk of clinical deterioration and mortality during the early hours after the acute cardiovascular event and suggest some potential strategies to further explore gaps in the literature.

Exploring the nurses' experiences in recognising and managing clinical deterioration in emergency patients: A qualitative study

BACKGROUND

Emergency Department (ED) patients are particularly at a high risk of deterioration. The frontline nurses are key players in identifying and responding to deterioration events; however, few studies have sought to explore the whole process of recognition and management of clinical deterioration by emergency nurses.

OBJECTIVES

The aim of this study was to explore the experiences of emergency nurses and provide a whole picture of how they recognise and manage clinical deterioration.

METHODS

A qualitative descriptive study involving 11 senior nurses and seven junior nurses was conducted in the ED of a 3000-bed tertiary general hospital using semistructured interviews. The interviews were transcribed and thematically analysed.

FINDINGS

Four salient themes emerged from the data analysis. The first, 'early recognition and response', revealed the importance of vital signs assessment in recognising and responding to clinical deterioration. The second, 'information transfer', depicted the skills and difficulties of transferring information in escalations of care. The third, 'abilities, education, and training', presented the abilities that emergency nurses should have and their perspectives on training. The fourth, 'support culture', described the major role of senior nurses in collaboration with colleagues in the ED.

CONCLUSIONS

This study explored the experiences of emergency nurses in recognising and managing clinical deterioration. The findings illuminate the need to support the critical role of emergency nurses, with an emphasis on their abilities and continuous interprofessional collaboration training to improve the recognition and management of clinical deterioration.

Developing a consensus-based definition of out-of-hospital clinical deterioration: A Delphi study

BACKGROUND

Clinical deterioration is a time-critical medical emergency requiring rapid recognition and intervention. Deteriorating patients are seen across various healthcare settings, including the out-of-hospital (OOH) environment. OOH care is an evolving area of medicine where decisions are made regarding priority and timing of clinical interventions, ongoing management, and transport to appropriate care. To date, the literature lacks a standardised definition of OOH clinical deterioration.

OBJECTIVE

The objective of this study was to create a consensus-based definition of OOH clinical deterioration informed by emergency medicine health professionals.

METHODS

A Delphi study consisting three rounds was conducted electronically between June 2020 and January 2021. The expert panel consisted of 30 clinicians, including emergency physicians and paramedics.

RESULTS

A consensus-based definition of OOH clinical deterioration was identified as changes from a patient's baseline physiological status resulting in their condition worsening. These changes primarily take the form of measurable vital signs and assessable symptoms but should be evaluated in conjunction with the history of events and pertinent risk factors. Clinicians should be suspicious that a patient could deteriorate when changes occur in one or more of the following vital signs: respiratory rate, heart rate, blood pressure, Glasgow Coma Scale, oxygen saturation, electrocardiogram, and skin colour. Almost all participants (92%) indicated an early warning system would be helpful to assist timely recognition of deteriorating patients.

CONCLUSION

The creation of a consensus-based definition of OOH clinical deterioration can serve as a starting point for the development and validation of OOH-specific early warning systems. Moreover, a standardised definition allows meaningful comparisons to be made across health services and ensures consistency in future research. This study has shown recognition of OOH clinical deterioration to be a complex issue requiring further research. Improving our understanding of key factors contributing to deterioration can assist timely recognition and intervention, potentially reducing unnecessary morbidity and mortality.

Improving escalation of deteriorating patients through cognitive task analysis: Understanding differences between work-as-prescribed and work-as-done

BACKGROUND

Appropriate care escalation requires the detection and communication of in-hospital patient deterioration. Although deterioration in the ward environment is common, there continue to be patient deaths where problems escalating care have occurred. Learning from the everyday work of health care professionals (work-as-done) and identifying performance variability may provide a greater understanding of the escalation challenges and how they overcome these. The aims of this study were to i) develop a representative model detailing escalation of care ii) identify performance variability that may negatively or positively affect this process and iii) examine linkages between steps in the escalation process.

METHODS

Thirty Applied Cognitive Task Analysis interviews were conducted with clinical experts (>4 years' experience) including Ward Nurses (n = 7), Outreach or Sepsis Nurses (n = 8), Nurse Manager or Consultant (n = 6), Physiotherapists (n = 4), Advanced Practitioners (n = 4), and Doctor (n = 1) from two National Health Service hospitals and analysed using Framework Analysis. Task-related elements of care escalation were identified and represented in a Functional Resonance Analysis Model.

FINDINGS

The NEWS2's clinical escalation response constitutes eight unique tasks and illustrates work-as-prescribed, but our interview data uncovered an additional 24 tasks (n = 32) pertaining to clinical judgement, decisions or processes reflecting work-as-done. Over a quarter of these tasks (9/32, 28 %) were identified by experts as cognitively challenging with a high likelihood of performance variability. Three out of the nine variable tasks were closely coupled and interdependent within the Functional Resonance Analysis Model ('synthesising data points', 'making critical decision to escalate' and 'identifying interim actions') so representing points of potential escalation failure. Data assimilation from different clinical information systems with poor usability was identified as a key cognitive challenge.

CONCLUSION

Our data support the emphasis on the need to retain clinical judgement and suggest that future escalation protocols and audit guidance require in-built flexibility, supporting staff to incorporate their expertise of the patient condition and the clinical environment. Improved information systems to synthesise the required data surrounding an unwell patient to reduce staff cognitive load, facilitate decision-making, support the referral process and identify actions are required. Fundamentally, reducing the cognitive load when assimilating core escalation data allows staff to provide better and more creative care. Study registration (ISRCTN 38850) and ethical approval (REC Ref 20/HRA/3828; CAG-20CAG0106).

Frequency of and associations with alterations of medical emergency team calling criteria in a teaching hospital emergency department

BACKGROUND

Medical emergency team (METs), activated by vital sign-based calling criteria respond to deteriorating patients in the hospital setting. Calling criteria may be altered where clinicians feel this is appropriate. Altered calling criteria (ACC) has not previously been evaluated in the emergency department (ED) setting.

OBJECTIVES

The objectives of this study were to (i) describe the frequency of ACC in a teaching hospital ED and the number and type of vital signs that were modified and (ii) associations between ACC in the ED and differences in the baseline patient characteristics and adverse outcomes including subsequent MET activations, unplanned intensive care unit (ICU) admissions and death within 72 h of admission.

METHODS

Retrospective observational study of patients presenting to an academic, tertiary hospital ED in Melbourne, Australia between January 1st, 2019 and December 31st, 2019. The primary outcome was frequency and nature of ACC in the ED. Secondary outcomes included differences in baseline patient characteristics, frequency of MET activation, unplanned ICU admission, and mortality in the first 72 h of admission between those with and without ACC in the ED.

RESULTS

Amongst 14 159 ED admissions, 725 (5.1%) had ACC, most frequently for increased heart or respiratory rate. ACC was associated with older age and increased comorbidity. Such patients had a higher adjusted risk of MET activation (odds ratio [OR]: 3.14, 95% confidence interval [CI]: 2.50-3.91, p = <0.001), unplanned ICU admission (OR: 1.97, 95% CI: 1.17-3.14, p = 0.016), and death (OR: 3.87, 95% CI: 2.08-6.70, p = 0.020) within 72 h.

CONCLUSIONS

ACC occurs commonly in the ED, most frequently for elevated heart and respiratory rates and is associated with worse patient outcomes. In some cases, ACC requires consultant involvement, more frequent vital sign monitoring, expeditious inpatient team review, or ICU referral.

Prognostic impact of the e-TAPSE ratio in intermediate-high risk pulmonary embolism patients

We assess the prognostic role of a new index (Age-T index), based on age and the tricuspid annular plane systolic excursion (TAPSE) for the estimation of 30-day mortality and risk of 48-h clinical deterioration since admission, in intermediate-high risk Pulmonary Embolism (PE) patients. A post-hoc analysis of intermediate-high risk PE patients enrolled in the Italian Pulmonary Embolism Registry (IPER) (Trial registry: ClinicalTrials.gov; No.: NCT01604538) was performed. The Age-T index was calculated as the ratio between age and TAPSE. The primary outcome was the 30-day mortality risk while the risk of clinical deterioration within 48 h in the same patients was chosen as the secondary outcome. Among 450 intermediate-high risk PE patients (mean age 71.4 ± 13.8 years, 298 males), 40 (8.8%) experienced clinical deterioration within 48 h since admission and 32 (7.1%) died within 30-day. Receiver operating characteristic analysis established ≥ 4.9 as the optimal cut-off value for the Age-T index in predicting 30-day mortality (AUC of 0.76 ± 0.1). Sensitivity, specificity, PPV and NPV were 81.2, 85.6, 30.2 and 98.3%, respectively. Multivariate Cox regression analysis showed that an Age-T index ≥ 4.9 predicts 30-day mortality (HR: 3.24, 95% CI: 1.58-4.96, p < 0.001) and was also associated with a significantly higher risk of 48-h clinical deterioration (HR: 2.02, 95% CI 1.96-2.08, p < 0.0001) in intermediate-high risk PE patients. Age-T Index appears as a useful, bed-side and non-invasive prognostic tool to identify intermediate-high risk PE patients at higher risk of death and/or 48-h clinical deterioration.

