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Dosage Regimen Designs: Nomograms and Tabulations

Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Constructing and Validating a Dynamic Nomogram to Predict Response to Bariatric Surgery: A Multicenter Retrospective

Wenfei Diao1,2, Yongquan Chen1,3, Luansheng Liang4

  • 1Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China.

Obesity Surgery
|July 15, 2023
PubMed
Summary

This study developed a nomogram to predict bariatric surgery outcomes. The model uses age, BMI, and fasting glucose to identify patients likely to have a suboptimal response, aiding personalized treatment strategies.

Keywords:
Bariatric SurgeryNomogramObesity

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Area of Science:

  • Bariatric Surgery Outcomes
  • Predictive Modeling in Medicine
  • Metabolic Surgery Research

Background:

  • Suboptimal response is a significant challenge in bariatric surgery.
  • Predicting individual patient outcomes is crucial for surgical success.
  • Developing personalized models enhances patient care and surgical planning.

Purpose of the Study:

  • To develop a nomogram for predicting response to bariatric surgery.
  • To identify key factors influencing bariatric surgery outcomes.
  • To create a tool for personalized prediction of surgical success.

Main Methods:

  • Retrieved data from 509 patients across 6 centers (2019-2020).
  • Classified patients based on %TWL (Total Weight Loss) at 1 year post-surgery.
  • Constructed and validated a web-based nomogram using logistic regression, ROC, and calibration curves.

Main Results:

  • Identified 11.0% of patients with suboptimal response.
  • Key predictors for suboptimal response included advanced age, lower pre-operative BMI, and smaller waist circumference.
  • The nomogram achieved an AUC of 0.829 (internal) and 0.798 (external) for predicting response.

Conclusions:

  • Age, BMI, and fasting glucose are critical factors influencing bariatric surgery response.
  • The developed nomogram demonstrates good predictive accuracy and adaptability.
  • This tool can aid in personalized prediction of bariatric surgery outcomes.