Decisional Conflict Among Chinese Family Caregivers Prior to ECMO Initiation: A Cross-Sectional Study in Critical Care Nursing

Decisional Conflict Among Chinese Family Caregivers Prior to ECMO Initiation: A Cross-Sectional Study in Critical Care Nursing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Decisional Conflict Among Chinese Family Caregivers Prior to ECMO Initiation: A Cross-Sectional Study in Critical Care Nursing suqin Xiao, Hongsuo Liu, Lingpeng TANG, Qin XIONG, jiang Rong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6971796/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Background The initiation of extracorporeal membrane oxygenation (ECMO) therapy for critically ill patients often necessitates complex, high-stakes decision-making by family members, who typically have limited preparation. This study aimed to quantify the prevalence of decisional conflict among Chinese family caregivers of patients prior to ECMO initiation and to identify modifiable biopsychosocial factors contributing to this conflict. Methods A convenience sample of 169 family members of patients undergoing ECMO treatment was recruited from a tertiary hospital in Jiangxi Province, China. Data were collected using validated instruments: the Decisional Conflict Scale, Decision Preparedness Scale, Decision Fatigue Scale, Social Support Scale, and Wake Forest Physician Trust Scale. Univariate and multivariate logistic regression analyses were performed to identify factors associated with decisional conflict. Results The incidence of decisional conflict among family members was 63.3%. Univariate analysis revealed significant associations (P < 0.05) between decisional conflict and family monthly income, understanding of the patient's condition and ECMO treatment, decision fatigue, decision preparedness, physician trust, and social support. Multivariate analysis identified decision fatigue as a significant risk factor (OR = 1.44, 95% CI: 1.00-2.06). Conversely, understanding of ECMO treatment (OR = 0.18, 95% CI: 0.06–0.58), physician trust (OR = 3.15, 95% CI: 1.51–5.57), and social support (OR = 0.85, 95% CI: 0.72–0.99) were identified as protective factors. Conclusions A substantial proportion of Chinese family caregivers of ECMO patients experience high levels of decisional conflict. Clinical interventions should prioritize enhancing information provision, mitigating decision fatigue, fostering physician trust, and strengthening social support systems to improve decision quality and ultimately optimize patient outcomes in this critical care setting. Clinical trial number Not applicable. Health sciences/Diseases Health sciences/Risk factors Extracorporeal Membrane Oxygenation Decision Conflict Family Members Surrogate Decision-Making Critical Care Nursing Cross-Sectional Study Background Decisional conflict, a pivotal concept in healthcare decision-making, describes the psychological distress experienced when individuals face choices involving inherent risks, uncertain outcomes, and potential conflicts with personal values [ 1 ]. This conflict is characterized by uncertainty regarding available options, insufficient information, ambiguity in personal values, and perceived external pressure [ 2 ]. Elevated levels of decisional conflict can lead to delayed decisions or post-decision regret, negatively impacting the quality of care and potentially escalating doctor-patient disputes [ 3 ]. In critical care environments, where life-altering decisions must frequently be made under severe time constraints, decisional conflict carries particularly profound consequences [ 4 ]. Extracorporeal membrane oxygenation (ECMO) is a sophisticated life-support technology that provides respiratory and/or circulatory support for patients experiencing severe cardiopulmonary failure by circulating blood through an external artificial lung [ 5 ]. Since its inception, ECMO has evolved from an experimental intervention into a standard rescue therapy for refractory respiratory and cardiac failure. The global COVID-19 pandemic significantly accelerated ECMO utilization, with over 10,000 COVID-19 patients receiving ECMO support between 2020 and 2022 [ 6 ]. However, ECMO technology presents substantial clinical challenges. Determining the optimal timing for initiation requires precise identification of the "therapeutic window." The treatment itself is associated with serious complications, including bleeding, thrombosis, and infection [ 7 , 8 ]. A systematic review reported complication rates ranging from 10–45% across different ECMO modalities, with major bleeding events occurring in approximately 30% of cases [ 9 ]. Furthermore, the considerable financial burden of ECMO treatment adds another layer of complexity; the average daily cost ranges from 5,000 to 10,000 USD, with total hospitalization costs often exceeding $ 150,000 per case [ 10 ]. These characteristics transform ECMO treatment decisions into complex, multidimensional dilemmas encompassing medical, ethical, and economic considerations. In clinical practice, family members often serve as surrogate decision-makers, navigating intricate scenarios that demand balancing the patient's survival probability against quality-of-life considerations, assessing financial capacity, addressing ethical challenges, and managing intense psychological stress within limited timeframes [ 11 ]. A qualitative study involving family members of ECMO candidates revealed that surrogate decision-makers frequently experience significant emotional burden, information overload, and value conflicts during this critical decision-making process [ 12 ]. This multifactorial decision-making environment frequently exacerbates decisional conflict, which has been shown to potentially delay optimal treatment timing [ 13 ]. Research indicates that such delays can cause patients to miss critical therapeutic windows, increase complication risks, and ultimately impact survival outcomes [ 14 ]. The ethical dimensions of ECMO decision-making have garnered increasing attention in recent literature. Key ethical challenges in ECMO therapy include the complexities of informed consent, resource allocation considerations, and end-of-life decision-making [ 15 ]. Obtaining informed consent for ECMO is particularly challenging due to the emergent nature of many cases, the technical complexity of the intervention, and the difficulty in accurately prognosticating outcomes [ 16 ]. Surrogate decision-makers often struggle to fully comprehend the implications of ECMO therapy, including potential complications, long-term quality-of-life outcomes, and scenarios involving withdrawal of support [ 17 ]. Despite the critical role of family members in ECMO authorization, existing research has predominantly focused on the clinical outcomes of ECMO technology itself, with insufficient attention to the decision-making processes, particularly regarding family members' decisional conflicts. A systematic review on ethical issues in extracorporeal life support identified only 12 studies specifically addressing family decision-making experiences [ 18 ]. This knowledge gap is particularly pronounced in non-Western healthcare contexts, where unique family dynamics, cultural values, and healthcare delivery systems can significantly influence decision-making processes [ 19 ]. Current literature on decisional conflicts among family members of ECMO patients remains limited, with most studies employing qualitative methodologies and small sample sizes. This limits their ability to comprehensively reflect decisional conflict patterns and provide robust quantitative analysis of influencing factors. While the integration of decision science frameworks into critical care research offers promising avenues for understanding and addressing decisional conflict, it remains underutilized, especially for time-sensitive interventions like ECMO [ 17 ]. Given the high-risk nature, treatment intensity, and cultural specificity of ECMO decisions, this study utilized validated instruments, including the Decisional Conflict Scale, Decision Preparedness Scale, Decision Fatigue Scale, Social Support Scale, and Wake Forest Physician Trust Scale, to systematically analyze the current status and influencing factors of decisional conflicts among family members prior to ECMO initiation. This comprehensive approach addresses a significant gap in the literature by providing quantitative data on decisional conflict prevalence and identifying modifiable factors that can serve as targets for intervention. The findings from this study offer empirical evidence for developing clinical decision support systems and optimizing physician-family communication, ultimately contributing to improved quality of critical care decision-making and patient outcomes. Methods Design and participants This cross-sectional study employed a convenience sampling method to recruit 169 family members of patients scheduled for ECMO treatment. Participants were enrolled from the Department of Critical Care Medicine at a tertiary hospital in Jiangxi Province, China, between January 2024 and April 2025. Sample size calculation was based on the 10 events per variable (EPV) criterion, considering 7 candidate predictors in the regression model. Accounting for an anticipated 52% decision conflict rate (derived from pilot data) and a 15% potential attrition rate, the minimum required sample size was determined to be 169. Inclusion criteria for family members were : (1) patients met ECMO indications and had signed informed consent; (2) direct relatives of the patient (prioritized by decision-making hierarchy: spouse > parents or children > siblings); (3) age ≥ 18 years, with basic communication and comprehension abilities; (4) full participation in doctor-patient communication prior to ECMO initiation; and (5) voluntary signing of research informed consent. Exclusion criteria included: (1) involvement in medical disputes or legal proceedings; (2) previously diagnosed cognitive dysfunction or mental illness; and (3) severe hearing or language impairment. The study protocol was approved by the hospital’s ethics committee (approval number: 1IT[2024]Clinical Ethics Review No. 339) and al methods were performed in accordance with the relevant guidelines and regulations. All participants provided written informed consent. Measurements General information Demographic and clinical information was collected through a self-designed questionnaire based on a comprehensive literature review. This included family members’ gender, age, education level, marital status, monthly per capita family income, place of residence, level of understanding regarding the patient’s condition and ECMO treatment, and prior experience caring for intensive care unit (ICU) patients. This section was completed by the family members themselves. Decisional Conflict Scale The Decisional Conflict Scale developed by O’Connor [20] and validated in Chinese by Li Yu [21], was used to assess decisional conflict. The scale comprises 16 items across five dimensions: being informed, clarifying values, support, decision uncertainty, and effective decision-making. A 5-point Likert scale is used, where 0 represents “yes” and 4 represents “no.” Total scores are converted to a percentage, with scores <25 indicating no decisional conflict and ≥25 indicating the presence of decisional conflict. In this study, the Cronbach’s α coefficient for the DCS was 0.922, demonstrating high internal