Intensive Care Unit Admitting Patterns in the Veterans Affairs Health Care System

Chen, Lena M.; Render, Marta L.; Sales, Anne E.; Kennedy, Edward H.; Wiitala, Wyndy L.; Hofer, Timothy P.

doi:10.1001/archinternmed.2012.2606

Cited by 89 publications

(81 citation statements)

References 41 publications

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“…All patients were reported to have excellent premorbid functioning prior to ICU admission, with a median Barthel score of 20 (range [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The median APACHE II score was 17 (IQR 13-23, range 4-43) with the median ISS in the trauma patients being 21 (IQR 10-34, range 1-59).…”

Section: Resultsmentioning

confidence: 99%

“…There was a short median ICU length of stay (LOS) of 3.0 days (IQR 1.0-6.0 days, range 0-59.0 days). In terms of functional outcome, the median 30-day Barthel score was 20 (range [7][8][9][10][11][12][13][14][15][16][17][18][19][20], reflecting excellent functional status.…”

Section: Resultsmentioning

confidence: 99%

“…The study population demographics reflect this distribution with the ICU population being young (mean age: 43 years) in comparison with highincome countries. [6][7][8]10 With nearly three-quarters of admissions coming from emergency cases, 30% of cases resulting from trauma, and close on 60% of surgical ICU admissions occurring postoperatively, the tremendous burden of unplanned admissions is evident. Data from this study have been significantly influenced by the inclusion of a cardiothoracic ICU where the vast majority of work was elective.…”

Section: Interpretation and Implicationsmentioning

confidence: 99%

“…2 Only 25% of these were in the public sector, which provides medical care for 84% of the population. High-income countries are able to collect and report intensive care demographic and outcome data, often using a national database, [5][6][7][8][9][10] thereby allowing for disease surveillance and outcome improvement. South Africa has yet to develop this capacity.…”

Section: Introductionmentioning

confidence: 99%

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Outcomes 30 days after ICU admission: the 30DOS study

Wise

Vasconcellos

Skinner

et al. 2017

Southern African Journal of Anaesthesia and Analgesia

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Background:The spectrum of illness and long-term outcome of critically ill patients admitted to intensive care units (ICUs) in South Africa remains largely unknown. Objectives: This study was designed to provide data on ICU outcomes and disease burden in public sector ICUs in KwaZuluNatal. The primary objective was to describe 30-day mortality of all patients admitted to participating sites. Secondary objectives included clinical demographics and spectrum of illness amongst these patients, and testing a template to demonstrate feasibility of such data collection. Methods: 30DOS was a multicentre, prospective, observational cohort study conducted over 30 days. An a priori decision was made to report study results separately for adults and paediatric patients. This article reports the results for adult patients. The complete 30-DOS study included 11 ICUs in six hospitals. All adult patients admitted to study ICUs were included. Patients were followed up telephonically by a research assistant. Data on patient demographics, preadmission functional scoring, injury severity scoring, co-morbidities, admission diagnosis/es, organ support, and outcome were collected. Results: A total of 228 adults were included. The majority of admissions (73.7%) occurred on an emergency basis, with 68.4% occurring in the postoperative period. Approximately half were for non-communicable disease (49.6%), followed by trauma (29.0%) and infectious disease (21.5%). There were a total of 59 (25.9%) deaths within the first 30 days after admission. In-ICU mortality was 19.7%. There were 12 (5.3%) in-hospital deaths following discharge and two (0.9%) out-of-hospital deaths. Thirtyday survival was known for 174 (76.3%) admissions with a 33.9% mortality rate. Conclusion: Overall in-ICU mortality was 19.7% with a large trauma burden in a young population. Thirty-day mortality was 33.9%. Information pertaining to patient demographics and spectrum of illness provided novel information to further the understanding of the demand placed on critical care resources within South Africa.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Interpretation and Implicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Outcomes 30 days after ICU admission: the 30DOS study

Wise

Vasconcellos

Skinner

et al. 2017

Southern African Journal of Anaesthesia and Analgesia

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“…These issues are particularly problematic, given variation in ICU admission patterns across hospitals (23)(24)(25). Since hospitals differ widely on the basis of both their propensity to treat high-risk patients outside the ICU (26,27) and their propensity to admit low-risk patients to the ICU (20), a measurement framework based on mortality for all patients admitted to the ICU may provide limited insight into overall hospital performance for the critically ill. To empirically evaluate this issue, we used a statewide database of both ICU and ward patients to assess variation in hospital-level performance assessment when measured on different "types" of critically ill patients: low-severity patients in the ICU, high-severity patients in the ICU, and high-severity patients receiving care solely on the hospital ward.…”

mentioning

confidence: 99%

The Effect of Intensive Care Unit Admission Patterns on Mortality-based Critical Care Performance Measures

Barbash

Le²,

Pike³

et al. 2016

Annals ATS

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Rationale: Current mortality-based critical care performance measurement focuses on intensive care unit (ICU) admissions as a single group, conflating low-severity and high-severity ICU patients for whom performance may differ and neglecting severely ill patients treated solely on hospital wards.Objectives: To assess the relationship between hospital performance as measured by risk-standardized mortality for severely ill ICU patients, less severely ill ICU patients, and severely ill patients outside the ICU.Methods: Using a statewide, all-payer dataset from the Pennsylvania Healthcare Cost Containment Council, we analyzed discharge data for patients with nine clinical conditions with frequent ICU use. Using a validated severity-of-illness measure, we categorized hospitalized patients as either high severity (predicted probability of in-hospital death in top quartile) or low severity (all others). We then created three mutually exclusive groups: high-severity ICU admissions, low-severity ICU admissions, and high-severity ward patients. We used hierarchical logistic regression to generate hospital-specific 30-day riskstandardized mortality rates for each group and then compared hospital performance across groups using Spearman's rank correlation. Measurements and Main Results:We analyzed 87 hospitals with 22,734 low-severity ICU admissions (mean per hospital, 261 6 187), 10,991 high-severity ICU admissions (mean per hospital, 126 6 105), and 6,636 high-severity ward patients (mean per hospital, 76 6 48). We found little correlation between hospital performance for high-severity ICU patients versus low-severity ICU patients (r = 0.15; P = 0.17). There were 29 hospitals (33%) that moved up or down at least two quartiles of performance across the ICU groups. There was weak correlation between hospital performance for highseverity ICU patients versus high-severity ward patients (r = 0.25; P = 0.02). There were 24 hospitals (28%) that moved up or down at least two quartiles of performance across the high-severity groups.Conclusions: Hospitals that perform well in caring for highseverity ICU patients do not necessarily also perform well in caring for low-severity ICU patients or high-severity ward patients, indicating that risk-standardized mortality rates for ICU admissions as a whole offer only a narrow window on a hospital's overall performance for critically ill patients.

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