Impact of intensive care unit discharge time on patient outcome

Impact of Introducing a PACU24 Concept on the Perioperative Outcome of Patients with Advanced Ovarian Cancer Treated with Cytoreductive Surgery

Introduction

Patients with ovarian cancer who undergo multivisceral surgery usually require intensive care monitoring postoperatively. In view of the ever-fewer numbers of high-care/intensive care beds and the introduction of fast-track treatment concepts, it is increasingly being suggested that these patients should be cared for postoperatively in 24-h Post Anesthesia Care Units (PACU24). No analyses have been carried out to date to investigate whether such a postoperative care concept might be associated with a potential increase in postoperative complications in this patient cohort.

Methods

A PACU24 unit was set up in our institution in 2015 and it has become the primary postoperative care pathway for patients with ovarian cancer who have undergone cytoreductive (debulking) surgery. A structured, retrospective analysis of data from patients treated before (control group) and after (PACU group) the introduction of this care concept was carried out, with a particular focus on postoperative complications and secondary admission to an intensive care unit where necessary.

Results

The data of 42 patients were analyzed for the PACU group and 45 patients for the control group. According to the analysis, the preoperative and surgical data of both groups were comparable (age, ASA, BMI, FIGO stage, duration of surgery, blood loss). The Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM score) as a measure for the risk of postoperative complications was higher in the PACU group (11.1% vs. 9.7%, p = 0.001). Patients in the PACU group underwent bowel resection with anastomosis significantly more often (76.3% vs. 33.3%, p < 0.001), although the extent of surgery was otherwise comparable. The total number, type and severity of postoperative complications and the duration of the overall stay in hospital did not differ between the two groups. None of the patients required secondary transfer from the PACU or normal ward to an intensive care unit (ICU).

Summary

Our data support the assumption that the care concept of transferring patients to a PACU24 represents a safe and cost-saving care pathway for the postoperative care of patients even after complex gynecological-oncological procedures.

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.

Perioperative Risk Factors for Intensive Care Unit Readmissions and Mortality After Cardiac Surgery

OBJECTIVE

The aim of this study was to identify perioperative risk factors associated with intensive care unit readmission and in-hospital death after cardiac surgery.

DESIGN

Retrospective analysis using a multivariate regression model to identify independent risk factors for intensive care unit [ICU] readmission and in-hospital mortality.

SETTING

The study was carried out in a single tertiary-care hospital.

PARTICIPANTS

This was an analysis of 2,789 adult patients.

INTERVENTIONS

All patients underwent cardiac surgery and were admitted to the intensive care unit perioperatively at the General Hospital Vienna.

MEASUREMENTS AND MAIN RESULTS

Among the 2,789 patients included in the analysis, 167 (6%) were readmitted to the intensive care unit during the same hospital stay. Preoperative risk factors associated with ICU readmission included end-stage renal failure (odds ratio [OR] 2.80, 95% CI: 1.126-6.964), arrhythmia (OR 1.59, 95% CI: 1.019-2.480), chronic obstructive pulmonary disease (OR 1.51, 95% CI: 1.018-2.237), age >80 (OR 2.55, 95% CI: 1.189-5.466), and European System for Cardiac Operative Risk Evaluation II >8 (OR 1.40, 95% CI: 1.013-1.940). Readmitted patients were more likely to die than nonreadmitted patients (OR 5.3, 95% CI: 3.284-8.558). In-hospital mortality in readmitted patients was 19.2%, whereas that in the nonreadmitted study population was 5.1%.

CONCLUSION

Preoperative risk assessment is crucial for identifying cardiac surgery patients at risk of ICU readmission and in-hospital death. The potentially modifiable risk factors pinpointed by this study call for the optimization of care before surgery and after ICU discharge.

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.

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.

A scheduling-based methodology for improving patient perceptions of quality of care in intensive care units

Research has found that hospitals with better scores on patient experience of care surveys have better patient safety records and outcomes. Therefore, targeting ways of improving patient experience of care is becoming relevant for hospitals not only for the patient health outcomes but also for the financial implications. Therefore, the goal of this paper is to develop new operation management strategies for improving patient experience of care in intensive care units (ICUs). A new scheduling-based methodology is developed that considers two of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey dimensions, doctor communication and discharge information. Two hypotheses are studied. The first hypothesis postulates that to improve doctor communication with the patient, a nurse must be present in the patient room when the doctor performs ward rounds. The second hypotheses states that to improve the patient-doctor communication of discharge information aspect, doctors must see the patient expected to be discharged early in the day. A computational study is performed to gather insights and to measure the performance of the scheduling-based methodology on a case study from an intensive care unit located in a hospital in central Texas. The results show hospital improvement in the studied dimensions of the HCAHPS survey after 1 year of the hospital adoption of the study recommendations.

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.

Preventing Early Bouncebacks to the Neurointensive Care Unit: A Retrospective Analysis and Quality Improvement Pilot

BACKGROUND

Early unplanned readmissions of "bouncebacks" to intensive care units are a healthcare quality metric and result in higher mortality and greater cost. Few studies have examined bouncebacks to the neurointensive care unit (neuro-ICU), and we sought to design and implement a quality improvement pilot to reduce that rate.

METHODS

First, we performed a retrospective chart review of 504 transfers to identify potential bounceback risk factors. Risk factors were assessed on the day of transfer by the transferring physician identifying patients as "high risk" or "low risk" for bounceback. "High-risk" patients underwent an enhanced transfer process emphasizing interdisciplinary communication and rapid assessment upon transfer during a 9-month pilot.