consistency Decision Fatigue Scale Decision Fatigue Scale developed by Hickman et al. [22] and translated into Chinese by Pan Guocui et al. [23] for use with ICU patients’ family members in 2020, this single-dimension scale consists of 9 items. A 4-point Likert scale (0 = “strongly disagree” to 3 = “strongly agree”) is used, yielding a total score range of 0 to 27. Higher scores indicate greater levels of decision fatigue. The Cronbach’s α coefficient in this study was 0.912, indicating good internal consistency.. Decision Preparedness Scale The Decision Preparedness Scale was Originally developed by O’Connor and revised by Bennett [24]. this scale assesses an individual’s preparedness for decision-making. The Chinese version, validated by Wan Junli et al. in 2020[25], is a single-dimension scale with 10 items. A 5-point Likert scale (1 = “none at all” to 5 = “very much”) is used. After percentage conversion, scores below 60 indicate poor decision preparedness, while higher scores suggest greater preparedness. The Cronbach’s α coefficient in this study was 0.959. Perceived Social Support Scale The original PSSS, developed by Zimet et al. [26] in 1988 (Cronbach’s α = 0.880), assesses perceived social support. The Chinese version, translated and revised by Jiang Qianjin et al. [27] in 1999, measures multidimensional perceptions of social support. It includes two dimensions: family internal support (4 items) and family external support (8 items), totaling 12 items. Each item uses a 7-point Likert scale (1 = “strongly disagree” to 7 = “strongly agree”), with total scores ranging from 12 to 84. Higher scores indicate higher levels of perceived social support. The Cronbach’s α coefficient in this study was 0.902.. Wake Forest Physician Trust Scale Developed by Hall et al. [28] and translated by Dong Enhong et al. [29], this scale assesses patient trust in physicians. It comprises 10 items across two dimensions: benevolence (5 items) and technical competence (5 items). A 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”) is used, with higher total scores indicating greater physician trust. The Cronbach’s α coefficient in this study was 0.812. Data collection and quality control methods The screening of study participants was conducted in two stages. In the first stage, researchers reviewed patients’ medical records to confirm hospitalization days, disease status, and the presence of a substitute decision-maker. In the second stage, for patients meeting ECMO indications, their substitute decision-makers were contacted for secondary eligibility screening. Upon confirming eligibility, researchers invited family members to a private conversation room prior to the patient’s ECMO initiation. The research purpose and methods were explained in detail, and informed consent was obtained. Researchers provided clear instructions and precautions for completing the questionnaires, ensuring family members fully understood before proceeding. All questionnaire completion processes were supervised by researchers, who provided on-site explanations for any questions. Upon completion, each questionnaire was checked for completeness, and any missing items were addressed. In the Chinese cultural context, substitute decision-making responsibilities are typically shared among family members. Data analysis Statistical analyses were performed using Microsoft Excel 2021 and IBM SPSS Statistics (version 26.0). Continuous variables with a normal distribution were presented as mean ± standard deviation (SD), while non-normally distributed data were expressed as median (interquartile range, IQR). Categorical variables were summarized using frequencies and percentages. Based on Decisional Conflict Scale (DCS) scores, family members were categorized into two groups: “No decisional conflict” (< 25 points) and “Decisional conflict present” (≥ 25 points). Univariate analyses employed Mann-Whitney U tests for non-parametric data, independent samples t-tests for normally distributed continuous variables, and Pearson’s chi-square tests for categorical variables. Variables demonstrating statistical significance (P < 0.05) in univariate analyses were subsequently entered into binary logistic regression modeling. The statistical significance threshold was set at α = 0.05 (two-tailed). Results General information of patients' families General information of patients' family members is detailed in Table 1 Decision readiness, decision fatigue, decision conflict, physician trust and social support scores of patients' families The average score of Decision Readiness Scale is (28.64±6.66), which is in the middle level. The average score of the Decision Fatigue Scale was (16.39±3.37), which was moderately high. The Decision Conflict Scale score (29.63±6.73) was at a high level, the mean Physician Trust Scale score (28.35±7.36) was at an intermediate level, and the mean Social Support Scale score (49.76±12.87) was at a moderately high level. Univariate analysis of factors influencing decision-making conflicts among patients' families The influencing factors with statistically significant differences in the information related to the patient's family members were: monthly income of the whole family, your level of knowledge about the patient's disease status and information related to ECMO treatment, decision fatigue, decision readiness, total score of trust, and social support were statistically significant, as shown in Table 2. Multifactorial analysis of factors influencing decision-making conflict among family members of ICU patients Taking the existence of decision-making conflict (non-existence = 0, existence = 1) as the dependent variable, the independent variables that were statistically significant in the univariate analysis were analyzed by binary logistic regression, in which decision fatigue, decision preparation Physician trust, social support original value substitution, categorical independent variable assignment is shown in Table 4, the analysis results show that the Omnibus test of the model coefficients P < 0.05, indicating that the model has significance, the analysis results show that the degree of your knowledge of the patient's disease status and information related to the treatment of ECMO, decision-making fatigue, decision-making readiness, physician trust, social support is the influence of the decision-making conflict, a total of explaining the total variance of of 64.5%, as shown in Table 3. Table1: Demographic Characteristics of Family Caregivers (n=169) Variable Category n % Education Level Primary school or below 22 13.0 Junior high school / Vocational school 21 12.4 High school / Technical secondary 42 24.9 College / Bachelor's degree 69 40.8 Master's degree or higher 15 8.9 Monthly Household Income (RMB) ＜3 000 0 0.0 3000～5000 15 8.9 5000～8000 76 45.0 8000～10000 77 45.6 ＞10000 1 0.6 ECMO Knowledge Level Uninformed 23 13.6 Basic understanding 55 32.5 Moderate understanding 47 27.8 Thorough understanding 44 26.0 Prior ICU Care Experience No 132 78.1 Yes 37 21.9 Payment Method Government-funded 15 8.9 Health insurance 153 90.5 Self-pay 1 0.6 Table 2. Factors Associated with Decisional Conflict Among ECMO Caregivers (n=169) Variable Category Decisional Conflict Statistic P No Conflict（n=62） No Conflict (n=107) Education Level Primary school or below 9(14.5) 13(12.1) 0.724 1) 0.948 Junior high/Vocational school 7(11.3) 14(13.1) High school/Technical secondary 14(22.6) 28(26.2) College/Bachelor's 27(43.5) 42(39.3) Master's or higher 5(8.1) 10(9.3) Monthly Income (RMB) ＜3 000 0(0.0) 0(0.0) 39.751 1) <0.001 3000～5000 1(1.6) 14(13.1) 5000～8000 13(21.0) 63(58.9) 8000～10000 47(75.8) 30(28.0) ＞10000 1(1.6) 0(0.0) ECMO Knowledge Uninformed 0(0.0) 23(21.5) 95.583 1) <0.001 Basic understanding 2(3.2) 53(49.5) Moderate understanding 20(32.3) 27(25.2) Thorough understanding 40(64.5) 4(3.7) Prior ICU Experience No 44(71.0) 88(82.2) 2.918 1) 0.088 Yes 18(29.0) 19(17.8) Payment Method Government-funded 7(11.3) 8(7.5) 2.497 1) 0.287 Health insurance 54(87.1) 99(92.5) Self-pay 1(1.6) 0(0.0) Decision Fatigue Total Score M(P25, P75) 14(12.8, 14) 18(16，20) -8.712 2) <0.001 Decision Readiness M(P25，P75) 35.5(32, 39) 25(23， 27) -8.798 2) <0.001 Physician Trust Total Score M(P25，P75) 38(27, 41) 25(22， 26) -8.162 2) <0.001 Social Support Total Score M(P25，P75) 67(48, 72) 43(39，46) -8.223 2) <0.001 1)X²; 2)Z Table 3. Multivariate Analysis of Factors Influencing Decision Conflict among Patients' Family Members Variable B Std. Error Wald χ2 P OR 95%CI Lower Upper Decision Fatigue 0.364 0.184 3.916 0.048 1.439 1.003 2.063 Decision Readiness -0.168 0.081 4.245 0.039 0.846 0.721 0.992 Physician Trust 1.149 0.374 9.415 0.002 3.154 1.514 6.57 Social Support -0.814 0.233 12.171 0.000 0.443 0.280 0.70 ECMO Knowledge Level -1.711 0.533 10.306 0.001 0.181 0.064 0.514 Constant 15.551 6.234 6.223 0.013 —— Table 4. Variable Coding Scheme for Independent Variables Independent Variable Coding Method Monthly household income 0 = ¥10,000 Knowledge about patient's disease and ECMO-related information 0 = Uninformed; 1 = Moderately informed; 2 = Well informed Decision fatigue Raw scores entered Decision readiness Raw scores entered Physician trust Raw scores entered Social support Raw scores entered Discussion This study found that 63.3% of family members experienced significant decisional conflict before ECMO initiation, with a mean score of 29.63 ± 6.73. This prevalence exceeds previously reported rates among general ICU family members (24.32 ± 15.19) by Qiao et al[ 30 ]. and medical ICU family members (21.9 ± 14.8) by Miller et al[ 31 ]. The elevated conflict level may be attributed to several factors. ECMO represents a high-cost, high-risk intervention with complex selection criteria and prognostic uncertainty, creating substantial information asymmetry between healthcare providers and family members. Additionally, advance care planning remains underutilized in China, with only 12.3% of end-stage patients participating in such discussions, leaving family members without clear guidance during emergency decisions. Furthermore, ECMO often necessitates multi-organ support considerations, requiring family members to weigh survival benefits against complication risks within limited timeframes, intensifying decision pressure[ 32 ]. Our findings demonstrate that family members' understanding of the patient's condition and ECMO treatment significantly influences decisional conflict levels, consistent with previous research[ 32 , 33 ]. Information deficiency represents a primary source of decisional conflict in high-risk, high-uncertainty contexts like ECMO decision-making. When family members lack accurate understanding of the patient's condition, potential benefits and risks of ECMO, possible complications, and prognosis, they frequently experience anxiety, fear, and uncertainty[ 34 ], which substantially increase decision difficulty and conflict. Previous research has shown that effective physician-family communication and comprehensive information disclosure significantly enhance family members' disease comprehension and treatment plan understanding[ 35 ]. With more complete information, family members can more rationally evaluate treatment options, develop realistic expectations, and reduce anxiety stemming from information asymmetry. Furthermore, transparent communication builds trust between healthcare providers and families, increasing confidence in medical team recommendations