Factors associated with increased risk of readmission to intensive care in Australia

Developing a reflection and analysis tool (We-ReAlyse) for readmissions to the intensive care unit: A quality improvement project

BACKGROUND

Readmissions to the intensive care unit are associated with poorer patient outcomes and health prognoses, alongside increased lengths of stay and mortality risk. To improve quality of care and patients' safety, it is essential to understand influencing factors relevant to specific patient populations and settings. A standardized tool for systematic retrospective analysis of readmissions would help healthcare professionals understand risks and reasons affecting readmissions; however, no such tool exists.

PURPOSE

This study's purpose was to develop a tool (We-ReAlyse) to analyze readmissions to the intensive care unit from general units by reflecting on affected patients' pathways from intensive care discharge to readmission. The results will highlight case-specific causes of readmission and potential areas for departmental- and institutional-level improvements.

METHOD

A root cause analysis approach guided this quality improvement project. The tool's iterative development process included a literature search, a clinical expert panel, and a testing in January and February 2021.

RESULTS

The We-ReAlyse tool guides healthcare professionals to identify areas for quality improvement by reflecting the patient's pathway from the initial intensive care stay to readmission. Ten readmissions were analyzed by using the We-ReAlyse tool, resulting in key insights about possible root causes like the handover process, patient's care needs, the resources on the general unit and the use of different electronic healthcare record systems.

CONCLUSIONS

The We-ReAlyse tool provides a visualization/objectification of issues related to intensive care readmissions, gathering data upon which to base quality improvement interventions. Based on the information on how multi-level risk profiles and knowledge deficits contribute to readmission rates, nurses can target specific quality improvements to reduce those rates.

IMPLICATIONS FOR CLINICAL PRACTICE AND RESEARCH

With the We-ReAlyse tool, we have the opportunity to collect detailed information about ICU readmissions for an in-depth analysis. This will allow health professionals in all involved departments to discuss and either correct or cope with the identified issues. In the long term, this will allow continuous, concerted efforts to reduce and prevent ICU readmissions. To obtain more data for analysis and to further refine and simplify the tool, it may be applied to larger samples of ICU readmissions. Furthermore, to test its generalizability, the tool should be applied to patients from other departments and other hospitals. Adapting it to an electronic version would facilitate the timely and comprehensive collection of necessary information. Finally, the tool's emphasis comprises reflecting on and analyzing ICU readmissions, allowing clinicians to develop interventions targeting the identified problems. Therefore, future research in this area will require the development and evaluation of potential interventions.

Development and validation of an interpretable 3 day intensive care unit readmission prediction model using explainable boosting machines

Background

Intensive care unit (ICU) readmissions are associated with mortality and poor outcomes. To improve discharge decisions, machine learning (ML) could help to identify patients at risk of ICU readmission. However, as many models are black boxes, dangerous properties may remain unnoticed. Widely used post hoc explanation methods also have inherent limitations. Few studies are evaluating inherently interpretable ML models for health care and involve clinicians in inspecting the trained model.

Methods

An inherently interpretable model for the prediction of 3 day ICU readmission was developed. We used explainable boosting machines that learn modular risk functions and which have already been shown to be suitable for the health care domain. We created a retrospective cohort of 15,589 ICU stays and 169 variables collected between 2006 and 2019 from the University Hospital Münster. A team of physicians inspected the model, checked the plausibility of each risk function, and removed problematic ones. We collected qualitative feedback during this process and analyzed the reasons for removing risk functions. The performance of the final explainable boosting machine was compared with a validated clinical score and three commonly used ML models. External validation was performed on the widely used Medical Information Mart for Intensive Care version IV database.

Results

The developed explainable boosting machine used 67 features and showed an area under the precision-recall curve of 0.119 ± 0.020 and an area under the receiver operating characteristic curve of 0.680 ± 0.025. It performed on par with state-of-the-art gradient boosting machines (0.123 ± 0.016, 0.665 ± 0.036) and outperformed the Simplified Acute Physiology Score II (0.084 ± 0.025, 0.607 ± 0.019), logistic regression (0.092 ± 0.026, 0.587 ± 0.016), and recurrent neural networks (0.095 ± 0.008, 0.594 ± 0.027). External validation confirmed that explainable boosting machines (0.221 ± 0.023, 0.760 ± 0.010) performed similarly to gradient boosting machines (0.232 ± 0.029, 0.772 ± 0.018). Evaluation of the model inspection showed that explainable boosting machines can be useful to detect and remove problematic risk functions.

Conclusions

We developed an inherently interpretable ML model for 3 day ICU readmission prediction that reached the state-of-the-art performance of black box models. Our results suggest that for low- to medium-dimensional datasets that are common in health care, it is feasible to develop ML models that allow a high level of human control without sacrificing performance.

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.

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

Background

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

Aims

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

Patients and Methods

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

Results

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

Conclusions

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

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..

Repeated maternal ICU admission: results from a nationwide analysis

OBJECTIVE

To determine the rate and profile of repeated maternal ICU admissions during or after pregnancy and to compare the characteristics of these women's first and second ICU admissions.

METHODS

A descriptive analysis from the French national hospital discharge database that included all women admitted to an ICU during pregnancy or within 42 days after delivery, between 2010 and 2014.

RESULTS

During the 5-year study period, there were 371 women with more than one maternal ICU admission, representing 2.5% of all women admitted during or after pregnancy (371/15,096) and a 0.9 per 10,000 deliveries (371/4,030,409) rate of repeated maternal ICU admission. Compared with women with only one maternal ICU admission, those with repeated maternal ICU admissions were more often admitted during the pregnancy rather than during or after the delivery stay (P < 0.001), for organ failure or sepsis (P < 0.001), and with a SAPS-II score >25 (P < 0.001). Women with repeated admissions were usually readmitted for the same indications and had similar SAPS-II scores. Half of ICU readmissions occurred within 72 hours of first ICU discharge, with similar causes and levels of severity for both stays.

