Background: One of the most severe complications of liposuction and fat grafting is pulmonary fat embolism (PFE). However, most healthcare workers are not familiar with PFE. We performed a systematic review to describe the details of PFE. Methods: PubMed, EMBASE, and Google Scholar were searched up to October 2022. Further analysis focused on clinical, diagnostic, and outcome parameters. Results: A total of 40 patients from 19 countries were included. Chest computed tomography (CT) yielded 100% accuracy in the diagnosis of PFE. More than 90% of the deceased died within 5 days after surgery, and in 69% of patients, onset of symptoms occurred within 24 h after surgery. The proportions of patients who required mechanical ventilation, had a cardiac arrest event, or died among all patients and among those whose onset of symptoms occurred within 24 h after surgery were 76%, 38%, and 34% versus 86%, 56%, and 54%, respectively. Conclusions: The earlier the onset of symptoms was, the more severe the clinical course was. Once a patient presents with PFE-related symptoms, surgery should be halted, supportive care initiated, and chest CT used to diagnose PFE. According to our review results, if a patient with PFE survives the initial episode without permanent sequelae, a complete recovery can be anticipated.

Background: This meta-analysis compared the effects of intravenous Tranexamic acid (TXA) and a placebo on hemostasis, hospital course, and complications in adult patients undergoing various urologic surgeries. Methods: The literature was extensively searched using various databases. The primary outcomes were standardized mean differences (SMDs) of intraoperative blood loss and odds ratios (ORs) of necessary blood product transfusion. The secondary outcomes included SMDs of operative time, SMDs of decreased hemoglobulin levels at 24 hours after surgery, and ORs of thromboembolic events. Results: The meta-analysis included 13 randomized controlled trials (RCT) comprising 1814 participants in total. The SMD of intraoperative blood loss for TXA versus placebo was −0.705 (95% confidence interval [CI]: −1.113 to −0.297). The pooled ORs of transfusion in the TXA group compared with the placebo group was 0.426 (95% CI: 0.290–0.625). These findings indicated a significantly lower intraoperative blood loss and a reduced need for transfusion following intravenous TXA. The pooled ORs of thromboembolic events in the TXA group compared with the placebo group was 0.664 (95% CI: 0.146–3.024). Conclusions: Intravenous TXA can reduce intraoperative blood loss, decrease the need for transfusion, and shorten operative time, and it does not increase the risk of thromboembolic events.

The accurate assessment of pupillary light reflex (PLR) is essential for monitoring critically ill patients, particularly those with traumatic brain injury or stroke and those in postoperative care. Smartphone-based pupillometers represent a potentially cost-effective solution for addressing this need. We developed a smartphone pupillometer application (app) and evaluated its effectiveness against the penlight test and quantitative pupillometry. This study included 50 volunteers aged >20 years and excluded individuals with neurologic or ophthalmic conditions. The app captured pupillary images by displaying a red circle on the screen, and an algorithm processed these images to calculate the pupil constriction percentage (PCP). The results revealed that the smartphone app often required multiple attempts for successful image acquisition. The obtained PCPs were consistently smaller and less variable than those obtained using the penlight test and a commercial pupillometer (app vs penlight for the right eye: 27.0% [27.0%–8.0%] vs 33.0% [32.3%–39.3%]; app vs pupillometer for the right eye: 27.0% [27.0%–28.0%] vs 35.0% [31.8%–38.3%]; app vs penlight for the left eye: 29.0% [28.0%–29.0%] vs 33.0% [29.8%–34.3%]; app vs pupillometer for the left eye: 29.0% [28.0%–29.0%] vs 36.0% [30.8%–38.0%]; P <.001 for all). Notably, the penlight and the pupillometer exhibited comparable PCPs (right eye: penlight vs pupillometer: 33.0% [32.3%–39.3%] vs 35.0% [31.8%–38.3%], P = .469; left eye: penlight vs pupillometer: 33.0% [29.8%–34.3%] vs 36.0% [30.8%–38.0%], P = .148). The app requires further refinement to yield results comparable to those of established methods. Future iterations can include alternative measurement strategies and dynamic assessment. Penlight and quantitative pupillometry remain indispensable as established tools for PLR.

Background and Objectives: We developed a machine learning algorithm to analyze trauma-related data and predict the mortality and chronic care needs of patients with trauma. Materials and Methods: We recruited admitted patients with trauma during 2015 and 2016 and collected their clinical data. Then, we subjected this database to different machine learning techniques and chose the one with the highest accuracy by using cross-validation. The primary endpoint was mortality, and the secondary endpoint was requirement for chronic care. Results: Data of 5871 patients were collected. We then used the eXtreme Gradient Boosting (xGBT) machine learning model to create two algorithms: a complete model and a short-term model. The complete model exhibited an 86% recall for recovery, 30% for chronic care, 67% for mortality, and 80% for complications; the short-term model fitted for ED displayed an 89% recall for recovery, 25% for chronic care, and 41% for mortality. Conclusions: We developed a machine learning algorithm that displayed good recall for the healthy recovery group but unsatisfactory results for those requiring chronic care or having a risk of mortality. The prediction power of this algorithm may be improved by implementing features such as age group classification, severity selection, and score calibration of trauma-related variables.

Smartphones are currently extensively used worldwide, and advances in hardware quality have engendered improvements in smartphone image quality, which is occasionally comparable to the quality of medical imaging systems. This paper proposes two algorithms for pupil recognition: a stateful-service-based pupil recognition mechanism and color component low-pass filtering algorithm. The PRSSM algorithm can determine pupil diameters in images captured in indoor natural light environments, and the CCLPF algorithm can determine pupil diameters in those captured outdoors under sunlight. The PRSSM algorithm converts RGB colors into the hue saturation value color space and performs adaptive thresholding, morphological operations, and contour detection for effectively analyzing the diameter of the pupil. The CCLPF algorithm derives the average matrix for the red components of eye images captured in outdoor environments. It also performs low-pass filtering, morphological and contour detection operations, and rule-of-thumb correction. This algorithm can effectively analyze pupil diameter in outdoor natural light. Traditional ruler-based measurements of pupil diameter were used as the reference to verify the accuracy of the PRSSM and CCLPF algorithms and to compare their accuracy with that of the other algorithm. The errors in pupil diameter data were smaller for the PRSSM and CCLPF algorithms than for the other algorithm.