Preoperative anxiety and its association with resilience of surgical patients in the preoperative waiting area: A latent profile analysis

Preoperative anxiety and its association with resilience of surgical patients in the preoperative waiting area: A latent profile analysis | 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 Research Article Preoperative anxiety and its association with resilience of surgical patients in the preoperative waiting area: A latent profile analysis Xiaowen Shen, Min Wu, Rong Wang, Silan Yang, Yuwei Wang, Suwan Dai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4639305/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Preoperative anxiety is a specific type of anxiety that focuses on concerns and worries related to anesthesia and surgical procedures. However, there has been limited research conducted on the various aspects of this phenomenon, especially in waiting areas. Objective Using latent profile analysis (LPA), this study aimed to identify various preoperative anxiety profiles among surgical patients in the preoperative waiting area. Additionally, this study aimed to explore the demographic and resilience factors associated with different preoperative anxiety profiles. Methods and Materials A cross-sectional study was conducted at comprehensive hospitals in Jiaxing, Zhejiang Province, China. Three hundred seventy-three (373) surgical patients were included in the analyses. Surgical patients completed the demographic characteristics questionnaire, the Amsterdam Preoperative Anxiety and Information Scale (APAIS-C), and the Connor-Davidson Resilience Scale (CD-RISC) while waiting for surgery in the waiting area. Results The LPA results indicated that the data were most consistent for four types of solutions: high preoperative anxiety-medium information need, high preoperative anxiety-low information need, low preoperative anxiety-medium information need, and low preoperative anxiety-low information need. One-way ANOVA revealed significant differences between the four groups with respect to resilience. Sex, education background, tumor diagnosis, sleep quality and surgical experience significantly predicted profile membership. Conclusion There is heterogeneity in preoperative anxiety among surgical patients in the waiting area. When supporting surgical patients in waiting areas, nurses should consider the level of psychological resilience of patients in addition to assessing demographic factors to identify high-risk subgroups to warrant intervention. preoperative anxiety latent profile analysis surgical patients preoperative waiting area resilience Figures Figure 1 Introduction According to the World Health Organization (WHO), approximately 312.9 million surgical procedures are performed worldwide each year, and this number is on the rise[1]. While surgery is often effective in diagnosing or treating a patient's condition, it can also trigger various stress reactions, with anxiety being the most common response[2]. Preoperative anxiety is a multifaceted emotional response that patients experience before surgery and is characterized by feelings of tension, anxiety, worry, and fear[3]. This anxiety can have physio-pathological effects, such as increased heart rate, body sweating, high blood pressure, and difficulty sleeping[4]. Research has indicated that a significant number of surgical patients, ranging from 40% to 80%, experience anxiety before their procedure[5-6]. Excessive preoperative anxiety can have negative consequences, such as requiring a higher dosage of anesthesia during surgery[7-8], prolonging the recovery time[9], reducing patient satisfaction with the surgery and increasing the risk of postoperative complications[10]. In recent years, developing countries have been placing more emphasis on the psychological well-being and experiences of patients[11]. Chinese medical professionals are also increasingly recognizing the importance of addressing preoperative anxiety in patients and seeking effective intervention methods. However, existing research has not yet identified the most suitable target for intervention[12-14]. Therefore, it is essential to analyze the key factors that contribute to preoperative anxiety in surgical patients to establish a theoretical basis for developing effective intervention measures. Studies have shown that the level of preoperative anxiety in patients varies across different stages of surgery; specifically, patients tend to experience greater anxiety in waiting areas than in wards[15]. The preoperative waiting area serves as a transitional space between the ward and the operating room, where patients spend a significant amount of time prior to surgery. In this unfamiliar and special medical environment, patients commonly exhibit various adverse psychological reactions[16]. Although existing research has primarily concentrated on analyzing the preoperative anxiety levels of patients awaiting surgery in the ward, few investigations have been conducted on the anxiety levels of patients in the waiting area. Consequently, our study focused on examining the anxiety levels of surgical patients within the waiting area. The medical field is increasingly integrating positive psychology, and researchers have started to focus on the role of resilience in preventing preoperative anxiety. Resilience refers to an individual's ability to maintain stable psychological and physical functioning when faced with challenging life events and adversity[17]. Lazarus's stress interaction theory suggests that people with different personality traits evaluate and respond to stress differently, leading to varying physiological and psychological reactions[18]. Resilience, as a personality trait, can help mitigate the negative effects of stress on health and promote positive adaptation[19]. Having higher levels of resilience is associated with more positive cognitive assessments, which can contribute to better mental health outcomes for patients. A study by Chavez TJ[20] revealed that patients with high resilience who underwent knee arthroscopy demonstrated significantly lower levels of preoperative anxiety than those with low resilience. Previous research on preoperative anxiety in surgical patients has focused primarily on variable-centered research approaches. These approaches sought to examine the significant individual traits that contribute to anxiety. However, these approaches often involve the study of these traits in isolation and overlook the influence of individual-specific factors. To address this issue, latent profile analysis (LPA) offers a statistical analysis approach that revolves around individual-level analysis rather than aggregate data. LPA utilizes latent categorical variables to explore relationships between external continuous variables and aims at establishing local independence among observed variables. Although LPA has been extensively utilized in the fields of education and psychology, no studies have investigated the underlying characteristics of preoperative anxiety in patients awaiting surgery in the waiting area. Therefore, this study utilizes latent class analysis with the following aims: 1)Investigate various profiles of preoperative anxiety among patients waiting in the waiting area. 2)Identifying these profiles and further analyzing the factors influencing anxiety levels 3) Develop targeted intervention strategies based on the distinctive features of each profile, providing valuable references for future interventions. Study design and setting This was a cross-sectional study conducted from November 2023 to December 2023 in a tertiary hospital in Jiaxing, China. Convenience sampling was employed to recruit eligible patients. Previous research recommended a minimum sample size of 300-500 for LPA studies[21], and thus, a total of 396 surgical patients were included in this study. The inclusion criteria were as follows: (a) patients who underwent surgery, (b) patients aged 18 years or older, and (c) patients who were clearly conscious and willing to participate. The exclusion criteria were patients who (a) underwent emergency surgery, (b) had severe mental illness or cognitive impairment, or (c) had a communication barrier preventing normal interaction with researchers or completing the questionnaire. Data collection procedure The survey we conducted took place in a waiting area. Prior to distributing the questionnaires, we provided a thorough explanation of the purpose, significance, and methodology to ensure the participants' understanding. We also informed them that the entire survey would take approximately 15-20 minutes and obtained informed consent and signatures. Participants were made aware of the voluntary nature of the investigation and assured of the anonymity and confidentiality of their data. Younger patients were given the option to complete the online questionnaires using electronic devices such as mobile phones or iPads, utilizing the online survey platform in China www.wjx.cn, while older patients who were not accustomed to screens were provided with paper questionnaires. In total, 396 questionnaires were distributed, and after excluding 23 incomplete or invalid questionnaires, we obtained a total of 373 valid questionnaires, resulting in a response rate of 94.19%. This study received approval from the Institutional Review Board at the local hospital, with reference number 2022-KY-239. Measures General information questionnaire The self-designed demographic questionnaire collected information on gender, age, marital status, and educational background. Patients completed the questionnaire on their own, while trained nurses collected the clinical data of the patients. Amsterdam Preoperative Anxiety and Information Scale (APAIS) The Chinese version of the APAIS[11] was utilized to assess preoperative anxiety. This tool consists of six self-reported items and comprises two subscales: the anxiety subscale (items 1, 2, 4, and 5) and the information needs subscale (items 3 and 6). Each item is rated on a scale from 1 point (none at all) to 5 points (very obvious). To calculate the APAIS score, the scores of all the items are summed. The anxiety scale ranges from 4 to 20 points, with a score of ≥12 indicating the presence of anxiety. The information need scale ranges from 2 to 10 points, where a score of 2 to 4 indicates no or low information needs, 5 to 7 indicates intermediate information needs, 8 to 10 indicates high information needs, and higher scores on both scales indicate higher levels of anxiety and information needs. The Cronbach’s α of the APAIS is 0.905. Connor-Davidson Resilience Scale (CD-RISC) The Chinese version of the CD-RISC[22] was utilized to evaluate the participants’ level of psychological resilience. The CD-RISC is a questionnaire comprising 25 items rated on a 5-point Likert scale ranging from "never" to "almost always." It consists of three dimensions: resilience (items 11-23), strength (items 1, 5, 7, 8, 9, 10, 24, and 25), and optimism (items 2, 3, 4, and 6). The total score on the scale can range from 0 to 100 points, with a higher score indicating greater resilience. In this study, the Cronbach’s α of the CD-RISC was 0.908. Statistical analysis The data analysis was conducted using SPSS (version 21.0)[23] and Mplus (version 8.7)[24]. To examine common method bias in the data, Harman's single-factor test was used. The results showed that the variance explained by the first common factor was 31.91%, which is less than the threshold of 40%[25]. This indicates that there was no significant common method bias present in this study. Data analysis is typically divided into three steps. First, descriptive analysis was conducted to calculate general demographic information, preoperative anxiety scores, and resilience scores. Categorical data are expressed in terms of frequency and percentage, while continuous data are expressed as the mean ± standard deviation (SD). Second, potential profile