Out-of-hours discharge from intensive care, in-hospital mortality and intensive care readmission rates: a systematic review and meta-analysis

Circumstantial risk factors for death after intensive care unit-to-unit inter-hospital transfer-a Swedish registry study

BACKGROUND

Unit-to-unit transfer of critically ill patients infers hazards that may cause adverse events. Circumstantial factors associated with mortality after intensive care include days in the ICU, night-time or weekend discharge and capacity transfer as compared to other reasons for transfer. Distance travelled may also constitute an indirect risk. The aim of this study was to assess potential associations between these circumstantial factors and the risk of death 30 days after transfer.

METHODS

Data from 2015 to 2019 was retrieved from the Swedish Intensive Care Registry. Logistic regression was used for risk analysis.

RESULTS

Among 4,327 patients, 965 (22%) were deceased 30 days after transfer. 1351 patients undergoing capacity transfer had a higher morbidity than patients transferred for other reasons. Using univariable logistic regression, days spent in the referring ICU before transfer, capacity transfer as compared to clinical transfer and repatriation as well as SAPS3 in the receiving ICU were associated with a higher risk of death at 30 days. However, after multivariable regression with adjustment for ICD-10 diagnosis and Standardised Mortality Rate in the receiving ICU, these associations were lost.

CONCLUSION

Our results suggest that inter-hospital transfer is safe to carry out at any time of day and over shorter as well as longer distances.

Who gets the bed: Factors influencing the intensive care exit block: A qualitative study

BACKGROUND

Patient flow problems, including discharge delay and after-hours discharge, have been a consistently major issue, especially for intensive care units (ICUs). Evidence suggests that discharge delay and after-hours discharge are associated with increased ICU and hospital length of stay, leading to worsened patient outcomes and increased healthcare costs. They can also increase ICU readmission and post-ICU mortality. The factors influencing discharge processes are not well elucidated.

OBJECTIVE

This study aimed to explore the barriers and facilitators to the ICU patient discharge processes in adult ICUs.

METHODS

This qualitative exploratory multisite observational study was conducted in three regional adult ICUs in Queensland, Australia. We used staff interviews, fieldnotes, and document analysis as data collection techniques. Data analysis commenced with a deductive content analysis using the Structure, Process, and Outcomes framework. Following this, an inductive process was taken using the Theoretical Domains Framework.

FINDINGS

We conducted 59 staff interviews and analysed the discharge documents across three sites. Four domains, including context and resources, beliefs about consequences, social/professional role and identity, and behaviour regulation, were strongly related to the factors that influenced the discharge processes. The findings revealed barriers to discharge, including finding the right bed, disconnected and ineffective information systems, ineffective communication and coordination within and across teams and departments, and uncertainty and inconsistency in discharge decision making. Facilitators included clarity on professional roles in ICU discharge, effective communication within the ICU team, and context specific strategies to support the discharge processes.

CONCLUSIONS

The findings provide an in-depth understanding of the barriers and facilitators to the ICU discharge processes. Multifaceted strategies should be considered to facilitate and manage ICU discharge safely and efficiently, including the use of clearer discharge criteria and guidelines, digital systems that aid communication and coordination, and early planning of ICU patient discharge.

Prevalence and risk factors associated within 48-hour unplanned paediatric intensive care unit readmissions: An integrative review

BACKGROUND

Unplanned paediatric intensive care unit (PICU) readmission is associated with increased morbidity/mortality, hospital length of stay, and health service cost and is recognised as a key performance indicator of quality-of-care delivery. However, research evidence on unplanned PICU readmission risk factors is limited, and results were inconsistent across studies.

AIM

The aim of this experiment was to collate and synthesise unplanned within-48-h PICU readmission prevalence and associated risk factors.

METHODS

An integrative review was conducted, guided by a five-stage framework. Seven electronic databases were searched (2013-30th June 2023). Studies published in English with full-text accessibility and detailed methodologies were included. The quality of included studies was critically appraised using the Joanna Briggs Institute checklists. Prevalence and risk factors were extracted, synthesised, and presented narratively.

RESULTS

Ten studies met eligibility criteria and reported a varied readmission rate from 0.008% to 6.49%. Fifteen types of significant risk factors were extracted. Twelve consistently cited risk factors were age, weight, complex chronic conditions, admission source, unplanned admission, PICU length of stay, positive pressure ventilation, discharge disposition, oxygen requirements, respiratory rate, heart rate, and Glasgow Coma Score at discharge. Of the 12, five predictors were classified as modifiable factors, including discharge disposition, oxygen requirement, abnormal respiratory rate, abnormal heart rate, and decreased Glasgow Coma Score at discharge.

CONCLUSION

This review acknowledges the complexity of confounding factors impacting unplanned PICU readmission and the lack of standardisation examining potential risk factors. The five modifiable factors are suggestive of clinical instability and premature PICU discharge. Patients with modifiable risk factors should have their readiness for discharge re-evaluated. Scaffolding support to manage patients at risk of readmission includes senior bedside nursing allocation, use of PICU outreach services, and 1:2 nurse-to-patient ratios in the ward setting, which are warranted to ensure patient safety.

Critical Care Challenges and Mortality Predictors in Older Adults: A Comprehensive Cohort Analysis

PURPOSE

 As the population ages, critically ill older adults increasingly face complications and require more healthcare resources during hospitalization. Since post-ICU (intensive care unit) mortality is an important consideration, particularly in elderly populations, this study aims to assess whether advanced age impacts ICU and post-ICU mortality by comparing outcomes between patients aged 81 years and above with those below 81 years.

METHODS

 This retrospective study analyzed data from 3,821 ICU patients treated at the Unidade Local de Saúde de Lisboa Ocidental between 2015 and 2023. Key variables included age, gender, ICU length of stay, and severity scores (APACHE [Acute Physiology and Chronic Health Evaluation] II, SAPS [Simplified Acute Physiology Score] II/III, SOFA [Sequential Organ Failure Assessment]). Patients with incomplete records, readmissions, ICU stays shorter than 24 hours, or those under 18 years of age were excluded.

RESULTS

 Mortality was significantly higher in patients aged 81 years and above compared to those under 81. Among patients aged 81 and above, ICU mortality was 22% (152 deaths), compared to 13% (342 deaths) in the younger group. Similarly, post-ICU mortality was 20% (138 deaths) for the older group, substantially higher than the 5% (131 deaths) observed in patients below 81 years. The SAPS II and SOFA scores were critical predictors of mortality. Even after adjusting for these scores, older patients still showed higher mortality rates.

CONCLUSION

This study demonstrated that advanced age is a major factor influencing mortality in critically ill patients, particularly among those aged 81 years and above. These patients faced higher mortality rates both during ICU stays and after discharge, emphasizing the importance of age-specific strategies in managing critically ill elderly populations.

Interventions to improve patient admission and discharge practices in adult intensive care units: A systematic review

OBJECTIVES

To identify and synthesise interventions and implementation strategies to optimise patient flow, addressing admission delays, discharge delays, and after-hours discharges in adult intensive care units.

METHODS

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. Five electronic databases, including CINAHL, PubMed, Emcare, Scopus, and the Cochrane Library, were searched from 2007 to 2023 to identify articles describing interventions to enhance patient flow practices in adult intensive care units. The Critical Appraisal Skills Program (CASP) tool assessed the methodological quality of the included studies. All data was synthesised using a narrative approach.

SETTING

Adult intensive care units.

RESULTS

Eight studies met the inclusion criteria, mainly comprising quality improvement projects (n = 3) or before-and-after studies (n = 4). Intervention types included changing workflow processes, introducing decision support tools, publishing quality indicator data, utilising outreach nursing services, and promoting multidisciplinary communication. Various implementation strategies were used, including one-on-one training, in-person knowledge transfer, digital communication, and digital data synthesis and display. Most studies (n = 6) reported a significant improvement in at least one intensive care process-related outcome, although fewer studies specifically reported improvements in admission delays (0/0), discharge delays (1/2), and after-hours discharge (2/4). Two out of six studies reported significant improvements in patient-related outcomes after implementing the intervention.

