Study for Machine-Learning Based Perioperative Risk Assessment and Prediction System for geriatric patients

Conditions

Perioperative Risk Assessment

L. L. Wang, Xinyu Guo, Haoyue Shi, Yuehang Ma, Han Bao et al. (10 authors)

BMC Medical Informatics and Decision Making2025-04-156 citations

A simple machine learning model for the prediction of acute kidney injury following noncardiac surgery in geriatric patients: a prospective cohort study.

Peng X, Zhu T, Chen Q, Zhang Y, Zhou R, Li K, Hao X.

2024-06-255 citations

10.1186/s12877-024-05148-1

Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study.

Peng X, Zhu T, Wang T, Wang F, Li K, Hao X.

2022-09-109 citations

10.1186/s12871-022-01827-x

Interventions

Gold Standard:Clinical outcomes

Index test:perioperative&#32

risk&#32

assessment&#32

method&#32

and&#32

event&#32

prediction&#32

model

Eligibility

Sex/Gender

All

Age

65 Years to No maximum

Inclusion criteria

Inclusion criteria: Aged >=65 years old male and female; geriatric patients who plan to undergo surgery under general anesthesia

Exclusion criteria

Exclusion criteria: Patients with inaccessible preoperative assessment data, such as emergency patients with severe traumatic hemorrhage, DCD donors, liver and kidney transplant recipients, etc

Design outcomes

Primary

Measure	Time frame
death;ICU stay time>24h;Myocardial infarction;Cardiac arrest;postoperative pulmonary complications;Heart failure;Delirium;Acute renal injury;Moderate to severe pain;	—

Countries

China

Contacts

Public ContactTao Zhu

West China Hospital, Sichuan University

xwtao_zhu@sina.cn+86 18980601552

Outcome results

None listed