Consequences of discharges from intensive care at night

Off-Hours Intensive Care Unit Transfer Is Associated With Increased Mortality and Failure to Rescue

BACKGROUND

Cardiac postoperative intensive care unit (ICU) beds are a limited resource, and when a patient no longer requires this level of care they are quickly transferred out. We hypothesized that complications and ICU readmission increased when transfer occurred during off-hours compared with regular work hours.

METHODS

From 2010 to 2021, patients who underwent a Society of Thoracic Surgeons index operation at a single center were assigned a group based on their ICU transfer time, defined as when they physically arrived on the acute care floor. Patients were stratified into off-hours vs regular hours by their transfer time. Off-hours was defined as 9 pm to 5 am. Risk-adjusted multivariable logistic regression analyzed the association of ICU readmission, postoperative complications, operative mortality, and failure to rescue by group.

RESULTS

The cohort included 5951 patients (off-hours n = 292 [4.9%], regular-hours n = 5659 [95.1%]). Patients in the off-hours group had significantly greater odds of risk-adjusted ICU readmission (odds ratio 1.99, 95% CI 1.25-3.04, P < .002) and mortality (odds ratio 3.88, 95% CI 2.27-6.33, P < .001). In the major complications subgroup (Off-hours n = 55, Regular-hours n = 603), Off-hours transfer was associated with increased mortality (failure to rescue) (odds ratio 3.05, 95% CI 1.58-5.69, P = .001).

CONCLUSIONS

Off-hours ICU to floor transfer was associated with increased postoperative complications, ICU readmission, and mortality, suggesting that the timing of ICU transfer may impact outcomes. This elucidates targets for quality and process improvement for our center and others facing the same resource constraints.

Comprehensive Improvement of Cardiology Inpatient Transfers: A Bed-availability Triggered Approach

Introduction

Patient transfers pose a potential risk during hospitalizations. Structured communication practices are necessary to ensure effective handoffs, but occur amidst competing priorities and constraints. We sought to design and implement a multidisciplinary process to enhance communication between pediatric cardiovascular intensive care unit and cardiology floor teams with a comprehensive approach evaluating efficiency, safety, and culture.

Methods

We conducted a prospective quality improvement study to enact a bed-availability triggered bedside handoff process. The primary aim was to reduce the time between handoff and unit transfer. Secondary metrics captured the impact on safety (reported safety events, overnight transfers, bounce backs, and I-PASS utilization), efficiency (transfer latency, unnecessary patient handoffs, and cumulative time providers were engaged in handoffs), and culture (team members perceptions of satisfaction, collaboration, and handoff efficiency via survey data).

Results

Eighty-two preimplementation surveys, 26 stakeholder interviews, and 95 transfers were completed during the preintervention period. During the postintervention period, 145 handoffs were audited. We observed significant reductions in transfer latency, unnecessary handoffs, and cumulative provider handoff time. Overnight transfers decreased, and no negative impact was observed in reported safety events or bouncebacks. Survey results showed a positive impact on collaboration, efficiency, and satisfaction among team members.

Conclusions

Developing safer handoff practices require a collaborative, structured, and stepwise approach. Advances are attainable in high-volume centers, and comprehensive measurement of change is necessary to ensure a positive impact on the overall patient and provider environment.

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.

A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units

This paper targets a major challenge of how to effectively allocate medical resources in intensive care units (ICUs). We trained multiple regression models using the Medical Information Mart for Intensive Care III (MIMIC III) database recorded in the period between 2001 and 2012. The training and validation dataset included pneumonia, sepsis, congestive heart failure, hypotension, chest pain, coronary artery disease, fever, respiratory failure, acute coronary syndrome, shortness of breath, seizure and transient ischemic attack, and aortic stenosis patients’ recorded data. Then we tested the models on the unseen data of patients diagnosed with coronary artery disease, congestive heart failure or acute coronary syndrome. We included the admission characteristics, clinical prescriptions, physiological measurements, and discharge characteristics of those patients. We assessed the models’ performance using mean residuals and running times as metrics. We ran multiple experiments to study the data partition’s impact on the learning phase. The total running time of our best-evaluated model is 123,450.9 mS. The best model gives an average accuracy of 98%, highlighting the location of discharge, initial diagnosis, location of admission, drug therapy, length of stay and internal transfers as the most influencing patterns to decide a patient’s readiness for discharge.

Impact of Intensive Care Unit Discharge Delay on Liver Transplantation Outcomes

Background: For general intensive care unit (ICU) patients, ICU discharge delay (ICUDD) has been associated with an increased hospital length of stay (LOS) and the acquisition of multi-resistant organism (MRO) infections. The impact of ICUDD on liver transplant (LT) recipients is unknown. Methods: We retrospectively studied consecutive adult LT between 2011 and 2019. ICUDD was defined as >8 h between a patient being cleared for discharge to ward and the patient leaving the ICU. Results: 550 patients received LT and the majority (68.5%) experienced ICUDD. The median time between clearance for ward and the patient leaving the ICU was 25.6 h. No donor or recipient variables were associated with ICUDD. Patients cleared for discharge early in the week (Sunday-Tuesday) and those discharged outside routine work hours were more likely to experience ICUDD (p = 0.001 and p < 0.001, respectively). The median hospital LOS was identical (18 days, p = 0.96) and there were no differences in other patient outcomes. Patients who became colonized with MRO in the ICU spent a longer time there compared to those who remained MRO-free (9 vs. 6 days, p < 0.001); however, this was not due to ICUDD. Conclusion: ICUDD post-LT is common and does not prolong hospital LOS. ICUDD is not associated with adverse patient outcomes or MRO colonization.

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.

Intensive care unit to unit capacity transfers are associated with increased mortality: an observational cohort study on patient transfers in the Swedish Intensive Care Register

Background

Transfers from one intensive care unit (ICU) to another ICU are associated with increased length of intensive care and hospital stay. Inter-hospital ICU transfers are carried out for three main reasons: clinical transfers, capacity transfers and repatriations. The aim of the study was to show that different ICU transfers differ in risk-adjusted mortality rate with repatriations having the least risk.