Transforming nursing assessment in acute hospitals: A cluster randomised controlled trial of an evidence-based nursing core assessment (the ENCORE trial)

BACKGROUND

Patient safety is threatened when early signs of clinical deterioration are missed or not acted upon. This research began as a clinical-academic partnership established around a shared concern of nursing physical assessment practices on general wards and delayed recognition of clinical deterioration. The outcome was the development of a complex intervention facilitated at the ward level for proactive nursing surveillance.

METHODS

The evidence-based nursing core assessment (ENCORE) trial was a pragmatic cluster-randomised controlled trial. We hypothesised that ward intervention would reduce the incidence of patient rescue events (medical emergency team activations) and serious adverse events. We randomised 29 general wards in a 1:2 allocation, across 5 Australian hospitals to intervention (n = 10) and usual care wards (n = 19). Skilled facilitation over 12 months enabled practitioner-led, ward-level practice change for proactive nursing surveillance. The primary outcome was the rate of medical emergency team activations and secondary outcomes were unplanned intensive care unit admissions, on-ward resuscitations, and unexpected deaths. Outcomes were prospectively collected for 6 months following the initial 6 months of implementation. Analysis was at the patient level using generalised linear mixed models to account for clustering by ward.

RESULTS

We analysed 29,385 patient admissions to intervention (n = 11,792) and control (n = 17,593) wards. Adjusted models for overall effects suggested the intervention increased the rate of medical emergency team activations (adjusted incidence rate ratio 1.314; 95 % confidence interval 0.975, 1.773), although the confidence interval was compatible with a marginal decrease to a substantial increase in rate. Confidence intervals for secondary outcomes included a range of plausible effects from benefit to harm. However, considerable heterogeneity was observed in intervention effects by patient comorbidity. Among patients with few comorbid conditions in the intervention arm there was a lower medical emergency team activation rate and decreased odds of unexpected death. Among patients with multimorbidity in the intervention arm there were higher rates of medical emergency team activation and intensive care unit admissions.

CONCLUSION

Trial outcomes have refined our assumptions about the impact of the ENCORE intervention. The intervention appears to have protective effects for patients with low complexity where frontline teams can respond locally. It also appears to have redistributed medical emergency team activations and unplanned intensive care unit admissions, mobilising higher rates of rescue for patients with multimorbidity.

TRIAL REGISTRATION NUMBER

ACTRN12618001903279 (Date of registration: 22/11/2018; First participant recruited: 01/02/2019).

General ward nurses detection and response to clinical deterioration in three hospitals at the Kenyan coast: a convergent parallel mixed methods study

BACKGROUND

In low and middle-income countries like Kenya, critical care facilities are limited, meaning acutely ill patients are managed in the general wards. Nurses in these wards are expected to detect and respond to patient deterioration to prevent cardiac arrest or death. This study examined nurses' vital signs documentation practices during clinical deterioration and explored factors influencing their ability to detect and respond to deterioration.

METHODS

This convergent parallel mixed methods study was conducted in the general medical and surgical wards of three hospitals in Kenya's coastal region. Quantitative data on the extent to which the nurses monitored and documented the vital signs 24 h before a cardiac arrest (death) occurred was retrieved from patients' medical records. In-depth, semi-structured interviews were conducted with twenty-four purposefully drawn registered nurses working in the three hospitals' adult medical and surgical wards.

RESULTS

This study reviewed 405 patient records and found most of the documentation of the vital signs was done in the nursing notes and not the vital signs observation chart. During the 24 h prior to death, respiratory rate was documented the least in only 1.2% of the records. Only a very small percentage of patients had any vital event documented for all six-time points, i.e. four hourly. Thematic analysis of the interview data identified five broad themes related to detecting and responding promptly to deterioration. These were insufficient monitoring of vital signs linked to limited availability of equipment and supplies, staffing conditions and workload, lack of training and guidelines, and communication and teamwork constraints among healthcare workers.

CONCLUSION

The study showed that nurses did not consistently monitor and record vital signs in the general wards. They also worked in suboptimal ward environments that do not support their ability to promptly detect and respond to clinical deterioration. The findings illustrate the importance of implementation of standardised systems for patient assessment and alert mechanisms for deterioration response. Furthermore, creating a supportive work environment is imperative in empowering nurses to identify and respond to patient deterioration. Addressing these issues is not only beneficial for the nurses but, more importantly, for the well-being of the patients they serve.

Risk factors and predicting nomogram for the clinical deterioration of non-severe community-acquired pneumonia

BACKGROUND

Currently, there remains insufficient focus on non-severe community-acquired pneumonia (CAP) patients who are at risk of clinical deterioration, and there is also a dearth of research on the related risk factors. Early recognition of hospitalized patients at risk of clinical deterioration will be beneficial for their clinical management.

METHOD

A retrospective study was conducted in The First Affiliated Hospital of Wenzhou Medical University, China, spanning from January 1, 2018 to April 30, 2022, and involving a total of 1,632 non-severe CAP patients. Based on whether their condition worsened within 72 h of admission, patients were divided into a clinical deterioration group and a non-clinical deterioration group. Additionally, all patients were randomly assigned to a training set containing 75% of patients and a validation set containing 25% of patients. In the training set, risk factors for clinical deterioration in patients with non-severe CAP were identified by using LASSO regression analysis and multivariate logistic regression analysis. A nomogram was developed based on identified risk factors. The effectiveness of the nomogram in both the training and validation sets was assessed using Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

Age, body mass index (BMI), body temperature, cardiovascular comorbidity, respiratory rate, LDH level, lymphocyte count and D-dimer level were identified as risk factors associated with the clinical deterioration of non-severe CAP within 72 h of admission. The area under curve (AUC) value of the nomogram was 0.78 (95% CI: 0.74-0.82) in the training set and 0.75 (95% CI: 0.67-0.83) in the validation set. Furthermore, the calibration curves for both the training and validation sets indicated that the predicted probability of clinical deterioration aligned with the actual probability. Additionally, DCA revealed clinical utility for the nomogram at a specific threshold probability.

CONCLUSION

The study successfully identified the risk factors linked to the clinical deterioration of non-severe CAP and constructed a nomogram for predicting the probability of deterioration. The nomogram demonstrated favorable predictive performance and has the potential to aid in the early identification and management of non-severe CAP patients at elevated risk of deterioration.

From pilot to a multi-site trial: refining the Early Detection of Deterioration in Elderly Residents (EDDIE +) intervention

BACKGROUND

Early Detection of Deterioration in Elderly Residents (EDDIE +) is a multi-modal intervention focused on empowering nursing and personal care workers to identify and proactively manage deterioration of residents living in residential aged care (RAC) homes. Building on successful pilot trials conducted between 2014 and 2017, the intervention was refined for implementation in a stepped-wedge cluster randomised trial in 12 RAC homes from March 2021 to May 2022. We report the process used to transition from a small-scale pilot intervention to a multi-site intervention, detailing the intervention to enable future replication.

METHODS

The EDDIE + intervention used the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to guide the intervention development and refinement process. We conducted an environmental scan; multi-level context assessments; convened an intervention working group (IWG) to develop the program logic, conducted a sustainability assessment and deconstructed the intervention components into fixed and adaptable elements; and subsequently refined the intervention for trial.

RESULTS

The original EDDIE pilot intervention included four components: nurse and personal care worker education; decision support tools; diagnostic equipment; and facilitation and clinical support. Deconstructing the intervention into core components and what could be flexibly tailored to context was essential for refining the intervention and informing future implementation across multiple sites. Intervention elements considered unsustainable were updated and refined to enable their scalability. Refinements included: an enhanced educational component with a greater focus on personal care workers and interactive learning; decision support tools that were based on updated evidence; equipment that aligned with recipient needs and available organisational support; and updated facilitation model with local and external facilitation.

CONCLUSION

By using the i-PARIHS framework in the scale-up process, the EDDIE + intervention was tailored to fit the needs of intended recipients and contexts, enabling flexibility for local adaptation. The process of transitioning from a pilot to larger scale implementation in practice is vastly underreported yet vital for better development and implementation of multi-component interventions across multiple sites. We provide an example using an implementation framework and show it can be advantageous to researchers and health practitioners from pilot stage to refinement, through to larger scale implementation.

TRIAL REGISTRATION

The trial was prospectively registered with the Australia New Zealand Clinical Trial Registry (ACTRN12620000507987, registered 23/04/2020).

Breaches of pre-medical emergency team call criteria in an Australian hospital

Objectives and outcomes

To evaluate the 24hrs before medical emergency team (MET) calls to examine: 1) the frequency, nature, and timing of pre-MET criteria breaches; 2) differences in characteristics and outcomes between patients who did and didn't experience pre-MET breaches.

Design

Retrospective observational study November 2020-June 2021.

Setting

Tertiary referral Australian hospital.

Participants

Adults (≥18 years) experiencing MET calls.

