A latent profile analysis of health-related quality of life in patients with aplastic anemia

A latent profile analysis of health-related quality of life in patients with aplastic anemia | 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 A latent profile analysis of health-related quality of life in patients with aplastic anemia Guibin Wu, Xiao Li, Xiang Ren, Jinbo Huang, Xiaoxiao Zhang, Taimei Liang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4566671/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 Concerns over health-related quality of life (HRQOL) in patients with aplastic anemia (AA) have been increasing worldwide. However, most researches on HRQOL in AA patients have ignored individual-level variability. Thus, our study was designed to explore practical classification of HRQOL and related variables among AA patients. Methods A cross-sectional study was conducted from May 2022 to March 2023, utilizing convenience sampling to enroll AA patients. Data of HRQOL, sociodemographic characteristics, and clinical variables were collected. Latent profile analysis (LPA) was used to analyze the latent categories of HRQOL in AA patients, utilizing scores from eight subscales of the Medical Outcomes Study 36-Item Short Form Health Survey version 2.0. Results A total of 229 patients completed the survey and were included in the analysis. The LPA results showed significantly individual differences and identified three subgroups of HRQOL: Group 1, poor HRQOL with role emotional limitation (n = 54, 23.58%); Group 2, moderate HRQOL with role physical limitation (n = 56, 24.45%), and Group 3, good HRQOL (n = 119, 51.97%), respectively among AA patients. Childless, no comorbidities, transfusion independence, no AA-related symptoms, and higher annual household income were associated with Group 3, whereas higher Eastern Cooperative Oncology Group performance status scores were associated with Group 1. Conclusions The findings of our study revealed significant heterogeneity in HRQOL among AA patients, providing valuable information for tailoring interventions to meet individual needs, especially for those in the poor HRQOL with role emotional limitation group. Anemia aplastic Health-related quality of life Latent profile analysis Figures Figure 1 Figure 2 Background Aplastic anemia (AA) is a bone marrow failure disorder characterized by peripheral pancytopenia and hypocellular bone marrow caused by many different etiologies[ 1 ]. In North America and Europe, the yearly prevalence of AA is estimated to be approximately 2.0 per million people[ 2 , 3 ], while in China, the prevalence of AA is 7.4 per million people[ 4 ]. AA may occur in individuals of all age groups, with a notable prevalence among people ages 15 to 25 and 65 to 69, and there is no substantial disparity in the prevalence of AA between males and females[ 5 , 6 ]. AA is characterized by a complex pathophysiology, a challenging therapy process, and a substantial disease burden, particularly for severe AA (SAA) and very severe AA (VSAA), which are characterized by acute onset, rapid progression, and high mortality[ 7 ], posing a critical threat to patients’ lives and health. Researches on AA patients have shown significant improvements in survival rates since the introduction of hematopoietic stem cell transplantation (HSCT) and immunosuppressive therapy (IST) with anti-thymocyte globulin in the 1980s and 1990s[ 8 – 10 ]. Studies have shown that SAA patients treated with IST have a 4-year survival rate exceeding 80%[ 11 ], while those who receive haploidentical HSCT have a 9 year failure-free survival and overall survival rates of 85.4% and 84.0%, respectively[ 12 , 13 ]. Nevertheless, the diagnosis of AA can be a traumatic experience for both patients and their families. Patients with AA may endure not only the undesired consequences of the disease itself but also the adverse effects resulting from associated treatments. These effects frequently emerge as pathological problems, including infections, bleeding, anemia and malnutrition[ 14 , 15 ]. Additionally, patients may face significant psychological pressure due to the financial strain associated with prolonged treatment and the unpredictability of disease prognosis[ 16 ]. By extension, the disease hinders their ability to resume normal family and social roles, potentially leading to psychological distress, including anxiety, depression, and even suicidal[ 17 ]. As a result, HRQOL for patients is profoundly diminished. Hence, the complete evaluation and enhancement of HRQOL for AA patients has emerged as an important concern. Several studies have been conducted to investigate the HRQOL of AA patients, with a predominant focus on specific facets of HRQOL (such as fatigue[ 14 ]) or the independent analysis of subdomains with HRQOL instruments through means-comparing tests or regression analysis[ 11 , 13 ]. However, the utilization