Clinical trial NCT05986474: Development of Synthetic Medical Data Generation Technology to Predict Postoperative Complications
Official title: Development of Synthetic Medical Data Generation Technology to Predict Postoperative Complications
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Acute Kidney Injury
Surgery-Complications
Nephropathy
Lead sponsor: Seoul National University Hospital
Sponsor class: OTHER
First posted: 2023-08-14
Enrollment: 410000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients over 18 years who underwent non-cardiothoracic and non-vascular surgery

Brief summary: \<Development of synthetic medical data generation technology to predict postoperative complications\>

In order to develop a model for predicting the occurrence of complications after surgery, it is necessary to establish a cohort along with statistical indicators related to the occurrence of complications. This study aims to combine synthetic medical data based on actual clinical data and develop a predictive model based on synthetic medical data.

This will allow researchers to conduct research only with synthetic data without dealing with actual medical data, allowing them to use and process data without legal constraints, and to create as much data as they want based on various preprocessed, standardized, and labeled raw data.

Patients from three hospitals in Korea (Seoul National University Hospital, Seoul National University Bundang Hospital, Seoul Metropolitan City-Boramae Medical Center) were enrolled for the study.

Medical data (both clinical and laboratory) from 410,000 patients who were conducted surgery between 2005 and 2020 were collected to evaluate the performance of the prediction model using AKI-based prediction model development and external verification.

Based on the collected patient data, synthetic medical data were combined using the machine learning algorithm, and the anonymity and re-identification of the synthesized medical data were evaluated.

Also, the development of AI-based prediction model using synthetic medical data and the actual medical data model were compared.


Eligibility criteria: Inclusion Criteria:

* Patients over 18 years.
* Non-cardiothoracic and non-vascular surgery from five departments (general surgery, obstetrics and gynecology (OBGY), urologic surgery, neurosurgery, and orthopedic surgery)

Exclusion Criteria:

* 1) no information of baseline (≤90 days before surgery) or follow-up (≤7 days after surgery) renal function
* 2) exclusive surgery, including surgeries of deceased patients or surgery that directly affect renal function (partial or total nephrectomy, kidney transplantation)
* 3) preoperative advanced kidney dysfunction, including preoperative serum creatinine (SCr) ≥4.0 mg/dL, baseline eGFR (estimated glomerular filtration rate) \<15 mL/min/1.73 m2, preoperative kidney replacement therapy history, or AKI history within 2 weeks of surgery
* 4) surgery other than general or spinal anesthesia (local anesthesia or monitored anesthesia care)
* 5) missing covariates.

Primary outcomes:
measure: Acute kidney injury
timeFrame: After surgery, within 7 days
measure: Acute kidney disease
timeFrame: After surgery, within 3 months
Secondary outcomes:
Other outcomes:
Locations:
city: Seoul
country: Korea, Republic of
facility: Seoul National University Hospital
Study officials:
References: