Using machine learning to predict early readmission following esophagectomy

Siavash Bolourani, Mohammad A. Tayebi, Li Diao, Ping Wang, Vihas Patel, Frank Manetta, Paul C. Lee

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Objective: To establish a machine learning (ML)-based prediction model for readmission within 30 days (early readmission or early readmission) of patients based on their profile at index hospitalization for esophagectomy. Methods: Using the National Readmission Database, 383 patients requiring early readmission out of a total of 2037 esophagectomy patients alive at discharge in 2016 were identified. Early readmission risk factors were identified using standard statistics and after the application of ML methodology, the models were interpreted. Results: Early readmission after esophagectomy connoted an increased severity score and risk of mortality. Chronic obstructive pulmonary disease and malnutrition as well as postoperative prolonged intubation, pneumonia, acute kidney failure, and length of stay were identified as factors most contributing to increased odds of early readmission. The reasons for early readmission were more likely to be cardiopulmonary complications, anastomotic leak, and sepsis/infection. Patients with upper esophageal neoplasms had significantly higher early readmission and patients who received pyloroplasty/pyloromyotomy had significantly lower early readmission. Two ML models to predict early readmission were generated: 1 with 71.7% sensitivity for clinical decision making and the other with 84.8% accuracy and 98.7% specificity for quality review. Conclusions: We identified risk factors for early readmission after esophagectomy and introduced ML-based techniques to predict early readmission in 2 different settings: clinical decision making and quality review. ML techniques can be utilized to provide targeted support and standardize quality measures.

Original languageEnglish
Pages (from-to)1926-1939.e8
JournalJournal of Thoracic and Cardiovascular Surgery
Volume161
Issue number6
DOIs
StatePublished - Jun 2021
Externally publishedYes

Keywords

  • decesion tree
  • esophagectomy
  • logistic model
  • machine learning
  • prediction models
  • pyloromyotomy

Fingerprint

Dive into the research topics of 'Using machine learning to predict early readmission following esophagectomy'. Together they form a unique fingerprint.

Cite this