Readmission to a surgical intensive care unit: incidence, outcome and risk factors

The association of the Sequential Organ Failure Assessment score at intensive care unit discharge with intensive care unit readmission in the cardiac intensive care unit

AIMS

Unplanned intensive care unit (ICU) readmissions contribute to increased morbidity, mortality, and healthcare costs. The severity of patient illness at ICU discharge may predict early ICU readmission. Thus, in this study, we investigated the association of cardiac ICU (CICU) discharge Sequential Organ Failure Assessment (SOFA) score with unplanned CICU readmission in patients admitted to the CICU.

METHODS AND RESULTS

We retrospectively reviewed the hospital medical records of 4659 patients who were admitted to the CICU from 2012 to 18. Sequential Organ Failure Assessment scores at CICU admission and discharge were obtained. The predictive performance of organ failure scoring was evaluated by using area under the receiver operating characteristic (AUROC) curves. The primary outcome was unplanned CICU readmission. Of the 3949 patients successfully discharged from the CICU, 184 (4.7%) had an unplanned CICU readmission or they experienced a deteriorated condition but died without being readmitted to the CICU (readmission group). The readmission group had significantly higher rates of organ failure in all organ systems at both CICU admission and discharge than the non-readmission group. The AUROC of the discharge SOFA score for CICU readmission was 0.731, showing good predictive performance. The AUROC of the discharge SOFA score was significantly greater than that of either the initial SOFA score (P = 0.020) or the Acute Physiology and Chronic Health Evaluation II score (P < 0.001). In the multivariable regression analysis, SOFA score, overweight or obese status, history of heart failure, and acute heart failure as reasons for ICU admission were independent predictors of unplanned ICU readmission during the same hospital stay.

CONCLUSION

The discharge SOFA score may identify patients at a higher risk of unplanned CICU readmission, enabling targeted interventions to reduce readmission rates and improve patient outcomes.

Intensive care unit readmission in injured older adults: Modifiable risk factors and implications

BACKGROUND

Prior evaluations of intensive care unit (ICU) readmission among injured older adults have inconsistently identified risk factors, with findings limited by use of subanalyses and small sample sizes. This study aimed to identify risk factors for and implications of ICU readmission in injured older adults.

METHODS

This retrospective, single-center cohort study was conducted at a high-volume Level 1 trauma center and included injured older adult patients (65 years or older) requiring at least one ICU admission during hospitalization between 2013 and 2018. Patients who died <48 hours of admission were excluded. Exposures included patient demographics and clinical factors. The primary outcome was ICU readmission. Multivariable regression was used to identify risk factors for ICU readmission.

RESULTS

A total of 6,691 injured adult trauma patients were admitted from 2013 to 2018, 55.4% (n = 3,709) of whom were admitted to the ICU after excluding early deaths. Of this cohort, 9.1% (n = 339) were readmitted to the ICU during hospitalization. Readmitted ICU patients had a higher median Injury Severity Score (21 [interquartile range, 14-26] vs. 16 [interquartile range, 10-24]), with similar mechanisms of injury between the two groups. Readmitted ICU patients had a significantly higher mortality (19.5%) compared with single ICU admission patients (9.9%) ( p < 0.001) and higher rates of developing any complication, including delirium (61% vs. 30%, p < 0.001). On multivariable analysis, the factors associated with the highest risk of readmission were delirium (Relative Risk, 2.6; 95% confidence interval, 2.07-3.26) and aspiration (Relative Risk, 3.04; 95% confidence interval, 1.67-5.54). More patients in the single ICU admission cohort received comfort-focused care at the time of their death as compared with the ICU readmission cohort (93% vs. 85%, p = 0.035).

CONCLUSION

Readmission to the ICU is strongly associated with higher mortality for injured older adults. Efforts targeted at preventing respiratory complications and delirium in the geriatric trauma population may decrease the rates of ICU readmission and related mortality risk.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

The effect of family-centered care on unplanned emergency room visits, hospital readmissions and intensive care admissions after surgery: a root cause analysis from a prospective multicenter study in the Netherlands

BACKGROUND

Optimizing transitional care by practicing family-centered care might reduce unplanned events for patients who undergo major abdominal cancer surgery. However, it remains unknown whether involving family caregivers in patients' healthcare also has negative consequences for patient safety. This study assessed the safety of family involvement in patients' healthcare by examining the cause of unplanned events in patients who participated in a family involvement program (FIP) after major abdominal cancer surgery.

METHODS

This is a secondary analysis focusing on the intervention group of a prospective cohort study conducted in the Netherlands. Data were collected from April 2019 to May 2022. Participants in the intervention group were patients who engaged in a FIP. Unplanned events were analyzed, and root causes were identified using the medical version of a prevention- and recovery-information system for monitoring and analysis (PRISMA) that analyses unintended events in healthcare. Unplanned events were compared between patients who received care from family caregivers and patients who received professional at-home care after discharge. A Mann-Whitney U test was used to analyze data.

RESULTS

Of the 152 FIP participants, 68 experienced an unplanned event and were included. 112 unplanned events occurred with 145 root causes since some unplanned events had several root causes. Most root causes of unplanned events were patient-related factors (n = 109, 75%), such as patient characteristics and disease-related factors. No root causes due to inadequate healthcare from the family caregiver were identified. Unplanned events did not differ statistically (interquartile range 1-2) (p = 0.35) between patients who received care from trained family caregivers and those who received professional at-home care after discharge.

