Development and internal validation of a predictive risk model for anxiety after completion of treatment for early stage breast cancer

Informing nursing policy: An exploration of digital health research by nurses in England

AIMS

Digital health technologies are designed, implemented, and evaluated to support clinical practice, enable patients to self-manage illness, and further public and global health. Nursing and health policies often emphasise the importance of evidence-based digital health services to deliver better care. However, the contribution nurses make to digital health research in many countries is unknown. Hence, this study aims to examine digital health research conducted by nurses in England.

DESIGN

A bibliometric analysis.

METHODS

The CINAHL, MEDLINE, and Scopus databases were searched between 2000 and 2022, and supplemented with a hand search of nurses' research profiles. Results were screened by title, abstract, and full text against eligibility criteria. Data were extracted and bibliometric analysis used to summarise the findings.

RESULTS

Mental health nurses produced the most digital health research in England, followed by nurses working in community care, with several disciplines underrepresented or missing. Web/online health services or information was the most researched technology, followed by mobile health and telehealth. Nurses based in the south-east and north-west of England produced the most digital health research, with other regions less well represented.

CONCLUSION

Nurse leaders should support nurses to conduct more digital health research by providing dedicated time, funding, and professional development opportunities, particularly in under researched clinical areas, technologies, and geographic regions to further evidence-based practice and patient care. More digital nursing data is needed to support nurse led research in areas like artificial intelligence and data science. The findings supported the national Philips Ives Review by identifying areas of digital nursing research that need more investment in England.

Machine learning-based algorithms to predict severe psychological distress among cancer patients with spinal metastatic disease

BACKGROUND CONTEXT

Metastatic spinal disease is an advanced stage of cancer patients and often suffer from terrible psychological health status; however, the ability to estimate the risk probability of this adverse outcome using current available data is very limited.

PURPOSE

The goal of this study was to propose a precise model based on machine learning techniques to predict psychological status among cancer patients with spinal metastatic disease.

STUDY DESIGN/SETTING

A prospective cohort study.

PATIENT SAMPLE

A total of 1043 cancer patients with spinal metastatic disease were included.

OUTCOME MEASURES

The main outcome was severe psychological distress.

METHODS

The total of patients was randomly divided into a training dataset and a testing dataset on a ratio of 9:1. Patients' demographics, lifestyle choices, cancer-related features, clinical manifestations, and treatments were collected as potential model predictors in the study. Five machine learning algorithms, including XGBoosting machine, random forest, gradient boosting machine, support vector machine, and ensemble prediction model, as well as a logistic regression model were employed to train and optimize models in the training set, and their predictive performance was assessed in the testing set.

RESULTS

Up to 21.48% of all patients who were recruited had severe psychological distress. Elderly patients (p<0.001), female (p =0.045), current smoking (p=0.002) or drinking (p=0.003), a lower level of education (p<0.001), a stronger spiritual desire (p<0.001), visceral metastasis (p=0.005), and a higher Eastern Cooperative Oncology Group (ECOG) score (p<0.001) were significantly associated with worse psychological health. With an area under the curve (AUC) of 0.865 (95% CI: 0.788-0.941) and an accuracy of up to 0.843, the gradient boosting machine algorithm performed best in the prediction of the outcome, followed by the XGBooting machine algorithm (AUC: 0.851, 95% CI: 0.768-0.934; Accuracy: 0.826) and ensemble prediction (AUC: 0.851, 95% CI: 0.770-0.932; Accuracy: 0.809) in the testing set. In contrast, the AUC of the logistic regression model was only 0.836 (95% CI: 0.756-0.916; Accuracy: 0.783).

CONCLUSIONS

Machine learning models have greater predictive power and can offer useful tools to identify individuals with spinal metastatic disease who are experiencing severe psychological distress.

How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion

Psychological researchers often use standard linear regression to identify relevant predictors of an outcome of interest, but challenges emerge with incomplete data and growing numbers of candidate predictors. Regularization methods like the LASSO can reduce the risk of overfitting, increase model interpretability, and improve prediction in future samples; however, handling missing data when using regularization-based variable selection methods is complicated. Using listwise deletion or an ad hoc imputation strategy to deal with missing data when using regularization methods can lead to loss of precision, substantial bias, and a reduction in predictive ability. In this tutorial, we describe three approaches for fitting a LASSO when using multiple imputation to handle missing data and illustrate how to implement these approaches in practice with an applied example. We discuss implications of each approach and describe additional research that would help solidify recommendations for best practices. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Incidence, risk factors, prevention and treatment of postmastectomy pain syndrome in breast cancer: A multicenter study

BACKGROUND

Postmastectomy pain syndrome (PMPS) is a common postoperative condition after breast cancer surgery.

PURPOSE

The aim of this study was to investigate the incidence rate and risk factors of PMPS, and to propose prevention and treatment methods.

METHODS

The study included 1790 postoperative breast cancer patients from three hospitals from 2017 to 2021, of which 302 (13.0%) patients with PMPS were included in the study.

RESULTS

Age, breast surgery type, axillary surgery type and radiotherapy are the risk factors of PMPS. Age, radiotherapy and chemotherapy affect the pain degree of PMPS during movement.

CONCLUSIONS

For breast cancer patients with high risk factors, pain should be actively prevented during perioperative period. Oral pharmacological agents, multidisciplinary combination therapy, local anesthetics and regional anesthesia are the most common treatment of PMPS.