Topical tacrolimus and periodontal therapy in the management of a case of oral chronic GVHD characterized by specific gingival localization.

Background. Chronic graft versus host disease (cGVHD) is a complication following bone marrow transplantation. The oral lesions are difficult to control with a systemic pharmacological therapy. Case Description. A 63-year-old female patient, who underwent an allogeniec transplantation for acute myeloid leukemia, developed a chronic oral and cutaneous GVHD. The patient was treated with topical tacrolimus 0.1%, twice daily for two months, and underwent a protocol of oral hygiene characterized by 3 appointments of scaling, root planning, and daily oral hygiene instructions. The patient showed marked resolution of gingival lesions and a significant improvement of related pain and gingival inflammatory indexes. Clinical Implications. This case report suggests that treatment with topical tacrolimus and professional oral hygiene may be helpful in the management of chronic oral GVHD with severe gingival involvement

Incidence, predictors and cerebrovascular consequences of leaflet thrombosis after transcatheter aortic valve implantation: A systematic review and meta-analysis

OBJECTIVES: We examined the incidence, the impact of subsequent cerebrovascular events and the clinical or procedural predictors of leaflet thrombosis (LT) in patients undergoing transcatheter aortic valve implantation (TAVI). METHODS: MEDLINE/PubMed was systematically screened for studies reporting on LT in TAVI patients. Incidence [both clinical and subclinical, i.e. detected with computed tomography (CT)] of LT was the primary end point of the study. Predictors of LT evaluated at multivariable analysis and impact of LT on stroke were the secondary ones. RESULTS: Eighteen studies encompassing 11 124 patients evaluating incidence of LT were included. Pooled incidence of LT was 0.43% per month [5.16% per year, 95% confidence interval (CI) 0.21-0.72, I2 = 98%]. Pooled incidence of subclinical LT was 1.36% per month (16.32% per year, 95% CI 0.71-2.19, I2 = 94%). Clinical LT was less frequent (0.04% per month, 0.48% per year, 95% CI 0.00-0.19, I2 = 93%). LT increased the risk of stroke [odds ratio (OR) 4.21, 95% CI 1.27-13.98], and was more frequent in patients with a valve diameter of 28-mm (OR 2.89: 1.55-5.8), for balloon-expandable (OR 8: 2.1-9.7) or after valve-in-valve procedures (OR 17.1: 3.1-84.9). Oral anticoagulation therapy reduced the risk of LT (OR 0.43, 95% CI: 0.22-0.84, I2 = 64%), as well as the mean transvalvular gradient. CONCLUSIONS: LT represents an infrequent event after TAVI, despite increasing risk of stroke. Given its full reversal with warfarin, in high-risk patients (those with valve-in-valve procedures, balloon expandable or large-sized devices), a protocol which includes a control CT appears reasonable

Prediction of All-Cause Mortality Following Percutaneous Coronary Intervention in Bifurcation Lesions Using Machine Learning Algorithms

Stratifying prognosis following coronary bifurcation percutaneous coronary intervention (PCI) is an unmet clinical need that may be fulfilled through the adoption of machine learning (ML) algorithms to refine outcome predictions. We sought to develop an ML-based risk stratification model built on clinical, anatomical, and procedural features to predict all-cause mortality following contemporary bifurcation PCI. Multiple ML models to predict all-cause mortality were tested on a cohort of 2393 patients (training, n = 1795; internal validation, n = 598) undergoing bifurcation PCI with contemporary stents from the real-world RAIN registry. Twenty-five commonly available patient-/lesion-related features were selected to train ML models. The best model was validated in an external cohort of 1701 patients undergoing bifurcation PCI from the DUTCH PEERS and BIO-RESORT trial cohorts. At ROC curves, the AUC for the prediction of 2-year mortality was 0.79 (0.74–0.83) in the overall population, 0.74 (0.62–0.85) at internal validation and 0.71 (0.62–0.79) at external validation. Performance at risk ranking analysis, k-center cross-validation, and continual learning confirmed the generalizability of the models, also available as an online interface. The RAIN-ML prediction model represents the first tool combining clinical, anatomical, and procedural features to predict all-cause mortality among patients undergoing contemporary bifurcation PCI with reliable performance