Development of an automated, general-purpose prediction tool for postoperative respiratory failure using machine learning: A retrospective cohort study.

ABSTRACT:

Study objective

Postoperative respiratory failure is a major surgical complication and key quality metric. Existing prediction tools underperform, are limited to specific populations, and necessitate manual calculation. This limits their implementation. We aimed to create an improved, machine learning powered prediction tool with ideal characteristics for automated calculation.

Design, setting, and patients

We retrospectively reviewed 101,455 anesthetic procedures from 1/2018 to 6/2021. The primary outcome was the Standardized Endpoints in Perioperative Medicine consensus definition for postoperative respiratory failure. Secondary outcomes were respiratory quality metrics from the National Surgery Quality Improvement Sample, Society of Thoracic Surgeons, and CMS. We abstract

SUBMITTER: Kiyatkin ME 

PROVIDER: S-EPMC10529165 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature