Intensive care readmission: A contemporary review of the literature

Time-to-surgery paradigms: wait time and surgical outcomes in critically Ill patients who underwent emergency surgery for gastrointestinal perforation

BACKGROUND

Waiting time for emergency abdominal surgery have been known to be linked to mortality. However, there is no clear consensus on the appropriated timing of surgery for gastrointestinal perforation. We investigated association between wait time and surgical outcomes in emergency abdominal surgery.

METHODS

This single-center retrospective cohort study evaluated adult patients who underwent emergency surgery for gastrointestinal perforations between January 2003 and September 2021. Risk-adjusted restricted cubic splines modeled the probability of each mortality according to wait time. The inflection point when mortality began to increase was used to define early and late surgery. Outcomes among propensity-score matched early and late surgical patients were compared using percent absolute risk differences (RDs, with 95% CIs).

RESULTS

Mortality rates began to rise after 16 h of waiting. However, early and late surgery groups showed no significant differences in 30-day mortality (11.4% vs. 5.7%), ICU stay duration (4.3 ± 7.5 vs. 4.3 ± 5.2 days), or total hospital stay (17.4 ± 17.0 vs. 24.7 ± 23.4 days). Notably, patients waiting over 16 h had a significantly higher ICU readmission rate (8.6% vs. 31.4%). The APACHE II score was a significant predictor of 30-day mortality.

CONCLUSIONS

Although we were unable to reveal significant differences in mortality in the subgroup analysis, we were able to find an inflection point of 16 h through the RCS curve technique.

TRIAL REGISTRATION

Formal consent was waived due to the retrospective nature of the study, and ethical approval was obtained from the institutional research committee of our institution (B-2110-714-107) on 6 October 2021.

Patient transfer from intensive care units to general wards: An exploratory qualitative study of ward nurses' experiences of patient safety

AIM

Patient transfer from intensive care units (ICUs) to general wards is a routine part of hospital care. However, if the transfer is not optimal, it can result in increased readmissions to the ICU, increased stress and discomfort for the patient and, thus, a threat to patient safety. The aim of this study was to explore how general ward nurses experience patient safety during patient transfers from ICUs to general wards.

DESIGN

A qualitative design based on a phenomenological approach was used.

METHODS

Two focus group interviews were conducted with a total of eight nurses from a medical ward and a surgical ward at one hospital in Norway. The data were analysed using systematic text condensation.

RESULTS

The nurses' experiences of patient safety during patient transfer had four themes: (1) the importance of preparedness; (2) the importance of the handover of information; (3) stress and a lack of resources and (4) a feeling of two different worlds.

CONCLUSION

In order to promote patient safety, the informants highlighted the importance of being well prepared for transfer, and to have optimal handover of information. Stress, lack of resources and a feeling of two different worlds may pose threats to patient safety.

IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE

We suggest several intervention studies testing the effect of intervention improving patient safety during the transfer process are designed, and that increased knowledge in this area is used to develop local practice recommendations.

PATIENT OR PUBLIC CONTRIBUTION

The participants of this study were nurses and this contribution has been explained in the Data collection section. There was no patient contribution in this study.

The risk assessment tool for intensive care unit readmission: A systematic review and meta-analysis

OBJECTIVE

To review and evaluate existing risk assessment tools for intensive care unitreadmission.

METHODS

Nine electronic databases (Medline, CINAHL, Web of Science, Cochrane Library, Embase, Sino Med, CNKI, VIP, and Wan fang) were systematically searched from their inception to September 2022. Two authors independently extracted data from the literature included. Meta-analysis was performed under the bivariate modeling and summary receiver operating characteristic curve method.

RESULTS

A total of 29 studies were included in this review, among which 11 were quantitatively Meta-analyzed. The results showed Stability and Workload Index for Transfer: Sensitivity = 0.55, Specificity = 0.65, Area under curve = 0.63. And Early warning score: Sensitivity = 0.78, Specificity = 0.83, Area under curve = 0.88. The remaining tools included scores, nomograms, machine learning models, and deep learning models. These studies, with varying reports on thresholds, case selection, data preprocessing, and model performance, have a high risk of bias.

