Dataset Information

Effect of Boarding on Mortality in ICUs.

ABSTRACT: OBJECTIVES:Hospitals use a variety of strategies to maximize the availability of limited ICU beds. Boarding, which involves assigning patients to an open bed in a different subspecialty ICU, is one such practice employed when ICU occupancy levels are high, and beds in a particular unit are unavailable. Boarding disrupts the normal geographic colocation of patients and care teams, exposing patients to nursing staff with different training and expertise to those caring for nonboarders. We analyzed whether medical ICU patients boarding in alternative specialty ICUs are at increased risk of mortality. DESIGN:Retrospective cohort study using an instrumental variable analysis to control for unmeasured confounding. A semiparametric bivariate probit estimation strategy was employed for the instrumental model. Propensity score matching and standard logistic regression (generalized linear modeling) were used as robustness checks. SETTING:The medical ICU of a tertiary care nonprofit hospital in the United States between 2002 and 2012. PATIENTS:All medical ICU admissions during the specified time period. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:The study population consisted of 8,429 patients of whom 1,871 were boarders. The instrumental variable model demonstrated a relative risk of 1.18 (95% CI, 1.01-1.38) for ICU stay mortality for boarders. The relative risk of in-hospital mortality among boarders was 1.22 (95% CI, 1.00-1.49). GLM and propensity score matching without use of the instrument yielded similar estimates. Instrumental variable estimates are for marginal patients, whereas generalized linear modeling and propensity score matching yield population average effects. CONCLUSIONS:Mortality increased with boarding of critically ill patients. Further research is needed to identify safer practices for managing patients during periods of high ICU occupancy.

SUBMITTER: Stretch R

PROVIDER: S-EPMC5851814 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Effect of Boarding on Mortality in ICUs.

Stretch Robert R Della Penna Nicolás N Celi Leo Anthony LA Landon Bruce E BE

Critical care medicine 20180401 4

<h4>Objectives</h4>Hospitals use a variety of strategies to maximize the availability of limited ICU beds. Boarding, which involves assigning patients to an open bed in a different subspecialty ICU, is one such practice employed when ICU occupancy levels are high, and beds in a particular unit are unavailable. Boarding disrupts the normal geographic colocation of patients and care teams, exposing patients to nursing staff with different training and expertise to those caring for nonboarders. We ...[more]

PMID: 29252930

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Project description:Intensive care units (ICUs) function frequently at capacity, requiring incoming critically ill patients to be placed in alternate geographically distinct ICUs. In some medical ICU populations, "boarding" in an overflow ICU has been associated with increased mortality. We hypothesized that surgical ICU patients experience more complications when boarding in an overflow ICU and that the frequency of these complications are greatest in boarders farthest from the home unit (HU).A 5-year (June 2005 to June 2010) retrospective review of a prospectively maintained ICU database was performed, and demographics, severity of illness, length of stay, and incidence of ICU complications were extracted. Distances between boarding patients' rooms and the HU were measured. Complications occurring in patients located in the same floor (BUSF) and different floor (BUDF) boarding units were compared and stratified by distance from HU to the patient room. Logistic regression was used to develop control for known confounders.A total of 7,793 patients were admitted to the HU and 833 to a boarding unit (BUSF, n = 712; BUDF, n = 121). Boarders were younger, had a lower length of stay, and Acute Physiology and Chronic Health Evaluation II and were more often trauma/emergency surgery patients. Compared with in-HU patients, the incidence of aspiration pneumonia (2.2% vs. 3.6%, p < 0.01) was greater in BUSF patients and highest in those farthest from the HU (odds ratio [OR], 2.39; p = 0.01). Delirium occurred less often in HU than in BUDF patients (3.3% vs. 8.3 %, p < 0.01), and both delirium (OR, 6.09, p < 0.01) and ventilator-associated pneumonia (OR, 4.49, p < 0.05) were more frequent in patients farther from the HU.Certain ICU complications occur more frequently in boarding patients particularly if they are located on a different floor or far from the HU. When surgical ICU bed availability forces overflow admissions to non-home ICUs, greater interdisciplinary awareness, education, and training may be needed to ensure equivalent care and outcomes.Epidemiologic study, level III. Therapeutic study, level IV.

