Characteristic Clustering of Thrombolytic Agent Decision-making in Acute Ischemic Stroke and Analysis of Influencing Factors

doi:10.21203/rs.3.rs-4141626/v1

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Characteristic Clustering of Thrombolytic Agent Decision-making in Acute Ischemic Stroke and Analysis of Influencing Factors

https://doi.org/10.21203/rs.3.rs-4141626/v1

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Aim

To explore the categories of different decision-making psychological characteristics of thrombolysis proxy decision-makers in acute ischemic stroke and analyze the influencing factors of different categories.

Background

Thrombolysis proxy decision-makers suffered from decision-making dilemmas, which correlated with their multiple psychosocial factors.Identifying the psychological characteristics of different decision-makers may help healthcare professionals develop decision-support programs applicable to different categories of decision-makers. This is essential for improving decision-making efficiency, reducing dilemmas, and decreasing hospital delays.

Methods To

facilitate the selection of acute ischemic stroke patients and their proxy decision makers who attended the emergency department of a comprehensive tertiary hospital in Zhengzhou City, Henan Province, from April 2023 to December 2023 as the survey subjects, and to collect the survey subjects' general information questionnaire, perceived social support scale, state anxiety scale, and decision-making dilemma scale. The final clustering was completed using K-means cluster analysis, and the influencing factors were analyzed by unordered multiclassified logistic regression.

Results

Acute ischemic stroke thrombolysis proxy decision makers can be divided into "high social support-low anxiety-high trust-decision effective", "high social support-high anxiety-moderate trust-decision conflict", "high social support-high anxiety-low trust-decision delay" three categories, which were influenced by the decision maker's gender, number of decision makers, decision maker's literacy level, whether they lived with the patient, whether they were the patient's main financial source, the patient's age, NIHSS score, and the time of the onset of the disease.

Conclusion

There are differences in the decision-making psychological characteristics of different thrombolysis agent decision-makers, and healthcare professionals should provide personalized decision-making aids for their psychological characteristics to improve the efficiency of thrombolysis decision-making, and strive for valuable time to save patients' neurological function.

Implications for nursing policy and health policy

Cluster analysis can be used to classify thrombolytic agent decision makers into three categories based on psychological characteristics and decision-making challenges. This is the first step in developing targeted decision aids for thrombolytic agent decision makers in the future.

Biological sciences/Neuroscience/Social behaviour

Health sciences/Diseases/Neurological disorders/Cerebrovascular disorders/Stroke

acute ischemic stroke

thrombolysis

cluster analysis

influential factor

decision dilemma

Stroke is a major chronic non-communicable disease (NCD) that seriously jeopardizes the health of Chinese people and is the leading cause of death and disability among Chinese adults^[1]. Acute ischemic stroke (AIS) accounts for 80% of all strokes.^[2] Currently, receiving intravenous thrombolysis within the 4.5 h time window is the recommended treatment option for patients with AIS^[3], significantly improving patient prognosis and reducing disability^[4]. However, it is crucial to note the strict time window limitation in treating AIS. Specifically, for each minute of delay in initiating thrombolysis, there is an observed reduction in survival rates by 0.6% and an increase in the occurrence of intracranial hemorrhage by 0.3%.^[5]. A study showed that 43.24% of thrombolysis delays were caused by prolonged acquisition of informed consent^[6]. Due to the potential for delays in thrombolysis administration, certain countries, including the United States and Singapore, do not endorse the use of written informed consent forms. Instead, Singapore recommends the utilization of verbal consent.^{[7, 8]}. In response to escalating doctor-patient tensions and a rise in medical disputes within China, it has become necessary to implement measures to provide some legal protection for doctors. As a result, before the administration of thrombolysis, the patient's family must sign informed consent. However, this requirement is not explicitly stated^[9].

Surveys have shown that most thrombolysis decisions are performed by proxy decision-makers^{[10, 11]}. However, due to the uncertainty of the benefits and risks of thrombolysis, it is difficult for proxy decision-makers to sign informed consent within a short time, resulting in delayed thrombolysis^[6]. Our previous studies found that most thrombolysis proxy decision-makers suffered from decision-making dilemmas, which correlated with their multiple psychosocial factors^{[9, 12, 13]}. Meanwhile, shared decision-making is important to address the thrombolysis decision dilemma. Moreover, the previous shared decision-making models require interviews to understand the decision-making characteristics and needs of each patient/decision-maker and develop an individualized decision-support program^[14]. However, the principle of "one person, one program" shared decision-making does not extend to thrombolysis procedures that have strict time-window limitations. Therefore, a more viable approach in thrombolysis proxy decision-making would involve categorizing decision-makers and designing tailored decision-support programs that align with the specific characteristics of each category, thereby achieving a "one category, one program" framework. Consequently, identifying the psychological characteristics of different decision-makers may help healthcare professionals develop decision-support programs applicable to different categories of decision-makers. This is essential for improving decision-making efficiency, reducing dilemmas, and decreasing hospital delays.

