ObjectivesThis study aims to develop and internally validate a prediction model, which takes account of multivariable and comprehensive factors to predict the prolonged length of stay (LOS) in patients with lower extremity atherosclerotic disease (LEAD).DesignThis is a retrospective study.SettingChina.Participants, primary and secondary outcomesData of 1694 patients with LEAD from a retrospective cohort study between January 2014 and November 2021 were analysed. We selected nine variables and created the prediction model using the least absolute shrinkage and selection operator (LASSO) regression model after dividing the dataset into training and test sets in a 7:3 ratio. Prediction model performance was evaluated by calibration, discrimination and Hosmer-Lemeshow test. The effectiveness of clinical utility was estimated using decision curve analysis.ResultsLASSO regression analysis identified age, gender, systolic blood pressure, Fontaine classification, lesion site, surgery, C reactive protein, prothrombin time international normalised ratio and fibrinogen as significant predictors for predicting prolonged LOS in patients with LEAD. In the training set, the prediction model showed good discrimination using a 500-bootstrap analysis and good calibration with an area under the receiver operating characteristic of 0.750. The Hosmer-Lemeshow goodness of fit test for the training set had a p value of 0.354. The decision curve analysis showed that using the prediction model both in training and tests contributes to clinical value.ConclusionOur prediction model is a valuable tool using easily and routinely obtained clinical variables that could be used to predict prolonged LOS in patients with LEAD and help to better manage these patients in routine clinical practice.
ObjectivesHomocysteine (Hcy) level has been widely identified as a risk factor associated with adverse outcomes in patients with lower extremity atherosclerotic disease (LEAD). However, there are still some knowledge gaps in research on the association between Hcy level and downstream adverse outcomes, such as length of stay (LOS). This study aims to explore whether and to what extent Hcy level is associated with LOS in patients with LEAD.DesignRetrospective cohort study.SettingChina.Participants, primary and secondary outcomesWe conducted a retrospective cohort study of 748 patients from inpatients with LEAD between January 2014 and November 2021 at the First Hospital of China Medical University in China. We used a slew of generalised linear models to evaluate the association between Hcy level and LOS.ResultsThe patients’ median age was 68 years and 631 (84.36%) were males. A dose–response curve with an inflection point at 22.63 µmol/L was observed between Hcy level and LOS after the adjustment of potential confounders. LOS increased before Hcy level reached the inflection point (β: 0.36; 95% CI: 0.18 to 0.55; p<0.001).ConclusionOur results show that an Hcy level <22.63 µmol/L is associated with increased LOS in patients with LEAD, which was independent of some other risk factors. This might shed light on how Hcy can be used as a key marker in the comprehensive management of patients with LEAD during hospitalisation.
Health-related quality of life, sleep quality, morning and evening types, and internet addiction are of significant importance to the development of medical students, yet they have rarely been studied. Taking this into consideration, the study aimed to confirm latent profiles in health-related quality of life, sleep quality, morning and evening types, and internet addiction in medical students and investigate the characteristics of participants in each profile to provide suggestions for students’ health. This was an observational cross-sectional study including 1221 medical student subjects at China Medical University in 2019. Multiple correspondence analysis was the initial step to verify the correspondence, dispersion, and approximation of variable categories. Latent profile analysis was used to identify the multiple correspondences between the levels of variables. Three profiles were found, including: (1) The Low sleep quality profile was characterized by the lowest sleep quality among the three existing profiles. (2) The High health-related quality of life and Low internet addiction profile was characterized by the highest level of health-related quality of life but the lowest level of internet addiction. (3) The Low health-related quality of life and High internet addiction profile was characterized by the highest standardized values of internet addiction but the lowest standardized values of health-related quality of life. This study had important implications for improving student health and supported the medical universities and hospitals in implementing targeted policies based on distinctive student characteristics.
BACKGROUND Despite the increased development and use of mobile health (mHealth) devices during the COVID-19 pandemic, there is little knowledge of willingness of the Chinese people to use mHealth devices and the key factors associated with their use in the post–COVID-19 era. Therefore, a more comprehensive and multiangle investigation is required. OBJECTIVE We aimed to probe Chinese attitudes regarding the use of mHealth and analyze possible associations between the attitude of willingness to use mHealth devices and some factors based on the socioecological model. METHODS A survey was conducted using quota sampling to recruit participants from 148 cities in China between June 20 and August 31, 2022. Data from the survey were analyzed using multiple stepwise regression to examine the factors associated with willingness to use mHealth devices. Standardized regression coefficients (β) and 95% CIs were calculated using multiple stepwise regression. RESULTS The survey contained a collection of 21,916 questionnaires and 21,897 were valid questionnaires, with a 99.91% effective response rate. The median score of willingness to use mHealth in the post–COVID-19 era was 70 points on a scale from 0 to 100. Multiple stepwise regression results showed that the female gender (β=.03, 95% CI 1.04-2.35), openness personality trait (β=.05, 95% CI 0.53-0.96), higher household per capita monthly income (β=.03, 95% CI 0.77-2.24), and commercial and multiple insurance (β=.04, 95% CI 1.77-3.47) were factors associated with the willingness to use mHealth devices. In addition, people with high scores of health literacy (β=.13, 95% CI 0.53-0.68), self-reported health rating (β=.22, 95% CI 0.24-0.27), social support (β=.08, 95% CI 0.40-0.61), family health (β=.03, 95% CI 0.03-0.16), neighbor relations (β=.12, 95% CI 2.09-2.63), and family social status (β=.07, 95% CI 1.19-1.69) were more likely to use mHealth devices. CONCLUSIONS On the basis of the theoretical framework of socioecological model, this study identified factors specifically associated with willingness of the Chinese people to use mHealth devices in the post–COVID-19 era. These findings provide reference information for the research, development, promotion, and application of future mHealth devices.
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