Results

Observational cohort study of adult patients transferred between Swedish ICUs during 3 years (2016–2018) with follow-up ending September 2019. Primary and secondary end-points were survival to 30 days and 180 days after discharge from the first ICU. Data from 75 ICUs in the Swedish Intensive Care Register, a nationwide intensive care register, were used for analysis (89% of all Swedish ICUs), covering local community hospitals, district general hospitals and tertiary care hospitals. We included adult patients (16 years or older) admitted to ICU and subsequently discharged by transfer to another ICU. Only the first admission was used. Exposure was discharge to any other ICU (ICU-to-ICU transfer), whether in the same or in another hospital. Transfers were grouped into three predefined categories: clinical transfer, capacity transfer, and repatriation. We identified 15,588 transfers among 112,860 admissions (14.8%) and analysed 11,176 after excluding 4112 repeat transfer of the same individual and 300 with missing risk adjustment. The majority were clinical transfers (62.7%), followed by repatriations (21.5%) and capacity transfers (15.8%). Unadjusted 30-day mortality was 25.0% among capacity transfers compared to 14.5% and 16.2% for clinical transfers and repatriations, respectively. Adjusted odds ratio (OR) for 30-day mortality were 1.25 (95% CI 1.06–1.49 p = 0.01) for capacity transfers and 1.17 (95% CI 1.02–1.36 p = 0.03) for clinical transfers using repatriation as reference. The differences remained 180 days post-discharge.

Conclusions

There was a large proportion of ICU-to-ICU transfers and an increased odds of dying for those transferred due to other reasons than repatriation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-022-01003-x.

If early warning systems are used, would it be possible to estimate early clinical deterioration risk and prevent readmission to intensive care?

Background

Although the intensive care unit (ICU) admission criteria are specified clearly, it is difficult to make the decision of discharge from ICU.

Aims

The purpose of this study is to test whether or not early warning scores will allow us to estimate early clinical deterioration within 24 hours and predict readmission to intensive care. A total of 1330 patients were included in the retrospective study.

Patients and Methods

All the patients' age, gender, ICU hospitalization reasons and Acute Physiological and Chronic Health Evaluation (APACHE II) scores were recorded. National Early Warning Score (NEWS) and VitalpacTM early warning score (VIEWS) scores were calculated using the physiological and neurological examination records. Discharge NEWS and VIEWS values of the patients who were readmitted to intensive care 24 hours after discharge were compared with the patients who were not readmitted to intensive care. The statistical analysis was performed using the IBM SPSS version 21 package software.

Results

Age average of all the patients was 64.3 ± 20.8 years. The number of the patients who were readmitted to intensive care was 118 (8.87%). When examining the factors that affect early clinical deterioration, it was found that advanced age, high APACHE II scores, higher NEWS and VIEWS scores, lower DAP values and the patient's transfer from the ward were significantly predictive (P < 0.05).

Conclusions

In this study, high NEWS and VIEWS are strong scoring systems that can be used in estimating early clinical deterioration risk and are easy-to-use and less time consuming.

The effect of COVID-19 epidemic on vital signs in hospitalized patients: a pre-post heat-map study from a large teaching hospital

The Lombardy SARS-CoV-2 outbreak in February 2020 represented the beginning of COVID-19 epidemic in Italy. Hospitals were flooded by thousands of patients with bilateral pneumonia and severe respiratory, and vital sign derangements compared to the standard hospital population. We propose a new visual analysis technique using heat maps to describe the impact of COVID-19 epidemic on vital sign anomalies in hospitalized patients. We conducted an electronic health record study, including all confirmed COVID-19 patients hospitalized from February 21st, 2020 to April 21st, 2020 as cases, and all non-COVID-19 patients hospitalized in the same wards from January 1st, 2018 to December 31st, 2018. All data on temperature, peripheral oxygen saturation, respiratory rate, arterial blood pressure, and heart rate were retrieved. Derangement of vital signs was defined according to predefined thresholds. 470 COVID-19 patients and 9241 controls were included. Cases were older than controls, with a median age of 79 vs 76 years in non survivors (p =  < 0.002). Gender was not associated with mortality. Overall mortality in COVID-19 hospitalized patients was 18%, ranging from 1.4% in patients below 65 years to about 30% in patients over 65 years. Heat maps analysis demonstrated that COVID-19 patients had an increased frequency in episodes of compromised respiratory rate, acute desaturation, and fever. COVID-19 epidemic profoundly affected the incidence of severe derangements in vital signs in a large academic hospital. We validated heat maps as a method to analyze the clinical stability of hospitalized patients. This method may help to improve resource allocation according to patient characteristics.

The role of antimicrobial resistance on long-term mortality and quality of life in critically ill patients: a prospective longitudinal 2-year study

Background

In the recent era, antimicrobial resistance has been identified as one of the most important threats to human health worldwide. The rapid emergence of antibiotic-resistant pathogens (ABRP) in the modern intensive care unit (ICU) also represents a “nightmare scenario” with unknown clinical consequences. In the Greek ICU, in particular, gram negative ABRPs are now considered endemic. However, the possible longitudinal impact of ABRPs on long-term outcomes of ICU patients has not yet been determined.

Methods

In this two-year (January 2014-December 2015) single-centre observational longitudinal study, 351 non-neurocritical ICU patients ≥ 18 year-old were enrolled. Patients’ demographic, clinical and outcome data were prospectively collected. Quality-adjusted life years (QALY) were calculated at 6, 12, 18 and 24 months after ICU admission.

Results

Fifty-eight patients developed infections due to ABRP (ABRP group), 57 due to non-ABRP (non-ABRP group), and 236 demonstrated no infection (no-infection group) while in ICU. Multiple regression analysis revealed that multiple organ dysfunction syndrome score (OR: 0.676, 95%CI 0.584–0.782; P < 0.001) and continuous renal replacement therapy (OR: 4.453, 95%CI 1.805–10.982; P = 0.001) were the only independent determinants for ABRP infections in ICU. Intra-ICU, 90-day and 2-year mortality was 27.9%, 52.4% and 61.5%, respectively. Compared to the non-ABRP and no-infection group, the ABRP group demonstrated increased intra-ICU, 90-day and 2-year mortality (P ≤ 0.022), worse 2-year survival rates in ICU patients overall and ICU survivor subset (Log-rank test, P ≤ 0.046), and poorer progress over time in 2-year QALY kinetics in ICU population overall, ICU survivor and 2-year survivor subgroups (P ≤ 0.013). ABRP group was further divided into multi-drug and extensively-drug resistant subgroups [MDR (n = 34) / XDR (n = 24), respectively]. Compared to MDR subgroup, the XDR subgroup demonstrated increased ICU, 90-day and 2-year mortality (P ≤ 0.031), but similar 90-day and 2-year QALYs (P ≥ 0.549). ABRP infections overall (HR = 1.778, 95% CI 1.166–2.711; P = 0.008), as well as XDR [HR = 1.889, 95% CI 1.075–3.320; P = 0.027) but not MDR pathogens, were independently associated with 2-year mortality, after adjusting for several covariates of critical illness.

Conclusions

The present study may suggest a significant association between ABRP (especially XDR) infections in ICU and increased mortality and inability rates for a prolonged period post-discharge that requires further attention in larger-scale studies.

A retrospective observational study of daytime and nighttime transfers from the intensive care unit: through the lens of critical care response teams

PURPOSE

To evaluate the impact of nighttime compared with daytime transfers from the intensive care unit (ICU) on mortality in a hospital with a critical care response team (CCRT).

