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

Cost-effectiveness analysis of CTZ/TAZ for the treatment of ventilated hospital-acquired bacterial pneumonia and ventilator-associated bacterial pneumonia in Japan

BACKGROUND

Resistant bacterial infections, particularly those caused by gram-negative pathogens, are associated with high mortality and economic burdens. Ceftolozane/tazobactam demonstrated efficacy comparable to meropenem in patients with ventilated hospital-acquired bacterial pneumonia in the ASPECT-NP study. One cost-effectiveness analysis in the United States revealed that ceftolozane/tazobactam was cost effective, but no Japanese studies have been conducted. Therefore, the objective of this study was to assess the cost-effectiveness of ceftolozane/tazobactam compared to meropenem for patients with ventilated hospital-acquired bacterial pneumonia/ventilator-associated bacterial pneumonia from a health care payer perspective.

METHODS

A hybrid decision-tree Markov decision-analytic model with a 5-year time horizon were developed to estimate costs and quality-adjusted life-years and to calculate the incremental cost-effectiveness ratio associated with ceftolozane/tazobactam and meropenem in the treatment of patients with ventilated hospital-acquired bacterial pneumonia/ventilator-associated bacterial pneumonia. Clinical outcomes were based on the ASPECT-NP study, costs were based on the national fee schedule of 2022, and utilities were based on published data. One-way sensitivity analysis and probabilistic sensitivity analysis were also conducted to assess the robustness of our modeled estimates.

RESULTS

According to our base-case analysis, compared with meropenem, ceftolozane/tazobactam increased the total costs by 424,731.22 yen (£2,626.96) and increased the quality-adjusted life-years by 0.17, resulting in an incremental cost-effectiveness ratio of 2,548,738 yen (£15,763.94) per quality-adjusted life-year gained for ceftolozane/tazobactam compared with meropenem. One-way sensitivity analysis showed that although the incremental cost-effectiveness ratio remained below 5,000,000 yen (£30,925) for most of the parameters, the incremental net monetary benefit may have been less than 0 depending on the treatment efficacy outcome, especially the cure rate and mortality rate for MEPM and mortality rate for CTZ/TAZ. 53.4% of the PSA simulations demonstrated that CTZ/TAZ was more cost-effective than MEPM was.

CONCLUSION

Although incremental cost-effectiveness ratio was below ￥5,000,000 in base-case analysis, whether ceftolozane/tazobactam is a cost-effective alternative to meropenem for ventilated hospital-acquired bacterial pneumonia/ventilator-associated bacterial pneumonia in Japan remains uncertain. Future research should examine the unobserved heterogeneity across patient subgroups and decision-making settings, to characterise decision uncertainty and its consequences so as to assess whether additional research is required.

Patients' experiences of preparing for transfer from the intensive care unit to a hospital ward: A multicentre qualitative study

BACKGROUND

The transfer from an intensive care unit (ICU) to a regular ward often causes confusion and stress for patients and family members. However, little is known about the patients' perspective on preparing for the transfer.

AIM

The purpose of the study was to describe patients' experiences of preparing for transfer from an ICU to a ward.

STUDY DESIGN

Individual interviews with 14 former ICU patients from three urban hospitals in Stockholm, Sweden were conducted 3 months after hospital discharge. Qualitative content analysis was used to interpret the interview transcripts. Reporting followed the consolidated criteria for reporting qualitative research checklist.

RESULTS

The results showed that the three categories, the discharge decision, patient involvement, and practical preparations were central to the patients' experiences of preparing for the transition from the intensive care unit to the ward. The discharge decision was associated with a sense of relief, but also worry about what would happen on the ward. The patients felt that they were not involved in the decision about the discharge or the planning of their health care. To handle the situation, patients needed information about planned care and treatment. However, the information was often sparse, delivered from a clinician's perspective, and therefore not much help in preparing for transfer.

CONCLUSIONS

ICU patients experienced that they were neither involved in the process of forthcoming care nor adequately prepared for the transfer to the ward. Relevant and comprehensible information and sufficient time to prepare were needed to reduce stress and promote efficient recovery.

RELEVANCE TO CLINICAL PRACTICE

The study suggests that current transfer strategies are not optimal, and a more person-centred discharge procedure would be beneficial to support patients and family members in the transition from the ICU to the ward.

Intensive care unit readmission and unexpected death after emergency general surgery

Background

Intensive care unit (ICU) readmission and unexpected death are closely associated with increased length of hospitalization and total mortality. However, data about readmission or unexpected death after discharge from ICU in patients who have undergone emergency general surgery (EGS) is very limited.

Methods

In total, 1133 patients who underwent EGS were identified in the Multiparameter Intelligent Monitoring in Intensive Care IV (MIMIC-IV) database. Of these 1133 patients, 124 underwent readmission into the ICU or death unexpectedly after their initial discharge. The clinical characteristics of the patients were investigated. A logistic regression model was implemented for the analysis of the independent risk factors associated with ICU readmission or unexpected death. A nomogram model was established to predict the risk of ICU readmission or unexpected death within 72 h after EGS.

Results

Peripheral vascular disease and atrial fibrillation, vasopressor requirement, a higher respiratory rate or heart rate, a lower pulse oxygen saturation or a platelet count of <150 K/μL and a relatively low Glasgow coma scale score in the last 24 h before ICU discharge were independent risk factors for ICU readmission or death within 72 h. The nomogram had moderate accuracy with an area under the curve of 0.852, which had a stronger prediction power than the Stability and Workload Index for Transfer (SWIFT) score, a classic prediction model for ICU readmission risk.

