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Related Experiment Video

Updated: Apr 23, 2026

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
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Bounceback branchpoints: using conditional inference trees to analyze readmissions.

Mark Hartney1, Yazhuo Liu1, Vic Velanovich1

  • 1Department of Surgery, University of South Florida, Tampa, FL.

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Identifying risks for colorectal surgery readmissions is crucial. Patients with deep infections or reoperations discharged early face significantly higher 30-day readmission rates, especially those with organ space infections.

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Area of Science:

  • Surgery
  • Medical Informatics
  • Public Health

Background:

  • 30-day readmission after colorectal surgery poses a significant challenge.
  • Identifying predictive risk factors is essential for improving patient outcomes and reducing healthcare costs.

Purpose of the Study:

  • To identify key risk factors associated with 30-day readmission in patients undergoing colorectal surgery.
  • To develop a predictive model for readmission using retrospective data.

Main Methods:

  • Utilized the 2011 American College of Surgery National Surgical Quality Improvement Program database.
  • Employed logistic regression and conditional inference trees (CTREES) with subsampled records to identify combined risks.
  • Analyzed 30,412 records, identifying 3,228 readmissions.

Main Results:

  • CTREES demonstrated high predictive accuracy for readmission in specific patient subgroups.
  • Patients requiring reoperation during hospitalization had a high likelihood of readmission (n=496).
  • Early discharge (≤5 days) for patients with deep space infections predicted >99% readmission risk (220/222); early discharge (≤8 days) for those returning to the OR predicted >90% readmission (253/271).

Conclusions:

  • Conditional inference trees (CTREES) effectively identify high-risk patient subgroups for 30-day readmission after colorectal surgery.
  • Early discharge of patients with deep space infections or those requiring reoperation significantly increases readmission risk.
  • CTREES can reveal hidden patterns in retrospective surgical data, aiding in risk stratification and intervention development.