CONCLUSION

Organisational-level strategies, such as protocols and alert systems, were frequently employed to improve patient flow within ICUs, while healthcare professional-level strategies to enhance communication were less commonly used. While most studies improved ICU processes, only half succeeded in significantly reducing discharge delays and/or after-hours discharges, and only a third reported improved patient outcomes, highlighting the need for more effective interventions.

IMPLICATIONS FOR CLINICAL PRACTICE

The findings of this review can guide the development of evidence-based, targeted, and tailored interventions aimed at improving patient and organisational outcomes.

Outcomes of traumatically injured patients after nighttime transfer from the intensive care unit

Background

Prior studies have associated nighttime transfer of patients from the intensive care unit (ICU) with increased morbidity. This study sought to examine this relationship in traumatically injured patients, as this has not been previously performed.

Methods

A retrospective review of traumatically injured patients admitted to a Level I Trauma Center's ICU from January 2021 to September 2022 was performed. "Day shift" (DS) was defined as 07:00 to 19:00 and "night shift" (NS) as 19:01 to 06:59. The time of transfer completion was based on the time of the patient arrival at the destination unit. The univariate analysis compared patients with completed transfers during DS and NS. Multivariate logistic regression was performed to predict readmission to the ICU.

Results

A total of 1,800 patients were included in the analysis, with 608 patients that had completed transfers during NS, and 1,192 during DS. Both groups were similar, with no significant differences in age, sex, Injury Severity Score (ISS), mechanism of injury, or median total comorbidities. The NS group had a longer median time to transfer completion (10.1 (IQR 5.5-13.6) hours vs 5.1 (IQR 2.9-8.4) hours; p<0.001). A significantly higher proportion of the NS group had a readmission to the ICU (60 (10.0%) vs 86 (7.0%); p=0.03) or a major complication (72 (11.9%) vs 107 (9.0%); p=0.048). When controlling for age, comorbidities, ISS, time to bed assignment and to transfer completed, and ICU length of stay, transfer completion during NS was associated with 1.56 times higher odds of having an ICU readmission (OR 1.56 (95% CI 1.05, 2.33); p=0.03).

Conclusions

Trauma patients transferred from the ICU during NS experienced longer delays, readmission to the ICU, and major complications significantly more often. With increasing hospital bed shortages, patient transfers must be analyzed to minimize worsened outcomes, especially in traumatically injured patients.

Level of evidence

Level III, therapeutic/care management.

Sleep in the intensive and intermediate care units: Exploring related factors of delirium, benzodiazepine use and mortality

AIM OF THE STUDY

The primary purpose was to examine sleep difficulties and delirium in the Intensive and Intermediate Care Unit. Secondarily, factors impacting night-time sleep duration and quality, mortality, and the impact of benzodiazepine use on sleep outcomes were investigated.

MATERIALS AND METHODS

This retrospective study encompassed data from 323 intensive and intermediate care unit admissions collected in the Netherlands, spanning from November 2018 to May 2020. Sleep quality was measured using the Richards-Campbell Sleep Questionnaire. Night-time sleep duration was nurse-reported. We investigated associations of these sleep outcomes with age, sex, length-of-stay, natural daylight, disease severity, mechanical ventilation, benzodiazepine use, and delirium using Generalized Estimating Equations models. Associations with one-year post-discharge mortality were analyzed using Cox regression.

RESULTS

Night-time sleep duration was short (median 4.5 hours) and sleep quality poor (mean score 4.9/10). Benzodiazepine use was common (24 % of included nights) and was negatively associated with night-time sleep duration and quality (B = -0.558 and -0.533, p <.001). Delirium and overnight transfers were negatively associated with sleep quality (B = -0.716 and -1.831, p <.05). The day-to-night sleep ratio was higher in the three days before delirium onset than in non-delirious individuals (p <.05). Age, disease severity and female sex were associated with increased one-year mortality. Sleep quality was negatively, but not-significantly, associated with mortality (p =.070).

CONCLUSIONS

Night-time sleep in the critical care environment has a short duration and poor quality. Benzodiazepine use was not associated with improved sleep. Sleep patterns change ahead of delirium onset.

IMPLICATIONS FOR CLINICAL PRACTICE

Consistent sleep monitoring should be part of routine nursing practice, using a validated instrument like the Richards-Campbell Sleep Questionnaire. Given the lack of proven efficacy of benzodiazepines in promoting sleep in critical care settings, it is vital to develop more effective sleep treatments that include non-benzodiazepine medication and sleep hygiene strategies.

Understanding medication safety involving patient transfer from intensive care to hospital ward: a qualitative sociotechnical factor study

OBJECTIVE

To understand the sociotechnical factors affecting medication safety when intensive care patients are transferred to a hospital ward. Consideration of these medication safety factors would provide a theoretical basis, on which future interventions can be developed and evaluated to improve patient care.

DESIGN

Qualitative study using semistructured interviews of intensive care and hospital ward-based healthcare professionals. Transcripts were anonymised prior to thematic analysis using the London Protocol and Systems Engineering in Patient Safety V.3.0 model frameworks.

SETTING

Four north of England National Health Service hospitals. All hospitals used electronic prescribing in intensive care and hospital ward settings.

PARTICIPANTS

Intensive care and hospital ward healthcare professionals (intensive care medical staff, advanced practitioners, pharmacists and outreach team members; ward-based medical staff and clinical pharmacists).

RESULTS

Twenty-two healthcare professionals were interviewed. We identified 13 factors within five broad themes, describing the interactions that most strongly influenced the performance of the intensive care to hospital ward system interface. The themes were: Complexity of process performance and interactions; Time pressures and considerations; Communication processes and challenges; Technology and systems and Beliefs about consequences for the patient and organisation.

CONCLUSIONS

The complexity of the interactions on the system performance and time dependency was clear. We make several recommendations for policy change and further research based on improving: availability of hospital-wide integrated and functional electronic prescribing systems, patient flow systems, sufficient multiprofessional critical care staffing, knowledge and skills of staff, team performance, communication and collaboration and patient and family engagement.

Clinical frailty and polypharmacy in older emergency critical care patients: a single-centre retrospective case series

BACKGROUND AND OBJECTIVES

Admission of complex and frail patients to critical care units is common. Little is known about the relationship between clinical frailty and polypharmacy measures in critical care patients or how a critical care admission affects polypharmacy.We sought to: (1) Describe the extent and relationship between clinical frailty and polypharmacy in a cohort of older emergency general critical care patients, and to (2) Describe the effect of the critical care pathway on patient polypharmacy measures.

METHODS

A retrospective evaluation was undertaken in all patients ≥70 years of age, admitted as emergencies to the general critical care units of a single large UK academic hospital, over a 2-year period (March 2016 to February 2018) (n=762). Patient Clinical Frailty Scale (CFS) and polypharmacy measures on admission were described and association was tested. Medication changes and documentation on care transitions were analysed in a randomly selected convenience cohort of critical care survivors (n=77).

RESULTS

On admission patients had a median of 9 (5;12) medicines, of which a median of 3 (2;5) were high-risk medicines. Polypharmacy (5-9 medicines) and hyperpolypharmacy (≥10 medicines) occurred in 80.7% (615/762) and 43.2% (329/762) of patients, respectively. A degree of frailty was the standard (median CFS 4 (3;5)) with 45.7% (348/762) CFS 4-5 and 20% (153/762) CFS ≥6. The patient median CFS increased by 1 with polypharmacy classification increments (p<0.001). In the survivor cohort, a median of 6 (4;8) and 5 (4;8) medication changes occurred on critical care and hospital discharges, respectively. A minority of patients had detailed medication continuity plans on care transitions.

CONCLUSIONS

Polypharmacy and frailty were very common in this UK single-centre cohort of older emergency critical care patients. There was a significant association between the degree of polypharmacy and frailty score. The critical care pathway created extensive changes in patient medication therapy. Medication changes on care transitions often lacked detailed documentation.

