Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.

Abstract

Objective: Delirium is a severe condition, often underreported and linked to adverse outcomes such as increased mortality and prolonged hospitalization. Despite its significance, delirium prediction is often hindered by underreporting and inconsistent labeling, highlighting the need for models trained on reliably labeled data (ground truth). This review examines (i) practices for determining labels in delirium prediction models and (ii) how study designs affect label quality, aiming to identify key considerations for improving model reliability.

Materials and methods: A search of Cochrane, PubMed, and IEEE identified 120 studies that met the inclusion criteria.

Results: To establish the ground truth, 40.8% of studies used routine data, while 42.5% used primary data. The Confusion Assessment Method (CAM) was the most widely used assessment tool (60. 0%). Label and data leakage occurred in 35.0% of studies. High Risk of Bias (RoB) was a recurring issue, with 31.7% of studies lacking sufficient reporting and 36.7% showing inadequate outcome determination. Studies using primary data had lower RoB, whereas those with unclear label sources displayed higher RoB.

Discussion: Our findings underscore the importance of careful planning in determining the ground truth frequently neglected in existing studies. To address these challenges, we provide a decision support flowchart to guide the development of more accurate and reliable prediction models.

Conclusion: This review uncovers significant variability in labeling methods and discusses how this may affect delirium prediction model reliability. Highlighting the importance of addressing underreporting bias and providing guidance for developing more robust models.