Results

Breaches in pre-MET criteria occurred prior to 1886/2255 (83.6%) MET calls, and 1038/1281 (81.0%) of the first MET calls. Patients with pre-MET breaches were older (median [IQR] 72 [57-81] vs 66 [56-77] yrs), more likely to be admitted from home (87.8% vs 81.9%) and via the emergency department (73.0% vs 50.2%), but less likely to be for full resuscitation after (67.3% vs 76.5%) the MET. The three most common pre-MET breaches were low SpO2 (48.0%), high pulse rate (39.8%), and low systolic blood pressure (29.0%) which were present for a median (IQR) of 15.4 (7.5-20.8), 13.2 (4.3-21.0), and 12.6 (3.5-20.1) hrs before the MET call, respectively. Patients with pre-MET breaches were more likely to need intensive care admission within 24 h (15.6 vs 11.9%), have repeat MET calls (33.3 vs 24.7%), and die in hospital (15.8 vs 9.9%).

Conclusions

Four-fifths of MET calls were preceded by pre-MET criteria breaches, which were present for many hours. Such patients were older, had more limits of treatment, and experienced worse outcomes. There is a need to improve goals of care documentation and pre-MET management of clinical deterioration.

Pre-medical emergency team activations - Patient characteristics, outcomes and predictors of deterioration

BACKGROUND

Pre-medical emergency team (MET) calls are an increasingly common tier of Rapid Response Systems, but the epidemiology of patients who trigger a Pre-MET is not well understoof.

OBJECTIVES

This study aims to examine the epidemiology and outcomes of patients who trigger a pre-MET activation and identify risk factors for further deterioration.

METHODS

This is a retrospective cohort study of pre-MET activations in a university-affiliated metropolitan hospital in Australia, between 13 April 2021 and 4 October 2021. A multivariable regression model was used to identify variables associated with further deterioration, defined as a MET call or Code Blue within 24 h of pre-MET activation.

RESULTS

From a total of 39 664 admissions, there were 7823 pre-MET activations (197.2 per 1000 admissions). Compared to inpatients that did not trigger a pre-MET, the patients were older (68.8 vs 53.8 years, p < 0.001), were more likely to be male (51.0 vs 47.6%, p < 0.001), had an emergency admission (70.1% vs 53.3%, p < 0.001), and were under a medical specialty (63.7 vs 54.9%, p < 0.001). They had a longer hospital length of stay (5.6 vs 0.4 d, p < 0.001) and higher in-hospital mortality (3.4% vs 1.0%, p < 0.001). A pre-MET was more likely to progress to a MET call or Code Blue if it was activated for fever, cardiovascular, neurological, renal, or respiratory criteria (p < 0.001), if the patient was under a paediatric team (p = 0.018), or if there had been a MET call or Code Blue prior to the pre-MET activation (p < 0.001).

CONCLUSION

Pre-MET activations affect almost 20% of hospital admissions and are associated with a higher risk of mortality. Certain characteristics may predict further deterioration to a MET call or Code Blue, suggesting the potential for early intervention via clinical decision support systems.

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.

Clinicians' use and perceptions of the pre-medical emergency team tier of one rapid response system: A mixed-methods study

BACKGROUND

The pre-medical emergency team (pre-MET) tier of rapid response systems facilitates early recognition and treatment of deteriorating ward patients using ward-based clinicians before a MET review is needed. However, there is growing concern that the pre-MET tier is inconsistently used.

OBJECTIVE

This study aimed to explore clinicians' use of the pre-MET tier.

METHODS

A sequential mixed-methods design was used. Participants were clinicians (nurses, allied health, doctors) caring for patients on two wards of one Australian hospital. Observations and medical record audits were conducted to identify pre-MET events and examine clinicians' use of the pre-MET tier as per hospital policy. Clinician interviews expanded on understandings gained from observation data. Descriptive and thematic analyses were performed.

RESULTS

Observations identified 27 pre-MET events for 24 patients that involved 37 clinicians (nurses = 24, speech pathologist = 1, doctors = 12). Nurses initiated assessments or interventions for 92.6% (n = 25/27) of pre-MET events; however, only 51.9% (n = 14/27) of pre-MET events were escalated to doctors. Doctors attended pre-MET reviews for 64.3% (n = 9/14) of escalated pre-MET events. Median time between escalation of care and in-person pre-MET review was 30 min (interquartile range: 8-36). Policy-specified clinical documentation was partially completed for 35.7% (n = 5/14) of escalated pre-MET events. Thirty-two interviews with 29 clinicians (nurses = 18, physiotherapists = 4, doctors = 7) culminated in three themes: Early Deterioration on a Spectrum, A Safety Net, and Demands Versus Resources.

CONCLUSIONS

There were multiple gaps between pre-MET policy and clinicians' use of the pre-MET tier. To optimise use of the pre-MET tier, pre-MET policy must be critically reviewed and system-based barriers to recognising and responding to pre-MET deterioration addressed.

Implementation evaluation of a rapid response system in a regional emergency department: a dual-methods study using the behaviour change wheel

BACKGROUND

Failure to recognise and respond to clinical deterioration is a major cause of high mortality events in emergency department (ED) patients. Whilst there is substantial evidence that rapid response teams reduce hospital mortality, unplanned intensive care admissions, and cardiac arrests on in-patient settings, the use of rapid response teams in the ED is variable with poor integration of care between emergency and specialty/intensive care teams.

OBJECTIVES

The aim of this study was to evaluate uptake and impact of a rapid response system on recognising and responding to deteriorating patients in the ED and identify implementation factors and strategies to optimise future implementation success.

METHODS

A dual-methods design was used to evaluate an ED Clinical Emergency Response System (EDCERS) protocol implemented at a regional Australian ED in June 2019. A documentation audit was conducted on patients eligible for the EDCERS during the first 3 months of implementation. Quantitative data from documentation audit were used to measure uptake and impact of the protocol on escalation and response to patient deterioration. Facilitators and barriers to the EDCERS uptake were identified via key stakeholder engagement and consultation. An implementation plan was developed using the Behaviour Change Wheel for future implementation.

RESULTS

The EDCERS was activated in 42 (53.1%) of 79 eligible patients. The specialty care team were more likely to respond when the EDCERS was activated than when there was no activation ([n = 40, 50.6%] v [n = 26, 32.9%], p = 0.01). Six facilitators and nine barriers to protocol uptake were identified. Twenty behaviour change techniques were selected and informed the development of a theory-informed implementation plan.

CONCLUSION

Implementation of the EDCERS protocol resulted in high response rates from specialty and intensive care staff. However, overall uptake of the protocol by emergency staff was poor. This study highlights the importance of understanding facilitators and barriers to uptake prior to implementing a new intervention.

Development and validation of an instrument for measuring junior nurses' recognition and response abilities to clinical deterioration (RRCD)

BACKGROUND

Nurses of all levels are expected to be competent in managing clinical deterioration. Given their limited experience and basic-level knowledge, there is a concern about junior nurses' clinical and patient management skills. However, junior nurses' abilities to recognise and respond to clinical deterioration have not been adequately explored because of the absence of a comprehensive tool.

OBJECTIVES

The aim of this study was to develop a new self-assessment scale to assess the junior nurses' recognition and response abilities to clinical deterioration and to examine its reliability and validity.

METHODS

Scale items were based on literature reviews and interviews. The preliminary scale was generated through two rounds of expert review. A panel of five experts evaluated content validity. After a pilot study, the questionnaire was distributed to 168 junior nurses via convenience sampling. Subsequent statistical analysis of results included construct validity, internal consistency, and test-retest reliability.

RESULTS

Six factors were included, and 69.310% of the total variance was explained by the 25 items comprising the scale. The Cronbach's alpha coefficient was 0.905 (95% confidence interval [CI]: 0.812-0.979) for the overall scale and 0.655-0.838 for its subscales. The Guttman split-half reliability was 0.856 (95% CI: 0.806-0.894). The test-retest reliability of the scale was 0.878 (95% CI: 0.836-0.911).

CONCLUSION

We developed a scale for measuring the abilities of junior nurses to recognise and respond to clinical deterioration and confirmed its reliability and validity. More experimental studies are needed to further evaluate this instrument.

Improving the management of medical emergency team calls due to suspected infections: A before-after study

Objective

To introduce a management guideline for sepsis-related MET calls to increase lactate and blood culture acquisition, as well as prescription of appropriate antibiotics.

Design

Prospective before (Jun-Aug 2018) and after (Oct-Dec 2018) study was designed.

Setting

A public university linked hospital in Melbourne, Australia.

Participants

Adult patients with MET calls related to sepsis/infection were included.

Main outcome measures

The primary outcome measure was the proportion of MET calls during which both a blood culture and lactate level were ordered. Secondary outcomes included the frequency with which new antimicrobials were commenced by the MET, and the presence and class of administered antimicrobials.

Results

There were 985 and 955 MET calls in the baseline and after periods, respectively. Patient features, MET triggers, limitations of treatment and disposition after the MET call were similar in both groups. Compliance with the acquisition of lactates (p = 0.101), respectively. There was a slight reduction in compliance with lactate acquisition in the after period (97% vs 99%; p = 0.06). In contrast, there was a significant increase in acquisition of blood cultures in the after period (69% vs 78%; p = 0.035).

