以CUSUM方法分析达芬奇机器人肺叶切除术的学习曲线
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摘要
目的探讨109例达芬奇机器人肺叶切除术的学习曲线。方法以累积和分析法(cumulative sum analysis,CUSUM)分析沈阳军区总医院2012年3月至2015年2月完成的第1例至第109例达芬奇机器人肺叶切除术的手术时间。采用多元线性回归排除影响手术时间的因素。对CUSUM学习曲线进行拟合,以R~2判断拟合优度。比较学习曲线不同阶段的手术时间、术中出血量、淋巴结清扫、术后并发症、术后带管时间和住院时间的差异。结果手术时间随手术例数的累积呈逐渐下降趋势。学习曲线最佳拟合为三次方曲线,拟合优度系数R~2=0. 847,拟合方程:CUSUM(n)=0. 002×n~3-0. 323×n~2+13. 360×n-18. 195(n为手术例数)。拟合曲线在手术例数累积至第28例时达到顶点,以此为分界将学习曲线划分为两个阶段,其手术时间(P=0. 022)、术中出血量(P=0. 014)、清扫淋巴结数目(P=0. 022)、术后并发症(P=0. 015)、术后带管时间(P=0. 025)和住院时间(P=0. 005)均存在显著的统计学差异。结论通过CUSUM分析法对达芬奇机器人肺叶切除术的学习曲线进行精确分析,表明掌握该技术须累积的手术例数为28例。
Objective This study aims to investigate the learning curve of da Vinci robot-assisted pulmonary lobectomy. Methods A cumulative sum analysis( CUSUM) was performed on the operation time( OT) of the 1 st to the 109 thpatients who underwent da Vinci robot-assisted pulmonary lobectomy from Mar.2012 to Feb. 2015 in our hospital. A multivariate linear regression was performed,in order to exclude the bias factors on OT. The CUSUM learning curve was modeled by curve fitting and R~2 was used to testify the goodness. The different phases of the learing curve was compared on OT,estimated blood loss( EBL),lymph nodes dissecting,complications,drainage time and hospital stay. Results The OT decreased with the accumulation of cases. The CUSUM learning curve was best modeled as a cubic curve with the equation:CUSUM( n) = 0. 002 × n~3-0. 323 × n~2+ 13. 360 × n-18. 195,which had a higher R~2 value of 0. 847. The fitting curve reached the top at the 28 ~(th)case. As a cut-off point,the 28 ~(th)case di Vided the learning curve into two phases. There were statistical differences in OT( P = 0. 022),EBL( P = 0. 014),numbers of lymph nodes dissected( P = 0. 022),complications( P = 0. 015),drainage time( P = 0. 025) and hospital stay( P = 0. 005). Conclusions The learning curve of da Vinci robot-assisted pulmonary lobectomy can be defined by CUSUM analysis,which suggested from our data that the surgeons need finish about 28 cases to master the technique.
Objective This study aims to investigate the learning curve of da Vinci robot-assisted pulmonary lobectomy. Methods A cumulative sum analysis( CUSUM) was performed on the operation time( OT) of the 1 st to the 109 thpatients who underwent da Vinci robot-assisted pulmonary lobectomy from Mar.2012 to Feb. 2015 in our hospital. A multivariate linear regression was performed,in order to exclude the bias factors on OT. The CUSUM learning curve was modeled by curve fitting and R~2 was used to testify the goodness. The different phases of the learing curve was compared on OT,estimated blood loss( EBL),lymph nodes dissecting,complications,drainage time and hospital stay. Results The OT decreased with the accumulation of cases. The CUSUM learning curve was best modeled as a cubic curve with the equation:CUSUM( n) = 0. 002 × n~3-0. 323 × n~2+ 13. 360 × n-18. 195,which had a higher R~2 value of 0. 847. The fitting curve reached the top at the 28 ~(th)case. As a cut-off point,the 28 ~(th)case di Vided the learning curve into two phases. There were statistical differences in OT( P = 0. 022),EBL( P = 0. 014),numbers of lymph nodes dissected( P = 0. 022),complications( P = 0. 015),drainage time( P = 0. 025) and hospital stay( P = 0. 005). Conclusions The learning curve of da Vinci robot-assisted pulmonary lobectomy can be defined by CUSUM analysis,which suggested from our data that the surgeons need finish about 28 cases to master the technique.
引文
1王述民.达芬奇机器人在肺癌根治术中的应用现状及展望[J].中国肿瘤,2014,23(9):736-742.
2 Veronesi G. Robotic surgery for the treatment of early-stage lung cancer[J]. Current Opinion in Oncology,2013,25(2):107-114.
3 Toker A. Robotic thoracic surgery:from the perspectives of european chest surgeons[J]. Journal of Thoracic Disease,2014,6(2):211-216.
4 Ricciardi S,Cardillo G,Zirafa CC,et al. Robotic lobectomies:when and why[J]. Journal of Visualized Surgery,2017,3(112):1-7.
5 Yamashita S,Yoshida Y,Iwasaki A. Robotic surgery for thoracic disease[J]. Annals of Thoracic&Cardiovascular Surgery Official Journal of the Association of Thoracic&Cardiovascular Surgeons of Asia,2016,22(1):1-5.
6 李欣,侯纯升,白洋,等.单孔腹腔镜胆囊切除术的学习曲线:累积求和分析法[J].中国微创外科杂志,2016,16(3):241-244.
