Retrospective external validation of the Mayo Delirium Prediction tool in a Swiss cohort of medical and surgical inpatients

BACKGROUND: Delirium is a frequent complication in hospitalised patients, associated with long-term cognitive impairment, prolonged hospital stays, and increased mortality. Early risk assessment is essential for implementing preventive strategies. The Mayo Delirium Prediction (MDP) tool, developed using data from a large academic hospital in the United States, includes separate models for medical and surgical patients. The MDP is available in its original and recalibrated versions. However, its performance in non-US population remains unknown. The objective of this study is to externally validate the MDP tool in a cohort of hospitalised patients from a Swiss private hospital. METHODS: This retrospective validation study used routinely collected clinical data from adult medical and surgical inpatients admitted to a Swiss hospital in May and June 2023. Delirium diagnosis was based on the Delirium Observation Screening Scale (DOSS). Predictive performance was assessed using the Area Under the Receiver Operating Characteristic curve (AUROC) and calibration plots. Additional sensitivity analyses were performed to assess the model's robustness and mitigate possible biases in the evaluation. RESULTS: The original medical MDP tool achieved the best predictive performance in the external validation cohort of 947 patients, with an AUROC of 0·87 (95% CI: 0·85-0·90). The original surgical MDP tool showed relatively lower performance in the external validation cohort of 1212 patients, with an AUROC of 0·73 (95% CI: 0·69-0·78). Performance remained acceptable across all sensitivity analyses, with only a modest reduction in AUROC. The original MDP tool consistently outperformed recalibrated versions across all scenarios. CONCLUSIONS: The MDP tool, particularly the medical model, maintained strong predictive performance in an independent setting. These findings support its generalisability and potential integration into routine clinical workflows for delirium prevention.

Authors

Institutions

Publication Details

Journal
BMC Medical Informatics and Decision Making
Published
2026-06-29
DOI
https://doi.org/10.1186/s12911-026-03660-5
Primary Topic
Intensive Care Unit Cognitive Disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Retrospective external validation of the Mayo Delirium Prediction tool in a Swiss cohort of medical and surgical inpatients

Felix Buddeberg, Michela Venturini, Benjamin T. Dodsworth, Sandeep R. Pagali et al.
BMC Medical Informatics and Decision Making
Intensive Care Unit Cognitive Disorders
article

Retrospective external validation of the Mayo Delirium Prediction tool in a Swiss cohort of medical and surgical inpatients

Felix Buddeberg, Michela Venturini, Benjamin T. Dodsworth, Sandeep R. Pagali, Nayeli Schmutz Gelsomino, Martin Zozman, Simone Pascale Wildhaber, Mary-Anne Kedda, Marius Möller, Reto Stocker
article en

Abstract

BACKGROUND: Delirium is a frequent complication in hospitalised patients, associated with long-term cognitive impairment, prolonged hospital stays, and increased mortality. Early risk assessment is essential for implementing preventive strategies. The Mayo Delirium Prediction (MDP) tool, developed using data from a large academic hospital in the United States, includes separate models for medical and surgical patients. The MDP is available in its original and recalibrated versions. However, its performance in non-US population remains unknown. The objective of this study is to externally validate the MDP tool in a cohort of hospitalised patients from a Swiss private hospital. METHODS: This retrospective validation study used routinely collected clinical data from adult medical and surgical inpatients admitted to a Swiss hospital in May and June 2023. Delirium diagnosis was based on the Delirium Observation Screening Scale (DOSS). Predictive performance was assessed using the Area Under the Receiver Operating Characteristic curve (AUROC) and calibration plots. Additional sensitivity analyses were performed to assess the model's robustness and mitigate possible biases in the evaluation. RESULTS: The original medical MDP tool achieved the best predictive performance in the external validation cohort of 947 patients, with an AUROC of 0·87 (95% CI: 0·85-0·90). The original surgical MDP tool showed relatively lower performance in the external validation cohort of 1212 patients, with an AUROC of 0·73 (95% CI: 0·69-0·78). Performance remained acceptable across all sensitivity analyses, with only a modest reduction in AUROC. The original MDP tool consistently outperformed recalibrated versions across all scenarios. CONCLUSIONS: The MDP tool, particularly the medical model, maintained strong predictive performance in an independent setting. These findings support its generalisability and potential integration into routine clinical workflows for delirium prevention.

BMC Medical Informatics and Decision Making
University of Basel (CH), Line Corporation (Japan) (JP), Klinik Hirslanden (CH), Ernst Basler + Partner (Switzerland) (CH), Mayo Clinic in Arizona (US)
Good health and well-being
Openalex Percentile: Top 7%
Intensive Care Unit Cognitive Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.