Conclusions

Introducing a sepsis management guideline and enhanced linkage with an AMS program increased blood culture acquisition and decreased broad spectrum antimicrobial use but didn't change in-hospital mortality.

Risk of in-hospital Deterioration for Children with Single Ventricle Physiology

Children with single ventricle physiology (SV) are at high risk of in-hospital morbidity and mortality. Identifying children at risk for deterioration may allow for earlier escalation of care and subsequently decreased mortality.We conducted a retrospective chart review of all admissions to the pediatric cardiology non-ICU service from 2014 to 2018 for children < 18 years old. We defined clinical deterioration as unplanned transfer to the ICU or inpatient mortality. We selected children with SV by diagnosis codes and defined infants as children < 1 year old. We compared demographic, vital sign, and lab values between infants with and without a deterioration event. We evaluated vital sign and medical therapy changes before deterioration events.Among infants with SV (129 deterioration events over 225 admissions, overall 25% with hypoplastic left heart syndrome), those who deteriorated were younger (p = 0.001), had lower baseline oxygen saturation (p = 0.022), and higher baseline respiratory rate (p = 0.022), heart rate (p = 0.023), and hematocrit (p = 0.008). Median Duke Pediatric Early Warning Score increased prior to deterioration (p < 0.001). Deterioration was associated with administration of additional oxygen support (p = 0.012), a fluid bolus (p < 0.001), antibiotics (p < 0.001), vasopressor support (p = 0.009), and red blood cell transfusion (p < 0.001).Infants with SV are at high risk for deterioration. Integrating baseline and dynamic patient data from the electronic health record to identify the highest risk patients may allow for earlier detection and intervention to prevent clinical deterioration.

Vital signs-based deterioration prediction model assumptions can lead to losses in prediction performance

OBJECTIVE

Vital signs-based models are complicated by repeated measures per patient and frequently missing data. This paper investigated the impacts of common vital signs modeling assumptions during clinical deterioration prediction model development.

STUDY DESIGN AND SETTING

Electronic medical record (EMR) data from five Australian hospitals (1 January 2019-31 December 2020) were used. Summary statistics for each observation's prior vital signs were created. Missing data patterns were investigated using boosted decision trees, then imputed with common methods. Two example models predicting in-hospital mortality were developed, as follows: logistic regression and eXtreme Gradient Boosting. Model discrimination and calibration were assessed using the C-statistic and nonparametric calibration plots.

RESULTS

The data contained 5,620,641 observations from 342,149 admissions. Missing vitals were associated with observation frequency, vital sign variability, and patient consciousness. Summary statistics improved discrimination slightly for logistic regression and markedly for eXtreme Gradient Boosting. Imputation method led to notable differences in model discrimination and calibration. Model calibration was generally poor.

CONCLUSION

Summary statistics and imputation methods can improve model discrimination and reduce bias during model development, but it is questionable whether these differences are clinically significant. Researchers should consider why data are missing during model development and how this may impact clinical utility.

Continuous monitoring of patients in and after the acute admission ward to improve clinical pathways: study protocol for a randomized controlled trial (Optimal-AAW)

BACKGROUND

Because of high demand on hospital beds, hospitals seek to reduce patients' length of stay (LOS) while preserving the quality of care. In addition to usual intermittent vital sign monitoring, continuous monitoring might help to assess the patient's risk of deterioration, in order to improve the discharge process and reduce LOS. The primary aim of this monocenter randomized controlled trial is to assess the effect of continuous monitoring in an acute admission ward (AAW) on the percentage of patients who are discharged safely.

METHODS

A total of 800 patients admitted to the AAW, for whom it is equivocal whether they can be discharged directly after their AAW stay, will be randomized to either receive usual care without (control group) or with additional continuous monitoring of heart rate, respiratory rate, posture, and activity, using a wearable sensor (sensor group). Continuous monitoring data are provided to healthcare professionals and used in the discharge decision. The wearable sensor keeps collecting data for 14 days. After 14 days, all patients fill in a questionnaire to assess healthcare use after discharge and, if applicable, their experience with the wearable sensor. The primary outcome is the difference in the percentage of patients who are safely discharged home directly from the AAW between the control and sensor group. Secondary outcomes include hospital LOS, AAW LOS, intensive care unit (ICU) admissions, Rapid Response Team calls, and unplanned readmissions within 30 days. Furthermore, facilitators and barriers for implementing continuous monitoring in the AAW and at home will be investigated.

DISCUSSION

Clinical effects of continuous monitoring have already been investigated in specific patient populations for multiple purposes, e.g., in reducing the number of ICU admissions. However, to our knowledge, this is the first Randomized Controlled Trial to investigate effects of continuous monitoring in a broad patient population in the AAW.

TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT05181111 . Registered on 6 January 2022. Start of recruitment: 7 December 2021.

Comparing Continuous with Periodic Vital Sign Scoring for Clinical Deterioration Using a Patient Data Model

To evaluate a minute-by-minute monitoring algorithm against a periodic early warning score (EWS) in detecting clinical deterioration and workload. Periodic EWSs suffer from large measurement intervals, causing late detection of deterioration. This might be prevented by continuous vital sign monitoring with a real-time algorithm such as the Visensia Safety Index (VSI). This prospective comparative data modeling cohort study (NCT04189653) compares continuous algorithmic alerts against periodic EWS in continuous monitored medical and surgical inpatients. We evaluated sensitivity, frequency, number of warnings needed to evaluate (NNE) and time of initial alert till escalation of care (EOC): Rapid Response Team activation, unplanned ICU admission, emergency surgery, or death. Also, the percentage of VSI alerting minutes was compared between patients with or without EOC. In 1529 admissions continuous VSI warned for 55% of EOC (95% CI: 45-64%) versus 51% (95% CI: 41-61%) by periodic EWS. NNE for VSI was 152 alerts per detected EOC (95% CI: 114-190) compared to 21 (95% CI: 17-28). It generated 0.99 warnings per day per patient compared to 0.13. Time from detection score till escalation was 8.3 hours (IQR: 2.6-24.8) with VSI versus 5.2 (IQR: 2.7-12.3) hours with EWS (P=0.074). The percentage of warning VSI minutes was higher in patients with EOC than in stable patients (2.36% vs 0.81%, P<0.001). Although sensitivity of detection was not significantly improved continuous vital sign monitoring shows potential for earlier alerts for deterioration compared to periodic EWS. A higher percentage of alerting minutes may indicate risk for deterioration.

The effectiveness of education strategies for nurses to recognise and manage clinical deterioration: A systematic review

OBJECTIVES

To identify, critically appraise and synthesise evidence on the efficacy of education strategies for nurses to recognise and manage clinical deterioration, as well as provide recommendations for standardised educational programmes.

DESIGN

A systematic review of quantitative studies.

METHODS

Quantitative studies published in English between 1 January 2010 and 14 February 2022 were chosen from nine databases. Studies were included if they reported education strategies for nurses to recognise and manage clinical deterioration. The quality appraisal was performed using the Quality Assessment Tool for Quantitative Studies, developed by the Effective Public Health Practice Project. The data were extracted and the findings were integrated into a narrative synthesis.

RESULTS

Altogether, 37 studies published in 39 eligible papers were included in this review, encompassing 3632 nurses. Most education strategies were determined to be effective, and outcome measures can be divided into three types: nurse outcomes; system outcomes; and patient outcomes. The education strategies could be divided into simulation and non-simulation interventions, and six interventions were in-situ simulations. Retention of knowledge and skills during the follow-up after education was determined in nine studies, with the longest follow-up interval totalling 12 months.

CONCLUSIONS

Education strategies can improve nurses' ability and practice to recognise and manage clinical deterioration. Simulation combined with a structured prebrief and debrief design can be viewed as a routine simulation procedure. Regular in-situ education determined long-term efficacy in response to clinical deterioration, and future studies can use an education framework to guide regular education practice and focus more on nurses' practice and patient outcomes.

Evaluation of machine learning-based models for prediction of clinical deterioration: A systematic literature review

BACKGROUND AND OBJECTIVE

Early identification of patients at risk of deterioration can prevent life-threatening adverse events and shorten length of stay. Although there are numerous models applied to predict patient clinical deterioration, most are based on vital signs and have methodological shortcomings that are not able to provide accurate estimates of deterioration risk. The aim of this systematic review is to examine the effectiveness, challenges, and limitations of using machine learning (ML) techniques to predict patient clinical deterioration in hospital settings.

METHODS

A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and meta-Analyses (PRISMA) guidelines using EMBASE, MEDLINE Complete, CINAHL Complete, and IEEExplore databases. Citation searching was carried out for studies that met inclusion criteria. Two reviewers used the inclusion/exclusion criteria to independently screen studies and extract data. To address any discrepancies in the screening process, the two reviewers discussed their findings and a third reviewer was consulted as needed to reach a consensus. Studies focusing on use of ML in predicting patient clinical deterioration that were published from inception to July 2022 were included.

