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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Related Experiment Video

Updated: Jan 17, 2026

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A Framework for Patient Prioritization in Bariatric Surgery Using a Predictive Success Score Versus a First-In

Fellipe S Mocellin1, Jeruza L Neyeloff1, Flávio S Fogliatto2

  • 1PostGraduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Federative Republic of Brazil.

Value in Health Regional Issues
|September 20, 2025
PubMed
Summary

Prioritizing patients for bariatric surgery using a predictive success score improves health outcomes and is cost-effective compared to a first-come, first-served queue. This strategy enhances life-years and reduces cardiovascular mortality.

Keywords:
bariatric surgerycost-effectiveness analysishealthcare accesshealthcare resource allocationobesity treatment

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Area of Science:

  • Health Economics
  • Surgical Outcomes Research
  • Public Health Policy

Background:

  • Bariatric surgery is crucial for obesity management, but resource limitations in low- and middle-income countries create access challenges.
  • Existing literature focuses on effectiveness and economic comparisons, lacking decision-analytic studies on patient prioritization.
  • A prior predictive scoring system for surgical success needs cost-effectiveness evaluation against standard queue-based access.

Purpose of the Study:

  • To evaluate the cost-effectiveness of a predictive success score system for prioritizing bariatric surgery patients.
  • To compare the predictive score approach against the traditional "first in, first out" queue system.
  • To assess the impact on health outcomes and resource allocation under varying supply constraints.

Main Methods:

  • Developed a Monte Carlo microsimulation branching model in RStudio.
  • Assessed costs and quality-adjusted life-years (QALYs) from the Brazilian National Health System perspective.
  • Compared a cohort prioritized by predictive success scores against a cohort using a standard queue, across six supply constraints.

Main Results:

  • The incremental cost-effectiveness ratio (ICER) for the success score ranged from R$227 to R$2883, below typical willingness-to-pay thresholds.
  • Prioritization by score resulted in longer total life-years and reduced cardiovascular mortality.
  • Slightly higher costs were observed for surgical procedures and chronic illness management in the score-prioritized group.

Conclusions:

  • Prioritizing bariatric surgery candidates based on predicted success offers a cost-effective strategy to enhance health outcomes.
  • This approach is viable across diverse healthcare system supply constraints.
  • The predictive scoring system presents a promising alternative to traditional queue-based patient allocation.