ArticleEuropean journal of drug metabolism and pharmacokinetics2022
Development and Validation of a Length- and Habitus-Based Method of Ideal and Lean Body Weight Estimation for Adults Requiring Urgent Weight-Based Medical Intervention.
Article in European journal of drug metabolism and pharmacokinetics, 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. Cited by 3 papers.
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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.
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.
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.
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
Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Development and characterization of a first-in-class adjustable-dose gene therapy system.Gene · 2024Article
- Discrepancies among equations to estimate the glomerular filtration rate for drug dosing decision making in aged patients: a cross sectional study.International journal of clinical pharmacy · 2024Article
- Estimating Lean Body Weight in Adults With the PAWPER XL-MAC Tape Using Actual Measured Weight as an Input Variable.Cureus · 2022Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveAccurate drug dosing in obese patients requires an estimation of ideal body weight (IBW) or lean body weight (LBW) for dosing hydrophilic medications. Erroneous weight estimates during the management of adults requiring weight-based treatment may contribute to poor outcomes. Existing methods of IBW and LBW estimation or measurement are very difficult to use during emergency care. A new point-of-care model would be useful to provide rapid estimates of IBW and LBW for this purpose.
methodsA model was derived based on the PAWPER XL-MAC tape, a pediatric weight estimation system which uses recumbent length and mid-arm circumference to estimate IBW and LBW. The new adult model was used to generate IBW and LBW estimations for a derivation sample (n = 33155) and a validation sample (n = 5926) from National Health and Nutrition Examination Survey (NHANES) datasets. The model was developed in two steps, with calibration at each step, before being validated on an unused dataset. The outcome measure was to achieve >95% of IBW and LBW estimations within 20% of recognized reference standards (P20 > 95%) and >70% of estimations within 10% of these standards (P10 > 70%).
resultsThe new model achieved a P20 of 100% and a P10 of 99.9% for IBW and a P20 of 98.3% and a P10 of 78.3% for LBW. This accuracy was maintained in both sexes, all ages, all ethnic groups, all lengths, and in all habitus types, except for the severely obese female subgroup.
conclusionsThe modified PAWPER XL-MAC model proved to be an accurate method of IBW and LBW estimation. It could, therefore, have an important role in facilitating emergency drug dose calculations in obese adult patients.
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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.