目的 探讨姑息性右室-肺动脉连接术在重症紫绀型先天性心脏病治疗中的临床应用。 方法 回顾性分析郑州市第七人民医院心脏外科 2011 年 1 月至 2015 年 1 月期间所有行姑息性右室-肺动脉连接术治疗的重症紫绀型先天性心脏病患者 25 例的临床资料，其中男 17 例、女 8 例，年龄 31（5～108）个月，体重 3.5～37.2（12.82±6.73）kg。 结果 25 例姑息性右室-肺动脉连接术后早期死亡 2 例（术后 30 d 内），早期死亡率 8.0%（2/25）。患者术后动脉血氧饱和度与术前差异有统计学意义（62.43%±7.83%vs. 81.62%±6.25%，P<0.05）。术后随访 6 个月至 3 年（每 3 个月复查一次超声心动图），23 例患者 McGoon 比值（1.05±0.14vs. 1.61±0.18，P<0.05）和 Nakata 指数[（112.37±14.38）mm2/m2 vs. （165.74±22.62） mm2/m2，P<0.05]均明显上升，且差异有统计学意义。17 例患者行二期根治手术治疗。 结论 姑息性右室-肺动脉连接术能够有效促进重症紫绀型先天性心脏病患者的自身肺血管床发育，为行二期根治术创造条件。
ObjectiveTo investigate the clinical effect of Maze Ⅳ in the treatment of elderly patients with valvular heart disease and persistent atrial fibrillation (AF).MethodsWe retrospectively analyzed the clinical data of 78 elderly patients with cardiac valve disease combined with persistent AF in our hospital from 2017 to 2018. The patients were allocated to two groups including a trial group (n=37) and a control group (n=41). There were 21 males and 16 females aged 61 to 74 (65.2±2.5) years in the trial group. There were 23 males and 18 females aged 62 to 76 (64.8±3.3) years in the control group. The clinical effects of the two groups were compared.ResultsThere was no statistical difference in baseline data between the two groups (P>0.05). The aortic occlusion time, extracorporeal circulation time, and operation time of the trial group were longer than those of the control group with statistical differences (P<0.05). There was no statistical difference in postoperative ventilator assistance time, complication rate, mortality, ICU retention time, perioperative drainage, red blood cell transfusion volume, or length of hospital stay between the two groups (P>0.05). At the time of discharge, postoperaive 1-month, 3-month, 6-month, and 12-month, the maintenance rates of sinus rhythm in the control group were statistically different from those of the trial group (P<0.05). Compared with the control group, left atrial diameter, left ventricular end diastolic diameter and the decrease of pulmonary artery systolic blood pressure were statistically different (P<0.05).ConclusionMaze Ⅳ is safe and effective in the treatment of elderly patients with valvular heart disease and persistent AF, which is conducive to the recovery and maintenance of sinus rhythm, and is beneficial to the remodeling of the left atrium and left ventricle and the reduction of pulmonary systolic blood pressure with improvement of life quality of the patients.
Objective To develop an automatic diagnostic tool based on deep learning for lumbar spine stability and validate diagnostic accuracy. Methods Preoperative lumbar hyper-flexion and hyper-extension X-ray films were collected from 153 patients with lumbar disease. The following 5 key points were marked by 3 orthopedic surgeons: L4 posteroinferior, anterior inferior angles as well as L5 posterosuperior, anterior superior, and posterior inferior angles. The labeling results of each surgeon were preserved independently, and a total of three sets of labeling results were obtained. A total of 306 lumbar X-ray films were randomly divided into training (n=156), validation (n=50), and test (n=100) sets in a ratio of 3∶1∶2. A new neural network architecture, Swin-PGNet was proposed, which was trained using annotated radiograph images to automatically locate the lumbar vertebral key points and calculate L4, 5 intervertebral Cobb angle and L4 lumbar sliding distance through the predicted key points. The mean error and intra-class correlation coefficient (ICC) were used as an evaluation index, to compare the differences between surgeons’ annotations and Swin-PGNet on the three tasks (key point positioning, Cobb angle measurement, and lumbar sliding distance measurement). Meanwhile, the change of Cobb angle more than 11° was taken as the criterion of lumbar instability, and the lumbar sliding distance more than 3 mm was taken as the criterion of lumbar spondylolisthesis. The accuracy of surgeon annotation and Swin-PGNet in judging lumbar instability was compared. Results ① Key point: The mean error of key point location by Swin-PGNet was (1.407±0.939) mm, and by different surgeons was (3.034±2.612) mm. ② Cobb angle: The mean error of Swin-PGNet was (2.062±1.352)° and the mean error of surgeons was (3.580±2.338)°. There was no significant difference between Swin-PGNet and surgeons (P>0.05), but there was a significant difference between different surgeons (P<0.05). ③ Lumbar sliding distance: The mean error of Swin-PGNet was (1.656±0.878) mm and the mean error of surgeons was (1.884±1.612) mm. There was no significant difference between Swin-PGNet and surgeons and between different surgeons (P>0.05). The accuracy of lumbar instability diagnosed by surgeons and Swin-PGNet was 75.3% and 84.0%, respectively. The accuracy of lumbar spondylolisthesis diagnosed by surgeons and Swin-PGNet was 70.7% and 71.3%, respectively. There was no significant difference between Swin-PGNet and surgeons, as well as between different surgeons (P>0.05). ④ Consistency of lumbar stability diagnosis: The ICC of Cobb angle among different surgeons was 0.913 [95%CI (0.898, 0.934)] (P<0.05), and the ICC of lumbar sliding distance was 0.741 [95%CI (0.729, 0.796)] (P<0.05). The result showed that the annotating of the three surgeons were consistent. The ICC of Cobb angle between Swin-PGNet and surgeons was 0.922 [95%CI (0.891, 0.938)] (P<0.05), and the ICC of lumbar sliding distance was 0.748 [95%CI(0.726, 0.783)] (P<0.05). The result showed that the annotating of Swin-PGNet were consistent with those of surgeons. ConclusionThe automatic diagnostic tool for lumbar instability constructed based on deep learning can realize the automatic identification of lumbar instability and spondylolisthesis accurately and conveniently, which can effectively assist clinical diagnosis.