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This study aimed to test the psychometric properties of the Self-Care of Chronic Illness Inventory and the Self-Care Self-Efficacy scale in patients with cancer.
A multisite cross-sectional validation study was conducted.
Between November 2022 and July 2023, a convenience sample of 318 patients with cancer were enrolled in five Italian inpatient and outpatient facilities. Confirmatory factor analysis was performed on the three scales of the Self-Care of Chronic Illness Inventory and the Self-Care Self-Efficacy scale. Internal consistency was tested using Cronbach's alpha for unidimensional scales and McDonald's Omega for multidimensional scales. Construct validity was assessed with the global health status by Pearson's correlation. The COnsensus-based Standards for the selection of health Measurement INstruments reporting guidelines were followed for the reporting process.
Three hundred fourteen patients were included (median age: 55.5 years; male: 53.82%). Confirmatory factor analysis showed supportive fit indices for the three Self-Care of Chronic Illness Inventory scales (CFI: 0.977–1.000; SRMR: 0.004–0.78) and the Self-Care Self-Efficacy scale (CFI: 1.000; SRMR: 0.014). All scales demonstrated adequate internal consistency (0.89–0.99) and test–retest reliability (0.85–0.95). Construct validity was confirmed through correlations between Self-Care Self-Efficacy, each Self-Care of Chronic Illness Inventory scale, and global health status.
The Self-Care of Chronic Illness Inventory and Self-Care Self-Efficacy scales demonstrated excellent psychometric qualities and construct validity when administered to patients with cancer. Future research should explore self-care behaviours across different diseases and cultural contexts.
These tools can help develop targeted educational programs, improving patient outcomes.
Currently, there is a lack of knowledge regarding self-care behaviours in patients with cancer. These tools enable healthcare professionals to identify patient needs, design personalised interventions, and monitor their effectiveness over time.
No patient or public contribution.
To assess the capability of a convolutional neural network trained by transfer learning on anterior segment optical coherence tomography (AS-OCT) images, Placido-disk corneal topography images and external photographs to predict age and biological sex.
Development of a deep learning model trained on retrospectively collected data using transfer learning.
A multicentre secondary care public health trust based in London.
We included 557,468 scans from 40,592 eyes of 20,542 patients. Data were extracted from all patients who underwent MS-39 imaging within our trust from October 2020 to March 2023.
Primary outcome measures for biological sex classification included accuracy, precision, recall, F1-score and area under the receiver operating curve (ROC-AUC). Primary outcome measures for age prediction were Pearson correlation coefficients (r), coefficients of determination (R²) and the mean absolute error (MAE) to evaluate the predictive performance. The secondary outcome was to visualise and interpret the model’s decision-making process through the construction of saliency maps.
For age prediction, the MAEs for the Placido, AS-OCT and external photograph models were 5.2, 5.1 and 6.2 years, respectively. For gender classification, the same models achieved ROC-AUCs of 0.88, 0.73 and 0.81, respectively. No difference in performance was found in the analysis of corneas with pathological topography. The saliency maps highlighted the peri-limbal cornea for age prediction and the central cornea for gender discrimination.
Our study demonstrates that deep learning models can extract age and gender information from anterior segment images. These findings support the concept that the anterior segment, like the retina, encodes important biological information. Future research should explore whether these models can predict specific systemic conditions.
Cardiac amyloidosis (CA) is a rare and underdiagnosed disease associated with a high mortality rate. Although, in the last decade, there has been increasing attention in the literature to the relationship between CA and psychological distress in patients, the evidence on this association has not yet been systematised. Therefore, this study aims to fill this gap.
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic review was conducted.
PubMed, ScienceDirect, Scopus and Web of Science were searched, with the last update conducted on 23 September 2024, and no time restrictions were applied.
Studies had to meet the following inclusion criteria to be included: (1) original quantitative research; (2) published in peer-reviewed journals written in English; (3) explore and report the relationship between CA and psychological distress or compare a clinical group with a control group and (4) investigate psychological distress through reliable and validated measures.
One author extracted the data, which was then double-checked by another, and data were reported both in tabular and textual form. The included studies were critically evaluated using the Appraisal Tool for Cross-Sectional Studies.
Through the research process, a total of 14 articles were selected. The quality assessment scores ranged from 12 to 18 (M=16.21±1.42). Overall, the results underline a significant presence of psychological distress in patients with CA. Moreover, while disease severity was not found to be associated with psychological distress in CA patients in all studies considered, more heterogeneous results emerged regarding the association between the severity of cardiac symptoms and psychological distress.
Results suggest that psychological distress is an important aspect to be considered when dealing with CA patients. Integrating psychological assessment and support may improve patient outcomes by reducing disease burden and enhancing treatment adherence.
CRD42023446913.
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