and reducing decisional burden. The results of this study identify decision fatigue as a significant risk factor for decisional conflict, with higher fatigue levels strongly associated with increased conflict. Decision fatigue encompasses emotional distress and cognitive impairment resulting from repeated decision-making demands, typically accompanied by diminished cognitive capacity and heightened emotional burden, negatively affecting decision quality and process[ 32 ]. In ECMO decision contexts, family members must process substantial complex information regarding treatment necessity, potential risks, and expected outcomes. Information overload and outcome uncertainty significantly exacerbate decision fatigue, further escalating decisional conflict. These findings suggest that healthcare providers should monitor family members' decision-making capacity and implement targeted interventions to alleviate fatigue[ 36 ]. Strategies may include simplifying information presentation, providing structured emotional support, and appropriately distributing decision responsibilities among family members. Additionally, our study demonstrates that family members with lower decision preparedness scores experience higher decisional conflict, consistent with previous research on ICU patients' families showing that decision preparedness negatively predicts conflict[ 28 ]. This relationship likely stems from two factors. First, ICU treatment decisions typically arise suddenly with significant time pressure, forcing family members into surrogate decision-making roles with minimal preparation. Second, critical care decisions involve complex, variable considerations requiring comprehensive assessment of the patient's condition, treatment options, and prognosis. These uncertainties frequently generate confusion and contradiction during decision processes, intensifying conflict. Therefore, clinical practice should strengthen decision support for family members through clear information guidance, assistance with risk-benefit evaluation, and psychological support to reduce decisional pressure. Our results further indicate that social support significantly influences decisional conflict among family members before ECMO initiation, with higher support levels associated with lower conflict, consistent with findings by Lee et al[ 37 ]. Social support provides emotional comfort, informational assistance, and practical help, reducing psychological pressure and decision burden. Strong internal family support promotes consensus-building and reduces opinion differences, enhancing medical decision consistency. External support helps family members better understand treatment plans and strengthen decision confidence through professional information and encouragement. Consequently, establishing multi-level support systems in clinical practice, particularly strengthening emotional and informational support, is recommended[ 38 ]. Healthcare institutions should facilitate effective communication channels between medical staff and family members, ensuring timely access to comprehensive information about treatment plans and patient status, thereby reducing anxiety from information asymmetry. Furthermore, our findings reveal a significant negative correlation between decisional conflict and physician trust, indicating that higher trust levels are associated with lower conflict. In the complex, high-risk ECMO context, strong trust enhances family members' acceptance of physician recommendations, effectively alleviating decision distress caused by information asymmetry, prognostic uncertainty, and treatment risk concerns[ 39 ]. This suggests that trust represents not only an important factor affecting decisional conflict but also plays a crucial role in implementing complex treatment plans. Therefore, establishing efficient communication mechanisms and promoting trust-building in physician-family relationships are particularly important in clinical practice. By clearly conveying treatment plans, thoroughly explaining risks and expected outcomes, and demonstrating both professional competence and compassionate care, healthcare providers can enhance family trust. This study's quantitative approach to decisional conflict in Chinese ECMO family caregivers addresses a notable gap in the literature, which has largely relied on qualitative studies with smaller sample sizes. By employing validated instruments and robust statistical analysis, we provide empirical evidence that can inform the development of targeted interventions. The findings suggest that a multi-faceted approach, integrating enhanced information provision, fatigue management, trust-building initiatives, and social support strengthening, is essential for improving decision quality and reducing decisional conflict in this vulnerable population. Limitations This study utilized a convenience sampling method from a single tertiary hospital in Jiangxi Province, which may limit the generalizability of the findings to other regions or healthcare settings in China, or to other cultural contexts. Future research could benefit from multi-center studies with larger and more diverse samples to enhance external validity. Additionally, while this study identified several modifiable factors, the cross-sectional design precludes the establishment of causal relationships. Longitudinal studies are needed to explore the dynamic interplay between these factors and decisional conflict over time, and to evaluate the effectiveness of interventions aimed at reducing decisional conflict. Conclusions This study demonstrates that family members of patients undergoing ECMO treatment experience relatively high levels of decisional conflict before treatment initiation. Key influencing factors include understanding of the patient's condition and ECMO treatment, decision fatigue, decision preparedness, physician trust, and social support. These findings provide important implications for clinical practice, suggesting the need for comprehensive decision support interventions addressing multiple domains of the decision-making experience. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of nanchang University First Affiliated Hospital ( (approval number: 1IT[2024]Clinical Ethics Review No. 339) and registered in the National Universal Health Security Platform Medical Research Registration and Filing Information System. All research subjects signed informed consent forms. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The First Affiliated Hospital of Nanchang University 2024 Clinical Research Funding Program for Cultivation, YFYLCYJPY202413 Authors' contributions Xiao Suqin (XSQ) and Liu Hongsuo (LHS) conceptualized and designed the study. Tang Lingpeng (TLP), Xiong Qin (XQ), and Dou Hong (HD) coordinated data collection, performed statistical analyses, and validated the dataset. Xiao Suqin (XSQ) and Dou Hong (HD) interpreted the findings, drafted the initial manuscript, and revised it critically for intellectual content. All authors contributed to manuscript refinement, approved the final version, and agreed to be accountable for all aspects of the work. Acknowledgements We would like to thank all the family members who participated in this study and the medical staff who assisted with the research. References Koedoot N, Molenaar S, Oosterveld P, et al. The decisional conflict scale: further validation in two samples of Dutch oncology patients)[J]. Patient Educ Couns. 2001 Dec 1;45(3):187-93. Janis I I , Mann L .Decisional Problems. (Book Reviews: Decision Making. A Psychological Analysis of Conflict, Choice, and Commitment)[J].Science, 1977, 197:1355-1356. White DB, Angus DC, Shields AM, et al. A Randomized Trial of a Family-Support Intervention in Intensive Care Units. N Engl J Med. 2018 Jun 21;378(25):2365-2375. Brodie D , Bacchetta M .Extracorporeal membrane oxygenation for ARDS in adults.[J].New England Journal of Medicine, 2011, 365(20):1905-14. Lobeck I N , Bondoc A , Nolan H ,et al.Extracorporeal Membrane Oxygenation in a Patient with Biliary Atresia: Case and Review of Extracorporeal Life Support Organization Data[J].ASAIO J. 2018 Nov/Dec;64(6):e191-e195. Balasubramanian P , Ghimire M , Pattnaik H ,et al.Clinical Outcomes With Extracorporeal Membrane Oxygenation for Interstitial Lung Disease: Systematic Review and Meta-Analysis[J]. 2024 Dec 1;70(12):1025-1032. Harvey M J , Gaies M G , Prosser L A .US and International In-Hospital Costs of Extracorporeal Membrane Oxygenation: a Systematic Review[J].Applied Health Economics & Health Policy, 2015 Aug;13(4):341-57. Ramanathan K .Ethical challenges of adult ECMO.[J].Springer Singapore, 2021 Apr;37(Suppl 2):303-308. Jaramillo C , Braus N .How Should ECMO Initiation and Withdrawal Decisions Be Shared?[J].2019 May 1;21(5):E387-393. Riggs K R , Becker L B , Sugarman J .Ethics in the use of extracorporeal cardiopulmonary resuscitation in adults[J].Resuscitation, 2015 Jun;91:73-5. Schou A , Mlgaard J , Andersen L W ,et al.Ethics in extracorporeal life support: a narrative review[J].Critical Care, 2021, 25(1):256. Yang X , Lin Y , Tang A ,et al.Tough choices: the experience of family members of critically ill patients participating in ECMO treatment decision-making: a descriptive qualitative study[J].BMC Medical Informatics and Decision Making, 2025, 25(1):1-10. Yang X , Lin Y , Tang A ,et al.Tough choices: the experience of family members of critically ill patients participating in ECMO treatment decision-making: a descriptive qualitative study[J].BMC Medical Informatics and Decision Making, 2025, 25(1):1-10. Kon A A .POINT: Is It Ethically Justifiable to Withdraw Extracorporeal Membrane Oxygenation Against the Wishes of a Patient With Decision-Making Capacity When Cure Is Not Possible? Yes[J].Chest, 2024, 166(6):1281-1283. Stephens A L , Bruce C R .Setting Expectations for ECMO: Improving Communication Between Clinical Teams and Decision Makers.[J].Methodist DeBakey cardiovascular journal, 2018, 14(2):120-125. Elwyn G , Durand M A , Song J ,et al.A three-talk model for shared decision making: multistage consultation process[J].BMJ, 2017 Nov 6;359:j4891. Lupattelli A, Branquinho M, Cardoso C, et al. Psychometric properties of the Decisional Conflict Scale in a sample of perinatal women with depressive symptoms: A study in Portugal and Norway.[J] Patient Educ Couns. 2024 Oct;127:108337. Aissaoui N , Caudron J , Leprince P ,et al. Right–left ventricular interdependence: a promising predictor of successful extracorporeal membrane oxygenation (ECMO) weaning after assistance for refractory cardiogenic shock[J].Intensive Care Medicine, 2017 Apr;43(4):592-594. Abrams D, Garan AR, Abdelbary A, et al. Position paper for the organization of ECMO programs for cardiac failure in adults[J].Intensive care medicine, 2018 Jun;44(6):717-729. O'Connor AM. Validation of a decisional conflict scale[J]. Med Decis Making. 1995;15(1):25-30. Li Y. Construction and application research of treatment decision aid program for early primary liver cancer patients [dissertation]. Shanghai: Second Military Medical University; 2017. Hickman RL Jr, Pignatiello GA, Tahir S. Evaluation of the Decisional Fatigue Scale Among Surrogate Decision Makers of the Critically Ill. West J Nurs Res. 2018;40(2):191-208. Pan GC, Li M. Chinese translation of the Decision Fatigue Scale and evaluation of its reliability and validity when applied to ICU patients' family members[J]. Nursing Journal. 