Dataset Information

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

ABSTRACT: 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.Empirically derive the optimal interval between ICU discharge and readmission for purposes of considering ICU readmission as an ICU quality indicator.Retrospective cohort study.A total of 214,692 patients discharged from 157 US ICUs participating in the Project IMPACT database, 2001-2008.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.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.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.

SUBMITTER: Brown SE

PROVIDER: S-EPMC3714373 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Brown Sydney E S SE Ratcliffe Sarah J SJ Halpern Scott D SD

Medical care 20130801 8

<h4>Background</h4>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.<h4>Objective</h4>Empirically derive the optimal interval between ICU discharge and readmission for purposes of considering ICU readmission as an ICU quality indicator.<h4>Research design</h4>Retrospective cohort study.<h4>Subjects</h4>A total of 214,692 patient ...[more]

PMID: 23698182

Similar Datasets

Project description:BackgroundVariation 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.MethodsWe 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.ResultsThe 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.ConclusionsWe 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.

Project description:IntroductionEarly discharge from the ICU is desirable because it shortens time in the ICU and reduces care costs, but can also increase the likelihood of ICU readmission and post-discharge unanticipated death if patients are discharged before they are stable. We postulated that, using eICU® Research Institute (eRI) data from >400 ICUs, we could develop robust models predictive of post-discharge death and readmission that may be incorporated into future clinical information systems (CIS) to assist ICU discharge planning.MethodsRetrospective, multi-center, exploratory cohort study of ICU survivors within the eRI database between 1/1/2007 and 3/31/2011.Exclusion criteriaDNR or care limitations at ICU discharge and discharge to location external to hospital. Patients were randomized (2?1) to development and validation cohorts. Multivariable logistic regression was performed on a broad range of variables including: patient demographics, ICU admission diagnosis, admission severity of illness, laboratory values and physiologic variables present during the last 24 hours of the ICU stay. Multiple imputation was used to address missing data. The primary outcomes were the area under the receiver operator characteristic curves (auROC) in the validation cohorts for the models predicting readmission and death within 48 hours of ICU discharge.Results469,976 and 234,987 patients representing 219 hospitals were in the development and validation cohorts. Early ICU readmission and death was experienced by 2.54% and 0.92% of all patients, respectively. The relationship between predictors and outcomes (death vs readmission) differed, justifying the need for separate models. The models for early readmission and death produced auROCs of 0.71 and 0.92, respectively. Both models calibrated well across risk groups.ConclusionsOur models for death and readmission after ICU discharge showed good to excellent discrimination and good calibration. Although prospective validation is warranted, we speculate that these models may have value in assisting clinicians with ICU discharge planning.

Project description:Good quality indicators should have face validity, relevance to patients, and be able to be measured reliably. Beyond these general requirements, good quality indicators should also have certain statistical properties, including sufficient variability to identify poor performers, relative insensitivity to severity adjustment, and the ability to capture what providers do rather than patients' characteristics. We assessed the performance of candidate indicators of ICU quality on these criteria. Indicators included ICU readmission, mortality, several length of stay outcomes, and the processes of venous-thromboembolism and stress ulcer prophylaxis provision.Retrospective cohort study.One hundred thirty-eight U.S. ICUs from 2001-2008 in the Project IMPACT database.Two hundred sixty-eight thousand eight hundred twenty-four patients discharged from U.S. ICUs.None.We assessed indicators' (1) variability across ICU-years; (2) degree of influence by patient vs. ICU and hospital characteristics using the Omega statistic; (3) sensitivity to severity adjustment by comparing the area under the receiver operating characteristic curve (AUC) between models including vs. excluding patient variables, and (4) correlation between risk adjusted quality indicators using a Spearman correlation. Large ranges of among-ICU variability were noted for all quality indicators, particularly for prolonged length of stay (4.7-71.3%) and the proportion of patients discharged home (30.6-82.0%), and ICU and hospital characteristics outweighed patient characteristics for stress ulcer prophylaxis (?, 0.43; 95% CI, 0.34-0.54), venous thromboembolism prophylaxis (?, 0.57; 95% CI, 0.53-0.61), and ICU readmissions (?, 0.69; 95% CI, 0.52-0.90). Mortality measures were the most sensitive to severity adjustment (area under the receiver operating characteristic curve % difference, 29.6%); process measures were the least sensitive (area under the receiver operating characteristic curve % differences: venous thromboembolism prophylaxis, 3.4%; stress ulcer prophylaxis, 2.1%). None of the 10 indicators was clearly and consistently correlated with a majority of the other nine indicators.No indicator performed optimally across assessments. Future research should seek to define and operationalize quality in a way that is relevant to both patients and providers.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets