村山 陵子

Background: Ultrasound guidance increases the success rate of peripheral intravenous catheter placement. However, the longer time required to obtain ultrasound-guided access poses difficulties for ultrasound beginners. Notably, interpretation of ultrasonographic images is considered as one of the main reasons of difficulty in using ultrasound for catheter placement. Therefore, an automatic vessel detection system (AVDS) using artificial intelligence was developed. This study aimed to investigate the effectiveness of AVDS for ultrasound beginners in selecting puncture points and determine suitable users for this system. Methods: In this crossover experiment involving the use of ultrasound with and without AVDS, we enrolled 10 clinical nurses, including 5 with some experience in peripheral intravenous catheterization using ultrasound-aided methods (categorized as ultrasound beginners) and 5 with no experience in ultrasound and less experience in peripheral intravenous catheterization using conventional methods (categorized as inexperienced). These participants chose two puncture points (those with the largest and second largest diameter) as ideal in each forearm of a healthy volunteer. The results of this study were the time required for the selection of puncture points and the vein diameter of the selected points. Results: Among ultrasound beginners, the time required for puncture point selection in the right forearm second candidate vein with a small diameter (<3 mm) was significantly shorter when using ultrasound with AVDS than when using it without AVDS (mean, 87 vs 247 s). Among inexperienced nurses, no significant difference in the time required for all puncture point selections was found between the use of ultrasound with and without AVDS. In the vein diameter, significant difference was shown only in the absolute difference at left second candidate among inexperienced participants. Conclusion: Ultrasonography beginners needed less time to select the puncture points in a small diameter vein using ultrasound with AVDS than without AVDS.

Despite the widespread use of peripheral intravenous catheters, unscheduled catheter failure before completion of treatment occurs frequently. If a large vein is selected, catheter failures may be prevented despite administering a highly irritant drug. In this study, we attempted to use a catheter that can be placed in a large upper arm vein. The new catheter was 88 mm long but had no guidewire to reduce contamination risk. This study aimed to evaluate the safety of the first-in-human trial for the new catheter with the administration of highly irritant drugs. This study was conducted at a university hospital in Tokyo, Japan. Eight Japanese adults were hospitalized adults with planned administration of non-cancer drugs with high irritant potential using a peripheral catheter. A trained nurse catheterized with the new catheter in the upper arm using ultrasonography. The catheterization site was monitored by staff and a research nurse once every 24 hours for up to 7 days. No adverse events or catheter failure occurred and the catheter placement success rate was 100%. In two patients, a temporary occlusion alarm of the infusion pump occurred, possibly due to the flexion of the catheter base. The new peripheral intravenous catheter did not interrupt medical treatments as is common after placement, but safety administered the irritant drugs. However, because this catheter may be easily affected by the contraction of the muscle at the fixation position, the position and method of catheter fixation in the upper arm need to be carefully considered.

Background: Peripheral intravenous catheter (PIVC) insertion often fails on the first attempt. Risk factors include small vein size and dehydration, causing vein deformation and displacement due to puncture resistance of the vessel. The authors developed a short, thin-tipped bevel needle and compared its puncture performance with needles of four available PIVCs using an ex vivo model. Methods: The PIVC with the thin-tipped short bevel needle was compared to four available PIVCs using an ex vivo model which simulated the cephalic vein of the human forearm. The ex vivo model consisted of a porcine shoulder and porcine internal jugular vein, and was used for evaluation of the rate of vein deformation and vessel displacement during needle insertion. Results: An ex vivo model was created with a vessel diameter of 2.7–3.7 mm and a depth of 2–5 mm. The thin-tipped short bevel PIVC needle was associated with a significantly lower compressive deformation rate and venous displacement compared to the needles of the other four PIVCs. Conclusion: The thin-tipped short bevel needle induced lower compressive deformation and displacement of the vein than the conventional needles. This needle has the potential to improve the first-attempt success rate of peripheral intravenous catheterization in patients with difficult venous access.

OBJECTIVES: Complications due to peripheral intravenous catheters (PIVC) can be assessed using ultrasound imaging; however, it is not routinely conducted due to the need for training in image reading techniques. This study aimed to develop and validate a system that automatically measures blood vessel diameters on ultrasound images using artificial intelligence (AI) and provide recommendations for selecting an implantation site. DESIGN: Pilot study. SETTING: The University of Tokyo Hospital, Japan. PRIMARY AND SECONDARY OUTCOME MEASURES: First, based on previous studies, the vessel diameter was calculated as the mean value of the maximum long diameter plus the maximum short diameter orthogonal to it. Second, the size of the PIVC to be recommended was evaluated based on previous studies. For the development and validation of an automatic detection tool, we used a fully convoluted network for automatic estimation of vein location and diameter. The agreement between manually generated correct data and automatically estimated data was assessed using Pearson's product correlation coefficient, systematic error was identified using the Bland-Altman plot, and agreement between catheter sizes recommended by the research nurse and those recommended by the system was evaluated. RESULTS: Through supervised machine learning, automated determination was performed using 998 ultrasound images, of which 739 and 259 were used as the training and test data set, respectively. There were 24 false-negatives indicating no arteries detected and 178 true-positives indicating correct detection. Correlation of the results between the system and the nurse was calculated from the 178 images detected (r=0.843); no systematic error was identified. The agreement between the sizes of the PIVC recommended by the research nurse and the system was 70.2%; 7% were underestimated and 21.9% were overestimated. CONCLUSIONS: Our automated AI-based image processing system may aid nurses in assessing peripheral veins using ultrasound images for catheterisation; however, further studies are still warranted.t.