Out-of-hours consultant cover and case-mix-adjusted mortality in intensive care

Blunt, Mark; Burchett, K

doi:10.1016/s0140-6736(00)02634-9

Cited by 133 publications

(68 citation statements)

References 3 publications

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“…4 However, previous studies of the eff ectiveness of nighttime intensivists have yielded mixed results. [5][6][7][8][9] One retrospective cohort study found that among 22 US ICUs with low-intensity daytime physician staffi ng (ie, absence of routine care by intensivists during the day), ICUs that employed in-hospital intensivists at night had lower risk-adjusted mortality than those without nighttime intensivists. No such differences were seen in ICUs with high-intensity daytime staffi ng (ie, mandatory involvement of intensivists as primary physicians or consultants).…”

mentioning

confidence: 99%

Nighttime Intensivist Staffing, Mortality, and Limits on Life Support

Kerlin

Harhay

Kahn

et al. 2015

Chest

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confidence: 99%

Nighttime Intensivist Staffing, Mortality, and Limits on Life Support

Kerlin

Harhay

Kahn

et al. 2015

Chest

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“…The presence of a critical care team (1, 2), a full-time director (3), a closed unit (4-7), 24-hour intensivist coverage (8), a stable nurse-patient ratio around the clock (1), higher hospital volume (9), pharmacist consultation on ICU rounds (10), computerized physician order entry (11), and good nursephysician interaction (12) have all been associated with improved patient outcome. A recent systematic review indicated that high-intensity physician staffing, where intensivists manage or comanage all patients, was associated with reduced hospital and ICU mortality and hospital and ICU length of stay (13).…”

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confidence: 99%

Effect of Intensive Care Unit Organizational Model and Structure on Outcomes in Patients with Acute Lung Injury

Treggiari¹,

Martin²,

Yanez³

et al. 2007

Am J Respir Crit Care Med

123

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Rationale: Prior studies supported an association between intensive care unit (ICU) organizational model or staffing patterns and outcome in critically ill patients. Objectives: To examine the association of closed versus open models with patient mortality across adult ICUs in King County (WA). Methods: Cohort study of patients with acute lung injury (ALI). Measurements and Main Results: ICU structure, organization, and patient care practices were assessed using self-administered mail questionnaires completed by the medical director and nurse manager. We defined closed ICUs as units that required patient transfer to or mandatory patient comanagement by an intensivist and open ICUs as those relying on other organizational models. Outcomes were obtained from the King County Lung Injury Project, a populationbased cohort of patients with ALI. The main endpoint was hospital mortality. Of 24 eligible ICUs, 13 ICUs were designated closed and 11 open. Complete survey data were available for 23 (96%) ICUs. Higher physician and nurse availability was reported in closed versus open ICUs. A total of 684 of 1,075 (63%) of patients with ALI were cared for in closed ICUs. After adjusting for potential confounders, patients with ALI cared for in closed ICUs had reduced hospital mortality (adjusted odds ratio, 0.68; 95% confidence interval, 0.53, 0.89; P 5 0.004). Consultation by a pulmonologist in open ICUs was not associated with improved mortality (adjusted odds ratio, 0.94; 95% confidence interval, 0.74, 1.20; P 5 0.62). These findings were robust for varying assumptions about the study population definition. Conclusions: Patients with ALI cared for in a closed-model ICU have reduced mortality. These data support recommendations to implement structured intensive care in the United States.

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“…Good examples of higher order influences are the organisation and quality of care delivered on an individual ICU. There is good evidence to suggest that the presence of a specialist in intensive care [4] and a ÔclosedÕ ICU [5] improve outcome. Such factors are rarely captured in regression models.…”

Section: Predictor Variables Selectionmentioning

confidence: 99%

Uncertainty and scoring systems

Ridley

2002

Anaesthesia

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Summary Estimating risks for individual patients facilitates communication with patients, relatives and colleagues, and determines whether further treatment is futile. The process of estimating risks involves mathematics (i.e. scoring systems) and human experience and expertise. Understanding how risks are estimated is important because prognostication is an integral part of any medical specialty. In the USA, such treatment limitation or withdrawal decisions were made on only 7% of all intensive care unit patients but this represented 47% of all deaths on such units. In the UK, data reported by the Intensive Care National Audit and Research Centre suggest that although treatment limitation decisions are made on only 11.8% of patients, this accounts for over 50% of deaths on intensive care. Scoring systems offer a useful adjunct in identifying futility but there are important inherent weaknesses that limit their performance. This review aims to discuss some of these limitations.

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Out-of-hours consultant cover and case-mix-adjusted mortality in intensive care

Cited by 133 publications

References 3 publications

Nighttime Intensivist Staffing, Mortality, and Limits on Life Support

Nighttime Intensivist Staffing, Mortality, and Limits on Life Support

Effect of Intensive Care Unit Organizational Model and Structure on Outcomes in Patients with Acute Lung Injury

Uncertainty and scoring systems

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