RESULTS

Within the retrospective cohort, 34 of 504 (4.7%) transfers required higher levels of care within 48 h. Respiratory failure and sepsis/hypotension were the most common reasons for bounceback among this group. During the intervention, 8 of 225 (3.6%) transfers bounced back, all of who were labeled "high risk." Being "high risk" was associated with a risk of bounceback (OR not calculable, p = 0.02). Aspiration risk (OR 6.9; 95% CI 1.6-30, p = 0.010) and cardiac arrhythmia (OR 7.1; 95% CI 1.6-32, p = 0.01) were independent predictors of bounceback in multivariate analysis. Bounceback rates trended downward to 2.8% in the final phase (p for trend 0.09). Eighty-five percent of providers responded that the pilot should become standard of care.

CONCLUSION

Patients at high risk for bounceback after transfer from the neuro-ICU can be identified using a simple tool. Early augmented multidisciplinary communication and care for high-risk patients may improve their management in the hospital.

[Unplanned admission or readmission to the intensive care unit : Avoidable or fateful?]

BACKGROUND

Unplanned admissions or readmissions to the intensive care unit lead to a poorer outcome and present medical, logistic and economic challenges for a clinic. How often and what are the reasons for readmission to the intensive care unit? Which strategies and guidelines to avoid readmission are recommended.

MATERIAL AND METHODS

Analysis and discussion of available studies and recommendations of national and international societies.

RESULTS

Many studies show that unplanned admissions and readmissions to the intensive care unit represent an independent risk factor for a poor outcome for patients. Different factors that increase the probability of readmission can be identified. Structural changes concerning the normal wards, intensive care unit or the clinic internal emergency service could positively effect readmission rates and/or patient outcome while other studies failed to show any effect of these arrangements.

CONCLUSION

Patient transition from the intensive care unit to a lower level of care is a critical point of time and has to be accompanied by a high quality handover. Unstable patients on normal wards have to be identified and treated as soon as possible but effects of standardized medical emergency teams are controversial.

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.

A Shift in Time Saves ……

How to cite this article: Srinivasan S. A Shift in Time Saves ……. Indian J Crit Care Med 2019;23(3):109-110.

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.

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.

Going back to the ward-transitioning care back to the ward team

Transition of care from the intensive care unit (ICU) to the ward is usually an indication of the patient's improving clinical status, but is also a time when patients are particularly vulnerable. The transition between care teams poses a higher risk of medical error, which can be mitigated by safe and complete patient handoff and medication reconciliation. ICU readmissions are associated with increased mortality as well as ICU and hospital length of stay (LOS); however tools to accurately predict ICU readmission risk are limited. While there are many mechanisms in place to carefully identify patients appropriate for transfer to the ward, the optimal timing of transfer can be affected by ICU strain, limited resources such as ICU beds, and overall hospital capacity and flow leading to suboptimal transfer times or delays in transfer. The patient and family perspectives should also be considered when planning for transfer from the ICU to the ward. During times of transition, families will meet a new care team, experience uncertainty of future care plans, and adjust to a different daily routine which can lead to increased stress and anxiety. Additionally, a subset of patients, such as those with new technology, require additional multidisciplinary support, education and care coordination which can contribute to longer hospital LOS if not addressed proactively early in the hospitalization while the patient remains in the ICU. In this review article, we describe key components of the transfer from ICU to the ward, discuss current strategies to optimize timing of patient transfers, explore strategies to partner with patients and families during the transfer process, highlight patient populations where additional considerations are needed, and identify future areas of exploration which could improve the care transition from the ICU to the ward.

Lessons learned and new directions regarding Discharge Direct from Adult Intensive Care Units Sent Home (DISH): A narrative review

Purpose

To perform a narrative review of the literature regarding the discharge of patients directly to home (DDH) from the intensive care unit, and to identify patient characteristics and clinical outcomes associated with this practice.

Methods

We searched MEDLINE and EMBASE from 1946 to present. We also manually searched the references of relevant articles. A two-step review process with three independent reviewers was used to identify relevant articles based on predetermined inclusion/exclusion criteria.

Results

Four studies were included in the final review. Two studies were retrospective and two studies were prospective that shared data from the same patient cohort. All were single center studies. Two of the four studies outlined clinical outcomes associated with DDH.

Conclusions

This study highlights the relative dearth in the literature regarding the increasingly common practice of DDH, underscores the importance of further studies in this area, and identifies future important foci of research.

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.

Investigating Immediate and Intermediate Patient Outcomes Following Transfer From the Acute Medicine Unit at the Western General Hospital, Edinburgh: A Prospective Cohort Study

BACKGROUND

An ageing population is placing increasing pressure on acute medical units (AMUs), necessitating frequent, and often inappropriate transfer of patients. We identified a gap in the literature, with similar studies relating to either cardiac or intensive care settings, with another, reporting frequency of movement and adverse outcomes in the elderly. The aim of this study was to ascertain whether patients admitted to the AMU and who are moved "out of hours" (22:00-06:59) experience adverse outcomes opposed to patients moved "within hours."

