Prediction of Postoperative Length of Hospital Stay Based on Differences in Nursing Narratives in Elderly Patients with Epithelial Ovarian Cancer

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.

Exploring Nursing Care for Patients With COVID-19 Using International Classification for Nursing Practice–Based Nursing Records

“…When comparing the ICNP concept extracted from nursing statements of ovarian cancer patients who underwent curative surgery with those of patients with COVID-19, the most common concepts in ovarian cancer patients included urination, food supply, bowel mobility, and pain. 22 Patients with COVID-19 had significantly more concepts related to respiratory status, such as sputum, dyspnea, oxygen therapy, and blood oxygen saturation, and concepts related to body temperature, including shiver and fever than ovarian cancer patients. Interestingly, it is notable that although loss of smell or taste are important clinical symptoms of COVID-19, [33][34][35] no nursing statements related to loss of smell or taste were found, especially in ward settings where patients can eat.…”

“…As expected, our results differed from those of other studies that analyzed nursing statements of patients with other diseases. When comparing the ICNP concept extracted from nursing statements of ovarian cancer patients who underwent curative surgery with those of patients with COVID-19, the most common concepts in ovarian cancer patients included urination, food supply, bowel mobility, and pain 22 . Patients with COVID-19 had significantly more concepts related to respiratory status, such as sputum, dyspnea, oxygen therapy, and blood oxygen saturation, and concepts related to body temperature, including shiver and fever than ovarian cancer patients.…”

“…In addition, the postoperative hospital LOS of patients with ovarian cancer was predicted using nursing narratives mapped to the International Classification for Nursing Practice (ICNP), which is one of the nursing terminologies. 22 As a concept-oriented terminology, ICNP is flexible and extensible for expressing the full complexity of nursing information. 23,24 The ICNP was designed and evolved to describe in a uniform way different aspects of nursing practices as multiaxial representation, the ICNP 7-Axis Model including focus, judgment, means, action, time, location, and client.…”

“…One study 21 demonstrated that the number of nursing diagnoses based on the NANDA-International can be used as a potent independent predictor of length of stay (LOS). In addition, the postoperative hospital LOS of patients with ovarian cancer was predicted using nursing narratives mapped to the International Classification for Nursing Practice (ICNP), which is one of the nursing terminologies 22 …”

Artificial intelligence and machine learning in pain research: a data scientometric analysis

“…The smallest ML approach where the analyzed data instances corresponded to the reported small sample size was predicting the length of hospital stays from nursing reports of n = 33 patients using a recurrent neural network long short-term memory with 2 hidden layers, which provided about 75% accuracy. 37 The largest study addressed cardiovascular risk and included pain only as a feature contributing to the prediction. 3 The second largest study, however, was closely related to pain research analyzing healthcare data from 392,492 patients with long-term back pain problems.…”