BackgroundSevere forefoot deformities, particularly those involving the dorsum of the foot, cause inconvenience in daily activities of living including moderate pain on the dorsal aspect of the contracted foot while walking and difficulty in wearing nonsupportive shoes due to toe contractures. This paper presents clinical results of reconstruction of severe forefoot deformity using the anterolateral thigh (ALT) free flap.MethodsSevere forefoot deformities were reconstructed using ALT flaps in 7 patients (8 cases) between March 2012 and December 2015. The mean contracture duration was 28.6 years.ResultsAll the flaps survived completely. The size of the flaps ranged from 8 cm × 5 cm to 19 cm × 8 cm. The mean follow-up period was 10 months (range, 7 to 15 months). There was no specific complication at both the recipient and donor sites. There was one case where the toe contracture could not be completely treated after surgery. All of the patients were able to wear shoes and walk without pain. Also, the patients were highly satisfied with cosmetic results.ConclusionsThe ALT flap may be considered ideal for the treatment of severe forefoot deformity.
Clustering stroke patients with similar characteristics to predict subsequent vascular outcome events is critical. This study aimed to compare several clustering methods, particularly a deep neural network-based model, and identify the best clustering method with a maximally distinct 1-year outcome in patients with ischemic stroke. Prospective stroke registry data from a comprehensive stroke center from January 2011 to July 2018 were retrospectively analyzed. Patients with acute ischemic stroke within 7 days of onset were included. The primary outcomes were the composite of all strokes (either hemorrhagic or ischemic), myocardial infarction, and all-cause mortality within one year. Neural network-based clustering models (deep lifetime clustering) were compared with other clustering models (k-prototype and semi-supervised clustering, SSC) and a conventional risk score (Stroke Prognostic Instrument-II, SPI-II) to obtain a distinct distribution of 1-year vascular events. Ultimately, 7,650 patients were included, and the 1-year primary outcome event occurred in 13.1%. The DLC-Kuiper UB model had a significantly higher C-index (0.674), log-rank score (153.1), and Brier score (0.08) than the other cluster models (SSC and DLC-MMD) and the SPI-II score. There were significant differences in primary outcome events among the 3 clusters (41.7%, 13.4%, and 6.5% in clusters 0, 1, and 2, respectively) when the DLC-Kuiper UB model was used. A neural network-based clustering model, the DLC-Kuiper UB model, can improve the clustering of stroke patients with a maximally distinct distribution of 1-year vascular outcomes among each cluster. Further studies are warranted to validate this deep neural network-based clustering model in ischemic stroke.
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