of these methodologies may ignore a crucial aspect of HRQOL among AA patients: the vast heterogeneity of HRQOL among AA patients[ 18 ]. This heterogeneity can be attributed to individual differences in self-management and treatment adherence. For example, some AA patients might retain optimal physical performance following effective therapy but may suffer from social dysfunction, whereas others might experience poor sleep quality or hypertrichosis. The heterogeneity among AA patients is not captured by the overall score and cannot be accurately modeled when subscales are analyzed independently, as information regarding the relationship between these subscales would be lost[ 19 ]. Therefore, it is crucial to assess the heterogeneity of HRQOL in AA patients. Recently, many studies have used latent profile analysis (LPA) to analyze the heterogeneity of HRQOL in the field of oncology[ 20 , 21 ]. LPA is a statistical methodology that employs subject values on many variables to discover homogenous subgroups or classes of individuals within a heterogeneous population[ 22 ]. Unfortunately, the degree to which heterogeneity impacts our understanding of HRQOL among AA patients is still unknown. Therefore, the important aim of this study was to utilize LPA to explore potential classifications of HRQOL among AA patients. Then, we analyzed the sociodemographic and disease-related variables linked to different subgroups. Methods Study design and participants This cross-sectional study enrolled AA patients from the Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College from May 2022 to March 2023 through convenience sampling. Participants were eligible if they were aged over 18 years old with a diagnosis of AA according to the “Guidelines for the Diagnosis and Management of Aplastic Anemia in China (2022)[ 6 ]”. Individuals with profound consciousness impairments or psychiatric conditions were excluded. When conducting multivariate logistic regression analysis, it was recommended that the sample size should be at least 5–10 times greater than the number of independent variables[ 23 ]. In this study, 21 independent variables were included. To ensure the accuracy and efficacy of the model, as well as to account for a 10% loss-to-follow-up rate, a total of 258 patients were recruited. All patients provided written informed consent, and the study was approved by the Ethics Committee of the Institute of Hematology, Chinese Academy of Medical Sciences and Peking Union Medical College in accordance with the guidelines of the Declaration of Helsinki (No.QTJC2022035-EC-1). Data collection To maintain the quality of the questionnaire results, the interviewers received training provided by the researchers to ensure a comprehensive understanding of the questionnaire content and scoring method. Interviewers were instructed to adhere to the established inclusion and exclusion criteria when recruiting participants. The purpose and methods of the survey were explained to patients before the survey, and only those who agreed to participate were enrolle d. All participants completed the questionnaires independently. Measures HRQOL HRQOL was measured using the Medical Outcomes Study 36-Item Short Form Health Survey version 2.0 (SF-36 v2), originally developed by the Boston Health Research Institute in 1988[ 24 ], and subsequently revised in 1996 to improve response accuracy[ 25 ]. The SF-36 v2, a 36-item questionnaire that individuals complete to assess their HRQOL, is a globally acknowledged instrument for evaluating QOL. The instrument consists of 8 subscales that assess several aspects of HRQOL, including physical functioning (PF), mental health (MH), role emotional (RE), role physical (RP), general health (GH), social functioning (SF), vitality (VT), and bodily pain (BP). Conversion computations are required to derive the subscale scores. The subscales conversion score can be calculated using the following formula: (raw score - lowest possible score) / (highest possible score - lowest possible score) × 100. The raw score is obtained by summing the scores of the subscale items[ 26 ]. Researchers from China (Li et al.) have successfully translated the SF-36 v2 into the Chinese language and culturally adapted it, ensuring its accuracy and appropriateness. They have also validated its reliability and validity, making it extensive utilization of the Chinese version of the SF-36 v2 in various domains[ 27 ]. Sociodemographic information The sociodemographic information included gender (male, female), age, marital status (married, single/divorced/widowed), education level (senior or lower, college or higher), annual household income (the total income earned by all family members over a year), having children or not, healthcare payment status (insured, uninsured), employment status (employed, unemployed), smoking history (yes or no), and drinking history (yes or no). Clinical