CONCLUSION

Although the rate of women with repeated maternal ICU admissions was low, their initial stay had a specific profile of causes of admission and greater severity compared with the stay of women admitted only once. The pattern and similar characteristics of both first and second ICU admission and the short interval for readmission suggests that some ICU discharges may have been potentially premature.

Factors and Outcomes of Intensive Care Unit Readmission in Elderly Patients

INTRODUCTION

An increase in age has been observed among patients admitted to the intensive care unit (ICU). Age is a well-known risk factor for ICU readmission and mortality. However, clinical characteristics and risk factors of ICU readmission of elderly patients (≥65 years) have not been studied.

METHODS

This retrospective single-center cohort study was conducted in a total of 122-bed ICU of a tertiary care hospital in Seoul, Korea. A total of 85,413 patients were enrolled in this hospital between January 1, 2007, and December 31, 2017. The odds ratio of readmission and in-hospital mortality was calculated by logistic regression analysis.

RESULTS

Totally, 29,503 patients were included in the study group, of which 2,711 (9.2%) had ICU readmissions. Of the 2,711 readmitted patients, 472 patients were readmitted more than once (readmitted 2 or more times to the ICU, 17.4%). In the readmitted patient group, there were more males, higher sequential organ failure assessment (SOFA) scores, and hospitalized for medical reasons. Length of stay (LOS) in ICU and in-hospital were longer, and 28-day and in-hospital mortality was higher in readmitted patients than in nonreadmitted patients. Risk factors of ICU readmission included the ICU admission due to medical reason, SOFA score, presence of chronic heart disease, diabetes mellitus, chronic kidney disease, transplantation, use of mechanical ventilation, and initial ICU LOS. ICU readmission and age (over 85 years) were independent predictors of in-hospital mortality on multivariable analysis. The delayed ICU readmission group (>72 h) had higher in-hospital mortality than the early readmission group (≤72 h) (20.6 vs. 16.2%, p = 0.005).

CONCLUSIONS

ICU readmissions occurred in 9.2% of elderly patients and were associated with poor prognosis and higher mortality.

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

BACKGROUND

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

OBJECTIVE

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

DESIGN

Retrospective chart review.

PARTICIPANTS

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

MAIN MEASURES

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

KEY RESULTS

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

CONCLUSION

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

Causes, Risk Factors and Outcomes of Patients Readmitted to the Intensive Care Unit After Esophageal Cancer Surgery: A Retrospective Cohort Study

BACKGROUND

Readmission to intensive care unit (ICU) after esophageal cancer surgery is a major concern and can be associated with increased adverse outcomes. This study aims to explore causes, risk factors and early outcomes.

METHODS

We performed a monocentric retrospective analysis in 1140 patients who received esophageal cancer surgery in a higher volume surgeon group between January 2016 and December 2019, at Shanghai Chest Hospital. Univariate and multivariate analysis were performed to identify risk factors, and 1:4 propensity score matching (PSM) analysis was conducted to compare early outcomes.

RESULTS

The incidence of ICU readmission was about 3.8% (43 of 1140). The most common cause was respiratory failure, found in 30 patients (70%). ICU readmission mainly occurred within 3 days after surgery, accounting for 46.5% (20 of 43), with the median length of stay was 3 days. Multivariate analysis identified heavy smoking (odds ratio[OR] = 2.445, 95% CI = 1.128 to 5.301, P = 0.024), intraoperative hypoxemia (OR = 2.461, 95% CI = 1.078 to 5.621, P = 0.033), mechanical ventilation during initial ICU stay (OR = 16.036, 95% CI = 7.332 to 35.074, P < 0.001), postoperative anemia (OR = 3.993, 95% CI = 1.893 to 8.420, P < 0.001) and unplanned reoperation (OR = 45.378, 95% CI = 13.023 to 158.122, P < 0.001) as independent risk factors for ICU readmission. Compared with no-readmitted patients, patients readmitted to ICU were associated with increased postoperative pulmonary complications (44.2% vs 97.7%, P < 0.001), prolonged median length of hospital stay (9[7-11] vs 19[13-30], P < 0.001) and ICU stay (1[1-3] vs 7[4-11], P < 0.001), higher hospitalization expenses (14,916 ± 3483 vs 19,850 ± 7595 dollars, P < 0.001) and 30-day readmission rates (1.8% vs 9.3%, P = 0.011). After 1:4 PSM, the baseline characteristics were comparable and the matched results were similar.

CONCLUSIONS

This study identified five independent risk factors for ICU readmission, which were associated with adverse early outcomes. Preemptive attention given to pulmonary complications within three days after surgery may be important to prevent patients from ICU readmission.

Serum creatinine as a predictor of mortality in patients readmitted to the intensive care unit after cardiac surgery: a retrospective cohort study in China

BACKGROUND

Patients readmitted to the intensive care unit (ICU) after cardiac surgery have a high mortality rate. The relationship between renal function and in-hospital mortality in readmitted patients has not been well demonstrated.

METHODS

We retrospectively evaluated cardiac surgery patients who were readmitted to the ICU at least once. Data on serum creatinine levels before surgery and on the day of ICU readmission were collected. The estimated glomerular filtration rate (eGFR) was calculated according to the creatinine-based Chronic Kidney Disease-Epidemiology Collaboration equation. We used logistic regression models and restricted cubic spline curves with four knots (5%, 35%, 65%, 95%) to investigate the relationship between renal function indicators and mortality.

RESULTS

Of the 184 patients evaluated, 30 patients died during hospitalization, yielding a mortality rate of 16.30%. Cardiac dysfunction (n=84, 45.65%) and respiration disorder (n=51, 27.72%) were the most common reasons for ICU readmission. Creatinine [odds ratio (OR): 1.14, 95% confidence interval (CI): 1.07-1.25] and eGFR (OR: 0.95, 95% CI: 0.93-0.98) were independently associated with in-hospital mortality after adjusting for various confounders. Both creatinine level and eGFR had a linear association with in-hospital mortality (P for non-linearity ˃0.05).

