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      Associations of Intensive Care Unit Capacity Strain with Disposition and Outcomes of Patients with Sepsis Presenting to the Emergency Department

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          Abstract

          <p class="first" id="d1473868e344"> <b>Rationale:</b> Intensive care unit (ICU) capacity strain refers to the potential limits placed on an ICU’s ability to provide high-quality care for all patients who may need it at a given time. Few studies have investigated how fluctuations in ICU capacity strain might influence care outside the ICU. </p><p id="d1473868e349"> <b>Objectives:</b> To determine whether ICU capacity strain is associated with initial level of inpatient care and outcomes for emergency department (ED) patients hospitalized for sepsis. </p><p id="d1473868e354"> <b>Methods:</b> We performed a retrospective cohort study of patients with sepsis admitted from the ED to a medical ward or ICU at three hospitals within the University of Pennsylvania Health System between 2012 and 2015. Patients were excluded if they required life support therapies, defined as invasive or noninvasive ventilatory support or vasopressors, at the time of admission. The exposures were four measures of ICU capacity strain at the time of the ED disposition decision: ICU occupancy, ICU turnover, ICU census acuity, and ward occupancy. The primary outcome was the decision to admit to a ward or to an ICU. Secondary analyses assessed the association of ICU capacity strain with in-hospital outcomes, including mortality. </p><p id="d1473868e359"> <b>Results:</b> Among 77,142 hospital admissions from the ED, 3,067 patients met the study’s eligibility criteria. The ICU capacity strain metrics varied between and within study hospitals over time. In unadjusted analyses, ICU occupancy, ICU turnover, ICU census acuity, and ward occupancy were all negatively associated with ICU admission. In the fully adjusted model including patient-level covariates, only ICU occupancy remained associated with ICU admission (odds ratio, 0.87; 95% confidence interval, 0.79–0.96; <i>P</i> = 0.005), such that a 10% increase in ICU occupancy (e.g., one additional patient in a 10-bed ICU) was associated with a 13% decrease in the odds of ICU admission. Among the subset of patients admitted initially from the ED to a medical ward, ICU occupancy at the time of admission was associated with increased odds of hospital mortality (odds ratio, 1.61; 95% confidence interval, 1.21–2.14; <i>P</i> = 0.001). </p><p id="d1473868e370"> <b>Conclusions:</b> The odds that patients in the ED with sepsis who do not require life support therapies will be admitted to the ICU are reduced when those ICUs experience high occupancy but not high levels of other previously explored measures of capacity strain. Patients with sepsis admitted to the wards during times of high ICU occupancy had increased odds of hospital mortality. </p>

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          Risk-adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.

          To develop a risk-adjustment methodology that maximizes the use of automated physiology and diagnosis data from the time period preceding hospitalization. : Retrospective cohort study using split-validation and logistic regression. Seventeen hospitals in a large integrated health care delivery system. Patients (n = 259,699) hospitalized between January 2002 and June 2005. Inpatient and 30-day mortality. Inpatient mortality was 3.50%; 30-day mortality was 4.06%. We tested logistic regression models in a randomly chosen derivation dataset consisting of 50% of the records and applied their coefficients to the validation dataset. The final model included sex, age, admission type, admission diagnosis, a Laboratory-based Acute Physiology Score (LAPS), and a COmorbidity Point Score (COPS). The LAPS integrates information from 14 laboratory tests obtained in the 24 hours preceding hospitalization into a single continuous variable. Using Diagnostic Cost Groups software, we categorized patients as having up to 40 different comorbidities based on outpatient and inpatient data from the 12 months preceding hospitalization. The COPS integrates information regarding these 41 comorbidities into a single continuous variable. Our best model for inpatient mortality had a c statistic of 0.88 in the validation dataset, whereas the c statistic for 30-day mortality was 0.86; both models had excellent calibration. Physiologic data accounted for a substantial proportion of the model's predictive ability. Efforts to support improvement of hospital outcomes can take advantage of risk-adjustment methods based on automated physiology and diagnosis data that are not confounded by information obtained after hospital admission.
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            ICU occupancy and mechanical ventilator use in the United States.

            Detailed data on occupancy and use of mechanical ventilators in U. S. ICU over time and across unit types are lacking. We sought to describe the hourly bed occupancy and use of ventilators in U.S. ICUs to improve future planning of both the routine and disaster provision of intensive care.
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              Risk-adjusting hospital mortality using a comprehensive electronic record in an integrated health care delivery system.

              Using a comprehensive inpatient electronic medical record, we sought to develop a risk-adjustment methodology applicable to all hospitalized patients. Further, we assessed the impact of specific data elements on model discrimination, explanatory power, calibration, integrated discrimination improvement, net reclassification improvement, performance across different hospital units, and hospital rankings. Retrospective cohort study using logistic regression with split validation. A total of 248,383 patients who experienced 391,584 hospitalizations between January 1, 2008 and August 31, 2011. Twenty-one hospitals in an integrated health care delivery system in Northern California. Inpatient and 30-day mortality rates were 3.02% and 5.09%, respectively. In the validation dataset, the greatest improvement in discrimination (increase in c statistic) occurred with the introduction of laboratory data; however, subsequent addition of vital signs and end-of-life care directive data had significant effects on integrated discrimination improvement, net reclassification improvement, and hospital rankings. Use of longitudinally captured comorbidities did not improve model performance when compared with present-on-admission coding. Our final model for inpatient mortality, which included laboratory test results, vital signs, and care directives, had a c statistic of 0.883 and a pseudo-R of 0.295. Results for inpatient and 30-day mortality were virtually identical. Risk-adjustment of hospital mortality using comprehensive electronic medical records is feasible and permits one to develop statistical models that better reflect actual clinician experience. In addition, such models can be used to assess hospital performance across specific subpopulations, including patients admitted to intensive care.
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                Author and article information

                Journal
                Annals of the American Thoracic Society
                Annals ATS
                American Thoracic Society
                2329-6933
                2325-6621
                November 2018
                November 2018
                : 15
                : 11
                : 1328-1335
                Affiliations
                [1 ]Division of Pulmonary, Allergy, and Critical Care
                [2 ]Center for Clinical Epidemiology and Biostatistics
                [3 ]Palliative and Advanced Illness Research Center
                [4 ]Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania; and
                [5 ]Division of Research, Kaiser Permanente, Oakland, California
                [6 ]Center for Community and Population Health, Department of Family Medicine and Community Health
                [7 ]Center for Emergency Care Policy and Research, Department of Emergency Medicine, and
                [8 ]Department of Medical Ethics and Health Policy, Perelman School of Medicine, and
                Article
                10.1513/AnnalsATS.201804-241OC
                6850726
                30113865
                6a700adc-3e61-42cd-a823-e6eb9c729c71
                © 2018
                History

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