IMPORTANCE Blood loss from surgical procedures is a major issue worldwide as the demand for blood products is increasing. Tranexamic acid is an antifibrinolytic agent commonly used to reduce intraoperative blood loss.OBJECTIVE To systematically examine the role of tranexamic acid in reducing intraoperative blood loss and postoperative edema and ecchymosis among patients undergoing primary elective rhinoplasty.DATA SOURCES A systematic review and meta-analysis was undertaken in an academic medical setting using Medline, Embase, and Google Scholar from inception to June 30, 2018. All references of included articles were screened for potential inclusion. The search was mapped to Medical Subject Headings, and the following terms were used to identify potential articles: reconstruction or rhinoplasty and tranexamic acid or anti-fibrinolysis or antifibrinolysis and bleeding or ecchymosis or bruising or edema or complications.STUDY SELECTION The population of interest consisted of adult patients undergoing primary elective rhinoplasty. The intervention was the use of tranexamic acid. The control group was composed of patients receiving a placebo. Primary outcomes were intraoperative blood loss and postoperative edema and ecchymosis. In vitro or animal studies were excluded, and only English-language articles were included.DATA EXTRACTION AND SYNTHESIS The PRISMA guidelines were followed, and articles were assessed using the Cochrane Collaboration's tool for assessing risk of bias and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidelines. Random-effects meta-analysis was performed to determine the overall effect size. MAIN OUTCOMES AND MEASURESThe primary outcomes were intraoperative blood loss and postoperative edema and ecchymosis.RESULTS Five studies (comprising 332 patients) were included in the qualitative analysis, all of which were randomized clinical trials published within the past 5 years. The mean (SD) patient age was 27 ( 7) years (age range, 16-42 years), while the mean (SD) sample size was 66 (19) (range, 50-96). Meta-analysis of 4 studies (271 patients) indicated that tranexamic acid treatment resulted in a mean reduction in intraoperative blood loss of −41.6 mL (95% CI, −69.8 to −13.4 mL) compared with controls (P = .004). Three studies indicated that postoperative edema and ecchymosis were reduced with tranexamic acid treatment compared with controls; however, there was no significant difference compared with corticosteroid use. Four studies were considered of high methodological quality, with a low risk of bias. The overall quality of evidence was high.CONCLUSIONS AND RELEVANCE Tranexamic acid has the ability to significantly reduce intraoperative blood loss and postoperative edema and ecchymosis among patients undergoing primary elective rhinoplasty.LEVEL OF EVIDENCE 4.

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Introduction: We sought to train and validate an automated machine learning algorithm for ICH segmentation and volume calculation using multicenter data. Methods: An open-source 3D deep machine learning algorithm “DeepMedic” was trained using manually segmented ICH from 208 CT scans (129 patients) from the multicenter PREDICT study. The algorithm was then validated with 125 manually segmented CT scans (48 patients) from the SPOTLIGHT study. Manual segmentation was performed with Quantomo semi-automated software. ABC/2 was measured for all studies by two neuroradiologists. Accuracy of DeepMedic segmentation was assessed using the Dice similarity coefficient. Analysis was stratified by presence of IVH. Intraclass correlation (ICC) with 95% confidence intervals (CI) assessed agreement between manual vs. DeepMedic segmentation volume; and manual segmentation and ABC/2 volume. Bland-Altman charts were analyzed for ABC/2 and DeepMedic vs. manual segmentation volumes. Results: DeepMedic demonstrated high segmentation accuracy in the training cohort (median Dice 0.96; IQR 0.95 - 0.97) and in the validation cohort (median Dice 0.91; IQR 0.86 - 0.94). Dice coefficients were not significantly different between patients with IVH in the training cohort; however was significantly worse in the validation cohort in patients with IVH (Wilcoxon p<0.001). Agreement was significantly better between DeepMedic and manual segmentation (PREDICT: ICC 0.99 [95%CI 0.99 -1.00]; SPOTLIGHT: ICC 0.98 [95%CI 0.97 - 0.99]) than between ABC/2 and manual segmentation (PREDICT: ICC 0.92 [95%CI 0.89 - 0.95]; SPOTLIGHT: ICC 0.95 [95%CI 0.93-0.97]). Improved accuracy of DeepMedic was demonstrated in Bland-Altman charts (Fig 1). Conclusion: ICH machine learning segmentation with DeepMedic is feasible and accurate; and demonstrates greater agreement with manual segmentation compared to ABC/2 volumes. Accuracy of the machine learning algorithm however is limited in patients with IVH.

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