CONCLUSION

Based on the insights from the root-cause analysis in this prospective multicenter study, it appears that unplanned emergency room visits and hospital readmissions are not related to the active involvement of family caregivers in surgical follow-up care. Moreover, surgical follow-up care by trained family caregivers during hospitalization was not associated with increased rates of unplanned adverse events. Hence, the concept of active family involvement by proficiently trained family caregivers in postoperative care appears safe and feasible for patients undergoing major abdominal surgery.

Prognostic factors for critically ill surgical patients with unplanned intensive care unit readmission: Developing a novel predictive scoring model for predicting readmission

BACKGROUND

Unplanned readmission to the surgical intensive care unit has been demonstrated to worsen patient outcomes. Our objective was to identify risk factors and outcomes associated with unplanned surgical intensive care unit readmission and to develop a predictive scoring model to identify patients at high risk of readmission.

METHODS

We retrospectively analyzed patients admitted to the surgical intensive care unit (2020-2021) and categorized them as either with or without unplanned readmission.

RESULTS

Of 1,112 patients in the derivation cohort, 76 (6.8%) experienced unplanned surgical intensive care unit readmission, with sepsis being the leading cause of readmission (35.5%). Patients who were readmitted had significantly higher in-hospital mortality rates than those who were not. Multivariate analysis identified congestive heart failure, high Sequential Organ Failure Assessment-Hepatic score, use of carbapenem during surgical intensive care unit stay, as well as factors before surgical intensive care unit discharge such as inadequate glycemic control, positive fluid balance, low partial pressure of oxygen in arterial blood/fraction of inspired oxygen ratio, and receipt of total parenteral nutrition as independent predictors for unplanned readmission. The scoring model developed using these predictors exhibited good discrimination between readmitted and non-readmitted patients, with an area under the curve of 0.74. The observed rates of unplanned readmission for scores of <4 points and ≥4 points were 4% and 20.2% (P < .001), respectively. The model also demonstrated good performance in the validation cohort, with an area under the curve of 0.74 and 19% observed unplanned readmission rate for scores ≥4 points.

CONCLUSION

Besides congestive heart failure, clinicians should meticulously re-evaluate critical variables such as the Sequential Organ Failure Assessment-Hepatic score, partial pressure of oxygen in arterial blood/fraction of inspired oxygen ratio, glycemic control, and fluid status before releasing the patient from the surgical intensive care unit. It is crucial to determine the reasons for using carbapenems during surgical intensive care unit stay and the causes for the inability to discontinue total parenteral nutrition before discharging the patient from the surgical intensive care unit.

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.

Survival status and predictors of mortality among patients admitted to surgical intensive care units of Addis Ababa governmental hospitals, Ethiopia: A multicenter retrospective cohort study

Introduction

Critical care is a serious global healthcare burden. Although a high number of surgical patients are being admitted to the surgical intensive care unit (SICU), the mortality remained high, particularly in low and middle-income countries. However, there is limited data in Ethiopia. Therefore, this study aimed to investigate the survival status and predictors of mortality in surgical patients admitted to the SICUs of Addis Ababa governmental hospitals, Ethiopia.

Methods

A multicenter retrospective cohort study was conducted on 410 surgical patients admitted to the SICUs of three government hospitals in Addis Ababa selected using a simple random sampling from February 2017 to February 2020. The data were entered into Epidata version 4.6 and imported to STATA/MP version 16 for further analysis. Bi-variable and multivariable Cox regression models were fitted in the analysis to determine the predictor variables. A hazard ratio (HR) with a 95% confidence interval (CI) was computed, and variables with a p-value <0.05 were considered statistically significant.

Results

From a sample of 410 patients, 378 were included for final analysis and followed for a median follow-up of 5 days. The overall mortality among surgical patients in the SICU was 44.97% with an incidence rate of 5.9 cases per 100 person-day observation. Trauma (AHR = 1.83, 95% CI: 1.19-2.08), Glasgow coma score (GCS) <9 (AHR = 2.06, 95% CI: 1.28-3.31), readmission to the SICU (AHR = 3.52, 95% CI: 2.18-5.68), mechanical ventilation (AHR = 2.52, 95% CI: 1.23-5.15), and creatinine level (AHR = 1.09, 95% CI: 1.01-1.18) were found to be significantly associated with mortality in the SICU.

Conclusion

The mortality of surgical patients in the SICU was high. Trauma, GCS <9 upon admission, readmission to the SICU, mechanical ventilation, and increased in the creatinine level on admission to the SICU were the identified predictors of mortality in the SICU.

Routine laboratory parameters predict intensive care unit admission and hospitalization in patients suffering stab injuries

Background

Knife crime has increased considerably in recent years in Northern Europe. Affected patients often require immediate surgical care due to traumatic organ injury. Yet, little is known about clinically relevant routine laboratory parameters in stab injury patients and how these are associated with intensive care unit (ICU) admission, hospitalization and number of surgeries.

Methods

We retrospectively analyzed 258 stab injury cases between July 2015 and December 2021 at an urban Level I Trauma Center. Annual and seasonal incidences, injury site, injury mechanism, Injury Severity Score (ISS), and surgical management were evaluated. First, correlations between routine laboratory parameters for hematology, coagulation, and serum biochemistry (peak, and Δ (change from admission to peak within 3 days following admission)) and length of hospital stay, ICU stay, and number of surgeries were assessed using Spearman's rank correlation coefficients. Second, multivariable Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were conducted to identify parameters predictive of clinical outcomes. Third, longitudinal developments of routine laboratory parameters were assessed during hospital admission.

