In spinal fusion surgery, total blood loss (TBL) is composed of visible blood loss from the surgical field and wound drainage, and hidden blood loss (HBL). Until now, no published studies exist reporting the effect of topical use of tranexamic acid (tTXA) on HBL in patients undergoing posterior lumbar spinal fusion surgery. This study aimed to explore the effect of tTXA on HBL during primary posterior lumbar spinal fusion surgery. Between September 2014 and September 2016, 100 adult patients (age > 18 years) with lumbar disc herniation or lumbar spinal stenosis undergoing primary posterior lumbar instrumented spinal fusions at 1 institution were divided into tTXA and control groups. The primary outcome was HBL. Secondary outcomes include TBL, intraoperative blood loss (IBL), postoperative blood loss (PBL), hemoglobin (HGB) levels on preoperative (Pre-op) and postoperatively (days 1–3, POD1, POD2, POD3, respectively), and amount of allogeneic blood transfusion. Complications occurring perioperatively until hospitalization discharge were also collected. In the tTXA group (n = 50 patients), wound surface was soaked with TXA (1 g in 100 mL saline solution) for 5 minutes before wound closure. For the control group (n = 50 patients), wound surface was soaked with the same volume of normal saline. There were no significant differences in demographics, surgical traits between the 2 groups. There were no significant differences in IBL or perioperative blood transfusion requirements between the 2 groups. However, in the tTXA group, TBL, PBL, and HBL were significantly lower than those in the control group (550 ± 268 vs 833 ± 298 mL, 53.5 ± 43.9 vs 136.7 ± 87.9 mL, 356.7 ± 254.8 vs 501.1 ± 216.9 mL, P < .001, respectively). HGB levels were significantly higher in the tTXA group (P < .001) on POD1 and had a slower decline on POD2 and POD3 than the control group. No complications associated with TXA were observed. From these data, we conclude that tTXA can effectively reduce HBL, without significant complications in adult patients undergoing posterior lumbar spinal fusion surgery.

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The ubiquity of smartphones and their rich set of on-board sensors has created many exciting new opportunities, where smartphones are used as powerful computing platforms to sense and analyze pervasive data. One important application of mobile sensing is activity recognition based on smartphone inertial sensors, which is a fundamental building block for a variety of scenarios, such as indoor pedestrian tracking, mobile health care, and smart cities. Although many approaches have been proposed to address the human activity recognition problem, several challenges are still present: 1) people's motion modes are very different for different individuals; 2) there is only a very limited amount of training data; 3) human activities can be arbitrary and complex, thus handcrafted feature engineering often fails to work; and 4) the recognition accuracy tends to be limited due to confusing activities. To tackle those challenges, in this paper, we propose a human activity recognition framework based on convolutional neural networks (CNNs) with two convolutional layers using the smartphone-based accelerometer, gyroscope, and magnetometer. To solve the confusion between highly similar activities like going upstairs and walking, this paper presents a novel ensemble model of CNN to further improve the identification accuracy. The extensive experiments have been conducted using 235 977 sensory samples from a total of 100 subjects. The results have shown that the classification accuracy of the proposed model can be up to 96.11%, which proves the effectiveness of the proposed model. INDEX TERMSConvolutional neural network, human activity recognition, sensor data, smartphone.

Our research is a case-control study and the level of proof is III.

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