Exploring patient delay in people with chronic kidney disease: A cross-sectional study.

To examine the factors that contribute to patient delays among individuals with chronic kidney disease (CKD) and offer insights to help develop specific risk management strategies. Conducted as a cross-sectional study between September 2021 and April 2022, this study used a convenient sampling technique to select 245 individuals diagnosed with CKD from a Grade 3 Class A hospital located in Shanxi Province. These individuals were chosen as the subjects of the study. The research participants underwent an investigation using several assessment tools, including socio-demographic information questionnaire, medical behavior, the social support rating scale, the simplified coping style questionnaire, and the General Self-efficacy Scale. The study revealed that 35.4% of individuals with CKD experienced patient delay (the interval between the initial onset and the time of seeking medical attention being longer than or equal to 3 months). Through a multifactorial logistic regression analysis, it was determined that various factors independently influenced patient delay in patients with CKD. These factors included the level of knowledge about CKD, educational level, frequency of attending physical examinations, severity of initial symptoms, social support, self-efficacy, positive coping, and negative coping. Numerous factors contribute to the Patient Delay. To effectively enhance awareness and coping abilities regarding CKD in high-risk groups, it is essential to implement focused and continuous interventions throughout the medical seeking process.

A new simple model for prediction of hospital mortality in patients with intracerebral hemorrhage.

BACKGROUND: The current prognostic models for mortality and functional outcome after intracerebral hemorrhage (ICH) are not simple enough. To predict the outcome of ICH, a new simple model, ICH index (ICHI), was established and evaluated in this study. METHODS: Medical records of all cases with ICH in our hospital from January 2008 to August 2009 were reviewed. Multiple linear regression analyses were used to assess the contributions of independent variables to hospital mortality after ICH. RESULTS: Age, serum glucose, white blood cell counts (WBC), and Glasgow Coma Scale (GCS) score were found to be greatly associated with mortality. A formula of ICH index [ICHI = age (years)/10 + glucose (mmol/L) + WBC (10(9) /L) - GCS score] was established. Furthermore, the receiver operating characteristic (ROC) analyses were performed to estimate the predictive value of the ICHI. The model showed an area under the ROC curve (AURC) of 0.923 (95% CI: 0.883-0.963, P < 0.001). The best cut-off value of ICHI for mortality was 18, which gave sensitivity, specificity, and Youden's index of 0.65, 0.95, and 0.60, respectively. The hospital mortality was extremely increased when 18 < ICHI < 28 (mortality 72.0%) and when ICHI ≥ 28 (mortality 100%), in contrast with overall mortality (21.6%). CONCLUSION: The ICHI can be a simple predictive model and complementary to other prognostic models.