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

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.

Incidence and predictors of readmission to the cardiac surgery intensive care unit: A retrospective cohort study in Greece

INTRODUCTION:

Readmission in the intensive care unit (ICU) is a significant morbidity index, which has been related to poor patient outcomes

AIM:

To identify the preoperative and intraoperative risk factors for readmission in the cardiac surgery ICU.

METHODS:

We conducted a retrospective cohort study of 595 consecutive patients who were admitted to the cardiac surgery ICU of a tertiary hospital of Athens — Greece during the one-year period (September 2011-September 2012). Data collection was carried out, retrospectively, by the use of a short questionnaire and based on the review of medical and nursing patient records at December 2012.

RESULTS:

The incidence of ICU readmission was 3.7% (22/595). Respiratory disorders were the main reason for readmission (45.4%). Readmitted patients had a significantly higher in-hospital mortality compared to those requiring no readmission (P < 0.001). Multivariate analysis revealed that female gender [for males odds ratio (OR) 0.37, 95% confidence interval (CI) 0.15-0.89], high logistic EuroSCORE (OR 1.02, 95% CI 1.00-1.04), prolonged cardiopulmonary (CPB) duration (OR 1.01, 95% CI 1.00-1.02) and preoperative renal failure (OR 1.02, 95% CI 1.00-1.05) were the independent risk factors for readmission to the cardiac surgery ICU.

CONCLUSIONS:

One intraoperative and three preoperative variables are associated strongly with higher probability for ICU readmission. Shorter CPB duration could contribute to lower ICU readmission incidence. In addition, the early identification of high risk patients for readmission in the cardiac surgery ICU could encourage both the more efficient healthcare planning and resources allocation.

Frequency, risk factors, and outcomes of early unplanned readmissions to PICUs

OBJECTIVES

To determine the rate of unplanned PICU readmissions, examine the characteristics of index admissions associated with readmission, and compare outcomes of readmissions versus index admissions.

DESIGN

Retrospective cohort analysis.

SETTING

Ninety North American PICUs that participated in the Virtual Pediatric Intensive Care Unit Systems.

PATIENTS

One hundred five thousand four hundred thirty-seven admissions between July 2009 and March 2011.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Unplanned PICU readmission within 48 hours of index discharge was the primary outcome. Summary statistics, bivariate analyses, and mixed-effects logistic regression model with random effects for each hospital were performed.There were 1,161 readmissions (1.2%). The readmission rate varied among PICUs (0-3.3%), and acute respiratory (56%), infectious (35%), neurological (28%), and cardiovascular (20%) diagnoses were often present on readmission. Readmission risk increased in patients with two or more complex chronic conditions (adjusted odds ratio, 1.72; p < 0.001), unscheduled index admission (adjusted odds ratio, 1.37; p < 0.001), and transfer to an intermediate unit (adjusted odds ratio, 1.29; p = 0.004, compared with ward). Trauma patients had a decreased risk of readmission (adjusted odds ratio, 0.67; p = 0.003). Gender, race, insurance, age more than 6 months, perioperative status, and nighttime transfer were not associated with readmission. Compared with index admissions, readmissions had longer median PICU length of stay (3.1 vs 1.7 d, p < 0.001) and higher mortality (4% vs 2.5%, p = 0.002).

CONCLUSIONS

Unplanned PICU readmissions were relatively uncommon, but were associated with worse outcomes. Several patient and admission characteristics were associated with readmission. These data help identify high-risk patient groups and inform risk-adjustment for standardized readmission rates.

Failure events in transition of care for surgical patients

BACKGROUND

Unexpected clinical deterioration (failure events) in surgical patients on standard nursing units (WARDs) could have a significant impact on eventual survival. We sought to investigate failure events requiring intensive care (surgical ICU [SICU]) transfer of surgical patients on WARDs in a single-center academic setting.