Establishing critical care nursing research priorities for three Australian regional public hospitals: A mixed method priority setting study

OBJECTIVE

To determine key priorities for critical care nursing research in three Australian regional public hospitals, representing the shared priorities of healthcare professionals and patient representatives.

METHODS

A three phase priority setting study, including consensus methods (nominal group), survey, qualitative interviews and focus groups were conducted between May 2021 and March 2022. Healthcare professionals and patient representatives from critical care units in regional public hospitals in Australia participated. A patient representative contributed to research design and co-authored this paper.

RESULTS

In phase one, 29 research topics were generated. In phase two, during a nominal group ranking process, the top 5 priority areas for each site were identified. In the final phase, three themes from focus groups and interviews included patient flow through intensive care, patient care through intensive care journey and intensive care patient recovery.

CONCLUSION

Identifying context specific research priorities through a priority setting exercise provides insight into the topics that are important to healthcare professionals and to patients in critical care. The top research priorities for nursing research in critical care in regional Australian hospitals include patient flow, patient recovery, and evidence based patient care through the intensive care journey, such as delirium management, pain and sedation, and mobilisation. These shared priorities will be used to guide future nursing research in critical care over the next 3-5 years.

IMPLICATIONS FOR CLINICAL PRACTICE

The method we used in identifying the research priorities can be used by other researchers and clinicians; close collaboration among researchers and clinicians will be beneficial for practice improvement; and how we can be reassured that our practice is evidence based is worthy of attention.

Effect of Admission and Discharge Times on Hospital Mortality in Patients With Sepsis

OBJECTIVES

To assess whether the time of admission/discharge time from the ICU and weekend admission are independently associated with hospital mortality in critically ill patients with sepsis.

DESIGN

Retrospective study. Each 24-hour period (08:00 to 07:59 hr) was split into three time periods, defined as "day" (08:00 to 16:59 hr), "evening" (17:00 to 23:59 hr), and "night" (00:00 to 07:59 hr). Weekends were defined as 17:00 hours on Friday to 07:59 hours on Monday. Multivariate logistic regression models were conducted to assess the association between the ICU admission/discharge time, weekend admission, and hospital mortality.

SETTING

Single-center ICUs in China.

PATIENTS

Characteristics and clinical outcomes of 1,341 consecutive septic patients admitted to the emergency ICU, general ICU, or cardiovascular ICU in a tertiary teaching hospital were collected.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

ICU mortality rates were 5.8%, 11.9%, and 10.6%, and hospital mortality rates were 7.3%, 15.6%, and 17.1% during the day, evening, and night time, respectively. Hospital mortality was adjusted for patient to nurse (P/N) ratio, disease severity, Charlson index, age, gender, mechanical ventilation, and shock. Notably, ICU admission time and weekend admission were not predictors of mortality after adjustment. The P/N ratio at admission was significantly associated with mortality ( p < 0.05). The P/N ratio and compliance with the Surviving Sepsis Campaign (SSC) were significantly correlated. After risk adjustment for illness severity at time of ICU discharge and Charlson index, the time of discharge was no longer a significant predictor of mortality.

CONCLUSIONS

ICU admission/discharge time and weekend admission were not independent risk factors of hospital mortality in critically ill patients with sepsis. The P/N ratio at admission, which can affect the compliance rate with SSC, was a predictor of hospital survival. Unstable state on transfer from the ICU was the main risk factor for in-hospital death. These findings may have implications for the management of septic patients.

Incidence, risk and impact of ICU readmission on patient outcomes and resource utilisation in tertiary level ICUs in Nepal: A cohort study

Background: Readmissions to Intensive Care Units (ICUs) result in increased morbidity, mortality, and ICU resource utilisation (e.g. prolonged mechanical ventilation), and as such, is a widely utilised metric of quality of critical care. Most of the evidence on incidence, characteristics, associated risk factors and attributable outcomes of unplanned readmission to ICU are from studies performed in high-income countries This study explores the determinants of risk attributable to unplanned ICU readmission in four ICUs in Kathmandu, Nepal. Methods: The registry-embedded eCRF reported data on case mix, severity of illness, in-ICU interventions (including organ support), ICU outcome, and readmission characteristics. Data were captured in all adult patients admitted between September 2019 and February 2021. Population and ICU encounter characteristics were compared between those with and without readmission. Independent risk factors for readmission were assessed using univariate analysis. Results: In total 2955 patients were included in the study. Absolute unplanned ICU readmission rate was 5.69 % (n=168) for all four ICUs. Median time from ICU discharge to readmission was 3 days (IQR=8,1). Of those readmitted, 29.17% (n=49) were discharged at night following their index admission. ICU mortality was higher following readmission to ICU(p=0.016) and mortality was increased further in patients whose primary index discharge was at night(p= 0.019).  Primary diagnosis, age, and use of organ support in the first 24hrs of index admission were all independently attributable risk factors for readmission. Conclusions: Unplanned ICU readmission rates were adversely associated with significantly poorer outcomes, increased ICU resource utilisation. Clinical and organisational characteristics influenced risk of readmission and outcome.

Neonatal intensive care unit occupancy rate and probability of discharge of very preterm infants

OBJECTIVE

To assess the association of NICU occupancy with probability of discharge and length of stay (LOS) among infants born <33 weeks gestational age (GA).

STUDY DESIGN

Retrospective study of 3388 infants born 23-32 weeks GA, admitted to five Level 3/4 NICUs (2014-2018) and discharged alive. Standardized ratios of observed-to-expected number of discharges were calculated for each quintile of unit occupancy. Multivariable linear regression models were used to assess the association between occupancy and LOS.

RESULTS

At the lowest unit occupancy quintiles (Q1 and Q2), infants were 12% and 11% less likely to be discharged compared to the expected number. At the highest unit occupancy quintile (Q5), infants were 20% more likely to be discharged. Highest occupancy (Q5) was also associated with a 4.7-day (95% CI 1.7, 7.7) reduction in LOS compared Q1.

CONCLUSION

NICU occupancy was associated with likelihood of discharge and LOS among infants born <33 weeks GA.

Predictive Modeling for Readmission to Intensive Care: A Systematic Review

To evaluate the methodologic rigor and predictive performance of models predicting ICU readmission; to understand the characteristics of ideal prediction models; and to elucidate relationships between appropriate triage decisions and patient outcomes.

DATA SOURCES

PubMed, Web of Science, Cochrane, and Embase.

STUDY SELECTION

Primary literature that reported the development or validation of ICU readmission prediction models within from 2010 to 2021.

DATA EXTRACTION

Relevant study information was extracted independently by two authors using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool. Data sources, modeling methodology, definition of outcomes, performance, and risk of bias were critically evaluated to elucidate relevant relationships.

DATA SYNTHESIS

Thirty-three articles describing models were included. Six studies had a high overall risk of bias due to improper inclusion criteria or omission of critical analysis details. Four other studies had an unclear overall risk of bias due to lack of detail describing the analysis. Overall, the most common (50% of studies) source of bias was the filtering of candidate predictors via univariate analysis. The poorest performing models used existing clinical risk or acuity scores such as Acute Physiologic Assessment and Chronic Health Evaluation II, Sequential Organ Failure Assessment, or Stability and Workload Index for Transfer as the sole predictor. The higher-performing ICU readmission prediction models used homogenous patient populations, specifically defined outcomes, and routinely collected predictors that were analyzed over time.

CONCLUSIONS

Models predicting ICU readmission can achieve performance advantages by using longitudinal time series modeling, homogenous patient populations, and predictor variables tailored to those populations.