RESULTS

A total of 29 primary studies that evaluated ML models to predict patient clinical deterioration were identified. After reviewing these studies, we found that 15 types of ML techniques have been employed to predict patient clinical deterioration. While six studies used a single technique exclusively, several others utilised a combination of classical techniques, unsupervised and supervised learning, as well as other novel techniques. Depending on which ML model was applied and the type of input features, ML models predicted outcomes with an area under the curve from 0.55 to 0.99.

CONCLUSIONS

Numerous ML methods have been employed to automate the identification of patient deterioration. Despite these advancements, there is still a need for further investigation to examine the application and effectiveness of these methods in real-world situations.

Challenges to recognising patients at risk of out-of-hospital clinical deterioration

BACKGROUND

The acute derangement of physiological function is a time-critical medical emergency requiring prompt recognition. As autonomous practitioners in resource scarce, high-risk environments, clinical deterioration can impose complex and increased clinical demands on paramedics. Early recognition is imperative to facilitating proactive responses to mitigate adverse effects. This study aimed to determine if clinicians can meet consensus regarding meaningful clinical factors for recognising to out-of-hospital (OOH) clinical deterioration risk.

METHODS

A three-round electronic Delphi study was conducted between June 2020 and January 2021. The expert panel was composed of 30 clinicians, including paramedics and emergency physicians. Participants were presented with eight clinically diverse case vignettes addressing various clinical factors related to OOH clinical deterioration.

RESULTS

Participants identified various challenges related to the recognition of OOH clinical deterioration. Although participants were able to meet consensus on most clinical factors related to deterioration, consensus was not achieved where cases had a combination of factors including: medical aetiology, subtle vital sign changes, non-specific complaints, age-extreme patients, and presence of co-morbidities.

CONCLUSIONS

This study demonstrated that clinicians face various challenges to recognising clinical deterioration in the OOH setting. Better understanding these challenging patient cohorts could assist to increase awareness and improve early recognition of OOH clinical deterioration.

Managing deteriorating patients with a physiotherapy critical care outreach service: A mixed-methods study

BACKGROUND

Critical care outreach teams support ward staff to manage patients who are seriously ill or after discharge from the intensive care unit (ICU). Respiratory deterioration is a common reason for (re)admission to the ICU. Physiotherapists are health professionals with skills to address acute respiratory concerns. Experienced respiratory physiotherapists play a role in supporting junior clinicians, particularly in managing deteriorating patients on the ward.

OBJECTIVES

The objective of this study was to evaluate a novel respiratory physiotherapy critical care outreach-style service. The primary objective was to describe service referrals and the patient cohort. Other objectives were to compare the effects of this model of care on ICU readmission rates to a historical cohort and explore clinician perceptions of the model of care and its implementation.

METHODS

A new physiotherapy model of care worked alongside an existing nurse-led outreach service to support physiotherapists with the identification and management of patients at risk of respiratory deterioration or ICU (re)admission. Purpose-built and pre-existing databases were used for prospective data collection and for a historical ICU readmissions control group. Questionnaires and semistructured group interviews were utilised to evaluate clinician satisfaction and perceptions.

RESULTS

The service accepted referrals for 274 patients in 6 months (on average 2.25/working day; commonly after trauma [29%] and abdominal surgery [19%]). During the implementation period of the model of care, fewer preventable respiratory ICU readmissions were reported (n = 1/20) than in the historical cohort (n = 6/19: Fisher's exact test, p < 0.05). Likelihood of respiratory ICU readmission, compared to all-cause readmissions, was not affected (intervention: 31%, historical control: 41%; odds ratio: 0.63 [95% confidence interval: 0.29 to 1.4]). Postimplementation surveys and focus groups revealed clinicians highly valued the support and perceived a positive impact on patient care.

CONCLUSIONS

Critical care outreach-style physiotherapy services can be successfully implemented and are positively perceived by clinicians, but any effect on ICU readmissions is unclear.

Rapid response teams: A review of data collection practice in Victoria, Australia

BACKGROUND

Successful implementation of rapid response teams (RRTs) requires robust data collection and reporting processes. However, there is variation in data collection practice in RRT activity between hospitals, leading to difficulties in quality review, collaboration and research. Although a standardised RRT data collection model would be a key step in addressing this, there is uncertainty regarding existing RRT data collection practice across Victoria.

OBJECTIVES

This study was endorsed by Safer Care Victoria (SCV) to evaluate existing RRT data collection practice across Victoria.

METHODOLOGY

Between 2016 and 2017, hospitals in Victoria were surveyed on data collection practice for RRT activity. Data collected included the fields populated and the mode of data collection. Qualitative content analysis, utilising a blend of pre-existing frameworks and ground-up data-driven approaches for derivation of a coding frame, was used to identify common categories. Validation of the analysis and results was performed by consultation with stakeholder groups.

RESULTS

Twenty five hospitals across 18 health networks contributed data, with a mix of tertiary (9/25), metropolitan (11/25) and rural (5/25) hospitals. Seven hospitals collected data electronically, the remainder using paper with abstraction to electronic spreadsheets. None of the hospitals linked with existing hospital data systems to reduce manual data entry requirements. Dataset size varied from 16 to 97 variables but demonstrated content consistency and could be mapped onto seven key categories (comprising antecedent, afferent, event, post-event, audit, context and patient data). Within each category, there was substantial variation in terminology and variable values, but consistency in the collection of a certain subset of variables.

CONCLUSION

Despite broad variation in data collection practice, existing datasets can be readily mapped into seven key categories, with the consistent collection of a subset of variables within each category. These variables could inform the development of a minimum dataset within a standardised RRT reporting framework and accommodate data submission from hospitals of differing resource bases.

Mean arterial pressure predicts 48 h clinical deterioration in intermediate-high risk patients with acute pulmonary embolism

AIMS

We assess the prognostic role of mean arterial pressure (MAP) for 48 h clinical deterioration in intermediate-high risk pulmonary embolism (PE) patients after admission.

METHODS AND RESULTS

A post hoc analysis of intermediate-high-risk PE and intermediate-low-risk PE patients enrolled in the Italian Pulmonary Embolism Registry (IPER) (Trial registry: ClinicalTrials.gov; No.: NCT01604538) was performed. Clinical deterioration within 48 h was defined as patient worsening from a stable to an unstable haemodynamic condition, need of catecholamine infusion, endotracheal intubation, or cardiopulmonary resuscitation. Of 450 intermediate-high risk PE patients (mean age 71.4 ± 13.8 years, 298 males), 40 (8.8%) experienced clinical deterioration within 48 h from admission. Receiver operating characteristic analysis established the optimal cut-off value for MAP, as a predictor of 48 h clinical deterioration, ≤81.5 mmHg [area under curve (AUC) of 0.77 ± 0.3] with sensitivity, specificity, positive predictive value, and negative predictive value were 77.5, 95.0, 63.2, and 97.7%, respectively. Multivariate Cox regression analysis showed that independent risk factors for 48 h clinical deterioration were age [hazard ratio (HR): 1.26, 95% confidence interval (CI): 1.19-1.28, P < 0.0001], history of heart failure (HR: 1.76, 95% CI: 1.72-1.81, P < 0.0001), simplified Pulmonary Embolism Severity Index (HR: 1.52, 95% CI: 1.49-1.58, P = 0.001), systemic thrombolysis (HR: 0.54, 95% CI: 0.30-0.65, P < 0.0001), and a MAP of ≤81.5 mmHg at admission (HR: 3.25, 95% CI: 1.89-5.21, P < 0.0001). The deteriorating group had a significantly higher risk of 30-day mortality (HR: 2.61, 95% CI: 2.54-2.66, P < 0.0001) compared with the non-deteriorating group.

CONCLUSION

The mean arterial pressure appears to be a useful, bedside, and non-invasive prognostic tool potentially capable of promptly identifying intermediate-high risk PE patients at higher risk of 48 h clinical deterioration.

A novel predictive analytics score reflecting accumulating disease burden - an investigation of the cumulative CoMET score

Predictive analytics tools variably take into account data from the electronic medical record, lab tests, nursing charted vital signs and continuous cardiorespiratory monitoring data to deliver an instantaneous score that indicates patient risk or instability. Few, if any, of these tools reflect the risk to a patient accumulated over the course of an entire hospital stay. Current approaches fail to best utilize all of the cumulatively collated data regarding the risk or instability sustained by the patient. We have expanded on our instantaneous CoMET predictive analytics score to generate the cumulative CoMET score (cCoMET), which sums all of the instantaneous CoMET scores throughout a hospital admission relative to a baseline expected risk unique to that patient. We have shown that higher cCoMET scores predict mortality, but not length of stay, and that higher baseline CoMET scores predict higher cCoMET scores at discharge/death. cCoMET scores were higher in males in our cohort, and added information to the final CoMET when it came to the prediction of death. In summary, we have shown that the inclusion of all repeated measures of risk estimation performed throughout a patients hospital stay adds information to instantaneous predictive analytics, and could improve the ability of clinicians to predict deterioration, and improve patient outcomes in so doing.