METHODS

We performed a retrospective observational study of ICU patients transferred between January 2011 and July 2013 who received CCRT follow-up. The transferred patients were divided into cohorts of daytime and nighttime transfers. A multivariable logistic regression model was used to identify independent predictors of mortality after ICU transfer.

RESULTS

There were 1,857 patients included in the study. With the exception of Multiple Organ Dysfunction Score on admission, transfers to a step-down unit, and lower urine output, there were no differences in the baseline characteristics, clinical events identified by CCRTs, and the number of CCRT interventions performed between daytime and nighttime transfers. Patients transferred at night were at higher risk of death in the univariate analysis but not in the multivariate analysis. Independent predictors of mortality included older age (odds ratio [OR], 1.02; 95% confidence interval [CI], 1.002 to 1.04), transfer to a medical service (OR, 1.96; 95% CI, 1.11 to 3.43), CCRT identification of hypoxemic respiratory failure (OR, 5.86; 95% CI, 3.11 to 11.04), decreased level of consciousness (OR, 3.14; 95% CI, 1.23 to 8.02), hypotension (OR, 3.69; 95% CI, 1.36 to 10.01), and longer CCRT duration of follow-up (OR, 1.02; 95% CI, 1.004 to 1.03).

CONCLUSIONS

Nighttime transfer from the ICU was not an independent predictor of mortality. We identified unique predictors of mortality, including clinical events that CCRTs identified in patients immediately after ICU transfer. Future studies are required to validate these predictors of mortality in transferred ICU patients.

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.

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.

Associations between hospital occupancy, intensive care unit transfer delay and hospital mortality

PURPOSE

Hospital occupancy (HospOcc) pressures often lead to longer intensive care unit (ICU) stay after physician recognition of discharge readiness. We evaluated the relationships between HospOcc, extended ICU stay, and patient outcomes.

MATERIALS AND METHODS

7-year retrospective cohort study of 8500 alive discharge encounters from 4 adult ICUs of a tertiary hospital. We estimated associations between i) HospOcc and ICU transfer delay; and ii) ICU transfer delay and hospital mortality.

RESULTS

Median (IQR) ICU transfer delay was 4.8 h (1.6-11.7), 1.4% (119) suffered in-hospital death, and 4% (341) were readmitted. HospOcc was non-linearly related with ICU transfer delay, with a spline knot at 80% (mean transfer delay 8.8 h [95% CI: 8.24, 9.38]). Higher HospOcc level above 80% was associated with longer transfer delays, (mean increase 5.4% per % HospOcc increase; 95% CI, 4.7 to 6.1; P < .001). Longer ICU transfer delay was associated with increasing odds of in-hospital death or ICU readmission (odds ratio 1.01 per hour; 95% CI 1.00 to 1.01; P = .04) but not with ICU readmission alone (OR 1.01 per hour; 95% CI 1.00 to 1.01, P = .14).

CONCLUSIONS

ICU transfer delay exponentially increased above a threshold hospital occupancy and may be associated with increased hospital mortality.

Intensive care unit occupancy and premature discharge rates: A cohort study assessing the reporting of quality indicators

PURPOSE

ICU occupancy fluctuates. High levels may disadvantage patients. Currently, occupancy is benchmarked annually which may inaccurately reflect strained units. Outcomes potentially sensitive to occupancy include premature (early) ICU discharge and non-clinical transfer (NCT). This study assesses the association between daily occupancy and these outcomes, and evaluates benchmarking care across Scotland using daily occupancy.

MATERIALS AND METHODS

Population: all Scottish ICU patients, 2006-2014.

EXPOSURE

bed occupancy per unit-day; Outcomes: proportion of early discharges and NCTs.

DESIGN

Retrospective cohort study. Outcome rates were calculated above various occupancy thresholds. Polynomial regression visualised associations, and inflection points between occupancy and outcomes. Spearman's rho correlations between occupancy measures and outcomes were reported.

RESULTS

65,472 discharges occurred over 57,812 unit-days. 1954(3.0%) discharges were early; 429 (0.7%) were NCTs. Early discharge rates above 70%, 80% and 90% occupancy were 3.9%, 5.0% and 7.5% respectively. Occupancies at which outcome rates greatly increased were near 80% for early discharge, and 90% for NCT. Mean annual occupancy was not correlated with outcomes; annual proportion of days ≥90% occupancy correlated most strongly (early discharge rho = 0.46,p < .001; NCT rho = 0.31, p < .001).

CONCLUSIONS

We demonstrate a clear association between daily ICU occupancy and early discharge/NCT. Daily occupancy may better benchmark care quality than mean annual occupancy.

Patient, family and provider experiences with transfers from intensive care unit to hospital ward: a multicentre qualitative study

BACKGROUND

Transfer of patient care from an intensive care unit (ICU) to a hospital ward is often challenging, high risk and inefficient. We assessed patient and provider perspectives on barriers and facilitators to high-quality transfers and recommendations to improve the transfer process.

METHODS

We conducted semistructured interviews of participants from a multicentre prospective cohort study of ICU transfers conducted at 10 hospitals across Canada. We purposively sampled 1 patient, 1 family member of a patient, 1 ICU provider, and 1 ward provider at each of the 8 English-speaking sites. Qualitative content analysis was used to derive themes, subthemes and recommendations.

RESULTS

The 35 participants described 3 interrelated, overarching themes perceived as barriers or facilitators to high-quality patient transfers: resource availability, communication and institutional culture. Common recommendations suggested to improve ICU transfers included implementing standardized communication tools that streamline provider-provider and provider-patient communication, using multimodal communication to facilitate timely, accurate, durable and mutually reinforcing information transfer; and developing procedures to manage delays in transfer to ensure continuity of care for patients in the ICU waiting for a hospital ward bed.

INTERPRETATION

Patient and provider perspectives attribute breakdown of ICU-to-ward transfers of care to resource availability, communication and institutional culture. Patients and providers recommend standardized, multimodal communication and transfer procedures to improve quality of care.

Effect of Boarding on Mortality in ICUs

OBJECTIVES

Hospitals use a variety of strategies to maximize the availability of limited ICU beds. Boarding, which involves assigning patients to an open bed in a different subspecialty ICU, is one such practice employed when ICU occupancy levels are high, and beds in a particular unit are unavailable. Boarding disrupts the normal geographic colocation of patients and care teams, exposing patients to nursing staff with different training and expertise to those caring for nonboarders. We analyzed whether medical ICU patients boarding in alternative specialty ICUs are at increased risk of mortality.

DESIGN

Retrospective cohort study using an instrumental variable analysis to control for unmeasured confounding. A semiparametric bivariate probit estimation strategy was employed for the instrumental model. Propensity score matching and standard logistic regression (generalized linear modeling) were used as robustness checks.