Results

In 2021, significantly more stab injuries were recorded compared with previous years and occurred less during winter compared with other seasons. Mean ISS was 8.3 ± 7.3, and ISS was positively correlated with length of hospital and ICU stay (r = 0.5-0.8, p < 0.001). Aspartate transaminase (AST) (Δ) (r = 0.690), peak C-reactive protein (CrP) (r = 0.573), and erythrocyte count (Δ) (r = 0.526) showed the strongest positive correlations for length of ICU stay for penetrating, thoracoabdominal, and organ injuries, respectively. No correlations were observed between routine laboratory parameters and number of surgeries. For patients with penetrating injuries, LASSO-selected predictors of ICU admission included ISS, pH and lactate at admission, and Δ values for activated partial thromboplastin time (aPTT), K+, and erythrocyte count. CrP levels on day 3 were significantly higher in patients with penetrating (p = 0.005), thoracoabdominal (p = 0.041), and organ injuries (p < 0.001) compared with those without.

Conclusion

Our data demonstrate an increase in stab injury cases in 2021 and an important link between changes in routine laboratory parameters and ICU admission and hospitalization. Monitoring ISS and changes in AST, CrP, erythrocyte count, pH, lactate, aPTT, and K+ may be useful to identify patients at risk and adjust surgical and ICU algorithms early on.

Transitional Care Programs for Patients with High Nursing Activity Scores Reduce Unplanned Readmissions to Intensive Care Units

Background and Objectives: The main objective of a transitional care program (TCP) is to detect patients with early deterioration following intensive care unit (ICU) discharge in order to reduce unplanned ICU readmissions. Consensus on the effectiveness of TCPs in preventing unscheduled ICU readmissions remains lacking. In this case study assessing the effectiveness of TCP, we focused on the association of unplanned ICU readmission with high nursing activities scores (NASs), which are considered a risk factor for ICU readmission. Materials and Methods: This retrospective observational study analyzed the data of patients admitted to a single-center ICU between January 2016 and December 2019, with an NAS of >53 points at ICU discharge. The following data were extracted: patient characteristics, ICU treatment, acute physiology and chronic health evaluation II (APACHE II) score at ICU admission, Charlson comorbidity index (CCI), 28-day mortality rate, and ICU readmission rate. The primary outcome was the association between unplanned ICU readmissions and the use of a TCP. The propensity score (PS) was calculated using the following variables: age, sex, APACHE II score, and CCI. Subsequently, logistic regression analysis was performed using the PS to evaluate the outcomes. Results: A total of 143 patients were included in this study, of which 87 (60.8%) participated in a TCP. Respiratory failure was the most common cause of unplanned ICU readmission. The unplanned ICU readmission rate was significantly lower in the TCP group. In the logistic regression model, TCP (odds ratio, 5.15; 95% confidence interval, 1.46−18.2; p = 0.01) was independently associated with unplanned ICU readmission. Conclusions: TCP intervention with a focus on patients with a high NAS (>53 points) may prevent unplanned ICU readmission.

Risk Factors for Unplanned ICU Readmission Among Trauma Patients: Age Matters

To determine the frequency of unplanned ICU readmission (UIR) among adult (18-64) and elderly (65+) trauma patients and to compare the risk factors for UIR and its clinical impact between age groups.

DESIGN

Retrospective cohort study using clinical data from a statewide trauma registry.

SETTING

All accredited trauma centers in Pennsylvania.

PATIENTS

Consecutive adult and elderly trauma patients requiring admission from the emergency department to the ICU between 2012 and 2017.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Among the 48,340 included in the analysis, 49.5% were elderly and 3.8% experienced UIR. UIR was 1.7 times more likely among elderly patients and was associated with increased hospital length of stay in both age groups. UIR was associated with an absolute increased risk of hospital mortality of 6.1% among adult patients and 16.9% among elderly patients experiencing UIR. In addition to overall injury severity and burden of preexisting medical conditions, specific risk factors for UIR were identified in each age group. In adult but not elderly patients, UIR was significantly associated with history of stroke, peptic ulcer disease, cirrhosis, diabetes, and malignancy. In elderly but not adult patients, UIR was also significantly associated with chronic kidney disease.

CONCLUSIONS

UIR is associated with worse clinical outcomes in both adult and elderly trauma patients, but risk factors and the magnitude of impact differ between age groups. Interventions to mitigate the risk of UIR that take into account patients' age group and specific risk factors may improve outcomes.

Safety of Elderly Fallers: Identifying Associated Risk Factors for 30-Day Unplanned Readmissions Using a Clinical Data Warehouse

BACKGROUND

Hospital readmissions are a major problem in the older people as they are frequent, costly, and life-threatening. Falls among older adults are the leading cause of injury, deaths, and emergency department visits for trauma.

OBJECTIVE

The main objective was to determine risk factors associated with a 30-day readmission after index hospital admission for fall-related injuries.

METHODS

A retrospective nested case-control study was conducted. Data from elderly patients initially hospitalized for fall-related injuries in 2019, in 11 of the Greater Paris University Hospitals and discharged home, were retrieved from the clinical data warehouse. Cases were admission of elderly patients who subsequently experienced a readmission within 30 days after discharge from the index admission. Controls were admission of elderly patients who were not readmitted to hospital.