CONCLUSION

Renal function is significantly associated with the in-hospital mortality of patients readmitted to the ICU after cardiac surgery, as evidenced by the independent correlation of both creatinine and eGFR with in-hospital mortality.

The development of a measurement instrument focusing on team collaboration in patient transfer processes

Purpose

Team collaboration is essential to ensure the quality of care and patient safety when critically ill patients are transferred from an intensive care unit (ICU) to a general ward. Measuring team collaboration in the patient transfer process can help gain insights into how team collaboration is perceived and how it can be improved. The purpose of this paper is to describe the development and testing of a questionnaire aiming to measure perceived team collaboration in the patient transfer process from ICU to the general ward. This study also aims to analyze the results to see how the survey could help improve team collaboration within ICU transitional care.

Design/methodology/approach

Statements, factors and main areas intended to measure perceived team collaboration were developed from a theory. The questionnaire was tested in two ICUs at two hospitals located in Sweden, and the results were analyzed statistically.

Findings

The results showed that the questionnaire could be used for measuring perceived team collaboration in this process. The results from the survey gave insights that can be useful when improving team collaboration in ICU transitional care.

Research limitations/implications

The collaboration between two research subjects, Nursing Science and Quality Management, has given new perspectives in how cultural and systemic differences and opportunities can help improving team collaboration in ICU transitional care, by shifting focus from the individual to team, culture, system, process and continuous improvement.

Practical implications

The developed questionnaire can be used to measure perceived team collaboration and to identify areas for improving team collaboration in the ICU transitional care process.

Originality/value

There is a sparse amount of research about measuring team collaboration in ICU transitional care, and this study contributes to filling this research gap.

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.

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.

The Impact of Mechanical Ventilation Duration on the Readmission to Intensive Care Unit: A Population-Based Observational Study

Background

If the duration of mechanical ventilation (MV) is related with the intensive care unit (ICU) readmission must be clarified. The purpose of this study was to elucidate if prolonged MV duration increases ICU readmission rate.

Methods

The present observational cohort study analyzed national healthcare claims data from 2006 to 2015. Critically ill patients who received MV in the ICU were classified into five groups according to the MV duration: MV for <7 days, 7–13 days, 14–20 days, 21–27 days, and ≥28 days. The rate and risk of the ICU readmission were estimated according to the MV duration using the unadjusted and adjusted analyses.

Results

We found that 12,929 patients had at least one episode of MV in the ICU. There was a significant linear relationship between the MV duration and the ICU readmission (R²=0.85, p=0.025). The total readmission rate was significantly higher as the MV duration is prolonged (MV for <7 days, 13.9%; for 7–13 days, 16.7%; for 14–20 days, 19.4%; for 21–27 days, 20.4%; for ≥28 days, 35.7%; p<0.001). The analyses adjusted by covariables and weighted with the multinomial propensity scores showed similar results. In the adjusted regression analysis with a Cox proportional hazards model, the MV duration was significantly related to the ICU readmission (hazard ratio, 1.058 [95% confidence interval, 1.047–1.069], p<0.001).

Conclusion

The rate of readmission to the ICU was significantly higher in patients who received longer durations of the MV in the ICU. In the clinical setting, closer observation of patients discharged from the ICU after prolonged periods of MV is required.

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.

Effect of post-hospital discharge telephonic intervention on hospital readmissions in a privately insured population in Australia

Objective The aim of the present study was to evaluate the effect of telephone support after hospital discharge to reduce early hospital readmission among members of the disease management program My Health Guardian (MHG) offered by the Hospitals Contribution Fund of Australia (HCF). Methods A quasi-experimental retrospective design compared 28-day readmissions of patients with chronic disease between two groups: (1) a treatment group, consisting of MHG program members who participated in a hospital discharge (HODI) call; and (2) a comparison group of non-participating MHG members. Study groups were matched for age, gender, length of stay, index admission diagnoses and prior MHG program exposure. Adjusted incidence rate ratios (IRR) and odds ratios (OR) were estimated using zero-inflated negative binomial and logistic regression models respectively. Results The treatment group exhibited a 29% lower incidence of 28-day readmissions than the comparison group (adjusted IRR 0.71; 95% confidence interval (CI) 0.59-0.86). The odds of treatment group members being readmitted at least once within 28 days of discharge were 25% lower than the odds for comparison members (adjusted OR 0.75; 95% CI 0.63-0.89). Reduction in readmission incidence was estimated to avoid A$713730 in cost. Conclusions The HODI program post-discharge telephonic support to patients recently discharged from a hospital effectively reduced the incidence and odds of hospital 28-day readmission in a diseased population. What is known about the topic? High readmission rates are a recognised problem in Australia and contribute to the over 600000 potentially preventable hospitalisations per year. What does this paper add? The present study is the first study of a scalable intervention delivered to an Australian population with a wide variety of conditions for the purpose of reducing readmissions. The intervention reduced 28-day readmission incidence by 29%. What are the implications for practitioners? The significant and sizable effect of the intervention support the delivery of telephonic support after hospital discharge as a scalable approach to reduce readmissions.

Variation in Case-Mix Adjusted Unplanned Pediatric Cardiac ICU Readmission Rates

OBJECTIVES

To identify modifiable factors leading to unplanned readmission and characterize differences in adjusted unplanned readmission rates across hospitals.

DESIGN

Retrospective cohort study using prospectively collected clinical registry data SETTING:: Pediatric Cardiac Critical Care Consortium clinical registry.