METHODS

Data was extracted from TrakCare-a unified, web-based healthcare information system- which facilitates real-time bed management processes. This prospective cohort study was carried out at the Western General Hospital (WGH), Edinburgh. The final cohort (n = 219) was split into two groups (out of hours vs. within hours) for statistical analysis. Specific sub-group analysis was used to supplement findings, with eight sub-groups, each defined by a 3-hr time frame around the 24-hr clock.

RESULTS

The final cohort after application of exclusion criteria was n = 219 (female: n = 114, median age = 76; male: n = 105, median age = 75). There was a significant difference in length of stay (LoS) between boarded and non-boarded patients who were: (1) moved out of hours (2) moved within hours (p = 0.003). Remainder of patient outcome results (readmission at 7 and 30 days respectively; mortality during admission, and at 7 and 30 days) were not statistically significant.

CONCLUSION

We revealed a significant difference in LoS between patients moved within and out with hours; the "out of hours" patient group-was significantly less than that of the "within" hours group.

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.

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.

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.

Development and implementation of a clinical pathway for cardiac surgery in the intensive care unit: Effects on protocol adherence

RATIONALE, AIMS AND OBJECTIVES

Cardiac surgery (CS) is facilitated by multiple perioperative guidelines and protocols. Use of a clinical pathway (CP) may facilitate the care of these patients.

METHODS

This is a pre-post design study in the ICU of a tertiary referral centre. A CP for CS patients in the ICU was developed by ICU-nurses and enabled them to execute proactively predefined actions in accordance with and within the preset boundaries which were part of a variance report. A tailored implementation strategy was used. Primary outcome measure was protocol adherence above 80% on the domains of blood pressure control, action on chest tube blood loss and electrolyte control within the CP.

RESULTS

In a 4-month period, 84 consecutive CP patients were included and compared with 162 matched control patients admitted in the year before implementation; 3 patients were excluded. Propensity score was used as matching parameter. CP patients were more likely to receive early adequate treatment for derangements in electrolytes (96% vs 47%, P < .001), blood pressure (90% vs 49%, P < .001) and adequate treatment for chest tube blood loss (90% vs 10%, P < .001). We found no differences in hospital and ICU LOS, ICU readmission or mortality.

CONCLUSION

Use of the CP improved postoperative ICU treatment for cardiac surgical patients. Implementation of a CP and the use of a special variance report could be a blueprint for the implementation and use of a CP in low-volume high complex surgery.

Patient transfer from the intensive care unit to a general ward

The transfer of patients from the intensive care unit (ICU) to a general ward can present several challenges for nurses. Such patients are at high risk of adverse outcomes, including readmission to the ICU, and increased nosocomial infections and mortality, with a resultant increase in hospital costs. This article explores the challenges of transferring patients from the ICU and uses evidence to examine ways to address them to ensure optimal care for a complex patient group. Transfer time, factors affecting general ward care, handover processes, recognition of deterioration and education, intensive care outreach, and the psychological factors affecting these patients are examined.

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.

Changes in Efficiency and Safety Culture After Integration of an I-PASS-Supported Handoff Process

BACKGROUND AND OBJECTIVES

Recent publications have shown improved outcomes associated with resident-to-resident handoff processes. However, the implementation of similar handoff processes for patients moving between units and teams with expansive responsibilities presents unique challenges. We sought to determine the impact of a multidisciplinary standardized handoff process on efficiency, safety culture, and satisfaction.

METHODS

A prospective improvement initiative to standardize handoffs during patient transitions from the cardiovascular ICU to the acute care unit was implemented in a university-affiliated children's hospital.

RESULTS

Time between verbal handoff and patient transfer decreased from baseline (397 ± 167 minutes) to the postintervention period (24 ± 21 minutes) (P < .01). Percentage positive scores for the handoff/transitions domain of a national culture of safety survey improved (39.8% vs 15.2% and 38.8% vs 19.6%; P = .005 and 0.03, respectively). Provider satisfaction improved related to the information conveyed (34% to 41%; P = .03), time to transfer (5% to 34%; P < .01), and overall experience (3% to 24%; P < .01). Family satisfaction improved for several questions, including: "satisfaction with the information conveyed" (42% to 70%; P = .02), "opportunities to ask questions" (46% to 74%; P < .01), and "Acute Care team's knowledgeabout my child's issues" (50% to 73%; P = .04). No differences in rates of readmission, rapid response team calls, or mortality were observed.

CONCLUSIONS

Implementation of a multidisciplinary I-PASS-supported handoff process for patients transferring from the cardiovascular ICU to the acute care unit resulted in improved transfer efficiency, safety culture scores, and satisfaction of providers and families.

Is weekend discharge associated with hospital readmission?

BACKGROUND

Although recent evidence suggests worse outcomes for patients admitted to the hospital on a weekend, the impact of weekend discharge is less understood.

METHODS

Utilizing the 2012 California Office of Statewide Health Planning and Development database, the impact of weekend discharge on 30-day hospital readmission rates for patients admitted with acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumonia (PNA) was investigated.

RESULTS

Out of 266,519 patients, 60,097 (22.5%) were discharged on a weekend. Unadjusted 30-day hospital readmission rates were similar between weekend and weekday discharges (AMI: 21.9% vs 21.9%; CHF: 15.4% vs 16.0%; PNA: 12.1% vs 12.4%). Patients discharged on a weekday had a longer length of stay and were more often discharged to a skilled nursing facility. However, in multivariable logistic regression models, weekend discharge was not associated with readmission (AMI: odds ratio [OR] 1.02 [95% CI: 0.98-1.06]; CHF: OR 0.99 [95% CI: 0.94-1.03]; PNA: OR 1.02 (95% CI: 0.98-1.07)).

