Thomas H McCoy1, Victor M Castro2, Kamber L Hart3, Roy H Perlis4. 1. Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA. Electronic address: thmccoy@partners.org. 2. Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA. Electronic address: vcastro@partners.org. 3. Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA. Electronic address: klhart@partners.org. 4. Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA. Electronic address: rperlis@partners.org.
Abstract
OBJECTIVE: Delirium is a common condition associated with increased morbidity and mortality. Medication side effects are a possible source of modifiable delirium risk and provide an opportunity to improve delirium predictive models. This study characterized the risk for delirium diagnosis by applying a previously validated algorithm for calculating central nervous system adverse effect burden arising from a full medication list. METHOD: Using a cohort of hospitalized adult (age 18-65) patients from the Massachusetts All-Payers Claims Database, we calculated medication burden following hospital discharge and characterized risk of new coded delirium diagnosis over the following 90 days. We applied the resulting model to a held-out test cohort. RESULTS: The cohort included 62,180 individuals of whom 1.6% (1019) went on to have a coded delirium diagnosis. In the training cohort (43,527 individuals), the medication burden feature was positively associated with delirium diagnosis (OR = 5.75, 95% CI 4.34-7.63) and this association persisted (aOR = 1.95; 1.31-2.92) after adjusting for demographics, clinical features, prescribed medications, and anticholinergic risk score. In the test cohort, the trained model produced an area under the curve of 0.80 (0.78-0.82). This performance was similar across subgroups of age and gender. CONCLUSION: Aggregating brain-related medication adverse effects facilitates identification of individuals at high risk of subsequent delirium diagnosis.
OBJECTIVE: Delirium is a common condition associated with increased morbidity and mortality. Medication side effects are a possible source of modifiable delirium risk and provide an opportunity to improve delirium predictive models. This study characterized the risk for delirium diagnosis by applying a previously validated algorithm for calculating central nervous system adverse effect burden arising from a full medication list. METHOD: Using a cohort of hospitalized adult (age 18-65) patients from the Massachusetts All-Payers Claims Database, we calculated medication burden following hospital discharge and characterized risk of new coded delirium diagnosis over the following 90 days. We applied the resulting model to a held-out test cohort. RESULTS: The cohort included 62,180 individuals of whom 1.6% (1019) went on to have a coded delirium diagnosis. In the training cohort (43,527 individuals), the medication burden feature was positively associated with delirium diagnosis (OR = 5.75, 95% CI 4.34-7.63) and this association persisted (aOR = 1.95; 1.31-2.92) after adjusting for demographics, clinical features, prescribed medications, and anticholinergic risk score. In the test cohort, the trained model produced an area under the curve of 0.80 (0.78-0.82). This performance was similar across subgroups of age and gender. CONCLUSION: Aggregating brain-related medication adverse effects facilitates identification of individuals at high risk of subsequent delirium diagnosis.
Authors: Esther S Oh; Dale M Needham; Roozbeh Nikooie; Lisa M Wilson; Allen Zhang; Karen A Robinson; Karin J Neufeld Journal: Ann Intern Med Date: 2019-09-03 Impact factor: 25.391
Authors: Hassan Khouli; Alfred Astua; Wen Dombrowski; Faiz Ahmad; Peter Homel; Janet Shapiro; Jagdeep Singh; Ravi Nallamothu; Humaira Mahbub; Edward Eden; Joel Delfiner Journal: Crit Care Med Date: 2011-04 Impact factor: 7.598
Authors: E R Marcantonio; G Juarez; L Goldman; C M Mangione; L E Ludwig; L Lind; N Katz; E F Cook; E J Orav; T H Lee Journal: JAMA Date: 1994-11-16 Impact factor: 56.272