A systematic review of tools for predicting severe adverse events following patient discharge from intensive care units

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.

Multivariate Methods for Genetic Variants Selection and Risk Prediction in Cardiovascular Diseases

Over the last decade, high-throughput genotyping and sequencing technologies have contributed to major advancements in genetics research, as these technologies now facilitate affordable mapping of the entire genome for large sets of individuals. Given this, genome-wide association studies are proving to be powerful tools in identifying genetic variants that have the capacity to modify the probability of developing a disease or trait of interest. However, when the study’s goal is to evaluate the effect of the presence of genetic variants mapping to specific chromosomes regions on a specific phenotype, the candidate loci approach is still preferred. Regardless of which approach is taken, such a large data set calls for the establishment and development of appropriate analytical methods in order to translate such knowledge into biological or clinical findings. Standard univariate tests often fail to identify informative genetic variants, especially when dealing with complex traits, which are more likely to result from a combination of rare and common variants and non-genetic determinants. These limitations can partially be overcome by multivariate methods, which allow for the identification of informative combinations of genetic variants and non-genetic features. Furthermore, such methods can help to generate additive genetic scores and risk stratification algorithms that, once extensively validated in independent cohorts, could serve as useful tools to assist clinicians in decision-making. This review aims to provide readers with an overview of the main multivariate methods for genetic data analysis that could be applied to the analysis of cardiovascular traits.

Description of PICU Unplanned Readmission

OBJECTIVES

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

DESIGN

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

SETTING

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

PATIENTS

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

MEASUREMENTS AND MAIN RESULTS

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

CONCLUSIONS

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

Improving timely medical reviews for patients discharged from intensive care

Transferring patients from the intensive care unit (ICU) to a general ward is commonly associated with error and adverse events, and is one of the most challenging and high-risk transitions of care. Patients discharged from ICUs often require sustained intensive multi-disciplinary team input, part of which can be provided by nurse or clinician-led outreach teams. Unfortunately, due to a lack of resources many institutions do not have such programmes. We work in one such hospital with no ICU outreach service for recently discharged patients.

We noted that a disproportionate number of patients recently discharged from the ICU needed acute medical reviews by on-call evening and overnight junior doctors. Furthermore we noted that many of these patients had not been reviewed by their medical team after having arrived onto the general ward from the ICU.

We aimed to foster a fundamental culture change within junior doctors to review patients within six hours of arrival onto a ward from the ICU. We introduced simple and low-cost interventions that included educational sessions for junior doctors and ward-based nurses, as well as posters that acted as visual reminders in relevant departments.

Overall, the number of patients discharged from the ICU to general wards that were reviewed within six hours improved from 22% to 70% in the space of six months. In the same period, the number of patients requiring an acute medical review by the evening or overnight on-call junior doctor dropped from 14% to 0%.

Whilst our project is not necessarily appropriate for many larger institutions that already have outreach teams in place, it is certainly applicable to other similar sized smaller hospitals. We hope that others who face the same inherent barriers are inspired to implement similar projects, to bring about positive change, and ultimately improve the safety of their patients.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Comparison of Unplanned Intensive Care Unit Readmission Scores: A Prospective Cohort Study

PURPOSE

Early discharge from the intensive care unit (ICU) may constitute a strategy of resource consumption optimization; however, unplanned readmission of hospitalized patients to an ICU is associated with a worse outcome. We aimed to compare the effectiveness of the Stability and Workload Index for Transfer score (SWIFT), Sequential Organ Failure Assessment score (SOFA) and simplified Therapeutic Intervention Scoring System (TISS-28) in predicting unplanned ICU readmission or unexpected death in the first 48 hours after discharge from the ICU.

METHODS

We conducted a prospective cohort study in a single tertiary hospital in southern Brazil. All adult patients admitted to the ICU for more than 24 hours from January 2008 to December 2009 were evaluated. SWIFT, SOFA and TISS-28 scores were calculated on the day of discharge from the ICU. A stepwise logistic regression was conducted to evaluate the effectiveness of these scores in predicting unplanned ICU readmission or unexpected death in the first 48 hours after discharge from the ICU. Moreover, we conducted a direct accuracy comparison among SWIFT, SOFA and TISS-28 scores.

RESULTS

A total of 1,277 patients were discharged from the ICU during the study period. The rate of unplanned ICU readmission or unexpected death in the first 48 hours after discharge from the ICU was 15% (192 patients). In the multivariate analysis, age (P = 0.001), length of ICU stay (P = 0.01), cirrhosis (P = 0.03), SWIFT (P = 0.001), SOFA (P = 0.01) and TISS-28 (P<0.001) constituted predictors of unplanned ICU readmission or unexpected death. The SWIFT, SOFA and TISS-28 scores showed similar predictive accuracy (AUC valueswere 0.66, 0.65 and 0.67, respectively; P = 0.58) [corrected].

CONCLUSIONS

SWIFT, SOFA and TISS-28 on the day of discharge from the ICU have only moderate accuracy in predicting ICU readmission or death. The present study did not find any differences in accuracy among the three scores.

