ABSTRACT
BACKGROUND: Deep sternal wound infection and mediastinitis following sternotomy are associated with significant morbidity and mortality, and often require sternal reconstruction by plastic surgeons. Despite this patient population having a substantial risk of venous thromboembolism, there are no reports of the incidence of venous thromboembolism in patients undergoing sternal reconstruction. The authors sought to evaluate the incidence of venous thromboembolism in sternal reconstruction patients and to identify common risk factors for venous thromboembolism in this patient population. METHODS: A single-center retrospective review was completed of all patients who underwent sternal reconstruction by plastic surgeons between January 2012 and July 2020. Demographic data, antiplatelet and anticoagulant use, 2005 Caprini score, operative time, bleeding events, and postoperative venous thromboembolism events were recorded. RESULTS: A total of 44 patients were identified for analysis. The average 2005 Caprini score for the cohort was 10.9. In total, 93.2% of patients received perioperative antiplatelet and anticoagulant therapy (either chemoprophylaxis or systemic). Two patients developed postoperative venous thromboembolism events, for a total venous thromboembolism rate of 4.6%. Four patients had bleeding events requiring reoperation. No deaths were reported from either of these complications. CONCLUSIONS: Patients undergoing sternal reconstruction are at a high risk for venous thromboembolism and postoperative bleeding events. Despite the growing body of literature on venous thromboembolism in various surgical populations, the optimal management of thromboembolic risk in patients with high Caprini scores undergoing sternal reconstruction requires additional investigation.
ABSTRACT
Curation and interpretation of copy number variants identified by genome-wide testing is challenged by the large number of events harbored in each personal genome. Conventional determination of phenotypic relevance relies on patterns of higher frequency in affected individuals versus controls; however, an increasing amount of ascertained variation is rare or private to clans. Consequently, frequency data have less utility to resolve pathogenic from benign. One solution is disease-specific algorithms that leverage gene knowledge together with variant frequency to aid prioritization. We used large-scale resources including Gene Ontology, protein-protein interactions and other annotation systems together with a broad set of 83 genes with known associations to epilepsy to construct a pathogenicity score for the phenotype. We evaluated the score for all annotated human genes and applied Bayesian methods to combine the derived pathogenicity score with frequency information from our diagnostic laboratory. Analysis determined Bayes factors and posterior distributions for each gene. We applied our method to subjects with abnormal chromosomal microarray results and confirmed epilepsy diagnoses gathered by electronic medical record review. Genes deleted in our subjects with epilepsy had significantly higher pathogenicity scores and Bayes factors compared to subjects referred for non-neurologic indications. We also applied our scores to identify a recently validated epilepsy gene in a complex genomic region and to reveal candidate genes for epilepsy. We propose a potential use in clinical decision support for our results in the context of genome-wide screening. Our approach demonstrates the utility of integrative data in medical genomics.
