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To systematically identify and appraise existing risk prediction models for EN aspiration in adult inpatients.
A systematic search was conducted across PubMed, Web of Science Core Collection, Embase, Cochrane Library, CINAHL, China National Knowledge Infrastructure (CNKI), Wanfang Database, China Biomedical Literature Database (CBM) and VIP Database from inception to 1 March 2025.
Systematic review of observational studies.
Two researchers independently performed literature screening and data extraction using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). The Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed to evaluate both the risk of bias and the clinical applicability of the included models.
A total of 17 articles, encompassing 29 prediction models, were included. The incidence of aspiration was 9.45%–57.00%. Meta-analysis of high-frequency predictors identified the following significant predictors of aspiration: history of aspiration, depth of endotracheal intubation, impaired consciousness, sedation use, nutritional risk, mechanical ventilation and gastric residual volume (GRV). The area under the curve (AUC) was 0.771–0.992. Internal validation was performed in 12 studies, while both internal and external validation were conducted in 5 studies. All studies demonstrated a high risk of bias, primarily attributed to retrospective design, geographic bias (all from different parts of China), inadequate data analysis, insufficient validation strategies and lack of transparency in the research process.
Current risk prediction models for enteral nutrition-associated aspiration show moderate to high discriminative accuracy but suffer from critical methodological limitations, including retrospective design, geographic bias (all models derived from Chinese cohorts, limiting global generalisability) and inconsistent outcome definitions.
Recognising the high bias of existing models, prospective multicentre data and standardised diagnostics are needed to develop more accurate and clinically applicable predictive models for enteral nutrition malabsorption.
Not applicable.
PROSPERO: CRD420251016435
To explore mothers' specific discharge preparation needs for preterm infants born before 32 gestational weeks, providing a foundation for developing effective discharge education programmes.
A qualitative descriptive design.
A semi-structured interview was conducted of 16 mothers of preterm infants less than 32 weeks gestation within 1 week post-discharge in March–June 2024. Directed content analysis was conducted using the Integrated Theory of Health Behaviour Change framework to code, categorise, and identify themes within the interview data.
Mothers provided rich, practical, experience-driven feedback regarding discharge preparedness needs. The interview resulted in three emergent themes related to the theory's constructs: maternal needs for knowledge acquisition, multifaceted social support, and adjusting learning strategies. These encompass sub-themes such as observing infant behaviour and health status, basic care knowledge, complex medical care guidance; support from medical staff, family members, fellow parents, community healthcare providers, and Wechat platform tools; learning time arrangement, and preferred learning approaches.
This study explored the discharge preparation needs of mothers with premature infants less than 32 weeks gestation. A nurse-led multidisciplinary team should tailor education programmes, emphasising care knowledge, multifaceted social support, and flexible learning. Future research should assess programme effectiveness on maternal and infant outcomes.
The study's results provided targeted guidance for clinical nursing education, enhancing mothers' readiness for preterm infant discharge and facilitating a smoother NICU-to-home transition.
These findings provide important guidance for nurse-led tailored discharge education and preparation services, thereby promoting improvements in clinical nursing practice and the development of nursing education.
The COREQ checklist was used for reporting.
Four mothers of premature infants (< 32 weeks gestation) provided feedback on the interview guide in the design phase, refining it for the target population, without joining the main study.
To describe the lived decision-making experiences of parents during the first 6 months after their children's new cancer diagnoses.
Descriptive phenomenological study.
This study was conducted from 2022 to 2023 at an academic teaching hospital in Taiwan. Parents of children newly diagnosed with cancer within the previous 6 months were recruited using purposive sampling. Data were collected via in-depth interviews with 18 participants and subsequently analysed using the Giorgi method.
Three major themes emerged: (1) making decisions without choices, with the subthemes of parallel universes and realities and overwhelming information and unanswered questions; (2) deferring decisions to expert judgements, with the subthemes of trust in professionals, working together, and seeing the future; and (c) balancing quality of life and survival, with the subthemes of confronting and suffering, mental preparation and worry, and being a strong supporter and carrying burdens. Hope for their children's survival sustained parents, empowering them to become steadfast sources of strength and support. Through hope, they transitioned from feeling helpless to actively advocating and assuming the primary caregiver role.
