Background: With the rapid progression of population aging, the number of frail individuals is steadily rising, making frailty a pressing public health issue that demands urgent attention. Compared to individuals with a single cardiovascular-metabolic disease, patients with cardiovascular-metabolic multimorbidity (CMM) are more prone to developing frailty. This study aimed to develop and validate a predictive model for assessing frailty risk in older adult patients with CMM.Methods: The data came from participants in the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS). The study population comprised individuals aged 60 years and older with CMM and complete frailty scale measurements. Frailty status was evaluated using the Fried Frailty Scale. 26 indicators, including socio-demographic characteristics, lifestyle factors, overall health condition, and psychological well-being. The entire sample was randomly allocated into training and validation sets at a 7:3 ratio. LASSO regression and logistic regression was conducted to evaluate factors associated with frailty. A nomogram was constructed using the identified predictors to predict outcomes. The discrimination, accuracy, and clinical effectiveness of the model were evaluated by the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis (DCA).Results: The study included 2,164 older adult CMM participants, with 387 (17.88%) displaying frailty symptoms. Binary logistic regression analyses revealed that depression, social activity, history of falls, life satisfaction, ADL scores, cognitive function, age and the number of CMDs were significantly associated with frailty. These eight factors were incorporated into the nomogram model, and the AUC values for the predictive model were 0.816 (95% CI = 0.787–0.848) and 0.816 (95% CI = 0.786–0.846) for the training and validation sets, respectively, indicating effective discrimination. Hosmer-Lemeshow test results showed p = 0.073 and p = 0.245 (both > 0.05), with calibration curves indicating strong alignment between the model’s predictions and actual outcomes. The DCA demonstrated the model’s substantial clinical utility.Conclusion: The nomogram prediction model developed in this research is a reliable and effective tool for assisting clinicians in identifying frailty in older adult CMM patients at an early stage, providing a scientific foundation for individualized health management and intervention.

Introduction: Early prediction of multiple organ dysfunction syndrome (MODS) secondary to severe heat stroke (SHS) is crucial for improving patient outcomes. This study aims to develop and validate a risk prediction model for those patients based on immediate assessment indicators on ICU admission.Methods: Two hundred eighty-four cases with SHS in our hospital between July 2009 and April 2024 were retrospectively reviewed, and categorized into non-MODS and MODS groups. Logistic regression analyses were performed to identify risk factors for MODS, and then to construct a risk prediction model, which was visualized by a nomogram. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow (HL) test, calibration curve, and decision curve analysis (DCA). Finally, the AUCs of the prediction model was compared with other scoring systems.Results: Acute gastrointestinal injury (AGI), heart rate (HR) >100 bpm, a decreased Glasgow Coma Scale (GCS) score, and elevated total bilirubin (TBil) within the first 24 h of ICU admission are identified as independent risk factors for the development of MODS in SHS patients. The model demonstrated good discriminative ability, and the AUC was 0.910 (95% CI: 0.856–0.965). Applying the predictive model to the internal validation dataset demonstrated good discrimination with an AUC of 0.933 (95% CI: 0.880–0.985) and good fit and calibration. The DCA of this model showed a superior clinical net benefit.Discussion: The risk prediction model based on AGI, HR, GCS, and TBil shows robust predictive performance and clinical utility, which could serve as a reference for assessing and screening the risk of MODS in SHS patients.

As the development and utilization of the ocean have progressed through a new historical period, human-ocean practice activities have become more intense, sea-related public affairs have become increasingly complex, maritime conflicts and natural disasters have occurred frequently, and maritime rescue has become increasingly important. The level of core competence of operating room(OR) nurses in maritime disaster emergency medical teams (MDEMTs) directly affects the efficiency of rescue, but there is a lack of research on the core competence of OR nurses in MDEMTs. This study aims to explore the competencies of OR nurses in MDEMTs based on the Iceberg Model. Between November 2024 and January 2025, purposive and snowball sampling techniques were combined to conduct interviews with 12 nurses, 4 surgeons, 1 anesthesiologist, and 3 care managers involved in related roles. All participants were members of MDEMTs who had previously taken part in a maritime exercise or rescue operation. Thematic analysis followed Colaizzi’s seven-step method, based on data collected through semistructured interviews. Eleven generic core competencies for operating room nurses and eleven competencies specific to the MDEMTs were identified through thematic analysis, collectively forming the core competency framework for OR nurses in MDEMTs. Based on the Iceberg Model, these competencies were categorized into four domains: professional knowledge, practical skills, comprehensive competence, and professional and personal traits. OR nurses in MDEMTs are required to demonstrate a wide range of core competencies, and competency-based training should be prioritized to meet the evolving demands of maritime rescue operations. The online version contains supplementary material available at 10.1186/s12912-025-03969-y.

Head nurses are frontline leaders who directly shape nurses’ work environments, staff well-being, and patient outcomes. Previous studies suggest that inclusive leadership behaviours of head nurse exert a positive influence on nursing practice. Yet, little is known about the factors of head nurses’ use of inclusive leadership in daily practice. The purpose of this study was to explore the factors influencing head nurses’ implementation of inclusive leadership from the perspectives of multiple stakeholders, using a qualitative descriptive approach guided by the diffusion of innovation theory. A qualitative descriptive study was adopted. Semi-structured interviews were conducted between October 2024 and March 2025 from stakeholders of inclusive leadership which included 7 chief nursing officers, 8 head nurses, and 10 nurses. Thematic analysis was applied to analyze the data. The participants were recruited via purposive and snowball sampling methods. 12 themes related to head nurses’ implementation of inclusive leadership were identified through thematic analysis, collectively forming the core framework for understanding this leadership practice. Based on the Diffusion of Innovation theory, these themes were classified into 5 major constructs: relative advantage, compatibility, complexity, trialability, and observability. There are certain factors that promote and hinder the implementation of inclusive leadership by head nurses. Future research should explore the potential of inclusive leadership in nursing management and ensure its alignment with clinical practice. Not applicable.