Evidence map›Paper›PMID 36277563›Full record

ArticleCureus2022

Estimating Lean Body Weight in Adults With the PAWPER XL-MAC Tape Using Actual Measured Weight as an Input Variable.

Mike Wells, Lara N Goldstein

3 registry-linked trialsOpen access · diamondAbstract read
In one paragraph

Article in Cureus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 83% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06645548 nawithdrawnnot on this mapstarted 2029, after this paper: background citation

Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 3 - Measure the Accuracy of Weight Estimations by The 3D Camera System in Acutely Ill or Injured Emergency Department Patients and Compare This Accuracy Against That of Standard Care.

TypeinterventionalSponsorFlorida Atlantic UniversityRan2029 to 2030Enrolled0ConditionsBody Weights and Measures, Body Weight in the Overweight and Obese Class - I Population, Drug Dose, Weight EstimateArmsWeight estimation using 3D camera
NCT06646120 withdrawnnot on this mapstarted 2025, after this paper: background citation

Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care. Study 1 - Establish a Model Using a Single 3D Camera Image of a Supine Patient to Accurately Estimate TBW, IBW And LBW.

TypeobservationalSponsorFlorida Atlantic UniversityRan2025 to 2026Enrolled0ConditionsBody Weights and Measures, Body Weight in the Overweight and Obese Class - I Population
NCT06646133 withdrawnnot on this mapstarted 2027, after this paper: background citation

Machine Learning and 3D Image-Based Modeling for Real-Time Body Weight and Body Composition Estimation During Emergency Medical Care: Study 2 - Measure the Accuracy and Speed of Weight Estimations Using a 3D Camera System During Simulated Medical Emergencies

TypeobservationalSponsorFlorida Atlantic UniversityRan2027 to 2028Enrolled0ConditionsBody Weights and Measures, Body Weight in the Overweight and Obese Class - I Population, Weight Estimation, Emergency Drug DosingArms3D camera weight estimate, Standard methods of weight estimation
3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Mike WellsDepartment of Emergency Medicine, Florida Atlantic University Charles E. Schmidt College of Medicine, Boca Raton, USA.
Lara N GoldsteinDepartment of Emergency Medicine, HCA Florida Aventura Hospital, Aventura, USA.
Aventura Hospital and Medical Center · USFlorida Atlantic University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Accurate drug dose calculation in obese patients requires an estimation of lean body weight (LBW) for dosing hydrophilic medications. Inaccurate weight estimates during the management of critically ill obese adults may contribute to inaccurate drug doses and consequential poor outcomes. Existing methods of LBW estimation or measurement may be very difficult or impossible to use during emergency care. A point-of-care model that could provide rapid, accurate estimates of LBW would, therefore, be of significant clinical value. Methods A model was derived based using the adult version of the PAWPER XL-MAC tape. This derived model used recumbent length and measured total body weight (TBW) to estimate LBW. The derived model was used to generate LBW estimations in a random sample from National Health and Nutrition Examination Survey (NHANES) datasets (n=33,215). The benchmark outcome measure was to achieve >95% of LBW estimations within 20% of DXA-measured fat-free mass (P20>95%) and >70% of estimations within 10% of DXA-measured fat-free mass (P10>70%). Results The new model achieved a P20 of 99.7% and a P10 of 86.4% for LBW in the pooled sample and exceeded the minimum accuracy standards. This accuracy was maintained in both sexes, all ages, all ethnic groups, all lengths and in all habitus types. Conclusions The modified PAWPER XL-MAC model, using TBW as an input variable, proved to be an accurate method of LBW estimation. It could potentially have an important role in facilitating emergency drug dose calculations in critically ill or injured obese adult patients.

Indexed as

drug dosinglean body weightobesitypawper tapepharmacology

Identifiers

PMID36277563
PMCPMC9576633
OpenAlexW4296309089

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.