Factors related to immediate response to symptoms in patients with stroke or transient ischaemic attack

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.

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

OBJECTIVE

To explore the factors influencing decision-making delay in seeking medical care for patients with acute ischemic stroke (AIS) in rural areas.

METHODS

From September to December 2021, we conducted a questionnaire survey of 260 patients with AIS who were hospitalized in the neurology departments of four county-level hospitals in Daqing. A decision-tree and logistic regression model was used to investigate the elements contributing to decision-making delays.

RESULTS

This study found that the decision-making delay rate for rural patients with AIS was 71.5%. The results of the univariate analysis showed that factors associated with decision-making delay included educational level, National Institute of Health stroke scale (NIHSS) score, self-assessed health, monthly income, social support, attitude toward medical help-seeking, health belief, and family dynamics (P < 0.05). Further, we combined logistic regression (LR) and decision-tree (DT) models for multivariate analysis, and finally obtained five factors affecting decision-making delay in AIS patients in rural areas: disease severity, health belief, monthly income (common factors), educational level (only DT model), and social support (only LR model).

CONCLUSIONS

This study found that a few variables, including disease severity, educational level, monthly income, health belief, and social support, affected rural AIS patients' decision-making delay in seeking medical care.

PRACTICE IMPLICATIONS

To achieve the goal of reducing decision-delay and increasing thrombolysis rate, this study thoroughly examined the influencing factors of decision-making delay in seeking medical care of rural AIS patients from various angles. This analysis provides guidance for medical and healthcare professionals on how to best provide future health education for the high-risk population for stroke in rural areas.