information The clinical information included Eastern Cooperative Oncology Group performance status (ECOG-PS), comorbidity (heart failure, diabetes, hypertension, etc.), transfusion-dependence (transfusion-dependence was defined as at least one transfusion of platelets or red blood cells every 8 weeks, on average, for a duration of 4 months[ 6 ]), AA-related symptoms (fever, bleeding, fatigue, etc.), medication adherence status (we asked, “How often have you taken the prescription given by your doctor in the past three months?” The potential responses were nearly all of the time (> 90%, classified as “excellent”), most of the time (75%, classified as “good”), approximately half the time (50%, classified as “fair”), or less than half the time (< 50%, classified as “poor”)[ 28 ], hemoglobin (HGB), neutrophil (N), platelet (PLT), disease severity (non-severe AA (NSAA), SAA, VSAA), time since diagnosis (years), and regular follow-up status (yes or no). Statistical analyses Statistics were analyzed using SPSS 26.0 and Mplus 7.0. Continuous variables that followed a normal distribution were presented as the mean with standard deviations and were compared using one-way ANOVA. Conversely, non-normally distributed variables were compared using the Kruskal-Wallis H test and were reported as medians with inter-quartile range (IQR). The Spearman correlation test was used to analyze the correlation. Categorical variables were depicted as frequency counts and percentages, with Chi-square tests or Fisher tests employed for group comparisons. LPA was used to analyze the latent categories of HRQOL levels among AA patients based on the eight subscales scores of the SF-36 v2. The iterative procedure began with a one-class model and progressively expanded the number of categories until optimal fit indices were achieved. The indicators for model fitting included the following: (1) the model fit improved as the akaike information criterion (AIC), bayesian information criterion (BIC), and sample size-adjusted BIC (aBIC) decreased; (2) The p < 0.05 for the lo-mendell-rubin likelihood ratio test (LMR) and bootstrapped likelihood ratio test (BLRT) indicated that the current model fit (N = k) was better than that of the former model (N = k-1); and (3) entropy (which ranges between 0 and 1) was greater than 0.8, indicating a good model fit[ 29 ]. The final classification of the model was determined based on model fit metrics and clinical significance[ 30 ]. Afterwards, we used multivariable logistic regression analysis to examine how sociodemographic and clinical variables influenced various categories of HRQOL. Results Sociodemographic and clinical characteristics of the participants Among 258 eligible participants, 29 individuals were excluded due to incomplete responses, resulting in a final 229 (88.76%) individuals who completed the survey. The median age of the participants was 30 (IQR 22.00–40.50) years, with over half being male (55.45%), married (54.15%), childless (55.46%), unemployed (54.59%), had no history of smoking (51.97%), free of comorbidities (52.40%), and without AA-related symptoms (52.84%). The majority of participants were diagnosed with NSAA (79.91%), reported a history of drinking alcohol (69.87%), good/excellent compliance with medication (78.17%), and regular follow-up (75.11%). More than one-third of participants (41.05%) possessed a college education or higher, and a smaller percentage (18.34%) were uninsured. The median time since diagnosis was 6.00 (IQR 4.00-10.80) years, and the household annual income of the patients was 7.12 (IQR 5.00-7.12) million Chinese yuan. The PF domain exhibited the highest median score (85.00, IQR 70.00,95.00), while the GH domain displayed the lowest median score (60.00, IQR 45.00,77.00), and slight variation in the median scores among the eight dimensions. According to the correlation analysis, there were strong, positive correlations between PF and RP ( r = 0.545, p ≤ 0.001 ), RE and RP ( r = 0.612, p ≤ 0.001 ), GH and VT ( r = 0.505, p ≤ 0.001 ), and MH and VT ( r = 0.623, p ≤ 0.001 ). Selection and naming of the models We examined potential profile models ranging from 1 to 6, as presented in Table 1 . The AIC, BIC, and aBIC showed a decrease as the number of class profiles increased. The entropy values for model 1 to model 6 all exceeded than 0.8. However, the LMR of Model 2 and Model 6 did not significantly differ. Model 3 and Model 5, which had entropy values closest to 1, exhibited statistically significant results for both LMR (Model 3: p = 0.005; Model 5: p = 0.023) and BLRT (Model 3: p < 0.001; Model 5: p < 0.001). Additionally, they met the requirement of having at least 3% of cases in each potential category[ 19 ]. However, for Model 5, the minimum number of samples was 14, which may considerably restrict its representativeness. After comparing the model fit indices of each model, Model 3 was selected