Conclusions

In critically ill patients who undergo EGS, ICU readmission or unexpected death within 72 h can be predicted using a nomogram model based on eight parameters including physiological and laboratory test values in the last 24 h before discharge and comorbidities. ICU physicians should prudently assess patients to make effective discharge decisions.

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

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

Risk factors for hospital mortality in intensive care unit survivors: a retrospective cohort study

BACKGROUND

Deaths can occur after a patient has survived treatment for a serious illness in an intensive care unit (ICU). Mortality rates after leaving the ICU can be considered indicators of health care quality. This study aims to describe risk factors and mortality of surviving patients discharged from an ICU in a university hospital.

METHODS

Retrospective cohort study carried out from January 2017 to December 2018. Data on age, sex, length of hospital stay, diagnosis on admission to the ICU, hospital discharge outcome, presence of infection, and Simplified Acute Physiology Score (SAPS) III prognostic score were collected. Infected patients were considered as those being treated for an infection on discharge from the ICU. Patients were divided into survivors and non-survivors on leaving the hospital. The association between the studied variables was performed using the logistic regression model.

RESULTS

A total of 1,025 patients who survived hospitalization in the ICU were analyzed, of which 212 (20.7%) died after leaving the ICU. When separating the groups of survivors and non-survivors according to hospital outcome, the median age was higher among non-survivors. Longer hospital stays and higher SAPS III values were observed among non-survivors. In the logistic regression, the variables age, length of hospital stay, SAPS III, presence of infection, and readmission to the ICU were associated with hospital mortality.

CONCLUSIONS

Infection on ICU discharge, ICU readmission, age, length of hospital stay, and SAPS III increased risk of death in ICU survivors.

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

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

Explainable Machine-Learning Model for Prediction of In-Hospital Mortality in Septic Patients Requiring Intensive Care Unit Readmission

INTRODUCTION

Septic patients requiring intensive care unit (ICU) readmission are at high risk of mortality, but research focusing on the association of ICU readmission due to sepsis and mortality is limited. The aim of this study was to develop and validate a machine learning (ML) model for predicting in-hospital mortality in septic patients readmitted to the ICU using routinely available clinical data.

METHODS

The data used in this study were obtained from the Medical Information Mart for Intensive Care (MIMIC-IV, v1.0) database, between 2008 and 2019. The study cohort included patients with sepsis requiring ICU readmission. The data were randomly split into a training (75%) data set and a validation (25%) data set. Nine popular ML models were developed to predict mortality in septic patients readmitted to the ICU. The model with the best accuracy and area under the curve (A.C.) in the validation cohort was defined as the optimal model and was selected for further prediction studies. The SHAPELY Additive explanations (SHAP) values and Local Interpretable Model-Agnostic Explanation (LIME) methods were used to improve the interpretability of the optimal model.

RESULTS

A total of 1117 septic patients who had required ICU readmission during the study period were enrolled in the study. Of these participants, 434 (38.9%) were female, and the median (interquartile range [IQR]) age was 68.6 (58.4-79.2) years. The median (IQR) ICU interval duration was 2.60 (0.64-5.78) days. After feature selection, 31 of 47 clinical factors were ultimately chosen for use in model construction. Of the nine ML models tested, the best performance was achieved with the random forest (RF) model, with an A.C. of 0.81, an accuracy of 85% and a precision of 62% in the validation cohort. The SHAP summary analysis revealed that Glasgow Coma Scale score, urine output, blood urea nitrogen, lactate, platelet count and systolic blood pressure were the top six most important factors contributing to the RF model. Additionally, the LIME method demonstrated how the RF model works in terms of explaining risk of death prediction in septic patients requiring ICU readmission.

CONCLUSION

The ML models reported here showed a good prognostic prediction ability in septic patients requiring ICU readmission. Of the features selected, the parameters related to organ perfusion made the largest contribution to outcome prediction during ICU readmission in septic patients.

Epidemiology and Outcomes of Patients Readmitted to the Intensive Care Unit After Cardiac Intensive Care Unit Admission

Readmission to the intensive care unit (ICU) during the index hospitalization is associated with poor outcomes in medical or surgical ICU survivors. Little is known about critically ill patients with acute cardiovascular conditions cared for in a cardiac intensive care unit (CICU). We sought to describe the incidence, risk factors, and outcomes of all ICU readmissions in patients who survived to CICU discharge. We retrospectively reviewed Mayo Clinic patients from 2007 to 2015 who survived the index CICU admission and identified patients with a second ICU stay during their index hospitalization; these patients were categorized as ICU transfers (patients who went directly from the CICU to another ICU) or ICU readmissions (patients initially transferred from the CICU to the ward, and then back to an ICU). Among 9,434 CICU survivors (mean age 67 years), 138 patients (1.5%) had a second ICU stay during the index hospitalization: 60 ICU transfers (0.6%) and 78 ICU readmissions (0.8%). The most common indications for ICU readmission were respiratory failure and procedure/surgery. On multivariable modeling, respiratory failure, severe acute kidney injury, and Charlson Comorbidity Index at the time of discharge from the index ICU stay were associated with ICU readmission. Death during the first ICU readmission (n = 78) occurred in 7.7% of patients. In-hospital mortality was higher for patients with a second ICU stay. In conclusion, few CICU survivors have a second ICU stay during their index hospitalization; these patients are at a higher risk of in-hospital and 1-year mortality. Respiratory failure, severe acute kidney injury, and higher co-morbidity burden identify CICU survivors at elevated risk of ICU readmission.