PATIENTS

Patients admitted to a pediatric cardiac ICU at Pediatric Cardiac Critical Care Consortium hospitals.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We examined pediatric cardiac ICU encounters in the Pediatric Cardiac Critical Care Consortium registry from October 2013 to March 2016. The primary outcomes were early (< 48 hr from pediatric cardiac ICU transfer) and late (2-7 d) unplanned readmission. Generalized logit models identified independent predictors of unplanned readmission. We then calculated observed-to-expected ratios of unplanned readmission and identified higher-than or lower-than-expected unplanned readmission rates for those with an observed-to-expected ratios greater than or less than 1, respectively, and a 95% CI that did not cross 1. Of 11,301 pediatric cardiac ICU encounters (16 hospitals), 62% were surgical, and 18% were neonates. There were 175 (1.6%) early unplanned readmission, and 300 (2.7%) late unplanned readmission, most commonly for respiratory (31%), or cardiac (28%) indications. In multivariable analysis, unique modifiable factors were associated with unplanned readmission. Although shorter time between discontinuation of vasoactive infusions and pediatric cardiac ICU transfer was associated with early unplanned readmission, nighttime discharge was independently associated with a greater likelihood of late unplanned readmission. Two hospitals had lower-than-expected unplanned readmission in both the early and late categories, whereas two other hospitals were higher-than-expected in both.

CONCLUSIONS

This analysis demonstrated time from discontinuation of critical care therapies to pediatric cardiac ICU transfer as a significant, modifiable predictor of unplanned readmission. We identified two hospitals with lower-than-expected adjusted rates of both early and late unplanned readmission, suggesting that their systems are well designed to prevent unplanned readmission. This offers the possibility of disseminating best practices to other hospitals through collaborative learning.

The impact of the introduction of a toxicology service on the intensive care unit

Objective: To examine the impact of a clinical toxicology service on toxicology patients admitted to an intensive care department Methods: The authors performed a retrospective chart audit of all patients presenting to Cairns Emergency Department (ED) over a five-year period from 2011 to 2016 with a toxicology diagnosis requiring Intensive Care Unit (ICU) admission. They were divided into two groups: pre-toxicology (1 April 2011 to 30 September 2012), and post-toxicology service (1 October 2012 to 31 of March 2016) introduction. Patients were identified using ED and ICU databases. Patient charts were manually searched, and data entered on a preformatted data extraction tool. The data were statistically compared pre- versus post-toxicology service introduction using univariate (t-tests and Pearson's Chi Square) and multivariate modelling. Where appropriate, continuous variables were log transformed to enable parametric analyses. Results: There were 37 patients in the pre-toxicology and 102 in the post-toxicology group, with an increased median APACHE III J score in the post toxicology group (39 vs. 49). The introduction of a toxicology service was associated with statistically significant reductions in median ICU length of stay (LOS) (32.9 vs. 20.6 h), median duration of mechanical ventilation (29.1 vs. 20.6 h) and median time to psychiatry review (19.4 vs. 6.7 h). The reduction in ICU LOS remained statistically significant (p = 0.036) when adjusted by sex, age and duration of mechanical ventilation. There was neither increase in mortality, nor readmissions from EDSSU to ICU. Conclusions: This study has demonstrated that the introduction of a toxicology service was associated with a reduction in median ICU LOS, duration of mechanical ventilation and time to psychiatric review in patients with a toxicology diagnosis admitted to our ICU.

Risk prediction models for intensive care unit readmission: A systematic review of methodology and applicability

OBJECTIVE

We conducted a systematic review of primary models to predict intensive care unit (ICU) readmission.

REVIEW METHODS

We searched MEDLINE, PubMed, Scopus, and Embase for studies on the development of ICU readmission prediction models that are published until January 2017. Data were extracted on the source of data, participants, outcomes, candidate predictors, sample size, missing data, methods for model development, and measures of model performance and model evaluation. The quality and applicability of the included studies were assessed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies.

RESULTS

We identified five studies describing the development of the primary prediction models of ICU readmission. Studies ranged in size from 343 to 704,963 patients with the mean age of 58.0-68.9 years. The proportion of readmission ranged from 2.5% to 9.6%. The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.66-0.81. None of the studies performed external validations. The quality scores ranged from 42 to 54 out of 62, and the applicability scores from 24 to 32 out of 38.

CONCLUSION

We identified five prediction models for ICU readmission. However, owing to the numerous methodological and reporting deficiencies in the included studies, physicians using these models should interpret the predictions with precautions until an external validation study shows the acceptable level of calibration and accuracy of these models.

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.

The continuum of critical care

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

Post-intensive care unit respiratory failure in older patients liberated from intensive care unit and ventilator: The predictive value of the National Early Warning Score on intensive care unit discharge

AIM

The older adult population is continuously growing worldwide and there is increasing use of medical recourse in older patients, especially for those requiring intensive care unit (ICU) care and mechanical ventilation (MV). The present study aimed to investigate the burden and predictors of post-ICU respiratory failure in older ICU patients weaned from MV.

METHODS

In the present retrospective study, older ICU patients aged ≥60 years, who were successfully weaned from MV and discharged to the general ward from the ICU of Taipei Veterans General Hospital, Taipei, Taiwan, in 2011, were included. Biomarkers on ICU discharge, as well as the National Early Warning Score (NEWS) were recorded and calculated. The outcome measure was post-ICU respiratory failure before day 14 (PIRF-14) requiring reinstitution of MV. Logistical regression was used to assess the predictors for PIRF-14.

RESULTS

Of 272 patients included, 23 (8.5%) developed PIRF-14. The post-ICU in-hospital mortality rates were 47.8% and 6.8% in patients with and without PIRF-14 (adjusted OR 12.597, 95% CI 4.368-36.331). In a multivariate analysis, the levels of NEWS and hemoglobin on ICU discharge were independent predictors for PIRF-14 (adjusted OR 1.273, 95% CI 1.076-1.507 and 0.645, 95% CI 0.474-0.879). In particular, patients with a NEWS of ≥10 and subsequent PIRF-14 had a 15-fold increased risk of mortality as compared with those without both factors (adjusted OR 15.418, 95% CI 4.344-54.720).

CONCLUSIONS

PIRF-14 is associated with high mortality in older ICU patients, and NEWS is a significant predictor for PIRF-14, which could be used to early identify patients at risk of post-ICU respiratory failure in the specific population. Geriatr Gerontol Int 2019; 19: 317-322.

