Clinica chimica acta; international journal of clinical chemistry · 2025
Development and validation of a support vector machine-based nomogram for diagnosis of obstetric antiphospholipid syndrome.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Qi Xuan, Han Yan, Zhang Yue, Ma Nianqiang, Liu Zhifeng, Zhai Jiajia, Guo Huifang
Аннотация
BACKGROUND: Antiphospholipid Syndrome (APS) is a systemic autoimmune disorder characterized by arterial or venous thrombosis and/or pregnancy complications. This study aims to develop a diagnostic model for Obstetric APS (OAPS) using the Support Vector Machine (SVM) algorithm.
METHODS: Data were retrospectively collected from 102 patients with OAPS and 80 healthy controls (HC). Utilizing random sampling, patients were randomly allocated into a training set and a validation set. The training set comprised 72 OAPS patients and 52 HCs, while the validation set included 30 OAPS patients and 24 HCs. Univariate logistic regression analysis and the LASSO method were employed to screen feature variables. Subsequently, the selected feature variables were used to construct a diagnostic model based on the SVM algorithm, which was then validated within the training set.
RESULTS: An optimal subset comprising 12 clinical features was curated. This ensemble of clinical features exhibited formidable predictive efficacy within both the training and validation datasets, as evidenced by Area Under the Curve (AUC) values of 0.969 and 0.942, sensitivities of 0.875 and 0.867, and specificities of 0.929 and 0.875, respectively. Furthermore, the nomogram generated a Concordance Index (C-index) of 0.851 across the entire dataset. Decision curve analysis demonstrates that the combined nomogram and TAT nomogram offer greater net benefit compared to nomograms based on other individual clinical indicators within the dataset.
CONCLUSION: The SVM-based model can effectively diagnose patients with OAPS.
METHODS: Data were retrospectively collected from 102 patients with OAPS and 80 healthy controls (HC). Utilizing random sampling, patients were randomly allocated into a training set and a validation set. The training set comprised 72 OAPS patients and 52 HCs, while the validation set included 30 OAPS patients and 24 HCs. Univariate logistic regression analysis and the LASSO method were employed to screen feature variables. Subsequently, the selected feature variables were used to construct a diagnostic model based on the SVM algorithm, which was then validated within the training set.
RESULTS: An optimal subset comprising 12 clinical features was curated. This ensemble of clinical features exhibited formidable predictive efficacy within both the training and validation datasets, as evidenced by Area Under the Curve (AUC) values of 0.969 and 0.942, sensitivities of 0.875 and 0.867, and specificities of 0.929 and 0.875, respectively. Furthermore, the nomogram generated a Concordance Index (C-index) of 0.851 across the entire dataset. Decision curve analysis demonstrates that the combined nomogram and TAT nomogram offer greater net benefit compared to nomograms based on other individual clinical indicators within the dataset.
CONCLUSION: The SVM-based model can effectively diagnose patients with OAPS.