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Updated: Jul 16, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
The Confusion Assessment Method in action: Implementation of a protocol to increase delirium screening and diagnosis
Thiago Junqueira Avelino-Silva1, José Adenaldo Santos Bittencourt2, César Gomes Miguel2
1Laboratorio de Investigacao Medica em Envelhecimento, Serviço de Geriatria, Departamento de Clínica Médica, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina da Universidade de Sao Paulo. Av. Dr. Enéas Carvalho de Aguiar 155, Prédio dos Ambulatórios, 8° Andar, Setor Azul, LIM-66, São Paulo, SP, 05304-000, Brazil; Global Brain Health Institute, University of California San Francisco. Attention: GBHI Memory and Aging Center, MC: 1207 1651 4th St, 3rd Floor, San Francisco, CA 94143, USA.
Abstract:
The use of the Confusion Assessment Method (CAM) for delirium assessment in real-life can be inconsistent. We examined the impact of a protocol on delirium screening and detection in hospitalized older adults using the CAM. We analyzed data from 32,338 admissions to a quaternary hospital between 2018 and 2022. We assessed the percentage of admissions screened for delirium, adherence to daily screening, positive screening, and overlap with ICD-10 coding. The percentage of admissions screened for delirium increased from 74% in 2018 to 98.7% in 2022. Adherence to daily screening was achieved in 24.5% of admissions, and the percentage of positive screenings fluctuated between 8.4% and 11.5%. Among the admissions with a delirium-related ICD-10 code, 32% had a positive screening, 62% were negative, and 6% remained unscreened. While implementing a protocol increased the proportion of admissions screened for delirium, adherence to daily screening and consistency of positive delirium screenings remain areas for improvement.
