Abstract

Objectives The current study sought to evaluate whether nursing narratives can be used to predict postoperative length of hospital stay (LOS) following curative surgery for ovarian cancer.

Methods A total of 33 patients, aged over 65 years, underwent curative surgery for newly diagnosed ovarian cancer between 2008 and 2012. Based on the median postoperative LOS, patients were divided into two groups: long-stay (>12 days; n = 13) and short-stay (≤12 days; n = 20). Patterns in nursing narratives were examined and compared through a quantitative analysis. Specifically, the total number (TN) of narratives pertaining to care and the standardized number (SN), which was calculated by dividing the TN by the LOS, were compared. Experts evaluated the relevance of the phrases extracted. LOS was then predicted using machine learning techniques.

Results The median postoperative LOS was 18 days (interquartile range [IQR]: 16–24 days) in the long-stay group and 9.5 days (IQR: 8–11.25 days) in the short-stay group. In the long-stay group, surgery duration was longer. Overall, patients in the long-stay group showed a higher volume of nursing narratives compared with patients in the short-stay group (SN: 68 vs. 46, p = 0.021). Thirty-two of the most frequently used nursing narratives were selected from 998 uniquely defined nursing narratives. Multiple t-tests were used to compare the TN and real standardized number (RSN; minimum p < 0.1). Mean and standard deviation of classification results of long-short term memory recurrent neural networks for long and short stays were 0.7774 (0.105), 0.745 (0.098), 0.739 (0.107), and 0.765 (0.115) for F1-measure, precision, recall, and area under the receiver operating characteristic, respectively. Agreement between the differential narratives as assessed by statistical methods and the expert response was low (52.6% agreement; McNemar's test p = 0.012).

Conclusions Statistical tests showed that nursing narratives that utilized the words “urination,” “food supply,” “bowel mobility,” or “pain” were related to hospital stay in elderly females with ovarian cancer. Additionally, machine learning effectively predicted LOS.

Summary The current study sought to determine whether elements of nursing narratives could be used to predict postoperative LOS among elderly ovarian cancer patients. Results indicated that nursing narratives that used the words “urination,” “food supply,” “bowel mobility,” and “pain” significantly predicted postoperative LOS in the study population. Additionally, it was found that machine learning could effectively predict LOS based on quantitative characteristics of nursing narratives.

Ethical Approval

The study was approved by the institutional review board of Seoul National University Bundang Hospital. Written, informed consents were waived (IRB no. B-1504/294-106 for the case-control study and IRB no. B-1506/302-301 for the survey study).

中文翻译：

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根据老年上皮性卵巢癌患者的护理叙述差异预测术后住院时间。

摘要

目的 本研究旨在评估是否可以采用护理学方法来预测卵巢癌根治性手术后的住院时间（LOS）。

方法 2008年至2012年，共33例65岁以上的初诊卵巢癌患者接受了根治性手术。根据术后LOS的中位数，将患者分为两组：长期（> 12天；n  = 13） ）和短期住宿（≤12天；n  = 20）。通过定量分析检查并比较了护理叙述中的模式。具体而言，比较了与护理相关的叙述总数（TN）和通过将TN除以LOS计算出的标准化数目（SN）。专家评估了提取的短语的相关性。然后使用机器学习技术预测LOS。

结果 长住组术后中位LOS为18天（四分位间距[IQR]：16-24天），短住组为9.5天（IQR：8-11.25天）。在长住组中，手术时间较长。总体而言，与短期住院患者相比，长期住院患者的护理叙述量更高（SN：68 vs. 46，p  = 0.021）。从998个唯一定义的护理叙述中选择了32个最常用的护理叙述。使用多个t检验比较TN和实际标准化数字（RSN；最小p <0.1）。F1量度，精度，召回率的长期和短期停留长期短期记忆循环神经网络分类结果的均值和标准差分别为0.7774（0.105），0.745（0.098），0.739（0.107）和0.765（0.115） ，以及接收器工作特性下的面积。通过统计学方法评估的差异性叙述与专家回复之间的一致性较低（52.6％一致性； McNemar检验p  = 0.012）。

结论 统计测试表明，使用“排尿”，“食物供应”，“大便动向”或“疼痛”等词语的护理叙述与老年女性卵巢癌患者的住院时间有关。此外，机器学习可有效预测LOS。

总结 当前的研究试图确定护理叙述的内容是否可以用来预测老年卵巢癌患者的术后LOS。结果表明，使用“排尿”，“食物供应”，“运动性”和“疼痛”这两个词的护理叙述可以显着预测研究人群的术后LOS。此外，还发现机器学习可以根据护理叙述的定量特征有效地预测LOS。

道德批准

该研究得到首尔国立大学盆唐医院机构审查委员会的批准。放弃书面知情同意书（病例对照研究的IRB编号B-1504 / 294-106，调查研究的IRB编号B-1506 / 302-301）。