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.

Predicting ICU readmission among surgical ICU patients: Development and validation of a clinical nomogram

BACKGROUND

Unplanned intensive care unit readmission within 72 hours is an established metric of hospital care quality. However, it is unclear what factors commonly increase the risk of intensive care unit readmission in surgical patients. The objective of this study was to evaluate predictors of readmission among a diverse sample of surgical patients and develop an accurate and clinically applicable nomogram for prospective risk prediction.

METHODS

We retrospectively evaluated patient demographic characteristics, comorbidities, and physiologic variables collected within 48 hours before discharge from a surgical intensive care unit at an academic center between April 2010 and July 2015. Multivariable regression models were used to assess the association between risk factors and unplanned readmission back to the intensive care unit within 72 hours. Model selection was performed using lasso methods and validated using an independent data set by receiver operating characteristic area under the curve analysis. The derived nomogram was then prospectively assessed between June and August 2017 to evaluate the correlation between perceived and calculated risk for intensive care unit readmission.

RESULTS

Among 3,109 patients admitted to the intensive care unit by general surgery (34%), transplant (9%), trauma (43%), and vascular surgery (14%) services, there were 141 (5%) unplanned readmissions within 72 hours. Among 179 candidate predictor variables, a reduced model was derived that included age, blood urea nitrogen, serum chloride, serum glucose, atrial fibrillation, renal insufficiency, and respiratory rate. These variables were used to develop a clinical nomogram, which was validated using 617 independent admissions, and indicated moderate performance (area under the curve: 0.71). When prospectively assessed, intensive care unit providers' perception of respiratory risk was moderately correlated with calculated risk using the nomogram (ρ: 0.44; P < .001), although perception of electrolyte abnormalities, hyperglycemia, renal insufficiency, and risk for arrhythmias were not correlated with measured values.

CONCLUSION

Intensive care unit readmission risk for surgical patients can be predicted using a simple clinical nomogram based on 7 common demographic and physiologic variables. These data underscore the potential of risk calculators to combine multiple risk factors and enable a more accurate risk assessment beyond perception alone.

Patients admitted via the emergency department to the intensive care unit: An observational cohort study

OBJECTIVE

Timely and appropriate assessment and management within the ED impacts patient outcomes including in-hospital mortality and length of stay (LOS). Within the ED, several processes facilitate timely recognition of the need for intensive care unit (ICU) admission. This study describes characteristics and outcomes for patient presentations admitted to ICU from ED, categorised by Australasian Triage Score (ATS), ICU admission time and ICU admission source.

METHODS

A retrospective observational cohort study with linked health data of adult ICU admissions during 2012. Outcomes measured included: ED, ICU and hospital LOS, time to see ED clinician, ICU readmission and ICU and hospital mortality rates.

RESULTS

In total, 423 ICU admissions occurred within 24 h of ED arrival; 395 were admitted directly to ICU; 28 were admitted to the ward before ICU admission. ATS 3/4/5 patients comprised 26.7% of ICU admissions and experienced longer waits to be seen, longer total ED LOS, shorter ICU LOS and a lower mortality rate than those triaged ATS 1/2. Compared to ICU admissions during business hours, admissions outside hours did not differ significantly for any outcome measured. Patients admitted to the ward before ICU experienced longer waits to be seen and longer ED LOS.

CONCLUSION

Most patients are appropriately identified in ED as requiring ICU admission, although around one in four were triaged ATS 3/4. Patients admitted to the ward first tended to have poorer outcomes than those directly admitted to ICU. Factors predicting the need for ICU admission should be identified to support clinical decision-making.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The outcomes and prognostic factors of the patients with unplanned intensive care unit readmissions

This retrospective cohort study investigated the outcomes of patients with unplanned intensive care unit (ICU) readmission.All of the patients readmitted to ICU within 48 hours between 2010 and 2016 were enrolled.A total of 99 patients early readmitted to ICU were identified and their mean age of the patients was 68.8 ± 14.8 years. Respiratory failure was the most common cause of ICU readmission (n = 48, 48.5%), followed by acute myocardial ischemia or worsening heart failure (n = 25, 25.3%), sepsis (n = 22, 22.2%), gastrointestinal disease (n = 16, 16.2%), and neurologic disease (n = 11, 11.1%). The median length of stay in the ICU and hospital was 7 (IQR, 4-11.5) and 32 (IQR, 15.5-48.5) days, respectively. A total of 34 patients died during the hospital stay and the rate of in-hospital mortality was 34.3%. Patients with higher APACHE II scores (adjusted odds ratio [OR], 1.17; 95% CI, 1.02-1.33), underlying malignancy (adjusted OR, 4.70; 95% CI, 1.19-18.57), and cardiovascular organ dysfunction (adjusted OR, 5.14; 95% CI, 1.24-21.38) were more likely to die.The mortality rate of ICU readmission patients was high, especially for those with higher APACHE II score, underlying malignancy and cardiovascular organ dysfunction.

Predicting medical emergency team calls, cardiac arrest calls and re-admission after intensive care discharge: creation of a tool to identify at-risk patients