Clinical and Laboratory Profile of COVID-19 Pneumonia Patients With a Complicated Post-Intensive Care Unit Hospital Course

Background

Characteristics of intensive care unit (ICU) downgrades who experience a complicated post-ICU ward course (ICU return or floor death) and the incidence of this phenomenon have not been examined in ICU survivors of coronavirus disease 2019 (COVID-19) pneumonia. The aim of the present study was to establish the rate of a complicated post-ICU ward course among survivors of COVID-19 pneumonia and describe the associated patient, ICU management, and serum biomarker characteristics. An additional aim was to compare these parameters between those who experienced a complicated post-ICU course and those who did not.

Methods

This was a retrospective study of patients who were admitted to the ICU with COVID-19 pneumonia and were downgraded to a hospital floor at the end of their initial ICU stay. Patients were divided based on a complicated or uncomplicated post-ICU course. Groups were compared with respect to relevant clinical variables. Serum biomarker levels were compared on day of ICU exit and were trended in the days preceding the downgrade. Ward stay of patients who had a complicated course was examined for notable floor events surrounding their decompensation.

Results

Eighteen out of 99 downgraded patients (18%) experienced a complicated post-ICU course, among them there were 14 returns (14%) and four deaths (4%). They had higher Charlson Comorbidity Index, higher Acute Physiology and Chronic Health Evaluation (APACHE) IV score, as well as higher D-dimer and C-reactive protein (CRP) at ICU departure. They were less likely to have received therapeutic anticoagulation and convalescent plasma during their ICU stay. On multivariable analysis, these parameters except D-dimer remained independently associated with a complicated course. Review of biomarker trends preceding ICU exit demonstrated an upward trajectory of D-dimer, CRP, and lactate dehydrogenase (LDH) in the complicated course group not mirrored by the uncomplicated course group. Examination of notable floor events leading up to decompensation revealed that in 50% the ward course was characterized by new cardiac disturbances.

Conclusions

Our rate of ward death among ICU downgrades was similar to pre-COVID data, but the rate of ICU return was higher. Complicated post-ICU course patients were exhibiting upward biomarker trends at ICU exit, and their ward stay was punctuated by acute cardiac abnormalities.

Nursing workload on the day of discharge from the intensive care unit is associated with readmission

PURPOSE

To verify whether there is an association between the Nursing Activities Score (NAS) on the day of discharge from the intensive care unit and readmission..

MATERIALS AND METHODS

A retrospective cohort study of all patients admitted to the intensive care unit of Hospital Ernesto Dornelles, Porto Alegre, Brazil, who were discharged to the ward from October 2018 to December 2019. We collected demographic and clinical variables of the patients and the Nursing Activities Scoreon the day of discharge. Patients were followed up until the day of hospital discharge or death.

RESULTS

We included 1045 patients in the final sample. One hundred eighty-eight (18.0%) patients were readmitted, in addition there were two (0.2%) unexpected deaths that occurred in the ward. The median NAS was 59.9 (50.9-67.3), which was higher in the bivariate analysis in patients who were readmitted (64.0, 55.7-71.4) than in patients who were not readmitted (58.7, 49.7-66.1) (p < 0.001). Patients with a Nursing Activities Score  ≥ 60.0 and < 60.0 had rates of readmission of 23.4% and 12.7%, respectively (p < 0.001). After multivariable adjustment, the Nursing Activities Score at discharge maintained an association with readmission. In addition, in the Cox regression, the Nursing Activities Score as a dichotomous variable was independently associated with readmission (adjusted HR 1.560; CI 1.146-2.125; p = 0.005).

CONCLUSIONS

We found that the nursing workload, assessed by the Nursing Activities Score at the time of discharge from the intensive care unit, was associated with risk of readmission..

Predicting Readmission to Intensive Care After Cardiac Surgery Within Index Hospitalization: A Systematic Review

Readmission to the cardiac intensive care unit after cardiac surgery has significant implications for both patients and healthcare providers. Identifying patients at risk of readmission potentially could improve outcomes. The objective of this systematic review was to identify risk factors and clinical prediction models for readmission within a single hospitalization to intensive care after cardiac surgery. PubMed, MEDLINE, and EMBASE databases were searched to identify candidate articles. Only studies that used multivariate analyses to identify independent predictors were included. There were 25 studies and five risk prediction models identified. The overall rate of readmission pooled across the included studies was 4.9%. In all 25 studies, in-hospital mortality and duration of hospital stay were higher in patients who experienced readmission. Recurring predictors for readmission were preoperative renal failure, age >70, diabetes, chronic obstructive pulmonary disease, preoperative left ventricular ejection fraction <30%, type and urgency of surgery, prolonged cardiopulmonary bypass time, prolonged postoperative ventilation, postoperative anemia, and neurologic dysfunction. The majority of readmissions occurred due to respiratory and cardiac complications. Four models were identified for predicting readmission, with one external validation study. As all models developed to date had limitations, further work on larger datasets is required to develop clinically useful models to identify patients at risk of readmission to the cardiac intensive care unit after cardiac surgery.

Adverse Events After Transition From ICU to Hospital Ward: A Multicenter Cohort Study

OBJECTIVES

To examine adverse events and associated factors and outcomes during transition from ICU to hospital ward (after ICU discharge).

DESIGN

Multicenter cohort study.

SETTING

Ten adult medical-surgical Canadian ICUs.

