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Objective of this study was to identify factors that impact the successful implementation of integrated care for older adults with multimorbidity in China.Patients and Methods: From July 2022 to May 2023, 33 healthcare providers and managers involved in the delivery and management of healthcare services for older adults with multimorbidity were recruited from Zhejiang Province, China using purposeful and maximum variation sampling methods. Semi-structured, face-to-face in-depth interviews were conducted by the same interviewer in the participants' native Chinese language until data saturation was reached. Inductive thematic analysis was used to analyze the data, and then, themes were mapped onto six dimensions using the Rainbow Model of Integrated Care to allow for a comprehensive view of the study's findings.Results: Eleven themes were generated as facilitators and barriers to integrated care for older adults with multimorbidity in China. These themes include (1) clinical integration: patient-centered care, (2) professional integration: interdisciplinary teams and training, (3) organizational integration: resources and accessibility, (4) system integration: community and funds, incentives, and health insurance, (5) functional integration: electronic health record systems, workforce, and guidelines, and (6) normative integration: shared mission.Conclusion: Guided by the Rainbow Model of Integrated Care, various factors at both micro, meso, and macro levels that impact the implementation of integrated care for older adults with multimorbidity in the Chinese context have been identified in this study. The strategies for future interventions and policies should focus on promoting facilitators and addressing barriers. [ABSTRACT FROM AUTHOR]

Purpose: To summarize adverse healthcare outcomes experienced by older adults with multimorbidity and barriers perceived by stakeholders regarding the healthcare systems primarily designed to address individual health conditions. Healthcare elements that aim to provide coordinated, continuous, and comprehensive services for this population were also identified.Patients and Methods: We applied the methodology framework developed by Arksey and O'Malley to guide the review. The three-step search strategy was used to identify relevant English reviews that focused on adverse healthcare outcomes and barriers encountered by older adults with multimorbidity and other stakeholders regarding the single-disease-focused healthcare systems, as well as those concentrated on healthcare elements that aim to provide coordinated, continuous, and comprehensive services for older adults with multimorbidity. Five electronic databases, including PubMed/Medline, CINAHL, Web of Science Core Collection, Cochrane Library, and Embase, were systematically searched from database inception to February 2022. A standardized table was used to extract data. Thematic analysis was then conducted under the guidance of the Rainbow Model of Integrated Care and the Chronic Care Model.Results: Twenty reviews were included in this study. Therapeutic competitions, high healthcare service utilization, and high healthcare costs were three adverse healthcare outcomes experienced by patients. Both patients and healthcare professionals faced various barriers. Other stakeholders, including informal caregivers, healthcare managers, and policymakers, also perceived several barriers. Numerous healthcare elements were identified that may contribute to optimized services. The elements most frequently mentioned included the implementation of shared decision-making, comprehensive geriatric assessments, and individual care plans.Conclusion: This study conducted a comprehensive overview of the current knowledge related to healthcare for older adults with multimorbidity. In the future, it is necessary to develop more coordinated, continuous, and comprehensive healthcare service delivery models based on the healthcare needs of older adults with multimorbidity and the specific characteristics of different countries. [ABSTRACT FROM AUTHOR]

Purpose: Machine learning (ML) techniques have emerged as a promising tool to predict risk and make decisions in different medical domains. We aimed to compare the predictive performance of machine learning-based methods for 4-year risk of metabolic syndrome in adults with the previous model using logistic regression. Patients and Methods: This was a retrospective cohort study that employed a temporal validation strategy. Three popular ML techniques were selected to build the prognostic models. These techniques were artificial neural networks, classification and regression tree, and support vector machine. The logistic regression algorithm and ML techniques used the same five predictors. Discrimination, calibration, Brier score, and decision curve analysis were compared for model performance. Results: Discrimination was above 0.7 for all models except classification and regression tree model in internal validation, while the logistic regression model showed the highest discrimination in external validation (0.782) and the smallest discrimination differences. The logistic regression model had the best calibration performance, and ANN also showed satisfactory calibration in internal validation and external validation. For overall performance, logistic regression had the smallest Brier score differences in internal validation and external validation, and it also had the largest net benefit in external validation. Conclusion: Overall, this study indicated that the logistic regression model performed as well as the flexible ML-based prediction models at internal validation, while the logistic regression model had the best performance at external validation. For clinical use, when the performance of the logistic regression model is similar to ML-based prediction models, the simplest and more interpretable model should be chosen. [ABSTRACT FROM AUTHOR]