Efficacy of the APACHE II score at ICU discharge in predicting post-ICU mortality and ICU readmission in critically ill surgical patients

In this study, we evaluated the efficacy of the discharge Acute Physiology and Chronic Health Evaluation (APACHE) II score in predicting post-intensive care unit (ICU) mortality and ICU readmission during the same hospitalisation in a surgical ICU. Of 1190 patients who were admitted to the ICU and stayed >48 hours between October 2007 and March 2010, 23 (1.9%) died and 86 (7.2%) were readmitted after initial ICU discharge, with 26 (3.0%) admitted within 48 hours. The area under the receiver operating characteristics curve of the discharge and admission APACHE II scores in predicting in-hospital mortality was 0.631 (95% confidence interval [CI] 0.603 to 0.658) and 0.669 (95% CI 0.642 to 0.696), respectively (P=0.510). The area under the receiver operating characteristics curve of discharge and admission APACHE II scores for predicting all forms of readmission was 0.606 (95% CI 0.578 to 0.634) and 0.574 (95% CI 0.545 to 0.602), respectively (P=0.316). The area under the receiver operating characteristics curve of discharge APACHE II score in predicting early ICU readmissions was, however, higher than that of admission APACHE II score (0.688 [95% CI 0.660 to 0.714] versus 0.505 [95% CI 0.476 to 0.534], P=0.001). The discharge APACHE II score (odds ratio [OR] 1.1, 95% CI 1.01 to 1.22, P=0.024), unplanned ICU readmission (OR 20.0, 95% CI 7.6 to 53.1, P=0.001), eosinopenia at ICU discharge (OR 6.0, 95% CI 1.34 to 26.9, P=0.019), and hospital length-of-stay before ICU admission (OR 1.02, 95% CI 1.01 to 1.03, P=0.021) were significant independent factors in predicting post-ICU mortality. This study suggests that the discharge APACHE II score may be useful in predicting post-ICU mortality and is superior to the admission APACHE II score in predicting early ICU readmission in surgical ICU patients.

A scoping review of patient discharge from intensive care: opportunities and tools to improve care

BACKGROUND

We conducted a scoping review to systematically review the literature reporting patient discharge from ICUs, identify facilitators and barriers to high-quality care, and describe tools developed to improve care.

METHODS

We searched Medline, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials. Data were extracted on the article type, study details for research articles, patient population, phase of care during discharge, and dimensions of health-care quality.

RESULTS

From 8,154 unique publications we included 224 articles. Of these, 131 articles (58%) were original research, predominantly case series (23%) and cohort (16%) studies; 12% were narrative reviews; and 11% were guidelines/policies. Common themes included patient and family needs/experiences (29% of articles) and the importance of complete and accurate information (26%). Facilitators of high-quality care included provider-patient communication (30%), provider-provider communication (25%), and the use of guidelines/policies (29%). Patient and family anxiety (21%) and limited availability of ICU and ward resources (26%) were reported barriers to high-quality care. A total of 47 tools to facilitate patient discharge from the ICU were identified and focused on patient evaluation for discharge (29%), discharge planning and teaching (47%), and optimized discharge summaries (23%).

CONCLUSIONS

Common themes, facilitators and barriers related to patient and family needs/experiences, communication, and the use of guidelines/policies to standardize patient discharge from ICU transcend the literature. Candidate tools to improve care are available; comparative evaluation is needed prior to broad implementation and could be tested through local quality-improvement programs.

Readmission to medical intensive care units: risk factors and prediction

PURPOSE

The objectives of this study were to find factors related to medical intensive care unit (ICU) readmission and to develop a prediction index for determining patients who are likely to be readmitted to medical ICUs.

MATERIALS AND METHODS

We performed a retrospective cohort study of 343 consecutive patients who were admitted to the medical ICU of a single medical center from January 1, 2008 to December 31, 2012. We analyzed a broad range of patients' characteristics on the day of admission, extubation, and discharge from the ICU.

RESULTS

Of the 343 patients discharged from the ICU alive, 33 (9.6%) were readmitted to the ICU unexpectedly. Using logistic regression analysis, the verified factors associated with increased risk of ICU readmission were male sex [odds ratio (OR) 3.17, 95% confidence interval (CI) 1.29-8.48], history of diabetes mellitus (OR 3.03, 95% CI 1.29-7.09), application of continuous renal replacement therapy during ICU stay (OR 2.78, 95% CI 0.85-9.09), white blood cell count on the day of extubation (OR 1.13, 95% CI 1.07-1.21), and heart rate just before ICU discharge (OR 1.03, 95% CI 1.01-1.06). We established a prediction index for ICU readmission using the five verified risk factors (area under the curve, 0.76, 95% CI 0.66-0.86).

CONCLUSION

By using specific risk factors associated with increased readmission to the ICU, a numerical index could be established as an estimation tool to predict the risk of ICU readmission.

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.

Stakeholder views regarding patient discharge from intensive care: Suboptimal quality and opportunities for improvement

OBJECTIVE

To provide the first description of intensive care unit (ICU) discharge practices from the perspective of Canadian ICU administrators, and ICU providers from Canada, the United States and the United Kingdom.