We aimed to develop a predictive model for intensive care unit (ICU)-discharged patients at risk of post-ICU deterioration. We performed a retrospective, single-centre cohort observational study by linking the hospital admission, patient pathology, ICU, and medical emergency team (MET) databases. All patients discharged from the Alfred Hospital ICU to wards between July 2012 and June 2014 were included. The primary outcome was a composite endpoint of any MET call, cardiac arrest call or ICU re-admission. Multivariable logistic regression analysis was used to identify predictors of outcome and develop a risk-stratification model. Four thousand, six hundred and thirty-two patients were included in the study. Of these, 878 (19%) patients had a MET call, 51 (1.1%) patients had cardiac arrest calls, 304 (6.5%) were re-admitted to ICU during the same hospital stay, and 964 (21%) had MET calls, cardiac arrest calls or ICU re-admission. A discriminatory predictive model was developed (area under the receiver operating characteristic curve 0.72 [95% confidence intervals {CI} 0.70 to 0.73]) which identified the following factors: increasing age (odds ratio [OR] 1.012 [95% CI 1.007 to 1.017] <i>P</i> <0.001), ICU admission with subarachnoid haemorrhage (OR 2.26 [95% CI 1.22 to 4.16] <i>P</i>=0.009), admission to ICU from a ward (OR 1.67 [95% CI 1.31 to 2.13] <i>P</i> <0.001), Acute Physiology and Chronic Health Evaluation (APACHE) III score without the age component (OR 1.005 [95% CI 1.001 to 1.010] <i>P</i>=0.025), tracheostomy on ICU discharge (OR 4.32 [95% CI 2.9 to 6.42] <i>P</i> <0.001) and discharge to cardiothoracic (OR 2.43 [95%CI 1.49 to 3.96] <i>P</i> <0.001) or oncology wards (OR 2.27 [95% CI 1.05 to 4.89] <i>P</i>=0.036). Over the two-year period, 361 patients were identified as having a greater than 50% chance of having post-ICU deterioration. Factors are identifiable to predict patients at risk of post-ICU deterioration. This knowledge could be used to guide patient follow-up after ICU discharge, optimise healthcare resources, and improve patient outcomes and service delivery.

Preventable readmission to intensive care unit in critically ill cancer patients

BACKGROUND

Readmission to intensive care unit (ICU) after discharge to ward has been reported to be associated with increased hospital mortality and longer length of stay (LOS). The objective of this study was to investigate whether ICU readmission are preventable in critically ill cancer patients.

METHODS

Data of patients who readmitted to intensive care unit (ICU) at National Cancer Center/Cancer Hospital of Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC) between January 2013 and November 2016 were retrospectively collected and reviewed.

RESULTS

A total of 39 patients were included in the final analysis, and the overall readmission rate between 2013 and 2016 was 1.32% (39/2,961). Of 39 patients, 32 (82.1%) patients were judged as unpreventable and 7 (17.9%) patients were preventable. There were no significant differences in duration of mechanical ventilation, ICU LOS, hospital LOS, ICU mortality and in-hospital mortality between patients who were unpreventable and preventable. For 24 early readmission patients, 7 (29.2%) patients were preventable and 17 (70.8%) patients were unpreventable. Patients who were late readmission were all unpreventable. There was a trend that patients who were preventable had longer 1-year survival compared with patients who were unpreventable (100% vs. 66.8%, log rank=1.668, P=0.196).

CONCLUSION

Most readmission patients were unpreventable, and all preventable readmissions occurred in early period after discharge to ward. There were no significant differences in short term outcomes and 1-year survival in critically ill cancer patients whose readmissions were preventable or not.

Biomarkers as predictors of mortality in critically ill patients with solid tumors

Biochemical markers produced by the affected organ or body in response to disease have gained high clinical value due to assess disease development and being excellent predictors of morbidity and mortality. The aim of this study is to analyze different biochemical markers in critically cancer patients and to determine which of them can be used as predictors of mortality. This is a prospective, cross-sectional study conducted at a University Hospital in Porto Alegre - RS. Screening was done to include patients in the study. Serum biochemical markers obtained in the first 24 hours of Intensive Care Unit hospitalization were analyzed. A second review of medical records occurred after three months objected to identify death or Unit discharged. A sample of 130 individuals was obtained (control group n = 65, study group n = 65). In the multivariate model, serum magnesium values ​​OR = 3.97 (1.17; 13.5), presence of neoplasia OR = 2.68 (95% CI 1.13; 6.37) and absence of sepsis OR = 0.31 (95% CI 0.12; 0.79) were robust predictors of mortality. The association of solid tumors, sepsis presence and alteration in serum magnesium levels resulted in an increased chance of mortality in critically ill patients.

Ethical aspects of admission or non-admission to the intensive care unit

The question of admission and non-admission to the intensive care unit (ICU) raises several ethical questions. There is a fine line between the risk of loss-of-opportunity for the patient in case of non-admission, and the risk of unreasonable therapeutic obstinacy, in case of unjustified admission. Similar difficulties arise in decisions regarding re-admission or non-re-admission, with the sole difference that the intensivists already know the patient and his/her medical history. This information can help inform the decision when re-admission is being considered. Intensive, i.e., life-sustaining care should be implemented after shared reflection involving the caregivers, the patient and the family, and the same applies for non-implementation of these same therapies. Anticipating admission or non-admission to the ICU in case of acute organ failure, or in case of potential deterioration represents a major challenge for our discipline in the coming years.

Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach

OBJECTIVES

Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event.

SETTING

A single academic, tertiary care hospital in the UK.

PARTICIPANTS

A set of 3326 ICU episodes collected between October 2014 and August 2016. All records were of patients who visited an ICU at some point during their stay. We excluded patients who were ≤16 years of age; visited ICUs other than the general and neurosciences ICU; were missing crucial electronic patient record measurements; or had indeterminate ICU discharge outcomes or very early or extremely late discharge times. After exclusion, 2018 outcome-labelled episodes remained.

PRIMARY AND SECONDARY OUTCOME MEASURES

Area under the receiver operating characteristic curve (AUROC) for prediction of unplanned ICU readmission or in-hospital death within 48 hours of first ICU discharge.

RESULTS

In 10-fold cross-validation, an ensemble predictor was trained on data from both the target hospital and the Medical Information Mart for Intensive Care (MIMIC-III) database and tested on the target hospital's data. This predictor discriminated between patients with the unplanned ICU readmission or death outcome and those without this outcome, attaining mean AUROC of 0.7095 (SE 0.0260), superior to the purpose-built Stability and Workload Index for Transfer (SWIFT) score (AUROC=0.6082, SE 0.0249; p=0.014, pairwise t-test).