7 Khan N,Abboudi H,Khan MS,et al. Measuring the surgical“learning curve”:methods,variables and competency[J]. Bju International,2014,113(3):504-508.
8 Bokhari MB, Patel CB. Learning curve for robotic-assisted laparoscopic colorectal surgery[J]. Surgical Endoscopy,2011,25(3):855-860.
9 Wang S,Xu S,Liu B. Resection of huge retrosternal goiter through a novel combined cervical and robot-assisted approach[J].Artificial Organs,2014,38(5):431-433.
10 宋德胜,于在诚,胡旭,等.单向式全胸腔镜肺癌根治术探讨[J/CD].中华腔镜外科杂志(电子版),2014,7(6):3-5.
11 刘博,汪明敏,许世广,等.达芬奇机器人纵隔肿瘤切除术的学习曲线[J].中国胸心血管外科临床杂志,2017,24(2):127-131.
12 刘星池,许世广,徐惟,等.达芬奇机器人非小细胞肺癌手术疗效分析[J/CD].中华腔镜外科杂志(电子版),2017,10(2):97-101.
13 张晋昕,李河.回归分析中定性变量的赋值[J].循证医学,2005,5(3):169-171.
14 肖雪梦,张应应.三种回归方法在消除多重共线性及预测结果的比较[J].统计与决策,2015,24(444):75-78.
15 Meyer M,Gharagozloo F,Tempesta B,et al. The learning curve of robotic lobectomy[J]. International Journal of Medical Robotics&Computer Assisted Surgery,2012,8(4):448-452.
16 Veronesi G,Galetta D,Maisonneuve P,et al. Four-arm robotic lobectomy for the treatment of early-stage lung cancer[J]. Journal of Thoracic&Cardiovascular Surgery,2010,140(1):19-25.
17 MOzyurtkan,Kaba E,Toker A. What happens while learning robotic lobectomy for lung cancer[J]. Vis Surg,2017,3(27):1-4.
18 Velezcubian FO, Ng EP, Fontaine JP, et al. Robotic-assisted videothoracoscopic surgery of the lung[J]. Cancer Control Journal of the Moffitt Cancer Center,2015,22(3):314-325.
2 Veronesi G. Robotic surgery for the treatment of early-stage lung cancer[J]. Current Opinion in Oncology,2013,25(2):107-114.
3 Toker A. Robotic thoracic surgery:from the perspectives of european chest surgeons[J]. Journal of Thoracic Disease,2014,6(2):211-216.
4 Ricciardi S,Cardillo G,Zirafa CC,et al. Robotic lobectomies:when and why[J]. Journal of Visualized Surgery,2017,3(112):1-7.
5 Yamashita S,Yoshida Y,Iwasaki A. Robotic surgery for thoracic disease[J]. Annals of Thoracic&Cardiovascular Surgery Official Journal of the Association of Thoracic&Cardiovascular Surgeons of Asia,2016,22(1):1-5.
6 李欣,侯纯升,白洋,等.单孔腹腔镜胆囊切除术的学习曲线:累积求和分析法[J].中国微创外科杂志,2016,16(3):241-244.
7 Khan N,Abboudi H,Khan MS,et al. Measuring the surgical“learning curve”:methods,variables and competency[J]. Bju International,2014,113(3):504-508.
8 Bokhari MB, Patel CB. Learning curve for robotic-assisted laparoscopic colorectal surgery[J]. Surgical Endoscopy,2011,25(3):855-860.
9 Wang S,Xu S,Liu B. Resection of huge retrosternal goiter through a novel combined cervical and robot-assisted approach[J].Artificial Organs,2014,38(5):431-433.
10 宋德胜,于在诚,胡旭,等.单向式全胸腔镜肺癌根治术探讨[J/CD].中华腔镜外科杂志(电子版),2014,7(6):3-5.
11 刘博,汪明敏,许世广,等.达芬奇机器人纵隔肿瘤切除术的学习曲线[J].中国胸心血管外科临床杂志,2017,24(2):127-131.
12 刘星池,许世广,徐惟,等.达芬奇机器人非小细胞肺癌手术疗效分析[J/CD].中华腔镜外科杂志(电子版),2017,10(2):97-101.
13 张晋昕,李河.回归分析中定性变量的赋值[J].循证医学,2005,5(3):169-171.
14 肖雪梦,张应应.三种回归方法在消除多重共线性及预测结果的比较[J].统计与决策,2015,24(444):75-78.
15 Meyer M,Gharagozloo F,Tempesta B,et al. The learning curve of robotic lobectomy[J]. International Journal of Medical Robotics&Computer Assisted Surgery,2012,8(4):448-452.
16 Veronesi G,Galetta D,Maisonneuve P,et al. Four-arm robotic lobectomy for the treatment of early-stage lung cancer[J]. Journal of Thoracic&Cardiovascular Surgery,2010,140(1):19-25.
17 MOzyurtkan,Kaba E,Toker A. What happens while learning robotic lobectomy for lung cancer[J]. Vis Surg,2017,3(27):1-4.
18 Velezcubian FO, Ng EP, Fontaine JP, et al. Robotic-assisted videothoracoscopic surgery of the lung[J]. Cancer Control Journal of the Moffitt Cancer Center,2015,22(3):314-325.