Body Mass Index (BMI) Related Morbidity with Thyroid Surgery.

OBJECTIVES: The increase in incidence of thyroid cancer correlates with strict increases in body mass index (BMI) and obesity in the United States. Thyroid hormone dysregulation has been shown to precipitate circulatory volume, peripheral resistance, cardiac rhythm, and even cardiac muscle health. Theoretically, thyroid surgery could precipitate injury to the cardiopulmonary system. METHODS: The American College of Surgery National Quality Improvement Program database was queried for thyroidectomy cases in the 2007-2020 Participant User files. Continuous and categorical associations between BMI and cardiopulmonary complications were investigated as reported in the database. RESULTS: The query resulted 186,095 cases of thyroidectomy procedures in which the mean age was 51.3 years and sample was 79.3% female. No correlation was evident in univariate and multivariate analyses between BMI and the incidence of postoperative stroke or myocardial infarction. The incidence of complications was extremely low. However, risk of deep venous thrombosis correlated with BMI in the categorical, univariate, and multivariate (OR 1.036, CI 1.014-1.057, p < 0.01) regression analysis. Additionally, increased BMI was associated with increased risk of pulmonary embolism (PE) (OR 1.050 (1.030, 1.069), p < 0.01), re-intubation (OR 1.012 (1.002, 1.023), p = 0.02), and prolonged intubation (OR 1.031 (1.017, 1.045), p < 0.01). CONCLUSION: Despite the rarity of cardiopulmonary complications during thyroid surgery, patients with very high BMI carry a significant risk of deep venous thrombosis, PE, and prolonged intubation. LEVEL OF EVIDENCE: 3 Laryngoscope, 133:2823-2830, 2023.

Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis.

Background: Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease. Methods: We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360). Findings: We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0-94.1) and specificity was 91.7% (95% CI: 87.4-94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias. Interpretation: The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis. Systematic review registration: [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].