STUDY DESIGN

Surgical patients admitted to WARDs over a 12-month period, who developed failure events, were retrospectively reviewed. Time to deterioration since WARD arrival, clinical factors, notification chain, and outcomes were identified. A physician review panel determined the preventability of failure events.

RESULTS

Ninety-eight patients experienced 111 failure events requiring SICU transfer. Most patients (85%) were emergency admissions. Of 111 events, 90% had been previously discharged from an SICU or a postanesthesia care unit (PACU). Recognition of failure was by nursing (54%) and on routine physician rounds (34%). Rapid response or code blue alone was less common (12%). A second physician notification was needed in 29%, with delays due to failure to identify severity of illness. Most commonly, respiratory events prompted notification (77 of 111, 69%). Overall mortality was 26 of 98 (27%). Median time to failure was 2 days and was associated with early transfer from the SICU or PACU. Rapid response or code blue activation was associated with higher mortality than physician notification.

CONCLUSIONS

Patients most at risk for WARD failures were those with acute surgical emergencies or recently discharged from the SICU or PACU. Respiratory complications were the most common cause of WARD failure events. Many early failures may have been due to premature transfer from the SICU or PACU. Failure events on WARDs can have lethal consequences. Awareness, monitoring, and communication are important components of preventative measures.

The effects of discharge to an intermediate care unit after a critical illness: a 5-year cohort study

PURPOSE

The impact of the intermediate care unit (IMCU) on post-intensive care unit (ICU) outcomes is controversial.

MATERIALS AND METHODS

We analyzed admissions from January 2003 to December 2008 from a mixed ICU in a teaching hospital in Brazil with a high patient-to-nurse ratio (3.5:1 on the ICU, 11:1 on the IMCU, 20-25:1 on the ward). A retrospective propensity-matched analysis was performed with data from 690 patients who were discharged after at least 3 days of ICU stay.

RESULTS

Of the 690 patients, 160 (23%) were discharged to the IMCU. A total of 399 propensity-matched patients were compared: 298 were discharged to the ward and 101 were discharged to the IMCU. Ninety-day mortality rate was similar between the IMCU and ward patients (22% vs 18%, respectively, P = .37), as was the unplanned ICU readmission rate (P = .63). In a multivariate logistic regression, discharge to the IMCU had no effect on the 90-day mortality rate (P = .27).

CONCLUSIONS

In a resource-limited setting with a high patient-to-nurse ratio, discharge to IMCU had no impact on 90-day mortality rate and on unplanned readmission rate. The impact of discharge to the IMCU on the outcome for critically ill patients should be evaluated in further studies.

Admission factors associated with prolonged (>14 days) intensive care unit stay

PURPOSE

To describe the admission factors associated with prolonged (>14 days) intensive care unit (ICU) stay (PIS).

MATERIALS AND METHODS

Retrospective analysis of 3257 admissions during a 1.5-year period in a tertiary hospital. We tested the association between clinically relevant variables and PIS (>14 days) through binary logistic regression using the backward method. A Kaplan-Meier curve and the log-rank test were used to compare hospital outcomes for ICU survivors between patients with and without PIS.

RESULTS

In total, 6.6% of all admissions had a prolonged stay, consuming over 40% of all ICU bed-days. Illness severity; respiratory support at admission; performance status; readmission; admission from a ward, emergency room or other hospital; admission due to intracranial mass effect; severe chronic obstructive pulmonary disease; and the temperature at admission were all associated with PIS in a multivariate analysis. The created model had a good area under the curve (0.82) and was calibrated (Hosmer-Lemeshow test p = 0.431). Post hoc analysis on ICU survivors on in patients with at least two days of ICU stay yielded similar results. Hospital survival after ICU discharge was similar for patients with and without PIS (log-rank test p = 0.50).

CONCLUSION

A small number of ICU admissions consume a great proportion of ICU bed-days. Illness severity, a need for support and performance status are important predictors of PIS. Patients who survive a PIS have similar hospital mortality to patients with a shorter stay.