as the best-fitting model. The probabilities of correct attribution for a patient with AA to each category were 0.972, 0.956, and 0.996, respectively (Table 2 ). These results indicated that the optimal model derived from the potential profile analysis in this study exhibited reliability and a robust capacity for differentiation across categories. Table 1 Comparison of model fit evaluation results for the different groups AIC BIC aBIC Entropy p Class probability LMR BLRT 1 16380.721 16435.661 16384.951 2 15922.350 16008.193 15928.959 0.918 0.397 <0.001 0.314/0.686 3 15769.473 15886.220 15778.461 0.952 0.005 <0.001 0.236/0.245/0.520 4 15706.068 15853.718 15717.436 0.883 0.004 <0.001 0.245/0.188/0.236/0.332 5 15641.403 15819.957 15655.150 0.952 0.023 <0.001 0.114/0.197/0.170/0.061/0.459 6 15593.959 15803.416 15610.086 0.907 0.055 <0.001 0.114/0.061/0.197/0.170/0.135/0.323 AIC: the Akaike Information Criterion BIC: Bayesian Information Criterion aBIC: sample size-adjusted BIC LMR: Lo-Mendell-Rubin likelihood ratio test BLRT: Bootstrapped likelihood ratio test Table 2 Classification probabilities for the most likely latent group membership (Column) by latent group (Row) Group 1 Group 2 Group 3 Group 1 0.972 0.028 0.000 Group 2 0.042 0.956 0.001 Group 3 0.000 0.004 0.996 Statistically significant differences were observed in the PF, RP, BP, GH, VT, SF, RE, and MH scores for Model 3. The study analyzed the characteristics of three potential groups of HRQOL in AA patients by plotting line graphs of the eight subscale scores of the SF-36 v2 (Fig. 1 ). These groups were named based on the fluctuations in the mean values of the subscale items. 54 (23.58%) patients were in Group 1, exhibiting lower HRQOL scores compared to the other two groups. Additionally, this group was classified as the "poor HRQOL with RE limitations" group showed a bipartition trend in the RE subscale compared to the other two groups. There were 56 patients (24.45%) in Group 2 whose HRQOL scores fell between Group 1 and Group 3, and significantly lower score on RP. As a result, these participants were classified into the "moderate HRQOL with RP limitation" group. Group 3, consisting 119 patients (51.97%), exhibited higher HRQOL scores compared to the other two groups. Therefore, this group was named the "good HRQOL" group. Figure 2 shows the trends of HRQOL curves for all three groups. Predictors of HRQOL groups The findings of the intergroup comparison analysis demonstrated statistically disparities ( p < 0.05) in the three groups of HRQOL among AA patients in terms of household annual income, having children or not, ECOG-PS score, comorbidity, transfusion-dependence, AA-related symptoms, and time since diagnosis (Table 3 ). Table 3 Comparison of sociodemographic characteristics among the different HRQOL groups Variable poor HRQOL with RE limitation (n = 54) moderate HRQOL with RP limitation (n = 56) good HRQOL (n = 119) Fisher/χ 2 /H p Gender Male 30 34 63 0.932 0.628 Female 24 22 56 Age 32.00 (23.75, 40.00) 30.50 (23.25, 39.75) 29.00 (20.00, 39.00) 2.268 0.322 Marital status Single/Divorced/Widowed 20 24 61 3.294 0.193 Married 34 32 58 Education Senior or lower 33 35 67 0.741 0.691 college or higher 21 21 52 Annual household income (million Chinese Yuan) 6.00 (3.00, 7.12) 7.12 (5.25, 7.12) 7.12 (5.00, 10.00) 10.604 0.005 Children With 32 26 44 7.574 0.023 Without 22 30 75 Healthcare payment Insured 43 47 97 0.343 0.842 Uninsured 11 9 22 Employment Employed 26 24 54 0.311 0.856 Unemployed 28 32 65 Smoking history Yes 31 23 56 3.034 0.219 No 23 33 63 Drinking history Yes 41 38 81 1.232 0.540 No 13 18 38 ECOG-PS < 2 34 50 102 15.766 < 0.001 ≥ 2 20 6 17 Table 3 (Continues) Variable poor HRQOL with RE limitation (n = 54) moderate HRQOL with RP limitation (n = 56) good HRQOL (n = 119) Fisher/χ 2 /H p Comorbidity With 34 31 44 11.847 0.003 Without 20 25 75 Transfusion-dependence Yes 38 31 50 12.305 0.002 No 16 25 69 AA-related symptoms With 39 25 44 18.707 < 0.001 Without 15 31 75 Medication adherence Excellence 29 35 67 8.176 0.225 Good 16 10 22 Fair 6 9 28 Poor 3 2 2 HGB (g/L) 100.00 (60.00, 132.25) 81.50 (64.55, 122.50) 92.00 (64.00, 126.00) 0.529 0.767 N (×10 9 /L) 54.00 (25.50, 108.00) 66.00 (26.00, 186.00) 72.00 (28.00, 166.00) 1.422 0.491 PLT (×10 9 /L) 1.88 (0.99, 5.38) 2.02 (1.26, 6.17) 1.86 (1.08, 4.92) 1.593 0.451 Disease severity NSAA 40 43 90 2.743 0.605 SAA 11 12 20 VSAA 3 1 9 Time since diagnosis (years) 6.00 (3.00, 10.08) 5.00 (4.00, 7.30) 6.90 (4.10, 11.00) 9.393 0.009 Regular follow-up Yes 37 42 93 1.844 0.398 No 17 14 26 In this study, we utilized these statistically significant variables as independent variables and potential profile categories of HRQOL as dependent variables in an unordered multicategorical logistic regression analysis, with the “poor HRQOL with RE limitation” group served as the reference group. The results presented in Table 4 , indicated that