SETTING

The medical ICU of a tertiary care nonprofit hospital in the United States between 2002 and 2012.

PATIENTS

All medical ICU admissions during the specified time period.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The study population consisted of 8,429 patients of whom 1,871 were boarders. The instrumental variable model demonstrated a relative risk of 1.18 (95% CI, 1.01-1.38) for ICU stay mortality for boarders. The relative risk of in-hospital mortality among boarders was 1.22 (95% CI, 1.00-1.49). GLM and propensity score matching without use of the instrument yielded similar estimates. Instrumental variable estimates are for marginal patients, whereas generalized linear modeling and propensity score matching yield population average effects.

CONCLUSIONS

Mortality increased with boarding of critically ill patients. Further research is needed to identify safer practices for managing patients during periods of high ICU occupancy.

Examining the weekend effect across ICU performance metrics

BACKGROUND

Known colloquially as the "weekend effect," the association between weekend admissions and increased mortality within hospital settings has become a highly contested topic over the last two decades. Drawing interest from practitioners and researchers alike, a sundry of works have emerged arguing for and against the presence of the effect across various patient cohorts. However, it has become evident that simply studying population characteristics is insufficient for understanding how the effect manifests. Rather, to truly understand the effect, investigations into its underlying factors must be considered. As such, the work presented in this manuscript serves to address this consideration by moving beyond identification of patient cohorts to examining the role of ICU performance.

METHODS

Employing a comprehensive, publicly available database of electronic medical records (EMR), we began by utilizing multiple logistic regression to identify and isolate a specific cohort in which the weekend effect was present. Next, we leveraged the highly detailed nature of the EMR to evaluate ICU performance using well-established ICU quality scorecards to assess differences in clinical factors among patients admitted to an ICU on the weekend versus weekday.

RESULTS

Our results demonstrate the weekend effect to be most prevalent among emergency surgery patients (OR 1.53; 95% CI 1.19, 1.96), specifically those diagnosed with circulatory diseases (P<.001). Differences between weekday and weekend admissions for this cohort included a variety of clinical factors such as ventilatory support and night-time discharges.

CONCLUSIONS

This work reinforces the importance of accounting for differences in clinical factors as well as patient cohorts in studies investigating the weekend effect.

Vital signs on hospital discharge and re admission rates

BACKGROUND

Assessing the stability of a patient's vital signs in the 24 hours before discharge has been suggested as an objective and inexpensive way to determine safety for discharge.

AIM

To determine the association between unstable vital signs at the time of discharge with the readmission rate over a one-year period.

DESIGN

An observational cohort multi-center study at three urban community hospitals using electronic health record data collected from November 1, 2016, to October 30, 2017.

METHODS

A total of 29322 hospitalizations to medical floors with complete sets of vital signs were included. The final vital signs collected on the day of discharge were used for analysis. The readmission rates were compared using different variables such as age, sex, insurance payer (Medicare or Medicaid), discharge time, discharge disposition, length of stay at the hospital, the number, and type of abnormal vital signs at discharge.

RESULTS

Unstable vital signs at discharge were found in 2862 patients (9.8%). The readmission rate was highest in patients with two (11.3%) unstable vital signs compared to those with one (8.5%) and three or more (0%) instabilities. Patients with a combination of heart rate >100 beats/min and respiratory rate >20 breaths/min at discharge had a 14.1% seven-day readmission rate (P = 0.0057, Odds Ratio = 1.87, Confidence Interval = 1.19-2.95).

CONCLUSIONS

Vital sign instabilities in the 24 hours before discharge are associated with increased seven-day readmission rate.

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.

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.

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

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

Outcome-based Clinical Indicators for Intensive Care Medicine

The clinical indicator is a tool used to monitor the quality of health care. Its use in the Intensive Care Unit (ICU) is desirable for many reasons: the maintenance of minimum standards, the development of best practice and the delivery of cost-effective health care. The utility of clinical indicators in ICU is limited by the lack of universal, robust, transparent, evidence-based and risk-adjusted measures of quality, and the difficulties in defining “quality care” and “good outcome”. Monitoring of adverse events, system descriptors, and resource indicators is valuable but they have a limited relationship to the quality of care. ICU mortality prediction models provide a global measure of quality and, despite their inherent deficiencies, remain one of the most robust and useful clinical indicators.

Transfer Time from the Intensive Care Unit and Patient Outcome: A Retrospective Analysis from a Tertiary Care Hospital in India

Background and aims

Patients' outcome after ICU transfer reflect hospital's post-ICU care status. This study assessed association of after-hour ICU transfer on patient outcome.

Subjects and methods

Single-centre, retrospective analysis of data between March 2016 and April 2017 was performed at a tertiary-care hospital in India. Patient data were collected on all consecutive ICU admissions during study period. Patients were categorized according to ICU transfer time into daytime (08:00-19:59 hours) and after-hour (20:00-07:59 hours). Patients transferred to other ICUs/hospitals, died in ICU, or discharged home from ICU were excluded. Only ?rst ICU admission was considered for outcome analysis. Primary outcome-hospital mortality; secondary outcomes-ICU readmission and hospital length of stay (LOS). All analysis were adjusted for illness severity.

Results

Of 1857 patients admitted during study period,1356 were eligible for study; out of which 53.9% were males and 383(28%) patients transferred during after-hour. Mean age of two groups (daytime vs. after-hour 65.7±15.2 vs. 66.3±16.2 years) was similar (p = 0.7). Mean APACHE IV score was comparable between daytime vs. after-hour transfers (45.6±20.4 vs 46.8±22; p = 0.05). Unadjusted hospital mortality rate of after-hour-transfers was significantly higher compared to daytime-transfers (7.1% vs. 4.1%; p = 0.02). After adjustment with illness severity, after-hour-transfers were associated with significantly higher hospital mortality compared to daytime-transfers(aOR1.7, 95%CI 1.1,2.8; p = 0.04). Median duration of hospital LOS and ICU readmission though higher for after-hour-transfers, was not statistically significant in adjusted analysis (aOR^hospitalLOS1.1, 95% CI 0.8, 1.4, p = 0.5; aOR^readmission 1.6, 95% CI 0.9,2.7; p = 0.06, respectively).

Conclusion

After-hour-transfers from ICU is associated with significantly higher hospital mortality. Hospital LOS and readmission rates are similar for daytime and after-hour -transfers.

How to cite this article

Chatterjee S, Sinha S et al., Transfer Time from the Intensive Care Unit and Patient Outcome: A Retrospective Analysis from a Tertiary Care Hospital in India. Indian J Crit Care Med 2019;23(3):115-121.

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.