Incidence, risk and impact of unplanned ICU readmission on patient outcomes and resource utilisation in tertiary level ICUs in Nepal: A cohort study

Background: Unplanned readmissions to Intensive Care Units (ICUs) result in increased morbidity, mortality, and ICU resource utilisation (e.g. prolonged mechanical ventilation), and as such, is a widely utilised metric of quality of critical care. Most of the evidence on incidence, characteristics, associated risk factors and attributable outcomes of unplanned readmission to ICU are from studies performed in high-income countries This study explores the determinants of risk attributable to unplanned ICU readmission in four ICUs in Kathmandu, Nepal. Methods: The registry-embedded eCRF reported data on case mix, severity of illness, in-ICU interventions (including organ support), ICU outcome, and readmission characteristics. Data were captured in all adult patients admitted between September 2019 and February 2021. Population and ICU encounter characteristics were compared between those with and without readmission. Independent risk factors for readmission were assessed using univariate analysis. Results: In total 2948 patients were included in the study. Absolute unplanned ICU readmission rate was 5.60 % (n=165) for all four ICUs. Median time from ICU discharge to readmission was 3 days (IQR=8,1). Of those readmitted, 29.7% (n=49) were discharged at night following their index admission. ICU mortality was higher following readmission to ICU(p=0.016) and mortality was increased further in patients whose primary index discharge was at night(p= 0.019).  Primary diagnosis, age, and use of organ support in the first 24hrs of index admission were all independently attributable risk factors for readmission. Conclusions: Unplanned ICU readmission rates were adversely associated with significantly poorer outcomes, increased ICU resource utilisation. Clinical and organisational characteristics influenced risk of readmission and outcome.

Medication-related interventions to improve medication safety and patient outcomes on transition from adult intensive care settings: a systematic review and meta-analysis

BACKGROUND

Patients recovering from an episode in an intensive care unit (ICU) frequently experience medication errors on transition to the hospital ward. Structured handover recommendations often underestimate the challenges and complexity of ICU patient transitions. For adult ICU patients transitioning to a hospital ward, it is currently unclear what interventions reduce the risks of medication errors.The aims were to examine the impact of medication-related interventions on medication and patient outcomes on transition from adult ICU settings and identify barriers and facilitators to implementation.

METHODS

The systematic review protocol was preregistered on PROSPERO. Six electronic databases were searched until October 2020 for controlled and uncontrolled study designs that reported medication-related (ie, de-prescribing; medication errors) or patient-related outcomes (ie, mortality; length of stay). Risk of bias (RoB) assessment used V.2.0 and ROBINS-I Cochrane tools. Where feasible, random-effects meta-analysis was used for pooling the OR across studies. The quality of evidence was assessed by Grading of Recommendations, Assessment, Development and Evaluations.

RESULTS

Seventeen studies were eligible, 15 (88%) were uncontrolled before-after studies. The intervention components included education of staff (n=8 studies), medication review (n=7), guidelines (n=6), electronic transfer/handover tool or letter (n=4) and medicines reconciliation (n=4). Overall, pooled analysis of all interventions reduced risk of inappropriate medication continuation at ICU discharge (OR=0.45 (95% CI 0.31 to 0.63), I2=55%, n=9) and hospital discharge (OR=0.39 (95% CI 0.2 to 0.76), I2=75%, n=9). Multicomponent interventions, based on education of staff and guidelines, demonstrated no significant difference in inappropriate medication continuation at the ICU discharge point (OR 0.5 (95% CI 0.22 to 1.11), I2=62%, n=4), but were very effective in increasing de-prescribing outcomes on hospital discharge (OR 0.26 (95% CI 0.13 to 0.55), I2=67%, n=6)). Facilitators to intervention delivery included ICU clinical pharmacist availability and participation in multiprofessional ward rounds, while barriers included increased workload associated with the discharge intervention process.

CONCLUSIONS

Multicomponent interventions based on education of staff and guidelines were effective at achieving almost four times more de-prescribing of inappropriate medication by the time of patient hospital discharge. Based on the findings, practice and policy recommendations are made and guidance is provided on the need for, and design of theory informed interventions in this area, including the requirement for process and economic evaluations.

Patient Harm and Institutional Avoidability of Out-of-Hours Discharge From Intensive Care: An Analysis Using Mixed Methods

OBJECTIVES

Out-of-hours discharge from ICU to the ward is associated with increased in-hospital mortality and ICU readmission. Little is known about why this occurs. We map the discharge process and describe the consequences of out-of-hours discharge to inform practice changes to reduce the impact of discharge at night.

DESIGN

This study was part of the REcovery FoLlowing intensive CarE Treatment mixed methods study. We defined out-of-hours discharge as 16:00 to 07:59 hours. We undertook 20 in-depth case record reviews where in-hospital death after ICU discharge had been judged "probably avoidable" in previous retrospective structured judgment reviews, and 20 where patients survived. We conducted semistructured interviews with 55 patients, family members, and staff with experience of ICU discharge processes. These, along with a stakeholder focus group, informed ICU discharge process mapping using the human factors-based functional analysis resonance method.

SETTING

Three U.K. National Health Service hospitals, chosen to represent different hospital settings.

SUBJECTS

Patients discharged from ICU, their families, and staff involved in their care.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Out-of-hours discharge was common. Patients and staff described out-of-hours discharge as unsafe due to a reduction in staffing and skill mix at night. Patients discharged out-of-hours were commonly discharged prematurely, had inadequate handover, were physiologically unstable, and did not have deterioration recognized or escalated appropriately. We identified five interdependent function keys to facilitating timely ICU discharge: multidisciplinary team decision for discharge, patient prepared for discharge, bed meeting, bed manager allocation of beds, and ward bed made available.

CONCLUSIONS

We identified significant limitations in out-of-hours care provision following overnight discharge from ICU. Transfer to the ward before 16:00 should be facilitated where possible. Our work highlights changes to help make day time discharge more likely. Where discharge after 16:00 is unavoidable, support systems should be implemented to ensure the safety of patients discharged from ICU at night.

ICU Readmission in Cardiac Surgical Subset: A Problem Worth Pondering

Over the past decades, there have been noteworthy advancements in the cardiac surgical practice that have assisted fast-tracking and enhanced recovery after cardiac surgery (ERACS). With that said, intensive care unit (ICU) readmission in this high-risk patient cohort entails a significant morbidity–mortality burden. As an extension of the same, there has been a heightened emphasis on a comprehensive evaluation of the predisposition to readmission following a primary ICU discharge. However, the variability of the institutional perioperative practices and the research complexities compound our understanding of this heterogeneous outcome of readmission, which is intricately linked to both patient and organizational factors. Moreover, a discussion on ICU readmission in the recent times can only be rendered comprehensive when staged in close conjunction to the fast-tracking practices in cardiac surgery. From a more positive probing of the matter, a preventative outlook can likely mitigate a part of the larger problem of ICU readmission. Herein, focused cardiac prehabilitation programs can play a potential role given the emerging literature on the positive impact of the former on the most relevant readmission causes. Therefore, the index review article aims to address the subject of cardiac surgical ICU readmission, highlighting the magnitude and burden, the causes and risk-factors, and the research complexities alongside deliberating the topic in the present-day context of ERACS and cardiac prehabilitation.

Mortality and readmission rates of patients discharged in-hours and out-of-hours from a British ICU over a 3-year period

Excess in-hospital mortality following out-of-hours ICU discharge has been reported worldwide. From preliminary data, we observed that magnitude of difference may be reduced when patients discharged for end-of-life care or organ donation are excluded. We speculated that these patients may be disproportionately discharged out-of-hours, biasing results. We now compare in-hospital mortality and ICU readmission rates following discharge in-hours and out-of-hours over 3 years, excluding discharges for organ donation or end-of-life care. This single-centre retrospective study includes patients discharged alive following ICU admission between 01/07/2015–31/07/2018, excluding readmissions and discharges for end-of-life care/organ donation. A multiple logistic regression model was fitted to estimate adjusted odds ratio of death following out-of-hours versus in-hours discharge. Characteristics and outcomes for both groups were compared. 4678 patients were included. Patients discharged out-of-hours were older (62 vs 59 years, p < 0.001), with greater APACHE II scores (15.7 vs 14.4, p < 0.001), length of ICU stay (3.25 vs 3.00 days, p = 0.01) and delays to ICU discharge (736 vs 489 min, p < 0.001). No difference was observed in mortality (4.6% vs 3.7%, p = 0.25) or readmission rate (4.1% vs 4.2%, p = 0.85). In the multiple logistic regression model out-of-hours discharge was not associated with in-hospital mortality (OR = 1.017, 95% CI 0.682–1.518, p = 0.93). Our findings present a possible explanation for reported excess mortality following out-of-hours ICU discharge, related to inclusion of organ donation and end-of-life care patients in data sets rather than standards of care delivered out-of-hours. We are not aware of any other studies investigating the influence of this group on reported post-ICU mortality rates.