patients without AA-related symptoms or with a higher annual household income were more likely to be classified in the "moderate HRQOL with RP limitation" group. Conversely, patients with higher ECOG-PS scores were more likely to be categorized in the "poor HRQOL with RE limitation" group, and these findings were observed when comparing the "poor HRQOL with RE limitation" with the "moderate HRQOL with RP limitation" subgroups. Patients in the "good HRQOL" group, compared to those in the "poor HRQOL with RE limitation" group, were more likely to be childless, transfusion-independent, and had no comorbidities, AA-related symptoms and have a higher annual household income. Table 4 Logistic regression analysis of categories of HRQOL trajectories in AA patients Items β SE Wald χ 2 p OR 95% CI poor HRQOL with RE limitation vs moderate HRQOL with RP limitation Constant -3.477 0.918 14.351 <0.001 — — Children (ref = With) Without 0.663 0.428 2.399 0.121 1.941 0.839 ~ 4.494 Comorbidity (ref = With) Without 0.783 0.450 3.020 0.082 2.188 0.905 ~ 5.289 Transfusion-dependent (ref = Yes) NO 0.601 0.437 1.893 0.169 1.824 0.775 ~ 4.296 AA-related symptoms (ref = With) Without 1.253 0.443 8.010 0.005 3.500 1.470 ~ 8.332 Annua household income ( million Chinese yuan ) 0.162 0.062 6.943 0.008 1.176 1.043 ~ 1.328 Time since diagnosis (years) -0.038 0.047 0.659 0.417 0.963 0.879 ~ 1.055 ECOG-PS (ref ≥ 2) < 2 1.749 0.560 9.745 0.002 5.747 1.917 ~ 8.332 poor HRQOL with RE limitation vs good HRQOL Constant -4.797 0.893 28.878 <0.001 — — Children (ref = With) Without 1.063 0.404 6.927 0.008 2.895 1.312 ~ 6.389 Comorbidity (ref = With) Without 1.632 0.427 14.584 <0.001 5.113 2.213 ~ 11.814 Transfusion-dependent (ref = Yes) NO 1.155 0.409 7.978 0.005 3.174 1.424 ~ 7.074 AA-related symptoms (ref = With) Without 1.593 0.418 14.531 <0.001 4.918 2.168 ~ 11.156 Annual household income ( million Chinese yuan ) 0.194 0.060 10.343 0.001 1.214 1.079 ~ 1.367 Time since diagnosis (years) 0.060 0.036 2.759 0.097 1.062 0.989 ~ 1.140 ECOG-PS (ref ≥ 2) < 2 1.672 0.490 11.642 0.001 5.324 2.037 ~ 13.912 Discussion In this study, we explored the HRQOL of patients with AA by utilizing LPA to investigate the heterogeneity of their HRQOL and identify the factors contributing to the various latent groups. To our knowledge, this is the first study to use LPA to explore the heterogeneity of HRQOL among AA patients. Our study revealed significant individual differences of HRQOL among AA patients, who were classified into three groups: the poor HRQOL with RE limitation, the moderate HRQOL with RP limitation, and the good HRQOL subgroups, respectively. These characteristics used to define these groups were similar to those utilized by Lidia et al. to define the potential categories for exploring HRQOL among older age groups through LPA[ 31 ]. The HRQOL for the three groups, as determined by LPA, showed statistically significant, which indicated that the categorization results were reasonable to some extent. Our findings confirmed that household annual income, have children or not, comorbidities, transfusion-dependence and AA-related symptoms were all significant factors associated with the identified HRQOL groups. Group 1, which had members of all the diagnostic groupings, was the smallest group (23.58%). Focusing on each subscale of the SF-36 v2, Group 1 had the lowest RP and RE scores. Moreover, the RE subscale scores exhibited the greatest discrepancy with those of Group 2 and Group 3, indicating a polarized state. This implies that compared to PF, MH might be the primary factor influencing the lowest level of HRQOL for individuals with AA. AA patients experience psychological symptoms that impede their ability to engage in work and daily activities, potentially leading to reduced treatment adherence, poorer prognosis and lower HRQOL. Martin et al.[ 32 ] emphasized the unique impacts of both physical and mental illnesses on mortality and disability, argued that the lack of MH is equivalent to the absence of PF. Thus, it is imperative for medical staff in clinical practice to focus not only on physical symptoms of AA patients but also their psychological problems, and to provide tailored interventions to patients in need of help. The composition of Group 2 was very interesting. Despite displaying a positive psychological state, these patients still encountered notable limitation in RP, such as limited mobility and decreased independence, due to physical health issues. Compared with those in the other two groups, more than 50% of the patients in Group 2 exhibited a higher prevalence of comorbidities, transfusion-dependence, and had the shortest time since diagnosis. Notably, RP was strongly correlated with RE in AA patients in this study. Thus, it may be necessary for health professional to tailor individualized interventions for patients in this group in order to decrease the probability of progression to Group 1. Julius et al.