An empirical derivation of the optimal time interval for defining ICU readmissions

BACKGROUND

Intensive care unit (ICU) readmission rates are commonly viewed as indicators of ICU quality. However, definitions of ICU readmissions vary, and it is unknown which, if any, readmissions are associated with ICU quality.

OBJECTIVE

Empirically derive the optimal interval between ICU discharge and readmission for purposes of considering ICU readmission as an ICU quality indicator.

RESEARCH DESIGN

Retrospective cohort study.

SUBJECTS

A total of 214,692 patients discharged from 157 US ICUs participating in the Project IMPACT database, 2001-2008.

MEASURES

We graphically examined how patient characteristics and ICU discharge circumstances (eg, ICU census) were related to the odds of ICU readmissions as the allowable interval between ICU discharge and readmission was lengthened. We defined the optimal interval by identifying inflection points where these relationships changed significantly and permanently.

RESULTS

A total of 2242 patients (1.0%) were readmitted to the ICU within 24 hours; 9062 (4.2%) within 7 days. Patient characteristics exhibited stronger associations with readmissions after intervals >48-60 hours. By contrast, ICU discharge circumstances and ICU interventions (eg, mechanical ventilation) exhibited weaker relationships as intervals lengthened, with inflection points at 30-48 hours. Because of the predominance of afternoon readmissions regardless of time of discharge, using intervals defined by full calendar days rather than fixed numbers of hours produced more valid results.

DISCUSSION

It remains uncertain whether ICU readmission is a valid quality indicator. However, having established 2 full calendar days (not 48 h) after ICU discharge as the optimal interval for measuring ICU readmissions, this study will facilitate future research designed to determine its validity.

Predictive ability of the stability and workload index for transfer score to predict unplanned readmissions after ICU discharge

OBJECTIVE

Unplanned readmission of hospitalized patients to an ICU is associated with an increased mortality and hospital length of stay. The ability to identify patients at risk, who would benefit from prolonged ICU treatment, is limited. The aim of this study is to validate a previously published numerical index named the Stability and Workload Index for Transfer in a heterogeneous group of ICU patients.

DESIGN

In this retrospective data analysis, the Stability and Workload Index for Transfer score was calculated for all patients, and the ability of the score to predict readmission was compared with the original publication.

SETTING

Four ICUs, one intermediate care unit, and one postanesthesia care unit of the department of anesthesia and intensive care of a university hospital.

PATIENTS

All consecutive patients treated in one of the units.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Unplanned ICU readmissions or unexpected death within 7 days of ICU discharge. The data of 7,175 patients were included in the analysis. Five hundred ninety-six patients were readmitted or died within 7 days of discharge. The patients who are readmitted to the ICU are significantly older and have significantly higher scores that define the severity of disease at the time of admission and discharge of their first ICU stay. The source of admission for the initial ICU stay did not differ (p = 0.055), and the last Glasgow Coma Scale and the last PaO2/FIO2 ratio before discharge from the ICU were higher in patients who did not need a readmission to the ICU. The performance of the Stability and Workload Index for Transfer score is poor with an area under the receiver operator curve of 0.581 (95% CI, 0.556-0.605; p < 0.001).

CONCLUSIONS

Based on the data from our patients, the proposed Stability and Workload Index for Transfer score by Gajic et al is not ideal in aiding the clinician in the decision, if a patient can be discharged safely from the ICU and further research is necessary to define the patients at risk for readmission.

Unscheduled readmissions to the PICU: epidemiology, risk factors, and variation among centers

OBJECTIVES

To determine the rate of, and potential risk factors for, unscheduled PICU readmission and assess for variability among PICUs within the United States.

DESIGN AND DATA SOURCE

This retrospective cohort study used 2005-2008 data from 73 PICUs in the Virtual PICU Systems database.

