Vollmer Andreas, Nagler Simon, Hörner Marius, Hartmann Stefan, Brands Roman C, Breitenbücher Niko, Straub Anton, Kübler Alexander, Vollmer Michael, Gubik Sebastian, Lang Gernot, Wollborn Jakob, Saravi Babak
Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, 97070, Würzburg, Germany.
Department of Oral and Maxillofacial Surgery, University Hospital of Tübingen, 72076, Tübingen, Germany.
Heliyon. 2023 Oct 18;9(11):e20752. doi: 10.1016/j.heliyon.2023.e20752. eCollection 2023 Nov.
Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort.
We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning algorithms for the prediction modeling of prolonged LOS.
The algorithms reached accuracy values of 75.40 (radial basis function neural network) to 97.92 (Random Trees) for the training set and 64.90 (multilayer perceptron neural network) to 84.14 (Random Trees) for the testing set. The leading parameters predicting prolonged LOS were operation time, ischemia time, the graft used, the ASA score, the intensive care stay, and the pathological stages. The results revealed that patients who had a higher number of harvested lymph nodes (LN) had a lower probability of recurrence but also a greater LOS. However, patients with prolonged LOS were also at greater risk of recurrence, particularly when fewer (LN) were extracted. Further, LOS was more strongly correlated with the overall number of extracted lymph nodes than with the number of positive lymph nodes or the ratio of positive to overall extracted lymph nodes, indicating that particularly unnecessary lymph node extraction might be associated with prolonged LOS.
The results emphasize the need for a closer follow-up of patients who experience prolonged LOS. Prospective trials are warranted to validate the present results.
通过评估头颈癌手术患者住院时间延长（LOS）的可能性，可以改善医疗资源管理。本研究的目的是开发预测模型，用于确定癌症手术后患者的住院时间是否在队列的正常范围内。
我们对一个数据集进行了回顾性分析，该数据集由2017年至2022年期间在一所大学医学中心连续接受头颈癌手术的300例患者组成。住院时间延长被定义为超过队列第75百分位数的住院时间。进行特征重要性分析以评估住院时间延长的最重要预测因素。然后，我们构建了7种机器学习和深度学习算法用于住院时间延长的预测建模。
对于训练集，算法的准确率值在75.40（径向基函数神经网络）至97.92（随机树）之间，对于测试集，准确率值在64.90（多层感知器神经网络）至84.14（随机树）之间。预测住院时间延长的主要参数是手术时间、缺血时间、使用的移植物、美国麻醉医师协会（ASA）评分、重症监护停留时间和病理分期。结果显示，收获淋巴结（LN）数量较多的患者复发概率较低，但住院时间也较长。然而，住院时间延长的患者复发风险也更高，尤其是在提取的LN较少时。此外，住院时间与提取的淋巴结总数的相关性比与阳性淋巴结数量或阳性淋巴结与提取的总淋巴结的比例更强，这表明特别不必要的淋巴结提取可能与住院时间延长有关。
结果强调了对住院时间延长的患者进行更密切随访的必要性。有必要进行前瞻性试验以验证目前的结果。
