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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
Nurses commonly experience negative experiences after experiencing a patient safety event, triggering a domino effect on the nurses themselves, subsequent patients, and healthcare organisations, thus requires urgent attention.
To explore the mediating role of psychological capital and coping styles between neurotic personality and negative experiences of nurses' second victims, and to provide theoretical guidance for nursing administrators to develop targeted strategies to mitigate negative experiences of nurses' second victims.
In June–July 2023, a general information questionnaire, a neurotic personality subscale, the Chinese version of the Second Victim Experience and Support Scale, the Nurses' Psychological Capital Questionnaire, and the Coping Styles Questionnaire were used to conduct an online survey of 213 nurses' second victims and structural equation modelling was constructed to clarify the relationship between these elements.
Psychological capital and coping styles partially mediated the relationship between neurotic personality and negative experiences in the nurses' second victims, with a total indirect effect value of 0.203 and a total effect value of 0.303, for a mediating effect of 33.00%.
Neurotic personality and immature coping styles negatively predict the degree of negative experience, while psychological capital and mature coping styles positively predict the degree of negative experience. Psychological capital and coping styles play a partial mediating role between neurotic personality and negative experience.
After a patient safety incident, nursing managers can mitigate the negative experiences of nurses' second victims in patient safety incidents by reducing their neurotic personality tendencies, enhancing their level of psychological capital, and guiding them to adopt mature coping styles.
No patient or public contribution.
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.
Nurse-led telephone-based follow-up interventions play a role in patient follow-up, but at present, no meta-analysis has been found to assess the effectiveness of nurse-led, telephone follow-up interventions for patients with acute coronary syndrome.
This systematic review and meta-analysis aimed to evaluate the effectiveness of nurse-led telephone-based follow-up interventions on health outcomes in people with acute coronary syndromes.
Systematic review and meta-analysis of randomized controlled trials.
A comprehensive search of six databases: PubMed, Web of Science, Embase, Cochrane Library, CINAHL and Scopus was conducted from the inception of the databases to 30 September 2023. The Cochrane Risk of Bias Tool was used to assess the methodological quality of the included randomized controlled studies. Review Manager 5.4 and Stata 16.0 were used to conduct statistical analysis.
A total of 12 studies were included. Nurse-led telephone-based follow-up interventions may reduce systolic and diastolic blood pressure (MD = −2.55, 95% CI [−4.16, −0.94]) (MD = −2.15, 95% CI [−3.18, −1.12]) and low-density lipoprotein (MD = −9.06, 95% CI [−14.33, −3.79]) in patients with acute coronary syndrome. However, its effectiveness in controlling high-density lipoprotein (MD = 1.65, 95% CI [−4.30, 7.61]) and reducing total cholesterol (MD = −2.72, 95% CI [−7.57, 2.13]) was uncertain. In addition, the results showed that the nurse-led follow-up intervention did not play a role in improving anxiety (SMD = −0.20, 95% CI [−0.44, 0.04]) and depression (SMD = −0.07, 95% CI [−0.21, 0.06]) in patients with acute coronary syndrome, but it probably improved drug adherence (RR = 1.30, 95% CI [1.05, 1.60]) and smoking cessation (RR = 1.31, 95% CI [1.08, 1.60]).
The findings of this review suggest that nurse-led telephone-based follow-up interventions had a potentially positive effect on controlling blood pressure and low-density lipoprotein levels, as well as improving medication adherence and smoking cessation among patients with acute coronary syndrome, compared to usual care. However, the intervention did not appear to significantly impact high-density lipoprotein, total cholesterol, anxiety, and depression, indicating that further research in these areas will be necessary in the future.
PROSPERO (International Prospective Register of Systematic Reviews): CRD42023465894
With ambient listening systems increasingly adopted in healthcare, analyzing clinician-patient conversations has become essential. The Omaha System is a standardized terminology for documenting patient care, classifying health problems into four domains across 42 problems and 377 signs/symptoms. Manually identifying and mapping these problems is time-consuming and labor-intensive. This study aims to automate health problem identification from clinician-patient conversations using large language models (LLMs) with retrieval-augmented generation (RAG).
Using the Omaha System framework, we analyzed 5118 utterances from 22 clinician-patient encounters in home healthcare. RAG-enhanced LLMs detected health problems and mapped them to Omaha System terminology. We evaluated different model configurations, including embedding models, context window sizes, parameter settings (top k, top p), and prompting strategies (zero-shot, few-shot, and chain-of-thought). Three LLMs—Llama 3.1-8B-Instruct, GPT-4o-mini, and GPT-o3-mini—were compared using precision, recall, and F1-score against expert annotations.
The optimal configuration used a 1-utterance context window, top k = 15, top p = 0.6, and few-shot learning with chain-of-thought prompting. GPT-4o-mini achieved the highest F1-score (0.90) for both problem and sign/symptom identification, followed by GPT-o3-mini (0.83/0.82), while Llama 3.1-8B-Instruct performed worst (0.73/0.72).
Using the Omaha System, LLMs with RAG effectively automate health problem identification in clinical conversations. This approach can enhance documentation completeness, reduce documentation burden, and potentially improve patient outcomes through more comprehensive problem identification, translating into tangible improvements in clinical efficiency and care delivery.
Automating health problem identification from clinical conversations can improve documentation accuracy, reduce burden, and ensure alignment with standardized frameworks like the Omaha System, enhancing care quality and continuity in home healthcare.
FreshRSS 