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<p>Qiu et al. present a nomogram integrating demographic, anthropometric, and comorbidity variables to predict weight loss outcomes one year after bariatric surgery. While this tool has potential for patient counseling and shared decision-making, its applicability in heterogeneous clinical settings requires careful consideration. In this commentary, we provide insights from a surgical and translational research perspective, emphasizing the need for external validation across diverse ethnic and procedural cohorts, integration with perioperative nutritional and psychological metrics, and the role of dynamic modeling for long-term outcomes. We propose potential expansions, including adaptive machine learning approaches, preoperative metabolic imaging, and postoperative telemonitoring data, to refine prediction accuracy and clinical utility.</p>

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Letter to the Editor Regarding “A Nomogram for Prediction of Weight Loss Outcomes After Bariatric Surgery”

  • 期刊论文

  • 发表日期：

    2025年8月27日

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  • Schawanya Kaewpitoon Rattanapitoon,
  • Nav La,
  • Patpicha Arunsan,
  • Nathkapach Kaewpitoon Rattanapitoon

摘要

Qiu et al. present a nomogram integrating demographic, anthropometric, and comorbidity variables to predict weight loss outcomes one year after bariatric surgery. While this tool has potential for patient counseling and shared decision-making, its applicability in heterogeneous clinical settings requires careful consideration. In this commentary, we provide insights from a surgical and translational research perspective, emphasizing the need for external validation across diverse ethnic and procedural cohorts, integration with perioperative nutritional and psychological metrics, and the role of dynamic modeling for long-term outcomes. We propose potential expansions, including adaptive machine learning approaches, preoperative metabolic imaging, and postoperative telemonitoring data, to refine prediction accuracy and clinical utility.