A systematic review on quality of life (QoL) of patients with peritoneal metastasis (PM) who underwent pressurized intraperitoneal aerosol chemotherapy (PIPAC)

Background

Pressurized intraperitoneal aerosol chemotherapy (PIPAC) has recently emerged as a palliative alternative for patients with unresectable peritoneal metastasis (PM). Quality of life (QoL) has increasingly been used as an endpoint to evaluate treatment outcomes. This review aims to identify evidence on how PIPAC would impact the QoL of PM patients.

Content

A systematic review was performed on articles identified from Medline, EMBASE, PsycInfo, and Web of Sciences. A meta-analysis was conducted on further selected studies. ACROBAT-NRSI was attempted to assess the risk of bias (RoB).

Summary

Nine studies using the EORTC QLQ-C30 questionnaire to assess QoL after repeated PIPAC cycles were identified. Majority was found to be moderately biased and a great extent of heterogeneity was observed. Four studies on PM from either gastric cancer (GC) or epithelial ovarian cancer (EOC) were included for meta-analysis. In 31 GC patients and 104 EOC patients, QoL remained stable in 13/14 and 11/14 EORTC QLQ-C30 scales. PIPAC was inferior to cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) in global QoL and functioning but superior in symptom reduction.

Outlook

PIPAC is a well-tolerated option for most GC and EOC patients with irresectable PM. Future trials are warranted to confirm the findings.

Validation of manual to complement the transition of care at discharge from intensive care

ABSTRACT Objective To develop and validate an interprofessional manual for the transfer of care to critically ill adult patients. Method Methodological study, conducted from January to September 2019. The content of the manual was listed by the multidisciplinary team of an adult Intensive Care Unit, in southern Brazil. In the validation by the professionals, the content validity index (CVI) of the evaluation questions was calculated. Subsequently, a sample of 30 patients/caregivers evaluated the product, and the arithmetic mean of the questions was calculated. Results The manual addresses important information and care transition guidance for patients and caregivers, from admission to the intensive care to discharge to the inpatient unit. The professionals’ CVI ranged from 0.9 to 1. The arithmetic mean of 17 patients and 13 caregivers was 3.8. Final considerations The validated manual can be used as a complementary material for health education and qualify the transition of care.

Validação de manual para complementar a transição de cuidados na alta da terapia intensiva

RESUMO Objetivo Desenvolver e validar um manual interprofissional de transferência de cuidados ao paciente adulto crítico. Método Estudo metodológico, realizado de janeiro a setembro/2019. O conteúdo do manual foi elencado pela equipe multiprofissional de um Centro Terapia Intensiva adulto, do Sul do Brasil. Na validação pelos profissionais, foi calculado o índice de validade de conteúdo (IVC) das questões de avaliação. Posteriormente, amostra de 30 pacientes/cuidadores avaliou o produto, sendo calculada a média aritmética das questões. Resultados O manual aborda informações importantes e orientações de transição do cuidado, para pacientes e cuidadores, desde a admissão na terapia intensiva até a alta para unidade de internação. O IVC dos profissionais variou de 0,9 a 1. A média aritmética, de 17 pacientes e 13 cuidadores foi 3,8. Considerações finais O manual validado poderá ser utilizado como material complementar de educação em saúde e qualificar a transição de cuidados.

Accuracy of Clinicians' Ability to Predict the Need for Intensive Care Unit Readmission

Rationale: Determining when an intensive care unit (ICU) patient is ready for discharge to the ward is a complex daily challenge for any ICU care team. Patients who experience unplanned readmissions to the ICU have increased mortality, length of stay, and cost compared with those not readmitted during their hospital stay. The accuracy of clinician prediction for ICU readmission is unknown.Objectives: To determine the accuracy of ICU physicians and nurses for predicting ICU readmissionsMethods: We conducted a prospective study in the medical ICU of an academic hospital from October 2015 to September 2017. After daily rounding for patients being transferred to the ward, ICU clinicians (nurses, residents, fellows, and attendings) were asked to report the likelihood of readmission within 48 hours (using a 1-10 scale, with 10 being "extremely likely"). The accuracy of the clinician prediction score (1-10) was assessed for all clinicians and by clinician type using sensitivity, specificity, and area under the curve (AUC) for the receiver operating characteristic curve for predicting the primary outcome, which was ICU readmission within 48 hours of ICU discharge.Results: A total of 2,833 surveys was collected for 938 ICU-to-ward transfers, of which 40 (4%) were readmitted to the ICU within 48 hours of transfer. The median clinician likelihood of readmission score was 3 (interquartile range, 2-4). When physician and nurse likelihood scores were combined, the median clinician likelihood score had an AUC of 0.70 (95% confidence interval [CI], 0.62-0.78) for predicting ICU readmission within 48 hours. Nurses were significantly more accurate than interns at predicting 48-hour ICU readmission (AUC, 0.73 [95% CI, 0.64-0.82] vs. AUC, 0.60 [95% CI, 0.49-0.71]; P = 0.03). All other pairwise comparisons were not significantly different for predicting ICU readmission within 48 hours (P > 0.05 for all comparisons).Conclusions: We found that all clinicians surveyed in our ICU, regardless of the level of experience or clinician type, had only fair accuracy for predicting ICU readmission. Further research is needed to determine if clinical decision support tools would provide prognostic value above and beyond clinical judgment for determining who is ready for ICU discharge.

A Simple Scoring Tool to Predict Medical Intensive Care Unit Readmissions Based on Both Patient and Process Factors

BACKGROUND

Although many predictive models have been developed to risk assess medical intensive care unit (MICU) readmissions, they tend to be cumbersome with complex calculations that are not efficient for a clinician planning a MICU discharge.

OBJECTIVE

To develop a simple scoring tool that comprehensively takes into account not only patient factors but also system and process factors in a single model to predict MICU readmissions.

DESIGN

Retrospective chart review.

PARTICIPANTS

We included all patients admitted to the MICU of Robert Wood Johnson University Hospital, a tertiary care center, between June 2016 and May 2017 except those who were < 18 years of age, pregnant, or planned for hospice care at discharge.

MAIN MEASURES

Logistic regression models and a scoring tool for MICU readmissions were developed on a training set of 409 patients, and validated in an independent set of 474 patients.

KEY RESULTS

Readmission rate in the training and validation sets were 8.8% and 9.1% respectively. The scoring tool derived from the training dataset included the following variables: MICU admission diagnosis of sepsis, intubation during MICU stay, duration of mechanical ventilation, tracheostomy during MICU stay, non-emergency department admission source to MICU, weekend MICU discharge, and length of stay in the MICU. The area under the curve of the scoring tool on the validation dataset was 0.76 (95% CI, 0.68-0.84), and the model fit the data well (Hosmer-Lemeshow p = 0.644). Readmission rate was 3.95% among cases in the lowest scoring range and 50% in the highest scoring range.

CONCLUSION

We developed a simple seven-variable scoring tool that can be used by clinicians at MICU discharge to efficiently assess a patient's risk of MICU readmission. Additionally, this is one of the first studies to show an association between MICU admission diagnosis of sepsis and MICU readmissions.

Predictors of Early and Late Unplanned Intensive Care Unit Readmission: A Retrospective Cohort Study

PURPOSE

Intensive care unit (ICU) readmission is considered one of the major quality indicators of critical care. Reducing ICU readmission can improve patients' outcomes and optimize health resources, but there are limited data on the predictors of unplanned ICU readmission. This study aimed to identify the risk factors associated with unplanned ICU readmission within 48 hr (early) and after 48 hr (late) from ICU discharge.

DESIGN

Retrospective cohort study.

