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Latent Class Analysis of Specialized Palliative Care Needs in Adult Intensive Care Units from a Single Academic Medical Center

Wang, David; Ing, Caleb H.; Blinderman, Craig; Hua, May

Context: In the intensive care unit (ICU), 14% of patients meet criteria for specialized palliative care, but whether subgroups of patients differ in their palliative care needs is unknown.

Objectives: To use latent class analysis (LCA) to separate ICU patients into different classes of palliative care needs, and determine if such classes differ in their palliative care resource requirements.

Methods: Retrospective cohort study of ICU patients who received specialized palliative care, August 2013 – August 2015. Reason(s) for consultation were extracted from the initial note and entered into a LCA model to generate mutually exclusive patient classes. Differences in “high use” of palliative care (defined as having ≥ 5 palliative care visits) between classes was assessed using logistic regression, adjusting for age, race, Charlson comorbidity index and length of stay.

Results: In a sample of 689 patients, a four-class model provided the most meaningful groupings: 1) Pain and Symptom Management (n=218, 31.6%), 2) Goals of Care and Advance Directives (GCAD) (n=131, 19.0%), 3) All Needs (n=112, 16.3%) and 4) Supportive Care (n=228, 33.1%). In comparison to GCAD patients, all other classes were more likely to require “high use” of palliative care, (adjusted odds ratio (aOR) 2.61, [1.41-4.83] for “All Needs”, aOR 2.01 [1.16-3.50] for “Pain and Symptom Management”, aOR 1.94 [1.12-3.34] for “Supportive Care”).

Conclusion: Based on the initial reason for consultation, we identified four classes of palliative care needs amongst critically ill patients, and GCAD patients were least likely to be high-utilizers. These findings may help inform allocation of palliative care resources for this population.

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Also Published In

Title
Journal of Pain and Symptom Management
DOI
https://doi.org/10.1016/j.jpainsymman.2018.10.270

More About This Work

Academic Units
Anesthesiology
Epidemiology
Published Here
June 3, 2019

Notes

This is a pre-print of an article published in Journal of Pain and Symptom Management, January 2019.