Prediction Models for In-Hospital Delirium Using Routinely Collected Electronic Health Record Data: Systematic Review

The useful part

Delirium is a common and clinically important form of acute in-hospital mental status deterioration. Electronic health record (EHR)–based prediction models may support early identification and targeted prevention, but their methodological quality, validation rigor, and clinical readiness remain uncertain. Eligible studies developed, validated, or evaluated multivariable prediction models using routinely collected EHR or administrative data to predict acute mental status deterioration during adult hospital admissions.

How it works

• Risk of bias and applicability were assessed using PROBAST (Prediction Model Risk of Bias Assessment Tool).
• This systematic review aimed to synthesize and critically evaluate prediction models for in-hospital delirium developed using routinely collected EHR data, focusing on model characteristics, validation...
• Data extraction was informed by CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and TRIPOD (Transparent Reporting of a Multivariable...
• The evidence clustered into 4 overlapping prediction tasks: admission or early-stay risk stratification, perioperative or postoperative prediction, dynamic intensive care unit prediction, and external...
• Although eligibility criteria were broad, all included studies operationalized deterioration as delirium.

What to take from it

Model performance, validation, calibration, and implementation features were synthesized narratively. Most studies were retrospective cohorts (20/29, 69%) and were conducted in general ward, mixed ward–intensive care unit, intensive care unit, or...

Details worth keeping

Twenty-nine studies met the inclusion criteria.