METHODS

The authors identified 140 Canadian ICUs and administered a survey to ICU administrators (unit manager, director) to obtain an institutional perspective. Also surveyed were members of professional critical care associations in Canada, the United States and the United Kingdom, using membership distribution lists, to obtain a provider perspective.

RESULTS

A total of 118 ICU administrators (114 ICUs [81%]) and 737 ICU providers (denominator unknown) responded to the survey. Administrator and provider respondents reported that ICU physicians are primarily responsible for determining the timing (70% and 77%, respectively) and safety (94% and 96%) for patients discharged from ICU. The majority of respondents indicated that patient summaries (87% and 85%) and medication reconciliation (78% and 79%) were part of their institutions' discharge process. One-half of respondents reported the use of discharge protocols, while a minority indicated that checklists (46% and 44%), electronic tools (19% and 28%) or outreach follow-up (44% and 33%) were used. The majority of respondents rated current ICU discharge practices to be of medium quality (57% and 58% scored 3 on a five-point scale). Suggested opportunities for improvement included the information provided to patients and families (71% and 59%) and collaboration among hospital units (65% and 66%).

CONCLUSION

Findings from the present study revealed the complexity of the ICU discharge process, considerable practice variation, perception of only medium quality and several proposed opportunities for improvement.

Characteristics and outcomes for hospitalized patients with recurrent clinical deterioration and repeat medical emergency team activation*

OBJECTIVE

To describe the occurrence of recurrent clinical deterioration and repeat medical emergency team activation and assess its effect on processes and outcomes of care.

DESIGN

Retrospective cohort study.

SETTING

Two community hospitals and two tertiary care hospitals, Alberta, Canada.

PATIENTS

Consecutive hospitalized adult patients with sudden clinical deterioration and medical emergency team activation without admission to ICU.

INTERVENTION

None.

MEASUREMENT AND MAIN RESULTS

We compared ICU admission rates (admissions > 2 hr following index medical emergency team), hospital length of stay, and hospital mortality for a cohort of 3,200 patients with and without recurrent clinical deterioration following medical emergency team activation adjusting for patient, provider, and hospital characteristics.The cohort consisted of 3,200 patients. Ten percent of patients (n = 337) experienced recurrent clinical deterioration and repeat medical emergency team activation during their hospital stay. Patients more likely to experience recurrent clinical deterioration and repeat medical emergency team activation included those with chronic liver disease (odds ratio, 1.75; 95% CI, 1.14-2.69) or who received airway suctioning (odds ratio, 1.66; 95% CI, 1.23-2.25), noninvasive mechanical ventilation (odds ratio, 1.67; 95% CI, 0.94-2.94), or central IV catheter insertion (odds ratio, 1.81; 95% CI, 1.02-3.21) during the index medical emergency team activation. Patients with recurrent clinical deterioration were more likely than patients without recurrent clinical deterioration to be subsequently admitted to ICU (43% vs 13%; odds ratio, 6.11; 95% CI, 4.67-8.00; p < 0.01), to have longer lengths of hospital stay (median, 31 d vs 13 d; p < 0.01), and to die during their hospital stay (34% vs 23%; odds ratio, 1.98; 95% CI, 1.47-2.67; p < 0.01).

CONCLUSIONS

Recurrent clinical deterioration and repeat medical emergency team activation are common and associated with increased risk of subsequent ICU admission, increased length of hospital stay, and increased hospital mortality. It may be possible to identify patients at risk of recurrent clinical deterioration following medical emergency team activation and target interventions to improve patient care.

Mortality and postoperative care pathways after emergency gastrointestinal surgery in 2904 patients: a population-based cohort study

BACKGROUND

Emergency major gastrointestinal (GI) surgery carries a considerable risk of mortality and postoperative complications. Effective management of complications and appropriate organization of postoperative care may improve outcome. The importance of the latter is poorly described in emergency GI surgical patients. We aimed to present mortality data and evaluate the postoperative care pathways used after emergency GI surgery.

METHODS

A population-based cohort study with prospectively collected data from six Capital Region hospitals in Denmark. We included 2904 patients undergoing major GI laparotomy or laparoscopy surgery between January 1, 2009, and December 31, 2010. The primary outcome measure was 30 day mortality.

RESULTS

A total of 538 patients [18.5%, 95% confidence interval (CI): 17.1-19.9] died within 30 days of surgery. In all, 84.2% of the patients were treated after operation in the standard ward, with a 30 day mortality of 14.3%, and 4.8% were admitted to the intensive care unit (ICU) after a median stay of 2 days (inter-quartile range: 1-6). When compared with 'admission to standard ward', 'admission to standard ward before ICU admission' and 'ICU admission after surgery' were independently associated with 30 day mortality; odds ratio 5.45 (95% CI: 3.48-8.56) and 3.27 (95% CI: 2.45-4.36), respectively.

CONCLUSIONS

Mortality in emergency major GI surgical patients remains high. Failure to allocate patients to the appropriate level of care immediately after surgery may contribute to the high postoperative mortality. Future research should focus on improving risk stratification and evaluating the effect of different postoperative care pathways in emergency GI surgery.