Research has shown that patients (12–43%)^[15] and family members (20–40%)^[15] experience anxiety after a stroke. Anxiety increases the perception of risk and uncertainty of an individual about decisions^[17] and is one of the important factors influencing decision-making dilemmas. Social support helps provide emotional support and the necessary information and resources. It helps individuals in distress to improve their ability to cope with stress. Research has found that social support could reduce decision-making delays in healthcare-seeking behaviors for stroke patients^[18]. Cognitive impairment caused by stroke and the shock of the event increase anxiety in patients and their families^[19]. Increasing trust in physicians will reduce decision burden and regret[21], contributing to improved decision quality.

Therefore, this study uses cluster analysis to classify decision-makers into different categories based on their psychosocial characteristics (social support, anxiety, trust in physicians, decision-making dilemmas). Moreover, this study explores influencing factors to provide a reference for developing personalized decision aids and to shorten the decision time for intravenous thrombolysis.

2.1 Design

This study was a descriptive cross-sectional design and followed the guidelines of the Strengthening Reports of Observational Studies in Epidemiology (STROBE) for cross-sectional studies.

2.2 Participants

Conveniently selected AIS patients and their proxy decision-makers who attended the emergency department of a comprehensive tertiary-level hospital in Zhengzhou of Henan Province from April 2023 to December 2023 as survey participants. Patient inclusion criteria:(1) AIS was diagnosed according to the Chinese guidelines for endovascular treatment of AIS 2023^[2]; (2) the patient meets the indications for thrombolysis after assessment by the physician and written informed consent by proxy for thrombolysis; (3) age ≥ 18 years; (4) can communicate in Chinese. Exclusion criteria: the independent decision of the patient to receive thrombolysis. Proxy decision-maker inclusion criteria: signed informed consent for thrombolysis. Herein, the minimum sample size was calculated as 10 times the number of variables, and the number of variables included was 26. The minimum sample size of 289 cases was calculated based on a 10% failure rate.

2.3 Ethical Considerations

This study was approved by our hospital(2023-KY-1384). Informed consent was obtained from all subjects and/or their legal guardian(s).

2.4 Tools

2.4.1 Demographic and clinical information

The content of the questionnaire was designed according to the purpose of the study, including (1) general information about the patient and surrogate decision-makers, such as gender, age, and economic status; (2) disease-related information about the patients, such as National Institute of Health Stroke Scale (NIHSS) score at admission, whether they had a recurrent stroke and previous health status; (3) decision-making process characteristics, such as the number of people who participate in the decision, whether the patient is involved in the decision, and the role of the proxy decision-maker in the decision-making process.

2.4.2 State Anxiety Inventory

The State-Trait Inventory, revised by Spielberger et al.^[20] in 1983, is a self-report scale that directly reflects the subjective feelings of anxiety patients. The inventory consists of two subscales: the State Anxiety Inventory (S-AI) and the Trait Anxiety Inventory (T-AI). The State Anxiety Inventory assesses the state of anxiety in the immediate moment or at a particular time or situation. It can evaluate state anxiety in stressful situations. Therefore, the former 20 items of the questionnaire were used to assess the current level of anxiety of the proxy decision-maker. Ten items describe positive emotions and are reverse-scored. A 4-point Likert scale was used, with scores from 1 to 4 representing not at all, somewhat, moderately, and very significantly, respectively. A higher total score indicates a higher level of anxiety. The Cronbach's α coefficient for this scale in this study was 0.893.

2.4.3 Decision Dilemma Scale

The level of decision dilemma in thrombolysis decision-making by proxy decision-makers was assessed using the Decision Dilemma Scale^[21]. The scale consists of 5 dimensions and 16 entries on information, values, decision support, decision effectiveness, and decision certainty. A 5-point Likert scale was used, with scores ranging from 0 to 4 representing "yes, probably yes, not sure, probably not, and no." The transformed total score was 0 to 100. Subsequently, < 25 was classified as a valid decision, ≥ 25 was classified as the presence of a decision dilemma, 25–27.4 was classified as a decision conflict, and ≥ 37.5 was classified as a delayed or uncertain decision. The Cronbach's α coefficient for this scale in this study was 0.902.

2.4.4 Wake Forest Physician Trust Scale

The Chinese version of the Wake Forest Physician Trust Scale was revised in 2012 by Dong et al.^[22], was used to measure the level of trust of proxy decision-makers in the physician who communicated with them about informed consent. The scale consists of two dimensions and ten items, five items for each dimension of "benevolence" and "technical competence," and each item was rated on a 5-point Likert scale, with a score of 1 to 5 representing "strongly disagree to strongly agree," respectively. Higher scores represent a higher level of trust in the physician. The internal consistency reliability of the total scale was 0.89, and the retest reliability was 0.83. The Cronbach's α coefficient for this scale in this study was 0.904.

