Does patient volume affect clinical outcomes in adult intensive care units?

Kanhere, M. H.; Kanhere, Harsh; Cameron, A Scott; Maddern, Guy J.

doi:10.1007/s00134-012-2519-y

Cited by 48 publications

(36 citation statements)

References 57 publications

(165 reference statements)

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“…As suggested by Chieregato and colleagues and demonstrated elsewhere, regional networking and collaboration provides a mechanism for shared service improvement and quality assurance [24], potentially levelling differences in outcome [10]. This is consistent with the successful experience of critical care networks in many parts of England and Wales since 2001.…”

Section: Underlying Principlessupporting

confidence: 71%

“…A recent UK retrospective cohort study showed a positive volume-outcome relationship in 104 844 admissions requiring mechanical ventilation in general critical care units [9]. Volume-outcome benefits may be influenced by diagnostic subgroup and severity [9,10], indicating a possible rationale for selective escalation. Conversely, a study of 5131 nonsurgical admissions to US Veterans Administration hospitals, with shared organisational and governance attributes, showed unifomity of outcomes independent of case volume, suggesting that evidencebased practice and standardisation of staffing care may mitigate variation by unit size [11].…”

Section: Should Regional Icus Be a Primary Goal?mentioning

confidence: 99%

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Regionalisation of critical care: can we sustain an intensive care unit in every hospital?

Suntharalingam

Handy

Walsh³

2014

Anaesthesia

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Section: Underlying Principlessupporting

confidence: 71%

Section: Should Regional Icus Be a Primary Goal?mentioning

confidence: 99%

Regionalisation of critical care: can we sustain an intensive care unit in every hospital?

Suntharalingam

Handy

Walsh³

2014

Anaesthesia

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“…Because ANNs can be trained with data acquired in specific clinical contexts, they can consider local expertise, Table 4 Comparison of 1000 pairs of data sets from logistic regression and the multilayer perceptron (MLP) and radial basis function (RBF) models in predicting in-hospital mortality postoperative outcome, is crucial. Kanhere et al 22 applied a systematic search strategy to identify published studies of volume-outcome relationships in adult ICU patients. They concluded that, under the constraint of risk adjustment, certain subsets of ICU patients such as those requiring mechanical ventilation or those with severe sepsis or other high-risk conditions tend to have better outcomes in highvolume hospitals than in low-volume hospitals.…”

Section: Discussionmentioning

confidence: 99%

“…They concluded that, under the constraint of risk adjustment, certain subsets of ICU patients such as those requiring mechanical ventilation or those with severe sepsis or other high-risk conditions tend to have better outcomes in highvolume hospitals than in low-volume hospitals. 22 Treatment outcomes clearly depend not only on management of patients, but also on the skill and experience of individual physicians. Meanwhile, high-volume physicians in high-volume hospitals are most likely to achieve good outcomes for patients because such physicians are usually assisted by highly skilled and interdisciplinary care teams.…”

Section: Discussionmentioning

confidence: 99%

“…[22][23][24] Therefore, the treatment strategies of high-volume hospitals should be carefully analyzed and emulated. If in-hospital mortality is considered a benchmark, hospital volume, which is a major predictor of Table 6 Comparative performance indices of logistic regression and the multilayer perceptron (MLP) and radial basis function (RBF) models when using 100 new data sets to predict in-hospital mortality clinical trial data due to possible errors in the coding of primary diagnoses.…”

Section: Discussionmentioning

confidence: 99%

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In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