CONCLUSION

We cannot identify a tool that can be used directly in intensive care unit readmission risk assessment. Scores based on early warning score are moderately accurate in predicting readmission, but there is heterogeneity and publication bias that requires model adjustment for local factors such as resources, demographics, and case mix. Machine learning models present a promising modeling technique but have a high methodological bias and require further validation.

IMPLICATIONS FOR CLINICAL PRACTICE

Using reliable risk assessment tools is essential for the early identification of unplanned intensive care unit readmission risk in critically ill patients.  A reliable risk assessment tool must be developed, which is the focus of further research.

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.

Prognostic value of National Early Warning Score and Modified Early Warning Score on intensive care unit readmission and mortality: A prospective observational study

Background

Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) are widely used in predicting the mortality and intensive care unit (ICU) admission of critically ill patients. This study was conducted to evaluate and compare the prognostic value of NEWS and MEWS for predicting ICU readmission, mortality, and related outcomes in critically ill patients at the time of ICU discharge.

Methods

This multicenter, prospective, observational study was conducted over a year, from April 2019 to March 2020, in the general ICUs of two university-affiliated hospitals in Northwest Iran. MEWS and NEWS were compared based on the patients’ outcomes (including mortality, ICU readmission, time to readmission, discharge type, mechanical ventilation (MV), MV duration, and multiple organ failure after readmission) using the univariable and multivariable binary logistic regression. The receiver operating characteristic (ROC) curve was used to determine the outcome predictability of MEWS and NEWS.

Results

A total of 410 ICU patients were enrolled in this study. According to multivariable logistic regression analysis, both MEWS and NEWS were predictors of ICU readmission, time to readmission, MV status after readmission, MV duration, and multiple organ failure after readmission. The area under the ROC curve (AUC) for predicting mortality was 0.91 (95% CI = 0.88–0.94, P < 0.0001) for the NEWS and 0.88 (95% CI = 0.84–0.91, P < 0.0001) for the MEWS. There was no significant difference between the AUC of the NEWS and the MEWS for predicting mortality (P = 0.082). However, for ICU readmission (0.84 vs. 0.71), time to readmission (0.82 vs. 0.67), MV after readmission (0.83 vs. 0.72), MV duration (0.81 vs. 0.67), and multiple organ failure (0.833 vs. 0.710), the AUCs of MEWS were significantly greater (P < 0.001).

Conclusion

National Early Warning Score and MEWS values of >4 demonstrated high sensitivity and specificity in identifying the risk of mortality for the patients’ discharge from ICU. However, we found that the MEWS showed superiority over the NEWS score in predicting other outcomes. Eventually, MEWS could be considered an efficient prediction score for morbidity and mortality of critically ill patients.

Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing

Predicting ICU readmission risk will help physicians make decisions regarding discharge. We used discharge summaries to predict ICU 30-day readmission risk using text mining and machine learning (ML) with data from the Medical Information Mart for Intensive Care III (MIMIC-III). We used Natural Language Processing (NLP) and the Bag-of-Words approach on discharge summaries to build a Document-Term-Matrix with 3000 features. We compared the performance of support vector machines with the radial basis function kernel (SVM-RBF), adaptive boosting (AdaBoost), quadratic discriminant analysis (QDA), least absolute shrinkage and selection operator (LASSO), and Ridge Regression. A total of 4000 patients were used for model training and 6000 were used for validation. Using the bag-of-words determined by NLP, the area under the receiver operating characteristic (AUROC) curve was 0.71, 0.68, 0.65, 0.69, and 0.65 correspondingly for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. We then used the SVM-RBF model for feature selection by incrementally adding features to the model from 1 to 3000 bag-of-words. Through this exhaustive search approach, only 825 features (words) were dominant. Using those selected features, we trained and validated all ML models. The AUROC curve was 0.74, 0.69, 0.67, 0.70, and 0.71 respectively for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. Overall, this technique could predict ICU readmission relatively well.