CONCLUSIONS

Among patients in California with AMI, CHF, and PNA, discharge on a weekend was not associated with an increased hospital readmission rate.

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

Background

We aimed to determine the impact of nighttime discharge from the intensive care unit (ICU) to the ward on hospital mortality and readmission rates in consecutive critically ill patients admitted to five Canadian ICUs. We hypothesized that hospital mortality and readmission rates would be higher for patients discharged after hours compared with discharge during the day.

Methods

A multi-center retrospective cohort study was carried out at five hospitals in Edmonton, Canada, between July 2002 and December 2009. Nighttime discharge was defined as discharge from the ICU occurring between 07:00 pm and 07:59 am. Logistic regression analysis was used to explore the associations between nighttime discharge and outcomes.

Results

Of 19,622 patients discharged alive from the ICU, 3,505 (17.9 %) discharges occurred during nighttime. Nighttime discharge occurred more commonly among medical than surgical patients (19.9 % vs. 13.8 %, P < 0.001) and among those with more comorbid conditions, compared with daytime discharged patients. Crude hospital mortality (11.8 % versus 8.8 %, P < 0.001) was greater for nighttime discharged as compared to daytime discharged patients. In a multivariable analysis, after adjustment for comorbidities, diagnosis and source of admission, nighttime discharge remains associated with higher mortality (odds ratio [OR] 1.29; 95 % CI, 1.14 to 1.46, P < 0.001). This finding was robust in two sensitivity analyses examining discharges occurring between 00:00 am and 04:59 am (OR 1.28; 1.12–1.47; P < 0.001) and for those who died within 48 h of ICU discharge without readmission (OR 1.24; 1.07–1.42, P = 0.002). There was no difference in ICU readmission for nighttime compared with daytime discharges (7.4 % vs. 6.9 %, p = 0.26). However, rates were higher for nighttime discharges in community compared with tertiary hospitals (7.7 % vs. 5.7 %, P = 0.023).

Conclusions

In a large integrated health region, 1 in 5 ICU patients are discharged at nighttime, a factor with increasing occurrence during our study and shown to be independently associated with higher hospital mortality.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-015-1044-4) contains supplementary material, which is available to authorized users.

Time for critically ill patients to regain mobility after early mobilization in the intensive care unit and transition to a general inpatient floor

PURPOSE

The purpose of this study is to determine if patient mobility achievements in an intensive care unit (ICU) setting are sustained during subsequent phases of hospitalization, specifically after transferring to inpatient floors and on the day of hospital discharge.

MATERIALS AND METHODS

The study is an analysis of adult patients who stayed in the ICU for 48 hours or more during the second quarter of 2013. The study sample included 182 patients who transferred to a general inpatient floor after the ICU stay.

RESULTS

Patients experienced an average delay of 16 hours to regain or exceed chair level of mobility and 7 hours to regain ambulation level after transferring to an inpatient floor. One third of patients ambulated in the ICU, and those patients had significantly shorter post-ICU and hospital stays compared with patients who did not ambulate in the ICU. Delays in regaining mobility on the floor were modestly associated with initial Morse Fall Score and being male.

CONCLUSIONS

Mobility progression through the hospital course is imperative to improving patient outcomes. Study findings show the need for improvement in maintaining early ICU mobilization achievement during the crucial phase between ICU stay and hospital discharge.

The timing of discharge from the intensive care unit and subsequent mortality. A prospective, multicenter study

RATIONALE

Previous studies suggested an association between after-hours intensive care unit (ICU) discharge and increased hospital mortality. Their retrospective design and lack of correction for patient factors present at the time of discharge make this association problematic.

OBJECTIVES

To determine factors independently associated with mortality after ICU discharge.

METHODS

This was a prospective, multicenter, binational observational study involving 40 ICUs in Australia and New Zealand. Participants were consecutive adult patients discharged alive from the ICU between September 2009 and February 2010.

MEASUREMENTS AND MAIN RESULTS

We studied 10,211 patients discharged alive from the ICU. Median age was 63 years (interquartile range, 49-74), 6,224 (61%) were male, 5,707 (56%) required mechanical ventilation, and their median Acute Physiology and Chronic Health Evaluation III risk of death was 9% (interquartile range, 3-25%). A total of 8,539 (83.6%) patients were discharged in-hours (06:00-18:00) and 1,672 (16.4%) after-hours (18:00-06:00). Of these, 408 (4.8%) and 124 (7.4%), respectively, subsequently died in hospital (P < 0.001). After risk adjustment for markers of illness severity at time of ICU discharge including limitations of medical therapy (LOMT) orders, the time of discharge was no longer a significant predictor of mortality. The presence of a LOMT order was the strongest predictor of death (odds ratio, 35.4; 95% confidence interval, 27.5-45.6).

CONCLUSIONS

In this large, prospective, multicenter, binational observational study, we found that patient status at ICU discharge, particularly the presence of LOMT orders, was the chief predictor of hospital survival. In contrast to previous studies, the timing of discharge did not have an independent association with mortality.