2.4.5 Perceived Social Support Scale

This scale was developed by Zimet et al.^[23]. It is used to assess the self-understanding and self-perceived social support of an individual, such as the perceived level of support from family, friends, and others. The scale consists of 12 items. A 7-point Likert scale was used, with options ranging from "strongly disagree" to "strongly agree" on a scale of 1 to 7. Higher scores indicate a higher level of perceived social support by the individual. The Cronbach's α coefficient for this scale in this study was 0.875.

2.5 Data collection and quality control

Before data collection, researcher training was conducted for nurses responsible for thrombolysis, informing them about electronic and paper questionnaires and filling time and notes. Proxy decision-makers were informed the purpose of the study after signing the informed consent for thrombolysis. Additionally, a brief description of the study was presented to them. After that, they were asked to complete the questionnaire electronically or on paper. The nurse reviewed the completed form on the spot, and any missing items or incorrect information was rechecked with the respondent. The researcher obtained the necessary information from the patient, including their financial burden and pre-morbidity health status, by accessing the electronic medical record system within 48 h of the patient's hospital admission. Paper questionnaires were recorded by two persons to avoid any error.

2.6 Data analysis

Data were analyzed using SPSS 25.0 and SPSSAU online analysis (https://spssau.com/index.html) software. Perceived Social Support Scale, State Anxiety Inventory, Wake Forest Physician Trust Scale, and Decision Dilemma Scale scores were used as clustering variables. In addition, the optimal clustering results were analyzed by K-means clustering using SPSSAU. Count data were described with frequency and percentage, normal measures with mean ± standard deviation, and non-normal measures with median and quartiles. Comparison was performed between groups using clustering categories as a grouping variable. The χ² test or Fisher's exact probability method was used for count data, and the Kruskal-Wallis test was used for measurement data. Influence factor analyses were performed using SPSS 25.0 unordered multicategorical logistic regression models with a test level of α = 0.05.

3.1 Clustering results of psychological characteristics of thrombolysis proxy decision-makers

Cluster analysis classified the psychological characteristics of thrombolysis proxy decision-makers into three categories, which were named according to the characteristics of each scale score. The Wake Forest Physician Trust Scale scores 20–30 as low trust, 31–40 as moderate trust, and above 40 as high trust^[22]. Decision Dilemma Scale in which < 25 is classified as a valid decision, 25–37.4 is a conflicting decision, and ≥ 37.5 is a delayed decision. The higher scores on the Perceived Social Support Scale indicate higher social support and the higher scores on the State Anxiety Inventory indicate higher anxiety levels. Category 1 was named "high social support - low anxiety - high trust - effective decision-making," category 2 was named "high social support - high anxiety - medium trust - conflicting decision-making," and category 3 was named "high social support - high anxiety - low trust - delayed decision-making," according to the characteristics of the scores of each scale of the three categories of proxy decision-makers. The percentages of these three categories were 13.05%, 72.58%, and 14.36%, respectively (Table 1).

Table 1

Number of proxy decision-makers by category and scores on each scale (n = 383).
variant	Category 1	Category 2	Category 3	F	P
sample size	50 (13.05%)	278 (72.58%)	55 (14.36%)	/	/
Social Support score	68.44 ± 15.42	66.06 ± 12.50	61.04 ± 15.23	4.528	0.011
Anxiety score	33.44 ± 3.26	51.27 ± 6.00	56.43 ± 8.12	221.558	< 0.001
Trust in physicians	40.72 ± 3.99	39.55 ± 3.93	26.04 ± 4.42	276.484	< 0.001
Decision Dilemma score	21.54 ± 8.54	26.93 ± 7.87	56.36 ± 17.34	228.047	< 0.001

3.2 Univariate analysis of different psychological characteristics of thrombolysis proxy decision-makers

Based on the different psychological characteristics of thrombolysis proxy decision-makers as dependent variables, "high social support-low anxiety-high trust-decision effective," "high social support-high anxiety-medium trust-decision conflicting," "high social support-high anxiety-low trust-decision delayed" were assigned values of 1, 2, and 3, respectively. General and disease-related information about the decision-maker and the patient were independent variables in performing chi-square or rank-sum tests. The results showed that variables with significant differences between the three different psychological profile categories of thrombolysis proxy decision-makers included gender of the decision-maker, whether or not the decision-maker lived with the patient, whether or not the decision-maker was the major financial resource of the patient, patient occupation, time of onset, whether or not the stroke was a recurrence, number of decision-makers, decision-makers level of education, age of the patient, income of the patient before stroke onset, whether or not the decision-maker had paid for this treatment, and NIHSS scores (P < 0.05) (Table 3). Table 2 shows the independent variable assignment method.

