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Predictive Model for Postoperative Mortality

Predictive Model for Postoperative Mortality in Adult Emergency Surgical Patients Under General Anesthesia

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT02947789
Enrollment
740
Registered
2016-10-28
Start date
2016-09-30
Completion date
2017-02-28
Last updated
2017-05-11

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

Conditions

Postoperative Complications

Brief summary

Surgery has risk of morbidity and mortality. Risk factors include: patient factors; surgical factors; and anesthetic factors. The risk is much higher in emergency cases. The study of relevant risk factors can lead to improvement in patient management and reduction in mortality.

Detailed description

Objective: To identify risk factors for postoperative mortality within 3 days in adult patients undergoing emergency surgery and construct a predictive model. Methods: This will be a retrospective, exploratory and analytical study. All medical records of adult patients undergoing emergency surgery between January 2013 to December 2014 will be used to analyze for relevant risk factors using binary logistic regression analysis. A predictive model to predict postoperative morbidity within 3 days will be constructed.

Interventions

PROCEDUREEmergency surgery

Patients undergoing emergency surgery

Sponsors

Khon Kaen University
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Emergency surgery * Age =\> 18 years

Exclusion criteria

* Caesarean section * Incomplete medical record

Design outcomes

Primary

MeasureTime frameDescription
Postoperative mortality within 3 daysthrough study completion, an average of 1 weekPatient die postoperatively within 3 day3

Countries

Thailand

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026