A 24-Hour Postintensive Care Unit Transition-of-Care Model Shortens Hospital Stay

BACKGROUND

Patients discharged early from the medical intensive care unit (MICU) are at risk of deterioration, MICU readmission, and increased mortality. An earlier discharge to a medical ward is desirable to reduce costs but it may adversely affect outcomes. To address this problem, a new model for the MICU transition of care was implemented at our academic center: The MICU team continued to manage all patients transferred from the MICU to the medical ward for at least 24 hours.

METHODS

Data were collected for all MICU patients admitted 1 year before and 1 year after the intervention. Hospital length of stay (LOS) after transfer from the MICU, readmission rate, and mortality rate were compared before and after the intervention. A nonparsimonious propensity model based on 30 factors was used to identify matched preintervention and postintervention cohorts.

RESULTS

A total of 618 of the 848 patients admitted to the MICU were transferred to medical ward during the year prior to the implementation of the new model, and 600 of the 883 patients were transferred during the following year. Pre- and postintervention cohorts were well matched (n = 483 patients in each group). Poisson regression analysis showed a decrease in the hospital LOS after MICU transfer by 1.17 days (P < .001) without a significant change in adjusted mortality (lower by 1.9%, P = .181) and MICU readmission rates (lower by 2%, P = .264).

CONCLUSION

A new model for the post-MICU transition of care, with the MICU team continuing to manage all patients transferred to the medical ward for at least 24 hours, significantly decreased duration of hospital stay after MICU transfer without affecting MICU readmission and mortality rate. The implementation of this model may lower medical costs and make transition of care safer without adverse outcomes.

Readmission to the Intensive Care Unit in Times of Minimally Invasive Cardiac Surgery: Does Size Matter?

<p><b>Objectives:</b> It is well known that patients who undergo readmission to an intensive care unit (ICU) after cardiac surgery face an increased risk of morbidity and mortality. The present study sought to evaluate whether less invasive procedures might be associated with a reduction of this economically as well as individually important problem. The role of the quantity of ICU and intermediate care (IMC) beds was investigated as well.</p><p><b>Methods:</b> Altogether, we reviewed 5,333 patients who underwent cardiac surgery in our department between 2005 and 2010. The incidence of and reasons for readmission were determined with regard to individual subgroups, particularly comparing minimally invasive procedures with conventional strategies.</p><p><b>Results:</b> A total of 5,132 patients were primarily discharged from the ICU. Out of this group, 293 patients were readmitted to the ICU at least once. After readmission, the average length of stay in the hospital was 21.9 � 11.3 days compared to 12.8 � 5.0 days in all other patients. Comparing the readmission rate in separate years, it was evident that this rate decreased with a growing ICU and IMC capacity. In patients who underwent less invasive cardiac surgery (ie, minimally invasive cardiac surgery, off-pump coronary artery bypass grafting), the readmission rates were significantly lower than in the entirety of patients studied.</p><p><b>Conclusion:</b> Readmission to the ICU after cardiac surgery is associated with impaired outcome. Extended resources in terms of ICU and IMC capacity may positively influence this problem by decreasing the number of readmissions. Modern surgical strategies with less invasive procedures may be associated with a reduced incidence of readmission as well.</p>

A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care

Introduction

We sought to derive literature-based summary estimates of readmission to the ICU and hospital mortality among patients discharged alive from the ICU.

Methods

We searched MEDLINE, Embase, CINAHL and the Cochrane Central Register of Controlled Trials from inception to March 2013, as well as the reference lists in the publications of the included studies. We selected cohort studies of ICU discharge prognostic factors that in which readmission to the ICU or hospital mortality among patients discharged alive from the ICU was reported. Two reviewers independently abstracted the number of patients readmitted to the ICU and hospital deaths among patients discharged alive from the ICU. Fixed effects and random effects models were used to estimate the pooled cumulative incidence of ICU readmission and the pooled cumulative incidence of hospital mortality.

Results

The analysis included 58 studies (n = 2,073,170 patients). The majority of studies followed patients until hospital discharge (n = 46 studies) and reported readmission to the ICU (n = 46 studies) or hospital mortality (n = 49 studies). The cumulative incidence of ICU readmission was 4.0 readmissions (95% confidence interval (CI), 3.9 to 4.0) per 100 patient discharges using fixed effects pooling and 6.3 readmissions (95% CI, 5.6 to 6.9) per 100 patient discharges using random effects pooling. The cumulative incidence of hospital mortality was 3.3 deaths (95% CI, 3.3 to 3.3) per 100 patient discharges using fixed effects pooling and 6.8 deaths (95% CI, 6.1 to 7.6) per 100 patient discharges using random effects pooling. There was significant heterogeneity for the pooled estimates, which was partially explained by patient, institution and study methodological characteristics.

Conclusions

Using current literature estimates, for every 100 patients discharged alive from the ICU, between 4 and 6 patients on average will be readmitted to the ICU and between 3 and 7 patients on average will die prior to hospital discharge. These estimates can inform the selection of benchmarks for quality metrics of transitions of patient care between the ICU and the hospital ward.

Comparing the monitoring of patients transferred from a critical care unit to hospital wards at after-hours with day transfers: an exploratory, prospective cohort study

AIMS

To investigate possible factors related to patient monitoring to explain the higher mortality rates associated with after-hours transfers compared with daytime transfers from critical care units to the wards.

BACKGROUND

International research suggests that patients transferred from critical care units after-hours have a higher mortality rate than transfers during daytime, although the reasons remain unknown.

