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早期体重增加作为避免/限制性食物摄入障碍中重量恢复的预测因素
Taylor R Perry1,2, Kelly Cai3, David Freestone3
1Equip Health, Inc, 2659 State Street Suite 100 #1012, Carlsbad, CA, 92008, USA. taylor@equip.health.
Journal of eating disorders
|February 15, 2024
概括
早期体重增加预测了青少年的体重恢复,这些青少年患有避免/限制性食物摄入障碍 (ARFID),并接受了以家庭为基础的治疗 (FBT). 改进的统计模型包含了入院权重,提高了预测的准确性.
科学领域:
- 精神病学是一个精神病学.
- 临床心理学 临床心理学
- 儿科医学 儿科医学
背景情况:
- 基于家庭的治疗 (FBT) 是有效的青少年厌食症 (AN),早期体重增加预测缓解.
- 缺乏关于早期体重增加是否预测避免/限制性食物摄入障碍 (ARFID) 的重量恢复 (95%极端体重[EBW]) 的数据.
- 在ARFID中恢复体重的统计模型的预测准确性需要评估.
研究的目的:
- 确定早期体重增加是否预测FBT接受ARFID青少年20周体重恢复.
- 评估统计模型在预测重量恢复方面的准确性,并识别错误分类模式.
主要方法:
- 130名青少年 (57.7%的女性,70.0%的白人) 接受了虚拟的门诊FBT.
- 接收器操作特征 (ROC) 分析使用早期体重增加来预测20周95%的EBW.
- 逻辑回归包括入院%EBW和早期体重增加,与ROC分析进行了比较.
主要成果:
- 在第5周至少增加6.2磅,预计95%的EBW在20周 (AUC=0.72).
- ROC分析错误分类了35%的患者,主要预测成功发生时的失败 (61.6%).
- 后勤回归实现了更高的准确性 (AUC=0.90) 并提供了概率估计.
结论:
- 在接受FBT的ARFID患者中,早期体重增加是实现95%EBW在20周的显著预测因素.
- 统计模型的好处在于,在早期体重增加的同时,包括入院%EBW,以便更准确地预测重量恢复.
- 增强的预测模型对于识别可能实现体重恢复的ARFID患者至关重要.
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