RESULTS

Among 670 eligible index admissions, 127 (18.9%) were followed by readmission within 30 days after discharge. After multivariate analysis, men sex (odds ratio [OR] = 2.29, 95% confidence interval [CI] = 1.45-3.61), abnormal concentration of C-reactive protein, and anemia (OR = 2.22, 95% CI = 1.28-3.85; OR = 1.85, 95% CI = 1.11-3.11, respectively) were associated with a higher risk of readmission. Oppositely, having a traumatic injury at index admission decreased this risk (OR = 0.47, 95% CI = 0.28-0.81).

CONCLUSIONS

Reducing early unplanned readmission is crucial, especially in elderly patients susceptible to falls. Our results indicate that the probability of unplanned readmission is higher for patients with specific characteristics that should be taken into consideration in interventions designed to reduce this burden.

The Impact of an Intensivist-Led Critical Care Transition Program

Objective: Evaluate the impact of a post-discharge critical care transition program (CTP) on intensive care unit (ICU) readmission, in-hospital mortality, and six-month survival.

Methods: This was a prospective observational, single-center study, with a before-after design, in a critical care department in a tertiary hospital in Northern Portugal. Critically ill patients with ICU stay > 48 h or intermediate care stay >72 h or tracheostomized patients were included in the program. Historic controls included critically ill patients admitted in the six months prior to program implementation. The follow-up visit included a medical evaluation by an intensivist and a meeting with the attending physician. The primary outcome was critical care department readmission. Secondary outcomes were mortality at hospital discharge, 28-day, and six-month mortality. The readmission rate was compared between groups. Multivariate analysis and Kaplan-Meyer survival analysis were used to evaluate survival benefits.

Results: Between September 2020 and March 2021, 132 patients were included in the CTP. The Control group included 196 patients. The intensivist’s assessment led to management change in 15.1% of patients. The CTP group had a non-significant lower readmission rate (0.8% vs. 4.1%; p=0.09). Multivariate analysis showed a benefit for the CTP regarding in-hospital, 28-day, and six-month mortality. Kaplan-Meyer survival analysis showed improved survival in the CTP group.

Conclusions: The CTP reduced, non-significantly, the readmission rate, and significantly improved in-hospital and six-month mortality. Further analyses are needed to improve inclusion criteria and better allocate human resources.

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.

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.

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.

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.

Critical care transition programs on readmission or death: A systematic review and meta-analysis

BACKGROUND

Deterioration after ICU discharge may lead to readmission or even death. Interventions (eg, critical care transition programs) have been developed to improve the clinical handover between the ICU and the ward. We conducted a systematic review with meta-analysis and trial sequential analysis (TSA) according to Cochrane Handbook and Grading of recommendations, assessment, development and evaluations (GRADE) methodology to assess the impact of these interventions on readmission and death (PROSPERO, no CRD42019121746).

METHODS

We searched PubMed/MEDLINE, CINAHL, AMED, PsycINFO, and the Cochrane Central Register for Controlled Trials from inception until January 2019. We included historically controlled studies that evaluated critical care transition programs in adults discharged from the ICU. Readmission and in-hospital mortality were the primary outcomes. Risk of bias, publications bias, and the quality of evidence were assessed with the ROBINS-Itool, funnel plot and GRADE, respectively.

RESULTS

Fifteen observational studies were included (11 in meta-analysis). All studies had at least serious risk of bias. ICU discharge within a critical care transition program modestly reduced the risk of readmission (RR 0.78; 95% CI: 0.64-0.96; TSA-adjusted 95% CI: 0.59-1.03) but not in-hospital mortality (RR 0.82; 95% CI: 0.64-1.06; TSA-adjusted 95% CI: 0.49-1.37). There was substantial heterogeneity among studies. TSA indicated lack of firm evidence. The GRADE quality of evidence on outcomes was very low.

CONCLUSIONS

We found no clear benefit in terms of reducing risk of readmission or death after ICU discharge, however, with overall very low certainty of evidence.

The Modified Early Warning Score as a Predictive Tool During Unplanned Surgical Intensive Care Unit Admission

Background: The Modified Early Warning Score (MEWS) has been proposed to warn healthcare providers of potentially serious adverse events. We evaluated this scoring system during unplanned escalation of care in hospitalized surgical patients during a 1-year period.

Methods: Following institutional review board approval, all consecutive, unplanned surgical admissions into the surgical intensive care unit (SICU) during 2016 were entered into this study. MEWS and patient demographics during bedside evaluation for SICU admission were extracted from electronic medical records. Logistic regression was used to analyze the association of MEWS with the incidence of future mortality. P values were set at <0.01 for statistical significance.

Results: In this series of 263 consecutive patients, the incidence of mortality following unplanned escalation of care was 29.3% (confidence interval [CI] 24.1% to 35.0%), ranging from 22% to 57%, with all positive MEWS values. The association of MEWS with future mortality was not statistically significant (P=0.0107). A misclassification rate of 0.29 (CI 0.24 to 0.35) was observed with this association.

Conclusion: MEWS provided no clinical benefit as an early warning system, as mortality was elevated throughout the MEWS scale in this clinical setting. The high misclassification rate indicates MEWS does not provide discriminatory support for patients at risk for mortality.

Efficiency and efficacy of planning and care on a post-anesthesia care unit: a retrospective cohort study

BACKGROUND

In the post-anesthesia care unit in our hospital, selected postoperative patients receive care from anesthesiologists and nursing staff if these patients require intensive hemodynamic monitoring or treatment to stabilize vital functions (e.g., vasopressor use and mechanical ventilation support) during a one-night admission. We investigated the agreement between elective preoperative planning for post-anesthesia care unit admission and the postoperative reality, along with the consequences of planning failures.

