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AI-Based Risk Factor Analysis and Prediction Model of MACE in Elderly Hip Fracture Patients Postoperatively

Artificial Intelligence-Based Analysis of Risk Factors and Risk Prediction Model for Major Adverse Cardiovascular Events Following Hip Fracture Surgery in the Elderly

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT07709377
Enrollment
600
Registered
2026-07-16
Start date
2025-07-01
Completion date
2028-06-30
Last updated
2026-07-16

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

Conditions

Elderly, Hip Fracture, MACE, Perioperative Complications

Keywords

Hip fracture; MACE; Elderly; Perioperative complications

Brief summary

This is a multicenter ambispective observational cohort study led by Beijing Anzhen Hospital, Capital Medical University, with China-Japan Friendship Hospital serving as the external validation center. Major adverse cardiovascular events (MACE) occurring after surgery are common complications among older adults undergoing hip fracture surgery and are associated with poor long-term outcomes, including increased one-year mortality. Previous studies have not systematically evaluated perioperative risk factors or developed prediction models specifically for this population. In addition, conventional statistical approaches may have limited ability to account for complex interactions and nonlinear associations among multiple perioperative variables. Therefore, this study aims to identify risk factors for postoperative MACE and to develop and externally validate a machine learning-based prediction model. Patients aged 60 years or older who undergo surgical treatment for hip fracture will be included. The derivation cohort, established at Beijing Anzhen Hospital, will be used for model development and internal validation, whereas the independent external validation cohort, established at China-Japan Friendship Hospital, will be used to evaluate the generalizability and predictive performance of the developed model. The study will adopt a bidirectional cohort design: one portion of the study population will be identified retrospectively from electronic medical records, and the remaining participants will be consecutively enrolled prospectively following ethics committee approval. Postoperative outcomes will be ascertained from the end of surgery until the earliest occurrence of hospital discharge, death, or postoperative day 30. Standardized, de-identified data will be collected, including demographic characteristics, comorbidities, laboratory findings, surgical and anesthetic information, perioperative medications, and postoperative MACE outcomes. No study-specific intervention will be assigned, and all patients will receive routine clinical care. Data will be managed in accordance with institutional privacy policies and applicable data protection requirements. Candidate predictors will be evaluated using univariable analyses, least absolute shrinkage and selection operator regression, and random forest-based feature selection. Prediction models, including logistic regression, XGBoost, and LightGBM, will be developed in the derivation cohort. Internal validation will be performed using resampling methods, and external validation will be conducted using data from the independent validation center. Model performance will be assessed in terms of discrimination, calibration, and clinical utility using the area under the receiver operating characteristic curve, calibration plots, and other appropriate performance measures.

Interventions

None listed

Sponsors

Beijing Anzhen Hospital
Lead SponsorOTHER
China-Japan Friendship Hospital
CollaboratorOTHER

Study design

Observational model
COHORT
Time perspective
OTHER

Eligibility

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

Inclusion criteria

* Age ≥60 years; * Diagnosis of femoral neck fracture or intertrochanteric fracture, including subtrochanteric fracture; * Undergoing surgical treatment.

Exclusion criteria

* Pathological fracture; * High-energy trauma or multiple injuries; * Incomplete relevant clinical data; * Failure to provide written informed consent (prospective cohort only).

Design outcomes

Primary

MeasureTime frameDescription
Number of Participants With In-Hospital Postoperative Major Adverse Cardiac EventsFrom completion of surgery until hospital discharge, death, or postoperative day 30, whichever occurred first.Major adverse cardiac events were defined as a composite of cardiovascular death, nonfatal myocardial infarction, arrhythmia, and heart failure occurring during the postoperative hospital stay. Each participant was counted only once according to the first event occurring during the observation period.

Secondary

MeasureTime frameDescription
All-cause mortalityFrom completion of surgery until hospital discharge, death, or postoperative day 30, whichever occurred first.Number of Participants With death, including death attributable to cardiovascular complications.
Number of Participants With Nonfatal Myocardial InfarctionFrom completion of surgery until hospital discharge, death, or postoperative day 30, whichever occurred first.
Number of Participants With Heart FailureFrom completion of surgery until hospital discharge, death, or postoperative day 30, whichever occurred first.
Number of Participants With ArrhythmiaFrom completion of surgery until hospital discharge, death, or postoperative day 30, whichever occurred first.
Number of Participants With Angina PectorisFrom completion of surgery until hospital discharge, death, or postoperative day 30, whichever occurred first.
Number of Participants With StrokeFrom completion of surgery until hospital discharge, death, or postoperative day 30, whichever occurred first.
Number of Participants With Pulmonary EmbolismFrom completion of surgery until hospital discharge, death, or postoperative day 30, whichever occurred first.

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Jul 17, 2026