Project description:IntroductionIntensive Care Unit (ICU) risk prediction models are used to compare outcomes for quality improvement initiatives, benchmarking, and research. While such models provide robust tools in high-income countries, an ICU risk prediction model has not been validated in a low-income country where ICU population characteristics are different from those in high-income countries, and where laboratory-based patient data are often unavailable. We sought to validate the Mortality Probability Admission Model, version III (MPM0-III) in two public ICUs in Rwanda and to develop a new Rwanda Mortality Probability Model (R-MPM) for use in low-income countries.MethodsWe prospectively collected data on all adult patients admitted to Rwanda's two public ICUs between August 19, 2013 and October 6, 2014. We described demographic and presenting characteristics and outcomes. We assessed the discrimination and calibration of the MPM0-III model. Using stepwise selection, we developed a new logistic model for risk prediction, the R-MPM, and used bootstrapping techniques to test for optimism in the model.ResultsAmong 427 consecutive adults, the median age was 34 (IQR 25-47) years and mortality was 48.7%. Mechanical ventilation was initiated for 85.3%, and 41.9% received vasopressors. The MPM0-III predicted mortality with area under the receiver operating characteristic curve of 0.72 and Hosmer-Lemeshow chi-square statistic p = 0.024. We developed a new model using five variables: age, suspected or confirmed infection within 24 hours of ICU admission, hypotension or shock as a reason for ICU admission, Glasgow Coma Scale score at ICU admission, and heart rate at ICU admission. Using these five variables, the R-MPM predicted outcomes with area under the ROC curve of 0.81 with 95% confidence interval of (0.77, 0.86), and Hosmer-Lemeshow chi-square statistic p = 0.154.ConclusionsThe MPM0-III has modest ability to predict mortality in a population of Rwandan ICU patients. The R-MPM is an alternative risk prediction model with fewer variables and better predictive power. If validated in other critically ill patients in a broad range of settings, the model has the potential to improve the reliability of comparisons used for critical care research and quality improvement initiatives in low-income countries.

Project description:OBJECTIVES:Pediatric cardiac ICUs should be adept at treating both critical medical and surgical conditions for patients with cardiac disease. There are no case-mix adjusted quality metrics specific to medical cardiac ICU admissions. We aimed to measure case-mix adjusted cardiac ICU medical mortality rates and assess variation across cardiac ICUs in the Pediatric Cardiac Critical Care Consortium. DESIGN:Observational analysis. SETTING:Pediatric Cardiac Critical Care Consortium clinical registry. PATIENTS:All cardiac ICU admissions that did not include cardiac surgery. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:The primary endpoint was cardiac ICU mortality. Based on multivariable logistic regression accounting for clustering, we created a case-mix adjusted model using variables present at cardiac ICU admission. Bootstrap resampling (1,000 samples) was used for model validation. We calculated a standardized mortality ratio for each cardiac ICU based on observed-to-expected mortality from the fitted model. A cardiac ICU was considered a statistically significant outlier if the 95% CI around the standardized mortality ratio did not cross 1. Of 11,042 consecutive medical admissions from 25 cardiac ICUs (August 2014 to May 2017), the observed mortality rate was 4.3% (n = 479). Final model covariates included age, underweight, prior surgery, time of and reason for cardiac ICU admission, high-risk medical diagnosis or comorbidity, mechanical ventilation or extracorporeal membrane oxygenation at admission, and pupillary reflex. The C-statistic for the validated model was 0.87, and it was well calibrated. Expected mortality ranged from 2.6% to 8.3%, reflecting important case-mix variation. Standardized mortality ratios ranged from 0.5 to 1.7 across cardiac ICUs. Three cardiac ICUs were outliers; two had lower-than-expected (standardized mortality ratio <1) and one had higher-than-expected (standardized mortality ratio >1) mortality. CONCLUSIONS:We measured case-mix adjusted mortality for cardiac ICU patients with critical medical conditions, and provide the first report of variation in this quality metric within this patient population across Pediatric Cardiac Critical Care Consortium cardiac ICUs. This metric will be used by Pediatric Cardiac Critical Care Consortium cardiac ICUs to assess and improve outcomes by identifying high-performing (low-mortality) centers and engaging in collaborative learning.