PATIENTS

Patients were those admitted to one of the 10 ICUs from July 2014 to January 2016.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Two ICU physicians independently reviewed progress and consultation notes documented in the medical record within 7 days of patient's ICU discharge date to identify and classify adverse events. The adverse event data were linked to patient characteristics and ICU and ward physician surveys collected during the larger prospective cohort study. Analyses were conducted using multivariable logistic regression. Of the 451 patients included in the study, 84 (19%) experienced an adverse event, the majority (62%) within 3 days of transfer from ICU to hospital ward. Most adverse events resulted only in symptoms (77%) and 36% were judged to be preventable. Patients with adverse events were more likely to be readmitted to the ICU (odds ratio, 5.5; 95% CI, 2.4-13.0), have a longer hospital stay (mean difference, 16.1 d; 95% CI, 8.4-23.7) or die in hospital (odds ratio, 4.6; 95% CI, 1.8-11.8) than those without an adverse event. ICU and ward physician predictions at the time of ICU discharge had low sensitivity and specificity for predicting adverse events, ICU readmissions, and hospital death.

CONCLUSIONS

Adverse events are common after ICU discharge to hospital ward and are associated with ICU readmission, increased hospital length of stay and death and are not predicted by ICU or ward physicians.

Are there opportunities to improve care as patients transition through the cancer care continuum? A scoping review protocol

INTRODUCTION

Transitions in Care (TiC) are vulnerable periods in care delivery associated with adverse events, increased cost and decreased patient satisfaction. Patients with cancer encounter many transitions during their care journey due to improved survival rates and the complexity of treatment. Collectively, improving TiC is particularly important among patients with cancer. The objective of this scoping review is to synthesise and map the existing literature regarding TiC among patients with cancer in order to explore opportunities to improve TiC among patients with cancer.

METHODS AND ANALYSIS

This scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Review Extension and the Joanna Briggs Institute methodology. The PubMed cancer filter and underlying search strategy will be tailored to each database (Embase, Cochrane, CINAHL and PsycINFO) and combined with search terms for TiC. Grey literature and references of included studies will be searched. The search will include studies published from database inception until 9 February 2020. Quantitative and qualitative studies will be included if they describe transitions between any type of healthcare provider or institution among patients with cancer. Descriptive statistics will summarise study characteristics and quantitative data of included studies. Qualitative data will be synthesised using thematic analysis.

ETHICS AND DISSEMINATION

Our objective is to synthesise and map the existing evidence; therefore, ethical approval is not required. Evidence gaps around TiC will inform a programme of research aimed to improve high-risk transitions among patients with cancer. The findings of this scoping review will be published in a peer-reviewed journal and widely presented at academic conferences. More importantly, decision makers and patients will be provided a summary of the findings, along with data from a companion study, to prioritise TiC in need of interventions to improve continuity of care for patients with cancer.

Characteristics and outcomes of patients admitted to adult intensive care units in Hong Kong: a population retrospective cohort study from 2008 to 2018

Background

Globally, mortality rates of patients admitted to the intensive care unit (ICU) have decreased over the last two decades. However, evaluations of the temporal trends in the characteristics and outcomes of ICU patients in Asia are limited. The objective of this study was to describe the characteristics and risk adjusted outcomes of all patients admitted to publicly funded ICUs in Hong Kong over a 11-year period. The secondary objective was to validate the predictive performance of Acute Physiology And Chronic Health Evaluation (APACHE) IV for ICU patients in Hong Kong.

Methods

This was an 11-year population-based retrospective study of all patients admitted to adult general (mixed medical-surgical) intensive care units in Hong Kong public hospitals. ICU patients were identified from a population electronic health record database. Prospectively collected APACHE IV data and clinical outcomes were analysed.

Results

From 1 April 2008 to 31 March 2019, there were a total of 133,858 adult ICU admissions in Hong Kong public hospitals. During this time, annual ICU admissions increased from 11,267 to 14,068, whilst hospital mortality decreased from 19.7 to 14.3%. The APACHE IV standard mortality ratio (SMR) decreased from 0.81 to 0.65 during the same period. Linear regression demonstrated that APACHE IV SMR changed by − 0.15 (95% CI − 0.18 to − 0.11) per year (Pearson’s R = − 0.951, p < 0.001). Observed median ICU length of stay was shorter than that predicted by APACHE IV (1.98 vs. 4.77, p < 0.001). C-statistic for APACHE IV to predict hospital mortality was 0.889 (95% CI 0.887 to 0.891) whilst calibration was limited (Hosmer–Lemeshow test p < 0.001).

Conclusions

Despite relatively modest per capita health expenditure, and a small number of ICU beds per population, Hong Kong consistently provides a high-quality and efficient ICU service. Number of adult ICU admissions has increased, whilst adjusted mortality has decreased over the last decade. Although APACHE IV had good discrimination for hospital mortality, it overestimated hospital mortality of critically ill patients in Hong Kong.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40560-020-00513-9.

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

BACKGROUND

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

AIMS AND OBJECTIVES

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

DESIGN

Systematic review of quantitative studies and meta-analysis.

METHODS

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

RESULTS

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

CONCLUSION

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

RELEVANCE TO CLINICAL PRACTICE

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

Mortality in severely injured patients: nearly one of five non-survivors have been already discharged alive from ICU

BACKGROUND

Most trauma patients admitted to the hospital alive and die later on, decease during the initial care in the emergency department or the intensive care unit (ICU). However, a number of patients pass away after having been discharged from the ICU during the initial hospital stay. On first sight these cases could be seen as "failure to rescue" of potentially salvageable patients. A low rate of such patients might be a potential indicator of quality for trauma care on ICUs and surgical wards.

