Department of Anesthesia Surgery Department, Changsha Fourth Hospital, Changsha, 410006 Hunan, China.
Comput Math Methods Med. 2022 May 24;2022:5070518. doi: 10.1155/2022/5070518. eCollection 2022.
This research was aimed at analyzing the effect of humanized nursing intervention combined with computed tomography (CT) imaging in the surgical anesthesia of femur intertrochanteric fractures (FIF) in the elderly. An image reconstruction algorithm was proposed based on nonlocal mean (NLM) algorithm, which was named as ONLM, and its performance was analyzed. A total of 114 elderly patients with FIF were equally and randomly divided into a humanized nursing group (57 cases) and a routine nursing group (57 cases). They were performed with CT imaging scan based on the ONLM algorithm, and the clinical indicators of the two groups of patients were recorded. The root mean square error (RMSE) and mean absolute error (MAE) of the CT images constructed using the ONLM algorithm were significantly lower than those using NLM algorithm, edge filtering algorithm, and total variation model, while the peak signal-to-noise ratio (PSNR) was the opposite ( < 0.05). The operation time, hospitalization days, intraoperative blood loss, postoperative drainage, and anesthesia preparation time of patients in the humanized nursing group were significantly lower than those in the routine nursing group. The number of patients with excellent Harris scores in the humanized nursing group was higher than that in the routine nursing group, and the number of patients with poor Harris scores was lower ( < 0.05). The language pain score, facial pain score, and visual analog simulation (VAS) scores of patients in the humanized nursing group were significantly lower than those in the routine nursing group. The numbers of postoperative hip varus and fracture nonunion cases in the humanized nursing group were significantly more than those in the routine nursing group. In short, CT images constructed by the ONLM showed higher performance than those by the traditional algorithm. In addition, CT images constructed by ONLM combined with humanized nursing intervention could more effectively improve the cooperation of patients with surgical anesthesia, reduce surgical pain and fear of patients, improve the prognosis of patients, and lower the occurrence of adverse events.
本研究旨在分析人性化护理干预联合计算机断层扫描（CT）成像在老年股骨粗隆间骨折（FIF）手术麻醉中的效果。提出了一种基于非局部均值（NLM）算法的图像重建算法，命名为 ONLM，并对其性能进行了分析。将 114 例股骨粗隆间骨折老年患者等分为人性化护理组（57 例）和常规护理组（57 例），均采用基于 ONLM 算法的 CT 成像扫描，记录两组患者的临床指标。ONLM 算法构建的 CT 图像的均方根误差（RMSE）和平均绝对误差（MAE）明显低于 NLM 算法、边缘滤波算法和全变分模型，而峰值信噪比（PSNR）则相反（<0.05）。人性化护理组患者的手术时间、住院天数、术中出血量、术后引流量、麻醉准备时间明显低于常规护理组。人性化护理组患者的 Harris 评分优秀者明显多于常规护理组，Harris 评分较差者明显少于常规护理组（<0.05）。人性化护理组患者的语言疼痛评分、面部疼痛评分和视觉模拟评分（VAS）明显低于常规护理组。人性化护理组患者术后髋内翻和骨折不愈合的例数明显多于常规护理组。总之，ONLM 构建的 CT 图像比传统算法具有更高的性能。此外，ONLM 构建的 CT 图像结合人性化护理干预能更有效地提高手术麻醉患者的配合度，减轻患者的手术疼痛和恐惧，改善患者的预后，降低不良事件的发生。