Table 2

Methods of assigning values to independent variables.
independent variable	assignment
Gender of decision-makers	1 = Male, 2 = Female
Level of education of decision-makers	1 = Elementary and below, 2 = Middle school, 3 = High school, 4 = College and above
Whether or not they live with the patient	1 = Yes, 2 = No
Whether the decision-maker is the patient's primary economic source	1 = Yes, 2 = No
Patient's gender	1 = Male, 2 = Female
Patient occupation	1 = Incumbency, 2 = Unemployed, 3 = Retired
Time of stroke onset	Waking up from sleep in the morning (Z₁ = 1, Z₂ = 0, Z₃ = 0, Z₄ = 0), morning flare-ups (Z₁ = 0, Z₂ = 1, Z₃ = 0, Z₄ = 0), afternoon flare-ups (Z₁ = 0, Z₂ = 0, Z₃ = 1, Z₄ = 0), and onset of symptoms in the evening just before bedtime (Z₁ = 0, Z₂ =, Z₃ = 0, Z₄ = 1)

Table 3

Univariate analysis of the categories of psychological characteristics of thrombolysis proxy decision-makers [n = 383, cases (%)/M (P25, P75)].
Items	Number of cases	Category 1	Category 2	Category 3	χ²/Hc	P
Gender of decision-makers					6.135	0.047
male	252	27 (10.71)	193 (76.59)	32 (12.70)
women	131	23 (17.56)	85 (64.89)	23 (17.56)
Whether or not they live with the patient					15.352	< 0.001
Yes	241	42 (17.43)	173 (71.78)	26 (10.79)
No	142	8 (5.63)	105 (73.94)	29 (20.42)
Whether the decision-maker is the patient's primary economic source					21.133	< 0.001
Yes	181	10 (5.52)	136 (75.14)	35 (19.34)
No	202	40 (19.80)	142 (70.30)	20 (9.90)
Patient occupation					12.021	0.017
Incumbency	243	37 (15.23)	176 (72.43)	10 (10.31)
Unemployed	43	3 (6.98)	27 (62.79)	75 (77.32)
Retirement	97	10 (10.31)	13 (30.23)	12 (12.37)
Time of stroke onset					38.665	< 0.001
Wake up in the morning	60	6 (10.00)	40 (66.67)	14 (23.33)
Morning onset	127	22 (17.32)	102 (80.31)	3 (2.36)
Afternoon onset	95	16 (16.84)	69 (72.63)	10 (10.53)
Evening onset	101	6 (5.94)	67 (66.34)	28 (27.72)
Is it a recurrent stroke					10.665	0.005
Yes	241	27 (11.20)	169 (70.12)	45 (18.67)
No	142	23 (16.20)	109 (76.76)	10 (7.04)
Number of decision-makers	383	1 (0,2)	2 (0.8,2)	2 (1,3)	8.715	0.013
Level of education of decision-makers	383	4 (3, 5)	4 (3,4)	4 (3,4)	11.727	0.003
Patient's age	383	49 (38,66.80)	62 (54,69)	63 (52, 71)	15.767	< 0.001
Patient's income before stroke	383	3 (2,3)	2 (1,3)	1 (1,3)	13.459	0.001
Whether the decision-maker is a payer for this treatment	383	1 (1,2)	2 (1,2)	2 (1,2)	17.244	< 0.001
NIHSS score	383	2.5 (1,6)	4 (3,5.4)	5.302 (4,13)	22.150	< 0.001

3.3 Logistic regression analysis of factors influence decision-making dilemmas of thrombolysis proxy decision-makers with different psychological profiles

Statistically significant variables from the univariate analysis were included in the unordered multicategorical logistic regression to construct the predictive model. Table 4 illustrates the results of the initial model likelihood ratio test. From the model likelihood ratio test: before stroke, patient income, whether the decision-maker paid for this treatment, and whether it was a recurrent stroke (P > 0.05). The optimal model was established by screening the independent variables using the backward method^[24]. Table 5 shows the correlation test results of the variables included in the final model.

The likelihood ratio chi-square test of the final unordered multiclassified logistic regression model: χ² = 215.855, P < 0.001, indicating that the model is significant. The results showed that using "high social support-low anxiety-high trust-effective decision-making" as a control, the factors influencing the performance of decision-makers " high social support - high anxiety - medium trust - decision-making conflict," "high social support - high anxiety - low trust - delayed decision-making" included: number of decision-makers, decision-makers literacy level, age of patient, NIHSS score of patient, decision-makers gender, whether or not they live with the patient, whether or not the decision-maker is the main source of income of patient and the time of onset of the illness. (P < 0.05) (Table 6).

Table 4

Initial model likelihood ratio test.
Effect	Model fit condition	Likelihood ratio test
Effect	The − 2 log-likelihood of the simplified model	chi-square	degrees of freedom	significance
intercept	369.141a	0.000	0.000	.
Number of decision-makers	380.033	10.892	2.000	0.004
Level of education of decision-makers	401.493	32.351	2.000	0.000
Patient's age	385.277	16.136	2.000	0.000
Patient's income before stroke	370.063	0.922	2.000	0.631
Whether the decision-maker pays for this treatment	372.815	3.674	2.000	0.159
NIHSS score	391.683	22.542	2.000	0.000
Gender of decision-makers	400.005	30.864	2.000	0.000
Whether or not they live with the patient	406.768	37.627	2.000	0.000
Whether the decision-maker is the patient's primary economic source	400.657	31.516	2.000	0.000
Patient occupation	390.067	20.926	4.000	0.000
Time of stroke onset	439.730	70.589	6.000	0.000
Is it a recurrent stroke	373.356	4.215	2.000	0.122

Table 5. Final model likelihood ratio test.
Effect	Model fit condition	Likelihood ratio test
	The − 2 log-likelihood of the simplified model	chi-square	degrees of freedom	significance
intercept	379.374a	0.000	0.000	.
Number of decision-makers	398.929	19.555	2.000	0.000
Level of education of decision-makers	410.033	30.659	2.000	0.000
Patient's age	394.805	15.432	2.000	0.000
NIHSS	404.268	24.895	2.000	0.000
Gender of decision-maker	410.384	31.010	2.000	0.000
Whether or not they live with the patient	421.969	42.595	2.000	0.000
Whether the decision-maker is the patient's primary economic source	416.323	36.949	2.000	0.000
Patient occupation	399.998	20.625	4.000	0.000
Time of stroke onset	455.810	76.436	6.000	0.000

Table 6. Unordered multiclassification logistic regression analysis of clustering categories of psychological characteristics of thrombolysis proxy decision-makers.
Clustering category	Beta	Standard error	P	OR	95%CI
intercept	5.253	2.132	0.014
Number of decision-makers	1.095	0.268	< 0.001	2.989	1.766 ~ 5.059
Level of education of decision-makers	-1.359	0.311	< 0.001	0.257	0.14 ~ 0.473
Patient's age	0.087	0.025	< 0.001	1.091	1.039 ~ 1.145
NIHSS	0.233	0.101	0.021	1.262	1.036 ~ 1.538
Sex of decision-maker (control group = female)	-2.623	0.730	< 0.001	0.073	0.017 ~ 0.303
Whether living with the patient (control group = no)	-2.846	0.648	< 0.001	0.058	0.016 ~ 0.207
Whether the decision-maker is the patient's main financial source (control group = no)	3.322	0.718	< 0.001	27.720	6.785 ~ 113.252
Patient occupation (control group = retired)
Incumbency	-1.126	0.633	0.075	0.324	0.094 ~ 1.121
Unemployed	-0.391	0.957	0.682	0.676	0.104 ~ 4.408
Time of onset (control = evening onset)
wake up in the morning	-0.319	0.920	0.729	0.727	0.12 ~ 4.408
Morning onset	-3.115	0.835	< 0.001	0.044	0.009 ~ 0.228
Afternoon onset	-3.512	0.888	< 0.001	0.030	0.005 ~ 0.17
intercep	5.512	2.679	0.040
Number of decision-makers	1.115	0.304	< 0.001	3.048	1.678 ~ 5.536
Level of education of decision-makers	-1.565	0.384	< 0.001	0.209	0.098 ~ 0.444
Patient's age	0.076	0.030	0.011	1.079	1.018 ~ 1.145
NIHSS	0.369	0.105	< 0.001	1.447	1.178 ~ 1.776
Sex of decision-maker (control group = female)	-4.094	0.830	< 0.001	0.017	0.003 ~ 0.085
Whether living with the patient (control group = no)	-4.284	0.752	< 0.001	0.014	0.003 ~ 0.06
Whether the decision-maker is the patient's main financial source (control group = no)	4.526	0.853	< 0.001	92.344	17.344 ~ 491.673
Patient occupation (control group = retired)
Incumbency	-0.939	0.838	0.263	0.391	0.076 ~ 2.021
Unemployed	1.787	1.130	0.114	5.971	0.652 ~ 54.691
Time of onset (control = evening onset)
wake up in the morning	-0.593	1.004	0.555	0.553	0.077 ~ 3.957
Morning onset	-6.673	1.124	< 0.001	0.001	0.000 ~ 0.011
Afternoon onset	-4.725	0.994	< 0.001	0.009	0.001 ~ 0.062
McFadden R² :0.363;Nagelkerke R² :0.546;Cox & Snell R² :0.431

4.1 Psychological characterization of decision-making by different categories of thrombolysis proxy decision-makers

This study shows that all three types of decision-makers have high social support, and decision-makers with decision dilemmas (decision conflicts, decision delays) have higher anxiety levels.

Influenced by the collective culture in China, people emphasize interdependence and mutual support^[25]. This concept is reflected in the family and also in other social relations. Filial piety is a traditional virtue of Chinese culture, and it is the responsibility and obligation of children to support their parents^[25]. Therefore, individuals receive more support from their families. There is an old Chinese proverb, "A distant relative is not better than a close neighbor". The tradition reinforces a holistic view of the community and emphasizes the responsibility of neighbors to be accompanied, dependent, and caring for each other. So, all proxy decision-makers in this study perceived high social support.

High anxiety levels were associated with altered cognitive functioning, and decision-making is an important aspect of cognitive functioning. Anxiety is associated with a tendency to perceive more negatively^[17]. Therefore, there was a dilemma for the proxy decision-makers with high anxiety levels.

Specifically, the results of this study showed that the psychological character of the first category of decision-makers was "high social support - low anxiety - high trust - effective decision-making." High social support suggests that proxy decision-makers have a wider social network, which allows them to access information from multiple sources and increases their chance of being exposed to stroke-related knowledge^[18]. Social support can alleviate the perception of stress of an individual and help produce positive decision-making behavior. Decision-makers with low anxiety levels have fewer concerns^[18] and reduce risk-averse behavior ^[28], facilitating effective decision-making. Studies have shown that patients with high trust in their doctors have better treatment adherence, and it is easier for doctors and patients to reach a consensus on decision-making, producing effective decision-making^[29]. Therefore, we suggest that medical staff focus on improving their competence and meeting the expectations of patients to increase the trust level between doctors and patients. Expertise, caring, and communication are important indicators of interpersonal trust in physicians^[30]. Healthcare professionals should strengthen their professional skills and pay attention to developing psychological communication skills. In addition, sincerity is the basis of trust. So, healthcare professionals should be sincere and respectful when communicating with patients, improve their comprehensive quality, enhance the trust level of patients in the doctor, and promote a harmonious doctor-patient relationship to improve the efficiency of decision-making.

The present results showed that the psychological characteristics of the second group of decision-makers were "high social support anxiety- medium trust-decision conflict." Trust is the basis of shared decision-making between doctor and patient^[31], and medium-trust decision-makers may remain uncertain and hesitant about the treatment plan proposed by the doctor. In addition, high anxiety levels increase the uncertainty of proxy decision-makers about decision-making behavior. This contradicts its high social support that would have a clearer perception of the disease and knowledge of the importance of thrombolysis, ultimately leading to conflicting decisions. This is similar to the findings of Sun et al. ^[32]. Therefore, we suggest that medical educators integrate resources to improve the communication skills of healthcare professionals, improve their service attitudes, and continuously strengthen their professional knowledge. Enhancing patient trust in physicians, improving levels of medical support, and advancing patient trust through quality improvement initiatives have been identified as effective strategies for mitigating decision-making dilemmas faced by thrombolysis decision-makers and enhancing thrombolysis rates among patients with ischaemic stroke.

The third category of decision-makers is characterized by "high social support - high anxiety - low trust - delayed decision-making." Individuals with high levels of anxiety state perceive too much risk and are more likely to develop risk avoidance behaviors^[32]. In addition, this category of decision-makers has a low level of trust in physicians and doubts about treatment options, which leads to delays in thrombolysis decisions. Therefore, it is recommended that clinicians employ objective data when engaging in discussions with this category of decision-makers regarding informed consent for thrombolysis. By presenting objective data, clinicians can enhance the credibility of the medical information being conveyed to decision-makers, ultimately reducing their uncertainty and facilitating efficient decision-making.

4.2 Factors influencing different psychological characteristics of AIS thrombolysis proxy decision-makers

4.2.1 Decision-maker factors can influence the formation of different psychological characteristics of thrombolysis proxy decision-makers