METHODS

Data were collected from patients' electronic medical records in a 24-bed medical ICU at a tertiary academic medical center in Busan, South Korea. Among all the patients admitted to the medical ICU (n = 1,033) between January 2015 and December 2017, 739 eligible patients were analyzed. A multivariable multinomial logistic regression model was conducted to identify predictors of ICU readmission.

FINDINGS

Out of the 739 patients analyzed, 66 (8.9%) were readmitted to the medical ICU: 13 (1.8%) as early readmission and 53 (7.1%) as late readmission. Two significant predictors were identified for early readmission: ICU admission from the ward (odds ratio [OR] = 4.14; 95% confidence interval [CI] 1.25, 13.67) and mechanical ventilation support >14 days (OR = 13.25; 95% CI 1.78, 98.89). For late ICU admission, there were four risk factors: ICU admission from the ward (OR = 2.69; 95% CI 1.44, 5.05), tracheostomy placement (OR = 3.58; 95% CI 1.49, 8.59), mechanical ventilation support >14 days (OR = 4.77; 95% CI 1.67, 13.63), and continuous renal replacement therapy (OR = 4.57; 95% CI 2.42, 8.63).

CONCLUSIONS

To prevent unplanned ICU readmission in patients at high risk, it is necessary to investigate further the role of clinical judgment and communication within the ICU clinical team and institutional-level support regarding ICU readmission events.

CLINICAL RELEVANCE

Both ICU nurses and nurses in post-ICU settings should be aware of the potential risk factors associated with early and late ICU readmission. Predictors and readmission strategies may be different for early and late readmissions. Prospective multicenter studies are needed to examine how these factors influence post-ICU outcomes.

Association Between Afterhours Discharge From the Intensive Care Unit and Hospital Mortality: A Multi-Center Retrospective Cohort Study

BACKGROUND

There is conflicting evidence on the association between afterhours discharge from the intensive care unit (ICU) and hospital mortality. We examined the effects of afterhours discharge, including the potential effect of residual organ dysfunction, on hospital mortality in a large integrated health region.

METHODS

We performed a multi-center retrospective cohort study of 10,463 adults discharged from 9 mixed medical/surgical ICUs in Alberta from June 2012 to December 2014. We applied a 2-stage modeling strategy to investigate the association between afterhours discharge (19:00h to 07:59h) and post-ICU hospital mortality. We applied mixed-effect multi-variable linear regression to assess the relationship between discharge organ dysfunction and afterhours discharge. We then applied mixed-effect multi-variable logistic regression to evaluate the direct, indirect and integrated associations of afterhours discharge on hospital mortality and hospitalization duration.

RESULTS

Of 10,463 patients, 23.7% (n = 2,480) were discharged afterhours, of which 27.4% occurred on a holiday or weekend. This varied significantly by ICU size, type, and site. Patients discharged afterhours were more likely medical admissions, had greater multi-morbidity and illness acuity. A greater average SOFA score in the 72 hours prior to ICU discharge was not associated with afterhours discharge. However, a greater average SOFA score was associated with hospital mortality (adjusted-odds ratio [OR], 1.23; 95% CI, 1.18-1.28). Afterhours discharge was associated with higher hospital mortality (adjusted-OR, 1.19; 95% CI, 1.01-1.39), increased hospital stay (adjusted-risk ratio [RR], 1.10; 95% CI, 1.09-1.11) and increased post-ICU stay (adjusted-RR, 1.16; 95% CI, 1.14-1.17) when compared with workhours discharge.

CONCLUSIONS

Afterhours discharge is common, occurring in 1 in 4 discharges, and is widely variable across ICUs. Patients discharged afterhours have greater risk of hospital mortality and prolonged hospitalization.

Risk factors for readmission to ICU and analysis of intra-hospital mortality

INTRODUCTION

Critical patients, despite initial recovery in the intensive care unit (ICU), may require readmission to the ICU or even die in the same hospital episode. The objectives are to determine the incidence and to identify risk factors for ICU readmission, and to determine hospital mortality.

METHODS

Observational cohort study of all patients admitted consecutively for more than 24hours to the ICU of the University Hospital of Getafe between April 1, 2018 and September 30, 2018 and discharged alive from their first ICU admission.

RESULTS

Of the 164 patients alive at ICU discharge, 14 (8.5%) were readmitted to ICU (2.4% at≤48hours). The adjusted risk of ICU readmission was higher in patients with disabling neurological deficits prior to ICU admission [odds ratio (OR) 7.96, 95% confidence interval (CI) 1.55-40.92] or who received vasoactive drugs (OR 5.07, 95% CI 1.41-18.29) during their ICU stay. Readmitted patients had higher hospital mortality (4 of 14 [29%] versus 5 of 150 [3%], P<.001) and longer hospital stay (74.5 [37.5-99.75] days versus 16 [9-34] days, median [interquartile range], P=.001).

CONCLUSIONS

Patients with disabling neurological deficits prior to hospital admission or who received vasoactive drugs during their ICU stay have a higher risk of readmission to the ICU, which increases hospital stay and mortality.

Problems in care and avoidability of death after discharge from intensive care: a multi-centre retrospective case record review study

BACKGROUND

Over 138,000 patients are discharged to hospital wards from intensive care units (ICUs) in England, Wales and Northern Ireland annually. More than 8000 die before leaving hospital. In hospital-wide populations, 6.7-18% of deaths have some degree of avoidability. For patients discharged from ICU, neither the proportion of avoidable deaths nor the reasons underlying avoidability have been determined. We undertook a retrospective case record review within the REFLECT study, examining how post-ICU ward care might be improved.

METHODS

A multi-centre retrospective case record review of 300 consecutive post-ICU in-hospital deaths, between January 2015 and March 2018, in 3 English hospitals. Trained multi-professional researchers assessed the degree to which each death was avoidable and determined care problems using the established Structured Judgement Review method.

RESULTS

Agreement between reviewers was good (weighted Kappa 0.77, 95% CI 0.64-0.88). Discharge from an ICU for end-of-life care occurred in 50/300 patients. Of the remaining 250 patients, death was probably avoidable in 20 (8%, 95% CI 5.0-12.1) and had some degree of avoidability in 65 (26%, 95% CI 20.7-31.9). Common problems included out-of-hours discharge from ICU (168/250, 67.2%), suboptimal rehabilitation (167/241, 69.3%), absent nutritional planning (76/185, 41.1%) and incomplete sepsis management (50/150, 33.3%).

CONCLUSIONS

The proportion of deaths in hospital with some degree of avoidability is higher in patients discharged from an ICU than reported in hospital-wide populations. Extrapolating our findings suggests around 550 probably avoidable deaths occur annually in hospital following ICU discharge in England, Wales and Northern Ireland. This avoidability occurs in an elderly frail population with complex needs that current strategies struggle to meet. Problems in post-ICU care are rectifiable but multi-disciplinary.

TRIAL REGISTRATION

ISRCTN14658054.

After-Hours/Nighttime Transfers Out of the Intensive Care Unit and Patient Outcomes: A Systematic Review and Meta-Analysis

PURPOSE

We evaluated the effects of after-hours/nighttime patient transfers out of the ICU on patient outcomes, by performing a systematic review and meta-analysis (PROSPERO CRD 42017074082).

DATA SOURCES

MEDLINE, PubMed, EMBASE, Google Scholar, CINAHL, and the Cochrane Library from 1987-November 2019. Conference abstracts from the Society of Critical Care Medicine, American Thoracic Society, CHEST, Critical Care Canada Forum, and European Society of Intensive Care Medicine from 2011-2019.

DATA EXTRACTION

Observational or randomized studies of adult ICU patients were selected if they compared after-hours transfer out of the ICU to daytime transfer on patient outcomes. Case reports, case series, letters, and reviews were excluded. Study year, country, design, co-variates for adjustment, definitions of after-hours, mortality rates, ICU readmission rates, and hospital length of stay (LOS) were extracted.