After deciding to proceed with their children's cancer treatment, hope for survival becomes the strongest factor motivating parents to navigate, explore, and move forward in an environment filled with uncertainty. Psychological preparation and understanding ease parental anxiety.
The development of clear, structured care plans is recommended to help parents feel supported and transition early from novices to confident guides.
The findings of this study highlight the shift in parents' roles following children's cancer diagnoses from facing unavoidable decision-making to actively striving to balance children's quality of life with treatment outcomes. They provide guidance for the support of parents' engagement with decision-making plans in clinical practice.
Standards for Reporting Qualitative Research.
None.
Smart home technology, as an emerging innovation, holds significant potential to support proactive health by enabling accurate prediction and intelligent warning of health issues. This study aims to explore older adults' perceptions of adopting smart home technology to promote proactive health.
An exploratory qualitative study.
Focus groups and one-on-one interviews were held with 20 older adults recruited from a retirement activity center, a nursing home, and the geriatrics department of a tertiary hospital in China between June and October 2024. The interview transcripts were analysed using thematic analysis and further examined through the framework of the Technology Acceptance Model.
The analysis identified four themes: (1) The need for care is the primary determinant for older adults' consideration of adopting smart home technology. When care is needed, factors such as self-care ability, care from children and the caregiving capabilities of smart home technology play a crucial role in their decision-making process. (2) Older adults expect smart home technology to deliver essential healthcare services, including health monitoring and counselling, emergency assistance and emotional support. (3) Individual differences, interplay with life experiences, significantly influence older adults' willingness to adopt smart home technology. (4) The perceived effectiveness of technology, age-friendly design, potential technical malfunctions and privacy concerns are also critical factors affecting adoption decisions. All themes were also matched to perceived usefulness, perceived ease of use and attitude in the Technology Acceptance Model.
This study provides valuable insights into older adults' perspectives on adopting smart home technology and serves as a reference for its development in geriatric health management. To enhance the applicability of these technologies, nurses should collaborate with developers, integrating their expertise in elderly care and daily living needs.
The findings offer guidance for advancing smart home technology to better address the health needs of older adults. By integrating these technologies into practice, nurses can more effectively respond to the unique health conditions of older adults, optimise nursing workflows and enhance the overall quality of care. Ultimately, this ensures that older adults remain the primary beneficiaries of technological advancements in healthcare.
The study adhered to the Consolidated Criteria for Reporting Qualitative Research guidelines.
Limited patient and public involvement was incorporated, focusing on feedback on data analysis.
Frailty affects over 35% of maintenance haemodialysis (MHD) patients globally—2–3 times higher than the general elderly—and is strongly linked to higher mortality, hospitalisation, and functional decline. Despite its clinical impact, frailty is often underdiagnosed in dialysis settings due to inconsistent assessments and limited resources. Existing prediction models vary widely in predictors and methods, requiring systematic review to guide clinical use and improve risk-stratified care.
To systematically identify, describe, and evaluate the existing risk prediction models for frailty in patients undergoing MHD.
Systematic review and Methodological appraisal.
A comprehensive search was conducted across multiple databases—PubMed, Web of Science Core Collection, Embase, Cochrane Library, CINAHL, China Biomedical Literature Database (CBM), Wanfang Database, VIP Database—covering studies up to November 1, 2024.
Two researchers independently conducted literature searches, screening, and data extraction. They used the Prediction Model Risk of Bias Assessment Tool (PROBAST) to evaluate the risk of bias and the applicability of the included models.
Fifteen studies (21 models) were analysed, with sample sizes 141–786 and frailty incidence 11.00%–59.57%. Model AUCs ranged 0.720–0.998 (potential overfitting at extreme values). Key predictors included age, serum albumin, gender, Charlson comorbidity index, and activities of daily living scores. Methodological appraisal using PROBAST revealed moderate applicability but high bias risks: 53% of studies used retrospective designs, 95% lacked external validation, and limitations included small samples, non-standard variable selection, and inadequate handling of missing data.
While models demonstrate initial predictive utility, widespread bias and developmental-stage limitations hinder clinical application. Future research must prioritise TRIPOD-guided model development, emphasising large prospective cohorts, rigorous validation, and transparent reporting to enhance reliability and clinical utility in frailty risk stratification for MHD patients.
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