ICU beds: less is more? Yes

ICU beds: less is more? Yes

Thomas S. Valley & Danilo T. Noritomi 

Intensive Care Medicine (2020) Published: 25 April 2020

In these extraordinary times, when intensive care unit (ICU) capacity is being outpaced by the dangers of the COVID-19 pandemic, ICU beds are a precious resource. However, when this crisis subsides, we may be left with greatly expanded ICU capacity. We, as intensivists, must act as leaders for our health care systems as there will be an opportunity to reevaluate two core tenets of critical care: (1) the definition of an ICU bed, and (2) the ideal number of ICU beds…

Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay*

Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay*

by Weissman, Gary E.; Hubbard, Rebecca A.; Ungar, Lyle H.; Harhay, Michael O.; Greene, Casey S.; Himes, Blanca E.; Halpern, Scott D.  

Critical Care Medicine: July 2018 - Volume 46 - Issue 7 - p 1125–1132

Objectives: Early prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients’ goals of care. We evaluated the performance of logistic regression, gradient boosting machine, random forest, and elastic net regression models, with and without unstructured clinical text data, to predict a binary composite outcome of in-hospital death or ICU length of stay greater than or equal to 7 days using data from the first 48 hours of hospitalization. Design: Retrospective cohort study with split sampling for model training and testing. Setting: A single urban academic hospital. Patients: All hospitalized patients who required ICU care at the Beth Israel Deaconess Medical Center in Boston, MA, from 2001 to 2012. Interventions: None. Measurements and Main Results: Among eligible 25,947 hospital admissions, we observed 5,504 (21.2%) in which patients died or had ICU length of stay greater than or equal to 7 days. The gradient boosting machine model had the highest discrimination without (area under the receiver operating characteristic curve, 0.83; 95% CI, 0.81–0.84) and with (area under the receiver operating characteristic curve, 0.89; 95% CI, 0.88–0.90) text-derived variables. Both gradient boosting machines and random forests outperformed logistic regression without text data (p < 0.001), whereas all models outperformed logistic regression with text data (p < 0.02). The inclusion of text data increased the discrimination of all four model types (p < 0.001). Among those models using text data, the increasing presence of terms “intubated” and “poor prognosis” were positively associated with mortality and ICU length of stay, whereas the term “extubated” was inversely associated with them. Conclusions: Variables extracted from unstructured clinical text from the first 48 hours of hospital admission using natural language processing techniques significantly improved the abilities of logistic regression and other machine learning models to predict which patients died or had long ICU stays. Learning health systems may adapt such models using open-source approaches to capture local variation in care patterns.

The volume-outcome relationship in critically ill patients in relation to the ICU-to-hospital bed ratio

The volume-outcome relationship in critically ill patients in relation to the ICU-to-hospital-bed ratio. Critical Care Medicine, June 2015, Vol. 43(6), p.1239-45.

Sasabuchi, Y., et al.

http://journals.lww.com/ccmjournal/Abstract/2015/06000/The_Volume_Outcome_Relationship_in_Critically_Ill.13.aspx

A volume-outcome relationship in ICU patients has been suggested in recent studies. However, it is unclear whether the ICU-to-hospital bed ratio affects the volume-outcome relationship. The aim of this study is to investigate the relationship between hospital volume and in-hospital mortality of adult ICU patients in relation to the ratio of ICU beds to regular hospital beds.

Rationing in the intensive care unit: To disclose or disguise?

Rationing in the intensive care unit: To disclose or disguise? Critical care medicine, Jan 2012, Vol. 40(1), p.261-266.

Young, M.J., et al.

http://journals.lww.com/ccmjournal/Abstract/2012/01000/Rationing_in_the_intensive_care_unit___To_disclose.38.aspx

Growing pressures to ration intensive care unit beds and services pose novel challenges to clinicians. Whereas the question of how to allocate scarce intensive care unit resources has received much attention, the question of whether to disclose these decisions to patients and surrogates has not been explored.