DESIGN

A prospective exploratory study.

METHODS

Twenty-nine patients transferred from a UK critical care unit to a ward within the same hospital after-hours for 10 weeks beginning April 2009 were compared with 29 transfers during daytime hours matched on potentially confounding characteristics. UK Critical Care Unit transfer guidelines have remained unchanged since data collection. Outcomes were as follows: (i) frequency of nursing observations; (ii) time periods from transfer to first medical review; (iii) time period from transfer to first clinical observations; (iv) frequency of transfer to an inappropriate ward; (v) delayed transfers from Critical Care Unit to ward.

RESULTS

Using Wilcoxon's Rank test (two tail) to compare paired data from the matched groups, observations were recorded significantly less frequently within the first 12 hours for after-hours transfers. Time from transfer to first clinical observations was significantly longer for after-hour transfer patients. The delay from when the patient was ready for ward care and actual transfer was also longer for the after-hours transfer group.

CONCLUSIONS

Surveillance differences, including time to the first set of observations and frequency of observations in the first 12 hours, are potential factors that may explain the differential mortality associated with after-hours transfers.

Intensive care unit bounce back in trauma patients: an analysis of unplanned returns to the intensive care unit

BACKGROUND

Discharging patients from the intensive care unit (ICU) often requires complex decision making to balance patient needs with available resources. Unplanned return to the ICU ("bounce back" [BB]) has been associated with increased resource use and worse outcomes, but few data on trauma patients are available. The goal of this study was to review ICU BB and define ICU discharge variables that may be predictive of BB.

METHODS

Adults admitted to ICU and discharged alive to a ward from November 04, 2012, to September 9, 2012 (interval with no changes in coverage), were selected from our trauma registry. Patients with unplanned return to ICU (BB cases) were matched 1:2 with controls on age, Injury Severity Score (ISS), and duration of post-ICU stay. Data were collected by chart review then analyzed with univariate and conditional multivariate techniques.

RESULTS

Of 8,835 hospital admissions, 1,971 (22.3%) were discharged alive from ICU to a ward. Eighty-eight patients (4.5%) met our criteria for BB (male, 75%; mean [SD] age, 52.9 [21.9] years; mean [SD] ISS, 23.1 [10.2]). Most (71.6%) occurred within 72 hours. Mortality for BB cases was high (19.3%). Regression analysis showed that male sex (odds ratio, 2.9; p = 0.01), Glasgow Coma Scale [GCS] score of less than 9 (odds ratio, 22.3; p < 0.01), discharge during day shift (odds ratio, 6.9; p < 0.0001), and presence of one (odds ratio, 3.5; p = 0.03), two (odds ratio, 3.8; p = 0.03), or three or more comorbidities (odds ratio, 8.4; p < 0.001) were predictive of BB.

CONCLUSION

In this study, BB rate was 4.8%, and associated mortality was 19.3%. At the time of ICU discharge, male sex, a GCS score of less than 9, higher FIO2, discharge on day shift, and presence of one or more comorbidities were the strongest predictors of BB. A multi-institutional study is needed to validate and extend these results.

LEVEL OF EVIDENCE

Epidemiologic/prognostic study, level IV.

Delay of transfer from the intensive care unit: a prospective observational study of incidence, causes, and financial impact

INTRODUCTION

A paucity of literature exists regarding delays in transfer out of the intensive care unit. We sought to analyze the incidence, causes, and costs of delayed transfer from a surgical intensive care unit (SICU).

METHODS

An IRB-approved prospective observational study was conducted from January 24, 2010, to July 31, 2010, of all 731 patients transferred from a 20-bed SICU at a large tertiary-care academic medical center. Data were collected on patients who were medically ready for transfer to the floor who remained in the SICU for at least 1 extra day. Reasons for delay were examined, and extra costs associated were estimated.

RESULTS

Transfer to the floor was delayed in 22% (n = 160) of the 731 patients transferred from the SICU. Delays ranged from 1 to 6 days (mean, 1.5 days; median, 2 days). The extra costs associated with delays were estimated to be $581,790 during the study period, or $21,547 per week. The most common reasons for delay in transfer were lack of available surgical-floor bed (71% (114 of 160)), lack of room appropriate for infectious contact precautions (18% (28 of 160)), change of primary service (Surgery to Medicine) (7% (11 of 160)), and lack of available patient attendant ("sitter" for mildly delirious patients) (3% (five of 160)). A positive association was found between the daily hospital census and the daily number of SICU beds occupied by patients delayed in transfer (Spearman rho = 0.27; P < 0.0001).

CONCLUSIONS

Delay in transfer from the SICU is common and costly. The most common reason for delay is insufficient availability of surgical-floor beds. Delay in transfer is associated with high hospital census. Further study of this problem is necessary.

A strategy to enhance the safety and efficiency of handovers of ICU patients: study protocol of the pICUp study

Background

To use intensive care unit (ICU) facilities efficiently and ensure high quality of care, an optimal patient flow is necessary. Discharging patients relieves the pressure on ICU beds but the risk of premature discharge must be managed carefully. Suboptimal patient discharge may result in ICU readmissions and in patients’ death.

The aim of this study is to obtain insight into the safety and efficiency of current ICU discharge practices and into barriers and facilitators to the implementation of effective ICU discharge interventions, and to develop an implementation strategy tailored to the barriers and facilitators identified.