Chen

Shi

Lee

et al. 2013

American Journal of Critical Care

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Background Few studies have used pooled data for more than 2 years and few have analyzed data for patients receiving mechanical ventilation in Taiwan. Objective To validate the use of an artificial neural network model for predicting in-hospital mortality in patients receiving mechanical ventilation in Taiwan and to compare the predictive accuracy of the artificial neural network model with that of a logistic regression model. Methods Retrospective comparison of 1000 pairs of data sets processed by logistic regression and artificial neural network models based on initial clinical data for 213 945 patients receiving mechanical ventilation. For each pair of artificial neural network and logistic regression models, the area under the receiver operating characteristic curves, Hosmer-Lemeshow statistics, and accuracy rate were calculated and compared by using t tests. Global sensitivity analysis and sensitivity score approach were also used to assess the relative significance of input parameters in the system model and the relative importance of variables. Results Compared with the logistic regression model, the artificial neural network model had a better accuracy rate in 96.3% of cases, better Hosmer-Lemeshow statistics in 41.2% of cases, and a better area under the curve in 97.6% of cases. Hospital volume was the most influential (sensitive) variable affecting in-hospital mortality, followed by Charlson comorbidity index, length of stay, and hospital type. Conclusions Compared with the conventional logistic regression model, the artificial neural network model was more accurate in predicting in-hospital mortality and had higher overall performance indices.

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Effect of Published Scientific Evidence on Glycemic Control in Adult Intensive Care Units

et al. 2015

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IMPORTANCE Little is known about the deadoption of ineffective or harmful clinical practices. A large clinical trial (the Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation [NICE-SUGAR] trial) demonstrated that strict blood glucose control (tight glycemic control) in patients admitted to adult intensive care units (ICUs) should be deadopted; however, it is unknown whether deadoption occurred and how it compared with the initial adoption. OBJECTIVE To evaluate glycemic control in critically ill patients before and after the publication of clinical trials that initially suggested that tight glycemic control reduced mortality (Leuven I) but subsequently demonstrated that it increased mortality (NICE-SUGAR). DESIGN, SETTING, AND PARTICIPANTS Interrupted time-series analysis of 353 464 patients admitted to 113 adult ICUs from January 1, 2001, through December 31, 2012, in the United States using data from the Acute Physiology and Chronic Health Evaluation database. MAIN OUTCOMES AND MEASURES The physiologically most extreme blood glucose level on day 1 of ICU admission defined glycemic control as tight control (glucose level, 80-110 mg/dL; to convert to millimoles per liter, multiply by 0.0555), hypoglycemia (glucose level, <70 mg/dL), and hyperglycemia (glucose level, Ն180 mg/dL). Temporal changes in each marker were examined using mixed-effects segmented linear regression. RESULTS Before the publication of Leuven I, 17.2% (95% CI, 16.2%-18.2%) of ICU admissions had tight glycemic control, 3.0% (95% CI, 2.6%-3.5%) had hypoglycemia, and 40.2% (95% CI, 38.8%-41.5%) had hyperglycemia. After the publication of Leuven I, there were significant increases in the relative proportion of admissions with tight glycemic control (1.7% per quarter; 95% CI, 1.2%-2.3%; P < .001) and hypoglycemia (2.5% per quarter; 95% CI, 1.9%-3.2%; P < .001) and decreases in those with hyperglycemia (0.6% per quarter; 95% CI, 0.4%-0.9%; P < .001). Following the publication of NICE-SUGAR, there was no change in the proportion of patients with tight glycemic control or hyperglycemia. There was an immediate decrease in the relative proportion of patients with hypoglycemia (22.4%; 95% CI, 13.2%-30.1%; P < .001) but no subsequent change over time. CONCLUSIONS AND RELEVANCE Among patients admitted to adult ICUs in the United States, there was a slow steady adoption of tight glycemic control following publication of a clinical trial that suggested benefit, with little to no deadoption following a subsequent trial that demonstrated harm. There is an urgent need to understand and promote the deadoption of ineffective clinical practices.

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Does patient volume affect clinical outcomes in adult intensive care units?

Abstract: The results indicate that outcomes of certain subsets of ICU patients--especially those on mechanical ventilation, high-risk patients, and patients with severe sepsis--are better in high volume centres within the constraints of risk adjustments.

Cited by 48 publications

References 57 publications

Regionalisation of critical care: can we sustain an intensive care unit in every hospital?

Regionalisation of critical care: can we sustain an intensive care unit in every hospital?

In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

Effect of Published Scientific Evidence on Glycemic Control in Adult Intensive Care Units

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