analysis is used to classify the levels of preoperative anxiety among surgical patients in the waiting area. Various model fitting indices are employed, such as the Akaike information criterion (AIC), Bayesian information criterion (BIC), correct Bayesian information adjusted BIC (aBIC), Lo-Mendell-Rubin likelihood ratio test (LMR), bootstrapped likelihood ratio test (BLRT), and entropy. Lower AIC, BIC, and aBIC values indicate better model fit. A p value less than 0.05 for LMR and BLRT suggests that the K-class model is superior to the K-1 class model[26]. The entropy value ranges between 0 and 1, with values closer to 1 indicating higher accuracy. Third, we compared demographic differences and resilience scores among different profile groups via either the chi-square test or one-way ANOVA. Furthermore, multiple logistic regression was performed to examine the demographic factors that showed significant differences. In our analysis, a p value<0.05 was considered to indicate statistical significance. Results The study involved a diverse group of patients aged 18 to 85 years (mean age: 50.23±14.24 years). Approximately 60% of the participants were female. Among the subjects, the majority (62.5%, n=233) had a middle school education or below. Among all the participants, 61.1% (n=168) were currently employed. A significant proportion of the participants were married individuals (88.2%, n=329), and a large majority (89.8%, n=335) had children. Moreover, 62.2% (n=247) of the subjects had a history of surgery, 70% (n=261) had a history of anesthesia, and 53.6% (n=200) were cancer patients. The primary caregivers for the patients were their spouses, accounting for 57.1% (n=213) of the patients. Latent profile analysis Table 1 displays the model fitting indices for solutions ranging from one to six types. As the number of categories increases, the AIC, BIC, and aBIC gradually decrease. When five categories are maintained, the entropy value reaches its highest point, but the LMR value does not reach statistical significance (P>0.05). Consequently, the best latent feature model for preoperative anxiety in surgical patients consists of four categories. Table. 1 Fit indices for the latent profile analysis Model K AIC BIC aBIC Entropy LMR p value BLRT p value Probability of classes 1 class 12 7379.90 7426.96 7388.88 - - - 1 2 class 19 6067.28 6141.79 6081.50 0.94 0.0000 0.0000 0.521/0.482 3 class 26 5673.24 5775.20 5692.71 0.93 0.0000 0.0000 0.41/ 0.251/0.342 4 class 33 5516.94 5646.35 5541.65 0.95 0.02 0.0000 0.287/0.152/0.252/0.309 5 class 40 5334.878 5491.741 5364.833 0.93 0.41 0.0000 0.323/0.156/0.197/0.234 6 class 47 5126.221 5310.535 5161.418 0.954 0.1445 0.0000 0.252/0.063/0.196/0.155/0.183 Note:K, number of free parameters; Log (L), log likelihood; AIC, Akaike information criterion; BIC, Bayesian information criterion; aBIC, adjusted BIC; LMR, Lo-Mendell-Rubin test; BLRT, bootstrap likelihood ratio test; —, not applicable Latent class profiles Figure 1 displays the response probability graphs for the four-class model. The means of the six questions differ significantly among the latent profile memberships, as indicated in Table 2. Questions 1, 2, 4, and 5 pertain to patients' preoperative anxiety, while questions 3 and 6 relate to patients' preoperative information needs. In Class 1 (n=106, 28.7%), participants had an average preoperative anxiety score of 16.16±2.94 and an information need score of 7.53±1.097. Therefore, we classified Class 1 as the "high preoperative anxiety-medium information need" group. In Class 2 (n=56, 15.2%), the subscale score for preoperative anxiety was 13.00±2.71, which suggests a high level of anxiety within this group. However, the score for information need was 3.16±1.09, indicating a low level of information need. Thus, we termed Class 2 the "high preoperative anxiety-low information need" group. In Class 3, which consisted of 95 participants (25.2% of the total sample), the subscale score for anxiety was 6.84±2.26. This indicated that this group had low levels of preoperative anxiety. Additionally, the information need score for this group was 6.67±4.47, suggesting a medium level of information need. Therefore, we classified this group as the "low preoperative anxiety-medium information need" group. Finally, Class 4 (n=116, 30.9%), referred to as the low preoperative anxiety-low information need group, had a mean subscale score of 5.02±1.54 for anxiety and a mean information need score of 2.53±0.869. This group exhibited the lowest levels of preoperative anxiety across all the scales used. Predictor of latent profile membership Table 2 presents the results, which indicate significant differences among the four profiles across various factors. Specifically, significant differences were observed in terms of age (F=4.227, p=0.006), gender (c 2 =21.179, p=0.00), education background (c 2 =18.449, p=0.00), being a tumor patient or not (c 2 =8.649, p=0.034), previous surgical experience (c 2 =20.182, p=0.017), preoperative night sleep condition (c 2 =26.086, p=0.000), and caregiver type (c 2 =15.382, p=0.042). Table. 2 Differences in the demographic characteristics among the latent classes Variables Total sample C1 C2 C3 C4 F/ c 2 p Age (mean±SD) 50.23±14.74 50.03±13.451 51.29±15.55 46.16±15.26 53.23±14.45 4.227 .006 Gender 21.179 0.000 Male 155(41.6 ) 33(31.1 ) 16(28.6) 39(41.1) 67(0.58) Female 218(58.4 ) 73(68.9 ) 40(71.4) 56(58.9) 49(0.42) Marriage 2.683 .848 single 31(8.3) 10(9.4 ) 4(7.1) 10(10.5) 7(0.06) married 329(88.2) 90(84.9 ) 50(89.3) 82(86.3) 107(0.92) Divorced/widow 13(3.5 ) 4(3.8 ) 2(3.6) 5(5.3) 2(0.02) Educational background 18.449 .000 Secondary school or below 233(62.5) 67(63.2 ) 42(75) 43(45.3) 81(69.8) High school or above 140(37.5 ) 39(36.8 ) 14(25) 52(55.7) 35(30.2) Having children or not 2.664 0.446 Yes 335(89.8) 95(89.6 ) 50(89.3) 82(86.3) 108(0.93) No 38(10.2 ) 11(10.4 ) 6(10.7) 13(13.7) 8(0.07) Employment status 10.054 .122 Unemployed 65(17.4) 21(19.8 ) 14(25.0) 12(12.6) 18(0.16) employed 228(61.1) 67(63.2 ) 30(53.6) 66(69.5) 65(0.56) Retired 80(21.5 ) 18(17.0 ) 12(21.4) 17(17.9) 33(0.28) Tumor patient or not 8.649 .034 Yes 200(53.6 ) 54(50.9 ) 41(73.2) 53(55.8) 52(0.45) No 173(46.4) 52(49.1 ) 15(26.8) 42(44.2) 64(0.55) Surgical history 3.329 .485 Yes 247(66.2) 76(71.7 ) 43(76.8) 62(65.3) 80(0.69) No 126(33.8) 30(28.3 ) 13(23.2) 33(34.7) 36(0.31) Anesthesia history 2.446 0.344 Yes 261(70.0 ) 72(67.9 ) 42(75.0) 58(61.1) 75(0.65) No 112(30.0 ) 34(32.1) 14(25.0) 37(38.9) 41(0.35) Previous surgical experience 20.182 0.017 Good 14(3.8) 8(7.5) 1(1.8) 3(3.2) 2(1.7) Normal 49(13.1) 18(17.0) 9(16.1) 16(16.8) 6(5.2) Bad 188(50.4) 48(45.3) 32(57.1) 40(42.1) 68(58.6) None 122(32.7) 32(30.2) 14(25.0) 36(37.9) 40(34.5) Previous Anesthesia experience 11.539 0.241 Good 15(4.0) 4(3.8) 4(7.1) 6(6.3) 1(0.9) Normal 49(13.1) 17(16.0) 9(16.1) 13(13.7) 10(8.6) Bad 198(53.1) 56(52.8) 30(53.6) 44(46.3) 68(58.6) None 111(29.8) 29(27.4) 13(23.2) 32(33.7) 37(31.9) Preoperative night sleep condition 26.086 0.000 Good 195(52.3) 45(42.5) 24(42.9) 52(54.7) 74(63.8) Normal 98(26.3) 24(22.6) 18(32.1) 32(33.7) 24(20.7) Bad 80(21.4) 37(34.9) 14(25.0) 11(11.6) 18(15.5) Day surgery 1.088 0.780 Yes 96(25.7) 26(24.5) 12(21.4) 25(26.3) 33(28.4) No 277(74.3) 80(75.5) 44(78.6) 70(73.7) 83(71.6) Caregiver type 15.382 0.042 Parents 77(20.64 ) 22(20.8 ) 11(19.6) 24(25.3) 20(17.2) Couples 213(57.10 ) 55(51.9 ) 27(48.2) 60(63.2) 71(61.2) Children 83(22.25 ) 29(27.4 ) 18(32.1) 11(11.6) 25(21.6) C1:high preoperative anxiety -medium information need group; C2:high preoperative anxiety-low information need group; C3:low preoperative anxiety-medium information need group; C4:low preoperative anxiety-low information need group To explore the sociodemographic factors that predict membership in archive relationships, a multiple logistic regression analysis was conducted using the low anxiety, low information need group as the reference group (Table 3). The results showed that compared to male patients, female patients were more likely to be in the C1 and C2 groups. In addition, patients with a history of poor surgical experience or poor preoperative sleep quality were more likely to be assigned to Group 1. Tumor patients were more likely to be in the C2 group than in the C4 group. Furthermore, patients with higher levels of education were more likely to be in the C3 group. Table. 3 Predictor of latent profile membership Characteristics C1vs. C4 C2vs. C4 C3vs. C4 OR 95%CI p OR 95%CI p OR 95%CI p Age(mean±SD) 0.987 0.948-1.007 0.303 0.977 0.948-1.007 0.139 0.985 0.96-1.011 0.25 Gender female 2.61 1.405-4.848 0.002 2.209 1.047-4.66 0.038 1.851 0.992-3.453 0.053 Caregiver Parent 0.68 0.24-1.922 0.467 0.548 0.161-1.863 0.336 1.243 0.406-3.806 0.704 Couples 0.708 0.333-1.504 0.369 0.476 0.201-1.129 0.092 1.342 0.559-3.222 0.51 Educational background Secondary school or below 0.746 0.369-1.509 0.415 1.24 0.523-2.938 0.626 0.465 0.234-0.923 0.029 Tumor patient 1.751 yes 1.174 0.656-2.103 0.589 2.935 1.412-6.097 0.004 0.96-3.192 0.068 Surgical experience Bad 5.495 1.017-29.674 0.048 1.928 0.152-24.379 0.612 1.61 0.242-10.697 0.622 Normal 2.656 0.881-8.009 0.083 2.821 0.781-10.195 0.114 2.064 0.677-6.295 0.203 Good 0.757 0.394-1.455 0.403 1.124 0.505-2.499 0.775 0.667 0.346-1.285 0.226 Sleep quality Good 0.349 0.169-0.722 0.005 0.627 0.256-1.538 0.308 1.037 0.432-2.493 0.935 Normal 0.452 0.19-1.077 0.073 1.128 0.411-3.096 0.816 1.528 0.571-4.084 0.398 C1:high preoperative anxiety -medium information need group; C2:high preoperative anxiety-low information need group; C3:low preoperative anxiety-medium information need group; C4:low preoperative anxiety-low information need group Relationship between the latent profiles and resilience of surgical patients in the waiting area The results in Table 4 show that the resilience scores were significantly different among the profiles. Table 4 Comparison of resilience of surgical patients among different latent classes of preoperative anxiety Variables C1 M(SD) C2 M(SD) C3 M(SD) C4 M(SD) F p total score 61.60±11.548 62.05±8.156 67.28±11.178 67.23±10.010 8.344 .000 resilience 30.46±6.490 30.09±5.418 33.84±6.619 33.57±5.958 9.008 .000 optimism 9.97±2.131 9.39±1.691 9.72±2.019 10.02±1.769 3.083 .029 strength 21.78±3.947 22.57±2.507 23.47±3.417 23.65±3.445 6.481 .000 C1:high preoperative anxiety -medium information need group; C2:high preoperative anxiety-low information need group; C3:low preoperative anxiety-medium information need group; C4:low preoperative anxiety-low information need group Discussion The preoperative anxiety level of patients in the waiting area is above average The study revealed that patients in the waiting area displayed a noteworthy level of preoperative anxiety, with an average score of 14.46±6.19. Furthermore, the incidence of preoperative anxiety among the participants was 35.7%. Notably, the total preoperative anxiety score surpassed the findings reported by Eberhartet[27]. These results suggest that patients in the waiting area commonly experience elevated levels of preoperative anxiety. Therefore, it is crucial for medical staff to promptly recognize and address patients with high preoperative anxiety. Surgical patients in the waiting area exhibit group heterogeneity in their levels of preoperative anxiety This study aimed to analyze preoperative anxiety and information needs among surgical patients in a waiting area using a method called latent profile analysis. The analysis identified four distinct groups, namely, the high preoperative anxiety-medium information need group, which accounted for 28.7% of the sample. The high preoperative anxiety-low information need group represented 15.2% of the sample. The low preoperative anxiety-medium information need group comprised 25.2% of the sample. The low preoperative anxiety-low information need group made up 30.9% of the sample. According to the study, approximately 70% of surgical patients were categorized as