[ 33 ], stated that PF can influence MH through lifestyle choices and social capital. In clinical practice, healthcare professional can enhance patients' health through health investments (such as health education) and social interactions (such as patients’ families participate in decision-making). Furthermore, the composition of Group 2 highlighted the advantages of using LPA to analyze HRQOL, and identifying AA patients with this specific level of HRQOL solely based on the characteristics of the SF-36 v2 can pose a significant challenge. Group 3 was characterized by high levels of HRQOL, even exceeding the average HRQOL level of the general population (matched for age and sex[ 27 ]). Patients within this group demonstrated the highest scores across all subscales of the SF-36 v2 scale (Fig. 2 ). Moreover, Group 3 also have the most members of patients (51.97%), encompassing individuals from various diagnostic group, underscoring the prognostic significance of HRQOL for a substantial portion of AA patients. Response shift has been extensively documented in HRQOL researches[ 34 , 35 ], indicating that the experience of a condition such as cancer can alter survivors’ perceptions of their HRQOL[ 19 ]. AA patients who possess a redefined perception of HRQOL and adjusted expectations during the period from diagnosis to treatment may demonstrated unexpectedly elevated levels of HRQOL in this study. Furthermore, these patients exhibited favorable prognosis following prompt initiation of treatment, which may also explain the high proportion of patients in this group. Previous studies[ 36 , 37 ] have validated the clinical significance of the ECOG-PS score, indicating an association between a worse prognosis and higher scores, which was consistent with the findings of this study. Patients with higher ECOG-PS scores were more likely to be classified into the "poor HRQOL with RE limitation" group. This relationship can be attributed to the decline in physical condition, activity endurance, and overall PF associated as the ECOG-PS scores increased, which heightens the likelihood of experiencing severe somatic symptoms. On the other hand, the ECOG-PS can also indirectly impact the HRQOL of patients by affecting negative emotions[ 38 ]. The deterioration in physical health may lead to changes in patients’ original life status and negative emotions due to unfulfilled social roles, ultimately leading to a diminished HRQOL[ 33 ]. Due to its 5-level scale, the ECOG-PS score is simple and allows medical staff to evaluate a patient's physical status quickly[ 39 ]. It could be an effective tool for assessing and examining a patient's HRQOL in clinical practice. Consistent with previous studies, AA patients with higher annual household incomes tend to report higher levels of HRQOL[ 40 – 42 ]. However, our study diverges from prior studies by demonstrating that AA patients without children exhibited a higher HRQOL[ 43 , 44 ]. This discrepancy may be attributed to the reduced financial burden of patients free of child-rearing expenses. AA patients without financial pressure could receive a standardized treatment and free of negative emotions, resulting in greater HRQOL. Nevertheless, a study by Hurmuz et al.[ 43 ] indicated that intimate interpersonal relationships can provide emotional support, maintain and enhance patients' physiological functioning, and increase motivation for recovery. Thus, medical staff should prioritize the evaluation of AA patients’ financial capacity, promptly identify patients facing financial difficulties and increase the reasonable utilization of healthcare. Moreover, medical staff should encourage patients’ families to participate in patient care as much as possible to ensure that patients receive more positive social support[ 45 ]. Our study findings revealed that the likelihood of transfusion-independent patients being attributed to "good HRQOL" group was 3.174 times greater than that of patients being attributed to “poor HRQOL with RE limitation” group. Vaht et al.[ 8 ] discovered that the lifespan of AA patients with transfusion dependence was considerably shorter than that of those without. Frequent blood transfusions during treatment may result in complications such as transfusion-related infections, iron overload, and inefficient transfusions, which can significantly impact the survival and HRQOL of these patients[ 46 , 47 ]. Clinical staff should pay close attention to the HRQOL of AA patients who are transfusion-dependent. Furthermore, AA patients who are transfusion-dependent tend to have more severe disease and experience more clinical symptoms than those who are not[ 48 ]. The results of our study indicated that patients with AA-related symptoms were more likely to be in the "poor HRQOL with RE limitation" group. AA patients with clinical symptoms tend to be more concerned and excessively worried