DATA SYNTHESIS

We identified 3,398 studies. Thirty-one observational studies (1,418,924 patients) were selected for the systematic review and meta-analysis. Included studies had varying definitions of after-hours, with the after-hours period starting anytime between 16:00-22:00 and ending between 06:00-09:00. Approximately 16% of transfers occurred after-hours. After-hours transfers were associated with increased in-hospital mortality for both unadjusted (odds ratio [OR] 1.51, 95% confidence interval [CI] 1.30-1.75, I² = 96%, number of studies [n] = 26, P < 0.001, low certainty) and adjusted (OR 1.32, 95% CI 1.25-1.38, I ² = 33%, n = 10, P < 0.001, low certainty) data, compared to daytime transfers. They were also associated with increased ICU readmission (pooled unadjusted OR 1.28, 95% CI 1.18-1.38, I² = 85%, n = 17, P < 0.001, low certainty) and longer hospital LOS (standardized mean difference 0.13, 95% CI 0.09-0.18, I ² = 93%, n = 9, P < 0.001, low certainty), compared to daytime transfers.

CONCLUSIONS

After-hours transfers out of the ICU are associated with increased in-hospital mortality, ICU readmission, and hospital LOS, across many settings. While the certainty of evidence is low, future research is needed to reduce the number and effects of after-hours transfers.

Association between intensive care unit occupancy at discharge, afterhours discharges, and clinical outcomes: a historical cohort study

PURPOSE

There is a paucity of evidence evaluating whether intensive care unit (ICU) discharge occupancy is associated with clinical outcomes. It is unknown whether increased discharge occupancy leads to greater afterhours discharges and downstream consequences. We explore the association between ICU discharge occupancy and afterhours discharges, 72-hr readmission, and 30-day mortality.

METHODS

This single-centre, historical cohort study included all patients discharged from the Vancouver General Hospital ICU between 5 April 2010 and 13 September 2017. Data were obtained from the British Columbia Critical Care Database. Occupancy was defined as the number of ICU bed hours utilized divided by the available bed hours for that day. Any discharge between 22:00 and 6:59 was considered afterhours. Logistic regression models adjusting for important covariates were constructed.

RESULTS

We included 8,862 ICU discharges representing 7,288 individual patients. There were 1,180 (13.3%) afterhours discharges, 408 (4.6%) 72-hr readmissions, and 574 (6.5%) 30-day post-discharge deaths. Greater discharge occupancy was associated with afterhours discharges (per 10% increase: adjusted odds ratio [aOR], 1.12; 95% confidence interval [CI], 1.03 to 1.20; P = 0.005). Discharge occupancy was not associated with 72-hr readmission (per 10% increase: aOR, 0.97; 95% CI, 0.87 to 1.09; P = 0.62) or 30-day mortality (per 10% increase: aOR, 1.05; 95% CI, 0.95 to 1.16; P = 0.32). Afterhours discharge was not associated with 72-hr readmission (aOR, 1.15; 95% CI, 0.86 to 1.54; P = 0.34) or 30-day mortality (aOR, 1.05; 95% CI, 0.82 to 1.36; P = 0.69).

CONCLUSIONS

Greater ICU discharge occupancy was associated with a significant increase in afterhours discharges. Nevertheless, neither discharge occupancy nor afterhours discharge were associated with 72-hr readmission or 30-day mortality.

A Tool to Predict Readmission to the Intensive Care Unit in Surgical Critical Care Patients-The RISC Score

BACKGROUND

Readmission to the Intensive Care Unit (ICU) is associated with a high risk of in-hospital mortality and higher health care costs. Previously published tools to predict ICU readmission in surgical ICU patients have important limitations that restrict their clinical implementation. We sought to develop a clinically intuitive score that can be implemented to predict readmission to the ICU after surgery or trauma. We designed the score to emphasize modifiable predictors.

METHODS

In this retrospective cohort study, we included surgical patients requiring critical care between June 2015 and January 2019 at Beth Israel Deaconess Medical Center, Harvard Medical School, MA, USA. We used logistic regression to fit a prognostic model for ICU readmission from a priori defined, widely available candidate predictors. The score performance was compared with existing prediction instruments.

RESULTS

Of 7,126 patients, 168 (2.4%) were readmitted to the ICU during the same hospitalization. The final score included 8 variables addressing demographical factors, surgical factors, physiological parameters, ICU treatment and the acuity of illness. The maximum score achievable was 13 points. Potentially modifiable predictors included the inability to ambulate at ICU discharge, substantial positive fluid balance (>5 liters), severe anemia (hemoglobin <7 mg/dl), hyperglycemia (>180 mg/dl), and long ICU length of stay (>5 days). The score yielded an area under the receiver operating characteristic curve of 0.78 (95% CI 0.74-0.82) and significantly outperformed previously published scores. The performance of the underlying model was confirmed by leave-one-out cross-validation.

CONCLUSION

The RISC-score is a clinically intuitive prediction instrument that helps identify surgical ICU patients at high risk for ICU readmission. The simplicity of the score facilitates its clinical implementation across surgical divisions.

Assessment of Costs of Avoidable Delays in Intensive Care Unit Discharge

IMPORTANCE

Delays in transfer for discharge-ready patients from the intensive care unit (ICU) are increasingly described and contribute to strained capacity.

OBJECTIVE

To describe the epidemiological features and health care costs attributable to potentially avoidable delays in ICU discharge in a large integrated health care system.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cohort study was performed in 17 adult ICUs in Alberta, Canada, from June 19, 2012, to December 31, 2016. Participants were patients 15 years or older admitted to a study ICU during the study period. Data were analyzed from October 19, 2018, to May 20, 2020.

EXPOSURES

Avoidable time in the ICU, defined as the portion of total ICU patient-days accounted for by avoidable delay in ICU discharge (eg, waiting for a ward bed).

MAIN OUTCOMES AND MEASURES

The primary outcome was health care costs attributable to avoidable time in the ICU. Secondary outcomes were factors associated with avoidable time, in-hospital mortality, and measures of use of health care resources, including the number of hours in the ICU and the number of days of hospitalization. Multilevel mixed multivariable regression was used to assess associations between avoidable time and outcomes.

RESULTS

In total, 28 904 patients (mean [SD] age, 58.3 [16.8] years; 18 030 male [62.4%]) were included. Of these, 19 964 patients (69.1%) had avoidable time during their ICU admission. The median avoidable time per patient was 7.2 (interquartile range, 2.4-27.7) hours. In multivariable analysis, male sex (odds ratio [OR], 0.92; 95% CI, 0.87-0.98), comorbid hemiplegia or paraplegia (OR 1.47; 95% CI, 1.23-1.75), liver disease (OR 1.20; 95% CI, 1.04-1.37), admission Acute Physiology and Chronic Health Evaluation II score (OR, 1.03; 95% CI, 1.02-1.03), surgical status (OR, 0.90; 95% CI, 0.82-0.98), medium community hospital type (OR, 0.12; 95% CI, 0.04-0.32), and admission year (OR, 1.16; 95% CI, 1.13-1.19) were associated with avoidable time. The cumulative avoidable time was 19 373.9 days, with estimated attributable costs of CAD$34 323 522. Avoidable time accounted for 12.8% of total ICU bed-days and 6.4% of total ICU costs. Patients with avoidable time before ICU discharge showed higher unadjusted in-hospital mortality (1115 [5.6%] vs 392 [4.4%]; P < .001); however, in multivariable analysis, avoidable time was associated with reduced in-hospital mortality (adjusted hazard ratio, 0.74; 95% CI, 0.64-0.85). Results were similar in sensitivity analyses.

CONCLUSIONS AND RELEVANCE

In this study, potentially avoidable discharge delay occurred for most patients admitted to ICUs across a large integrated health system and translated into substantial associated health care costs.

Is my patient ready for a safe transfer to a lower-intensity care setting? Nursing complexity as an independent predictor of adverse events risk after ICU discharge

BACKGROUND

Patients discharged from intensive care units (ICUs) are at risk for adverse events (AEs). Establishing safe discharge criteria is challenging. No available criteria consider nursing complexity among risk factors.