2020;27(12):38-41. Bennett C, Graham ID, Kristjansson E, et al. Validation of a preparation for decision making scalee[J]. Patient Educ Couns. 2010;78(1):130-133. Wan JL, Bian W, Guo WM, et al. Reliability and validity study of the Chinese version of the Decision Preparedness Scale applied to visually impaired patientse[J]. China Medical Herald. 2020;17(09):49-52. Zimet GD, Dahlem NW, Zimet SG, et al. The Multidimensional Scale of Perceived Social Supporte[J]. J Pers Assess. 1988;52(1):30-41. Wang XD, Wang XL, Ma H. Mental Health Assessment Scale Manual. Beijing: Chinese Mental Health Journal Press; 1999. Hall A, Zheng B, Dugan E, et al. Measuring patients' trust in their primary care providerse[J]. Med Care Res Rev. 2002;59(3):293-318. Dong EH, Bao Y. Reliability and validity of the Chinese revised version of the Wake Forest Physician Trust Scalee[J]. Chinese Mental Health Journal. 2012;26(3):171-175. Qiao XT, Sui JJ, Wang KL, et al. Research on the current status and influencing factors of decisional conflict among ICU patient family memberse[J]. Chinese Journal of Emergency and Critical Care Nursing. 2024;5(8):677-683. Jia XL, Zhang YH, You Q, et al. Analysis of the current status and influencing factors of decisional conflict among ICU patient proxy decision-makerse[J]. Journal of Nursing. 2024;39(22):92-97. Decision-Making, Ethics, and End-of-Life Care in Pediatric Extracorporeal Membrane Oxygenation: A Comprehensive Narrative Reviewe[J]. Pediatr Crit Care Med. 2021;22(1):95-104. Yang XY, Tang AM, Lin Y, et al. A qualitative study on the experience of family members of critically ill patients participating in extracorporeal membrane oxygenation treatment decision-makinge[J]. Chinese Journal of Emergency and Critical Care Nursing. 2024;5(12):1068-1073. Sun WN, Hsu HT, Ko NY, Huang YT. Decision-Making Processes in Surrogates of Cancer Patients in a Taiwan Intensive Care Unite[J]. Int J Environ Res Public Health. 2020;17(12):4443. Gao YJ, Shan Y, Zhou Y, et al. Research progress on doctor-patient shared decision-making communicatione[J]. Chinese Nursing Management. 2021;21(01):156-160. Hickman RL, Pignatiello GA, Tahir S. Evaluation of the Decisional Fatigue Scale Among Surrogate Decision Makers of the Critically Ille[J]. West J Nurs Res. 2018;40(2):191-208. Lee J, Jung D, Choi M. Relationship of social support and decisional conflict to advance directives attitude in Korean older adults: A community-based cross-sectional studye[J]. Jpn J Nurs Sci. 2016;13(1):29-37. Zheng HY, Hu JL, Dong BJ, et al. Research progress on theoretical models related to shared decision-making among medical staff, nurses, and patientse[J]. Chinese Nursing Management. 2018;18(11):1575-1580. Soll RF, Ovelman C, McGuire W. The future of Cochrane Neonatale[J]. Early Hum Dev. 2020;150:105191. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviewers agreed at journal 23 Aug, 2025 Reviews received at journal 22 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers invited by journal 11 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Editor invited by journal 26 Jun, 2025 Submission checks completed at journal 26 Jun, 2025 First submitted to journal 25 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6971796","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":501284411,"identity":"aa5304b7-ab46-463f-ba58-53ff5d041bbb","order_by":0,"name":"suqin Xiao","email":"","orcid":"","institution":"Nanchang University First Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"suqin","middleName":"","lastName":"Xiao","suffix":""},{"id":501284412,"identity":"c570ce14-85e8-4e0a-8880-9a5c09dec8eb","order_by":1,"name":"Hongsuo Liu","email":"","orcid":"","institution":"Nanchang University First Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongsuo","middleName":"","lastName":"Liu","suffix":""},{"id":501284413,"identity":"529a3350-6b78-4ae2-a5c0-f7cb945473c4","order_by":2,"name":"Lingpeng TANG","email":"","orcid":"","institution":"Nanchang University First Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lingpeng","middleName":"","lastName":"TANG","suffix":""},{"id":501284414,"identity":"544d410e-9581-4ce0-9122-e550b8b7d91e","order_by":3,"name":"Qin XIONG","email":"","orcid":"","institution":"Nanchang University First Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"XIONG","suffix":""},{"id":501284415,"identity":"76a44c4b-7e80-44b2-a746-dddc51e94fcc","order_by":4,"name":"jiang Rong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3NIQvCQBTA8XcenIbTfDLQr3BDUIR9mBNhaUGL2DYRlrRP8ENo0ejEYDnMayrCkkEQRJtTi+mmTfD+8Mrj/XgAOt1PJuAEnLmL7SF8LcJ0ggLoWsgLbfENkXZCHP4ZqWUds5/3Vxh58rK7+VAqRAKdWwpSHxyfhGA0nJtDHyrFSGAjUBAeObVDQijB+ZmBfGhMIkEwVZPnF0YJjR/E/YxQaXNGKXkQwVOJjNu9cdcSnJFqcbBh5kju+4aSrJtT78hZQnDMrh2rXFg3l2cVAcjxzNsBSwZ5SgCQ3aFryolOp9P9eXfiyU5YTUCA5AAAAABJRU5ErkJggg==","orcid":"","institution":"Nanchang University First Affiliated Hospital","correspondingAuthor":true,"prefix":"","firstName":"jiang","middleName":"","lastName":"Rong","suffix":""},{"id":501284416,"identity":"18a73aab-ee83-4723-baa4-27b766f5b6ec","order_by":5,"name":"DOU Hong","email":"","orcid":"","institution":"The Operating Room of the First Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"DOU","middleName":"","lastName":"Hong","suffix":""}],"badges":[],"createdAt":"2025-06-25 07:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6971796/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6971796/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-28709-9","type":"published","date":"2025-12-11T15:59:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98243993,"identity":"826bc314-76b8-4e4f-97cc-55a6536f9e6e","added_by":"auto","created_at":"2025-12-15 16:12:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":971090,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6971796/v1/f2cf7b4e-4d75-4aff-8029-5152f5080ab5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decisional Conflict Among Chinese Family Caregivers Prior to ECMO Initiation: A Cross-Sectional Study in Critical Care Nursing","fulltext":[{"header":"Background","content":"\u003cp\u003eDecisional conflict, a pivotal concept in healthcare decision-making, describes the psychological distress experienced when individuals face choices involving inherent risks, uncertain outcomes, and potential conflicts with personal values [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This conflict is characterized by uncertainty regarding available options, insufficient information, ambiguity in personal values, and perceived external pressure [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Elevated levels of decisional conflict can lead to delayed decisions or post-decision regret, negatively impacting the quality of care and potentially escalating doctor-patient disputes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In critical care environments, where life-altering decisions must frequently be made under severe time constraints, decisional conflict carries particularly profound consequences [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eExtracorporeal membrane oxygenation (ECMO) is a sophisticated life-support technology that provides respiratory and/or circulatory support for patients experiencing severe cardiopulmonary failure by circulating blood through an external artificial lung [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Since its inception, ECMO has evolved from an experimental intervention into a standard rescue therapy for refractory respiratory and cardiac failure. The global COVID-19 pandemic significantly accelerated ECMO utilization, with over 10,000 COVID-19 patients receiving ECMO support between 2020 and 2022 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, ECMO technology presents substantial clinical challenges. Determining the optimal timing for initiation requires precise identification of the \"therapeutic window.\" The treatment itself is associated with serious complications, including bleeding, thrombosis, and infection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A systematic review reported complication rates ranging from 10\u0026ndash;45% across different ECMO modalities, with major bleeding events occurring in approximately 30% of cases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, the considerable financial burden of ECMO treatment adds another layer of complexity; the average daily cost ranges from 5,000 to 10,000 USD, with total hospitalization costs often exceeding \u003cspan\u003e$\u003c/span\u003e150,000 per case [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These characteristics transform ECMO treatment decisions into complex, multidimensional dilemmas encompassing medical, ethical, and economic considerations.\u003c/p\u003e\u003cp\u003eIn clinical practice, family members often serve as surrogate decision-makers, navigating intricate scenarios that demand balancing the patient's survival probability against quality-of-life considerations, assessing financial capacity, addressing ethical challenges, and managing intense psychological stress within limited timeframes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A qualitative study involving family members of ECMO candidates revealed that surrogate decision-makers frequently experience significant emotional burden, information overload, and value conflicts during this critical decision-making process [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This multifactorial decision-making environment frequently exacerbates decisional conflict, which has been shown to potentially delay optimal treatment timing [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Research indicates that such delays can cause patients to miss critical therapeutic windows, increase complication risks, and ultimately impact survival outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe ethical dimensions of ECMO decision-making have garnered increasing attention in recent literature. Key ethical challenges in ECMO therapy include the complexities of informed consent, resource allocation considerations, and end-of-life decision-making [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Obtaining informed consent for ECMO is particularly challenging due to the emergent nature of many cases, the technical complexity of the intervention, and the difficulty in accurately prognosticating outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Surrogate decision-makers often struggle to fully comprehend the implications of ECMO therapy, including potential complications, long-term quality-of-life outcomes, and scenarios involving withdrawal of support [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Despite the critical role of family members in ECMO authorization, existing research has predominantly focused on the clinical outcomes of ECMO technology itself, with insufficient attention to the decision-making processes, particularly regarding family members' decisional conflicts. A systematic review on ethical issues in extracorporeal life support identified only 12 studies specifically addressing family decision-making experiences [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This knowledge gap is particularly pronounced in non-Western healthcare contexts, where unique family dynamics, cultural values, and healthcare delivery systems can significantly influence decision-making processes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCurrent literature on decisional conflicts