METHODS

Retrospective analysis of the TraumaRegister DGU® with data from 2015 to 2017. Patients that died during the initial ICU stay were compared to those who were discharged from the initial ICU stay for at least 24 h but died later on.

RESULTS

A total of 82,313 trauma patients were included in the TraumaRegister DGU®. In total, 6576 patients (8.0%) died during their hospital stay. Out of those, 5481 were admitted to the ICU alive and 972 patients (17.7%) were discharged from ICU and died later on. Those were older (mean age: 77 vs. 68 years), less severely injured (mean ISS: 23.1 vs. 30.0 points) and had a longer mean ICU length of stay (10 vs. 6 days). A limitation of life-sustaining therapy due to a documented living will was present in 46.1% of all patients who died during their initial ICU stay and in 59.9% of patients who died after discharge from their initial ICU stay.

CONCLUSIONS

17.7% of all non-surviving severely injured trauma patients died within the hospital after discharge from their initial ICU treatment. Their death can partially be explained by a limitation of therapy due to a living will. In conclusion, the rate of such late deaths may partially represent patients that died of potentially avoidable or treatable complications.

A Comprehensive Review of the Outcome for Patients Readmitted to the ICU Following Trauma and Strategies to Decrease Readmission Rates

In recent years, there has been an emphasis on evaluating the outcomes of patients who have experienced an intensive care unit (ICU) readmission. This may in part be due to the Patient Protection and Affordable Care Act's Hospital Readmission Reduction Program which imposes financial sanctions on hospitals who have excessive readmission rates, informally known as bounceback rates. The financial cost associated with avoidable bounceback combined with the potentially preventable expenses can result in unnecessary financial strain. Within the hospital readmissions, there is a subset pertaining to unplanned readmission to the ICU. Although there have been studies regarding ICU bounceback, there are limited studies regarding ICU bounceback of trauma patients and even fewer proven strategies. Although many studies have concluded that respiratory complications were the most common factor influencing ICU readmissions, there is inconclusive evidence in terms of a broadly applicable strategy that would facilitate management of these patients. The purpose of this review is to highlight the outcomes of patients readmitted to the ICU and to provide an overview of possible strategies to aid in decreasing ICU readmission rates.

Subjective score and outcomes after discharge from the intensive care unit: a prospective observational study

Objective

Intensive care unit (ICU) discharge is a decision process that is usually performed subjectively. We evaluated whether a subjective score (Sabadell score) is associated with hospital outcomes.

Methods

We conducted a prospective cohort study from August 2014 to May 2015 at a tertiary-care private hospital in Brazil. We analyzed 425 patients who were discharged alive from the ICU to the wards. We used univariate and multivariate analysis to identify risk factors associated with a composite endpoint of worse outcomes (later ICU readmission or ward death) during the same hospitalization.

Results

Forty-three patients (10.1%) were readmitted after ICU discharge, and 19 died in the ward. Compared with patients with successful outcomes, those with the composite endpoint were older and more severely ill, had a nonsurgical reason for hospitalization, more frequently came from the ward, were less frequently independent during daily activities, had sepsis, had higher C-reactive protein concentrations at ICU admission, and had higher Sabadell scores at discharge. The multivariate analysis showed that sepsis and the Sabadell score were independently and significantly associated with worse outcomes.

Conclusion

Sepsis at admission and the Sabadell score were predictors of worse hospital outcomes. The Sabadell score might be a promising predictive tool.

[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.

Readmission and hospital mortality after ICU discharge of critically ill cancer patients

Background

Intensive care unit (ICU) readmission is generally associated with increased hospital stays and increased mortality. However, there are limited data on ICU readmission in critically ill cancer patients.

Method

We conducted a retrospective cohort study based on the prospective registry of all critically ill cancer patients admitted to the oncology medical ICU between January 2012 and December 2013. After excluding patients who were discharged to another hospital or decided to end-of-life care, we divided the enrolled patients into four groups according to the time period from ICU discharge to unexpected events (ICU readmission or ward death) as follows: no (without ICU readmission or death, n = 456), early (within 2 days, n = 42), intermediate (between 2 and 7 days, n = 64), and late event groups (after 7 days of index ICU discharge, n = 129). The independent risk factors associated with ICU readmission or unexpected death after ICU discharge were also analyzed using multinomial logistic regression model.

Results

There were no differences in the reasons for ICU readmission across the groups. ICU mortality did not differ among the groups, but hospital mortality was significantly higher in the late event group than in the early event group. Mechanical ventilation during ICU stay, tachycardia, decreased mental status, and thrombocytopenia on the day of index ICU discharge increased the risk of early ICU readmission or unexpected ward death, while admission through the emergency room and sepsis and respiratory failure as the reasons for index ICU admission were associated with increased risk of late readmission or unexpected ward death. Interestingly, recent chemotherapy within 4 weeks before index ICU admission was inversely associated with the risk of late readmission or unexpected ward death.

Conclusion

In critically ill cancer patients, patient characteristics predicting ICU readmission or unexpected ward death were different according to the time period between index ICU discharge and the events.

