OBJECTIVE: To develop a predictive equation that estimates the probability of life-supporting therapy among ICU monitor admissions and to explore its potential for reducing cost and improving ICU utilization. DESIGN: Prospective inception cohort analysis. PARTICIPANTS: Forty-two ICUs in 40 US hospitals with more than 200 beds and a consecutive sample of 17,440 ICU admissions. INTERVENTIONS: A multivariate equation was developed to estimate the probability of life support for ICU monitoring admissions during an entire ICU stay. MEASUREMENTS: Demographic, physiologic, and treatment information obtained during the first 24 h in the ICU and over the first 7 ICU days. RESULTS: The most important determinants of subsequent risk for life-supporting (active) treatment were diagnosis, the acute physiology score of APACHE III, age, operative status, and the patient's location and hospital length of stay before ICU admission. Among 8,040 ICU monitoring admissions, 6,180 (76.8%) had a low (< 10%) risk for receiving active treatment during the ICU stay; 95.6% received no subsequent active treatment. Review of outcomes and the type and amount of therapy received suggest that most low-risk ICU monitor admissions could be safely cared for in an intermediate care setting. CONCLUSION: Objective predictions can accurately identify groups of ICU admissions who are at a low risk for receiving life support. This capability can be used to assess ICU resource use and develop strategies for providing graded critical care services at a reduced cost.
OBJECTIVE: To develop a predictive equation that estimates the probability of life-supporting therapy among ICU monitor admissions and to explore its potential for reducing cost and improving ICU utilization. DESIGN: Prospective inception cohort analysis. PARTICIPANTS: Forty-two ICUs in 40 US hospitals with more than 200 beds and a consecutive sample of 17,440 ICU admissions. INTERVENTIONS: A multivariate equation was developed to estimate the probability of life support for ICU monitoring admissions during an entire ICU stay. MEASUREMENTS: Demographic, physiologic, and treatment information obtained during the first 24 h in the ICU and over the first 7 ICU days. RESULTS: The most important determinants of subsequent risk for life-supporting (active) treatment were diagnosis, the acute physiology score of APACHE III, age, operative status, and the patient's location and hospital length of stay before ICU admission. Among 8,040 ICU monitoring admissions, 6,180 (76.8%) had a low (< 10%) risk for receiving active treatment during the ICU stay; 95.6% received no subsequent active treatment. Review of outcomes and the type and amount of therapy received suggest that most low-risk ICU monitor admissions could be safely cared for in an intermediate care setting. CONCLUSION: Objective predictions can accurately identify groups of ICU admissions who are at a low risk for receiving life support. This capability can be used to assess ICU resource use and develop strategies for providing graded critical care services at a reduced cost.
Authors: Ari Moskowitz; Parth V Patel; Anne V Grossestreuer; Maureen Chase; Nathan I Shapiro; Katherine Berg; Michael N Cocchi; Mathias J Holmberg; Michael W Donnino Journal: Crit Care Med Date: 2017-11 Impact factor: 7.598
Authors: Annette Ilg; Ari Moskowitz; Varun Konanki; Parth V Patel; Maureen Chase; Anne V Grossestreuer; Michael W Donnino Journal: Ann Emerg Med Date: 2018-08-02 Impact factor: 5.721
Authors: Mark T Keegan; Bhargavi Gali; James Y Findlay; Julie K Heimbach; David J Plevak; Bekele Afessa Journal: BMC Surg Date: 2009-07-29 Impact factor: 2.102