among family members of ECMO patients remains limited, with most studies employing qualitative methodologies and small sample sizes. This limits their ability to comprehensively reflect decisional conflict patterns and provide robust quantitative analysis of influencing factors. While the integration of decision science frameworks into critical care research offers promising avenues for understanding and addressing decisional conflict, it remains underutilized, especially for time-sensitive interventions like ECMO [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven the high-risk nature, treatment intensity, and cultural specificity of ECMO decisions, this study utilized validated instruments, including the Decisional Conflict Scale, Decision Preparedness Scale, Decision Fatigue Scale, Social Support Scale, and Wake Forest Physician Trust Scale, to systematically analyze the current status and influencing factors of decisional conflicts among family members prior to ECMO initiation. This comprehensive approach addresses a significant gap in the literature by providing quantitative data on decisional conflict prevalence and identifying modifiable factors that can serve as targets for intervention. The findings from this study offer empirical evidence for developing clinical decision support systems and optimizing physician-family communication, ultimately contributing to improved quality of critical care decision-making and patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eDesign and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study employed a convenience sampling method to recruit 169 family members of patients scheduled for ECMO treatment. Participants were enrolled from the Department of Critical Care Medicine at a tertiary hospital in Jiangxi Province, China, between January 2024 and April 2025. Sample size calculation was based on the 10 events per variable (EPV) criterion, considering 7 candidate predictors in the regression model. Accounting for an anticipated 52% decision conflict rate (derived from pilot data) and a 15% potential attrition rate, the minimum required sample size was determined to be 169. Inclusion criteria for family members were\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003e(1) patients met ECMO indications and had signed informed consent; (2) direct relatives of the patient (prioritized by decision-making hierarchy: spouse \u0026gt; parents or children \u0026gt; siblings); (3) age \u0026ge; 18 years, with basic communication and comprehension abilities; (4) full participation in doctor-patient communication prior to ECMO initiation; and (5) voluntary signing of research informed consent. Exclusion criteria included: (1) involvement in medical disputes or legal proceedings; (2) previously diagnosed cognitive dysfunction or mental illness; and (3) severe hearing or language impairment. The study protocol was approved by the hospital\u0026rsquo;s ethics committee (approval number: 1IT[2024]Clinical Ethics Review No. 339) and \u0026nbsp;al methods were performed in accordance with the relevant guidelines and\u003c/p\u003e\n\u003cp\u003eregulations. All participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic and clinical information was collected through a self-designed questionnaire based on a comprehensive literature review. This included family members\u0026rsquo; gender, age, education level, marital status, monthly per capita family income, place of residence, level of understanding regarding the patient\u0026rsquo;s condition and ECMO treatment, and prior experience caring for intensive care unit (ICU) patients. This section was completed by the family members themselves.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecisional Conflict Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Decisional Conflict Scale developed by O\u0026rsquo;Connor [20] and validated in Chinese by Li Yu [21], was used to assess decisional conflict. The scale comprises 16 items across five dimensions: being informed, clarifying values, support, decision uncertainty, and effective decision-making. A 5-point Likert scale is used, where 0 represents \u0026ldquo;yes\u0026rdquo; and 4 represents \u0026ldquo;no.\u0026rdquo; Total scores are converted to a percentage, with scores \u0026lt;25 indicating no decisional conflict and \u0026ge;25 indicating the presence of decisional conflict. In this study, the Cronbach\u0026rsquo;s \u0026alpha; coefficient for the DCS was 0.922, demonstrating high internal consistency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecision Fatigue Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDecision Fatigue Scale developed by Hickman et al. [22] and translated into Chinese by Pan Guocui et al. [23] for use with ICU patients\u0026rsquo; family members in 2020, this single-dimension scale consists of 9 items. A 4-point Likert scale (0 = \u0026ldquo;strongly disagree\u0026rdquo; to 3 = \u0026ldquo;strongly agree\u0026rdquo;) is used, yielding a total score range of 0 to 27. Higher scores indicate greater levels of decision fatigue. The Cronbach\u0026rsquo;s \u0026alpha; coefficient in this study was 0.912, indicating good internal consistency..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecision Preparedness Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Decision Preparedness Scale was Originally developed by O\u0026rsquo;Connor and revised by Bennett [24]. this scale assesses an individual\u0026rsquo;s preparedness for decision-making. The Chinese version, validated by Wan Junli et al. in 2020[25], is a single-dimension scale with 10 items. A 5-point Likert scale (1 = \u0026ldquo;none at all\u0026rdquo; to 5 = \u0026ldquo;very much\u0026rdquo;) is used. After percentage conversion, scores below 60 indicate poor decision preparedness, while higher scores suggest greater preparedness. The Cronbach\u0026rsquo;s \u0026alpha; coefficient in this study was 0.959.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived Social Support Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original PSSS, developed by Zimet et al. [26] in 1988 (Cronbach\u0026rsquo;s \u0026alpha; = 0.880), assesses perceived social support. The Chinese version, translated and revised by Jiang Qianjin et al. [27] in 1999, measures multidimensional perceptions of social support. It includes two dimensions: family internal support (4 items) and family external support (8 items), totaling 12 items. Each item uses a 7-point Likert scale (1 = \u0026ldquo;strongly disagree\u0026rdquo; to 7 = \u0026ldquo;strongly agree\u0026rdquo;), with total scores ranging from 12 to 84. Higher scores indicate higher levels of perceived social support. The Cronbach\u0026rsquo;s \u0026alpha; coefficient in this study was 0.902..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWake Forest Physician Trust Scale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeveloped by Hall et al. [28] and translated by Dong Enhong et al. [29], this scale assesses patient trust in physicians. It comprises 10 items across two dimensions: benevolence (5 items) and technical competence (5 items). A 5-point Likert scale (1 = \u0026ldquo;strongly disagree\u0026rdquo; to 5 = \u0026ldquo;strongly agree\u0026rdquo;) is used, with higher total scores indicating greater physician trust. The Cronbach\u0026rsquo;s \u0026alpha; coefficient in this study was 0.812.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection and quality control methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe screening of study participants was conducted in two stages. In the first stage, researchers reviewed patients\u0026rsquo; medical records to confirm hospitalization days, disease status, and the presence of a substitute decision-maker. In the second stage, for patients meeting ECMO indications, their substitute decision-makers were contacted for secondary eligibility screening. Upon confirming eligibility, researchers invited family members to a private conversation room prior to the patient\u0026rsquo;s ECMO initiation. The research purpose and methods were explained in detail, and informed consent was obtained. Researchers provided clear instructions and precautions for completing the questionnaires, ensuring family members fully understood before proceeding. All questionnaire completion processes were supervised by researchers, who provided on-site explanations for any questions. Upon completion, each questionnaire was checked for completeness, and any missing items were addressed. In the Chinese cultural context, substitute decision-making responsibilities are typically shared among family members.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using Microsoft Excel 2021 and IBM SPSS Statistics (version 26.0). Continuous variables with a normal distribution were presented as mean \u0026plusmn; standard deviation (SD), while non-normally distributed data were expressed as median (interquartile range, IQR). Categorical variables were summarized using frequencies and percentages. Based on Decisional Conflict Scale (DCS) scores, family members were categorized into two groups: \u0026ldquo;No decisional conflict\u0026rdquo; (\u0026lt; 25 points) and \u0026ldquo;Decisional conflict present\u0026rdquo; (\u0026ge; 25 points). Univariate analyses employed Mann-Whitney U tests for non-parametric data, independent samples t-tests for normally distributed continuous variables, and Pearson\u0026rsquo;s chi-square tests for categorical variables. Variables demonstrating statistical significance (P \u0026lt; 0.05) in univariate analyses were subsequently entered into binary logistic regression modeling. The statistical significance threshold was set at \u0026alpha; = 0.05 (two-tailed).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGeneral information of patients\u0026apos; families\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneral information of patients\u0026apos; family members is detailed in Table 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecision readiness, decision fatigue, decision conflict, physician trust and social support scores of patients\u0026apos; families\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe average score of Decision Readiness Scale is (28.64\u0026plusmn;6.66), which is in the middle level. The average score of the Decision Fatigue Scale was (16.39\u0026plusmn;3.37), which was moderately high. The Decision Conflict Scale score (29.63\u0026plusmn;6.73) was at a high level, the mean Physician Trust Scale score (28.35\u0026plusmn;7.36) was at an intermediate level, and the mean Social Support Scale score (49.76\u0026plusmn;12.87) was at a moderately high level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate analysis of factors influencing decision-making conflicts among patients\u0026apos; families\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe influencing factors with statistically significant differences in the information related to the patient\u0026apos;s family members were: monthly income of the whole family, your level of knowledge about the patient\u0026apos;s disease status and information related to ECMO treatment, decision fatigue, decision readiness, total score of trust, and social support were statistically significant, as shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultifactorial analysis of factors influencing decision-making conflict among family members of ICU patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTaking the existence of decision-making conflict (non-existence = 0, existence = 1) as the dependent variable, the independent variables that were statistically significant in the univariate analysis were analyzed by binary logistic regression, in which decision fatigue, decision preparation Physician trust, social support original value substitution, categorical independent variable assignment is shown in Table 4, the analysis results show that the Omnibus test of the model coefficients P \u0026lt; 0.05, indicating that the model has significance, the analysis results show that the degree of your knowledge of the patient\u0026apos;s disease status and information related to the treatment of ECMO, decision-making fatigue, decision-making readiness, physician trust, social support is the influence of the decision-making conflict, a total of explaining the total variance of of 64.5%, as shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable1: Demographic Characteristics of Family Caregivers (n=169)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"109%\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 25px;\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003ePrimary school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eJunior high school / Vocational school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eHigh school / Technical secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eCollege / Bachelor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e40.