The Boarding Patient: Effects of ICU and Hospital Occupancy Surges on Patient Flow

Patients admitted to a hospital's intensive care unit (ICU) often endure prolonged boarding within the ICU following receipt of care, unnecessarily occupying a critical care bed, and thereby delaying admission for other incoming patients due to bed shortage. Using patient-level data over two years at two major academic medical centers, we estimate the impact of ICU and ward occupancy levels on ICU length of stay (LOS), and test whether simultaneous "surge occupancy" in both areas impacts overall ICU length of stay. In contrast to prior studies that only measure total LOS, we split LOS into two individual periods based on physician requests for bed transfers. We find that "service time" (when critically ill patients are stabilized and treated) is unaffected by occupancy levels. However, the less essential "boarding time" (when patients wait to exit the ICU) is accelerated during periods of high ICU occupancy and, conversely, prolonged when hospital ward occupancy levels are high. When the ICU and wards simultaneously encounter bed occupancies in the top quartile of historical levels-which occurs 5% of the time-ICU boarding increases by 22% compared to when both areas experience their lowest utilization, suggesting that ward bed availability dominates efforts to accelerate ICU discharges to free up ICU beds. We find no adverse effects of high occupancy levels on ICU bouncebacks, in-hospital deaths, or 30-day hospital readmissions, which supports our finding that the largely discretionary boarding period fluctuates with changing bed occupancy levels.

Identifying essential elements to include in Intensive Care Unit to hospital ward transfer summaries: A consensus methodology

PURPOSE

Transitions of care from the intensive care unit (ICU) to a hospital ward are high risk and contingent on effective communication. We sought to identify essential information elements to be included in an ICU to hospital ward transfer summary tool, and describe tool functionality and composition perceived to be important.

MATERIALS AND METHODS

A panel of 13 clinicians representing ICU and hospital ward providers used a modified Delphi process to iteratively review and rate unique information elements identified from existing ICU transfer tools through three rounds of review (two remote and one in person). Qualitative content analysis was conducted on transcribed audio recordings of the workshop to characterize tool functionality and composition.

RESULTS

A total of 141 unique information elements were reviewed of which 63 were identified by panelists as essential. Qualitative content analyses of panelist discussions identified three themes related to how information elements should be considered when developing an ICU transfer summary tool: 1) Flexibility, 2) Usability, and 3) Accountability.

CONCLUSION

We identified 63 distinct information elements identified as essential for inclusion in an ICU transfer summary tool to facilitate communication between providers during the transition of patient care from the ICU to a hospital ward.

Administrator Perspectives on ICU-to-Ward Transfers and Content Contained in Existing Transfer Tools: a Cross-sectional Survey

BACKGROUND

The transfer of critically ill patients from the intensive care unit (ICU) to hospital ward is challenging. Shortcomings in the delivery of care for patients transferred from the ICU have been associated with higher healthcare costs and poor satisfaction with care. Little is known about how hospital ward providers, who accept care of these patients, perceive current transfer practices nor which aspects of transfer they perceive as needing improvement.

OBJECTIVE

To compare ICU and ward administrator perspectives regarding ICU-to-ward transfer practices and evaluate the content of transfer tools.

DESIGN

Cross-sectional survey design.

PARTICIPANTS

We administered a survey to 128 medical and/or surgical ICU and 256 ward administrators to obtain institutional perspectives on ICU transfer practices. We performed qualitative content analysis on ICU transfer tools received from respondents.

KEY RESULTS

In total, 108 (77%) ICU and 160 (63%) ward administrators responded to the survey. The ICU attending physician was reported to be "primarily responsible" for the safety (93% vs. 91%; p = 0.515) of patient transfers. ICU administrators more commonly perceived discharge summaries to be routinely included in patient transfers than ward administrators (81% vs. 60%; p = 0.006). Both groups identified information provided to patients/families, patient/family participation during transfer, and ICU-ward collaboration as opportunities for improvement. A minority of hospitals used ICU-to-ward transfer tools (11%) of which most (n = 21 unique) were designed to communicate patient information between providers (71%) and comprised six categories of information: demographics, patient clinical course, corrective aids, mobility at discharge, review of systems, and documentation of transfer procedures.

CONCLUSION

ICU and ward administrators have similar perspectives of transfer practices and identified patient/family engagement and communication as priorities for improvement. Key information categories exist.

Elevated red blood cell distribution width at ICU discharge is associated with readmission to the intensive care unit

BACKGROUND

Red blood cell distribution width (RDW) is a predictor of mortality in critically ill patients. Our objective was to investigate the association between the RDW at ICU discharge and the risk of ICU readmission or unexpected death in the ward.

METHODS

A secondary analysis of prospectively collected data study was conducted including patients discharged alive from the ICU to the ward. The target variable was the RDW collected at ICU discharge. Elevated RDW was defined as an RDW > 16%. Outcomes of interest included readmission to the ICU, unexpected death in the ward and in-hospital death. Variables with a p-value <0.1 in the univariate analysis or with biological plausibility for the occurrence of the outcome were included in the Cox proportional hazards model for adjustment.

RESULTS

We included 813 patients. A total of 138 readmissions to the ICU and 44 unexpected deaths in the ward occurred. Elevated RDW at ICU discharge was independently associated with readmission to the ICU or unexpected death in the ward after multivariable adjustment (HR: 1.901; 95% CI 1.357-2.662). Other variables associated with this outcome included age, tracheostomy and mean corpuscular volume (MCV) at ICU discharge. Similar results were obtained after the exclusion of unexpected deaths in the ward (HR 1.940; CI 1.312-2.871) and for in-hospital deaths (HR 1.716; 95% CI 1.141-2.580).

CONCLUSIONS

Elevated RDW at ICU discharge is independently associated with ICU readmission and in-hospital death.