CONCLUSIONS

Despite the inherent difficulties, we demonstrate that a novel machine learning algorithm based on transfer learning could achieve good discrimination, over and above that of the treating clinicians or the value added by the SWIFT score. Accurate prediction of unplanned readmission could be used to target resources more efficiently.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Variation in rates of ICU readmissions and post-ICU in-hospital mortality and their association with ICU discharge practices

BACKGROUND

Variation in intensive care unit (ICU) readmissions and in-hospital mortality after ICU discharge may indicate potential for improvement and could be explained by ICU discharge practices. Our objective was threefold: (1) describe variation in rates of ICU readmissions within 48 h and post-ICU in-hospital mortality, (2) describe ICU discharge practices in Dutch hospitals, and (3) study the association between rates of ICU readmissions within 48 h and post-ICU in-hospital mortality and ICU discharge practices.

METHODS

We analysed data on 42,040 admissions to 82 (91.1%) Dutch ICUs in 2011 from the Dutch National Intensive Care Evaluation (NICE) registry to describe variation in standardized ICU readmission and post-ICU mortality rates using funnel-plots. We send a questionnaire to all Dutch ICUs. 75 ICUs responded and their questionnaire data could be linked to 38,498 admissions in the NICE registry. Generalized estimation equations analyses were used to study the association between ICU readmissions and post-ICU mortality rates and the identified discharge practices, i.e. (1) ICU discharge criteria; (2) bed managers; (3) early discharge planning; (4) step-down facilities; (5) medication reconciliation; (6) verbal and written handover; (7) monitoring of post-ICU patients; and (8) consulting ICU nurses. In all analyses, the outcomes were corrected for patient-related confounding factors.

RESULTS

The standardized rate of ICU readmissions varied between 0.14 and 2.67 and 20.8% of the hospitals fell outside the 95% control limits and 3.6% outside the 99.8% control limits. The standardized rate of post-ICU mortality varied between 0.07 and 2.07 and 17.1% of the hospitals fell outside the 95% control limits and 4.9% outside the 99.8% control limits. We could not demonstrate an association between the eight ICU discharge practices and rates of ICU readmissions or post-ICU in-hospital mortality. Implementing a higher number of ICU discharge practices was also not associated with better patient outcomes.

CONCLUSIONS

We found both variation in patient outcomes and variation in ICU discharge practices between ICUs. However, we found no association between discharge practices and rates of ICU readmissions or post-ICU mortality. Further research is necessary to find factors, which may influence these patient outcomes, in order to improve quality of care.

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.

Association of severity of illness and intensive care unit readmission: A systematic review

OBJECTIVES

To determine whether ICU readmission is associated with higher severity of illness scores in adult patients.

BACKGROUND

Readmissions to the intensive care unit (ICU) are associated with increased costs, morbidity, and mortality.

METHODS

We performed searches of MEDLINE, EMBASE, and grey literature databases. We selected studies reporting data from adults who were hospitalized in an ICU, received severity of illness scores, and were discharged from the ICU. Characteristics of readmitted and non-readmitted patients were examined.

RESULTS

We screened 4766 publications and included 31 studies in our analysis. In most studies, severity of illness scores were higher in patients readmitted to the ICU. Readmission was also associated with higher mortality and longer ICU and hospital stays. Excessive heterogeneity precluded the reporting of results in the form of a meta-analysis.

CONCLUSIONS

ICU readmission is associated with higher severity of illness scores during the same hospitalization in adult patients.

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.

Physical and Mental Health of Patients Immediately After Discharge From Intensive Care Unit and 24 Hours Later

Background:

Monitoring the health status of patients discharged from intensive care units is a crucial method of service evaluation.

Objectives:

This study aimed to assess the physical and mental health status of patients immediately after discharge from the ICU and 24 hours later.

Patients and Methods:

This descriptive comparative study was conducted on 104 patients discharged from the ICUs of a referral trauma center in Shiraz, Southwest Iran. Physical parameters, including respiratory rate, need for supplemental oxygen, heart rate, blood pressure, and need for cardiac monitoring, were assessed. Hospital anxiety and depression scale (HADS) was used for mental health evaluation. The mental and physical status of patients were assessed before ICU discharge and 24 hours later; data were recorded in information forms and were analyzed using SPSS statistical software version 17.

Results:

At the time of discharge, the respiratory rate of 28% of the participants was more than 24 minutes, and 95.2% received supplemental oxygen. However, after 24 hours these values decreased to 10% and 21.6%, respectively. The mean heart rate and systolic blood pressure were within the normal range at both time points. Additionally, 63% of the patients had anxiety scores above 11 at both time points, reflecting high anxiety. The number of patients who reported depression increased from 58.7% at ICU discharge to 69.6% after 24 hours.

Conclusions:

Despite the considerable improvement in most of the patients’ physical condition in the first 24 hours after discharge from ICU, a significant number of them remain at risk for the development of adverse effects from this transition. The high prevalence of mental health disorders in these patients reveals the necessity to conduct follow-up consultations.

Risk Factors and Outcomes Associated With Readmission to the Intensive Care Unit After Cardiac Surgery

Unplanned readmission to the intensive care unit (ICU) is associated with poor prognosis, longer hospital stay, increased costs, and higher mortality rate. In this retrospective study, involving 1368 patients, the risk factors for and outcomes of ICU readmission after cardiac surgery were analyzed. The readmission rate was 5.9%, and the most common reason for readmission was cardiac issues. Preoperative risk factors were comorbid conditions, mechanical ventilation, and admission route. Perioperative risk factors were nonelective surgery, duration of cardiopulmonary bypass, and longer operation time. Postoperative risk factors were prolonged mechanical ventilation time, new-onset arrhythmia, unplanned reoperation, massive blood transfusion, prolonged inotropic infusions, and complications. Other factors were high blood glucose level, hemoglobin level, and score on the Acute Physiology and Chronic Health Evaluation II. In-hospital stay was longer and late mortality was higher in the readmitted group. These data could help clinical practitioners create improved ICU discharge protocols or treatment algorithms to reduce length of stay or to reduce readmissions.