having high preoperative anxiety and/or medium information needs. This suggests that most patients who undergo surgery experience anxiety before the procedure and desire more information. Interestingly, the study revealed that Chinese patients generally had lower information needs than patients from other developed countries[28]. This difference could be attributed to the thorough preoperative education provided to patients, which likely increased their understanding of the surgery. Figure 1 shows the characteristics of the patients in the four groups, indicating that preoperative anxiety and information needs do not necessarily increase or decrease together and may be influenced by patient demographic factors. These findings emphasize the importance of further investigations to determine the underlying reasons for these observations[29]. Future studies could focus on understanding the psychological mechanisms utilized by these groups, which could provide insights to help reduce preoperative anxiety and information needs among patients. Female patients are more prone to experiencing high preoperative anxiety and having intermediate information needs. The present study revealed that female surgical patients are more prone to experiencing preoperative anxiety and have a greater need for information. This result supports earlier research suggesting that women have considerably greater levels of preoperative anxiety traits than men [29-30]. This may be because women tend to be more emotionally and psychologically vulnerable than men are, and they are more likely to openly express their emotions. Additionally, experimental studies have suggested that fluctuations in estrogen and progesterone levels could contribute to the observed differences among female patients [31]. Tumor patients are more likely to enter the high preoperative anxiety, low information need group Patients in the waiting area who are being treated for a tumor are more likely to have a higher level of preoperative anxiety and lower information needs. The reason for the higher level of preoperative anxiety in oncology patients is the increased worry and fear they experience following a cancer diagnosis regarding both the surgery and the disease itself. Previous studies have demonstrated that oncology surgeries are more challenging and carry a greater risk than other types of surgeries. As a result, patients require a longer hospital preparation time[30]. These patients often undergo multiple consultations and educational preparations, which leads to a greater level of knowledge. However, they also face a greater risk in terms of their disease and are more likely to adopt passive psychological defense mechanisms such as avoidance and surrender [31-32]. Patients with negative surgical experiences and preoperative insomnia are at a greater risk of falling into the category of high preoperative anxiety-intermediate need information. This study focused on the relationship between negative surgical experiences in the past and preoperative anxiety and information needs. Individuals who had previously undergone painful surgeries tended to experience higher levels of anxiety when facing future surgeries. This increased anxiety could be attributed to the stress and anticipation of encountering similar negative experiences again. Furthermore, the study revealed that patients with preoperative insomnia were more likely to have high preoperative anxiety and intermediate information needs than patients in other groups were. Poor sleep quality is recognized as a risk factor for anxiety[33-34], and previous research has linked poor sleep quality to impaired emotional control, negative emotional regulatory mechanisms, and heightened emotional responses[35]. As a result, these factors can contribute to symptoms of anxiety and depression in patients. It is crucial to acknowledge that this study is cross-sectional, thus providing only a momentary depiction of the link between insomnia and preoperative anxiety. Consequently, it cannot be established whether insomnia causes anxiety or if preoperative anxiety results in insomnia. To gain a more comprehensive understanding of this association, additional research is needed, specifically through longitudinal surveys. Exploring the long-term correlation between insomnia and preoperative anxiety is a significant pathway for future studies. Patients with higher levels of education are more inclined to fall into the category of low preoperative anxiety-high information need According to the present study, patients with a junior high school education or above had low preoperative anxiety but intermediate information needs. These findings emphasize the impact of education level on patients' perception and ability to handle the risks associated with surgery. Nigussie's study also supported the idea that a patient's education level can affect their assessment and coping mechanisms regarding surgery[36]. It has been suggested that patients with less education tend to take a more straightforward surgical approach, and providing them with excessive information may result in increased anxiety[37]. Relationship between potential categories of preoperative anxiety and psychological resilience The study revealed that patients with different levels of preoperative anxiety showed significant differences in their psychological resilience scores. Specifically, patients with high preoperative anxiety had the lowest psychological resilience scores, which supports previous research. Other studies have also demonstrated that individuals with low preoperative anxiety may possess greater resilience due to its positive impact on promoting the adoption of positive emotion regulation strategies[38]. Overall, individuals with high resilience are better equipped to actively adjust to unpleasant situations, thereby reducing negative emotions such as tension, anxiety, and depression. Recent research indicates that individuals who possess high levels of resilience are better equipped to regulate the release of stress hormones such as amines and cortisol when confronted with stressful or traumatic situations[39]. This is made possible through the engagement of different brain structures and neurotransmitters, ultimately leading to a reduction in anxiety-provoking circumstances. Given that resilience has been identified as a potential indicator of surgical outcomes, it is essential for nursing staff to promptly identify patients who display low mental resilience and offer appropriate interventions. By intervening early, healthcare professionals can help improve patients' levels of preoperative anxiety and enhance their overall quality of life following surgery. Conclusion Preoperative anxiety was more common in the waiting area. After analyzing various characteristics, the symptoms were categorized into four groups: preoperative high anxiety-intermediate information need, preoperative high anxiety-low information need, preoperative low anxiety-intermediate information need, and preoperative low anxiety-low information need. Female patients with poor preoperative sleep quality and previous negative surgical experiences were more likely to be categorized into the group with high anxiety-intermediate information needs. Female cancer patients, on the other hand, are prone to belonging to the group with high anxiety and low information needs. Female patients with a high school education or above are more likely to fall into the group with low anxiety-intermediate information needs. To address this issue, it is recommended that personalized management strategies be implemented based on the characteristics of each patient group. The aim is to reduce preoperative anxiety, cater to patient requirements, and enhance patient satisfaction. Declarations Consent for publication Not applicable in this section. Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Funding This research received funding from the Jiaxing Key Discipline of Medicine - Anesthesiology (2023-Zc-001), the Jiaxing Key Discipline of Medicine - Nursing (2023-Zc-007), and the National Oncology Clinical Key Specialty (2023-GJZK-001). Authors’ contributions All authors contributed to design of the study and approved the submitted version. RW and XWS participated in the design of the study and helped to revise the manuscript, MW collected the data, and drafted the manuscript, SWD、QHZ、YWW provided comments and ideas and proofed reading the manuscript. Acknowledgements We would like to express our deep gratitude to the patients who generously participated in this study. Their invaluable contributions have been essential in advancing our understanding and knowledge in this field. Additionally, we extend our sincere appreciation to the study site for their significant support and collaboration throughout the research process. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics declarations The study involving human participants were reviewed and approved by the Institutional Review Board of the Jiaxing hospital.(Number: 2022-KY-239). Informed consent to participate in this study was provided by the participants. All methods were carried out in accordance with relevant guidelines and regulations. References H. Aust, L. Eberhart, T. Sturm, M. Schuster, Y. Nestoriuc, F. Brehm, D. Rüsch, A cross-sectional study on preoperative anxiety in adults, J. Psychosom. Res. 111 (2018) 133-139. B. Agüero-Millan, R. Abajas-Bustillo, C. Ortego-Maté, Efficacy of nonpharmacologic interventions in preoperative anxiety: A systematic review of systematic reviews, J. Clin. Nurs. 32 (2023) 6229-6242. W.T. Kassahun, M. Mehdorn, T.C. Wagner, J. Babel, H. Danker, I. Gockel, The effect of preoperative patient-reported anxiety on morbidity and mortality outcomes in patients undergoing major general surgery, Sci Rep.12 (2022) 6312. R. Powell, N.W. Scott, A. Manyande, J. Bruce, C. Vögele, L.M. Byrne-Davis, M. Unsworth, C. Osmer, M. Johnston, Psychological preparation and postoperative outcomes for adults undergoing surgery under general anesthesia, Cochrane Database Syst Rev .2016 (2016) D8646. T. Renouf, A. Leary, T. Wiseman, Do psychological interventions reduce preoperative anxiety? Br J .Nurs .23 (2014) 1208-1212. H. Aust, D. Rüsch, M. Schuster, T. Sturm, F. Brehm, Y. 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Aubrun, Preoperative Anxiety in the Surgical Transfer and Waiting Area: A Cross-Sectional Mixed Method Study, J. Clin. Med. 11 (2022). P. Buonanno, A. Marra, C. Iacovazzo, M. Vargas, S. Nappi, A.U. de Siena, G. Servillo, Preoperative anxiety during COVID-19 pandemic: A single-center observational study and comparison with a historical cohort, Front Med (Lausanne) .9 (2022) 1062381. Southwick SM, Bonanno GA, Masten AS, Panter-Brick C, Yehuda R. Resilience definitions, theory, and challenges: interdisciplinary perspectives. Eur .J. Psychotraumatol. 5(2014)1-14. J.B. Lowe, Handbook of behavioral medicine Handbook of behavioral medicine1984. I. Robertson, C.L. Cooper, Resilience, Stress Health. 29 (2013) 175-176. T.J. Chavez, K.D. Garvey, J.E. Collins, N.A. Lowenstein, E.G. Matzkin, Resilience as a Predictor of Patient Satisfaction With Nonopioid Pain Management and Patient-Reported Outcome Measures After Knee Arthroscopy, Arthroscopy. 36 (2020) 2195-2201. S.L. Ferguson, W.G. Moore, D.M. Hull, Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers, Int. J. Behav. Dev. 44 (2019). X. Yu, J. Zhang, X.N. Yu, J.X. Zhang, FACTOR ANALYSIS AND PSYCHOMETRIC EVALUATION OF THE CONNOR-DAVIDSON RESILIENCE SCALE (CD-RISC) WITH CHINESE PEOPLE, Social Behavior & Personality: an international journal. 35 (2007) 19-30. P.M. Podsakoff, S.B. Mackenzie, J.Y. Lee, N.P. Podsakoff, Common method biases in behavioral research: a critical review of the literature and recommended remedies., J. Appl. Psychol. 88 (2003) 879-903. B. Muthén, Mplus user's guide (4th ed), (2007). L.L. Zhou H, Statistical remedies for common method biases, Adva Psychol Sci. (2004) 942-950. J.J. Dziak, S.T. Lanza, X. Tan, Effect Size, Statistical Power and Sample Size Requirements for the Bootstrap Likelihood Ratio Test in Latent Class Analysis, Struct. Equ. Modeling. 21 (2014) 534-552. L. Eberhart, H. Aust, M. Schuster, T. Sturm, M. Gehling, F. Euteneuer, D. Rüsch, Preoperative anxiety in adults - a cross-sectional study on specific fears and risk factors, Bmc Psychiatry. 20 (2020) 140. W.T. Kassahun, M. Mehdorn, T.C. Wagner, J. Babel, H. Danker, I. Gockel, The effect of preoperative patient-reported anxiety on morbidity and mortality outcomes in patients undergoing major general surgery, Sci Rep. 12 (2022) 6312. K.S. Gurusamy, J. Vaughan, B.R. Davidson, Formal education of patients about to undergo laparoscopic cholecystectomy, Cochrane Database Syst Rev .2014 (2014) D9933. Y. Yang, G. Sun, X. Dong, H. Zhang, C. Xing, Y. Liu, Preoperative anxiety in Chinese colorectal cancer patients: The role of social support, self-esteem and coping styles, J. Psychosom. Res. 121 (2019) 81-87. L. Xu, Q. Pan, R. Lin, Prevalence rate and influencing factors of preoperative anxiety and depression in gastric cancer patients in China: Preliminary study, J. Int. Med. Res. 44 (2016) 377-388. M. Di Giuseppe, R. Ciacchini, T. Micheloni, I. Bertolucci, L. Marchi, C. Conversano, Defense mechanisms in cancer patients: a systematic review, J. Psychosom. Res. 115 (2018) 76-86. D.L. McMakin, C.A. Alfano, Sleep and anxiety in late childhood and early adolescence, Curr Opin Psychiatry. 28 (2015) 483-489. G.N. Pires, A.G. Bezerra, S. Tufik, M.L. Andersen, Effects of acute sleep deprivation on state anxiety levels: a systematic review and meta-analysis, Sleep Med. 24 (2016) 109-118. M.Z.X.H. Ning, Effect of Acute Sleep Deprivation on Cognition and Emotion：an Updated Review, Chinese General Practice. 29 (2021) 3653-3659. S. Nigussie, T. Belachew, W. Wolancho, Predictors of preoperative anxiety among surgical patients in Jimma University Specialized Teaching Hospital, South Western Ethiopia, Bmc Surg. 14 (2014) 67. L. Li, S. Li, Y. Sun, S. Zhang, X. Zhang, H. Qu, Personalized Preoperative Education Reduces Perioperative Anxiety in Old Men with Benign Prostatic Hyperplasia: A Retrospective Cohort Study, Gerontology. 67 (2021) 177-183. Y. Shan, X. Liu, W. Chen, R. Chen, L. Jin, H. Sun, H. Lu, Predictors of psychological resilience trajectories in patients with knee arthroplasty: A longitudinal study, J. Adv. Nurs. 79 (2023) 1926-1938. M. Tang, H. Huang, S. Li, M. Zhou, Z. Liu, R. Huang, W. Liao, P. Xie, J. Zhou, Hippocampal proteomic changes of susceptibility and resilience to depression or anxiety in a rat model of chronic mild stress, Transl Psychiatry 9 (2019) 260. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4639305","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327516454,"identity":"1c66d3e9-43c6-472b-87ff-e358ff9b1425","order_by":0,"name":"Xiaowen Shen","email":"","orcid":"","institution":"First Hospital of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Xiaowen","middleName":"","lastName":"Shen","suffix":""},{"id":327516455,"identity":"cd9c9df3-912a-4c41-8595-330f98505604","order_by":1,"name":"Min Wu","email":"","orcid":"","institution":"First Hospital of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Wu","suffix":""},{"id":327516456,"identity":"9fb37e32-abb0-4a33-8284-5f9cda2d551f","order_by":2,"name":"Rong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYDACZobkBwkGNnJs7O0HiNTC3vDM4EFBmjEfz5kEIrXwHHwg+eDD4cR5Eg4GxOngn5GcYJBgcDi9TYIhgeFHxTbCWiRupCUA/ZKe2ybdeICx58xtIqy5kQOyxTq3TeZAAjNjGxFa5G/kf5BIMGBOZwORRGkxOHMgAajYOYF4LYbHG9KADkszbAMG8kGi/CJ3mCH54Y8/NvLy7e0HH/yoIMb7yOAAiepHwSgYBaNgFOACAMXFQBUK94F2AAAAAElFTkSuQmCC","orcid":"","institution":"First Hospital of Jiaxing","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Wang","suffix":""},{"id":327516457,"identity":"a0ad855b-f4e6-4137-acd9-561f234d7a41","order_by":3,"name":"Silan Yang","email":"","orcid":"","institution":"First Hospital of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Silan","middleName":"","lastName":"Yang","suffix":""},{"id":327516458,"identity":"6313c940-c4af-4a5d-ad1d-e08eac5907f7","order_by":4,"name":"Yuwei Wang","email":"","orcid":"","institution":"Jiaxing University","correspondingAuthor":false,"prefix":"","firstName":"Yuwei","middleName":"","lastName":"Wang","suffix":""},{"id":327516459,"identity":"e80c71cc-9a5b-4b6d-85ac-511bd36b3097","order_by":5,"name":"Suwan Dai","email":"","orcid":"","institution":"First Hospital of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Suwan","middleName":"","lastName":"Dai","suffix":""},{"id":327516460,"identity":"d7569107-5a4b-4e65-9790-ae933b9a485d","order_by":6,"name":"Qinghe Zhou","email":"","orcid":"","institution":"First Hospital of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Qinghe","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-06-26 02:21:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4639305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4639305/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60706430,"identity":"320da54a-ca9f-40bc-89b2-cd151b1905eb","added_by":"auto","created_at":"2024-07-19 19:28:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40551,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eItem means for the four-profile model of preoperative anxiety. note:item1-4 preoperative anxiety items\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4639305/v1/9426cb6b1e98c00b6face596.png"},{"id":70165606,"identity":"67613a96-9f8b-4f45-b42a-cb59df7a5e1d","added_by":"auto","created_at":"2024-11-29 05:39:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1125408,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4639305/v1/784fb521-221a-46a3-a155-6e29bcdfd0dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preoperative anxiety and its association with resilience of surgical patients in the preoperative waiting area: A latent profile analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the World Health Organization (WHO), approximately 312.9 million surgical procedures are performed worldwide each year, and this number is on the rise[1]. While surgery is often effective in diagnosing or treating a patient\u0026apos;s condition, it can also trigger various stress reactions, with anxiety being the most common response[2].\u003c/p\u003e\n\u003cp\u003ePreoperative anxiety is a multifaceted emotional response that patients experience before surgery and is characterized by feelings of tension, anxiety, worry, and fear[3]. This anxiety can have physio-pathological effects, such as increased heart rate, body sweating, high blood pressure, and difficulty sleeping[4]. Research has indicated that a significant number of surgical patients, ranging from 40% to 80%, experience anxiety before their procedure[5-6]. Excessive preoperative anxiety can have negative consequences, such as requiring a higher dosage of anesthesia during surgery[7-8], prolonging the recovery time[9], reducing patient satisfaction with the surgery and increasing the risk of postoperative complications[10]. In recent years, developing countries have been placing more emphasis on the psychological well-being and experiences of patients[11]. Chinese medical professionals are also increasingly recognizing the importance of addressing preoperative anxiety in patients and seeking effective intervention methods. However, existing research has not yet identified the most suitable target for intervention[12-14]. Therefore, it is essential to analyze the key factors that contribute to preoperative anxiety in surgical patients to establish a theoretical basis for developing effective intervention measures.\u003c/p\u003e\n\u003cp\u003eStudies have shown that the level of preoperative anxiety in patients varies across different stages of surgery; specifically, patients tend to experience greater anxiety in waiting areas than in wards[15]. The preoperative waiting area serves as a transitional space between the ward and the operating room, where patients spend a significant amount of time prior to surgery. In this unfamiliar and special medical environment, patients commonly exhibit various adverse psychological reactions[16]. Although existing research has primarily concentrated on analyzing the preoperative anxiety levels of patients awaiting surgery in the ward, few investigations have been conducted on the anxiety levels of patients in the waiting area. Consequently, our study focused on examining the anxiety levels of surgical patients within the waiting area.\u003c/p\u003e\n\u003cp\u003eThe medical field is increasingly integrating positive psychology, and researchers have started to focus on the role of resilience in preventing preoperative anxiety. Resilience refers to an individual\u0026apos;s ability to maintain stable psychological and physical functioning when faced with challenging life events and adversity[17]. Lazarus\u0026apos;s stress interaction theory suggests that people with different personality traits evaluate and respond to stress differently, leading to varying physiological and psychological reactions[18]. Resilience, as a personality trait, can help mitigate the negative effects of stress on health and promote positive adaptation[19]. Having higher levels of resilience is associated with more positive cognitive assessments, which can contribute to better mental health outcomes for patients. A study by Chavez TJ[20] revealed that patients with high resilience who underwent knee arthroscopy demonstrated significantly lower levels of preoperative anxiety than those with low resilience.\u003c/p\u003e\n\u003cp\u003ePrevious research on preoperative anxiety in surgical patients has focused primarily on variable-centered research approaches. These approaches sought to examine the significant individual traits that contribute to anxiety. However, these approaches often involve the study of these traits in isolation and overlook the influence of individual-specific factors. To address this issue, latent profile analysis (LPA) offers a statistical analysis approach that revolves around individual-level analysis rather than aggregate data. LPA utilizes latent categorical variables to explore relationships between external continuous variables and aims at establishing local independence among observed variables. Although LPA has been extensively utilized in the fields of education and psychology, no studies have investigated the underlying characteristics of preoperative anxiety in patients awaiting surgery in the waiting area.\u003c/p\u003e\n\u003cp\u003eTherefore, this study utilizes latent class analysis with the following aims:\u003c/p\u003e\n\u003cp\u003e1)Investigate various profiles of preoperative anxiety among patients waiting in the waiting area.\u003c/p\u003e\n\u003cp\u003e2)Identifying these profiles and further analyzing the factors influencing anxiety levels\u003c/p\u003e\n\u003cp\u003e3) Develop targeted intervention strategies based on the distinctive features of each profile, providing valuable references for future interventions.\u003c/p\u003e"},{"header":"Study design and setting ","content":"\u003cp\u003eThis was a cross-sectional study conducted from November 2023 to December 2023 in a tertiary hospital in Jiaxing, China. Convenience sampling was employed to recruit eligible patients. Previous research recommended a minimum sample size of 300-500 for LPA studies[21], and thus, a total of 396 surgical patients were included in this study. The inclusion criteria were as follows: (a) patients who underwent surgery, (b) patients aged 18 years or older, and (c) patients who were clearly conscious and willing to participate. The exclusion criteria were patients who (a) underwent emergency surgery, (b) had severe mental illness or cognitive impairment, or (c) had a communication barrier preventing normal interaction with researchers or completing the questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection procedure\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe survey we conducted took place in a waiting area. Prior to distributing the questionnaires, we provided a thorough explanation of the purpose, significance, and methodology to ensure the participants\u0026apos; understanding. We also informed them that the entire survey would take approximately 15-20 minutes and obtained informed consent and signatures. Participants were made aware of the voluntary nature of the investigation and assured of the anonymity and confidentiality of their data. Younger patients were given the option to complete the online questionnaires using electronic devices such as mobile phones or iPads, utilizing the online survey platform in China www.wjx.cn, while older patients who were not accustomed to screens were provided with paper questionnaires. In total, 396 questionnaires were distributed, and after excluding 23 incomplete or invalid questionnaires, we obtained a total of 373 valid questionnaires, resulting in a response rate of 94.19%. This study received approval from the Institutional Review Board at the local hospital, with reference number 2022-KY-239.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral information questionnaire\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe self-designed demographic questionnaire collected information on gender, age, marital status, and educational background. Patients completed the questionnaire on their own, while trained nurses collected the clinical data of the patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmsterdam Preoperative Anxiety and Information Scale (APAIS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Chinese version of the APAIS[11] was utilized to assess preoperative anxiety. This tool consists of six self-reported items and comprises two subscales: the anxiety subscale (items 1, 2, 4, and 5) and the information needs subscale (items 3 and 6). Each item is rated on a scale from 1 point (none at all) to 5 points (very obvious). To calculate the APAIS score, the scores of all the items are summed. The anxiety scale ranges from 4 to 20 points, with a score of \u0026ge;12 indicating the presence of anxiety. The information need scale ranges from 2 to 10 points, where a score of 2 to 4 indicates no or low information needs, 5 to 7 indicates intermediate information needs, 8 to 10 indicates high information needs, and higher scores on both scales indicate higher levels of anxiety and information needs. The Cronbach\u0026rsquo;s \u0026alpha; of the APAIS is 0.905.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConnor-Davidson Resilience Scale (CD-RISC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Chinese version of the CD-RISC[22] was utilized to evaluate the participants\u0026rsquo; level of psychological resilience. The CD-RISC is a questionnaire comprising 25 items rated on a 5-point Likert scale ranging from \u0026quot;never\u0026quot; to \u0026quot;almost always.\u0026quot; It consists of three dimensions: resilience (items 11-23), strength (items 1, 5, 7, 8, 9, 10, 24, and 25), and optimism (items 2, 3, 4, and 6). The total score on the scale can range from 0 to 100 points, with a higher score indicating greater resilience. In this study, the Cronbach\u0026rsquo;s \u0026alpha; of the CD-RISC was 0.908.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analysis was conducted using SPSS (version 21.0)[23] and Mplus (version 8.7)[24]. To examine common method bias in the data, Harman\u0026apos;s single-factor test was used. The results showed that the variance explained by the first common factor was 31.91%, which is less than the threshold of 40%[25]. This indicates that there was no significant common method bias present in this study.\u003c/p\u003e\n\u003cp\u003eData analysis is typically divided into three steps. First, descriptive analysis was conducted to calculate general demographic information, preoperative anxiety scores, and resilience scores. Categorical data are expressed in terms of frequency and percentage, while continuous data are expressed as the mean \u0026plusmn; standard deviation (SD). Second, potential profile analysis is used to classify the levels of preoperative anxiety among surgical patients in the waiting area. Various model fitting indices are employed, such as the Akaike information criterion (AIC), Bayesian information criterion (BIC), correct Bayesian information adjusted BIC (aBIC), Lo-Mendell-Rubin likelihood ratio test (LMR), bootstrapped likelihood ratio test (BLRT), and entropy. Lower AIC, BIC, and aBIC values indicate better model fit. A p value less than 0.05 for LMR and BLRT suggests that the K-class model is superior to the K-1 class model[26]. The entropy value ranges between 0 and 1, with values closer to 1 indicating higher accuracy.\u003c/p\u003e\n\u003cp\u003eThird, we compared demographic differences and resilience scores among different profile groups via either the chi-square test or one-way ANOVA. Furthermore, multiple logistic regression was performed to examine the demographic factors that showed significant differences. In our analysis, a p value\u0026lt;0.05 was considered to indicate statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study involved a diverse group of patients aged 18 to 85 years\u0026nbsp;(mean age: 50.23\u0026plusmn;14.24 years). Approximately 60% of the participants were female. Among the subjects, the majority (62.5%, n=233) had a middle school education or below. Among all the participants, 61.1% (n=168) were currently employed. A significant proportion of the participants were married individuals (88.2%, n=329), and a large majority (89.8%, n=335) had children. Moreover, 62.2% (n=247) of the subjects had a history of\u0026nbsp;surgery, 70% (n=261) had a history of anesthesia, and 53.6% (n=200) were cancer patients. The primary caregivers for the patients were their spouses, accounting for 57.1% (n=213) of the patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLatent profile analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 displays the model fitting indices for solutions ranging from one to six types. As the number of categories increases, the AIC, BIC, and aBIC gradually decrease. When five categories are maintained, the entropy value reaches its highest point, but the LMR value does not reach statistical significance (P\u0026gt;0.05). Consequently, the best latent feature model for preoperative anxiety in surgical patients consists of four categories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Fit indices for the latent profile analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"112%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.123711340206185%\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eaBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eBLRT\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\"\u003e\n \u003cp\u003eProbability of classes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e1 class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.123711340206185%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e7379.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e7426.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e7388.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e2 class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.123711340206185%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e6067.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e6141.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e6081.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\"\u003e\n \u003cp\u003e0.521/0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e3 class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.123711340206185%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5673.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5775.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5692.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\"\u003e\n \u003cp\u003e0.41/ 0.251/0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e4 class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.123711340206185%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5516.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5646.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5541.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\"\u003e\n \u003cp\u003e0.287/0.152/0.252/0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e5 class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.123711340206185%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5334.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5491.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5364.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\"\u003e\n \u003cp\u003e0.323/0.156/0.197/0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e6 class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.123711340206185%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5126.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5310.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5161.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.1445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.896907216494846%\"\u003e\n \u003cp\u003e0.252/0.063/0.196/0.155/0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote:K, number of free parameters; Log (L), log likelihood; AIC, Akaike information criterion; BIC, Bayesian information criterion; aBIC, adjusted BIC; LMR, Lo-Mendell-Rubin test; BLRT, bootstrap likelihood ratio test; \u0026mdash;, not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLatent class profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 displays the response probability graphs for the four-class model. The means of the six questions differ significantly among the latent profile memberships, as indicated in Table 2. Questions 1, 2, 4, and 5 pertain to patients\u0026apos; preoperative anxiety, while questions 3 and 6 relate to patients\u0026apos; preoperative information needs.\u003c/p\u003e\n\u003cp\u003eIn Class 1 (n=106, 28.7%), participants had an average preoperative anxiety score of 16.16\u0026plusmn;2.94 and an information need score of 7.53\u0026plusmn;1.097. Therefore, we classified Class 1 as the \u0026quot;high preoperative anxiety-medium information need\u0026quot; group.\u003c/p\u003e\n\u003cp\u003eIn Class 2 (n=56, 15.2%), the subscale score for preoperative anxiety was 13.00\u0026plusmn;2.71, which suggests a high level of anxiety within this group. However, the score for information need was 3.16\u0026plusmn;1.09, indicating a low level of information need. Thus, we termed Class 2 the \u0026quot;high preoperative anxiety-low information need\u0026quot; group.\u003c/p\u003e\n\u003cp\u003eIn Class 3, which consisted of 95 participants (25.2% of the total sample), the subscale score for anxiety was 6.84\u0026plusmn;2.26. This indicated that this group had low levels of preoperative anxiety. Additionally, the information need score for this group was 6.67\u0026plusmn;4.47, suggesting a medium level of information need. Therefore, we classified this group as the \u0026quot;low preoperative anxiety-medium information need\u0026quot; group.\u003c/p\u003e\n\u003cp\u003eFinally, Class 4 (n=116, 30.9%), referred to as the low preoperative anxiety-low information need group, had a mean subscale score of 5.02\u0026plusmn;1.54 for anxiety and a mean information need score of 2.53\u0026plusmn;0.869. This group exhibited the lowest levels of preoperative anxiety across all the scales used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictor of latent profile membership\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the results, which indicate significant differences among the four profiles across various factors. Specifically, significant differences were observed in terms of age (F=4.227, p=0.006), gender (c\u003csup\u003e2\u003c/sup\u003e=21.179, p=0.00), education background (c\u003csup\u003e2\u003c/sup\u003e=18.449, p=0.00), being a tumor patient or not (c\u003csup\u003e2\u003c/sup\u003e=8.649, p=0.034), previous surgical experience (c\u003csup\u003e2\u003c/sup\u003e=20.182, p=0.017), preoperative night sleep condition (c\u003csup\u003e2\u003c/sup\u003e=26.086, p=0.000), and caregiver type (c\u003csup\u003e2\u003c/sup\u003e=15.382, p=0.042).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable. 2 Differences in the demographic characteristics among the latent classes\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF/\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge (mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50.23\u0026plusmn;14.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50.03\u0026plusmn;13.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.29\u0026plusmn;15.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.16\u0026plusmn;15.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.23\u0026plusmn;14.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e155(41.6 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33(31.1 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16(28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39(41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67(0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e218(58.4 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73(68.9 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40(71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56(58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49(0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10(9.4 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7(0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e329(88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90(84.9 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50(89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82(86.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107(0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDivorced/widow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13(3.5 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4(3.8 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2(3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducational background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSecondary school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e233(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67(63.2 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43(45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81(69.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh school or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e140(37.5 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39(36.8 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52(55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35(30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHaving children or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e335(89.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95(89.6 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50(89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82(86.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108(0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38(10.2 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11(10.4 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6(10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13(13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8(0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65(17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21(19.8 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12(12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18(0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e228(61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67(63.2 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30(53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66(69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65(0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80(21.5 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18(17.0 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17(17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33(0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTumor patient or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e200(53.6 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54(50.9 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41(73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53(55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52(0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e173(46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52(49.1 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42(44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64(0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurgical history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e247(66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76(71.7 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43(76.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62(65.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80(0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e126(33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30(28.3 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13(23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33(34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36(0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnesthesia history \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e261(70.0 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72(67.9 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42(75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58(61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75(0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112(30.0 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34(32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37(38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41(0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrevious surgical experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49(13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18(17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9(16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16(16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e188(50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48(45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32(57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40(42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68(58.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122(32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32(30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36(37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40(34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrevious Anesthesia experience\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15(4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49(13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17(16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9(16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13(13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e198(53.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56(52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30(53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44(46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68(58.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111(29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29(27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13(23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32(33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37(31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePreoperative night sleep condition\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e195(52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45(42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24(42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52(54.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74(63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98(26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24(22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18(32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32(33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24(20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37(34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11(11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18(15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDay surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96(25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26(24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25(26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33(28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e277(74.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80(75.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44(78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70(73.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83(71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCaregiver type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77(20.64 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22(20.8 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11(19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24(25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20(17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCouples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e213(57.10 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55(51.9 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27(48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60(63.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71(61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChildren\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83(22.25 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29(27.4 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18(32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11(11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25(21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\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\u003c/div\u003e\n\u003cp\u003eC1:high preoperative anxiety -medium information need group; C2:high preoperative anxiety-low information need group; C3:low preoperative anxiety-medium information need group; C4:low preoperative anxiety-low information need group\u003c/p\u003e\n\u003cp\u003eTo explore the sociodemographic factors that predict membership in archive relationships, a multiple logistic regression analysis was conducted using the low anxiety, low information need group as the reference group (Table 3). The results showed that compared to male patients, female patients were more likely to be in the C1 and C2 groups. In addition, patients with a history of poor surgical experience or poor preoperative sleep quality were more likely to be assigned to Group 1. Tumor patients were more likely to be in the C2 group than in the C4 group. Furthermore, patients with higher levels of education were more likely to be in the C3 group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable. 3 Predictor of latent profile membership\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"630\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.10459587955626%\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.782884310618066%\" colspan=\"3\"\u003e\n \u003cp\u003eC1vs. C4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.535657686212361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.039619651347067%\" colspan=\"3\"\u003e\n \u003cp\u003eC2vs. C4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.535657686212361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.001584786053883%\" colspan=\"3\"\u003e\n \u003cp\u003eC3vs. C4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.687615526802219%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.157116451016636%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.393715341959335%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.957486136783734%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.872458410351202%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939001848428836%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.393715341959335%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.957486136783734%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.014787430683919%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.232902033271719%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.393715341959335%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eAge(mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e0.948-1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.948-1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e0.96-1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e1.405-4.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e2.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.047-4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e1.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e0.992-3.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eCaregiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eParent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e0.24-1.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.161-1.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e1.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e0.406-3.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eCouples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e0.333-1.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.201-1.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e1.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e0.559-3.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eEducational background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eSecondary school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e0.369-1.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.523-2.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e0.234-0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eTumor patient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e1.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e1.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e0.656-2.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e2.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e1.412-6.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e0.96-3.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eSurgical experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e5.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e1.017-29.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e1.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.152-24.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e0.242-10.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e2.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e0.881-8.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e2.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.781-10.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e2.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e0.677-6.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e0.394-1.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e1.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.505-2.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e0.346-1.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003eSleep quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003e\u0026nbsp;Good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e0.169-0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.256-1.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e0.432-2.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.126984126984127%\"\u003e\n \u003cp\u003e\u0026nbsp;Normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4603174603174605%\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.015873015873016%\"\u003e\n \u003cp\u003e0.19-1.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.619047619047619%\"\u003e\n \u003cp\u003e1.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003e0.411-3.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5396825396825395%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.317460317460318%\"\u003e\n \u003cp\u003e1.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.222222222222221%\"\u003e\n \u003cp\u003e0.571-4.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.349206349206349%\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eC1:high preoperative anxiety -medium information need group; C2:high preoperative anxiety-low information need group; C3:low preoperative anxiety-medium information need group; C4:low preoperative anxiety-low information need group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between the latent profiles and resilience of surgical patients in the waiting area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results in Table\u0026nbsp;4 show that the resilience scores were significantly different among the profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Comparison of resilience of surgical patients among different latent classes of preoperative anxiety\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.864818024263432%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.558058925476603%\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003cp\u003eM(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.54419410745234%\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003cp\u003eM(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71750433275563%\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003cp\u003eM(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.291161178509533%\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003cp\u003eM(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.864818024263432%\"\u003e\n \u003cp\u003etotal score \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.558058925476603%\"\u003e\n \u003cp\u003e61.60\u0026plusmn;11.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.54419410745234%\"\u003e\n \u003cp\u003e62.05\u0026plusmn;8.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71750433275563%\"\u003e\n \u003cp\u003e67.28\u0026plusmn;11.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.291161178509533%\"\u003e\n \u003cp\u003e67.23\u0026plusmn;10.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e8.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.864818024263432%\"\u003e\n \u003cp\u003eresilience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.558058925476603%\"\u003e\n \u003cp\u003e30.46\u0026plusmn;6.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.54419410745234%\"\u003e\n \u003cp\u003e30.09\u0026plusmn;5.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71750433275563%\"\u003e\n \u003cp\u003e33.84\u0026plusmn;6.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.291161178509533%\"\u003e\n \u003cp\u003e33.57\u0026plusmn;5.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e9.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.864818024263432%\"\u003e\n \u003cp\u003eoptimism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.558058925476603%\"\u003e\n \u003cp\u003e9.97\u0026plusmn;2.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.54419410745234%\"\u003e\n \u003cp\u003e9.39\u0026plusmn;1.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71750433275563%\"\u003e\n \u003cp\u003e9.72\u0026plusmn;2.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.291161178509533%\"\u003e\n \u003cp\u003e10.02\u0026plusmn;1.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e3.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.864818024263432%\"\u003e\n \u003cp\u003estrength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.558058925476603%\"\u003e\n \u003cp\u003e21.78\u0026plusmn;3.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.54419410745234%\"\u003e\n \u003cp\u003e22.57\u0026plusmn;2.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.71750433275563%\"\u003e\n \u003cp\u003e23.47\u0026plusmn;3.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.291161178509533%\"\u003e\n \u003cp\u003e23.65\u0026plusmn;3.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.838821490467938%\"\u003e\n \u003cp\u003e6.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.185441941074524%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eC1:high preoperative anxiety -medium information need group; C2:high preoperative anxiety-low information need group; C3:low preoperative anxiety-medium information need group; C4:low preoperative anxiety-low information need group\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eThe preoperative anxiety level of patients in the waiting area is above average\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study revealed that patients in the waiting area displayed a noteworthy level of preoperative anxiety, with an average score of 14.46\u0026plusmn;6.19. Furthermore, the incidence of preoperative anxiety among the participants was 35.7%. Notably, the total preoperative anxiety score surpassed the findings reported by Eberhartet[27]. These results suggest that patients in the waiting area commonly experience elevated levels of preoperative anxiety. Therefore, it is crucial for medical staff to promptly recognize and address patients with high preoperative anxiety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurgical patients in the waiting area exhibit group heterogeneity in their levels of preoperative anxiety\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to analyze preoperative anxiety and information needs among surgical patients in a waiting area using a method called latent profile analysis. The analysis identified four distinct groups, namely, the high preoperative anxiety-medium information need group, which accounted for 28.7% of the sample. The high preoperative anxiety-low information need group represented 15.2% of the sample. The low preoperative anxiety-medium information need group comprised 25.2% of the sample. The low preoperative anxiety-low information need group made up 30.9% of the sample.\u003c/p\u003e\n\u003cp\u003eAccording to the study, approximately 70% of surgical patients were categorized as having high preoperative anxiety and/or medium information needs. This suggests that most patients\u0026nbsp;who undergo surgery experience anxiety before the procedure and desire more information. Interestingly, the study revealed that Chinese patients generally had lower information needs than patients from other developed countries[28]. This difference could be attributed to the thorough preoperative education provided to patients, which likely increased their understanding of the surgery.\u0026nbsp;Figure 1 shows the characteristics of the patients in the four groups, indicating that preoperative anxiety and information needs do not necessarily increase or decrease together and may be influenced by patient demographic factors. These findings emphasize the importance of further investigations to determine the underlying reasons for these observations[29]. Future studies could focus on understanding the psychological mechanisms utilized by these groups, which could provide insights to help reduce preoperative anxiety and information needs among patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFemale patients are more prone to experiencing high preoperative anxiety and having intermediate information needs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study revealed that female surgical patients are more prone to experiencing preoperative anxiety and have a greater need for information. This result supports earlier research suggesting that women have considerably greater levels of preoperative anxiety traits than men [29-30]. This may be because women tend to be more emotionally and psychologically vulnerable than men are, and they are more likely to openly express their emotions. Additionally, experimental studies have suggested that fluctuations in estrogen and progesterone levels could contribute to the observed differences among female patients [31].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor patients are more likely to enter the high preoperative anxiety, low information need group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients in the waiting area who are being treated for a tumor are more likely to have a higher level of preoperative anxiety and lower information needs. The reason for the higher level of preoperative anxiety in oncology patients is the increased worry and fear they experience following a cancer diagnosis regarding both the surgery and the disease itself. Previous studies have demonstrated that oncology surgeries are more challenging and carry a greater risk than other types of surgeries. As a result, patients require a longer hospital preparation time[30]. These patients often undergo multiple consultations and educational preparations, which leads to a greater level of knowledge. However, they also face a greater risk in terms of their disease and are more likely to adopt passive psychological defense mechanisms such as avoidance and surrender [31-32].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients with negative surgical experiences and preoperative insomnia are at a greater risk of falling into the category of high preoperative anxiety-intermediate need information.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study focused on the relationship between negative surgical experiences in the past and preoperative anxiety and information needs. Individuals who had previously undergone painful surgeries tended to experience higher levels of anxiety when facing future surgeries. This increased anxiety could be attributed to the stress and anticipation of encountering similar negative experiences again. Furthermore, the study revealed that patients with preoperative insomnia were more likely to have high preoperative anxiety and intermediate information needs than patients in other groups were. Poor sleep quality is recognized as a risk factor for anxiety[33-34], and previous research has linked poor sleep quality to impaired emotional control, negative emotional regulatory mechanisms, and heightened emotional responses[35]. As a result, these factors can contribute to symptoms of anxiety and depression in patients.\u003c/p\u003e\n\u003cp\u003eIt is crucial to acknowledge that this study is cross-sectional, thus providing only a momentary depiction of the link between insomnia and preoperative anxiety. Consequently, it cannot be established whether insomnia causes anxiety or if preoperative anxiety results in insomnia. To gain a more comprehensive understanding of this association, additional research is needed, specifically through longitudinal surveys. Exploring the long-term correlation between insomnia and preoperative anxiety is a significant pathway for future studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients with higher levels of education are more inclined to fall into the category of low preoperative anxiety-high information need\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the present study, patients with a junior high school education or above had low preoperative anxiety but intermediate information needs. These findings emphasize the impact of education level on patients\u0026apos; perception and ability to handle the risks associated with surgery. Nigussie\u0026apos;s study also supported the idea that a patient\u0026apos;s education level can affect their assessment and coping mechanisms regarding surgery[36]. It has been suggested that patients with less education tend to take a more straightforward surgical approach, and providing them with excessive information may result in increased anxiety[37].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between potential categories of preoperative anxiety and psychological resilience\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study revealed that patients with different levels of preoperative anxiety showed significant differences in their psychological resilience scores. Specifically, patients with high preoperative anxiety had the lowest psychological resilience scores, which supports previous research. Other studies have also demonstrated that individuals with low preoperative anxiety may possess greater resilience due to its positive impact on promoting the adoption of positive emotion regulation strategies[38]. Overall, individuals with high resilience are better equipped to actively adjust to unpleasant situations, thereby reducing negative emotions such as tension, anxiety, and depression.\u003c/p\u003e\n\u003cp\u003eRecent research indicates that individuals who possess high levels of resilience are better equipped to regulate the release of stress hormones such as amines and cortisol when confronted with stressful or traumatic situations[39]. This is made possible through the engagement of different brain structures and neurotransmitters, ultimately leading to a reduction in anxiety-provoking circumstances. Given that resilience has been identified as a potential indicator of surgical outcomes, it is essential for nursing staff to promptly identify patients who display low mental resilience and offer appropriate interventions. By intervening early, healthcare professionals can help improve patients\u0026apos; levels of preoperative anxiety and enhance their overall quality of life following surgery.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePreoperative anxiety was more common in the waiting area. After analyzing various characteristics, the symptoms were categorized into four groups: preoperative high anxiety-intermediate information need, preoperative high anxiety-low information need, preoperative low anxiety-intermediate information need, and preoperative low anxiety-low information need. Female patients with poor preoperative sleep quality and previous negative surgical experiences were more likely to be categorized into the group with high anxiety-intermediate information needs. Female cancer patients, on the other hand, are prone to belonging to the group with high anxiety and low information needs. Female patients with a high school education or above are more likely to fall into the group with low anxiety-intermediate information needs. To address this issue, it is recommended that personalized management strategies be implemented based on the characteristics of each patient group. The aim is to reduce preoperative anxiety, cater to patient requirements, and enhance patient satisfaction.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable in this section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received funding from the Jiaxing Key Discipline of Medicine - Anesthesiology (2023-Zc-001), the Jiaxing Key Discipline of Medicine - Nursing (2023-Zc-007), and the National Oncology Clinical Key Specialty (2023-GJZK-001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to design of the study and approved the submitted version. RW and XWS participated in the design of the study and helped to revise the manuscript, MW collected the data, and drafted the manuscript, SWD、QHZ、YWW provided comments and ideas and proofed reading the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our deep gratitude to the patients who generously participated in this study. Their invaluable contributions have been essential in advancing our understanding and knowledge in this field. Additionally, we extend our sincere appreciation to the study site for their significant support and collaboration throughout the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study involving human participants were reviewed and approved by the Institutional Review Board of the Jiaxing hospital.(Number: 2022-KY-239). Informed consent to participate in this study was provided by the participants. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eH. Aust, L. 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Nurs. 79 (2023) 1926-1938.\u003c/li\u003e\n\u003cli\u003eM. Tang, H. Huang, S. Li, M. Zhou, Z. Liu, R. Huang, W. Liao, P. Xie, J. Zhou, Hippocampal proteomic changes of susceptibility and resilience to depression or anxiety in a rat model of chronic mild stress, Transl Psychiatry 9 (2019) 260.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"preoperative anxiety, latent profile analysis, surgical patients, preoperative waiting area, resilience","lastPublishedDoi":"10.21203/rs.3.rs-4639305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4639305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePreoperative anxiety is a specific type of anxiety that focuses on concerns and worries related to anesthesia and surgical procedures. However, there has been limited research conducted on the various aspects of this phenomenon, especially in waiting areas.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eUsing latent profile analysis (LPA), this study aimed to identify various preoperative anxiety profiles among surgical patients in the preoperative waiting area. Additionally, this study aimed to explore the demographic and resilience factors associated with different preoperative anxiety profiles.\u003c/p\u003e\u003ch2\u003eMethods and Materials\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted at comprehensive hospitals in Jiaxing, Zhejiang Province, China. Three hundred seventy-three (373) surgical patients were included in the analyses. Surgical patients completed the demographic characteristics questionnaire, the Amsterdam Preoperative Anxiety and Information Scale (APAIS-C), and the Connor-Davidson Resilience Scale (CD-RISC) while waiting for surgery in the waiting area.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe LPA results indicated that the data were most consistent for four types of solutions: high preoperative anxiety-medium information need, high preoperative anxiety-low information need, low preoperative anxiety-medium information need, and low preoperative anxiety-low information need. One-way ANOVA revealed significant differences between the four groups with respect to resilience. Sex, education background, tumor diagnosis, sleep quality and surgical experience significantly predicted profile membership.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere is heterogeneity in preoperative anxiety among surgical patients in the waiting area. When supporting surgical patients in waiting areas, nurses should consider the level of psychological resilience of patients in addition to assessing demographic factors to identify high-risk subgroups to warrant intervention.\u003c/p\u003e","manuscriptTitle":"Preoperative anxiety and its association with resilience of surgical patients in the preoperative waiting area: A latent profile analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 19:28:51","doi":"10.21203/rs.3.rs-4639305/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90bf760d-e62b-4a37-a000-636cd53c7a77","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-29T05:38:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 19:28:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4639305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4639305","identity":"rs-4639305","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}