Evidence-based characterization of the diagnostic and prognostic value of the hematological and immunological markers related to the epidemic of Coronavirus Disease 2019 (COVID-19) is critical to understand the clinical course of the infection and to assess in development and validation of biomarkers. Based on systematic search in Web of Science, PubMed, Scopus, and Science Direct up to April 22, 2020, a total of 52 eligible articles with 6,320 laboratory-confirmed COVID-19 cohorts were included. Pairwise comparison between severe versus mild disease, Intensive Care Unit (ICU) versus general ward admission and expired versus survivors were performed for 36 laboratory parameters. The pooled standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated using the DerSimonian Laird method/random effects model and converted to the Odds ratio (OR). The decision tree algorithm was employed to identify the key risk factor(s) attributed to severe COVID-19 disease. Cohorts with elevated levels of white blood cells (WBCs) (OR = 1.75), neutrophil count (OR = 2.62), D-dimer (OR = 3.97), prolonged prothrombin time (PT) (OR = 1.82), fibrinogen (OR = 3.14), erythrocyte sedimentation rate (OR = 1.60), procalcitonin (OR = 4.76), IL-6 (OR = 2.10), and IL-10 (OR = 4.93) had higher odds of progression to severe phenotype. Decision tree model (sensitivity = 100%, specificity = 81%) showed the high performance of neutrophil count at a cut-off value of more than 3.74x10⁹/L for identifying patients at high risk of severe COVID‐19. Likewise, ICU admission was associated with higher levels of WBCs (OR = 5.21), neutrophils (OR = 6.25), D-dimer (OR = 4.19), and prolonged PT (OR = 2.18). Patients with high IL-6 (OR = 13.87), CRP (OR = 7.09), D-dimer (OR = 6.36), and neutrophils (OR = 6.25) had the highest likelihood of mortality. Several hematological and immunological markers, in particular neutrophilic count, could be helpful to be included within the routine panel for COVID-19 infection evaluation to ensure risk stratification and effective management.

The influence of minimally invasive options has led to the application of new evolving techniques in thyroid surgery to eliminate visible neck scars. Here, we describe one author's experience with transaxillary robotic thyroidectomy and examine the effect of experience on determining the learning curve and improvements over time in operative performance. With IRB approval, a prospective analysis of our surgical experience was performed. All patients underwent robotic transaxillary thyroidectomy by a single surgeon between September 2009 and June 2011. Principal outcomes measures included length of hospital stay, incidence of complications, and effect of obesity on outcomes. One hundred consecutive operations were performed on 91 patients. Sixty-nine hemithyroidectomy, 22 total or near-total thyroidectomy, and 9 completion thyroidectomy procedures were performed. Of patients who underwent hemithyroidectomy, 21.7% were discharged within 4 hours; the remaining patients were discharged within 23 hours. Mean operative time for hemithyroidectomy was 108.1 ± 60.5 minutes, and for total or near-total thyroidectomy, mean operative time was 118.1 ± 51.3 minutes. Mean robot docking time was 9.1 ± 2.2 minutes for all cases. Obesity contributed to prolonged total operative time. Improvement in the length of time to perform components of the procedure was noted after 45 cases. Two cases required conversion to a cervical approach. There were no instances of permanent vocal cord palsy on postoperative laryngoscopy. Here we report the largest experience of robotic gasless thyroid surgery in the United States. This novel technique provides excellent cosmetic results and can be performed as an outpatient procedure in selected group of patients. It is feasible and safe, however, has a lengthy learning curve.

Background: As an immune modulator, vitamin D has been implicated in the coronavirus disease‐2019 (COVID‐19) outcome. We aim to systematically explore the association of vitamin D serum levels with COVID‐19 severity and prognosis.Methods: The standardized mean difference (SMD) or odds ratio and 95% confidence interval (CI) were applied to estimate pooled results from six studies. The prognostic performance of vitamin D serum levels for predicting adverse outcomes with detection of the best cutoff threshold was determined by receiver operating characteristic curve analysis. Decision tree analysis by combining vitamin D levels and clinical features was applied to predict severity in COVID‐19 patients.Results: Mean vitamin D serum level of 376 patients, was 21.9 nmol/L (95% CI = 15.36–28.45). Significant heterogeneity was found (I² = 99.1%, p < .001). Patients with poor prognosis (N = 150) had significantly lower serum levels of vitamin D compared with those with good prognosis (N = 161), representing an adjusted standardized mean difference of −0.58 (95% Cl = −0.83 to −0.34, p < .001).Conclusion: Serum vitamin D levels could be implicated in the COVID‐19 prognosis. Diagnosis of vitamin D deficiency could be a helpful adjunct in assessing patients' potential of developing severe COVID‐19. Appropriate preventative and/or therapeutic intervention may improve COVID‐19 outcomes.