METHODS AND MEASUREMENTS

Early (within 48 hr of PICU discharge) and late (later than 48 hr) unscheduled readmission rates were calculated. Hierarchical logistic regression, with a random intercept for site, was used to identify factors independently associated with early readmission. Significant random effects identified sites with an outlying risk of readmission, adjusting for patient and admission characteristics.

MAIN RESULTS

For 117,923 children meeting inclusion criteria, the unscheduled readmission rate was 3.7% with 38% (1.4%) occurring early. Half of early readmissions had the same primary diagnosis as the first admission. Patients with late readmissions had a higher mortality (6.6% vs 3.3%, p < 0.001) and longer median total PICU length of stay (11 d vs 6 d, p < 0.0001) than those with early readmission. Patient characteristics strongly associated with increased risk of early readmission included the following: age < 6 months, acute respiratory and renal disease, and several underlying chronic conditions such as liver disease, bone marrow transplant, airway stenosis, and abnormal antidiuretic hormone balances. An initial PICU admission that was unscheduled, originated from the general floor, or with a discharge time between 4 PM and 8 AM was associated with higher risk of readmission. A quarter of sites were identified as potential high (16%) or low (8%) outliers.

CONCLUSIONS

The rate of unscheduled PICU readmission was low but associated with worse outcomes. Patient and admission/discharge characteristics associated with increased risk of readmissions could be used to target high-risk populations or modifiable factors to improve outcome. Variation of risk among centers suggests room for improvement.

Unplanned pediatric intensive care unit readmissions: a single-center experience

PURPOSE

The purpose of the study was to compare patients readmitted to the pediatric intensive care unit (PICU) unexpectedly within 48 hours (early), more than 48 hours from transfer (late), or not readmitted during the same hospitalization.

MATERIALS AND METHODS

A retrospective study (2007-2009) was performed at a tertiary care pediatric academic hospital. Readmitted at-risk patients were grouped by timing of readmission, and a sample of nonreadmitted patients was randomly selected. Early readmissions were compared to late readmissions and to nonreadmissions.

RESULTS

Of 3805 eligible patients, 3.9% had an unplanned PICU readmission with almost half occurring within 48 hours. Median times to readmission were 21.5 hours (early) and 7 days (late). Compared with late readmissions, early readmissions were more often admitted from and transferred to a surgical service, transferred on a weekend, and readmitted with the same primary diagnosis. Compared with nonreadmitted patients, independent risk factors for early readmission were admission source and respiratory support at PICU transfer. Readmitted patients had longer total PICU and hospital lengths of stay than nonreadmitted patients. Late readmissions had a higher mortality than early readmissions.

CONCLUSIONS

Patients requiring an unplanned PICU readmission had worse outcomes than those without a readmission. Future studies should focus on identifying modifiable risk factors for targeted interventions.

The association between ICU readmission rate and patient outcomes

OBJECTIVE

To examine the association between ICU readmission rates and case-mix-adjusted outcomes.

DESIGN

Retrospective cohort study of ICU admissions from 2002 to 2010.

SETTING

One hundred five ICUs at 46 United States hospitals.

PATIENTS

Of 369,129 admissions, 263,082 were first admissions that were alive at ICU discharge and candidates for readmission.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The median unit readmission rate was 5.9% (intraquartile range 5.1%-7.0%). Across all admissions, hospital mortality for patients with and without readmission was 21.3% vs. 3.6%, mean ICU stay 4.9 days vs. 3.4 days, and hospital stay 13.3 days vs. 4.5 days, respectively. We stratified ICUs according to their readmission rate: high (>7%), moderate (5%-7%), and low (<5%) rates. Observed and case-mix-adjusted hospital mortality, ICU and hospital lengths of stay were examined by readmission rate strata. Observed outcomes were much worse in the high readmission rate units. But after adjusting for patient and institutional differences, there was no association between level of unit readmission rate and case-mix-adjusted mortality. The difference between observed and predicted mortality was -0.4%, 0.4%, and -1.1%, for the high, medium, and low readmission rate strata, respectively. Additionally, the difference between observed and expected ICU length of stay was approximately zero for the three strata.