Beyond objective measurements: Danish nurses' identification of hospitalized pediatric patients at risk of clinical deterioration - A qualitative study

PURPOSE

While the use of Pediatric Track and Trigger Tools as a standard to discriminate high level of urgency in pediatric care has received considerable attention, less focus has been given to other important factors such as nurses' clinical observations and judgement. The purpose of this study was to explore nurses' observational practice and focus on which non-measurable signs and symptoms nurses find important when identifying inpatient pediatric patients at risk of clinical deterioration.

DESIGN AND METHODS

This was an inductive qualitative study based on an interpretive description methodology. Data were obtained through participant observation of experienced nurses working in a Danish pediatric unit and focus group interviews with pediatric nurses. Field notes were taken, and focus group interviews were audio taped and transcribed. A thematic text condensation method was used to analyse data.

RESULTS

Findings revealed the following four main themes of non-measurable signs and symptoms that nurses find important when identifying children at risk of clinical deterioration: Colour and skin tone; sounds; movement patterns; behavioural signs.

CONCLUSIONS

This study suggest that pediatric patients show signs and symptoms that go beyond the objective measurements integrated in Pediatric Track and Trigger Tools and they should not be ignored as they are highly valuable to nurses who are responsible for observing inpatient pediatric patients at risk of clinical deterioration.

IMPLICATIONS

More empirical research on nurses' observational practice is recommended, especially research to identify the signs and symptoms - both measurable and non-measurable - that are significant to nurses at the bedside.

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.

Exploration of a rapid response team model of care: A descriptive dual methods study

BACKGROUND

Avoidable in-patient clinical deterioration results in serious adverse events and up to 80% are preventable. Rapid response systems allow early recognition and response to clinical deterioration.

OBJECTIVE

To explore the characteristics of a collaborative rapid response team model.

DESIGN

Dual methodology was used for this descriptive study.

SETTING

The study was conducted in a 500-bed tertiary referral hospital (Sydney, Australia).

PARTICIPANTS

Inpatients (>17 years) who received a rapid response team activation were included in an electronic medical audit. Participants were rapid response team members and nurses and medical doctors in two in-patient wards.

METHODS

A 12-month (January-December 2018) retrospective electronic health record audit and semi-structured interviews with nurses and medical doctors (July-August 2019) were conducted. Descriptive statistics summarised audit data. Interviews were transcribed and analysed thematically.

RESULTS

The rapid response team consulted for 2195 patients. Mean patient age was 67.9 years, and 46% of the sample was female. Activations (n = 4092) occurred most often in general medicine (n = 1124, 70.8%) units. Overall, 117 patients had >5 activations. The themes synthesised from interviews were i) managing patient deterioration before arrival of the rapid response team; ii) collaboratively managing patient deterioration at the bedside; iii) rapid response team guidance at the bedside; and iv) 'staff concern' rapid response activation.

CONCLUSIONS

Some patients received many activations, however few required treatment in critical care. The rapid response model was collaborative and supportive. The themes revealed a focus on patient safety, optimising early detection, and management of patient deterioration.

Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease

Background

The National Early Warning Score-2 (NEWS-2) is used to detect patient deterioration in UK hospitals but fails to take account of the detailed granularity or temporal trends in clinical observations. We used data-driven methods to develop dynamic early warning scores (DEWS) to address these deficiencies, and tested their accuracy in patients with respiratory disease for predicting (1) death or intensive care unit admission, occurring within 24 h (D/ICU), and (2) clinically significant deterioration requiring urgent intervention, occurring within 4 h (CSD).

Methods

Clinical observations data were extracted from electronic records for 31,590 respiratory in-patient episodes from April 2015 to December 2020 at a large acute NHS Trust. The timing of D/ICU was extracted for all episodes. 1100 in-patient episodes were annotated manually to record the timing of CSD, defined as a specific event requiring a change in treatment. Time series features were entered into logistic regression models to derive DEWS for each of the clinical outcomes. Area under the receiver operating characteristic curve (AUROC) was the primary measure of model accuracy.

Results

AUROC (95% confidence interval) for predicting D/ICU was 0.857 (0.852–0.862) for NEWS-2 and 0.906 (0.899–0.914) for DEWS in the validation data. AUROC for predicting CSD was 0.829 (0.817–0.842) for NEWS-2 and 0.877 (0.862–0.892) for DEWS. NEWS-2 ≥ 5 had sensitivity of 88.2% and specificity of 54.2% for predicting CSD, while DEWS ≥ 0.021 had higher sensitivity of 93.6% and approximately the same specificity of 54.3% for the same outcome. Using these cut-offs, 315 out of 347 (90.8%) CSD events were detected by both NEWS-2 and DEWS, at the time of the event or within the previous 4 h; 12 (3.5%) were detected by DEWS but not by NEWS-2, while 4 (1.2%) were detected by NEWS-2 but not by DEWS; 16 (4.6%) were not detected by either scoring system.

Conclusion

We have developed DEWS that display greater accuracy than NEWS-2 for predicting clinical deterioration events in patients with respiratory disease. Prospective validation studies are required to assess whether DEWS can be used to reduce missed deteriorations and false alarms in real-life clinical settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02130-6.

Frequency and nature of medical emergency team afferent limb failure in patients with documented vital sign abnormalities: A retrospective point prevalence study

BACKGROUND

Medical emergency team (MET) afferent limb failure is the presence of MET triggers and the absence of a documented MET call.

OBJECTIVES

The aim of this study was to measure and understand the frequency and nature of MET afferent limb failure in patients with documented vital sign abnormalities in an Australian major teaching hospital.

METHODS

A retrospective point prevalence study was conducted at a 600-bed teaching hospital in Melbourne, Australia. Data were collected for all adult inpatients (aged ≥18 years) on 13 wards (three general medicine, three surgical, and seven specialist wards) during a randomly selected 24-h period. Data were extracted from the electronic medical record.

RESULTS

There were 357 patients included in the study, with a median age of 72 y. Of the 9716 vital sign measures extracted, 0.9% fulfilled patient-specific MET activation criteria. There were 93 MET triggers documented in 36 patients: 25 patients experienced MET afferent limb failure. The major issues related to MET afferent limb failure were MET trigger modification processes, resolution of vital sign abnormalities, alternative escalation of care, and limitations of medical treatment orders without specific modifications to MET triggers.

CONCLUSIONS

Mandating MET activation for one aberrant vital sign at a single point in time warrants further assessment: lack of timely vital sign resolution may be a more appropriate trigger for MET calls and should be formally tested in future research. The frequency and effectiveness of alternative escalation pathways and local management of patients with MET triggers also warrant further investigation.

Exercise in Futility or do CART or MEWS Prevent Errors?

How to cite this article: Rao RMG. Exercise in Futility or do CART or MEWS Prevent Errors? Indian J Crit Care Med 2022;26(7):765-766.

Requirement Analysis for an Intelligent Warning System to Alarm the Rapid Response Team Prior to Patient Deterioration

The early warning system alarms the rapid response team (RRT) for clinical deterioration monitoring and prediction. Available systems do not perform well to decrease the number of ICU transfers or death. This study aimed to address the requirement of an intelligent warning system for timely and accurate RRT activation.

METHODOLOGY

A literature review was conducted in scientific databases to extract data. Then, a questionnaire was developed for experts' views collection (N=12). The collected data were analyzed using the Content Validity Ratio (CVR). According to the Lawshe table for the corresponding number of experts, the cut-off=0.56 for items to be accepted/rejected was considered. A schematic structure was suggested.

FINDINGS

The analysis of the extracted papers (N=24) and qualitative analysis addressed 44 requirements in the frame of five involved sub-systems, including a patient monitoring system, electronic health record, clinical decision support system, remote monitoring patient, and dashboard &registries. They were confirmed by meeting the least cut-off value (CVR= 0.86).

CONCLUSION

An integrated approach and technologies of IoT, deep and machine learning techniques, big data, advanced databases, and standards to create an intelligent EWS are required.

Selecting intervention content to target barriers and enablers of recognition and response to deteriorating patients: an online nominal group study

Background

Patients who deteriorate in hospital wards without appropriate recognition and/or response are at risk of increased morbidity and mortality. Track-and-trigger tools have been implemented internationally prompting healthcare practitioners (typically nursing staff) to recognise physiological changes (e.g. changes in blood pressure, heart rate) consistent with patient deterioration, and then to contact a practitioner with expertise in management of acute/critical illness. Despite some evidence these tools improve patient outcomes, their translation into clinical practice is inconsistent internationally. To drive greater guideline adherence in the use of the National Early Warning Score tool (a track-and-trigger tool used widely in the United Kingdom and parts of Europe), a theoretically informed implementation intervention was developed (targeting nursing staff) using the Theoretical Domains Framework (TDF) version 2 and a taxonomy of Behaviour Change Techniques (BCTs).

Methods

A three-stage process was followed: 1. TDF domains representing important barriers and enablers to target behaviours derived from earlier published empirical work were mapped to appropriate BCTs; 2. BCTs were shortlisted using consensus approaches within the research team; 3. shortlisted BCTs were presented to relevant stakeholders in two online group discussions where nominal group techniques were applied. Nominal group participants were healthcare leaders, senior clinicians, and ward-based nursing staff. Stakeholders individually generated concrete strategies for operationalising shortlisted BCTs (‘applications’) and privately ranked them according to acceptability and feasibility. Ranking data were used to drive decision-making about intervention content.