Reducing night-time discharge from intensive care. A nationwide improvement project with public display of ICU outcomes

PURPOSE

Discharge from an intensive care unit (ICU) during the night is an independent risk factor for adverse outcomes. A quality improvement project was conducted with the aim of reducing the incidence and the associated mortality after night-time discharge.

MATERIALS AND METHODS

ICUs that submitted data to the Swedish Intensive Care Registry (SIR) agreed to appoint night-time discharge as a national quality indicator with detailed public display on the internet of various discharge proportions and outcomes. The registry was then examined for trends during a 10-year period with use of multilevel mixed-effects models.

RESULTS

We analysed 163,371 patients who were discharged alive from 70 ICUs to a general ward within the same hospital during 2006-2015. The prevalence of night-time discharge fell from 7.0% (95% CI: 5.2 to 8.7%) in 2006 to 4.9% (95% CI: 4.3 to 5.5%) in 2015 (P = .035 for trend). The original increased risk of death within 30 days after night-time discharge in 2006-2010, OR 1.20 (95% CI: 1.01 to 1.42), disappeared in 2011-2015, OR 1.06 (95% CI: 0.96 to 1.17).

CONCLUSIONS

During the 10-year period of the quality improvement project, the annual prevalence and risk of death within 30-days after night-time discharge were reduced. The public display and feedback of audit data could have helped in achieving this.

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

PURPOSE

Discharge from an intensive care unit (ICU) out of hours is common. We undertook a systematic review and meta-analysis to explore the association between time of discharge and mortality/ICU readmission.

METHODS

We searched Medline, Embase, Web of Knowledge, CINAHL, the Cochrane Library and OpenGrey to June 2017. We included studies reporting in-hospital mortality and/or ICU readmission rates by ICU discharge "out-of-hours" and "in-hours". Inclusion was limited to patients aged ≥ 16 years discharged alive from a non-specialist ICU to a lower level of hospital care. Studies restricted to specific diseases were excluded. We assessed study quality using the Newcastle Ottowa Scale. We extracted published data, summarising using a random-effects meta-analysis.

RESULTS

Our searches identified 1961 studies. We included unadjusted data from 1,191,178 patients from 18 cohort studies (presenting data from 1994 to 2014). "Out of hours" had multiple definitions, beginning between 16:00 and 22:00 and ending between 05:59 and 09:00. Patients discharged out of hours had higher in-hospital mortality [relative risk (95% CI) 1.39 (1.24, 1.57) p < 0.0001] and readmission rates [1·30 (1.19, 1.42), p < 0.001] than patients discharged in hours. Heterogeneity was high (I2 90.1% for mortality and 90.2% for readmission), resulting from differences in effect size rather than the presence of an effect.

CONCLUSIONS

Out-of-hours discharge from an ICU is strongly associated with both in-hospital death and ICU readmission. These effects persisted across all definitions of "out of hours" and across healthcare systems in different geographical locations. Whether these increases in mortality and readmission result from patient differences, differences in care, or a combination remains unclear.

The association between outcome-based quality indicators for intensive care units

Purpose

To assess and improve the effectiveness of ICU care, in-hospital mortality rates are often used as principal quality indicator for benchmarking purposes. Two other often used, easily quantifiable, quality indicators to assess the efficiency of ICU care are based on readmission to the ICU and ICU length of stay. Our aim was to examine whether there is an association between case-mix adjusted outcome-based quality indicators in the general ICU population as well as within specific subgroups.

Materials and methods

We included patients admitted in 2015 of all Dutch ICUs. We derived the standardized in-hospital mortality ratio (SMR); the standardized readmission ratio (SRR); and the standardized length of stay ratio (SLOSR). We expressed association through Pearson’s correlation coefficients.

Results

The SMR ranged from 0.6 to 1.5; the SRR ranged from 0.7 to 2.1; and the SLOSR ranged from 0.7 to 1.3. For the total ICU population we found no significant associations. We found a positive, non-significant, association between SMR and SLOSR for admissions with low-mortality risk, (r = 0.25; p = 0.024), and a negative association between these indicators for admissions with high-mortality risk (r = -0.49; p<0.001).

Conclusion

Overall, we found no association at ICU population level. Differential associations were found between performance on mortality and length of stay within different risk strata. We recommend users of quality information to take these three outcome indicators into account when benchmarking ICUs as they capture different aspects of ICU performance. Furthermore, we suggest to report quality indicators for patient subgroups.

Impact on mortality of prompt admission to critical care for deteriorating ward patients: an instrumental variable analysis using critical care bed strain

PURPOSE

To estimate the effect of prompt admission to critical care on mortality for deteriorating ward patients.

METHODS

We performed a prospective cohort study of consecutive ward patients assessed for critical care. Prompt admissions (within 4 h of assessment) were compared to a 'watchful waiting' cohort. We used critical care strain (bed occupancy) as a natural randomisation event that would predict prompt transfer to critical care. Strain was classified as low, medium or high (2+, 1 or 0 empty beds). This instrumental variable (IV) analysis was repeated for the subgroup of referrals with a recommendation for critical care once assessed. Risk-adjusted 90-day survival models were also constructed.

RESULTS

A total of 12,380 patients from 48 hospitals were available for analysis. There were 2411 (19%) prompt admissions (median delay 1 h, IQR 1-2) and 9969 (81%) controls; 1990 (20%) controls were admitted later (median delay 11 h, IQR 6-26). Prompt admissions were less frequent (p < 0.0001) as strain increased from low (22%), to medium (15%) to high (9%); the median delay to admission was 3, 4 and 5 h respectively. In the IV analysis, prompt admission reduced 90-day mortality by 7.4% (95% CI 1.7-18.5%, p = 0.117) overall, and 16.2% (95% CI 1.1-31.3%, p = 0.036) for those recommended for critical care. In the risk-adjust survival model, 90-day mortality was similar.

CONCLUSION

After allowing for unobserved prognostic differences between the groups, we find that prompt admission to critical care leads to lower 90-day mortality for patients assessed and recommended to critical care.

Association between afterhours admission to the intensive care unit, strained capacity, and mortality: a retrospective cohort study

Background

Admission to the intensive care unit (ICU) outside daytime hours has been shown to be variably associated with increased morbidity and mortality. We aimed to describe the characteristics and outcomes of patients admitted to the ICU afterhours (22:00–06:59 h) in a large Canadian health region. We further hypothesized that the association between afterhours admission and mortality would be modified by indicators of strained ICU capacity.

Methods

This is a population-based cohort study of 12,265 adults admitted to nine ICUs in Alberta from June 2012 to December 2014. We used a path-analysis modeling strategy and mixed-effects multivariate regression analysis to evaluate direct and integrated associations (mediated through Acute Physiology and Chronic Health Evaluation (APACHE) II score) between afterhours admission (22:00–06:59 h) and ICU mortality. Further analysis examined the effects of strained ICU capacity and varied definitions of afterhours and weekend admissions. ICU occupancy ≥ 90% or clustering of admissions (≥ 0.15, defined as number of admissions 2 h before or after the index admission, divided by the number of ICU beds) were used as indicators of strained capacity.

