Deep Vein Thrombosis Risk Prediction after Total Hip Replacement at Kandou Hospital
DOI:
https://doi.org/10.46799/jhs.v7i10.2882Keywords:
deep vein thrombosis, total hip replacement, risk prediction, wells scoreAbstract
Deep vein thrombosis (DVT) is a clinically significant complication following total hip arthroplasty (THA), requiring a clear distinction between observed postoperative events and model-based risk estimates. This prospective cohort study enrolled 62 patients who underwent THA at Prof. Dr. R. D. Kandou Hospital in Manado, Indonesia, between January 2023 and December 2024, with a 90-day follow-up period. Clinical characteristics, Wells scores, D-dimer levels, fibrinogen levels, and Doppler ultrasonography findings were recorded. Descriptive findings indicated that 46 participants (74%) were male, 48 (77%) had elevated D-dimer levels, and all documented Doppler ultrasonography examinations yielded normal findings. A Monte Carlo simulation comprising 10,000 iterations was also performed using a logistic regression equation with coefficients reportedly derived from published literature rather than estimated from the study cohort. The simulation yielded a median predicted DVT prevalence of 7.3%, with a 95% prediction interval of 0%–29%. However, this estimate represented a simulation-derived outcome rather than the observed incidence of postoperative DVT. The available manuscript did not report the actual number of incident DVT events during the 90-day follow-up and contained an unresolved inconsistency between the exclusion of patients with active cancer and the reported inclusion of seven participants with active cancer. [AUTHOR DATA REQUIRED: Confirm the number of observed DVT events during follow-up and clarify the eligibility criteria concerning active cancer.] Overall, the study provided descriptive information on clinical characteristics and potential DVT risk factors in a local THA population, alongside an exploratory simulation-based risk estimation framework. Nevertheless, cohort-specific predictive associations, model discrimination, and calibration could not be established without verification of the observed clinical outcomes, model coefficients, and estimation procedures.
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