Surgical gestures as a method to quantify surgical performance and predict patient outcomes.
Surgical gestures as a method to quantify surgical performance and predict patient outcomes.
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DOI：
10.1038/s41746-022-00738-y
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发表时间：
2022-12-22
影响因子：
15.2
通讯作者：
中科院分区：
文献类型：
--
作者：
手术进行得如何影响患者的结果;然而，客观量化性能仍然是一个未解决的挑战。将手术过程分解为离散的器械-组织“手势”是理解手术的一种新兴方式。为了在性能是患者结局最重要因素的手术中建立这种范式，我们识别了来自两个国际医疗中心的80例保留神经的机器人辅助根治性前列腺切除术中执行的34，323个个人手势。手势被分类为九种不同的解剖手势（例如，热切）和四个支持手势（例如，收回）。我们的主要结果是确定影响患者根治性直肠癌切除术后1年勃起功能（EF）恢复的因素。我们发现，较少使用热切割和较多使用剥离/推送在统计学上与1年EF恢复的机会更高相关。我们的研究结果还显示了外科医生的经验和手势类型之间的相互作用，类似的手势选择导致不同的EF恢复率取决于外科医生的经验。为了进一步验证这一框架，两个团队使用手势序列与传统临床特征独立构建了不同的机器学习模型来预测1年EF。在这两个模型中，手势序列能够更好地预测1年EF（组1：AUC 0.77，95% CI 0.73-0.81;组2：AUC 0.68，95% CI 0.66-0.70）（组1：AUC 0.69，95% CI 0.65-0.73;组2：AUC 0.65，95% CI 0.62-0.68）。我们的研究结果表明，手势提供了一种客观地指示手术性能和结果的粒度方法。将这种方法应用于其他手术可能会发现改进手术的方法。
How well a surgery is performed impacts a patient’s outcomes; however, objective quantification of performance remains an unsolved challenge. Deconstructing a procedure into discrete instrument-tissue “gestures” is a emerging way to understand surgery. To establish this paradigm in a procedure where performance is the most important factor for patient outcomes, we identify 34,323 individual gestures performed in 80 nerve-sparing robot-assisted radical prostatectomies from two international medical centers. Gestures are classified into nine distinct dissection gestures (e.g., hot cut) and four supporting gestures (e.g., retraction). Our primary outcome is to identify factors impacting a patient’s 1-year erectile function (EF) recovery after radical prostatectomy. We find that less use of hot cut and more use of peel/push are statistically associated with better chance of 1-year EF recovery. Our results also show interactions between surgeon experience and gesture types—similar gesture selection resulted in different EF recovery rates dependent on surgeon experience. To further validate this framework, two teams independently constructe distinct machine learning models using gesture sequences vs. traditional clinical features to predict 1-year EF. In both models, gesture sequences are able to better predict 1-year EF (Team 1: AUC 0.77, 95% CI 0.73–0.81; Team 2: AUC 0.68, 95% CI 0.66–0.70) than traditional clinical features (Team 1: AUC 0.69, 95% CI 0.65–0.73; Team 2: AUC 0.65, 95% CI 0.62–0.68). Our results suggest that gestures provide a granular method to objectively indicate surgical performance and outcomes. Application of this methodology to other surgeries may lead to discoveries on methods to improve surgery.
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影响因子：
2.7
作者：
Hung, Andrew J.;Chen, Jian;Liu, Yan
通讯作者：
Liu, Yan
DOI：
10.1007/s00464-015-4070-8
发表时间：
2015-11-01
影响因子：
3.1
作者：
Aghazadeh, Monty A.;Jayaratna, Isuru S.;Goh, Alvin C.
通讯作者：
Goh, Alvin C.
DOI：
10.1016/j.juro.2017.07.081
发表时间：
2018-01
期刊：
The Journal of urology
影响因子：
--
作者：
Hung AJ;Chen J;Jarc A;Hatcher D;Djaladat H;Gill IS
通讯作者：
Gill IS
影响因子：
6.6
作者：
Ma, Runzhuo;Lee, Ryan S.;Hung, Andrew J.
通讯作者：
Hung, Andrew J.
影响因子：
6.6
作者：
Bhat, K. R. Seetharam;Moschovas, Marcio Covas;Patel, Vipul R.
通讯作者：
Patel, Vipul R.