4.2.1.1 Gender and number of decision-makers

This study showed that female decision-makers were more likely than male decision-makers to belong to the "high social support-high anxiety-moderate trust-decision conflict" and "high social support-high anxiety-low trust-decision delay" categories than to the "high social support-low anxiety-high trust-decision effective" category. Analyzing the causes, this may be related to the personality tendencies of different genders. Women are delicate, sensitive, often pessimistic about uncertain events, and tend to be more risk-averse in their decision-making^{[9, 33]}. When their physician informs that thrombolysis presents a risk of bleeding, female decision-makers may be hesitant to make decisions for concern that their decision will lead to adverse consequences, which may ultimately result in delayed decision-making. Thrombolysis proxy decision-makers with a growing number of decision-makers were more likely to be categorized as "high social support-high anxiety- medium trust-decision conflict" and "high social support-high anxiety-low trust-decision delay" than "high social support-low anxiety-high trust-decision effective." Previous studies found that the more people involved in decision-making, the more likely delay thrombolysis decisions^[34]. This may be explained by the fact that different individuals have different levels of understanding and acceptance of knowledge about thrombolysis, which prevents them from reaching a consensus in a limited period and delays decision-making about thrombolysis. Therefore, we suggest that healthcare professionals consider the specific psychology of female decision-makers when providing thrombolysis advice and provide personalized aids to help them make decisions. To mitigate the drawbacks associated with group decision-making. The selection of family members who possess a strong voice and effectively represent the patient in the decision-making process serves to mitigate uncertainty and conflict in decision-making.

4.2.1.2 Educational level of the decision-maker and whether they are the main provider of financial support to the patient

Herein, decision-makers with lower levels of education were more likely to be categorized as "high social support-high anxiety- medium trust-decision conflict" and "high social support-high anxiety-low trust-decision delay" than "high social support-low anxiety-high trust-decision effective." Failure to develop good communication with physicians results in decision-making dilemmas due to lower levels of health literacy and inadequate ability to understand and utilize information in low-educated decision-makers^[12]. Decision-makers who were the main financial resources of patients were more likely to be categorized as "high social support-high anxiety-medium trust-decision-making conflict" and "high social support-high anxiety-low trust-decision-making delayed" than as "high social support-low anxiety-high trust-decision-making effective." Research has shown that economic factors also influence decision-making^[18]. Most decision-makers who are the main financial resources of the patient were patient family members and had a better understanding of the affordability of family. Decision-makers made decisions that might be delayed by considering their financial situation. Therefore, we advise healthcare professionals to use understandable language to explain knowledge about thrombolysis when dealing with low-education decision-makers and to help them clarify their intention of whether or not to proceed with thrombolysis. Furthermore, healthcare professionals need to assess the financial situation of patients and take appropriate supportive measures to help thrombolysis decision-makers make quick decisions. The relevant departments should promote the improvement of medical insurance to enhance the ability of patients to resist the risk of illness and reduce decision-making dilemmas.

4.2.1.3 Whether decision-maker lives with the patient

Decision-makers who were not living with the patient were more likely to be categorized as "high social support-high anxiety- medium trust-decision-making conflict" and "high social support-high anxiety-low trust-decision-making delayed" than the "high social support-low anxiety-high trust-decision-making effective" category. By analyzing the reasons, decision-makers who were not living with the patient might have less knowledge of the daily health status of the patient. Those decision-makers were unprepared for sudden illnesses and could not easily cope with medical decisions, leading to delays in decision-making. On the other hand, decision-makers living with the patient were more knowledgeable about the physical and social condition of the patient. They were better prepared with pre-constructed mentalities and information about the possible disease conditions of the patient. When a patient presents with stroke symptoms and needs to be treated, it is possible to make full use of known information to make decisions in the best interests of the patient. Therefore, we advise healthcare professionals to select family members who have a better understanding of the health and social conditions of the patient as decision-makers for the thrombolysis informed consent conversation to promote a rapid consensus between doctors and patients, thus shortening the decision-making time for thrombolysis and improving the benefits of thrombolysis for patients.

4.2.2 Patient factors can influence the development of different psychological characteristics of thrombolysis proxy decision-makers

4.2.2.1 Age of patients

Herein, the results showed that patients with higher age were more likely to classify the thrombolysis decision-maker as "high social support-high anxiety-medium trust-decision conflict" and "high social support-high anxiety-low trust-decision delayed" than as "high social support-low anxiety-high trust-decision effective." Moreover, the decision dilemma of decision-makers was more severe. Elderly patients benefit less from thrombolysis and are at higher risk of adverse events with poor clinical prognosis, which may lead to delays in decision-making^[35]. The Chinese guidelines for stroke diagnosis and treatment released in 2018 and 2023 mention the age requirement for intravenous thrombolysis, which should be considered carefully for elderly patients^{[2, 36]}. However, studies have shown that elderly patients also benefit from thrombolysis, which can improve their prognosis.^[37] Therefore, healthcare professionals should keep a strict eye on the various conditions of elderly stroke patients. By assessing the individual benefit-risk and providing objective data, such as National Institutes of Health Stroke Scale (NIHSS) scores below 7 and NIHSS scores ≥ 7, accompanied by bleeding incidence rates of 1.9% and 3.9%, respectively reduces the tendency of decision-makers to overestimate the bleeding risk, which leads to delayed decision-making^[38].