METHODS

Data from records for 479 consecutive patients from June 1 to November 30, 2014, in a tertiary referral hospital were reviewed and analyzed. All patients admitted to PACU were included, along with patients scheduled to be referred to PACU but ultimately transferred to another ward. The primary outcome was the efficiency of planning PACU admission for elective patients. Secondary outcomes included secondary admissions to PACU or the intensive care unit (ICU) and 30-day morbidity and mortality.

RESULTS

Of the 479 included patients, 342 (71%) were admitted per preoperative planning. Five patients (1%) needed cardiopulmonary resuscitation, and six (1%) did not survive the follow-up period. Patients admitted to PACU because of a shortage of beds in the ICU had the highest readmission (20%) and mortality rates (20%) (P = 0.01).

CONCLUSIONS

Preoperative planning for PACU admission was off-target for 29%. However, efficient care always takes precedence over efficient planning. In particular, downgrading patients to PACU because of a shortage of beds in the ICU was associated with a mortality increase.

Surgical Intensive Care Unit Admission Variables Predict Subsequent Readmission

Intensive care unit (ICU) readmissions are associated with increased resource use. Defining predictors may improve resource use. Surgical ICU patients requiring readmission will have different characteristics than those who do not. We conducted a retrospective cohort study of a prospectively maintained database. The Acute Physiology and Chronic Health Evaluation (APACHE) IV quality database identified patients admitted January 1 through December 31, 2011. Patients were divided into groups: NREA = patients admitted to the ICU, discharged, and not readmitted versus REA = patients admitted to the ICU, discharged, and readmitted. Comparisons were made at index admission, not readmission. Categorical variables were compared by Fisher's exact testing and continuous variables by t test. Multivariate logistic regression identified independent predictors of readmission. There were 765 admissions. Seventy-seven patients required readmission 94 times (12.8% rate). Sixty-two patients died on initial ICU admission. Admission severity of illness was significantly higher (APACHE III score: 69.54 ± 21.11 vs 54.88 ± 23.48) in the REA group. Discharge acute physiology scores were equal between groups (47.0 ± 39.2 vs 44.2 ± 34.0, P = nonsignificant). In multivariate analysis, REA patients were more likely admitted to emergency surgery (odds ratio, 1.9; 95% confidence interval, 1.01 ± 3.5) more likely to have a history of immunosuppression (2.7, 1.4 ± 5.3) or higher Acute Physiology Score (1.02; 1.0 ± 1.03) than NREA. Patients who require ICU readmission have a different admission profile than those who do not “bounce back.” Understanding these differences may allow for quality improvement projects such as instituting different discharge criteria for different patient populations.

Application of electronic medical record-derived analytics in critical care: Rothman Index predicts mortality and readmissions in surgical intensive care unit patients

INTRODUCTION

The Rothman Index (RI) is an objective measurement of a patient's overall condition, automatically generated from 26 variables including vital signs, laboratory data, cardiac rhythms, and nursing assessments. The purpose of this study was to assess the validity of RI scores in predicting surgical ICU (SICU) readmission rates and mortality.

METHODS

We conducted a single-center retrospective analysis of surgical patients who were transferred from the SICU to the surgical floor from December 2014 to December 2016. Data included demographics, length of stay (LOS), mortality, and RI at multiple pretransfer and post-transfer time points.

RESULTS

A total of 1,445 SICU patients were transferred to the surgical floor; 79 patients (5.5%) were readmitted within 48 hours of transfer. Mean age was 52 years, and 67% were male. Compared to controls, patients readmitted to the SICU within 48 hours experienced higher LOS (29 vs. 11 days, p < 0.05) as well as higher mortality (2.5% vs. 0.6%, p < 0.05). Patients requiring readmission also had a lower RI at 72, 48, and 24 hours before transfer as well as at 24 and 48 hours after transfer (p < 0.05 for all). Rothman Index scores were categorized into higher-risk (<40), medium-risk (40-65), and lower-risk groups (>65); RI scores at 24 hours before transfer were inversely proportional to overall mortality (RI < 40 = 2.5%, RI 40-65 = 0.3%, and RI > 65 = 0%; p < 0.05) and SICU readmission rates (RI < 40 = 9%, RI 40-65 = 5.2%, and RI > 65 = 2.8%; p < 0.05). Patients transferred with RI scores greater than 83 did not require SICU readmission within 48 hours.

CONCLUSION

Surgical ICU patients requiring readmission within 48 hours of transfer have a significantly higher mortality and longer LOS compared to those who do not. Patients requiring readmission also have significantly lower pretransfer and post-transfer RI scores compared to those who do not. Rothman Index scores may be used as a clinical tool for evaluating patients before transfer from the SICU. Prospective studies are warranted to further validate use of this technology.

LEVEL OF EVIDENCE

Retrospective database review, level III.

Impact of Intensive Care Unit Readmissions on Patient Outcomes and the Evaluation of the National Early Warning Score to Prevent Readmissions: Literature Review

Background

Intensive care unit (ICU) readmissions have been shown to increase a patient’s in-hospital mortality and length of stay (LOS). Despite this, no methods have been set in place to prevent readmissions from occurring.

Objective

The aim of this literature review was to evaluate the impact of ICU readmission on patient outcomes and to evaluate the effect of using a risk stratification tool, the National Early Warning Score (NEWS), on ICU readmissions.