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.

Epidemiology of admissions to 11 stand-alone high-dependency care units in the UK

PURPOSE

High-dependency care units (HDUs) are a focus of research to optimize critical care resource allocation. HDUs provide a level of care between the general ward and the intensive care unit (ICU). However, few data report on the case mix and outcomes of patients in these units.

METHODS

Retrospective observational cohort study of patients admitted to 11 stand-alone HDUs in the UK from 2008 to 2011. We stratified patients by location prior to HDU admission and location on discharge from HDU, and we summarized the case mix, transitions of care, and mortality.

RESULTS

Of 9008 patients admitted to 11 stand-alone HDUs, 56.5% were male and the mean age was 62.7 ± 17.9 years. The majority of patients admitted to HDUs were non-surgical (59.3%), with 22.4 and 20.1% admitted from the ICU and general ward, respectively; 41.3% were admitted from the operating room or recovery suite. The median length of stay in HDU was 1.8 days (IQR 0.9-3.5) and in-HDU mortality was 5.1%. Among HDU survivors (n = 8551), 8.5% were discharged to an ICU, 80.9% to a general ward, and 10.6% to other care areas. For patients admitted to HDU from an ICU, only 5.8% were readmitted to ICU. Hospital mortality for the HDU population was 14.8%; for patients discharged to an ICU, hospital mortality was 43.6%.

CONCLUSIONS

In a sample of 11 stand-alone HDUs in the UK, patients are from many different hospital locations. Hospital mortality for patients requiring HDU care is high, particularly for patients who require transfer to an ICU.

Inflammatory and perfusion markers as risk factors and predictors of critically ill patient readmission

Objective

To assess the performance of central venous oxygen saturation, lactate, base deficit, and C-reactive protein levels and SOFA and SWIFT scores on the day of discharge from the intensive care unit as predictors of patient readmission to the intensive care unit.

Methods

This prospective and observational study collected data from 1,360 patients who were admitted consecutively to a clinical-surgical intensive care unit from August 2011 to August 2012. The clinical characteristics and laboratory data of readmitted and non-readmitted patients after discharge from the intensive care unit were compared. Using a multivariate analysis, the risk factors independently associated with readmission were identified.

Results

The C-reactive protein, central venous oxygen saturation, base deficit, and lactate levels and the SWIFT and SOFA scores did not correlate with the readmission of critically ill patients. Increased age and contact isolation because of multidrug-resistant organisms were identified as risk factors that were independently associated with readmission in this study group.

Conclusion

Inflammatory and perfusion parameters were not associated with patient readmission. Increased age and contact isolation because of multidrug-resistant organisms were identified as predictors of readmission to the intensive care unit.

Risk prediction of ICU readmission in a mixed surgical and medical population

BACKGROUND

Readmission to intensive care units (ICU) is accompanied with longer ICU stay as well as higher ICU, in-hospital and 30-day mortality. Different scoring systems have been used in order to predict and reduce readmission rates.

METHODS

The purpose of this study was to evaluate the Stability and Workload Index for Transfer (SWIFT) score as a predictor of readmission. Further, we wanted to study steps and measures taken at the ward prior to readmission.

RESULTS

This was a retrospective study conducted at the mixed surgical and medical ICU at Linköping University Hospital. One thousand sixty-seven patients >18 years were admitted to the ICU during 2 years and were included in the study. During the study period, 27 patients were readmitted to the ICU. Readmitted patients had a higher SWIFT score than the non-readmitted (16.1 ± 6.8 vs. 13.0 ± 7.5, p = 0.03) at discharge. The total ICU length of stay was longer (7.5 ± 7.5 vs. 2.9 ± 5.1, p = 0.004), and the 30-day mortality was higher (26 vs. 7 %, p < 0.001) for readmitted patients. Fifty-six percent of readmitted patients were assessed by the critical care outreach service (CCOS) at the ward prior to ICU readmission. A SWIFT score of 15 or more was associated with a significantly higher readmission rate (p = 0.03) as well as 30-day mortality (p < 0.001) compared to a score of ≤14.

CONCLUSIONS

A SWIFT score of 15 or more is associated with higher readmission rate and 30-day mortality. The SWIFT score could therefore be used for risk prediction for readmission and mortality at ICU discharge.

Assessing the utility of ICU readmissions as a quality metric: an analysis of changes mediated by residency work-hour reforms

BACKGROUND

ICU readmissions are associated with increased mortality and costs; however, it is unclear whether these outcomes are caused by readmissions or by residual confounding by illness severity. An assessment of temporal changes in ICU readmission in response to a specific policy change could help disentangle these possibilities. We sought to determine whether ICU readmission rates changed after 2003 Accreditation Council for Graduate Medical Education Resident Duty Hours reform ("reform") and whether there were temporally corresponding changes in other ICU outcomes.

METHODS

We used a difference-in-differences approach using Project IMPACT (Improved Methods of Patient Information Access of Core Clinical Tasks). Piecewise regression models estimated changes in outcomes immediately before and after reform in 274,491 critically ill medical and surgical patients in 151 community and academic US ICUs. Outcome measures included ICU readmission, ICU mortality, and in-hospital post-ICU-discharge mortality.

RESULTS

In ICUs with residents, ICU readmissions increased before reform (OR, 1.5; 95% CI, 1.22-1.84; P < .01), and decreased after (OR, 0.85; 95% CI, 0.73-0.98; P = .03). This abrupt decline in ICU readmissions after reform differed significantly from an increase in readmissions observed in ICUs without residents at this time (difference-in-differences P < .01). No comparable changes in mortality were observed between ICUs with vs without residents.

CONCLUSIONS

The changes in ICU readmission rates after reform, without corresponding changes in mortality, suggest that ICU readmissions are not causally related to other untoward patient outcomes. Instead, ICU readmission rates likely reflect operational aspects of care that are not patient-centered, making them less useful indicators of ICU quality.