Development of a Patient-Based Model for Estimating Operative Times for Robot-Assisted Radical Prostatectomy.
Development of a Patient-Based Model for Estimating Operative Times for Robot-Assisted Radical Prostatectomy.
复制标题
开发基于患者的模型来估计机器人辅助根治性前列腺切除术的手术时间。
DOI：
10.1089/end.2018.0249
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发表时间：
2018
影响因子：
2.7
通讯作者：
Guru,KhurshidA
中科院分区：
文献类型：
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作者：
Huben,NeilB;Hussein,AhmedA;May,PaulR;Whittum,Michelle;Krasowski,Collin;Ahmed,YoussefE;Jing,Zhe;Khan,Hijab;Kim,HyungL;Schwaab,Thomas;Underwood,Willie;Kauffman,EricC;Mohler,JamesL;Guru,KhurshidA
目的：开发一种预测机器人辅助根治性前列腺切除术（RARP）手术时间的方法，利用术前患者、疾病、手术过程和外科医生的变量来促进手术室（OR）的安排。方法：模型包括术前指标：体重指数（BMI）、美国麻醉医师学会评分、临床分期、国家综合癌症网络风险、前列腺重量、神经保留情况、淋巴结清扫程度和侧边、手术医师（共6名外科医师）。采用条件推理树方法拟合二叉决策树，预测操作时间。与手术时间最相关的变量采用排列试验确定。数据按导致手术时间平均差异最大的变量值进行分割。这个过程在结果数据上递归地重复。结果：共纳入rarp 1709条。与手术时间最密切相关的变量是外科医生（外科医生2和4-102分钟比外科医生1、3、5和6短，p< 0.001）。在外科医生2和4中，BMI与手术时间的相关性最强（p< 0.001）。在由外科医生1、3、5和6进行手术的患者中，RARP时间再次与外科医生进行RARP最密切相关。外科医生1、3、6比外科医生5平均快76分钟（p< 0.001）。以箱形图形式的回归树输出显示了根据患者、疾病、程序和外科医生指标的手术时间中位数和范围。结论：我们开发了一种基于患者、疾病和外科医生变量预测RARP手术时间的方法。该方法可用于质量控制、手术室调度和手术室效率最大化。
Objectives:To develop a methodology for predicting operative times for robot-assisted radical prostatectomy (RARP) using preoperative patient, disease, procedural, and surgeon variables to facilitate operating room (OR) scheduling.Methods:The model included preoperative metrics: body mass index (BMI), American Society of Anesthesiologists score, clinical stage, National Comprehensive Cancer Network risk, prostate weight, nerve-sparing status, extent and laterality of lymph node dissection, and operating surgeon (six surgeons were included in the study). A binary decision tree was fit using a conditional inference tree method to predict operative times. The variables most associated with operative time were determined using permutation tests. Data were split at the value of the variable that results in the largest difference in mean for surgical time across the split. This process was repeated recursively on the resultant data.Results:A total of 1709 RARPs were included. The variable most strongly associated with operative time was the surgeon (surgeons 2 and 4—102 minutes shorter than surgeons 1, 3, 5, and 6,p< 0.001). Among surgeons 2 and 4, BMI had the strongest association with surgical time (p< 0.001). Among patients operated by surgeons 1, 3, 5, and 6, RARP time was again most strongly associated with the surgeon performing RARP. Surgeons 1, 3, and 6 were on average 76 minutes faster than surgeon 5 (p< 0.001). The regression tree output in the form of box plots showed operative time median and ranges according to patient, disease, procedural, and surgeon metrics.Conclusion:We developed a methodology that can predict operative times for RARP based on patient, disease and surgeon variables. This methodology can be utilized for quality control, facilitate OR scheduling, and maximize OR efficiency.