Methods

A database search was performed on PubMed, Cumulative Index of Nursing and Allied Health Literature, Google Scholar, and ProQuest. In the initial search, 2028 articles were retrieved; after inclusion and exclusion criteria were applied, 12 articles were ultimately used in this literature review.

Results

This literature review found that patients readmitted to the ICU have an increased mortality rate and LOS at the hospital. The sample sizes in the reviewed studies ranged from 158 to 745,187 patients. Readmissions were most commonly associated with respiratory issues about 18% to 59% of the time. The NEWS has been shown to detect early clinical deterioration in a patient within 24 hours of transfer, with a 95% CI of 0.89 to 0.94 (P<.001), a sensitivity of 93.6% , and a specificity of 82.2%.

Conclusions

ICU readmissions are associated with worse patient outcomes, including hospital mortality and increased LOS. Without the use of an objective screening tool, the provider has been solely responsible for the decision of patient transfer. Assessment with the NEWS could be helpful in decreasing the frequency of inappropriate transfers and ultimately ICU readmission.

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.

The Critical Care Society of Southern Africa Consensus Statement on ICU Triage and Rationing (ConICTri)

Background

In South Africa (SA), intensive care is faced with the challenge of resource scarcity as well as an increasing demand for intensive care unit (ICU) services. ICU services are expensive, and practitioners in low- to middle-income countries experience daily the consequences of limited resources. Critically limited resources necessitate that rationing and triage (prioritisation) decisions are frequently necessary in SA, particularly in the publicly funded health sector.

Purpose

The purpose of this consensus statement is to examine key questions that arise when considering the status of ICU resources in SA, and more specifically ICU admission, rationing and triage decisions. The accompanying guideline in this issue is intended to guide frontline triage policy and ensure the best utilisation of intensive care in SA, while maintaining a fair distribution of available resources. Fair and efficient triage is important to ensure the ongoing provision of high-quality care to adult patients referred for intensive care.

Recommendations

In response to 14 key questions developed using a modified Delphi technique, 29 recommendations were formulated and graded using an adapted GRADE score. The 14 key questions addressed the status of the provision of ICU services in SA, the degree of resource restriction, the efficiency of resource management, the need for triage, and how triage could be most justly implemented. Important recommendations included the need to formally recognise and accurately quantify the provision of ICU services in SA by national audit; actively seek additional resources from governmental bodies; consider methods to maximise the efficiency of ICU care; evaluate lower level of care alternatives; develop a triage guideline to assist policy-makers and frontline practitioners to implement triage decisions in an efficient and fair way; measure and audit the consequence of triage; and promote research to improve the accuracy and consistency of triage decisions. The consensus document and guideline should be reviewed and revised appropriately within 5 years.

Conclusion

In recognition of the absolute need to limit patient access to ICU because of the lack of sufficient intensive care resources in public hospitals, recommendations and a guideline have been developed to guide policy-making and assist frontline triage decision-making in SA. These documents are not a complete plan for quality practice but rather the beginning of a long-term initiative to engage clinicians, the public and administrators in appropriate triage decision-making, and promote systems that will ultimately maximise the efficient and fair use of available ICU resources.

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.

Short- and long-term mortality in major non-cardiac surgical patients admitted to the intensive care unit

BACKGROUND

The aim of this register-based cohort study was to characterize patients admitted to the intensive care unit (ICU) following non-cardiac surgery and identify risk factors associated with 30-days, 90-days and 1-year mortality after ICU admission.

METHODS

Patients aged 18+ years admitted to the intensive care unit within 30-days of non-cardiac surgery at four Capital Region hospitals in Denmark between January 2005 and December 2014 were included. Patients were identified through the Danish National Patient Register. The outcomes were 30-days, 90-days and 1-year mortality after ICU admission. Unadjusted and multivariate logistic regression analyses were performed to identify independent risk factors of mortality.

RESULTS

The study included 3311 ICU patients. Gastrointestinal surgery accounted for 71.3%, orthopaedic surgery for 18.4% and urologic surgery for 10.2% of the population. For the total population, the median length of stay in hospital was 18 days (9-36, 25th-75th percentile) and 2 days (1-4, 25th-75th percentile) in the ICU. Thirty-days, 90-days and 1-year mortality were 37.8%, 44.5% and 51.2% respectively. Mortality within the ICU was 22.3% while the post-ICU in-hospital mortality was 19.4%. Higher age, comorbidity, delayed ICU admission, acute surgery, and gastrointestinal and orthopaedic surgery increased 30-days, 90-days and 1-year mortality.

CONCLUSIONS

Short- and long-term mortality in non-cardiac surgical patients admitted to the ICU is very high, especially among the elderly comorbid patients undergoing acute surgery. Future research should focus on targeting clinically modifiable risk factors and performing tailored treatment for these high-risk patients.

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

Purpose

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

Patients and methods

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

Results

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

Conclusion

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

ICU readmission of patients with cancer: Incidence, risk factors and mortality

PURPOSE

Few studies evaluated ICU readmission in cancer patients. This study aimed to describe the incidence and risk factors for ICU readmission in cancer patients and the association with mortality.

MATERIALS AND METHODS

A retrospective cohort study at a comprehensive cancer center, which included cancer patients who were discharged after their initial ICU admission over a 5-year period. The characteristics and outcomes of patients who required ICU readmission within 30 days of discharge were compared to those who did not require readmission during the study period. Multivariate analyses were performed to identify factors associated with readmission and to evaluate the association between readmission and mortality.

