Background: In the United States, national guidelines suggest that aggressive cancer care should be avoided in the final months of life. However, guideline compliance currently requires clinicians to make judgments based on their experience as to when a patient is nearing the end of their life. Machine learning (ML) algorithms may facilitate improved end-of-life care provision for patients with cancer by identifying patients at risk of short-term mortality. Objective: This study aims to summarize the evidence for applying ML in ≤1-year cancer mortality prediction to assist with the transition to end-of-life care for patients with cancer. Methods: We searched MEDLINE, Embase, Scopus, Web of Science, and IEEE to identify relevant articles. We included studies describing ML algorithms predicting ≤1-year mortality in patients of oncology. We used the prediction model risk of bias assessment tool to assess the quality of the included studies. Results: We included 15 articles involving 110,058 patients in the final synthesis. Of the 15 studies, 12 (80%) had a high or unclear risk of bias. The model performance was good: the area under the receiver operating characteristic curve ranged from 0.72 to 0.92. We identified common issues leading to biased models, including using a single performance metric, incomplete reporting of or inappropriate modeling practice, and small sample size. Conclusions: We found encouraging signs of ML performance in predicting short-term cancer mortality. Nevertheless, no included ML algorithms are suitable for clinical practice at the current stage because of the high risk of bias and uncertainty regarding real-world performance. Further research is needed to develop ML models using the modern standards of algorithm development and reporting.

PURPOSE: Despite their promises, digital innovations have scarcely translated to technologies used in routine clinical practice, making the identification of barriers to successful implementation a research priority. Low levels of transdisciplinary skills represent such a barrier but so far, this has not been evaluated and compared between information technology (IT) and health care specialists. In this study, we evaluated the level of digital health literacy among IT and health care specialists. MATERIALS AND METHODS: An anonymous questionnaire was distributed to staff at a breast cancer unit and an IT department of two German universities in December 2020. The survey questionnaire consisted of the previously validated eHealth Literacy Assessment Toolkit and additional questions with respect to age, profession, and career stage. Mann-Whitney or Wilcoxon rank-sum tests and two-sample chi-square tests were used for the analysis. RESULTS: The survey was completed by 113 individuals: 70 (61.9%) IT specialists and 43 (38.1%) health care specialists. Health care specialists scored significantly higher on the health-related scales and IT specialists scored significantly higher on the digitally related scales. No single participant identified themselves to have the highest level of literacy on all survey questions (n = 0 of 113; 0%). Only one person (n = 1 of 113; 0.9%) consistently reported a high or the highest level of literacy. CONCLUSION: Although IT and health care specialists showed great literacy in their respective disciplines, only few individuals combined both digital and health care literacy. Multidisciplinary teams and transdisciplinary curricula are crucial to bridge skill gaps between disciplines and to drive the implementation of digital health initiatives.

Objectives: The present study aimed at investigating the therapeutic effect of Salidroside on skeletal muscle atrophy in a rat model of cigarette smoking-induced chronic obstructive pulmonary disease (COPD) and its potential mechanisms. Methods: Male Wistar rats were randomized, and treated intraperitoneally (IP) with vehicle (injectable water) or a low, medium or high dose of Salidroside, followed by exposure to cigarette smoking daily for 16 weeks. A healthy control received vehicle injection and air exposure. Their lung function, body weights and gastrocnemius (GN) weights, grip strength and cross-section area (CSA) of individual muscular fibers in the GN were measured. The levels of TNF-α, IL-6, malondialdehyde (MDA), superoxide dismutase (SOD), glutathione (GSH) in serum and GN tissues as well as myostatin and myogenin expression in GN tissues were measured. Results: In comparison with that in the healthy control, long-term cigarette smoking induced emphysema, significantly impaired lung function, reduced body and GN weights and CSA values in rats, accompanied by significantly increased levels of TNF-α, IL-6 and MDA, but decreased levels of SOD and GSH in serum and GN tissues. Furthermore, cigarette smoking significantly up-regulated myostatin expression, but down-regulated myogenin expression in GN tissues. Salidroside treatment decreased emphysema, significantly ameliorated lung function, increased antioxidant, but reduced MDA, IL-6 and TNF-α levels in serum and GN tissues of rats, accompanied by decreased myostain, but increased myogenin expression in GN tissues. Conclusion: Salidroside mitigates the long-term cigarette smoking-induced emphysema and skeletal muscle atrophy in rats by inhibiting oxidative stress and inflammatory responses and regulating muscle-specific transcription factor expression.

Objectives: This study sought to evaluate advanced psychometric properties of the 15-item Economic Strain and Resilience in Cancer (ENRICh) measure of financial toxicity for cancer patients. Methods: We surveyed 515 cancer patients in the greater Houston metropolitan area using ENRICh from March 2019 to March 2020. We conducted a series of factor analyses alongside parametric and non-parametric item response theory (IRT) assessments using Mokken analysis and the graded response model (GRM). We utilized parameters derived from the GRM to run a simulated computerized adaptive test (CAT) assessment. Results: Among participants, mean age was 58.49 years and 278 (54%) were female. The initial round factor analysis results suggested a one-factor scale structure. Negligible levels of differential item functioning (DIF) were evident between eight items. Three items were removed due to local interdependence (Q3>+0.4). The original 11-point numerical rating scale did not function well, and a new 3-point scoring system was implemented. The final 12-item ENRICh had acceptable fit to the GRM (p<0.001; TLI = 0.94; CFI = 0.95; RMSEA = 0.09; RMSR = 0.06) as well as good scalability and dimensionality. We observed high correlation between CAT version scores and the 12-item measure (r = 0.98). During CAT, items 2 (money you owe) and 4 (stress level about finances) were most frequently administered, followed by items 1 (money in savings) and 5 (ability to pay bills). Scores from these four items alone were strongly correlated with that of the 12-item ENRICh (r = 0.96). Conclusion: These CAT and 4-item versions provide options for quick screening in clinical practice and low-burden assessment in research.

Background: We sought to predict clinically meaningful changes in physical, sexual, and psychosocial well-being for women undergoing cancer-related mastectomy and breast reconstruction 2 years after surgery using machine learning (ML) algorithms trained on clinical and patient-reported outcomes data. Patients and Methods: We used data from women undergoing mastectomy and reconstruction at 11 study sites in North America to develop three distinct ML models. We used data of ten sites to predict clinically meaningful improvement or worsening by comparing pre-surgical scores with 2 year follow-up data measured by validated Breast-Q domains. We employed ten-fold cross-validation to train and test the algorithms, and then externally validated them using the 11th site’s data. We considered area-under-the-receiver-operating-characteristics-curve (AUC) as the primary metric to evaluate performance. Results: Overall, between 1454 and 1538 patients completed 2 year follow-up with data for physical, sexual, and psychosocial well-being. In the hold-out validation set, our ML algorithms were able to predict clinically significant changes in physical well-being (chest and upper body) (worsened: AUC range 0.69–0.70; improved: AUC range 0.81–0.82), sexual well-being (worsened: AUC range 0.76–0.77; improved: AUC range 0.74–0.76), and psychosocial well-being (worsened: AUC range 0.64–0.66; improved: AUC range 0.66–0.66). Baseline patient-reported outcome (PRO) variables showed the largest influence on model predictions. Conclusions: Machine learning can predict long-term individual PROs of patients undergoing postmastectomy breast reconstruction with acceptable accuracy. This may better help patients and clinicians make informed decisions regarding expected long-term effect of treatment, facilitate patient-centered care, and ultimately improve postoperative health-related quality of life.