Objective To investigate disparities in physical restraint use in a U.S. intensive care unit (ICU) setting, focusing on the influence of demographic factors (ethnicity, sex, age), mental health diagnoses, intubation status, and ICU type. The study also examines trends before and after policy changes in 2014.

Methods This retrospective cross-sectional study uses MIMIC-IV data from adult ICU patients (2008–2022) at Beth Israel Deaconess Medical Center. The primary outcome is the proportion of ICU days with physical restraint. A binomial Generalized Linear Model (GLM) with a logit link function will be used to estimate associations between patient factors and the proportion of ICU time spent in restraints, modeling the number of days with restraint as a binomial outcome with the number of trials equal to the total ICU length of stay. Results will be reported as adjusted odds ratios with 95% confidence intervals. Temporal trends will be evaluated across predefined three-year intervals. Secondary analyses include binary restraint use (yes/no), death within 24 hours of restraint use (yes/no), interaction effects, and multiple sensitivity analyses.

The authors have declared no competing interest.

No specific funding was obtained for the delivery of this study. Maximin Lange is supported through a studentship by the London Interdisciplinary Social Science Doctoral Training Partnership (LISS DTP). Ben Carter is part funded through the NIHR Maudsley Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust in partnership with Kings College London This paper represents independent research funded by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and Kings College London. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. Leo Anthony Cell is funded by DS-I Africa U54 TW012043-01 and Bridge2AI OT2OD032701 and the National Science Foundation through ITEST #2148451. Tom Pollard is funded by NIH Bridge2AI OT2OD032701 and RO1 EB030362. Jesse Raffa is funded by Philips Healthcare. The funders had no role in study design, data collection, analysis, decision to publish or preparation of the manuscript.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

MIMIC-IV data was collected as part of routine clinical care. It has been deidentified and transformed. It is available to researchers who have completed training in human research and signed a data use agreement. It was approved for research by the institutional review boards of the Massachusetts Institute of Technology and BIDMC, who granted a waiver of informed consent and approved the sharing of the research resource.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Source code for SQL extraction code for the MIMIC-IV database will be made public on GitHub, along with all python code used for data analysis. MIMIC data is available on PhysioNet after completion of relevant research training and accreditation.