ArticleFrontiers in cardiovascular medicine2026
External validation of three risk prediction models for deep vein thrombosis in patients with acute stroke: a single-center cohort study.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: To evaluate the predictive performance of three previously established risk models for deep vein thrombosis (DVT) in patients with acute stroke and to support model selection in clinical practice. Methods: In this single-center spatial validation cohort study, patients diagnosed with acute stroke and admitted to the Stroke Center of South China Hospital of Shenzhen University between January 2023 and January 2025 were consecutively enrolled. Three DVT prediction models, previously identified by a systematic review conducted by our research team, were selected for external validation. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), Brier score, bootstrap calibration curves, and decision curve analysis (DCA). Results: A total of 1,270 patients with acute stroke were included, among whom 217 developed DVT, yielding an incidence of 17.08%. The AUCs were 0.699, 0.804, and 0.753 for the Shen Xiaofang, Lu Qiufang, and Xi Pan models, respectively. The Lu Qiufang model achieved the highest positive predictive value (48.4%), specificity (84.9%), accuracy (82.1%), and Youden index (0.536). All three models had negative predictive values exceeding 90%. The Brier scores were 0.182, 0.154, and 0.245. Calibration curves indicated that the Lu Qiufang model demonstrated the best goodness-of-fit, whereas the Shen Xiaofang and Xi Pan models exhibited systematic bias in certain risk intervals. DCA curves showed that the Lu Qiufang model provided greater net benefit within the threshold probability range of approximately 0.20-0.70, indicating superior clinical decision value. Conclusion: All three DVT risk prediction models demonstrated acceptable predictive performance in patients with acute stroke. Among them, the Lu Qiufang model showed comparatively superior discrimination, calibration, and clinical net benefit. However, given the single-center design of this study, further multicenter and cross-regional validation studies are warranted to confirm model transportability and generalizability across diverse healthcare settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.