A multi-center prospective cohort study of patient transfers from the intensive care unit to the hospital ward

PURPOSE

To provide a 360-degree description of ICU-to-ward transfers.

METHODS

Prospective cohort study of 451 adults transferred from a medical-surgical ICU to a hospital ward in 10 Canadian hospitals July 2014-January 2016. Transfer processes documented in the medical record. Patient (or delegate) and provider (ICU/ward physician/nurse) perspectives solicited by survey 24-72 h after transfer.

RESULTS

Medical records (100%) and survey responses (ICU physicians-80%, ICU nurses-80%, ward physicians-46%, ward nurses-64%, patients-74%) were available for most transfers. The median time from initiation to completion of transfer was 25 h (IQR 6-52). ICU physicians and nurses reported communicating with counterparts via telephone (78 and 75%) when transfer was requested (82 and 24%) or accepted (31 and 59%) and providing more elements of clinical information than ward physicians (mean 4.7 vs. 3.9, p < 0.001) and nurses (5.0 vs. 4.4, p < 0.001) reported receiving. Patients were more likely to report satisfaction with the transfer when they received more information (OR 1.32, 95% CI 1.18-1.48), had their questions addressed (OR 3.96, 95% CI 1.33-11.84), met the ward physician prior to transfer (OR 4.61, 95% CI 2.90-7.33), and were assessed by a nurse within 1 h of ward arrival (OR 4.70, 95% CI 2.29-9.66). Recommendations for improvement included having a documented care plan travel with the patient (all stakeholders), standardized face-to-face handover (physicians), avoiding transfers at shift change (nurses) and informing patients about pending transfers in advance (patients).

CONCLUSIONS

ICU-to-ward transfers are characterized by failures of patient flow and communication; experienced differently by patients, ICU/ward physicians and nurses, with distinct suggestions for improvement.

Patients Discharged From the Intensive Care Unit on a Dopamine Infusion-A Retrospective, Observational Study

OBJECTIVE

To assess the safety of discharging cardiac surgical patients from the intensive care unit (ICU) to wards while the patients are still receiving a dopamine infusion.

DESIGN

Retrospective, observational study.

SETTING

Cardiothoracic ICU of a tertiary academic hospital in the United Kingdom.

PARTICIPANTS

The study comprised all cardiac surgical patients older than 18 years and admitted between September 1, 2015 and September 16, 2016 to the ICU and subsequently discharged to a surgical ward. Patients were divided in the following 2 groups: a dopamine group with patients discharged with a dopamine infusion and a control group with patients discharged without any dopamine infusion.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The hospital mortality rate was comparable in both groups (0.7% in the dopamine group v 0.2% in the control group [p = 0.11]), despite that the median logistic EuroSCORE was significantly higher in the dopamine group (7.0 v 3.8 [p < 0.01]). The ICU readmission rate was higher in the dopamine group (6.6% v 2.4%; p < 0.01). ICU and hospital lengths of stay were longer in the dopamine group (1.7 v 0.9 days [p < 0.01] and 11.4 v 8.0 days [p < 0.01], respectively).

CONCLUSIONS

Despite a higher ICU readmission rate, ICU discharge of patients on dopamine infusion was not associated with increased mortality.

Readmissions to Intensive Care: A Prospective Multicenter Study in Australia and New Zealand

OBJECTIVES

To determine factors independently associated with readmission to ICU and the independent association of readmission with subsequent mortality.

DESIGN

Prospective multicenter observational study.

SETTING

Forty ICUs in Australia and New Zealand.

PATIENTS

Consecutive adult patients discharged alive from ICU to hospital wards between September 2009 and February 2010.

INTERVENTIONS

Measurement of hospital mortality.

MEASUREMENTS AND MAIN RESULTS

We studied 10,210 patients and 674 readmissions. The median age was 63 years (interquartile range, 49-74), and 6,224 (61%) were male. The majority of readmissions were unplanned (84.1%) but only deemed preventable in a minority (8.9%) of cases. Time to first readmission was shorter for unplanned than planned readmission (3.2 vs 6.9 d; p < 0.001). Primary diagnosis changed between admission and readmission in the majority of patients (60.2%) irrespective of planned (58.2%) or unplanned (60.6%) status. Using recurrent event analysis incorporating patient frailty, we found no association between readmissions and hospital survival (hazard ratios: first readmission 0.88, second readmission 0.90, third readmission 0.44; p > 0.05). In contrast, age (hazard ratio, 1.03), a medical diagnosis (hazard ratio, 1.43), inotrope use (hazard ratio, 3.47), and treatment limitation order (hazard ratio, 17.8) were all independently associated with outcome.

CONCLUSIONS

In this large prospective study, readmission to ICU was not an independent risk factor for mortality.

Admission factors associated with intensive care unit readmission in critically ill oncohematological patients: a retrospective cohort study

Objective

The purpose of our study was to determine the admission factors associated with intensive care unit readmission among oncohematological patients.

Methods

Retrospective cohort study using an intensive care unit database from a tertiary oncological center. The participants included 1,872 critically ill oncohematological patients who were admitted to the intensive care unit from January 2012 to December 2014 and who were subsequently discharged alive. We used univariate and multivariate analysis to identify the admission risk factors associated with later intensive care unit readmission.