Purpose: A prediction model for 4-year risk of metabolic syndrome in adults was previously developed and internally validated. However, external validity or generalizability for this model was not assessed so it is not appropriate for clinical application. We aimed to externally validate this model based on a retrospective cohort. Patients and Methods: A retrospective cohort design and a temporal validation strategy were used in this study based on a dataset from 1 January 2015 to 31 December 2018. Multiple imputation was used for missing values. Model performance was evaluated by using discrimination, calibration (calibration plot, calibration slope, and calibration intercept), overall performance (Brier score), and decision curve analysis. Results: In external validation, the C-statistic was 0.782 (95% CI, 0.771– 0.793). The calibration plot shows good calibration, calibration slope was 1.006 (95% CI, − 0.011– 1.063), and calibration intercept was − 0.045 (95% CI, − 0.113– 0.022). Brier score was 0.164.The discrimination and calibration of the prediction model were good in temporal external validation. Conclusion: The discrimination and calibration of the prediction model were satisfactory in the temporal external validation. However, clinicians should be aware that this prediction model was developed and validated in a tertiary setting. It is strongly recommended that further studies validate this model in international cohorts and large, prospective cohorts in different institutions. [ABSTRACT FROM AUTHOR]

Purpose: A prognostic prediction model for metabolic syndrome can help nurses or physicians evaluate the future individual absolute risk of MetS in order to develop personalized care strategies. We aimed to derive and internally validate a prognostic prediction model for 4-year risk of metabolic syndrome in adults. Patients and Methods: This was a retrospective cohort study conducted in a tertiary care setting, and the dataset was obtained from the Healthcare Information and Management Systems of a tertiary hospital. The cohort included Chinese adults attending health examination from 1 January 2011 to 31 December 2014. A total of 6793 participants without metabolic syndrome were included in the cohort and were followed up for 4 years. Available candidate predictors in the dataset were weight, MCV, MCH, AST, ALT, BMI, NGC, TC, serum uric acid, gender, smoking, WBC, LC, Hb, HCT, and age. A logistic regression model was adopted to build the risk equation, and bootstrapping was used when considering internal validation. Calibration, discrimination, and the clinical utility were calculated for the model's performance. Results: Of the 6793 participants, 1750 participants were diagnosed with metabolic syndrome within 4 years. The developed prediction model contained 5 predictors (body mass index, age, total cholesterol, alanine transaminase, and serum uric acid). After internal validation, the C-statistic was 0.783 (95% CI, 0.772– 0.795). Additionally, the current model had good calibration. Calibration slope was 0.995 (95% CI, 0.934– 1.058), and calibration intercept was − 0.008 (95% CI, − 0.088– 0.073). The Brier score was 0.156. The decision-curve analysis indicated that the prediction model provided greater net benefit than the default strategies of providing treatment or not providing treatment for all patients. Conclusion: A prognostic risk prediction model for determining 4-year risk of metabolic syndrome onset in adults was developed and internally validated. This model was based on routine clinical measurements that quantified individual future risk of metabolic syndrome. [ABSTRACT FROM AUTHOR]

Introduction: With the effective treatments of novel coronavirus disease 2019 (COVID-19), thousands of patients have recovered from COVID-19 globally. The public perceptions and views are vital to facilitate recovered COVID-19 patients reintegrate into society. In China, the rural population accounts for nearly 70% of the total population. Therefore, we chose to evaluate perceptions and views of rural residents towards COVID-19 recovered patients in China. Methods: Fifteen participants were sampled from a village with the severe COVID-19 epidemic in Zibo city, Shandong Province. The fifteen participants who lived in the village with COVID-19 recovered patients were included. They were over 18 years of age and were voluntary to participant in the study. A descriptive qualitative design using semi-structured telephone interviews was undertaken. Thematic analysis was undertaken. Results: Five main themes emerged from the data: (1) Perceived personal characteristics of COVID-19 recovered patients; (2) Perceived difficulties faced by COVID-19 recovered patients; (3) Perceptions on the social relationship with COVID-19 recovered patients; (4) Views on COVID-19 recovered patients going to public venues; (5) Views on helping COVID-19 recovered patients. Each theme was supported by several subthemes. Conclusion: Our study showed that discrimination and reduced social intimacy exist among rural residents. To improve their views or the situation, relevant departments could lead health educational programs and encourage supportive social connections. Through these strategic messaging, rural residents are expected to recognize that COVID-19 recovered patients need more social support, rather than discrimination and resistance, which helps recovered patients better return to society. [ABSTRACT FROM AUTHOR]

