International Journal of Molecular Medical Science, 2025, Vol.15, No.2, 54-68 http://medscipublisher.com/index.php/ijmms 60 Continued Table 2 Author (Year) Missing data processing Model development method Calibration method Proof technique Number of predictor Ultimate predictor Model performance Walker et al. (2021) Median Interpolation Method Logistic Regression - Internal Verification 11 CVC, VTE history, heart disease, blood gas test, infection, age, mean erythrocyte hemoglobin concentration, cancer, erythrocyte distribution width, lactic acid, surgery AUC 0.908, the Positive Predictive Value was 20.1% and the Negative Predictive Value was 99.5% Jaffray et al. (2022) Multiple Interpolation Method Logistic Regression - Internal Verification 5 Recent CVC implantation, immobility, congenital heart disease, length of stay ≥3 days prior to ICU admission, autoimmune disease/inflammatory status, or current history of infection AUC0.79 Connelly et al. (2016) - Logistic Regression Bayesian Information Guide External Verification 10 GCS, age, sex, intubation, ICU admission, blood transfusion, CVC, pelvic fracture, lower limb fracture, major surgery AUC0.945 Tiratrakoonseree et al. (2024) - Logistic Regression HosmerLemeshow Internal Verification 5 Congenital heart disease, known thrombosis, history of VTE and nephrotic syndrome, and a clinically significant presentation of chest pain AUC 0.809, the Sensitivity was 74.4%, and the specificity was 75.4% Note: “-”, not reported; KNN, the nearest neighbor algorithm of “k”; ISS: Trauma severity score; GCS: Glasgow Coma Scale; ASA: American Society of Anesthesiologists; we believe that AUC=0.5-0.7 is poor discrimination, 0.7-0.8 is moderate discrimination, 0.8-0.9 is good discrimination, and 0.9-1.0 is excellent discrimination
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