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A machine learning algorithm-based model for predicting postoperative ventilation failure in cardiac surgery: a medical records based retrospective study

A machine learning algorithm-based model for predicting postoperative ventilation failure in cardiac surgery

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2100054187
Enrollment
Unknown
Registered
2021-12-10
Start date
2021-12-07
Completion date
Unknown
Last updated
2022-11-14

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Heart surgery-related diseases

Interventions

Cardiac surgery group:No

Sponsors

West China Hospital of Sichuan University
Lead Sponsor

Eligibility

Sex/Gender
Male
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. Patients undergoing cardiac surgery (including interventional and open-heart surgery); 2. Aged >= 18 years; 3. Postoperative admission to the thoracic intensive care unit for more than 24 hours.

Exclusion criteria

Exclusion criteria: 1. Preoperative ventilation failure (preoperative mechanical ventilation or preoperative oxygen saturation <90%); 2. History of previous cardiac surgery; 3. Length of hospital stay less than 48 hours; 4. Death from non-pulmonary sources.

Design outcomes

Primary

MeasureTime frame
Respiratory failure;—

Secondary

MeasureTime frame
Length of hospital;Length of intensive care unit;Death;—

Countries

China

Contacts

Public ContactYu Pengming

West China Hospital of Sichuan University

13438201451@126.com+86 13438201451

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026