Influencing factors for decision-making delay in seeking medical care among acute ischemic stroke patients in rural areas

Analyzing prehospital delays in recurrent acute ischemic stroke: Insights from interpretable machine learning

OBJECTIVE

This study investigates prehospital delays in recurrent Acute Ischemic Stroke (AIS) patients, aiming to identify key factors contributing to these delays to inform effective interventions.

METHODS

A retrospective cohort analysis of 1419 AIS patients in Shenzhen from December 2021 to August 2023 was performed. The study applied the Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP) for identifying determinants of delay.

RESULTS

Living with others and lack of stroke knowledge emerged as significant risk factors for delayed hospital presentation in recurrent AIS patients. Key features impacting delay times included residential status, awareness of stroke symptoms, presence of conscious disturbance, diabetes mellitus awareness, physical weakness, mode of hospital presentation, type of stroke, and presence of coronary artery disease.

CONCLUSION

Prehospital delays are similarly prevalent among both recurrent and first-time AIS patients, highlighting a pronounced knowledge gap in the former group. This discovery underscores the urgent need for enhanced stroke education and management.

PRACTICE IMPLICATION

The similarity in prehospital delay patterns between recurrent and first-time AIS patients emphasizes the necessity for public health initiatives and tailored educational programs. These strategies aim to improve stroke response times and outcomes for all patients.

Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm

OBJECTIVE

To explore the factors affecting delayed medical decision-making in older patients with acute ischemic stroke (AIS) using logistic regression analysis and the Light Gradient Boosting Machine (LightGBM) algorithm, and compare the two predictive models.

METHODS

A cross-sectional study was conducted among 309 older patients aged ≥ 60 who underwent AIS. Demographic characteristics, stroke onset characteristics, previous stroke knowledge level, health literacy, and social network were recorded. These data were separately inputted into logistic regression analysis and the LightGBM algorithm to build the predictive models for delay in medical decision-making among older patients with AIS. Five parameters of Accuracy, Recall, F1 Score, AUC and Precision were compared between the two models.

RESULTS

The medical decision-making delay rate in older patients with AIS was 74.76%. The factors affecting medical decision-making delay, identified through logistic regression and LightGBM algorithm, were as follows: stroke severity, stroke recognition, previous stroke knowledge, health literacy, social network (common factors), mode of onset (logistic regression model only), and reaction from others (LightGBM algorithm only). The LightGBM model demonstrated the more superior performance, achieving the higher AUC of 0.909.

CONCLUSIONS

This study used advanced LightGBM algorithm to enable early identification of delay in medical decision-making groups in the older patients with AIS. The identified influencing factors can provide critical insights for the development of early prevention and intervention strategies to reduce delay in medical decisions-making among older patients with AIS and promote patients' health. The LightGBM algorithm is the optimal model for predicting the delay in medical decision-making among older patients with AIS.

Developmental trajectory of care dependency in older stroke patients

Background

Stroke is the leading cause of death in China. Older stroke survivors often have other chronic conditions, not only musculoskeletal deterioration due to age, but also changes in body image that can be brought on by stroke and other diseases, making them unable to take good care of themselves and dependent on others. The degree of dependency affects the rehabilitation progress of stroke survivors and shows dynamic changes that need to be recognized.

Objectives

This study investigates the trajectory of dependency changes in older stroke patients with comorbidities and analyze the influencing factors.

Methods

Grounded in the Chronic Illness Trajectory Framework (CITF), a longitudinal study was conducted from February 2023 to October 2023, tracking 312 older stroke patients with comorbidities admitted to two tertiary hospitals in Guangzhou. Care dependency levels were assessed using Care Dependency Scale on admission day 5 (T0), at discharge (T1), 1 month post-discharge (T2), and 3 months post-discharge (T3). Growth Mixture Model were utilized to identify trajectory categories, and both univariate analysis and multivariate logistic regression methods were employed to explore factors associated with different developmental trajectories.

Results

A total of four developmental trajectories were fitted, C1 (high independence-slow increased group, 52.0%), C2 (moderate independence-rapid increased group, 13.0%), C3 (moderate independence-slow increased group, 25.0%), and C4 (low independence-increased and decreased group, 10.0%). Length of hospital stay, place of residence, level of social support, residual functional impairments, NIHSS score, and BI index independently influence the trajectory categories.

Conclusion

There is heterogeneity in care dependency among older stroke patients with comorbidities. Most patients gradually reduce their dependency and become more independent, but others remain dependent for an extended period of time. It is recommended to focus on patients who live in rural areas, have low social support, have high admission NIHSS scores and have residual functional impairment, and provide them with personalized continuity of care and rehabilitation services in order to reduce care dependency and the burden of care, and to improve patients' quality of life.

Psychosocial markers of pre-hospital delay in patients with diabetic foot: A cross-sectional survey

AIM

This study aimed to determine the psychosocial markers associated with pre-hospital delay among patients with diabetic foot (DF).

DESIGN

This study has a cross-sectional design.

METHODS

The participants completed a questionnaire including pre-hospital time, demographic characteristics, Social Support Rate Scale, Brief Illness Perception Questionnaire and Type D Personality Scale-14. Bivariate and multivariate analyses were conducted to explore independent associations with pre-hospital delay.

RESULTS

Only 1.8% (3/164) of participants arrived at the hospital for medical care in 24 h of symptom onset. Patients with low utilization of social support (p = 0.029), low negative illness perceptions (p = 0.014) and high levels of negative affectivity (p = 0.009) are likely to arrive late at the clinic. Medical staff should pay attention to identifying diabetic patients' Type D personalities and take actions to improve their social support as well as illness perception, so as to reduce the occurrence of hospital delay.

IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE

Psychosocial factors play a vital role in the delay in seeking medical treatment for patients with DF. Medical staff need to improve patients' illness perception as well as self-management ability through health education. Importantly, key family members provide an emotional and psychological support system for diabetic patients. Therefore, nurses need to work with family members together to give information and psychological support during family visits. Additionally, building and maintaining trust with patients is crucial to encouraging individuals to express their concerns and worries. In this case, nurses may identify patients' negative emotions and conduct timely intervention, so as to achieve favourable outcomes.

PATIENT OR PUBLIC CONTRIBUTION

This study used a convenience sample of 164 participants with DF recruited from the wound clinic of Northern Jiangsu People's Hospital and Yangzhou Hospital of TCM in China.