Results

One hundred seventy-two patients (9.2% of 1,872 oncohematological patients discharged alive from the intensive care unit) were readmitted after intensive care unit discharge. The readmitted patients were sicker compared with the non-readmitted group and had higher hospital mortality (32.6% versus 3.7%, respectively; p < 0.001). In the multivariate analysis, the independent risk factors for intensive care unit readmission were male sex (OR: 1.5, 95% CI: 1.07 - 2.12; p = 0.019), emergency surgery as the admission reason (OR: 2.91, 95%CI: 1.53 - 5.54; p = 0.001), longer hospital length of stay before intensive care unit transfer (OR: 1.02, 95%CI: 1.007 - 1.035; p = 0.003), and mechanical ventilation (OR: 2.31, 95%CI: 1.57 - 3.40; p < 0.001).

Conclusions

In this cohort of oncohematological patients, we identified some risk factors associated with intensive care unit readmission, most of which are not amenable to interventions. The identification of risk factors at intensive care unit discharge might be a promising approach.

Readmission of ICU patients: A quality indicator?

PURPOSE

Readmission rate is frequently proposed as a quality indicator because it is related to both patient outcome and organizational efficiency. Currently available studies are not clear about modifiable factors as tools to reduce readmission rate.

MATERIAL AND METHODS

In a 14year retrospective cohort study of 19,750 ICU admissions we identified 1378 readmissions (7%). A multivariate logistic regression analysis for determinants of readmission within 24h, 48h, 72h and any time during hospital admission was performed with adjustment for patients' characteristics and initial admission severity scores.

RESULTS

In all models with different time points, patients with older age, a medical and emergency surgery initial admission and patients with higher SOFA score have a higher risk of readmission. Immunodeficiency was a predictor only in the at any time model. Confirmed infection was predicted in all models except the 24h model. Last day noradrenaline treatment was predicted in the 24 and 48h model. Mechanical ventilation on admission independently protected for readmission, which can be explained by the large number of cardiac surgery patients. All multivariate models had a moderate performance with the highest AUC of 0.70.

CONCLUSIONS

Readmission can be predicted with moderate precision and independent variables associated with readmission are age, severity of disease, type of admission, infection, immunodeficiency and last day noradrenaline use. The latter factor is the only one that can be modified and therefore readmission rate does not meet the criteria to be used as a useful quality indicator.

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.

Description of PICU Unplanned Readmission

OBJECTIVES

ICU readmission within 48 hours of discharge is associated with increased mortality. The objectives of this study were to describe the frequency of, factors associated with, and outcomes associated with unplanned PICU readmission.

DESIGN

A retrospective case-control study was performed. We evaluated 13 candidate risk factors and report patient outcomes following readmission. Subgroup analyses were performed for patients discharged from the cardiac PICU and medical-surgical PICU.

SETTING

The study was undertaken at the Hospital for Sick Children, Department of Critical Care Medicine.

PATIENTS

Eligible patients were discharged from the PICU to an inpatient ward between December 2006 and January 2013. Case patients were readmitted to the PICU within 48 hours of discharge.

MEASUREMENTS AND MAIN RESULTS

There were 10,422 eligible patient discharges; 264 (2.5%) were readmitted within 48 hours. In the univariable analysis, unplanned readmission was associated with PICU patient admissions of younger age, lower weight, greater duration of PICU stay, greater cumulative stay in PICU in the past 2 years, higher Pediatric Logistic Organ Dysfunction score on PICU discharge, discharge outside goal discharge time (06:00-11:59 hr), use of extracorporeal organ support during ICU stay, greater Bedside Pediatric Early Warning Score, at discharge and discharge from the cardiac PICU. In the multivariable analysis, the factors most significantly associated with unplanned PICU readmission were length of stay more than 48 hours, greater cumulative length of PICU stay in the past 2 years, discharge from cardiac PICU, and higher Pediatric Logistic Organ Dysfunction and Bedside Pediatric Early Warning Scores on index discharge. Mortality was 1.8 times (p = 0.03) higher in patients with an unplanned PICU readmission compared with patients during their index PICU admission.

CONCLUSIONS

The only potentially modifiable factors we found associated with PICU readmission within 48 hours of discharge were discharge time of day and the Pediatric Logistic Organ Dysfunction and Bedside Pediatric Early Warning Scores at the time of PICU discharge.

Critical care transition programs and the risk of readmission or death after discharge from ICU

PURPOSE

Critical care transition programs have been widely implemented to improve the safety of patient discharge from ICU, but have undergone limited evaluation. We sought to evaluate implementation of a critical care transition program on patient readmission to ICU (72 h) and mortality (14 days).

METHODS

Interrupted time series analysis of 32,234 consecutive adult patients discharged alive from medical-surgical ICUs in eight hospitals in two cities between January 1, 2002 and January 1, 2012. A multidisciplinary ICU provider team (physician, nurse, respiratory therapist) that serially evaluated each patient after ICU discharge was implemented in three hospitals in one city (study group), but not the five hospitals in the other city (control group). Temporal changes were examined using multivariable, segmented linear regression models.

RESULTS

After implementation of the program, there was an immediate non-significant decrease in the absolute proportion of patients readmitted to ICU in the study group (-0.4%, 95% CI -1.7 to +1.0%) and a non-significant increase in the absolute proportion of patients readmitted to ICU in the control group (+1.0%, 95% CI -0.3 to +2.2%). Subsequently, there were non-significant changes in the absolute proportion of patients readmitted to ICU in both the study (+0.1% per quarter; 95% CI, -0.1 to +0.2%) and control (-0.1 per quarter; 95% CI, -0.2 to +0.1%) groups over time. No significant changes were observed in mortality. The results were stable across patient subgroups.

CONCLUSIONS

Implementation of a critical care transition program was not associated with patient readmission to ICU or mortality.