Treatment goal weight in adolescents with anorexia nervosa: use of BMI percentiles.

Int J Eat Disord

Division of Adolescent Medicine, Schneider Children's Hospital, Albert Einstein College of Medicine, New Hyde Park, New York 94040, USA.

Published: May 2008

Objective: There is a lack of consensus as to how to determine treatment goal weight in the growing adolescent with anorexia nervosa (AN). Resumption of menses (ROM) is an indicator of biological health and weight at ROM can be used as a treatment goal weight. This study determined the BMI percentile for age at which ROM occurs.

Method: A secondary analysis of a prospective cohort study examining 56 adolescent females with AN, aged 12-19 years, followed every 3 months until ROM. BMI percentiles for age and gender at ROM were determined using the nutrition module of Epi Info 2002.

Results: At 1-year follow-up, 36 participants (64.3%) resumed menses and 20 (35.7%) remained amenorrheic. Mean BMI percentile at ROM was 27.1 (95% CI = 20.0-34.2). Fifty percent of participants who resumed menses, did so at a BMI percentile between the 14th and 39th percentile.

Conclusion: A BMI percentile range of 14th-39th percentile can be used to assign a treatment goal weight, with adjustments for prior weight, stage of pubertal development, and anticipated growth.

Download full-text PDF

Source
http://dx.doi.org/10.1002/eat.20503DOI Listing

Publication Analysis

Top Keywords

treatment goal
16
goal weight
16
bmi percentile
16
anorexia nervosa
8
bmi percentiles
8
resumed menses
8
weight
6
bmi
6
rom
6
percentile
5

Similar Publications

Importance: An increasing number of older adults are undergoing surgery. Older adults face significant challenges throughout the spectrum of perioperative care. No frameworks exist to support primary care clinicians in helping older adults navigate perioperative care beyond preoperative medical clearance.

View Article and Find Full Text PDF

Artificial intelligence (AI) and its subset, machine learning, have tremendous potential to transform health care, medicine, and population health through improved diagnoses, treatments, and patient care. However, the effectiveness of these technologies hinges on the quality and diversity of the data used to train them. Many datasets currently used in machine learning are inherently biased and lack diversity, leading to inaccurate predictions that may perpetuate existing health disparities.

View Article and Find Full Text PDF

Objective: The goal of this study was to better understand the epidemiology, clinical characteristics, and treatment outcomes of head and neck sarcomas using real-world data from Japan.

Methods: Using the Japanese Head and Neck Cancer Registry, we identified 438 patients who were pathologically diagnosed with head and neck sarcoma between 2011 and 2020. We compared epidemiological, clinical, and prognostic data for the different histological types of sarcoma.

View Article and Find Full Text PDF

Disclaimer: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!