Results

Of 12,265 admissions, 34.7% (n = 4251) occurred afterhours. The proportion of afterhours admissions varied amongst ICUs (range 26.7–37.8%). Patients admitted afterhours were younger (median (IQR) 58 (44–70) vs 60 (47–70) years, p < 0.0001), more likely to have a medical diagnosis (75.9% vs 72.1%, p < 0.0001), and had higher APACHE II scores (20.9 (8.6) vs 19.9 (8.3), p < 0.0001). Crude ICU mortality was greater for those admitted afterhours (15.9% vs 14.1%, p = 0.007), but following multivariate adjustment there was no direct or integrated effect on ICU mortality (odds ratio (OR) 1.024; 95% confidence interval (CI) 0.923–1.135, p = 0.658). Furthermore, direct and integrated analysis showed no association of afterhours admission and hospital mortality (p = 0.90) or hospital length of stay (LOS) (p = 0.27), although ICU LOS was shorter (p = 0.049). Early-morning admission (00:00–06:59 h) with ICU occupancy ≥ 90% was associated with short-term (≤ 7 days) and all-cause ICU mortality.

Conclusions

One-third of critically ill patients are admitted to the ICU afterhours. Afterhours ICU admission was not associated with greater mortality risk in most circumstances but was sensitive to strained ICU capacity.

Electronic supplementary material

The online version of this article (10.1186/s13054-018-2027-8) contains supplementary material, which is available to authorized users.

Prognostic impact of the time of admission and discharge from the intensive care unit

Objective

To determine the impact of the day and time of admission and discharge from the intensive care unit on mortality.

Methods

Prospective observational study that included patients admitted to the intensive care unit of the Hospital Maciel in Montevideo between April and November 2014.

Results

We analyzed 325 patients with an average age of 55 (36 - 71) years and a SAPS II value of 43 (29 - 58) points. No differences were found in the mortality of patients in the intensive care unit when time of admission (35% on the weekend versus 31% on weekdays, p = ns) or the hour of entry (35% at night versus 31% in the daytime, p = ns) were compared. The time of discharge was associated with higher hospital mortality rates (57% for weekend discharges versus 14% for weekday discharges, p = 0.000). The factors independently associated with hospital mortality after discharge from the intensive care unit were age > 50 years (OR 2.4, 95%CI, 1.1 - 5.4) and weekend discharge (OR 7.7, 95%CI, 3.8-15.6).

Conclusion

This study identified the time of discharge from the intensive care unit as a factor that was independently associated with hospital mortality.

Development and implementation of a risk identification tool to facilitate critical care transitions for high-risk surgical patients

Quality problem

Patients recently discharged from the intensive care unit (ICU) are at high risk for clinical deterioration.

Initial assessment

Unreliable and incomplete handoffs of complex patients contributed to preventable ICU readmissions. Respiratory decompensation was responsible for four times as many readmissions as other causes.

Choice of solution

Form a multidisciplinary team to address care coordination surrounding the transfer of patients from the ICU to the surgical ward.

Implementation

A quality improvement intervention incorporating verbal handoffs, time-sensitive patient evaluations and visual cues was piloted over a 1-year period in consecutive high-risk surgical patients discharged from the ICU. Process metrics and clinical outcomes were compared to historical controls.

Evaluation

The intervention brought the primary team and respiratory therapists to the bedside for a baseline examination within 60 min of ward arrival. Stakeholders viewed the intervention as such a valuable adjunct to patient care that the intervention has become a standard of care. While not significant, in a comparatively older and sicker intervention population, the rate of readmissions due to respiratory decompensation was 12.5%, while 35.0% in the control group (P = 0.28).

Lessons learned

The implementation of this ICU transition protocol is feasible and internationally applicable, and results in improved care coordination and communication for a high-risk group of patients.

Workload and severity of illness of patients on intensive care units with available intermediate care units: a multicenter cohort study

BACKGROUND

Intermediate Care Units (IMCU) are established in many hospitals to better match the requirements of patient care with respect to their personnel, equipment and other resources. This should relieve Intensive Care Unit (ICU) capacities for more severely ill patients and reduce readmissions to ICU. This study was conducted to investigate the effects of IMCU use on ICU populations.

METHODS

This is a retrospective analysis of the German National Registry of Intensive Care from the years 2000 to 2010.

RESULTS

We included 39 ICUs with high and 11 ICUs with low IMCU use. Patients in ICUs with high IMCU use were younger (mean age [high vs. low]: 60.5 vs. 64.5 years, P<0.001), while the severity of illness was higher (percentage of ventilated patients during ICU stay [high vs. low ICMU use]: 67.2% vs. 40.2%, P<0.001; patients ventilated >24 hours: 22% vs. 18%, P<0.001; mean therapeutic intervention scoring system-28 (TISS-28) score: 25.7 vs. 23.3, P<0.001). Readmission rates to ICU did not differ between ICU groups ([high vs. low]: 4.5% vs. 4.4%, P=0.25). ICUs with high IMCU use discharged 90.3% of all patients who were discharged to the IMCU or general ward between the regular workday hours of 06:00 and 14:59, while ICUs with low IMCU use discharged 83.8% of all patients discharged to the general ward in the same time period.

CONCLUSIONS

The use of IMCUs influences resource utilization of ICUs. Severity of illness and workload was higher in ICUs with high IMCU and more scheduled discharges occurred during the main working hours while readmission rates were similar.

Impossible decision? An investigation of risk trade-offs in the intensive care unit

In the intensive care unit (ICU), clinicians must often make risk trade-offs on patient care. For example, on deciding whether to discharge a patient before they have fully recovered in order to create a bed for another, sicker, patient. When misjudged, these decisions can negatively influence patient outcomes: yet it can be difficult, if not impossible, for clinicians to evaluate with certainty the safest course of action. Using a vignette-based interview methodology, a naturalistic decision-making approach was utilised to study this phenomena. The decision preferences of ICU clinicians (n = 24) for two common risk trade-off scenarios were investigated. Qualitative analysis revealed the sample of clinicians to reach different, and sometimes oppositional, decision preferences. These practice variations emerged from differing analyses of risk, how decisions were 'framed' (e.g. philosophies on care), past experiences, and perceptions of group and organisational norms. Implications for patient safety and clinical decision-making are discussed. Practitioner Summary: Physicians managing ICUs have to make rapid decisions with incomplete information and suboptimal resources. A qualitative vignette-based interview study examined how such decisions are made. We found physicians used a heterogeneous mixture of risk assessments, factual knowledge and prior experience to make judgements, which leads to potential for inconsistent decision-making.

