American journal of translational research · 2026
Risk factors and a predictive nomogram for in-hospital deep vein thrombosis in elderly patients with multiple chronic conditions.
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Luo Ying, Xu Manyu, Xu Zhiying, Ji Hongmei, Wu Yalei, Shen Huan, Guan Caifeng
Аннотация
OBJECTIVE: To explore the independent influencing factors of deep venous thrombosis (DVT) in elderly patients with multiple chronic conditions (MCCs) and construct a predictive nomogram for this population.
METHODS: A total of 689 elderly patients with MCCs who were hospitalized from January 2023 to March 2025 were retrospectively enrolled in this study. Patients were categorized into a DVT group (73 cases) and a non-DVT group (616 cases). The overall cohort was randomly split into a training set (482 cases) and an internal validation set (207 cases) at a 7:3 ratio. An additional 360 patients admitted from April 2025 to April 2026 were enrolled as an external validation cohort. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate the performance of the predictive model.
RESULTS: Age, MCCs number, polypharmacy, Padua score, limb muscle strength and DPR were identified as independent predicting factors for in-hospital DVT. The nomogram achieved an area under the curve (AUC) of 0.848. The AUC was 0.846 for the training set and 0.831 for the internal validation set, with good calibration. The external validation AUC was 0.807 with satisfactory calibration efficiency, and the DeLong test verified no significant difference in AUC values (P > 0.05). Furthermore, the model presented favorable clinical net benefits.
CONCLUSION: The validated nomogram established based on six routinely accessible clinical indicators can effectively and reliably predict the risk of in-hospital DVT in elderly patients with MCCs.
METHODS: A total of 689 elderly patients with MCCs who were hospitalized from January 2023 to March 2025 were retrospectively enrolled in this study. Patients were categorized into a DVT group (73 cases) and a non-DVT group (616 cases). The overall cohort was randomly split into a training set (482 cases) and an internal validation set (207 cases) at a 7:3 ratio. An additional 360 patients admitted from April 2025 to April 2026 were enrolled as an external validation cohort. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate the performance of the predictive model.
RESULTS: Age, MCCs number, polypharmacy, Padua score, limb muscle strength and DPR were identified as independent predicting factors for in-hospital DVT. The nomogram achieved an area under the curve (AUC) of 0.848. The AUC was 0.846 for the training set and 0.831 for the internal validation set, with good calibration. The external validation AUC was 0.807 with satisfactory calibration efficiency, and the DeLong test verified no significant difference in AUC values (P > 0.05). Furthermore, the model presented favorable clinical net benefits.
CONCLUSION: The validated nomogram established based on six routinely accessible clinical indicators can effectively and reliably predict the risk of in-hospital DVT in elderly patients with MCCs.