Effectiveness of caudal block in patients with spinal stenosis accompanied by redundant nerve root syndrome.

OBJECTIVE: Redundant nerve root syndrome (RNRS) is characterized by tortuous, elongated, and enlarged nerve roots in patients with lumbar spinal stenosis. This study was performed to evaluate the effects of caudal block in patients with RNRS and assess factors associated with RNRS. METHODS: Patients with lumbar spinal stenosis who underwent caudal block were retrospectively analyzed. A comparative analysis of pain reduction was conducted between patients with RNRS (Group R) and those without RNRS (Group C). Generalized estimating equation analysis was used to identify factors related to the treatment response. RNRS-associated factors were analyzed using logistic regression analysis. RESULTS: In total, 54 patients were enrolled (Group R, n = 22; Group C, n = 32). Group R had older patients than Group C. The caudal block showed less pain reduction in Group R than in Group C, but the difference was not statistically significant. Generalized estimating equation analysis showed that RNRS was the factor significantly associated with the treatment response. The dural sac anteroposterior diameter and left ligamentum flavum thickness were associated with RNRS in the logistic regression analysis. CONCLUSIONS: Caudal block tended to be less effective in patients with than without RNRS, but the difference was not statistically significant.

Clinical efficacy of short-term prewarming in elderly and adult patients: A prospective observational study.

Background: Short-term prewarming effectively reduces intraoperative hypothermia in adult patients. However, few data exist regarding its efficacy in elderly patients. Elderly people have a reduced ability to regulate their body temperature, which affects the efficacy of prewarming. This study aimed to compare the clinical efficacy of short-term pre-warming in elderly patients with that in adult patients. Methods: We enrolled 25 adult (20-50 years) and 25 elderly (> 65 years) patients scheduled for ureteroscopic stone surgery under general anaesthesia. All patients received preanaesthetic forced-air warming for 20 min. The core temperature was measured using an infrared tympanic thermometer during awakening and nasopharyngeal thermistors during anaesthesia. Incidence and severity of intraoperative hypothermia (< 36°C) was compared. Postoperative shivering and number of patients requiring active warming in the post-anaesthesia care unit were also assessed. Results: Intraoperative hypothermia was more frequent in elderly than in adult patients (58.3% vs. 12.0%; relative risk 2.6; 95% confidence interval 1.5 to 4.6; effect size h = 1.010; p = 0.001). The severity of intraoperative hypothermia showed a significant intergroup difference (p = 0.002). Postoperative shivering was more frequent in elderly than in adult patients (33.3% vs. 8.0%, p = 0.037). A greater number of elderly patients in the post-anaesthesia care unit required active warming (33.3% vs. 8.0%, p = 0.037). Conclusions: The effects of short-term prewarming on the prevention of hypothermia and maintenance of perioperative normothermia are not the same in the elderly and adult patients.

Risk Factors for Postoperative Hypothermia in Patients Undergoing Robot-Assisted Gynecological Surgery: A Retrospective Cohort Study.

Since postoperative hypothermia increases the morbidity and mortality rates of surgery, identifying its risk factors is an important part of perioperative management. Considering the increasing demand for robot-assisted surgery and other characteristics of conventional laparoscopic surgery, identifying the risk factors for hypothermia in robot-assisted surgery is necessary. However, this has not yet been clearly established. This study aimed to identify the risk factors and incidence rate of postoperative hypothermia in patients undergoing robot-assisted gynecological surgery. In total, 516 patients aged ≥ 19 years undergoing robot-assisted gynecological surgery at a single university hospital between January 2018 and November 2020 were retrospectively analyzed. Postoperative hypothermia was defined as 36.0°C or lower body temperature at the end of the surgery, and multivariate logistic regression analysis was performed to identify the risk factors for postoperative hypothermia. Among the 516 patients, the incidence rate of postoperative hypothermia was 28.1% in 145 patients. The independent risk factors for postoperative hypothermia included body mass index ≤ 22.9 kg/m2, baseline heart rate ≤ 73 rate/min, baseline body temperature ≤ 36.8°C, use of intraoperative nicardipine, and amount of administered intravenous fluid larger than 800 mL. Therefore, to prevent hypothermia in patients undergoing robot-assisted gynecological surgery, these risk factors must be considered.

Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver.

PURPOSE: Volumetry is used in polycystic kidney and liver diseases (PKLDs), including autosomal dominant polycystic kidney disease (ADPKD), to assess disease progression and drug efficiency. However, since no rapid and accurate method for volumetry has been developed, volumetry has not yet been established in clinical practice, hindering the development of therapies for PKLD. This study presents an artificial intelligence (AI)-based volumetry method for PKLD. MATERIALS AND METHODS: The performance of AI was first evaluated in comparison with ground-truth (GT). We trained a V-net-based convolutional neural network on 175 ADPKD computed tomography (CT) segmentations, which served as the GT and were agreed upon by 3 experts using images from 214 patients analyzed with volumetry. The dice similarity coefficient (DSC), interobserver correlation coefficient (ICC), and Bland-Altman plots of 39 GT and AI segmentations in the validation set were compared. Next, the performance of AI on the segmentation of 50 random CT images was compared with that of 11 PKLD specialists based on the resulting DSC and ICC. RESULTS: The DSC and ICC of the AI were 0.961 and 0.999729, respectively. The error rate was within 3% for approximately 95% of the CT scans (error<1%, 46.2%; 1%≤error<3%, 48.7%). Compared with the specialists, AI showed moderate performance. Furthermore, an outlier in our results confirmed that even PKLD specialists can make mistakes in volumetry. CONCLUSIONS: PKLD volumetry using AI was fast and accurate. AI performed comparably to human specialists, suggesting its use may be practical in clinical settings.