Effects of a medical emergency team follow-up programme on patients discharged from the medical intensive care unit to the general ward: a single-centre experience

Epidemiology and outcome of medical emergency team calls within 48 hours of intensive care unit discharge

BACKGROUND

Some patients experience early (within 48 h) clinical deterioration and medical emergency team (MET) review following intensive care unit (ICU) discharge. Few studies have explored early MET review, despite it being a recognised quality and safety indicator.

AIMS

To evaluate the (i) proportion of patients discharged from ICU receiving MET review and timing of reviews; (ii) characteristics of patients who received early MET review and (iii) predictors of early MET review and associations with clinical outcomes.

METHODS

This is a retrospective observational study of ICU discharges over 2 years in a tertiary hospital and involves descriptive and inferential statistics, including logistic regression analysis.

RESULTS

Of 3712 patients, 312 (8.4%) had an early MET review. Patients with cardiothoracic, cardiovascular, gastrointestinal and general surgical diagnoses, higher illness severity or who received invasive ventilation had a higher risk of early MET review. On multivariable analysis, early MET review was associated with an increased risk of ICU re-admission (odds ratio (OR) 6.76, 95% confidence interval (CI) 5.01-9.13, P < 0.001), in-hospital mortality (OR 3.62, 95% CI 2.19-5.99, P < 0.001) and discharge to a nursing home (OR 2.49, 95% CI 1.25-4.97, P = 0.01). Length of stay was longer in patients requiring early post-ICU MET review compared to those who did not (median 16 days vs. 10 days, P < 0.001).

CONCLUSIONS

One in 12 patients received post-ICU early MET review. This was more likely in patients who were invasively ventilated, had higher illness severity and had certain admission diagnoses. Such patients were at risk for worse outcomes. There is a need to identify reversible factors contributing to such increased risk.

Society of Critical Care Medicine Guidelines on Recognizing and Responding to Clinical Deterioration Outside the ICU: 2023

RATIONALE

Clinical deterioration of patients hospitalized outside the ICU is a source of potentially reversible morbidity and mortality. To address this, some acute care hospitals have implemented systems aimed at detecting and responding to such patients.

OBJECTIVES

To provide evidence-based recommendations for hospital clinicians and administrators to optimize recognition and response to clinical deterioration in non-ICU patients.

PANEL DESIGN

The 25-member panel included representatives from medicine, nursing, respiratory therapy, pharmacy, patient/family partners, and clinician-methodologists with expertise in developing evidence-based Clinical Practice Guidelines.

METHODS

We generated actionable questions using the Population, Intervention, Control, and Outcomes (PICO) format and performed a systematic review of the literature to identify and synthesize the best available evidence. We used the Grading of Recommendations Assessment, Development, and Evaluation Approach to determine certainty in the evidence and to formulate recommendations and good practice statements (GPSs).

RESULTS

The panel issued 10 statements on recognizing and responding to non-ICU patients with critical illness. Healthcare personnel and institutions should ensure that all vital sign acquisition is timely and accurate (GPS). We make no recommendation on the use of continuous vital sign monitoring among unselected patients. We suggest focused education for bedside clinicians in signs of clinical deterioration, and we also suggest that patient/family/care partners' concerns be included in decisions to obtain additional opinions and help (both conditional recommendations). We recommend hospital-wide deployment of a rapid response team or medical emergency team (RRT/MET) with explicit activation criteria (strong recommendation). We make no recommendation about RRT/MET professional composition or inclusion of palliative care members on the responding team but suggest that the skill set of responders should include eliciting patients' goals of care (conditional recommendation). Finally, quality improvement processes should be part of a rapid response system.

CONCLUSIONS

The panel provided guidance to inform clinicians and administrators on effective processes to improve the care of patients at-risk for developing critical illness outside the ICU.

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

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

Routine critical care step-down programmes: Systematic review and meta-analysis

BACKGROUND

Patients discharged from critical care to general hospital wards are vulnerable to clinical deterioration, critical care readmission, and death. In response, routine critical care stepdown programmes (CCSDPs) have been widely developed, which involve the review of all patients on general wards following discharge from critical care by multidisciplinary Outreach teams with critical care skills.

AIMS AND OBJECTIVES

This review aims to answer the question: do routine CCSDPs reduce readmission and/or mortality among patients discharged from critical care?

DESIGN

Systematic review of quantitative studies and meta-analysis.

METHODS

Six databases were comprehensively searched from inception (CENTRAL, Cochrane Reviews, MEDLINE, Embase, CINAHL and web of Science), alongside grey literature and trial registers. Studies investigating the effect of routine CCSDPs delivered by Outreach nurses on readmission and/or mortality following discharge from adult critical care to general hospital wards were included. Study quality was assessed using the Cochrane ROBINS-I tool.

RESULTS

Eight studies met the inclusion criteria, with data from 6 studies pooled in 3 meta-analyses. Among patients exposed to routine CCSDPs, pooled data estimated a statistically nonsignificant reduction in the risk of readmission to critical care (risk ratio [RR] 0.85; 95% confidence interval [CI] 0.66-1.09; P = .19), a statistically significant increase in the risk of readmission to critical care within 72 hours (RR 1.49; 95% CI 1.05-2.12; P = .03), a statistically non-significant reduction in risk of mortality following critical care discharge (RR 0.90; 95% CI 0.75-1.07; P = .22), and no association with mortality within 14 days of discharge.