A Generic Simulation Model of the Relative Cost-Effectiveness of ICU Versus Step-Down (IMCU) Expansion

BACKGROUND

Many jurisdictions are facing increased demand for intensive care. There are two long-term investment options: intensive care unit (ICU) versus step-down or intermediate care unit (IMCU) capacity expansion. Relative cost-effectiveness of the two investment strategies with regard to patient lives saved has not been studied to date.

METHODS

We expand a generic system dynamics simulation model of emergency patient flow in a typical hospital, populated with empirical evidence found in the medical and hospital administration literature, to estimate the long-term effects of expanding ICU versus IMCU beds on patient lives saved under a common assumption of 2.1% annual increase in hospital arrivals. Two alternative policies of expanding ICU by two beds versus introducing a two-bed IMCU are compared over a ten-year simulation period. Russel equation is used to calculate total cost of patients' hospitalization. Using two possible values for the ratio of ICU to IMCU cost per inpatient day and four possible values for the percentage of patients transferred from ICU to IMCU found in the literature, nine scenarios are compared against the baseline scenario of no capacity expansion.

RESULTS

Expanding ICU capacity by two beds is demonstrated as the most cost-effective scenario with an incremental cost-effectiveness ratio of 3684 (US $) per life saved against the baseline scenario. Sensitivity analyses on the mortality rate of patients in IMCU, direct transfer of IMCU-destined patients to the ward upon completing required IMCU length of stay in the ICU, admission of IMCU patient to ICU, adding two ward beds, and changes in hospital size do not change the superiority of ICU expansion over other scenarios.

CONCLUSIONS

In terms of operational costs, ICU beds are more cost effective for saving patients than IMCU beds. However, capital costs of setting up ICU versus IMCU beds should be considered for a complete economic analysis.

National Early Warning Score (NEWS) at ICU discharge can predict early clinical deterioration after ICU transfer

OBJECTIVE

This study aims to determine the ability of the National Early Warning Score at ICU discharge (NEWS_dc) to predict the development of clinical deterioration within 24h.

METHODS

A prospective observational study was conducted. The NEWS was immediately recorded before discharge (NEWS_dc). The development of early clinical deterioration was defined as acute respiratory failure or circulatory shock within 24h of ICU discharge. The discrimination of NEWS_dc and the best cut off value of NEWS_dc to predict the early clinical deterioration was determined.

RESULTS

Data were collected from 440 patients. The incidence of early clinical deterioration after ICU discharge was 14.8%. NEWS_dc was an independent predictor for early clinical deterioration after ICU discharge (OR 2.54; 95% CI 1.98-3.26; P<0.001). The AUROC of NEWS_dc was 0.92±0.01 (95% CI 0.89-0.94, P<0.001). A NEWS_dc>7 showed a sensitivity of 93.6% and a specificity of 82.2% to detect an early clinical deterioration after ICU discharge.

CONCLUSION

Among critically ill patients who were discharged from ICU, a NEWS_dc>7 showed the best sensitivity and specificity to detect early clinical deterioration 24h after ICU discharge.

Nurses' experience of the transfer of ICU patients to general wards: A great responsibility and a huge challenge

AIMS AND OBJECTIVES

The aim of the study was to describe nurses' experiences of patients' transition from ICUs to general wards and their suggestions for improvements.

BACKGROUND

In the ICU, the most seriously ill patients with life-threatening conditions and multiple organ dysfunction syndromes are cared for and carefully monitored by specially trained professionals using advanced techniques for the prevention of failure of vital functions. The transfer of ICU patients to general wards means a change from a high to a lower level, including the loss of one-to-one nursing and a reduction of visible monitoring equipment and general close attention.

DESIGN

A qualitative descriptive design.

METHODS

Eight nurses from three different inpatient units in Norway, five from a university hospital and three from a local hospital were selected through a convenience sample. Interviews with open questions were conducted, and qualitative content analysis was used to explore the data.

RESULTS

Nurses' experiences were described in one main category: ICU patients' transition-a great responsibility and a huge challenge, and two generic categories: (i) a challenging transition for nurses, patients and relatives and (ii) dialogue and competencies as tools for improvement, with six subcategories.

CONCLUSION

A number of factors affected patient care, such as poor cooperation, communication, reporting, expertise and clinical gaze. It was clear that the general wards had major challenges, and a number of improvements were suggested.

RELEVANCE TO CLINICAL PRACTICE

This study shows that there is still a gap between the ICU and general wards and that nurses continue to struggle with this. It is therefore important that the managers responsible for the quality of care together with the professionals take seriously the criticism in the present and previous studies and work towards a safe transition for patients.

Quality Indicators Compliance Survey in Indian Intensive Care Units

Context:

The quality of health care and outcomes of Intensive Care Unit (ICU) have been a major subject of discussion in the past decade. Quality indicators in ICUs maintain an order of uniformity and standard care of delivery across ICUs.

Aims:

In this study, we tried to analyze the percentage compliance of quality indicators in ICU across our country.

Methods:

Four hundred complete questionnaire forms were collected in two stages by means of conducting a survey and through email responses to the survey questionnaire. Data were tabulated and evaluated in percentage responses.

Results:

Monitoring of infection control measures such as hand hygiene (77%), monitoring of ICU-acquired infections (>75%), and quality and policy measures (>70%) were promising. Improvements are required in following end-of-life pathways (52%) and staffing patterns in ICU. ICU discharge timings (41%), standardized mortality ratio monitoring (39%), and multidisciplinary rounds (58%) in ICUs are few areas we need to develop further.

Conclusion:

The future of critical care looks promising with growing number of trained intensivists and hospitals functioning with an average ICU bed strength of 30–40. Such surveys need to be performed regularly to improve the patient care and safety across ICUs.

Intensive care discharge delay is associated with increased hospital length of stay: A multicentre prospective observational study

BACKGROUND

Some patients experience a delayed discharge from the intensive care unit (ICU) where the intended and actual discharge times do not coincide. The clinical implications of this remain unclear.

OBJECTIVE

To determine the incidence and duration of delayed ICU discharge, identify the reasons for delay and evaluate the clinical consequences.

METHODS

Prospective multi-centre observational study involving five ICUs over a 3-month period. Delay in discharge was defined as >6 hours from the planned discharge time. The primary outcome measure was hospital length stay after ICU discharge decision. Secondary outcome measures included ICU discharge after-hours, incidence of delirium, survival to hospital discharge, discharge destination, the incidence of ICU acquired infections, revocation of ICU discharge decision, unplanned readmissions to ICU within 72 hours, review of patients admitting team after ICU discharge decision.