Methods/design

This study exists of five phases. Phase A: analysis of routinely registered data on variation in ICU readmissions and hospital mortality after ICU discharge of all ICUs participating in the Dutch National Intensive Care Evaluation registry (n = 83). Phase B: systematic review of effective interventions aiming to improve the efficiency and safety of the ICU discharge process. Phase C: assessing the intervention adherence with a questionnaire survey among all Dutch ICUs (n = 90). Phase D: assessing barriers and facilitators to the implementation of effective ICU discharge interventions with a questionnaire survey among all Dutch intensivists (n = 700). The questionnaire will be based on barriers and facilitators identified by focus groups (n = 4) and individual interviews with professionals of ICUs and general wards and adult discharged ICU patients (n = 25 to 30). Phase E: systematic development of an implementation strategy based on the sampled data in phase A to D, and effective implementation strategies from the literature using the intervention mapping method.

Discussion

Using theory and empirical data, an implementation strategy will be developed to improve the safety and efficiency of the ICU discharge process. The developed strategy will be evaluated in a subsequent study. The knowledge obtained in this study should be used for further implementation of ICU discharge interventions, and can be used for implementation of handover interventions in other healthcare transition settings.

The association between ICU readmission rate and patient outcomes

OBJECTIVE

To examine the association between ICU readmission rates and case-mix-adjusted outcomes.

DESIGN

Retrospective cohort study of ICU admissions from 2002 to 2010.

SETTING

One hundred five ICUs at 46 United States hospitals.

PATIENTS

Of 369,129 admissions, 263,082 were first admissions that were alive at ICU discharge and candidates for readmission.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The median unit readmission rate was 5.9% (intraquartile range 5.1%-7.0%). Across all admissions, hospital mortality for patients with and without readmission was 21.3% vs. 3.6%, mean ICU stay 4.9 days vs. 3.4 days, and hospital stay 13.3 days vs. 4.5 days, respectively. We stratified ICUs according to their readmission rate: high (>7%), moderate (5%-7%), and low (<5%) rates. Observed and case-mix-adjusted hospital mortality, ICU and hospital lengths of stay were examined by readmission rate strata. Observed outcomes were much worse in the high readmission rate units. But after adjusting for patient and institutional differences, there was no association between level of unit readmission rate and case-mix-adjusted mortality. The difference between observed and predicted mortality was -0.4%, 0.4%, and -1.1%, for the high, medium, and low readmission rate strata, respectively. Additionally, the difference between observed and expected ICU length of stay was approximately zero for the three strata.

CONCLUSIONS

Patients readmitted to ICUs have increased hospital mortality and lengths of stay. After case-mix adjustment, there were no significant differences in standardized mortality or case-mix-adjusted lengths of stay between units with high readmission rates compared to units with moderate or low rates. The use of readmission as a quality measure should only be implemented if patient case-mix is taken into account.

Effects of intensivist coverage in a post-anaesthesia care unit on surgical patients' case mix and characteristics of the intensive care unit

Introduction

There is an increasing demand for intensive care in hospitals, which can lead to capacity limitations in the intensive care unit (ICU). Due to postponement of elective surgery or delayed admission of emergency patients, outcome may be negatively influenced. To optimize the admission process to intensive care, the post-anaesthesia care unit (PACU) was staffed with intensivist coverage around the clock. The aim of this study is to demonstrate the impact of the PACU on the structure of ICU-patients and the contribution to overall hospital profit in terms of changes in the case mix index for all surgical patients.

Methods

The administrative data of all surgical patients (n = 51,040) 20 months prior and 20 months after the introduction of a round-the-clock intensivist staffing of the PACU were evaluated and compared.

Results

The relative number of patients with longer length of stay (LOS) (more than seven days) in the ICU increased after the introduction of the PACU. The average monthly number of treatment days of patients staying less than 24 hours in the ICU decreased by about 50% (138.95 vs. 68.19 treatment days, P <0.005). The mean LOS in the PACU was 0.45 (± 0.41) days, compared to 0.27 (± 0.2) days prior to the implementation. The preoperative times in the hospital decreased significantly for all patients. The case mix index (CMI) per hospital day for all surgical patients was significantly higher after the introduction of a PACU: 0.286 (± 0.234) vs. 0.309 (± 0.272) P <0.001 CMI/hospital day.

Conclusions

The introduction of a PACU and the staffing with intensive care staff might shorten the hospital LOS for surgical patients. The revenues for the hospital, as determined by the case mix index of the patients per hospital day, increased after the implementation of a PACU and more patients can be treated in the same time, due to a better use of resources.

Intensive care unit readmissions in U.S. hospitals: patient characteristics, risk factors, and outcomes

OBJECTIVE

To examine which patient characteristics increase the risk for intensive care unit readmission and assess the association of readmission with case-mix adjusted mortality and resource use.

DESIGN

: Retrospective cohort study.

SETTING

Ninety-seven intensive and cardiac care units at 35 hospitals in the United States.