CONCLUSION

This review is unable to definitively conclude whether routine CCSDPs reduce critical care readmission or mortality following critical care discharge.

RELEVANCE TO CLINICAL PRACTICE

While the synthesized evidence does not suggest a change in policy and practice are warranted, neither does it support routine CCSDPs in the absence of high-quality evidence.

Critical care transition programs on readmission or death: A systematic review and meta-analysis

BACKGROUND

Deterioration after ICU discharge may lead to readmission or even death. Interventions (eg, critical care transition programs) have been developed to improve the clinical handover between the ICU and the ward. We conducted a systematic review with meta-analysis and trial sequential analysis (TSA) according to Cochrane Handbook and Grading of recommendations, assessment, development and evaluations (GRADE) methodology to assess the impact of these interventions on readmission and death (PROSPERO, no CRD42019121746).

METHODS

We searched PubMed/MEDLINE, CINAHL, AMED, PsycINFO, and the Cochrane Central Register for Controlled Trials from inception until January 2019. We included historically controlled studies that evaluated critical care transition programs in adults discharged from the ICU. Readmission and in-hospital mortality were the primary outcomes. Risk of bias, publications bias, and the quality of evidence were assessed with the ROBINS-Itool, funnel plot and GRADE, respectively.

RESULTS

Fifteen observational studies were included (11 in meta-analysis). All studies had at least serious risk of bias. ICU discharge within a critical care transition program modestly reduced the risk of readmission (RR 0.78; 95% CI: 0.64-0.96; TSA-adjusted 95% CI: 0.59-1.03) but not in-hospital mortality (RR 0.82; 95% CI: 0.64-1.06; TSA-adjusted 95% CI: 0.49-1.37). There was substantial heterogeneity among studies. TSA indicated lack of firm evidence. The GRADE quality of evidence on outcomes was very low.

CONCLUSIONS

We found no clear benefit in terms of reducing risk of readmission or death after ICU discharge, however, with overall very low certainty of evidence.

Transition of care at discharge from the Intensive Care Unit: a scoping review

OBJECTIVE

to map the available evidence on the components of the transition of care, practices, strategies, and tools used in the discharge from the Intensive Care Unit (ICU) to the Inpatient Unit (IU) and its impact on the outcomes of adult patients.

METHOD

a scoping review using search strategies in six relevant health databases.

RESULTS

37 articles were included, in which 30 practices, strategies or tools were identified for organizing and executing the transfer process, with positive or negative impacts, related to factors intrinsic to the Intensive Care Unit and the Inpatient Unit and cross-sectional factors regarding the staff. The analysis of hospital readmission and mortality outcomes was prevalent in the included studies, in which trends and potential protective actions for a successful care transition are found; however, they still lack more robust evidence and consensus in the literature.

CONCLUSION

transition of care components and practices were identified, in addition to factors intrinsic to the patient, associated with worse outcomes after discharge from the Intensive Care Unit. Discharges at night or on weekends were associated with increased rates of readmission and mortality; however, the association of other practices with the patient's outcome is still inconclusive.

To develop a regional ICU mortality prediction model during the first 24 h of ICU admission utilizing MODS and NEMS with six other independent variables from the Critical Care Information System (CCIS) Ontario, Canada

Background

Intensive care unit (ICU) scoring systems or prediction models evolved to meet the desire of clinical and administrative leaders to assess the quality of care provided by their ICUs. The Critical Care Information System (CCIS) is province-wide data information for all Ontario, Canada level 3 and level 2 ICUs collected for this purpose. With the dataset, we developed a multivariable logistic regression ICU mortality prediction model during the first 24 h of ICU admission utilizing the explanatory variables including the two validated scores, Multiple Organs Dysfunctional Score (MODS) and Nine Equivalents Nursing Manpower Use Score (NEMS) followed by the variables age, sex, readmission to the ICU during the same hospital stay, admission diagnosis, source of admission, and the modified Charlson Co-morbidity Index (CCI) collected through the hospital health records.

Methods

This study is a single-center retrospective cohort review of 8822 records from the Critical Care Trauma Centre (CCTC) and Medical-Surgical Intensive Care Unit (MSICU) of London Health Sciences Centre (LHSC), Ontario, Canada between 1 Jan 2009 to 30 Nov 2012. Multivariable logistic regression on training dataset (n = 4321) was used to develop the model and validate by bootstrapping method on the testing dataset (n = 4501). Discrimination, calibration, and overall model performance were also assessed.

Results

The predictors significantly associated with ICU mortality included: age (p < 0.001), source of admission (p < 0.0001), ICU admitting diagnosis (p < 0.0001), MODS (p < 0.0001), and NEMS (p < 0.0001). The variables sex and modified CCI were not significantly associated with ICU mortality. The training dataset for the developed model has good discriminating ability between patients with high risk and those with low risk of mortality (c-statistic 0.787). The Hosmer and Lemeshow goodness-of-fit test has a strong correlation between the observed and expected ICU mortality (χ² = 5.48; p > 0.31). The overall optimism of the estimation between the training and testing data set ΔAUC = 0.003, indicating a stable prediction model.

Conclusions

This study demonstrates that CCIS data available after the first 24 h of ICU admission at LHSC can be used to create a robust mortality prediction model with acceptable fit statistic and internal validity for valid benchmarking and monitoring ICU performance.

Electronic supplementary material

The online version of this article (doi:10.1186/s40560-016-0143-6) contains supplementary material, which is available to authorized users.