RESULTS

A total of 955 out of 1118 patients discharged were included in analysis. 49.9% of the patients discharge was delayed. The most common reason (74%) for delay in discharge was non-availability of ward bed. The median duration of the delay was 24 hours. On univariable analysis, the duration of hospital stay from the time of ICU discharge decision was significantly higher in patients who had ICU discharge delay (Median days-5 vs 6; p = 0.003). After-hours discharge was higher in patients whose discharge was delayed (34% Vs 10%; p<0.001). There was no statistically significant difference in the other secondary outcomes analysed. Multivariable analysis adjusting for known confounders revealed delayed ICU discharge was independently associated with increased hospital length of stay.

CONCLUSION

Half of all ICU patients experienced a delay in ICU discharge. Delayed discharge was associated with increased hospital length of stay.

Association between time of discharge from ICU and hospital mortality: a systematic review and meta-analysis

Background

Epidemiological studies have provided inconsistent results on whether intensive care unit (ICU) discharge at night and on weekends is associated with an increased risk of mortality. This systematic review and meta-analysis aimed to determine whether ICU discharge time was associated with hospital mortality.

Methods

The PubMed, Embase, and Scopus databases were searched to identify cohort studies that investigated the effects of discharge from the ICU on weekends and at night on hospital mortality, with adjustments for the disease severity at ICU admission or discharge. The primary meta-analysis focused on the association between nighttime ICU discharge and hospital mortality. The secondary meta-analysis examined the association between weekend ICU discharge and hospital mortality. Odds ratios (ORs) with 95% confidence intervals (CIs) were pooled using a random-effects model.

Results

We included 14 studies that assessed outcomes for nighttime versus daytime discharges among 953,312 individuals. Of these 14 studies, 5 evaluated outcomes for weekend versus weekday discharges (n = 70,883). The adjusted OR for hospital mortality was significantly higher among patients discharged during the nighttime, compared to patients discharged during the daytime (OR 1.31, 95% CI 1.25–1.38, P < 0.0001), and the studies exhibited low heterogeneity (I² = 33.8%, P = 0.105). There was no significant difference in the adjusted ORs for hospital mortality between patients discharged during the weekend or on weekdays (OR 1.03, 95% CI 0.88–1.21, P = 0.68), although there was significant heterogeneity between the studies in the weekday/weekend analysis (I² = 72.5%, P = 0.006).

Conclusions

Nighttime ICU discharge is associated with an increased risk of hospital mortality, while weekend ICU discharge is not. Given the methodological limitations and heterogeneity among the included studies, these conclusions should be interpreted with caution, and should be tested in further studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-016-1569-x) contains supplementary material, which is available to authorized users.

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

RATIONALE, AIMS AND OBJECTIVES

The aim of this study was to analyse the effects of the follow-up programme implemented by the Asan Medical Center Medical Emergency Team (MET).

METHOD

A quasi-experimental pre-post intervention design was used, retrospectively reviewed. The follow-up programme includes respiratory care, regular visits and communication between the attending doctors and MET nurse for patients discharged from the medical intensive care unit (MICU) to the general ward. This programme has been implemented since February 2013. Outcomes of patients before and at 1 year after the introduction of the programme were retrospectively reviewed.

RESULTS

A total of 1229 patients were enrolled and divided two groups (Before, n = 624; After the introduction of the programme, n = 625). Forty-six patients (3.7%) were readmitted to the ICU within 72 hours, and there was no significant difference found between the two groups (3.7% versus 3.7%, P = 0.996). Respiratory distress was the most common reason for readmission (67.4%). Cardiac arrest developed in four (0.6%) Before patients; whereas, no cardiac arrest occurred in the After group (0.0%, P = 0.062) cases. A total of 223 patients were discharged to the step-down units. The SOFA (sequential organ failure assessment) score was significantly higher in the step-down unit patients than general ward patients (4.9 ± 2.8 versus 6.2 ± 3.1, P = 0.000). In the analysis restricted to patients discharged to step-down units, unplanned ICU readmissions significantly decreased in the After group (9.3% versus 2.6%, P = 0.034).

CONCLUSIONS

The implementation of the MET follow-up programme did not change the rate of ICU readmission and cardiac arrest; however, its introduction was associated with the reduced ICU readmission of the high-risk patient populations discharged to the step-down unit.

Sustaining critical care: using evidence-based simulation to evaluate ICU management policies

Intensive Care Units (ICU) are costly yet critical hospital departments that should be available to care for patients needing highly specialized critical care. Shortage of ICU beds in many regions of the world and the constant fire-fighting to make these beds available through various ICU management policies motivated this study. The paper discusses the application of a generic system dynamics model of emergency patient flow in a typical hospital, populated with empirical evidence found in the medical and hospital administration literature, to explore the dynamics of intended and unintended consequences of such ICU management policies under a natural disaster crisis scenario. ICU management policies that can be implemented by a single hospital on short notice, namely premature transfer from ICU, boarding in ward, and general ward admission control, along with their possible combinations, are modeled and their impact on managerial and health outcome measures are investigated. The main insight out of the study is that the general ward admission control policy outperforms the rest of ICU management policies under such crisis scenarios with regards to reducing total mortality, which is counter intuitive for hospital administrators as this policy is not very effective at alleviating the symptoms of the problem, namely high ED and ICU occupancy rates that are closely monitored by hospital management particularly in times of crisis. A multivariate sensitivity analysis on parameters with diverse range of values in the literature found the superiority of the general ward admission control to hold true in every scenario.

Rationing in the intensive care unit in case of full bed occupancy: a survey among intensive care unit physicians

BACKGROUND

Internationally, there is no consensus on how to best deal with admission requests in cases of full ICU bed occupancy. Knowledge about the degree of dissension and insight into the reasons for this dissension is lacking. Information about the opinion of ICU physicians can be used to improve decision-making regarding allocation of ICU resources. The aim of this study was to: Assess which factors play a role in the decision-making process regarding the admission of ICU patients; Assess the adherence to a Dutch guideline pertaining to rationing of ICU resources; Investigate factors influencing the adherence to this guideline.

METHODS

In March 2013, an online questionnaire was sent to all ICU physician members (n = 761, in 90 hospitals) of the Dutch Society for Intensive Care.

RESULTS

166 physicians (21.8 %) working in 64 different Dutch hospitals (71.1 %) completed the questionnaire. Factors associated with a patient's physical condition and quality of life were generally considered most important in admission decisions. Scenario-based adherence to the Dutch guideline "Admission request in case of full ICU bed occupancy" was found to be low (adherence rate 50.0 %). There were two main reasons for this poor compliance: unfamiliarity with the guideline and disagreement with the fundamental approach underlying the guideline.

CONCLUSIONS

Dutch ICU physicians disagree about how to deal with admission requests in cases of full ICU bed occupancy. The results of this study contribute to the discussion about the fundamental principles regarding admission of ICU patients in case of full bed occupancy.