about any changes in their bodies[ 14 ], potentially exacerbating disease burden, resulting in poor HRQOL. Thus, health education should be improved in order to enhance patients’ awareness of disease understanding and provide tailor interventions for AA-related symptoms to prevent and minimize complications, alleviate disease burden, and improve patients’ HRQOL. Limitations Our study was designed initially as cross-sectional, preventing the determination of changes in HRQOL groups for AA patients over time and whether the predictors of their group status had changed. Second, the participants in our study were all recruited from a single hospital, which may restrict the generalizability of the results. Multicentre, large-sample studies are needed to further validate the present results. Last but not least, in our study, we only explored the effects of sociodemographic and disease factors on different groups of HRQOL in AA patients. However, other significant factors, such as social support and psychological resilience, weren’t evaluated in this study and warrant further investigation. Conclusions This study could make a contribution to the awareness of HRQOL among AA patients. Our study revealed that LPA offers notable benefits in addressing the complexity and diversity of data related to HRQOL measures among AA patients. Utilizing this model, we successfully categorized the HRQOL of AA patients into three subgroups: Group 1, poor HRQOL with role emotional limitation; Group 2, moderate HRQOL with role physical limitation, and Group 3, good HRQOL, respectively. Factors such as AA-related symptoms, household annual income, ECOG-PS score, children, comorbidities, and transfusion-dependence were found to significantly impact the patients’ HRQOL. The findings of our study also provided a better understanding of the heterogeneity in HRQOL among AA patients, it could be a useful to guide clinicians to timely identify patients at heightened risk for diminished HRQOL in the context of limited healthcare resources. Declarations Acknowledgements We extend our sincere gratitude to all participants who generously contributed their time and participation to this study. Author contributions GW, XL, YZ and WX designed the work. GW, XZ, TL, LS, MH, ZK, XL, LX and QZ collected the data. XL and GW analyzed and interpreted the data. XL, GW, XR, JH, YZ and WX drafted the manuscript, YZ and WX revised it. All authors read and approved the manuscript and declared no potential conflict of interest. Funding This study was funded by Chinese Nursing Association Research Project (ZHKY202115). Data availability The data supporting the findings of this study can be obtained from the corresponding author [YZ and WX], upon reasonable request. Ethics approval and consent to participate All patients provided written informed consent, and the study was approved by the Ethics Committees of the Institute of Hematology, Chinese Academy of Medical Sciences and Peking Union Medical College according to the guidelines of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors affirm that the research was carried out without any commercial or financial affiliations that might present a potential conflict of interest. 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J Clin Epidemiol. 1998;51(11):1189–202. https://doi.org/10.1016/s0895-4356(98)00111-5 . Morfeld M, Bullinger M, Nantke J, Brähler E. The version 2.0 of the SF-36 Health Survey: results of a population-representative study. Soz Praventivmed. 2005;50(5):292–300. https://doi.org/10.1007/s00038-005-4090-6 . Ware JE. Jr. SF-36 health survey update. Spine (Phila Pa 1976). 2000;25(24):3130–9. https://doi.org/10.1097/00007632-200012150-00008 . Li L, Wang HM, Shen Y, Chinese. SF-36 Health Survey: translation, cultural adaptation, validation, and normalisation. J Epidemiol Community Health. 2003;57(4):259–63. https://doi.org/10.1136/jech.57.4.259 . Whooley MA, de Jonge P, Vittinghoff E, Otte C, Moos R, Carney RM, et al. Depressive symptoms, health behaviors, and risk of cardiovascular events in patients with coronary heart disease. JAMA. 2008;300(20):2379–88. https://doi.org/10.1001/jama.2008.711 . Dziak JJ, Lanza ST, Tan X, Effect, Size. Statistical Power and Sample Size Requirements for the Bootstrap Likelihood Ratio Test in Latent Class Analysis. Struct Equ Model. 2014;21(4):534–52. https://doi.org/10.1080/10705511.2014.919819 . Kim SY. Determining the Number of Latent Classes in Single- and Multi-Phase Growth Mixture Models. Struct Equ Model. 2014;21(2):263–79. https://doi.org/10.1080/10705511.2014.882690 . Băjenaru L, Balog A, Dobre C, Drăghici R, Prada GI. Latent profile analysis for quality of life in older patients. BMC Geriatr. 2022;22(1):848. https://doi.org/10.1186/s12877-022-03518-1 . Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, et al. No health without mental health. lancet. 