Huettenbrink Clemens, Hitzl Wolfgang, Pahernik Sascha, Kubitz Jens, Popeneciu Valentin, Ell Jascha
Department of Urology, Nuremberg General Hospital, Paracelsus Medical University, 90419 Nuremberg, Germany.
Research and Innovation Management (RIM), Team Biostatistics and Publication of Clinical Trial Studies, Paracelsus Medical University, 5020 Salzburg, Austria.
J Pers Med. 2022 May 12;12(5):784. doi: 10.3390/jpm12050784.
When scheduling surgeries for urolithiasis, the lack of information about the complexity of procedures and required instruments can lead to mismanagement, cancellations of elective surgeries and financial risk for the hospital. The aim of this study was to develop, train, and test prediction models for ureterorenoscopy. Routinely acquired Computer Tomography (CT) imaging data and patient data were used as data sources. Machine learning models were trained and tested to predict the need for laser lithotripsy and to forecast the expected duration of ureterorenoscopy on the bases of 474 patients over a period from May 2016 to December 2019. Negative predictive value for use of laser lithotripsy was 92%, and positive predictive value 91% before application of the reject option, increasing to 97% and 94% after application of the reject option. Similar results were found for duration of surgery at ≤30 min. This combined prediction is possible for 54% of patients. Factors influencing prediction of laser application and duration ≤30 min are age, sex, height, weight, Body Mass Index (BMI), stone size, stone volume, stone density, and presence of a ureteral stent. Neuronal networks for prediction help to identify patients with an operative time ≤30 min who did not require laser lithotripsy. Thus, surgical planning and resource allocation can be optimised to increase efficiency in the Operating Room (OR).
在安排尿石症手术时，缺乏有关手术复杂性和所需器械的信息可能会导致管理不善、择期手术取消以及医院面临财务风险。本研究的目的是开发、训练和测试输尿管镜检查的预测模型。常规获取的计算机断层扫描（CT）成像数据和患者数据被用作数据源。基于2016年5月至2019年12月期间的474例患者，训练并测试了机器学习模型，以预测激光碎石术的需求并预测输尿管镜检查的预期持续时间。在应用拒绝选项之前，激光碎石术使用的阴性预测值为92%，阳性预测值为91%，应用拒绝选项后分别增至97%和94%。手术持续时间≤30分钟时也发现了类似结果。54%的患者可以进行这种联合预测。影响激光应用预测和持续时间≤30分钟的因素包括年龄、性别、身高、体重、体重指数（BMI）、结石大小、结石体积、结石密度以及输尿管支架的存在。用于预测的神经网络有助于识别手术时间≤30分钟且不需要激光碎石术的患者。因此，可以优化手术规划和资源分配，以提高手术室（OR）的效率。