RESULTS

Among 1582 patients discharged from the ICU, 313(19.8%) were readmitted after a median of 6 days. The most common readmission diagnoses were respiratory failure and sepsis. Mechanical ventilation (OR 5.80; 95% CI 4.29-7.84) and thrombocytopenia (OR 1.66; 95% CI 1.16-2.38), on the first ICU admission were associated with readmission. Readmission was associated with a higher risk of 28-day and 90-day mortality, (OR 3.02; CI 2.3-4.00) and (OR 3.47; 95% CI 2.69-4.49), respectively.

CONCLUSIONS

ICU readmission was associated with increased mortality. Mechanical ventilation and thrombocytopenia at the first admission were associated with ICU readmission.

Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data

RATIONALE

Patients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving risk stratification for patients transferred to the wards could have important benefits for critically ill hospitalized patients.

OBJECTIVES

We aimed to use a machine-learning technique to derive and validate an intensive care unit readmission prediction model with variables available in the electronic health record in real time and compare it to previously published algorithms.

METHODS

This observational cohort study was conducted at an academic hospital in the United States with approximately 600 inpatient beds. A total of 24,885 intensive care unit transfers to the wards were included, with 14,962 transfers (60%) in the training cohort and 9,923 transfers (40%) in the internal validation cohort. Patient characteristics, nursing assessments, International Classification of Diseases, Ninth Revision codes from prior admissions, medications, intensive care unit interventions, diagnostic tests, vital signs, and laboratory results were extracted from the electronic health record and used as predictor variables in a gradient-boosted machine model. Accuracy for predicting intensive care unit readmission was compared with the Stability and Workload Index for Transfer score and Modified Early Warning Score in the internal validation cohort and also externally using the Medical Information Mart for Intensive Care database (n = 42,303 intensive care unit transfers).

RESULTS

Eleven percent (2,834) of discharges to the wards were later readmitted to the intensive care unit. The machine-learning-derived model had significantly better performance (area under the receiver operating curve, 0.76) than either the Stability and Workload Index for Transfer score (area under the receiver operating curve, 0.65), or Modified Early Warning Score (area under the receiver operating curve, 0.58; P value < 0.0001 for all comparisons). At a specificity of 95%, the derived model had a sensitivity of 28% compared with 15% for Stability and Workload Index for Transfer score and 7% for the Modified Early Warning Score. Accuracy improvements with the derived model over Modified Early Warning Score and Stability and Workload Index for Transfer were similar in the Medical Information Mart for Intensive Care-III cohort.

CONCLUSIONS

A machine learning approach to predicting intensive care unit readmission was significantly more accurate than previously published algorithms in both our internal validation and the Medical Information Mart for Intensive Care-III cohort. Implementation of this approach could target patients who may benefit from additional time in the intensive care unit or more frequent monitoring after transfer to the hospital ward.

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.

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

Quality problem

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

Initial assessment

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

Choice of solution

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

Implementation

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

Evaluation

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

Lessons learned

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

A qualitative exploration of the discharge process and factors predisposing to readmissions to the intensive care unit

Background

Quantitative studies have demonstrated several factors predictive of readmissions to intensive care. Clinical decision tools, derived from these factors have failed to reduce readmission rates. The purpose of this study was to qualitatively explore the experiences and perceptions of physicians and nurses to gain more insight into intensive care readmissions.

Methods

Semi-structured interviews of intensive care unit (ICU) and general medicine care providers explored work routines, understanding and perceptions of the discharge process, and readmissions to intensive care. Participants included ten providers from the ICU setting, including nurses (n = 5), consultant intensivists (n = 2), critical care fellows (n = 3) and 9 providers from the general medical setting, nurses (n = 4), consulting physicians (n = 2) and senior resident physicians (n = 3). Principles of grounded theory were used to analyze the interview transcripts.

Results

Nine factors within four broad themes were identified: (1) patient factors – severity-of-illness and undefined goals of care; (2) process factors – communication, transitions of care; (3) provider factors – discharge decision-making, provider experience and comfort level; (4) organizational factors – resource constraints, institutional policies.

Conclusions

Severe illness predisposes ICU patients to readmission, especially when goals of care were not adequately addressed. Communication, premature discharge, and other factors, mostly unrelated to the patient were also perceived by physicians and nurses to be associated with readmissions to intensive care. Quality improvement efforts that focus on modifying or improving aspects of non-patient factors may improve outcomes for patients at risk of ICU readmission.

Electronic supplementary material

The online version of this article (10.1186/s12913-017-2821-z) contains supplementary material, which is available to authorized users.

Audit of Postoperative Surgical Intensive Care Unit Admissions

Objective

The objective of this study was to conduct an audit of Surgical Intensive Care Unit (SICU) for identifying the admission risk factors and evaluating the outcomes of postoperative surgical patients.

Design

This was a prospective, observational study.

Setting

This study was conducted at SICU of a 1500-bedded tertiary care university hospital in Western India.

Subject and Methods

Two hundred and forty patients admitted to the SICU postoperatively over a period of 15 months.

Results

Planned and unplanned postoperative SICU admission rate was 4.45% and 0.09% of the 5284 patients operated. Indications for planned admissions included preoperative medical illnesses, anticipated blood loss, and anticipated mechanical ventilation while unpredicted intraoperative hypotension was the principal cause of unplanned admittance. Univariate analysis for two groups of the American Society of Anesthesiologists (ASA) physical status indicated that advanced age, high American College of Cardiology/American Heart Association (ACC/AHA) surgical risk, emergency surgery, planned admissions, and mortality were related to high ASA class. Analysis for ACC/AHA surgical risk showed association of high ACC/AHA surgical risk with advanced age, male gender, high ASA physical status, emergency surgery, planned admissions, and mortality. High mortality was observed in patients with SICU stay of >7 days (75.86%) and readmitted cases (72.73%).