Project description:IMPORTANCE:Boarding in the emergency department (ED) is a critical indicator of quality of care for hospitals. It is defined as the time between the admission decision and departure from the ED. As a result of boarding, patients stay in the ED until inpatient beds are available; moreover, boarding is associated with various adverse events. STUDY OBJECTIVE:The objective of our systematic review was to determine whether ED boarding (EDB) time is associated with in-hospital mortality (IHM). METHODS:A systematic search was conducted in academic databases to identify relevant studies. Medline, PubMed, Scopus, Embase, Cochrane, Web of Science, Cochrane, CINAHL and PsychInfo were searched. We included all peer-reviewed published studies from all previous years until November 2018. Studies performed in the ED and focused on the association between EDB and IHM as the primary objective were included. Extracted data included study characteristics, prognostic factors, outcomes, and IHM. A search update in PubMed was performed in May 2019 to ensure the inclusion of recent studies before publishing. RESULTS:From the initial 4,321 references found through the systematic search, the manual screening of reference lists and the updated search in PubMed, a total of 12 studies were identified as eligible for a descriptive analysis. Overall, six studies found an association between EDB and IHM, while five studies showed no association. The last remaining study included both ICU and non-ICU subgroups and showed conflicting results, with a positive association for non-ICU patients but no association for ICU patients. Overall, a tendency toward an association between EDB and IHM using the pool random effect was observed. CONCLUSION:Our systematic review did not find a strong evidence for the association between ED boarding and IHM but there is a tendency toward this association. Further well-controlled, international multicenter studies are needed to demonstrate whether this association exists and whether there is a specific EDB time cut-off that results in increased IHM.

Project description:1) To show how to exploit the information contained in the trajectories of time-varying patient clinical data for dynamic predictions of mortality in the ICU; and 2) to demonstrate the additional predictive value that can be achieved by incorporating this trajectory information.DesignObservational, retrospective study of patient medical records for training and testing of statistical learning models using different sets of predictor variables.SettingMedical ICU at the Yale-New Haven Hospital.SubjectsElectronic health records of 3,763 patients admitted to the medical ICU between January 2013 and January 2015.InterventionsNone.Measurements and main resultsSix-hour mortality predictions for ICU patients were generated and updated every 6 hours by applying the random forest classifier to patient time series data from the prior 24 hours. The time series were processed in different ways to create two main models: 1) manual extraction of the summary statistics used in the literature (min/max/median/first/last/number of measurements) and 2) automated extraction of trajectory features using machine learning. Out-of-sample area under the receiver operating characteristics curve and area under the precision-recall curve ("precision" refers to positive predictive value and "recall" to sensitivity) were used to evaluate the predictive performance of the two models. For 6-hour prediction and updating, the second model achieved area under the receiver operating characteristics curve and area under the precision-recall curve of 0.905 (95% CI, 0.900-0.910) and 0.381 (95% CI, 0.368-0.394), respectively, which are statistically significantly higher than those achieved by the first model, with area under the receiver operating characteristics curve and area under the precision-recall curve of 0.896 (95% CI, 0.892-0.900) and 0.905 (95% CI, 0.353-0.379). The superiority of the second model held true for 12-hour prediction/updating as well as for 24-hour prediction/updating.ConclusionsWe show that statistical learning techniques can be used to automatically extract all relevant shape features for use in predictive modeling. The approach requires no additional data and can potentially be used to improve any risk model that uses some form of trajectory information. In this single-center study, the shapes of the clinical data trajectories convey information about ICU mortality risk beyond what is already captured by the summary statistics currently used in the literature.