OBJECTIVES

To investigate whether nursing complexity upon ICU discharge is an independent predictor for AEs.

METHODS

Prospective observational study. The Patient Acuity and Complexity Score (PACS) was developed to measure nursing complexity. Its predictive power for AEs was tested using multivariate regression analysis.

RESULTS

The final regression model showed a very-good discrimination power (AUC 0.881; p<0.001) for identifying patients who experienced AEs. Age, ICU admission reason, PACS, cough strength, PaCO₂, serum creatinine and sodium, and transfer to Internal Medicine showed to be predictive of AEs. Exceeding the identified PACS threshold increased by 3.3 times the AEs risk.

CONCLUSIONS

The level of nursing complexity independently predicts AEs risk and should be considered in establishing patient's eligibility for a safe ICU discharge.

Intensive Care Role in Disaster Management Critical Care Clinics

The "daily disasters" within the ebb and flow of routine critical care provide a foundation of preparedness for the less-frequent, larger events that affect most health care organizations at some time. Although large disasters can overwhelm, those who strengthen processes and habits through daily practice will be the best prepared to manage them.

The continuum of critical care

Until relatively recently, critical illness was considered as a separate entity and the intensive care unit (ICU), often a little cut-off from other areas of the hospital, was in many cases used as a last resort for patients so severely ill that it was no longer possible to care for them on the general ward. However, we are increasingly realizing that critical illness should be seen as just one part of the patient's disease trajectory and how the patient is managed before and after ICU admission has an important role to play in optimizing outcomes. Identifying critical illness early, before it reaches a stage where it is life-threatening, is a challenge and requires a combination of improved and more frequent or continuous monitoring of at-risk patients, staff training to recognize when a patient is deteriorating, a system to "call for help," and an effective response to that call. Critical care doctors are now widely available 24 h a day for consultation, and many hospitals have rapid response or medical emergency teams composed of staff trained in intensive care and with resuscitation skills who can attend patients on the ward who have been identified to be deteriorating, assess them to determine the need for ICU admission, and initiate further tests and/or initial therapy. Early intensivist input may also be important for patients undergoing interventions that are likely to result in ICU admission, e.g., transplantation or cardiac surgery. The patient's continuum after ICU discharge must also be taken into account during their ICU stay, with attempts made to limit the longer-term physical and psychological consequences of critical illness as much as possible. Minimal sedation, good communication, and early mobilization are three factors that can help patients survive their ICU stay with minimal sequelae.

Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK

OBJECTIVE

The primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.

DESIGN

We used two datasets of routinely collected patient data to test and improve on a set of previously proposed discharge criteria.

SETTING

Bristol Royal Infirmary general intensive care unit (GICU).

PATIENTS

Two cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from Medical Information Mart for Intensive Care (MIMIC)-III.

RESULTS

In both cohorts few successfully discharged patients met all of the discharge criteria. Both a random forest and a logistic classifier, trained using multiple-source cross-validation, demonstrated improved performance over the original criteria and generalised well between the cohorts. The classifiers showed good agreement on which features were most predictive of readiness-for-discharge, and these were generally consistent with clinical experience. By weighting the discharge criteria according to feature importance from the logistic model we showed improved performance over the original criteria, while retaining good interpretability.

CONCLUSIONS

Our findings indicate the feasibility of the proposed approach to ready-for-discharge classification, which could complement other risk models of specific adverse outcomes in a future decision support system. Avenues for improvement to produce a clinically useful tool are identified.

Use of National Early Warning Score for observation for increased risk for clinical deterioration during post-ICU care at a surgical ward

Purpose

Patients transferred from an intensive care unit (ICU) to a general ward are at risk for clinical deterioration. The aim of the study was to determine if an increase in National Early Warning Score (NEWS) value predicted worse outcomes in surgical ward patients previously treated in the ICU.

Patients and methods

A retrospective observational study was conducted in a cohort of gastrointestinal surgery patients after transfer from an ICU/high dependency unit (HDU). NEWS values were collected throughout the ward admission. Clinical deterioration was defined by ICU readmission or death. The ability of NEWS to predict clinical deterioration was determined using a linear mixed effect model.

Results

We included 124 patients, age 65.9±14.5, 60% males with an ICU Simplified Acute Physiology Score II 33.8±12.7. No patients died unexpectedly at the ward and 20 were readmitted to an ICU/HDU. The NEWS values increased by a mean of 0.15 points per hour (intercept 3.7, P<0.001) before ICU/HDU readmission according to the linear mixed effect model. NEWS at transfer from ICU was the only factor that predicted readmission (OR 1.32; 95% CI 1.01–1.72; P=0.04) at the time of admission to the ward.

Conclusion

Clinical deterioration of surgical patients was preceded by an increase in NEWS.

Protocol for a mixed-methods exploratory investigation of care following intensive care discharge: the REFLECT study

INTRODUCTION

A substantial number of patients discharged from intensive care units (ICUs) subsequently die without leaving hospital. It is unclear how many of these deaths are preventable. Ward-based management following discharge from ICU is an area that patients and healthcare staff are concerned about. The primary aim of REFLECT (Recovery Following Intensive Care Treatment) is to develop an intervention plan to reduce in-hospital mortality rates in patients who have been discharged from ICU.

METHODS AND ANALYSIS

REFLECT is a multicentre mixed-methods exploratory study examining ward care delivery to adult patients discharged from ICU. The study will be made up of four substudies. Medical notes of patients who were discharged from ICU and subsequently died will be examined using a retrospective case records review (RCRR) technique. Patients and their relatives will be interviewed about their post-ICU care, including relatives of patients who died in hospital following ICU discharge. Staff involved in the care of patients post-ICU discharge will be interviewed about the care of this patient group. The medical records of patients who survived their post-ICU stay will also be reviewed using the RCRR technique. The analyses of the substudies will be both descriptive and use a modified grounded theory approach to identify emerging themes. The evidence generated in these four substudies will form the basis of the intervention development, which will take place through stakeholder and clinical expert meetings.

ETHICS AND DISSEMINATION

Ethical approval has been obtained through the Wales Research and Ethics Committee 4 (17/WA/0107). We aim to disseminate the findings through international conferences, international peer-reviewed journals and social media.

TRIAL REGISTRATION NUMBER

ISRCTN14658054.

Nighttime intensive care unit discharge and outcomes: A propensity matched retrospective cohort study

Background

Nighttime ICU discharge, i.e., discharge from the ICU during the night hours, has been associated with increased readmission rates, hospital length of stay (LOS) and in-hospital mortality. We sought to determine the frequency of nighttime ICU discharge and identify whether nighttime ICU discharge is associated with worse outcomes in a private adult ICU located in Brazil.

Methods

Post hoc analysis of a cohort study addressing the effect of ICU readmissions on outcomes. This retrospective, single center, propensity matched cohort study was conducted in a medical-surgical ICU located in a private tertiary care hospital in São Paulo, Brazil. Based on time of transfer, patients were categorized into nighttime (7:00 pm to 6:59 am) and daytime (7:00 am to 6:59 pm) ICU discharge and were propensity-score matched at a 1:2 ratio. The primary outcome of interest was in–hospital mortality.

Results

Among 4,313 eligible patients admitted to the ICU between June 2013 and May 2015, 1,934 patients were matched at 1:2 ratio [649 (33.6%) nighttime and 1,285 (66.4%) daytime discharged patients]. The median (IQR) cohort age was 66 (51–79) years and SAPS III score was 43 (33–55). In-hospital mortality was 6.5% (42/649) in nighttime compared to 5.6% (72/1,285) in daytime discharged patients (OR, 1.17; 95% CI, 0.79 to 1.73; p = 0.444). While frequency of ICU readmission (OR, 0.95; 95% CI, 0.78 to 1.29; p = 0.741) and length of hospital stay did not differ between the groups, length of ICU stay was lower in nighttime compared to daytime ICU discharged patients [1 (1–3) days vs. 2 (1–3) days, respectively, p = 0.047].

Conclusion

In this propensity-matched retrospective cohort study, time of ICU discharge did not affect in-hospital mortality.