Results

Fifty BCTs (mapped in stage 1) were shortlisted to 14 (stage 2) and presented to stakeholders in nominal groups (stage 3) alongside example applications. Informed by ranking data from nominal groups, the intervention was populated with 12 BCTs that will be delivered face-to-face, to individuals and groups of nursing staff, through 18 applications.

Conclusions

A description of a theory-based behaviour change intervention is reported, populated with BCTs and applications generated and/or prioritised by stakeholders using replicable consensus methods. The feasibility of the proposed intervention should be tested in a clinical setting and the content of the intervention elaborated further to permit replication and evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08128-6.

Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care

BACKGROUND/OBJECTIVE

Between 10 and 25% patients are hospitalized or visit emergency department (ED) during home healthcare (HHC). Given that up to 40% of these negative clinical outcomes are preventable, early and accurate prediction of hospitalization risk can be one strategy to prevent them. In recent years, machine learning-based predictive modeling has become widely used for building risk models. This study aimed to compare the predictive performance of four risk models built with various data sources for hospitalization and ED visits in HHC.

METHODS

Four risk models were built using different variables from two data sources: structured data (i.e., Outcome and Assessment Information Set (OASIS) and other assessment items from the electronic health record (EHR)) and unstructured narrative-free text clinical notes for patients who received HHC services from the largest non-profit HHC organization in New York between 2015 and 2017. Then, five machine learning algorithms (logistic regression, Random Forest, Bayesian network, support vector machine (SVM), and Naïve Bayes) were used on each risk model. Risk model performance was evaluated using the F-score and Precision-Recall Curve (PRC) area metrics.

RESULTS

During the study period, 8373/86,823 (9.6%) HHC episodes resulted in hospitalization or ED visits. Among five machine learning algorithms on each model, the SVM showed the highest F-score (0.82), while the Random Forest showed the highest PRC area (0.864). Adding information extracted from clinical notes significantly improved the risk prediction ability by up to 16.6% in F-score and 17.8% in PRC.

CONCLUSION

All models showed relatively good hospitalization or ED visit risk predictive performance in HHC. Information from clinical notes integrated with the structured data improved the ability to identify patients at risk for these emergent care events.

Aboriginal perspectives on recognising clinical deterioration in their child and communicating concerns to clinicians

AIMS AND OBJECTIVES

To explore the perspectives of family members of Aboriginal children about a) their involvement in recognising clinical deterioration in a hospital setting and b) the effectiveness of a poster designed to promote family involvement.

BACKGROUND

To assist in the early recognition and response to clinical deterioration for hospitalised children, many escalation of care processes now include family involvement. Little is currently known about the perspectives of Australian Aboriginal families in recognising deterioration in their child and raising the alarm, or if current escalation of care systems meet the needs of Aboriginal families.

DESIGN

Qualitative pragmatist approach using semi-structured interviews.

METHODS

Seven interviews were conducted with five mothers and two grandmothers of Aboriginal children who were inpatients at a children's hospital. Thematic analysis was undertaken.

FINDINGS

Two themes were identified: Theme one was: Family role in recognising and responding to clinical deterioration, with two subthemes of knowing when to worry and communicating concerns. Participants reported that some families needed more knowledge to recognise clinical deterioration. Communication barriers between families and clinicians were identified. Theme two was: Effective visual communication with three subthemes of linguistic clarity, visual appeal and content.

CONCLUSIONS

Additional strategies are needed to promote effective communication between clinicians and families of Aboriginal children in hospital. Posters were considered effective, particularly if including a cultural connection, images and simplified language.

PRACTICE IMPLICATIONS

These insights provide important information for health professionals and health service managers to be aware that additional communication strategies are required to support Aboriginal family involvement in recognising clinical deterioration and escalation of care.

Evaluation of a digital system to predict unplanned admissions to the intensive care unit: A mixed-methods approach

Background

We have developed the Hospital Alerting Via Electronic Noticeboard (HAVEN) which aims to identify hospitalised patients most at risk of reversible deterioration. HAVEN combines patients’ vital-sign measurements with laboratory results, demographics and comorbidities using a machine learnt algorithm.

Objectives

The aim of this study was to identify variables or concepts that could improve HAVEN predictive performance.

Methods

This was an embedded, mixed methods study. Eligible patients with the five highest HAVEN scores in the hospital (i.e., ‘HAVEN Top 5′) had their medical identification details recorded. We conducted a structured medical note review on these patients 48 hours post their identifiers being recorded. Methods of constant comparison were used during data collection and to analyse patient data.

Results

The 129 patients not admitted to ICU then underwent constant comparison review, which produced three main groups. Group 1 were patients referred to specialist services (n = 37). Group 2 responded to ward-based treatment, (n = 38). Group 3 were frail and had documented treatment limitations (n = 47).

Conclusions

Digital-only validation methods code the cohort not admitted to ICU as ‘falsely positive’ in sensitivity analyses however this approach limits the evaluation of model performance. Our study suggested that coding for patients referred to other specialist teams, those with treatment limitations in place, along with those who are deteriorating but then respond to ward-based therapies, would give a more accurate measure of the value of the scores, especially in relation to cost-effectiveness of resource utilisation.

[Risk factors for clinical deterioration in patients admitted for COVID-19: A case-control study]

INTRODUCTION

There is controversy regarding the best predictors of clinical deterioration in COVID-19.

OBJECTIVE

This work aims to identify predictors of risk factors for deterioration in patients hospitalized due to COVID-19.

METHODS DESIGN

Nested case-control study within a cohort. Setting: 13 acute care centers of the Osakidetza-Basque Health Service. Participants: Patients hospitalized for COVID-19 with clinical deterioration-defined as onset of severe ARDS, ICU admission, or death-were considered cases. Two controls were matched to each case based on age. Sociodemographic data; comorbidities; baseline treatment; symptoms; date of onset; previous consultations; and clinical, analytical, and radiological variables were collected. An explanatory model of clinical deterioration was created by means of conditional logistic regression.

RESULTS

A total of 99 cases and 198 controls were included. According to the logistic regression analysis, the independent variables associated with clinical deterioration were: emergency department O2 saturation ≤90% (OR 16.6; 95%CI 4-68), pathological chest X-ray (OR 5.6; 95%CI 1.7-18.4), CRP >100 mg/dL (OR 3.62; 95%CI 1.62-8), thrombocytopenia with < 150,000 platelets (OR 4; 95%CI 1.84-8.6); and a medical history of acute myocardial infarction (OR 15.7; 95%CI, 3.29-75.09), COPD (OR 3.05; 95%CI 1.43-6.5), or HT (OR 2.21; 95%CI 1.11-4.4). The model's AUC was 0.86. On the univariate analysis, female sex and presence of dry cough and sore throat were associated with better clinical progress, but were not found to be significant on the multivariate analysis.

CONCLUSION

The variables identified could be useful in clinical practice for the detection of patients at high risk of poor outcomes.

Risk factors for clinical deterioration in patients admitted for COVID-19: A case-control study

INTRODUCTION

There is controversy regarding the best predictors of clinical deterioration in COVID-19.

OBJECTIVE

This work aims to identify predictors of risk factors for deterioration in patients hospitalized due to COVID-19.

METHODS DESIGN

Nested case-control study within a cohort.

SETTING

13 acute care centers of the Osakidetza-Basque Health Service.

PARTICIPANTS

patients hospitalized for COVID-19 with clinical deterioration-defined as onset of severe ARDS, ICU admission, or death-were considered cases. Two controls were matched to each case based on age. Sociodemographic data; comorbidities; baseline treatment; symptoms; date of onset; previous consultations; and clinical, analytical, and radiological variables were collected. An explanatory model of clinical deterioration was created by means of conditional logistic regression.

RESULTS

A total of 99 cases and 198 controls were included. According to the logistic regression analysis, the independent variables associated with clinical deterioration were: emergency department O2 saturation ≤90% (OR 16.6; 95%CI 4-68), pathological chest X-ray (OR 5.6; 95%CI 1.7-18.4), CRP > 100 mg/dL (OR 3.62; 95%CI 1.62-8), thrombocytopenia with <150,000 platelets (OR 4; 95%CI 1.84-8.6); and a medical history of acute myocardial infarction (OR 15.7; 95%CI, 3.29-75.09), COPD (OR 3.05; 95%CI 1.43-6.5), or HT (OR 2.21; 95%CI 1.11-4.4). The model's AUC was 0.86. On the univariate analysis, female sex and presence of dry cough and sore throat were associated with better clinical progress, but were not found to be significant on the multivariate analysis.

CONCLUSION

The variables identified could be useful in clinical practice for the detection of patients at high risk of poor outcomes.

Enablers of the successful implementation of simulation exercises: a qualitative study among nurse teachers in undergraduate nursing education

BACKGROUND

Simulation exercises are increasingly being used as a teaching method in the field of undergraduate nursing education. Thus, the present study sought to identify, describe and discuss enablers of the successful implementation of simulation exercises in undergraduate nursing education.