Purpose: A prognostic prediction model for metabolic syndrome can calculate the probability of risk of experiencing metabolic syndrome within a specific period for individualized treatment decisions. We aimed to provide a systematic review and critical appraisal on prognostic models for metabolic syndrome. Materials and Methods: Studies were identified through searching in English databases (PubMed, EMBASE, CINAHL, and Web of Science) and Chinese databases (Sinomed, WANFANG, CNKI, and CQVIP). A checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) and the prediction model risk of bias assessment tool (PROBAST) were used for the data extraction process and critical appraisal. Results: From the 29,668 retrieved articles, eleven studies meeting the selection criteria were included in this review. Forty-eight predictors were identified from prognostic prediction models. The c-statistic ranged from 0.67 to 0.95. Critical appraisal has shown that all modeling studies were subject to a high risk of bias in methodological quality mainly driven by outcome and statistical analysis, and six modeling studies were subject to a high risk of bias in applicability. Conclusion: Future model development and validation studies should adhere to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement to improve methodological quality and applicability, thus increasing the transparency of the reporting of a prediction model study. It is not appropriate to adopt any of the identified models in this study for clinical practice since all models are prone to optimism and overfitting. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the status of cardiac rehabilitation knowledge and attitude of the medical staff. Patients and Methods: A questionnaire survey of doctors and nurses was performed in the departments of cardiology of 7 teaching hospitals with attitudes, knowledge toward CR in Zhejiang Province, China, from April to September 2018, to assess Chinese medical staff for the degree of mastery and attitude toward cardiac rehabilitation. Results: A total of 160 questionnaires were distributed and 152 valid were collected. The responding individuals included 106 nurses (69.74%) and 46 doctors (30.26%). The scores of cardiac rehabilitation knowledge were 56.68± 5.06, and those of cardiac rehabilitation attitude were 15.19± 3.86. Subgroup analysis showed the lower the job title (P< 0.001), the lower the educational level (P = 0.011), and the shorter the years of specialty experience (P=0.005), the more negative the attitude toward cardiac rehabilitation. Conclusion: The medical staff has a good mastery of cardiac rehabilitation knowledge. But we also find that staffs with lower education level and job title, shorter specialty work experience were associated with a more negative attitude toward implement rehabilitation. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to translate the Roy adaptation model concept of adaptation level into measurable concepts when considering the health-related quality of life, social support, and self-efficacy of community-dwelling Chinese adults with metabolic syndrome. Patients and Methods: This was a descriptive cross-sectional study. Data were collected from 144 adults with metabolic syndrome in the suburban area of Hangzhou, China. Correlation analysis was performed to examine the relationships between environmental stimuli (sociodemographic characteristics), adaptation level (health-related quality of life and social support), and prerequisite of behavior (self-efficacy). Regression analysis was applied to test the effect of stimuli on adaptation level and their associations with behavior. Results: Adaptation level was potentially influenced by environmental stimuli and evaluated by the quality of life. Age and education level were related to physiological and psychological function. Besides, family income was related to physiological function. Thus, adaptation level may predict individuals' behavior towards illness management, with a percentage classification accuracy of 76.40%. The vitality and social functioning of the quality of life were identified as significant correlates of self-efficacy. Conclusion: Our findings demonstrate that individuals' internal and external resources should be considered when developing nursing interventions to improve patients' adaptation level. Future research should explore whether facilitating self-efficacy among community-dwelling adults with metabolic syndrome may positively influence an individual's adaptation process in health management. [ABSTRACT FROM AUTHOR]

Purpose: This study is aiming to investigate cardiovascular patients' attitudes towards self-management during hospitalization in China. Patients and Methods: Twenty-nine individuals living with cardiovascular disease from one designated Cardiology Department in Hangzhou, China, were recruited through a purposive sampling procedure. A qualitative descriptive methodology was used. Semi-structured interviews were also used to gain attitudes toward self-management. The interviews were audio-recorded, transcribed and analyzed by thematic analysis to develop the results. Results: Four themes were identified from the qualitative data: (1): Responsibilities of self-management; (2): Reflections on self-management; (3): Acknowledgement of self-management support; (4): Challenges in implementing and adherence to self-management. Additionally, interview data were also given to illustrate these main themes emerging during the analysis. Patients gradually took their responsibilities to manage chronic symptoms. During their self-management process, they did reflections to help correct their regiments through supportive interactions. Health system responsiveness, health disparities, social capital, and cultural setting were the main external factors influencing better self-management implementation and adherence. Conclusion: This study revealed the hospitalized cardiovascular patients' attitudes towards self-management in China. These findings emphasized the importance of patients' responsibility, reflections, and various social support receiving and pointed out specific external factors influencing the health outcomes and their quality of life. This study also proves the guide for the policymakers and health system better instructions to develop individually and culturally tailored advanced self-management interventions and programs. [ABSTRACT FROM AUTHOR]