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.

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

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

Novel Score for Stratifying Risk of Critical Care Needs in Patients With Intracerebral Hemorrhage

OBJECTIVE

To develop a risk prediction score identifying patients with intracerebral hemorrhage (ICH) at low risk for critical care.

METHODS

We retrospectively analyzed data of 451 patients with ICH between 2010 and 2018. The sample was randomly divided into a development and a validation cohort. Logistic regression was used to develop a risk score by weighting independent predictors of intensive care unit (ICU) needs according to strength of association. The risk score was tested in the validation cohort and externally validated in a dataset from another institution.

RESULTS

The rate of ICU interventions was 80.3%. Systolic blood pressure (SBP), Glasgow Coma Scale (GCS) score, intraventricular hemorrhage (IVH), and ICH volume were independent predictors of critical care, resulting in the following point assignments for the Intensive Care Triaging in Spontaneous Intracerebral Hemorrhage (INTRINSIC) score: SBP 160 to 190 mm Hg (1 point), SBP >190 mm Hg (3 points); GCS 8 to 13 (1 point), GCS <8 (3 points); ICH volume 16 to 40 cm3 (1 point), ICH volume >40 cm3 (2 points); and presence of IVH (1 point), with values ranging between 0 and 9. Among patients with a score of 0 and no ICU needs during their emergency department stay, 93.6% remained without critical care needs. In an external validation cohort of patients with ICH, the INTRINSIC score achieved an area under the receiver operating characteristic curve of 0.823 (95% confidence interval 0.782-0.863). A score <2 predicted the absence of critical care needs with 48.5% sensitivity and 88.5% specificity, and a score <3 predicted the absence of critical care needs with 61.7% sensitivity and 83.0% specificity.

CONCLUSION

The INTRINSIC score identifies patients with ICH who are at low risk for critical care interventions.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that the INTRINSIC score identifies patients with ICH at low risk for critical care interventions.

Predictors of Early and Late Unplanned Intensive Care Unit Readmission: A Retrospective Cohort Study

PURPOSE

Intensive care unit (ICU) readmission is considered one of the major quality indicators of critical care. Reducing ICU readmission can improve patients' outcomes and optimize health resources, but there are limited data on the predictors of unplanned ICU readmission. This study aimed to identify the risk factors associated with unplanned ICU readmission within 48 hr (early) and after 48 hr (late) from ICU discharge.

DESIGN

Retrospective cohort study.

METHODS

Data were collected from patients' electronic medical records in a 24-bed medical ICU at a tertiary academic medical center in Busan, South Korea. Among all the patients admitted to the medical ICU (n = 1,033) between January 2015 and December 2017, 739 eligible patients were analyzed. A multivariable multinomial logistic regression model was conducted to identify predictors of ICU readmission.

FINDINGS

Out of the 739 patients analyzed, 66 (8.9%) were readmitted to the medical ICU: 13 (1.8%) as early readmission and 53 (7.1%) as late readmission. Two significant predictors were identified for early readmission: ICU admission from the ward (odds ratio [OR] = 4.14; 95% confidence interval [CI] 1.25, 13.67) and mechanical ventilation support >14 days (OR = 13.25; 95% CI 1.78, 98.89). For late ICU admission, there were four risk factors: ICU admission from the ward (OR = 2.69; 95% CI 1.44, 5.05), tracheostomy placement (OR = 3.58; 95% CI 1.49, 8.59), mechanical ventilation support >14 days (OR = 4.77; 95% CI 1.67, 13.63), and continuous renal replacement therapy (OR = 4.57; 95% CI 2.42, 8.63).

CONCLUSIONS

To prevent unplanned ICU readmission in patients at high risk, it is necessary to investigate further the role of clinical judgment and communication within the ICU clinical team and institutional-level support regarding ICU readmission events.