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 25px;\"\u003e\n \u003cp\u003eMonthly Household Income (RMB) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e＜3 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e3000～5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e5000～8000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e8000～10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e45.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e＞10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 25px;\"\u003e\n \u003cp\u003eECMO Knowledge Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eUninformed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eBasic understanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e32.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eModerate understanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eThorough understanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e26.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003ePrior ICU Care Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e78.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 25px;\"\u003e\n \u003cp\u003ePayment Method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eGovernment-funded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eHealth insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e90.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003eSelf-pay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Factors Associated with Decisional Conflict Among ECMO Caregivers (n=169)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"112%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003eDecisional Conflict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eNo Conflict（n=62）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eNo Conflict\u003c/p\u003e\n \u003cp\u003e(n=107)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 20px;\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;Primary school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9(14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e13(12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.724\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eJunior high/Vocational school \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e14(13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eHigh school/Technical secondary \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e14(22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e28(26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eCollege/Bachelor\u0026apos;s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e27(43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e42(39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eMaster\u0026apos;s or higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e5(8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e10(9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 20px;\"\u003e\n \u003cp\u003eMonthly Income (RMB)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e＜3 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 11px;\"\u003e\n \u003cp\u003e39.751\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e3000～5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e14(13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e5000～8000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e13(21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e63(58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e8000～10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e47(75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e30(28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e＞10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 20px;\"\u003e\n \u003cp\u003eECMO Knowledge\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eUninformed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e23(21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 11px;\"\u003e\n \u003cp\u003e95.583\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eBasic understanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e53(49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eModerate understanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e20(32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e27(25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eThorough understanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e40(64.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e4(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003ePrior ICU Experience\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e44(71.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e88(82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2.918\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e18(29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e19(17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 20px;\"\u003e\n \u003cp\u003ePayment Method\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eGovernment-funded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e7(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e8(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2.497\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eHealth insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e54(87.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e99(92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eSelf-pay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e0(0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eDecision Fatigue Total Score\u003c/p\u003e\n \u003cp\u003eM(P25, P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e14(12.8, \u0026nbsp;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e18(16，20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-8.712\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eDecision Readiness\u003c/p\u003e\n \u003cp\u003eM(P25，P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e35.5(32, \u0026nbsp;39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e25(23，\u0026nbsp;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-8.798\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003ePhysician Trust Total Score\u003c/p\u003e\n \u003cp\u003eM(P25，P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e38(27, \u0026nbsp; \u0026nbsp; 41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e25(22，\u0026nbsp;26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-8.162\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eSocial Support Total Score\u003c/p\u003e\n \u003cp\u003eM(P25，P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e67(48, \u0026nbsp; \u0026nbsp; 72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e43(39，46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-8.223\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1)X\u0026sup2;; 2)Z\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Multivariate Analysis of Factors Influencing Decision Conflict among Patients\u0026apos; Family Members\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"112%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003eWald \u0026chi;2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eDecision Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e3.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eDecision Readiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e4.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003ePhysician Trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e9.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eSocial Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e12.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eECMO Knowledge Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e-1.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e10.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e15.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e6.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Variable Coding Scheme for Independent Variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"639\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eIndependent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 459px;\"\u003e\n \u003cp\u003eCoding Method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMonthly household income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 459px;\"\u003e\n \u003cp\u003e0 = \u0026lt;\u0026yen;3,000; 1 = \u0026yen;3,000-5,000; 2 = \u0026yen;5,000-8,000; 3 = \u0026yen;8,000-10,000; 4 = \u0026gt;\u0026yen;10,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eKnowledge about patient\u0026apos;s disease and ECMO-related information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 459px;\"\u003e\n \u003cp\u003e0 = Uninformed; 1 = Moderately informed; 2 = Well informed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDecision fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 459px;\"\u003e\n \u003cp\u003eRaw scores entered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDecision readiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 459px;\"\u003e\n \u003cp\u003eRaw scores entered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ePhysician trust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 459px;\"\u003e\n \u003cp\u003eRaw scores entered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSocial support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 459px;\"\u003e\n \u003cp\u003eRaw scores entered\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study found that 63.3% of family members experienced significant decisional conflict before ECMO initiation, with a mean score of 29.63\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73. This prevalence exceeds previously reported rates among general ICU family members (24.32\u0026thinsp;\u0026plusmn;\u0026thinsp;15.19) by Qiao et al[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. and medical ICU family members (21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8) by Miller et al[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The elevated conflict level may be attributed to several factors. ECMO represents a high-cost, high-risk intervention with complex selection criteria and prognostic uncertainty, creating substantial information asymmetry between healthcare providers and family members. Additionally, advance care planning remains underutilized in China, with only 12.3% of end-stage patients participating in such discussions, leaving family members without clear guidance during emergency decisions. Furthermore, ECMO often necessitates multi-organ support considerations, requiring family members to weigh survival benefits against complication risks within limited timeframes, intensifying decision pressure[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur findings demonstrate that family members' understanding of the patient's condition and ECMO treatment significantly influences decisional conflict levels, consistent with previous research[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Information deficiency represents a primary source of decisional conflict in high-risk, high-uncertainty contexts like ECMO decision-making. When family members lack accurate understanding of the patient's condition, potential benefits and risks of ECMO, possible complications, and prognosis, they frequently experience anxiety, fear, and uncertainty[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which substantially increase decision difficulty and conflict. Previous research has shown that effective physician-family communication and comprehensive information disclosure significantly enhance family members' disease comprehension and treatment plan understanding[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. With more complete information, family members can more rationally evaluate treatment options, develop realistic expectations, and reduce anxiety stemming from information asymmetry. Furthermore, transparent communication builds trust between healthcare providers and families, increasing confidence in medical team recommendations and reducing decisional burden.\u003c/p\u003e\u003cp\u003eThe results of this study identify decision fatigue as a significant risk factor for decisional conflict, with higher fatigue levels strongly associated with increased conflict. Decision fatigue encompasses emotional distress and cognitive impairment resulting from repeated decision-making demands, typically accompanied by diminished cognitive capacity and heightened emotional burden, negatively affecting decision quality and process[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In ECMO decision contexts, family members must process substantial complex information regarding treatment necessity, potential risks, and expected outcomes. Information overload and outcome uncertainty significantly exacerbate decision fatigue, further escalating decisional conflict. These findings suggest that healthcare providers should monitor family members' decision-making capacity and implement targeted interventions to alleviate fatigue[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Strategies may include simplifying information presentation, providing structured emotional support, and appropriately distributing decision responsibilities among family members.\u003c/p\u003e\u003cp\u003eAdditionally, our study demonstrates that family members with lower decision preparedness scores experience higher decisional conflict, consistent with previous research on ICU patients' families showing that decision preparedness negatively predicts conflict[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This relationship likely stems from two factors. First, ICU treatment decisions typically arise suddenly with significant time pressure, forcing family members into surrogate decision-making roles with minimal preparation. Second, critical care decisions involve complex, variable considerations requiring comprehensive assessment of the patient's condition, treatment options, and prognosis. These uncertainties frequently generate confusion and contradiction during decision processes, intensifying conflict. Therefore, clinical practice should strengthen decision support for family members through clear information guidance, assistance with risk-benefit evaluation, and psychological support to reduce decisional pressure.\u003c/p\u003e\u003cp\u003eOur results further indicate that social support significantly influences decisional conflict among family members before ECMO initiation, with higher support levels associated with lower conflict, consistent with findings by Lee et al[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Social support provides emotional comfort, informational assistance, and practical help, reducing psychological pressure and decision burden. Strong internal family support promotes consensus-building and reduces opinion differences, enhancing medical decision consistency. External support helps family members better understand treatment plans and strengthen decision confidence through professional information and encouragement. Consequently, establishing multi-level support systems in clinical practice, particularly strengthening emotional and informational support, is recommended[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Healthcare institutions should facilitate effective communication channels between medical staff and family members, ensuring timely access to comprehensive information about treatment plans and patient status, thereby reducing anxiety from information asymmetry.\u003c/p\u003e\u003cp\u003eFurthermore, our findings reveal a significant negative correlation between decisional conflict and physician trust, indicating that higher trust levels are associated with lower conflict. In the complex, high-risk ECMO context, strong trust enhances family members' acceptance of physician recommendations, effectively alleviating decision distress caused by information asymmetry, prognostic uncertainty, and treatment risk concerns[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This suggests that trust represents not only an important factor affecting decisional conflict but also plays a crucial role in implementing complex treatment plans. Therefore, establishing efficient communication mechanisms and promoting trust-building in physician-family relationships are particularly important in clinical practice. By clearly conveying treatment plans, thoroughly explaining risks and expected outcomes, and demonstrating both professional competence and compassionate care, healthcare providers can enhance family trust.\u003c/p\u003e\u003cp\u003eThis study's quantitative approach to decisional conflict in Chinese ECMO family caregivers addresses a notable gap in the literature, which has largely relied on qualitative studies with smaller sample sizes. By employing validated instruments and robust statistical analysis, we provide empirical evidence that can inform the development of targeted interventions. The findings suggest that a multi-faceted approach, integrating enhanced information provision, fatigue management, trust-building initiatives, and social support strengthening, is essential for improving decision quality and reducing decisional conflict in this vulnerable population.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study utilized a convenience sampling method from a single tertiary hospital in Jiangxi Province, which may limit the generalizability of the findings to other regions or healthcare settings in China, or to other cultural contexts. Future research could benefit from multi-center studies with larger and more diverse samples to enhance external validity. Additionally, while this study identified several modifiable factors, the cross-sectional design precludes the establishment of causal relationships. Longitudinal studies are needed to explore the dynamic interplay between these factors and decisional conflict over time, and to evaluate the effectiveness of interventions aimed at reducing decisional conflict.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that family members of patients undergoing ECMO treatment experience relatively high levels of decisional conflict before treatment initiation. Key influencing factors include understanding of the patient's condition and ECMO treatment, decision fatigue, decision preparedness, physician trust, and social support. These findings provide important implications for clinical practice, suggesting the need for comprehensive decision support interventions addressing multiple domains of the decision-making experience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of nanchang University First Affiliated Hospital ( (approval number: 1IT[2024]Clinical Ethics Review No. 339) and registered in the National Universal Health Security Platform Medical Research Registration and Filing Information System. All research subjects signed informed consent forms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe First Affiliated Hospital of Nanchang University 2024 Clinical Research Funding Program for Cultivation, YFYLCYJPY202413\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiao Suqin (XSQ) and Liu Hongsuo (LHS) conceptualized and designed the study. Tang Lingpeng (TLP), Xiong Qin (XQ), and Dou Hong (HD) coordinated data collection, performed statistical analyses, and validated the dataset. Xiao Suqin (XSQ) and Dou Hong (HD) interpreted the findings, drafted the initial manuscript, and revised it critically for intellectual content. All authors contributed to manuscript refinement, approved the final version, and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the family members who participated in this study and the medical staff who assisted with the research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKoedoot N, Molenaar S, Oosterveld P, et al. The decisional conflict scale: further validation in two samples of Dutch oncology patients)[J]. Patient Educ Couns. 2001 Dec 1;45(3):187-93. \u003c/li\u003e\n\u003cli\u003eJanis I I , Mann L .Decisional Problems. (Book Reviews: Decision Making. A Psychological Analysis of Conflict, Choice, and Commitment)[J].Science, 1977, 197:1355-1356.\u003c/li\u003e\n\u003cli\u003eWhite DB, Angus DC, Shields AM, et al. A Randomized Trial of a Family-Support Intervention in Intensive Care Units. N Engl J Med. 2018 Jun 21;378(25):2365-2375.\u003c/li\u003e\n\u003cli\u003eBrodie D , Bacchetta M .Extracorporeal membrane oxygenation for ARDS in adults.[J].New England Journal of Medicine, 2011, 365(20):1905-14.\u003c/li\u003e\n\u003cli\u003eLobeck I N , Bondoc A , Nolan H ,et al.Extracorporeal Membrane Oxygenation in a Patient with Biliary Atresia: Case and Review of Extracorporeal Life Support Organization Data[J].ASAIO J. 2018 Nov/Dec;64(6):e191-e195.\u003c/li\u003e\n\u003cli\u003eBalasubramanian P , Ghimire M , Pattnaik H ,et al.Clinical Outcomes With Extracorporeal Membrane Oxygenation for Interstitial Lung Disease: Systematic Review and Meta-Analysis[J]. 2024 Dec 1;70(12):1025-1032. \u003c/li\u003e\n\u003cli\u003eHarvey M J , Gaies M G , Prosser L A .US and International In-Hospital Costs of Extracorporeal Membrane Oxygenation: a Systematic Review[J].Applied Health Economics \u0026amp; Health Policy, 2015 Aug;13(4):341-57.\u003c/li\u003e\n\u003cli\u003eRamanathan K .Ethical challenges of adult ECMO.