• Andrew J Admon, Hannah Wunsch, Theodore J Iwashyna, and Colin R Cooke.
• 1Department of Internal Medicine, University of Michigan, Ann Arbor, MI. 2Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. 3Department of Anesthesia, University of Toronto, Toronto, ON, Canada. 4Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI. 5Center for Clinical Management Research, VA Ann Arbor Health System, Ann Arbor, MI. 6Center for Healthcare Outcomes and Policy, Institute of Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI.
• Crit. Care Med. 2017 Jan 1; 45 (1): 75-84.

ObjectiveHospitals vary widely in ICU admission rates across numerous medical diagnoses. The extent to which variability in ICU use is specific to individual diagnoses or is a function of the hospital, regardless of disease, is unknown.DesignRetrospective cohort study.SettingA total of 1,120 acute care hospitals with ICU capabilities.PatientsMedicare beneficiaries 65 years old or older admitted for five medical diagnoses (acute myocardial infarction, congestive heart failure, stroke, pneumonia, and chronic obstructive pulmonary disease) and a surgical diagnosis (hip fracture treated with arthroplasty) in 2010.InterventionsNone.Measurements And Main ResultsWe used multilevel models to calculate risk- and reliability-adjusted ICU admission rates, examined the correlation in ICU admission rates across diagnosis and calculated intraclass correlation coefficients and median odds ratios to quantify the variability in ICU admission rate that was attributable to hospitals. We also examined the ability of a high ICU-use hospital for one condition to predict high ICU use for other conditions. We identified 348,462 patients with one of the eligible conditions. ICU admission rates were positively correlated within hospitals for included medical diagnoses (r range, 0.38-0.59; p < 0.01). The top hospital quartile of ICU use for congestive heart failure had a sensitivity of 50-60% and specificity of 79-81% for detecting top quartile hospitals for each other conditions. After adjustment for patient and hospital characteristics, hospitals accounted for 17.6% (95% CI, 16.2-19.1%) of variability in ICU admission, corresponding to a median odds ratio of 2.3, compared to 25.8% (95% CI, 24.5-27.1%) and median odds ratio 2.8 for diagnosis. This suggests a patient with median baseline risk of ICU admission would more than double his/her odds of ICU admission if moving to a higher utilizing hospital.ConclusionsHospitals account for a significant proportion of variation independent of measured patient and hospital characteristics, suggesting the need for further work to evaluate the causes of variation at the hospital level and potential consequences of variation across hospitals.

30 keywords...

hide…