The goal of this randomized controlled clinical trial is to [learn about, test, compare etc.] in critically ill or injured cohorts of patients presenting to the Emergency Department. The main question[s] it aims to answer are:

• Are weight estimates from a 3D camera system more accurate than standard methods of weight estimation?
• Do patients who receive weight estimates with a 3D camera system have fewer drug dosing errors than patients receiving standard care?

Participants will either receive a weight estimate using a 3D camera system, or standard methods of care.

Researchers will compare the 3D camera group to those with standard care to see if the weight estimates are more accurate, to see if drug dosing is more accurate, and to compare the incidence of adverse events related to medications in each group.

Drug dosing errors can have a catastrophic effect in acutely ill patients such as stroke patients needing thrombolytic therapy or patients requiring urgent sedation. In an acutely ill patient, inaccurate weight estimates are a significant cause of dosing errors, and weight estimates that deviate by >10% from actual weight could make treatment itself life threatening. Inaccurate weight estimates lead to inaccurate drug doses, which can result in potentially fatal treatment failure (from subtherapeutic doses) or potentially fatal adverse events (from supratherapeutic doses). Nearly 75% of treatment failures in obese patients may be related to errors in weight estimation. When clinical care is time-sensitive, it may be impossible to obtain a measured weight in >50% of patients. In these circumstances, a rapid, accurate method for estimating weight is critical. One recent innovation is the use of a low-cost 3D camera system to estimate weight. The 3D camera device (e.g., Intel RealSense D415) is used to obtain a point cloud map of the patient's body, from which a weight estimate can be estimated based on algorithms derived using convoluted neural network analysis. Initial studies have been extremely promising in terms of the accuracy achievable by this system in estimating Total Body Weight (TBW).

The primary aim of this study is to measure the accuracy of weight estimations by the 3D camera system in acutely ill or injured ED patients and compare this accuracy against that of standard care. The researchers will compare the performance and downstream effects of weight estimation using the 3D camera system against standard care in a randomized controlled trial of acutely ill or injured adults presenting to the ED.

The key hypothesis is that the 3D camera system will provide real-time estimates of TBW, IBW and LBW in an emergency setting and will exceed the accuracy of existing methods of weight estimation.

Supporting non-clinical trial studies will establish the accuracy of the 3D camera system in laboratory conditions, and in simulated medical emergencies. However, its performance, and its impact on downstream drug dosing accuracy, needs to be established during emergency care in a real clinical setting. This study will provide an essential perspective about the accuracy and functioning of the 3D camera system as well as real-world weight estimation during emergency care. It will also describe the ability to measure weight using in-bed scales and to obtain weight estimations from patients themselves and family members in ED patients. The secondary objective, to determine the accuracy of drug doses in each arm of the study, will provide critical information on the need for alternative weight scalars in obese and morbidly obese patients presenting to the ED. The study will establish the need for standards and policies to guide dose scaling in obese patients in the ED. Information on the actual usage of drugs that should be scaled to TBW and those that should be scaled to LBW will provide useful real-world insight into the magnitude of the problem in the threat to patient safety by using a "one size fits all" approach to drug dose calculations for all patients, irrespective of weight status.

Acutely ill patients presenting to the ED of a large regional hospital, and who require weight-based drug therapy, will be enrolled in the study. They will be randomised to either receive a weight estimation using a 3D camera system (which will provide estimates of TBW, IBW and LBW), or to receive standard care. All other interventions and medical care will be standard care.

These patients will be followed for the first 72 hours of their hospital stay. The accuracy of the weight estimates will be compared between the groups, as will the drug dose accuracy, and any adverse events related to drug therapy.

Inclusion Criteria:

- Any patient presenting to the Emergency Department of the study site, who will require any form of weight-based intravenous drug therapy, and who will be admitted to the hospital.

Exclusion Criteria:

• Patients who are unable to provide consent.
• Patients whose medical treatment could be negatively impacted by participation in the study.

Data generated during this project will be shared in multiple different forums. Data will be shared locally at Florida Atlantic University through presentations. Results will be made available to the broader scientific community through publications and presentations at national and international meetings. Protocols and/or method(s) developed during the proposed study will be made readily available for educational, research, and non-profit purposes, provided that a data-sharing agreement is signed. Any other important techniques developed from this work will be made available electronically for all non-business purposes described above. The intended date for such availability will be less than 6 months after the fulfillment of the Specific Aims of this proposal. In addition, a dataset containing non-identifiable point cloud data and images, along with ground truth weight data, will be stored in a repository that can be accessed through a data-sharing agreement.

Within 6 months of the completion of the study.

Upon request.

Investigators and outcomes assessors will be blinded to the arm. Coded data will be used for masking.