CONCLUSIONS

Patients readmitted to ICUs have increased hospital mortality and lengths of stay. After case-mix adjustment, there were no significant differences in standardized mortality or case-mix-adjusted lengths of stay between units with high readmission rates compared to units with moderate or low rates. The use of readmission as a quality measure should only be implemented if patient case-mix is taken into account.

Vital dysfunctions after intensive care discharge: prevalence and impact on patient outcome

BACKGROUND

Patients discharged from the intensive care unit (ICU) are at increased risk for serious adverse events (SAEs). Recording vital functions and comprehending the consequences of altered vitals on general wards may be suboptimal. This potentially endangers recovery after successful intensive care. We aimed to determine the prevalence of vital dysfunctions after ICU discharge and their effect on patient outcome.

METHODS

A prospective observational study. Adult patients discharged from a tertiary referral hospital ICU to general wards without treatment limitations were visited 24 h afterwards; their vitals were measured and reported to ward staff. Attending ward nurse responsible for patient was interviewed.

RESULTS

The cohort consisted of 184 patients who had survived the first 24 h on the ward without complications (age: 57 ± 16 years; male: 68%). The prevalence of objectively measured vital dysfunctions was 15%, and the attending nurse had been unusually concerned about the patient in 19% of cases. Of the 184 patients, 9.8% subsequently suffered an SAE. In a multivariate logistic regression model, only vital dysfunctions (odds ratio 3.79; 95% confidence interval 1.18-12.2) and nurse concern (3.63; 1.17-11.3) were independently associated with an increased incidence of SAE. Medical emergency team (MET) assistance was never considered necessary by ward staff. Sensitivity of observed altered vitals on SAEs was 50% and specificity 89%. Sensitivity of nurse concern was 26%, specificity 84%.

CONCLUSIONS

Simple vital function measurement and attending ward nurse's subjective assessment facilitate early detection of post-ICU patients at risk. The threshold in seeking assistance through MET remains high.

The epidemiology of intensive care unit readmissions in the United States

RATIONALE

The incidence of intensive care unit (ICU) readmissions across the United States is unknown.

OBJECTIVES

To determine incidence of ICU readmissions in United States hospitals, and describe the distribution of time between ICU discharges and readmissions.

METHODS

This retrospective cohort study used 196,202 patients in 156 medical and surgical ICUs in 106 community and academic hospitals participating in Project IMPACT from April 1, 2001, to December 31, 2007. We used mixed-effects logistic regression, adjusting for patient and hospital characteristics, to describe how ICU readmission rates differed across patient types, ICU models, and hospital types.

MEASUREMENTS AND MAIN RESULTS

Measurements consisted of 48- and 120-hour ICU readmission rates and time to readmission. A total of 3,905 patients (2%) were readmitted to the ICU within 48 hours, and 7,171 (3.7%) within 120 hours. In adjusted analysis, there was no difference in ICU readmissions across patient types or ICU models. Among medical patients, those in academic hospitals had higher odds of 48- and 120-hour readmission than patients in community hospitals without residents (1.51 [95% confidence interval, 1.12-2.02] and 1.63 [95% confidence interval, 1.24-2.16]). Median time to ICU readmission was 3.07 days (interquartile range, 1.27-6.58). Closed ICUs had the longest times to readmission (3.55 d [interquartile range, 1.42-7.50]).

CONCLUSIONS

Approximately 2% and 4% of ICU patients discharged to the ward are readmitted within 48 and 120 hours, within a median time of 3 days. Medical patients in academic hospitals are more likely to be readmitted than patients in community hospitals without residents. ICU readmission rates could be useful for policy makers and investigations into their causes and consequences.