MASLD (Metabolic Dysfunction-Associated Steatotic Liver Disease)
Conditions
Brief summary
Study data were derived from existing electronic health records at our hospital's health examination center between January 1, 2018, and December 31, 2024. These records encompassed fatty liver-related checkups, liver ultrasound/elastography, laboratory tests, anthropometric measurements, and medical histories. Following ethical approval, these datasets were linked to mortality registry, hospitalization records, and liver-related events. This study does not alter participants' past or current clinical pathways, introduces no new examination items, and requires no additional follow-up visits.Concurrently, this study serves as an external implementation evaluation of a clinical prediction model. The original model was derived from prior analyses of NHANES, CHARLS, and VCTE datasets. Rather than re-developing the primary model within the hospital data, this study focuses on evaluating the real-world implementation readiness of the existing model, conducting phenotypic validation, and performing conditional outcome verification. The application of the model within the hospital cohort is strictly limited to research purposes and will not serve as the sole baseline for clinical diagnostic or therapeutic decision-making.
Interventions
None listed
Sponsors
Anhui Provincial Hospital
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Inclusion criteria
1. Age ≥ 18 years at the time of the health examination. 2. Presence of health examination records at our hospital's health examination center between January 1, 2018, and December 31, 2024. 3. Availability of fatty liver-related records, including hepatic steatosis indicated by liver ultrasound, controlled attenuation parameter (CAP), liver stiffness measurement (LSM), or screening-related checkup items. 4. Complete documentation of essential baseline characteristics, minimally including age, sex, and the exact date of the physical examination. 5. Possession of a unique in-hospital patient identifier or a study-specific identification code generated by the data management department to enable reliable deduplication and outcome linkage.
Exclusion criteria
1. Age \< 18 years. 2. Absence of critical data fields required to establish basic eligibility, such as missing records for age, sex, or the date of the health examination. 3. Presence of duplicate records that cannot be reliably resolved, or irresolvable conflicts during participant identity mapping. 4. Physiologically implausible data without a verifiable audit trail for correction, such as anthropometric indices, CAP, LSM, or liver enzymes falling outside biologically reasonable ranges. 5. Explicit prior refusal by the participant (opt-out) to allow their medical or health examination data to be utilized for clinical research. 6. Any other conditions or restrictions stipulated by the Institutional Review Board (IRB) or the hospital's data management department that preclude inclusion.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 5-year all-cause mortality | From each participant's baseline/index assessment through 5 years of follow-up | All-cause mortality is defined as death from any cause occurring within 5 years after each participant's baseline/index assessment. Mortality status is ascertained from the available linked mortality or longitudinal follow-up data. For time-to-event analyses, follow-up is measured from the baseline/index assessment to death from any cause; participants without a documented death are censored at the last known follow-up or at 5 years, whichever occurs first. |
Countries
China
Contacts
STUDY_CHAIRJizhou Wang, MD
The First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui 230000
Outcome results
None listed