CLINICAL RELEVANCE

Both ICU nurses and nurses in post-ICU settings should be aware of the potential risk factors associated with early and late ICU readmission. Predictors and readmission strategies may be different for early and late readmissions. Prospective multicenter studies are needed to examine how these factors influence post-ICU outcomes.

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

BACKGROUND

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

AIMS AND OBJECTIVES

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

DESIGN

Systematic review of quantitative studies and meta-analysis.

METHODS

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

RESULTS

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

CONCLUSION

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

RELEVANCE TO CLINICAL PRACTICE

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

Modified Early Warning Score as a predictor of intensive care unit readmission within 48 hours: a retrospective observational study

OBJECTIVE

To evaluate the hypothesis that the Modified Early Warning Score (MEWS) at the time of intensive care unit discharge is associated with readmission and to identify the MEWS that most reliably predicts intensive care unit readmission within 48 hours of discharge.

METHODS

This was a retrospective observational study of the MEWSs of discharged patients from the intensive care unit. We compared the demographics, severity scores, critical illness characteristics, and MEWSs of readmitted and non-readmitted patients, identified factors associated with readmission in a logistic regression model, constructed a Receiver Operating Characteristic (ROC) curve of the MEWS in predicting the probability of readmission, and presented the optimum criterion with the highest sensitivity and specificity.

RESULTS

The readmission rate was 2.6%, and the MEWS was a significant predictor of readmission, along with intensive care unit length of stay > 10 days and tracheostomy. The ROC curve of the MEWS in predicting the readmission probability had an AUC of 0.82, and a MEWS > 6 carried a sensitivity of 0.78 (95%CI 0.66 - 0.9) and specificity of 0.9 (95%CI 0.87 - 0.93).

CONCLUSION

The MEWS is associated with intensive care unit readmission, and a score > 6 has excellent accuracy as a prognostic predictor.

Impact of Glasgow Coma Scale scores on unplanned intensive care unit readmissions among surgical patients

Background

Physiological instability at discharge from intensive care units (ICU) is known to increase readmission rates among critically ill patients. However, associations between consciousness levels at discharge and readmission rates remain unclear. This study aimed to investigate the association between the Glasgow Coma Scale (GCS) score at discharge and unplanned ICU readmissions in surgical patients.

Methods

This retrospective cohort study in a single tertiary academic hospital analyzed the electronic health records of adults aged 18 years or older, who were discharged from the ICU between January 2012 and December 2018. The primary endpoint was unplanned readmission within 48 hours after discharge. Multivariable logistic regression analysis was performed.

Results

Among 9,512 patients, unplanned readmissions occurred in 161 (1.7%). At discharge, GCS and verbal response scores of ≤13 (vs. ≥14) were associated with 2.28-fold higher unplanned readmissions within 48 hours [odds ratio (OR): 2.35, 95% confidence interval (CI): 1.51-3.65, P<0.001]. Sensitivity analysis showed that verbal response scores of ≤4 (vs. 5) at ICU discharge were associated with 2.21-fold higher unplanned readmissions within 48 hours (OR: 2.21, 95% CI: 1.49-3.29, P<0.001), whereas eye or motor responses at time of ICU discharge were not significantly associated with unplanned readmissions (P>0.05).

Conclusions

In this surgical ICU population cohort, GCS scores at ICU discharge were significantly associated with unplanned readmissions within 48 hours. This association was stronger with GCS scores of ≤13 and with verbal response scores of ≤4 at time of discharge. These findings suggest that surgical ICU patients with GCS scores of ≤13 or verbal response scores of ≤4 should be monitored carefully for discharge in order to avoid unplanned ICU readmissions.

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.

Critical care outreach: A valuable resource?

BACKGROUND

Critical Care Outreach Services (CCOS) were recommended by the Department of Health in the United Kingdom in 2000. Despite being an established service, research studies have not explicitly demonstrated its efficacy.

AIM AND OBJECTIVES

To explore the impact of CCOS from the perspective of hospital ward staff to inform service improvement potential.

DESIGN

A pilot formative process evaluation was used to meet the study aims, including the development of a self-completion questionnaire.

METHODS

The exploratory questionnaire was distributed to a purposive sample of clinical staff (health care assistants, nurses, therapists, and doctors) on two medical and two surgical wards to establish the value of CCOS from the perspective of ward staff.

RESULTS

The questionnaire was distributed to 195 staff members, of who 58 replied (30%). A descriptive analysis of quantitative data and content analysis of free-text responses demonstrated that staff knew how and when to use the service, that it was highly valued by all members of the multidisciplinary team, and that CCOS were perceived to have diverse responsibilities. Service improvement suggestions included increased staffing and longer hours of operation.

CONCLUSIONS

Despite the lack of quality research supporting the efficacy of CCOS in previous research studies, the results of this project support the findings of previous evaluations that the value of the service lies in the support offered to ward staff and in the quality of care provided to patients.

RELEVANCE TO CLINICAL PRACTICE

Because of the size of this evaluation, it was impossible to draw any generalizable conclusions. However, results clearly indicate that value is given to the support that the Critical Care Outreach Service provides to ward staff.

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.

Role of Transitional Care Measures in the Prevention of Readmission After Critical Illness

Transitioning from the critical care unit to the medical-surgical care area is vital to patients' recovery and resolution of critical illness. Such transitions are necessary to optimize use of available hospital resources to meet patient care needs. One in 10 patients discharged from the intensive care unit are readmitted to the unit during their hospitalization. Critical care readmission is associated with significant increases in illness acuity, overall length of stay, and health care costs as well as a potential 4-fold increased risk of mortality. Patients with complex illness, multiple comorbid conditions, and a prolonged initial stay in the critical care unit are at an increased risk of being readmitted to the critical care unit and experiencing poor outcomes. Implementing nurse-driven measures that support continuity of care and consistent communication practices such as critical care outreach services, transitional communication tools, discharge planning, and transitional care units improves transitions of patients from the critical care environment and reduces readmission rates.

Decline in functional status after intensive care unit discharge is associated with ICU readmission: a prospective cohort study

OBJECTIVES

To compare the functional status at intensive care unit (ICU) discharge of patients who were later readmitted to the ICU and patients discharged home and to verify whether a decline in functional status is associated with ICU readmission.

DESIGN

Prospective cohort study.

SETTING

ICU at a tertiary teaching hospital.

PARTICIPANTS

Patients admitted to the ICU, ≥18 years old, submitted to invasive mechanical ventilation (IMV), and discharged to the ward.

INTERVENTIONS

Functional assessment at ICU discharge. Discharge Group (DG) (patients discharged home) and Readmission Group (RG) (patients who returned to the ICU) were compared with Mann-Whitney and Chi-square or Exact Fisher tests. Multiple logistic regression verified association.

MAIN OUTCOME MEASURES

Barthel Index, key pinch strength, clinical and demographic data.

RESULTS

Patients in the readmission group presented lower Barthel Index [Median 40 (IQR 20-75) vs 60 (33-83), P=0.033], greater relative variation (pre and post ICU) of the Barthel Index (P=0.04), lower key pinch strength [3.4 (1.8-4.5) vs 4.5 (2.7-6.8)kg·f, P=0.006] and higher APACHE II [18 (12-22) vs 15 (11-20), P=0.027]. Multiple regression found that the relative variation of the Barthel Index was independently associated with ICU readmission (P<0.001), as well as higher APACHE II (P=0.020), shorter IMV duration (P<0.001) and ICU admission without clear diagnosis (P=0.020). The Hosmer-Lemeshow test indicated good adjustment of the model (P=0.99).