4.2.2.2 NIHSS score and time of illness onset of patient

Herein, patients with high NIHSS scores were more likely to categorize decision-makers as "high social support-high anxiety- medium trust-decision conflict" and "high social support-high anxiety-low trust-decision delayed" than as "high social support-low anxiety-high trust-decision effective." The higher NIHSS score represents a greater risk of thrombolysis bleeding in stroke patients^[39], and the prognostic outcome after thrombolysis may be poor^{[40, 41]}. Decision-making is delayed due to excessive risk perception by the decision-maker^[13]. Compared with the "high social support-low anxiety-high trust-decision effective" type, patients with night-time onset were more likely than those with day-time onset to categorize the decision-maker as "high social support-high anxiety-medium trust-decision conflicted" and "high social support-high anxiety-low trust-decision delayed." Studies have shown that nocturnal onset in stroke patients is more likely to result in delayed decision-making^{[34, 42]}. This may be caused by the onset of the disease at night during sleep, making it more difficult for the patient and family members to detect the symptoms. Additionally, with limited access to healthcare, the fatigue of a long day at work tends to make decision-making at night less efficient than during the day. Therefore, we advise healthcare professionals to explore feasible targeted measures for patients with different time frames of onset to help thrombolysis decision-makers reduce the burden of thrombolysis decision-making.

Herein, cluster analysis classified the thrombolysis proxy decision-makers into three categories according to psychological characteristics and decision dilemmas. It was also found that general information such as the number of decision-makers, the level of education of the decision-maker, and the age of the patient, which can be quickly obtained in the prehospital setting, could predict decision-making dilemmas of thrombolysis proxy decision-makers with different psychological characteristics. Healthcare professionals should timely identify the decision-making dilemmas of thrombolysis decision-makers according to different psychological characteristics and take targeted supportive measures to enhance their confidence in decision-making and to make a quick and clear decision on thrombolysis.

This study still has some limitations. First, the data analyzed came from a cross-sectional survey of only one hospital, which is an insufficiently representative sample. Second, the decision-making satisfaction of thrombolysis proxy decision-makers was not investigated. In the future, multi-center, large-sample longitudinal surveys should be conducted to understand further the decision-making dilemmas, decision-making satisfaction, and influencing factors of decision-makers with different psychological characteristics.

The earlier the thrombolytic treatment is administered, the better the outcome. However, obtaining informed consent for thrombolysis remains a significant obstacle to the process of thrombolytic therapy in China. In the past, face-to-face interviews to understand the decision-making challenges of thrombolytic decision makers were time-consuming and not applicable to thrombolytic therapy scenarios. It is more feasible to classify thrombolysis proxy decision makers into different categories and develop appropriate decision aids based on the characteristics of each category. Cluster analysis can be used to classify thrombolytic agent decision makers into three categories based on psychological characteristics and decision-making challenges. This is the first step in developing targeted decision aids for thrombolytic agent decision makers in the future.

Author Contribution

All the authors contributed to this article. Designed the study:YLG、KKM、YJL and CXY;gathered data：WFF、KKM、LNG、XFD、CXY andYLG；analysed data:KKM、YLG、RKG and WFF;study supervision:YLG、YJL、KKM；drafted the manuscript:WFF、KKM and RKG；revised the manuscript:KKM、YLG、CXY. All authors approved the final version.

Data Availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Availability of data and materials：

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Characteristic Clustering of Thrombolytic Agent Decision-making in Acute Ischemic Stroke and Analysis of Influencing Factors

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Abstract

Aim

Background

Methods To

Results

Conclusion

Implications for nursing policy and health policy

1 Background

2 Methods

2.1 Design

2.2 Participants

2.3 Ethical Considerations

2.4 Tools

2.4.1 Demographic and clinical information

2.4.2 State Anxiety Inventory

2.4.3 Decision Dilemma Scale

2.4.4 Wake Forest Physician Trust Scale

2.4.5 Perceived Social Support Scale

2.5 Data collection and quality control

2.6 Data analysis

3 Results

3.1 Clustering results of psychological characteristics of thrombolysis proxy decision-makers

3.2 Univariate analysis of different psychological characteristics of thrombolysis proxy decision-makers

4 Discussion

4.1 Psychological characterization of decision-making by different categories of thrombolysis proxy decision-makers

4.2 Factors influencing different psychological characteristics of AIS thrombolysis proxy decision-makers

4.2.1 Decision-maker factors can influence the formation of different psychological characteristics of thrombolysis proxy decision-makers

4.2.1.1 Gender and number of decision-makers

4.2.1.3 Whether decision-maker lives with the patient

4.2.2 Patient factors can influence the development of different psychological characteristics of thrombolysis proxy decision-makers

4.2.2.1 Age of patients

4.2.2.2 NIHSS score and time of illness onset of patient

5 Conclusion

6 Limitations

7 Implications for nursing policy and health policy

Declarations

Author Contribution

Data Availability

References

Additional Declarations

Status:

Version 1