Conclusion

The need for postoperative critical care is significantly higher in males, elderly, patients with poor preoperative risk stratification scores, preexisting medical illness, major intraoperative hemorrhage, hypotension requiring inotropic support, perioperative respiratory problems and patients undergoing abdominal, trauma, and emergent surgeries. A larger study inclusive of other surgical subspecialties would aid in optimal decision-making for admissions to the SICU.

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.

Does the implementation of a novel intensive care discharge risk score and nurse-led inpatient review tool improve outcome? A prospective cohort study in two intensive care units in the UK

OBJECTIVE

To develop a clinical prediction model for poor outcome after intensive care unit (ICU) discharge in a large observational data set and couple this to an acute post-ICU ward-based review tool (PIRT) to identify high-risk patients at the time of ICU discharge and improve their acute ward-based review and outcome.

DESIGN

Retrospective patient cohort of index ICU admissions between June 2006 and October 2011 receiving routine inpatient review. Prospective cohort between March 2012 and March 2013 underwent risk scoring (PIRT) which subsequently guided inpatient ward-based review.

SETTING

Two UK adult ICUs.

PARTICIPANTS

4212 eligible discharges from ICU in the retrospective development cohort and 1028 patients included in the prospective intervention cohort.

INTERVENTIONS

Multivariate analysis was performed to determine factors associated with poor outcome in the retrospective cohort and used to generate a discharge risk score. A discharge and daily ward-based review tool incorporating an adjusted risk score was introduced. The prospective cohort underwent risk scoring at ICU discharge and inpatient review using the PIRT.

OUTCOMES

The primary outcome was the composite of death or readmission to ICU within 14 days of ICU discharge following the index ICU admission.

RESULTS

PIRT review was achieved for 67.3% of all eligible discharges and improved the targeting of acute post-ICU review to high-risk patients. The presence of ward-based PIRT review in the prospective cohort did not correlate with a reduction in poor outcome overall (P=0.876) or overall readmission but did reduce early readmission (within the first 48 hours) from 4.5% to 3.6% (P=0.039), while increasing the rate of late readmission (48 hours to 14 days) from 2.7% to 5.8% (P=0.046).

CONCLUSION

PIRT facilitates the appropriate targeting of nurse-led inpatient review acutely after ICU discharge but does not reduce hospital mortality or overall readmission rates to ICU.

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.

Critical Care Prognosis and Outcomes in Patients with Cancer

Advances in cancer treatment and patient survival are associated with increasing number of these patients requiring intensive care. Over the last 2 decades, there has been a steady improvement in the outcomes of critically ill patients with cancer. This review provides data on the use of the intensive care unit (ICU) and short and long-term outcomes of critically ill patients with cancer, the ICU system practices that influence patients outcomes, and the role of the different clinical variables in predicting the prognosis of these patients.

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.

Quality of life of critically ill patients in a developing country: a prospective longitudinal study

[Purpose] To evaluate the quality of life of critical illness survivors in a developing country over the time after hospital discharge and to assess the influence of clinical variables on quality of life. [Subjects and Methods] A prospective longitudinal study was conducted in a large, tertiary, public hospital in Sao Paulo, Brazil. We included patients ≥18 years old, hospitalized in the intensive care unit with ≥24 hours of invasive mechanical ventilation. Quality of life was assessed using the Medical Outcomes Study 36-Item Short Form Health Survey, which was applied by telephone interview at the first, third and sixth months after hospital discharge. [Results] 75 patients were included in the study. Quality of life improved progressively after hospital discharge; role-physical was the most compromised domain. The physical component was influenced by the age. Quality of life was not influenced by Apache II categorization, length of invasive mechanical ventilation, intensive care unit stay or hospital stay. [Conclusion] Survivors of critical illness in a developing country present poor quality of life, which improves over time. Age influenced the physical component of quality of life.

The Effect of Liaison Nurse Service on Patient Outcomes after Discharging From ICU: a Randomized Controlled Trial

Introduction: Recent studies suggest that liaison nurse intervention might be effective to solve the gap between intensive care unit and wards, but little studies are known about the effect of this intervention. The aim of this study was to investigate the effect of liaison nurse service on patient outcomes after discharging from intensive care unit.

Methods: In this single blinded randomized controlled trial, a total of 80 patients were selected by convenience sampling method from two teaching hospitals located in Tehran, Iran. Patients were randomly allocated to either the experimental or the control groups. Patients in the experimental group received post-ICU care from a liaison nurse and patients in the control group received the routine care. After the intervention, patients’ vital signs, level of consciousness, length of hospital stay, need for re-hospitalization in ICU, and satisfaction with care were measure. Data were analyzed by SPSS Ver.13 software.

Results: None of the participants experienced ICU re-hospitalization. According to the result and there were no significant differences between the study groups regarding heart rate, respiratory rate, systolic blood pressure, post-ICU level of consciousness, satisfaction with care, and length of hospitalization in medical-surgical wards. However, the study groups differed significantly in terms of body temperature.

Conclusion: Care services provided by an ICU liaison nurse has limited effects on patient outcomes. However, considering the contradictions among the studies, further studies are needed for providing clear evidence about the effectiveness of the liaison nurse strategy.

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.

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.