[J].Springer Singapore, 2021 Apr;37(Suppl 2):303-308. \u003c/li\u003e\n\u003cli\u003eJaramillo C , Braus N .How Should ECMO Initiation and Withdrawal Decisions Be Shared?[J].2019 May 1;21(5):E387-393.\u003c/li\u003e\n\u003cli\u003eRiggs K R , Becker L B , Sugarman J .Ethics in the use of extracorporeal cardiopulmonary resuscitation in adults[J].Resuscitation, 2015 Jun;91:73-5. \u003c/li\u003e\n\u003cli\u003eSchou A , Mlgaard J , Andersen L W ,et al.Ethics in extracorporeal life support: a narrative review[J].Critical Care, 2021, 25(1):256.\u003c/li\u003e\n\u003cli\u003eYang X , Lin Y , Tang A ,et al.Tough choices: the experience of family members of critically ill patients participating in ECMO treatment decision-making: a descriptive qualitative study[J].BMC Medical Informatics and Decision Making, 2025, 25(1):1-10.\u003c/li\u003e\n\u003cli\u003eYang X , Lin Y , Tang A ,et al.Tough choices: the experience of family members of critically ill patients participating in ECMO treatment decision-making: a descriptive qualitative study[J].BMC Medical Informatics and Decision Making, 2025, 25(1):1-10.\u003c/li\u003e\n\u003cli\u003eKon A A .POINT: Is It Ethically Justifiable to Withdraw Extracorporeal Membrane Oxygenation Against the Wishes of a Patient With Decision-Making Capacity When Cure Is Not Possible? Yes[J].Chest, 2024, 166(6):1281-1283.\u003c/li\u003e\n\u003cli\u003eStephens A L , Bruce C R .Setting Expectations for ECMO: Improving Communication Between Clinical Teams and Decision Makers.[J].Methodist DeBakey cardiovascular journal, 2018, 14(2):120-125.\u003c/li\u003e\n\u003cli\u003eElwyn G , Durand M A , Song J ,et al.A three-talk model for shared decision making: multistage consultation process[J].BMJ, 2017 Nov 6;359:j4891. \u003c/li\u003e\n\u003cli\u003eLupattelli A, Branquinho M, Cardoso C, et al. Psychometric properties of the Decisional Conflict Scale in a sample of perinatal women with depressive symptoms: A study in Portugal and Norway.[J] Patient Educ Couns. 2024 Oct;127:108337.\u003c/li\u003e\n\u003cli\u003eAissaoui N , Caudron J , Leprince P ,et al. Right\u0026ndash;left ventricular interdependence: a promising predictor of successful extracorporeal membrane oxygenation (ECMO) weaning after assistance for refractory cardiogenic shock[J].Intensive Care Medicine, 2017 Apr;43(4):592-594.\u003c/li\u003e\n\u003cli\u003eAbrams D, Garan AR, Abdelbary A, et al. Position paper for the organization of ECMO programs for cardiac failure in adults[J].Intensive care medicine, 2018 Jun;44(6):717-729.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Connor AM. Validation of a decisional conflict scale[J]. Med Decis Making. 1995;15(1):25-30.\u003c/li\u003e\n\u003cli\u003eLi Y. Construction and application research of treatment decision aid program for early primary liver cancer patients [dissertation]. Shanghai: Second Military Medical University; 2017.\u003c/li\u003e\n\u003cli\u003eHickman RL Jr, Pignatiello GA, Tahir S. Evaluation of the Decisional Fatigue Scale Among Surrogate Decision Makers of the Critically Ill. West J Nurs Res. 2018;40(2):191-208.\u003c/li\u003e\n\u003cli\u003ePan GC, Li M. Chinese translation of the Decision Fatigue Scale and evaluation of its reliability and validity when applied to ICU patients\u0026apos; family members[J]. Nursing Journal. 2020;27(12):38-41.\u003c/li\u003e\n\u003cli\u003eBennett C, Graham ID, Kristjansson E, et al. Validation of a preparation for decision making scalee[J]. Patient Educ Couns. 2010;78(1):130-133.\u003c/li\u003e\n\u003cli\u003eWan JL, Bian W, Guo WM, et al. Reliability and validity study of the Chinese version of the Decision Preparedness Scale applied to visually impaired patientse[J]. China Medical Herald. 2020;17(09):49-52.\u003c/li\u003e\n\u003cli\u003eZimet GD, Dahlem NW, Zimet SG, et al. The Multidimensional Scale of Perceived Social Supporte[J]. J Pers Assess. 1988;52(1):30-41.\u003c/li\u003e\n\u003cli\u003eWang XD, Wang XL, Ma H. Mental Health Assessment Scale Manual. Beijing: Chinese Mental Health Journal Press; 1999.\u003c/li\u003e\n\u003cli\u003eHall A, Zheng B, Dugan E, et al. Measuring patients\u0026apos; trust in their primary care providerse[J]. Med Care Res Rev. 2002;59(3):293-318.\u003c/li\u003e\n\u003cli\u003eDong EH, Bao Y. Reliability and validity of the Chinese revised version of the Wake Forest Physician Trust Scalee[J]. Chinese Mental Health Journal. 2012;26(3):171-175.\u003c/li\u003e\n\u003cli\u003eQiao XT, Sui JJ, Wang KL, et al. Research on the current status and influencing factors of decisional conflict among ICU patient family memberse[J]. Chinese Journal of Emergency and Critical Care Nursing. 2024;5(8):677-683.\u003c/li\u003e\n\u003cli\u003eJia XL, Zhang YH, You Q, et al. Analysis of the current status and influencing factors of decisional conflict among ICU patient proxy decision-makerse[J]. Journal of Nursing. 2024;39(22):92-97.\u003c/li\u003e\n\u003cli\u003eDecision-Making, Ethics, and End-of-Life Care in Pediatric Extracorporeal Membrane Oxygenation: A Comprehensive Narrative Reviewe[J]. Pediatr Crit Care Med. 2021;22(1):95-104.\u003c/li\u003e\n\u003cli\u003eYang XY, Tang AM, Lin Y, et al. A qualitative study on the experience of family members of critically ill patients participating in extracorporeal membrane oxygenation treatment decision-makinge[J]. Chinese Journal of Emergency and Critical Care Nursing. 2024;5(12):1068-1073.\u003c/li\u003e\n\u003cli\u003eSun WN, Hsu HT, Ko NY, Huang YT. Decision-Making Processes in Surrogates of Cancer Patients in a Taiwan Intensive Care Unite[J]. Int J Environ Res Public Health. 2020;17(12):4443.\u003c/li\u003e\n\u003cli\u003eGao YJ, Shan Y, Zhou Y, et al. Research progress on doctor-patient shared decision-making communicatione[J]. Chinese Nursing Management. 2021;21(01):156-160.\u003c/li\u003e\n\u003cli\u003eHickman RL, Pignatiello GA, Tahir S. Evaluation of the Decisional Fatigue Scale Among Surrogate Decision Makers of the Critically Ille[J]. West J Nurs Res. 2018;40(2):191-208.\u003c/li\u003e\n\u003cli\u003eLee J, Jung D, Choi M. Relationship of social support and decisional conflict to advance directives attitude in Korean older adults: A community-based cross-sectional studye[J]. Jpn J Nurs Sci. 2016;13(1):29-37.\u003c/li\u003e\n\u003cli\u003eZheng HY, Hu JL, Dong BJ, et al. Research progress on theoretical models related to shared decision-making among medical staff, nurses, and patientse[J]. Chinese Nursing Management. 2018;18(11):1575-1580.\u003c/li\u003e\n\u003cli\u003eSoll RF, Ovelman C, McGuire W. The future of Cochrane Neonatale[J]. Early Hum Dev. 2020;150:105191.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Extracorporeal Membrane Oxygenation, Decision Conflict, Family Members, Surrogate Decision-Making, Critical Care Nursing, Cross-Sectional Study","lastPublishedDoi":"10.21203/rs.3.rs-6971796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6971796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e The initiation of extracorporeal membrane oxygenation (ECMO) therapy for critically ill patients often necessitates complex, high-stakes decision-making by family members, who typically have limited preparation. This study aimed to quantify the prevalence of decisional conflict among Chinese family caregivers of patients prior to ECMO initiation and to identify modifiable biopsychosocial factors contributing to this conflict.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e A convenience sample of 169 family members of patients undergoing ECMO treatment was recruited from a tertiary hospital in Jiangxi Province, China. Data were collected using validated instruments: the Decisional Conflict Scale, Decision Preparedness Scale, Decision Fatigue Scale, Social Support Scale, and Wake Forest Physician Trust Scale. Univariate and multivariate logistic regression analyses were performed to identify factors associated with decisional conflict.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e The incidence of decisional conflict among family members was 63.3%. Univariate analysis revealed significant associations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between decisional conflict and family monthly income, understanding of the patient's condition and ECMO treatment, decision fatigue, decision preparedness, physician trust, and social support. Multivariate analysis identified decision fatigue as a significant risk factor (OR\u0026thinsp;=\u0026thinsp;1.44, 95% CI: 1.00-2.06). Conversely, understanding of ECMO treatment (OR\u0026thinsp;=\u0026thinsp;0.18, 95% CI: 0.06\u0026ndash;0.58), physician trust (OR\u0026thinsp;=\u0026thinsp;3.15, 95% CI: 1.51\u0026ndash;5.57), and social support (OR\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.72\u0026ndash;0.99) were identified as protective factors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e A substantial proportion of Chinese family caregivers of ECMO patients experience high levels of decisional conflict. Clinical interventions should prioritize enhancing information provision, mitigating decision fatigue, fostering physician trust, and strengthening social support systems to improve decision quality and ultimately optimize patient outcomes in this critical care setting.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical trial number\u003c/b\u003e Not applicable.\u003c/p\u003e","manuscriptTitle":"Decisional Conflict Among Chinese Family Caregivers Prior to ECMO Initiation: A Cross-Sectional Study in Critical Care Nursing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 15:13:00","doi":"10.21203/rs.3.rs-6971796/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-11T05:40:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T06:42:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T05:25:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60947720163589054457617757709903851504","date":"2025-08-25T09:25:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77880616168729324436386017381615078937","date":"2025-08-23T22:32:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-22T06:29:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181463434581209334548605464609556788875","date":"2025-08-21T15:42:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60058528246517330775872464984678859070","date":"2025-08-11T14:52:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T13:15:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T12:58:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-26T10:57:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-26T04:25:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-25T07:19:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"872d1b45-24e7-4dc9-8442-a4fb768136fb","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53247511,"name":"Health sciences/Diseases"},{"id":53247512,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-12-15T16:04:44+00:00","versionOfRecord":{"articleIdentity":"rs-6971796","link":"https://doi.org/10.1038/s41598-025-28709-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-11 15:59:02","publishedOnDateReadable":"December 11th, 2025"},"versionCreatedAt":"2025-08-19 15:13:00","video":"","vorDoi":"10.1038/s41598-025-28709-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-28709-9","workflowStages":[]},"version":"v1","identity":"rs-6971796","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6971796","identity":"rs-6971796","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}