METHODS

This study had a qualitative descriptive design and involved individual interviews conducted between November and December 2018 with six nurse teachers from three different university campuses in Norway. The transcribed interviews were analysed by means of a qualitative thematic analysis.

RESULTS

The majority of the interviewees wanted to offer more simulation exercises as part of their respective undergraduate nursing education programmes. Moreover, creating a safe environment, facilitating student-centred learning and promoting reflection were all identified by the interviewees as enablers of the successful implementation of simulation exercises.

CONCLUSIONS

The findings of this study indicate that nurse teachers consider simulation to be a valuable teaching method for improving students' learning outcomes. In addition, the findings could guide the future implementation of simulation exercises in undergraduate nursing education.

TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT04063319 . Protocol ID: 52110 Nursing Students' Recognition of and Response to Deteriorating Patients.

Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond

As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation. We must develop strategies and techniques to study the impact of AI-based technologies on patient care outcomes and the clinical workflow. This paper outlines key concepts for the intersection of nursing and precision predictive analytics monitoring.

Effects of patient deterioration simulation using inattentional blindness for final year nursing students: A randomized controlled trial

BACKGROUND

Patient deterioration should be detected early and responded appropriately for patient safety. It is necessary to strengthen situational awareness regarding patient deterioration. Inattentional blindness is a major factor that hinders situational awareness about patient deterioration in the clinical setting.

OBJECTIVES

To analyze the impact of patient deterioration simulation using inattentional blindness (PDS-IB) on situational awareness and patient safety competency-attitude among final year nursing students.

DESIGN

A randomized controlled trial.

PARTICIPANTS

Final year nursing students at a university in South Korea.

METHODS

Students were randomly assigned to an experimental or control group. The experimental group (n = 47) was given a PDS-IB. The control group (n = 44) received a simple patient deterioration simulation. Situational awareness and patient safety competency-attitude were measured at baseline, post intervention, and at 2 weeks follow-up. Data were analyzed using a two-way repeated measures ANOVA.

RESULTS

There were statistically significant group effects, time effects, and group and time interaction effects in situational awareness and patient safety competency-attitude.

CONCLUSION

PDS-IB is an effective educational strategy that increases situational awareness and patient safety competency-attitude in final year nursing students.

Development of an evidence-based ESCALATION system for recognition and response to paediatric clinical deterioration

AIM

The aim of this study was to develop an evidence-based paediatric early warning system for infants and children that takes into consideration a variety of paediatric healthcare contexts and addresses barriers to escalation of care.

METHODS

A three-stage intervention development framework consisted of Stage 1: evidence review, benchmarking, stakeholder (health professionals, decision-makers, and health consumers) engagement, and consultation; Stage 2: planning and coproduction by the researchers and stakeholders using action research cycles; and Stage 3: prototyping and testing.

RESULTS

A prototype evidence-based system incorporated human factor principles, used a structured approach to patient assessment, promoted situational awareness, and included family as well as clinician concern. Family involvement in detecting changes in their child's condition was supported by posters and flyers codesigned with health consumers. Five age-specific observation and response charts included 10 weighted variables and one unweighted variable (temperature) to convey a composite early warning score. The escalation pathway was supported by a targeted communication framework (iSoBAR NOW).

CONCLUSION

The development process resulted in an agreed uniform ESCALATION system incorporating a whole-system approach to promote critical thinking, situational awareness for the early recognition of paediatric clinical deterioration as well as timely and effective escalation of care. Incorporating family involvement was a novel component of the system.

Less is more: Detecting clinical deterioration in the hospital with machine learning using only age, heart rate, and respiratory rate

AIM

We sought to develop a machine learning analytic (eCART Lite) for predicting clinical deterioration using only age, heart rate, and respiratory data, which can be pulled in real time from patient monitors and updated continuously without need for additional inputs or cumbersome electronic health record integrations.

METHODS

We utilized a multicenter dataset of adult admissions from five hospitals. We trained a gradient boosted machine model using only current and 24-hour trended heart rate, respiratory rate, and patient age to predict the probability of intensive care unit (ICU) transfer, death, or the combined outcome of ICU transfer or death. The area under the receiver operating characteristic curve (AUC) was calculated in the validation cohort and compared to those for the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and eCARTv2, a previously-described, 27-variable, cubic spline, logistic regression model without trends.

RESULTS

Of the 556,848 included admissions, 19,509 (3.5%) were transferred to an ICU and 5764 (1.0%) died within 24 hours of a ward observation. eCART Lite significantly outperformed the MEWS, NEWS, and eCART v2 for predicting ICU transfer (0.79 vs 0.71, 0.74, and 0.78, respectively; p < 0.01) and the combined outcome (0.80 vs 0.72, 0.76, and 0.79, respectively; p < 0.01). Two of the strongest predictors were respiratory rate and heart rate.

CONCLUSION

Using only three inputs, we developed a tool for predicting clinical deterioration that is similarly or more accurate than commonly-used algorithms, with potential for use in inpatient settings with limited resources or in scenarios where low-cost tools are needed.

Treatments costs associated with inpatient clinical deterioration

AIMS

This study aimed to quantify the health economic treatment costs of clinical deterioration of patients within 72 h of admission via the emergency department.

METHODS

This study was conducted between March 2018 and February 2019 in two hospitals in regional New South Wales, Australia. All patients admitted via the emergency department were screened for clinical deterioration (defined as initiation of a medical emergency team call, cardiac arrest or unplanned admission to Intensive Care Unit) within 72 h through the site clinical deterioration databases. Patient characteristics, including pre-existing conditions, diagnosis and administrative data were collected.

RESULTS

1600 patients clinically deteriorated within 72 h of hospital admission. Linked treatment cost data were available for 929 (58%) of these patients across 352 Australian Refined Diagnosis Related Groups. The average (standard deviation) treatment costs for patients who deteriorated within 72 h was $26,778 ($34,007) compared to $7727 ($12,547). The average hospital length of stay of the deterioration group was nearly 8 days longer than patients without deterioration. When controlling for length of stay and Australian Refined Diagnosis Related Group codes, the incremental cost per episode of deterioration was $14,134.

CONCLUSION

Clinical deterioration within 72 h of admission is associated with increased treatment costs irrespective of diagnosis, hospital length of stay and age. Implementation of interventions known to prevent patient deterioration require evaluation.

Etiology and Timing of Postoperative Rapid Response Team Activations

In this retrospective cohort study we sought to evaluate the association between the etiology and timing of rapid response team (RRT) activations in postoperative patients at a tertiary care hospital in the southeastern United States. From 2010 to 2016, there were 2,390 adult surgical inpatients with RRT activations within seven days of surgery. Using multivariable linear regression, we modeled the correlation between etiology of RRT and timing of the RRT call, as measured from the conclusion of the surgical procedure. We found that respiratory triggers were associated with an increase in time after surgical procedure to RRT of 10.6 h compared to activations due to general concern (95% CI 3.9 - 17.3) (p = 0.002). These findings may have an impact on monitoring of postoperative patients, as well as focusing interventions to better respond to clinically deteriorating patients.

Increased time from physiological derangement to critical care admission associates with mortality

Background

Rapid response systems aim to achieve a timely response to the deteriorating patient; however, the existing literature varies on whether timing of escalation directly affects patient outcomes. Prior studies have been limited to using ‘decision to admit’ to critical care, or arrival in the emergency department as ‘time zero’, rather than the onset of physiological deterioration. The aim of this study is to establish if duration of abnormal physiology prior to critical care admission [‘Score to Door’ (STD) time] impacts on patient outcomes.

Methods

A retrospective cross-sectional analysis of data from pooled electronic medical records from a multi-site academic hospital was performed. All unplanned adult admissions to critical care from the ward with persistent physiological derangement [defined as sustained high National Early Warning Score (NEWS) > / = 7 that did not decrease below 5] were eligible for inclusion. The primary outcome was critical care mortality. Secondary outcomes were length of critical care admission and hospital mortality. The impact of STD time was adjusted for patient factors (demographics, sickness severity, frailty, and co-morbidity) and logistic factors (timing of high NEWS, and out of hours status) utilising logistic and linear regression models.

Results

Six hundred and thirty-two patients were included over the 4-year study period, 16.3% died in critical care. STD time demonstrated a small but significant association with critical care mortality [adjusted odds ratio of 1.02 (95% CI 1.0–1.04, p = 0.01)]. It was also associated with hospital mortality (adjusted OR 1.02, 95% CI 1.0–1.04, p = 0.026), and critical care length of stay. Each hour from onset of physiological derangement increased critical care length of stay by 1.2%. STD time was influenced by the initial NEWS, but not by logistic factors such as out-of-hours status, or pre-existing patient factors such as co-morbidity or frailty.

Conclusion

In a strictly defined population of high NEWS patients, the time from onset of sustained physiological derangement to critical care admission was associated with increased critical care and hospital mortality. If corroborated in further studies, this cohort definition could be utilised alongside the ‘Score to Door’ concept as a clinical indicator within rapid response systems.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03650-1.