Clinical Effectiveness of AI-Based Cardiac Arrest Prediction System for General Ward Patients (VITAL-MD): Protocol for a Multicenter Pragmatic Stepped Wedge Cluster Randomized Controlled Trial.

@article{kim2026clinical,
  title = {{Clinical Effectiveness of AI-Based Cardiac Arrest Prediction System for General Ward Patients (VITAL-MD): Protocol for a Multicenter Pragmatic Stepped Wedge Cluster Randomized Controlled Trial.}},
  author = {Mincheol Kim and Kyung Hoon Kim and Minsoo Kim and Sun Jung Kwon and Myung Soo Park and Ah Jin Kim and Man-Jong Lee and Mi Hwa Park and Hyung Keun Cha and Minhyeok Lee and In Beom Jeong and Soon Joo Wang and Sung Bak An and Jeen Hwa Lee and Kyung-Jae Cho and Jinhui Jang and HyoJin Jeong and Yoon Young Lee and Woori Han and VITAL-MD Study Group and Jung Soo Kim and Dongjoon Yoo},
  journal = {JMIR research protocols},
  year = {2026},
  month = oct,
  doi = {10.2196/104230},
  url = {https://europepmc.org/article/MED/42895816},
  note = {CC BY},
}

TY  - JOUR
TI  - Clinical Effectiveness of AI-Based Cardiac Arrest Prediction System for General Ward Patients (VITAL-MD): Protocol for a Multicenter Pragmatic Stepped Wedge Cluster Randomized Controlled Trial.
AU  - Mincheol Kim
AU  - Kyung Hoon Kim
AU  - Minsoo Kim
AU  - Sun Jung Kwon
AU  - Myung Soo Park
AU  - Ah Jin Kim
AU  - Man-Jong Lee
AU  - Mi Hwa Park
AU  - Hyung Keun Cha
AU  - Minhyeok Lee
AU  - In Beom Jeong
AU  - Soon Joo Wang
AU  - Sung Bak An
AU  - Jeen Hwa Lee
AU  - Kyung-Jae Cho
AU  - Jinhui Jang
AU  - HyoJin Jeong
AU  - Yoon Young Lee
AU  - Woori Han
AU  - VITAL-MD Study Group
AU  - Jung Soo Kim
AU  - Dongjoon Yoo
PY  - 2026
DA  - 2026/10/19
JO  - JMIR research protocols
DO  - 10.2196/104230
UR  - https://europepmc.org/article/MED/42895816
AB  - Background  In-hospital cardiac arrest (IHCA) remains a frequent and devastating event, and conventional early warning scores (EWSs) coupled with rapid response systems (RRSs) have shown inconsistent effects on patient-centered outcomes, largely owing to limited predictive accuracy and high false-alarm burden. AI-based EWSs (AI-EWSs) have demonstrated superior discrimination retrospectively, but high-quality prospective evidence in true external validation environments remains scarce. Existing AI-EWS trials have either been confined to internal-development settings or bundled the algorithm with specific implementation infrastructure, leaving the stand-alone effect of AI-based software as a medical device (AI-SaMD) on patient outcomes uncharacterized.Objective  This trial will evaluate whether implementation of AI-SaMD (VUNO Med-DeepCARS) as a stand-alone clinical decision-support tool in routine general-ward care reduces IHCA compared with usual care based on conventional EWSs.Methods  The VITAL-MD trial (validation of an AI-based clinical deterioration prediction model on patient outcomes through a stepped-wedge cluster randomized trial [SW-CRT] multicenter evaluation of DeepCARS) is a pragmatic, multicenter, SW-CRT conducted at 4 tertiary academic hospitals in the Republic of Korea. Six tertiary academic hospitals were assessed for eligibility based on inpatient capacity, RRS operating hours aligned with the national RRS pilot program, and no prior AI-SaMD use. All adult patients aged 19 years or older admitted to general wards during the 12-month trial period are included; patients younger than 19 years, or with no documented vital signs throughout hospitalization, are excluded. The 4 sites are randomized to sequentially transition from preimplementation (usual care) to postimplementation (AI-SaMD-guided care) in 4 steps separated by 7-day washout periods. DeepCARS generates real-time risk scores from 4 vital signs and integrates into the existing electronic medical record interface without altering RRS workflows. The primary outcome is the incidence of general-ward IHCA, defined per the Utstein guidelines, among the target cohort (defined as patients exceeding the alert threshold at least once). Assuming a baseline IHCA incidence of 6.70%, a 40% relative risk reduction, an intracluster correlation coefficient of 0.01, and 350 patients per cluster-period, 7002 target-cohort patients (total enrollment approximately 72,695) provide 80% power. Analyses follow the intention-to-treat principle using generalized linear mixed models adjusting for prespecified covariates, with prespecified sensitivity, subgroup, and compliance analyses.Results  The trial was registered with the Clinical Research Information Service (KCT0010243) on February 28, 2025. Recruitment was conducted from April 1, 2025, to March 31, 2026. All 4 sites completed transition to the postimplementation period, and data cleaning is currently underway before the final database lock, with primary results expected in 2027.Conclusions  VITAL-MD will provide the first large-scale, multicenter randomized evidence evaluating whether an AI-EWS, deployed as a stand-alone device in external validation environments and benchmarked against real-world standard-of-care practice, improves patient-centered outcomes for general-ward patients, advancing the evaluation of AI-SaMD from retrospective diagnostic accuracy toward prospective, action-anchored clinical utility.
SN  - 1929-0748
ER  -