PATIENTS

A total of 229,375 initial intensive care unit admissions during 2001 through 2009 who met inclusion criteria.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

For patients who were discharged alive and candidates for readmission, we compared the characteristics of those with and without a readmission. A multivariable logistic regression analysis was used to identify potential patient-level characteristics that increase the risk for subsequent readmission. We also evaluated case-mix adjusted outcomes by comparing observed and predicted values of mortality and length of stay for patients with and without intensive care unit readmission. Among 229,375 first admissions that met inclusion criteria, 13,980 (6.1%) were eventually readmitted. Risk factors associated with the highest multivariate odds ratio for unit readmission included location before intensive care unit admission, age, comorbid conditions, diagnosis, intensive care unit length of stay, physiologic abnormalities at intensive care discharge, and discharge to a step-down unit. After adjustment for risk factors, patients who were readmitted had a four-fold greater probability of hospital mortality and a 2.5-fold increase in hospital stay compared to patients without readmission.

CONCLUSIONS

Intensive care readmission is associated with patient factors that reflect a greater severity and complexity of illness, resulting in a higher risk for hospital mortality and a longer hospital stay. To improve patient safety, physicians should consider these risk factors when making intensive care discharge decisions. Because intensive care unit readmission correlates with more complex and severe illness, readmission rates require case-mix adjustment before they can be properly interpreted as quality measures.

A model to predict short-term death or readmission after intensive care unit discharge

OBJECTIVE

Early unplanned readmission to the intensive care unit (ICU) carries a poor prognosis, and post-ICU mortality may be related, in part, to premature ICU discharge. Our objectives were to identify independent risk factors for early post-ICU readmission or death and to construct a prediction model.

DESIGN

Retrospective analysis of a prospective database was done.

SETTING

Four ICUs of the French Outcomerea network participated.

PATIENTS

Patients were consecutive adults with ICU stay longer than 24 hours who were discharged alive to same-hospital wards without treatment-limitation decisions.

MAIN RESULTS

Of 5014 admitted patients, 3462 met our inclusion criteria. Age was 60.6 ± 17.6 years, and admission Simplified Acute Physiology Score II (SAPS II) was 35.1 ± 15.1. The rate of death or ICU readmission within 7 days after ICU discharge was 3.0%. Independent risk factors for this outcome were age, SAPS II at ICU admission, use of a central venous catheter in the ICU, Sepsis-related Organ Failure Assessment and Systemic Inflammatory Response Syndrome scores before ICU discharge, and discharge at night. The predictive model based on these variables showed good calibration. Compared with SAPS II at admission or Stability and Workload Index for Transfer at discharge, discrimination was better with our model (area under receiver operating characteristics curve, 0.74; 95% confidence interval, 0.68-0.79).

CONCLUSION

Among patients without treatment-limitation decisions and discharged alive from the ICU, 3.0% died or were readmitted within 7 days. Independent risk factors were indicators of patients' severity and discharge at night. Our prediction model should be evaluated in other ICU populations.

Redesigning the ICU nursing discharge process: a quality improvement study

PURPOSE

To evaluate the impact of a redesigned intensive care unit (ICU) nursing discharge process on ICU discharge delay, hospital mortality, and ICU readmission within 72 hours.

METHODS

A quality improvement study using a time series design and statistical process control analysis was conducted in one Australian general ICU. The primary outcome measure was hours of discharge delay per patient discharged alive per month, measured for 15 months prior to, and for 12 months after the redesigned process was implemented. The redesign process included appointing a change agent to facilitate process improvement, developing a patient handover sheet, requesting ward staff to nominate an estimated transfer time, and designing a daily ICU discharge alert sheet that included an expected date of discharge.

RESULTS

A total of 1,787 ICU discharges were included in this study, 1,001 in the 15 months before and 786 in the 12 months after the implementation of the new discharge processes. There was no difference in in-hospital mortality after discharge from ICU or ICU readmission within 72 hours during the study period. However, process improvement was demonstrated by a reduction in the average patient discharge delay time of 3.2 hours (from 4.6 hour baseline to 1.0 hours post-intervention).

CONCLUSIONS

Involving both ward and ICU staff in the redesign process may have contributed to a shared situational awareness of the problems, which led to more timely and effective ICU discharge processes. The use of a change agent, whose ongoing role involved follow-up of patients discharged from ICU, may have helped to embed the new process into practice.

Mortality associated with timing of admission to and discharge from ICU: a retrospective cohort study

Background

Although the association between mortality and admission to intensive care units (ICU) in the "after hours" (weekends and nights) has been the topic of extensive investigation, the timing of discharge from ICU and outcome has been less well investigated. The objective of this study was to assess effect of timing of admission to and discharge from ICUs and subsequent risk for death.

Methods

Adults (≥18 years) admitted to French ICUs participating in Outcomerea between January 2006 and November 2010 were included.

Results

Among the 7,380 patients included, 61% (4,481) were male, the median age was 62 (IQR, 49-75) years, and the median SAPS II score was 40 (IQR, 28-56). Admissions to ICU occurred during weekends (Saturday and Sunday) in 1,708 (23%) cases, during the night (18:00-07:59) in 3,855 (52%), and on nights and/or weekends in 4,659 (63%) cases. Among 5,992 survivors to ICU discharge, 903 (15%) were discharged on weekends, 659 (11%) at night, and 1,434 (24%) on nights and/or weekends. After controlling for a number of co-variates using logistic regression analysis, admission during the after hours was not associated with an increased risk for death. However, patients discharged from ICU on nights were at higher adjusted risk (odds ratio, 1.54; 95% confidence interval, 1.12-2.11) for death.

Conclusions

In this study, ICU discharge at night but not admission was associated with a significant increased risk for death. Further studies are needed to examine whether minimizing night time discharges from ICU may improve outcome.