Project description:OBJECTIVE:To develop a postoperative mortality case-mix adjustment model to facilitate assessment of cardiac ICU quality of care, and to describe variation in adjusted cardiac ICU mortality across hospitals within the Pediatric Cardiac Critical Care Consortium. DESIGN:Observational analysis. SETTING:Multicenter Pediatric Cardiac Critical Care Consortium clinical registry. PARTICIPANTS:All surgical cardiac ICU admissions between August 2014 and May 2016. The analysis included 8,543 admissions from 23 dedicated cardiac ICUs. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We developed a novel case-mix adjustment model to measure postoperative cardiac ICU mortality after congenital heart surgery. Multivariable logistic regression was performed to assess preoperative, intraoperative, and immediate postoperative severity of illness variables as candidate predictors. We used generalized estimating equations to account for clustering of patients within hospital and obtain robust SEs. Bootstrap resampling (1,000 samples) was used to derive bias-corrected 95% CIs around each predictor and validate the model. The final model was used to calculate expected mortality at each hospital. We calculated a standardized mortality ratio (observed-to-expected mortality) for each hospital and derived 95% CIs around the standardized mortality ratio estimate. Hospital standardized mortality ratio was considered a statistically significant outlier if the 95% CI did not include 1. Significant preoperative predictors of mortality in the final model included age, chromosomal abnormality/syndrome, previous cardiac surgeries, preoperative mechanical ventilation, and surgical complexity. Significant early postoperative risk factors included open sternum, mechanical ventilation, maximum vasoactive inotropic score, and extracorporeal membrane oxygenation. The model demonstrated excellent discrimination (C statistic, 0.92) and adequate calibration. Comparison across Pediatric Cardiac Critical Care Consortium hospitals revealed five-fold difference in standardized mortality ratio (0.4-1.9). Two hospitals had significantly better-than-expected and two had significantly worse-than-expected mortality. CONCLUSIONS:For the first time, we have demonstrated that variation in mortality as a quality metric exists across dedicated cardiac ICUs. These findings can guide efforts to reduce mortality after cardiac surgery.

Project description:BackgroundEmergency department overcrowding is associated with adverse clinical outcomes and poor patients and staff experience. Full capacity protocols enabling hallway boarding in internal wards are instituted to relieve emergency room overcrowding. The effect of hallway boarding on the clinical outcomes of all inpatients in the internal wards has not been studied. Early in 2016, a decision to enable hallway boarding in the internal wing in our medical center came into effect, comprising an abrupt change to the medical center's policy. The objective of this study is to examine the effect of hallway boarding on patients who were hospitalized in the internal wards.MethodsGeneral linear regression analysis, based on administrative data about admissions of patients, from January 2013 through September 2019, is used to compare in-hospital mortality, 30-day readmission and 30-day mortality rates, of inpatients hospitalized in two internal departments in our medical center, before and after the policy change.ResultsEight thousand five hundred eighty-three patients and 11,962 patients were admitted to internal departments A and B, before and after the policy change, respectively. Adjusted in-hospital mortality was lower after the policy change (OR 0.76, [CI, 0.65 to 0.90]), 30-day readmission was mildly higher (OR, 1.18 [CI, 1.00 to 1.40]) and no change in 30-day mortality was observed (OR 1.16 [CI, 0.88 to 1.53]). The results emanate from corresponding changes in department A. No apparent change was observed in the length of hospital stay in department A, while a shorter length of stay was observed in department B.ConclusionEnabling inpatient boarding in our medical center, effectively, had increased bed capacity and generated an increase in the volume of patients. It was associated with lower in-hospital mortality and an increased 30-day readmission, without increasing 30-day mortality. Since this is an observational study, conducted in a single center, further research is necessary to confirm and qualify these observations.

Dataset Information

Effect of Boarding on Mortality in ICUs.

Publications

Effect of Boarding on Mortality in ICUs.

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