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DOI: 10.1055/s-0040-1705122
Prediction of Postoperative Length of Hospital Stay Based on Differences in Nursing Narratives in Elderly Patients with Epithelial Ovarian Cancer
Funding S.A. received a grant from the National Research Foundation of Korea (NRF) by the Ministry of Science, ICT, and Future Planning (NRF-2013R1A1A3012306). S.J., K.D., and Y.H. received salaries from the grant. K.L. and H.-A.P. received consultation fees from the grant.Publication History
23 July 2019
16 January 2020
Publication Date:
29 April 2020 (online)
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).
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References
- 1 Collins SA, Cato K, Albers D. , et al. Relationship between nursing documentation and patients' mortality. Am J Crit Care 2013; 22 (04) 306-313
- 2 Park HA, Cho I, Ahn HJ. Use of narrative nursing records for nursing research. NI 2012 (2012) 2012; 2012: 316
- 3 Bjarnadottir RI, Lucero RJ. What can we learn about fall risk factors from EHR nursing notes? A text mining study. EGEMS (Wash DC) 2018; 6 (01) 21
- 4 Moss J, Andison M, Sobko H. An analysis of narrative nursing documentation in an otherwise structured intensive care clinical information system. AMIA Annu Symp Proc 2007; 2007: 543-547
- 5 Kim K, Jeong S, Lee K. , et al. Metrics for Electronic-Nursing-Record-Based Narratives: cross-sectional analysis. Appl Clin Inform 2016; 7 (04) 1107-1119
- 6 Lin JJ, Egorova N, Franco R, Prasad-Hayes M, Bickell NA. Ovarian cancer treatment and survival trends among women older than 65 years of age in the United States, 1995-2008. Obstet Gynecol 2016; 127 (01) 81-89
- 7 Fanfani F, Fagotti A, Salerno MG. , et al. Elderly and very elderly advanced ovarian cancer patients: does the age influence the surgical management?. Eur J Surg Oncol 2012; 38 (12) 1204-1210
- 8 Díaz-Montes TP, Zahurak ML, Giuntoli II RL. , et al. Surgical care of elderly women with ovarian cancer: a population-based perspective. Gynecol Oncol 2005; 99 (02) 352-357
- 9 Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997; 9 (08) 1735-1780
- 10 Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014; 15 (01) 1929-1958
- 11 Pedregosa F, Varoquaux G, Gramfort A. , et al. Scikit-learn: machine learning in Python. J Mach Learn Res 2011; 12: 2825-2830
- 12 Van Der Walt S, Colbert SC, Varoquaux G. The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 2011; 13 (02) 22-30