CONCLUSION

Readmitted patients presented poorer functional status and lower pinch strength. Relative variation of the Barthel Index was associated with ICU readmission despite other factors, as was higher APACHE II, shorter IMV duration and admission without clear diagnosis.

TRIAL REGISTRATION NUMBER

Not applicable.

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.

Healthcare Provider Perceptions of Causes and Consequences of ICU Capacity Strain in a Large Publicly Funded Integrated Health Region: A Qualitative Study

OBJECTIVES

Discrepancy in the supply-demand relationship for critical care services precipitates a strain on ICU capacity. Strain can lead to suboptimal quality of care and burnout among providers and contribute to inefficient health resource utilization. We engaged interprofessional healthcare providers to explore their perceptions of the sources, impact, and strategies to manage capacity strain.

DESIGN

Qualitative study using a conventional thematic analysis.

SETTING

Nine ICUs across Alberta, Canada.

SUBJECTS

Nineteen focus groups (n = 122 participants).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Participants' perspectives on strain on ICU capacity and its perceived impact on providers, families, and patient care were explored. Participants defined "capacity strain" as a discrepancy between the availability of ICU beds, providers, and ICU resources (supply) and the need to admit and provide care for critically ill patients (demand). Four interrelated themes of contributors to strain were characterized (each with subthemes): patient/family related, provider related, resource related, and health system related. Patient/family-related subthemes were "increasing patient complexity/acuity," along with patient-provider communication issues ("paucity of advance care planning and goals-of-care designation," "mismatches between patient/family and provider expectations," and "timeliness of end-of-life care planning"). Provider-related factor subthemes were nursing workforce related ("nurse attrition," "inexperienced workforce," "limited mentoring opportunities," and "high patient-to-nurse ratios") and physician related ("frequent turnover/handover" and "variations in care plan"). Resource-related subthemes were "reduced service capability after hours" and "physical bed shortages." Health system-related subthemes were "variable ICU utilization," "preferential "bed" priority for other services," and "high ward bed occupancy." Participants perceived that strain had negative implications for patients ("reduced quality and safety of care" and "disrupted opportunities for patient- and family-centered care"), providers ("increased workload," "moral distress," and "burnout"), and the health system ("unnecessary, excessive, and inefficient resource utilization").

CONCLUSIONS

Engagement with frontline critical care providers is essential for understanding their experiences and perspectives regarding strained capacity and for the development of sustainable strategies for improvement.

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.

Analysis of readmission rates to the intensive care unit after implementation of a rapid response team in a University Hospital

OBJECTIVES

To compare readmission rates to the intensive care unit (ICU) before and after the implementation of a rapid response team (RRT), and to identify risk factors for readmission.

DESIGN

A quasi-experimental before-after study was carried out.

SETTING

A University Hospital.

PATIENTS

All patients discharged from the ICU from January to December 2008 (control group) and from January 2010 to December 2012 (intervention group).

INTERVENTION

Implementation of an RRT.

MAIN VARIABLES OF INTEREST

The data included demographic parameters, diagnoses upon admission, ICU readmission, APACHE II, SOFA, and TISS 28 scores, and routine daily assessment by an RRT of patients discharged from the ICU.

RESULTS

During the study interval, 380 patients were analyzed in the period prior to the implementation of the RRT and 1361 after implementation. There was a tendency toward decreased readmission rates one year after RRT implementation. The APACHE II score and SOFA score at ICU discharge were independent factors associated to readmission, as well as clinical referral to the ICU.

CONCLUSIONS

The RRT intervention resulted in a sustained decrease in readmission rates one year after implementation of this service. The use of a specialized team in health institutions can be recommended for ICU survivors.

Readmission of ICU patients: A quality indicator?

PURPOSE

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Association 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.

Effects of a medical emergency team follow-up programme on patients discharged from the medical intensive care unit to the general ward: a single-centre experience

RATIONALE, AIMS AND OBJECTIVES

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

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

Introduction

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

Methods

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

Results

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

Conclusions

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