2007;370(9590):859–77. https://doi.org/10.1016/S0140-6736(07)61238-0 . Ohrnberger J, Fichera E, Sutton M. The relationship between physical and mental health: A mediation analysis. Soc Sci Med. 2017;195:42–9. https://doi.org/10.1016/j.socscimed.2017.11.008 . Sprangers MA, Schwartz CE. Integrating response shift into health-related quality of life research: a theoretical model. Soc Sci Med. 1999;48(11):1507–15. https://doi.org/10.1016/s0277-9536(99)00045-3 . Rapkin BD, Schwartz CE. Advancing quality-of-life research by deepening our understanding of response shift: a unifying theory of appraisal. Qual Life Res. 2019;28(10):2623–30. https://doi.org/10.1007/s11136-019-02248-z . Shibuki T, Mizuta T, Shimokawa M, Koga F, Ueda Y, Nakazawa J, et al. Prognostic nomogram for patients with unresectable pancreatic cancer treated with gemcitabine plus nab-paclitaxel or FOLFIRINOX: A post-hoc analysis of a multicenter retrospective study in Japan (NAPOLEON study). BMC Cancer. 2022;22(1):19. https://doi.org/10.1186/s12885-021-09139-y . Yan Z, Gu YY, Hu XD, Zhao Q, Kang HL, Wang M, et al. Clinical outcomes and safety of apatinib monotherapy in the treatment of patients with advanced epithelial ovarian carcinoma who progressed after standard regimens and the analysis of the VEGFR2 polymorphism. Oncol Lett. 2020;20(3):3035–45. https://doi.org/10.3892/ol.2020.11857 . Mei Hsien CC, Wan Azman WA, Md Yusof M, Ho GF, Krupat E. Discrepancy in patient-rated and oncologist-rated performance status on depression and anxiety in cancer: a prospective study protocol. BMJ Open. 2012;2(5):e001799. https://doi.org/10.1136/bmjopen-2012-001799 . Neeman E, Gresham G, Ovasapians N, Hendifar A, Tuli R, Figlin R, et al. Comparing Physician and Nurse Eastern Cooperative Oncology Group Performance Status (ECOG-PS) Ratings as Predictors of Clinical Outcomes in Patients with Cancer. Oncologist. 2019;24(12):e1460–6. https://doi.org/10.1634/theoncologist.2018-0882 . Ellepola S, Nadeesha N, Jayawickrama I, Wijesundara A, Karunathilaka N, Jayasekara P. Quality of life and physical activities of daily living among stroke survivors; cross-sectional study. Nurs Open. 2022;9(3):1635–42. https://doi.org/10.1002/nop2.1188 . Mohamadzadeh Tabrizi Z, Mohammadzadeh F, Davarinia Motlagh Quchan A, Bahri N. COVID-19 anxiety and quality of life among Iranian nurses. BMC Nurs. 2022;21(1):27. https://doi.org/10.1186/s12912-021-00800-2 . Babazadeh T, Dianatinasab M, Daemi A, Nikbakht HA, Moradi F, Ghaffari-Fam S. Association of Self-Care Behaviors and Quality of Life among Patients with Type 2 Diabetes Mellitus: Chaldoran County, Iran. Diabetes Metab J. 2017;41(6):449–56. https://doi.org/10.4093/dmj.2017.41.6.449 . Hurmuz M, Frandes M, Panfil AL, Stoica IP, Bredicean C, Giurgi-Oncu C, et al. Quality of Life in Patients with Chronic Psychotic Disorders: A Practical Model for Interventions in Romanian Mental Health Centers. Med (Kaunas). 2022;58(5):615. https://doi.org/10.3390/medicina58050615 . White R, Haddock G, Campodonico C, Haarmans M, Varese F. The influence of romantic relationships on mental wellbeing for people who experience psychosis: A systematic review. Clin Psychol Rev. 2021;86:102022. https://doi.org/10.1016/j.cpr.2021.102022 . Geue K, Götze H, Friedrich M, Leuteritz K, Mehnert-Theuerkauf A, Sender A, et al. Perceived social support and associations with health-related quality of life in young versus older adult patients with haematological malignancies. Health Qual Life Outcomes. 2019;17(1):145. https://doi.org/10.1186/s12955-019-1202-1 . Wang H, Dong Q, Fu R, Qu W, Ruan E, Wang G, et al. Recombinant human thrombopoietin treatment promotes hematopoiesis recovery in patients with severe aplastic anemia receiving immunosuppressive therapy. Biomed Res Int. 2015;2015:597293. https://doi.org/10.1155/2015/597293 . Zhou S, Sun L, Qian S, Ma Y, Ma R, Dong Y, et al. Iron overload adversely effects bone marrow haematogenesis via SIRT-SOD2-mROS in a process ameliorated by curcumin. Cell Mol Biol Lett. 2021;26(1):2. https://doi.org/10.1186/s11658-020-00244-7 . Helmer P, Hottenrott S, Steinisch A, Röder D, Schubert J, Steigerwald U, et al. Avoidable Blood Loss in Critical Care and Patient Blood Management: Scoping Review of Diagnostic Blood Loss. J Clin Med. 2022;11(2):320. https://doi.org/10.3390/jcm11020320 . 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4566671","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323234404,"identity":"46724cb8-5e7f-462d-9d77-eb0c093806a3","order_by":0,"name":"Guibin Wu","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences Institute of Hematology and Blood Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guibin","middleName":"","lastName":"Wu","suffix":""},{"id":323234405,"identity":"7cb2eb4a-0814-42e3-8ddd-8743146ce5d1","order_by":1,"name":"Xiao 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Blood Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Xu","suffix":""},{"id":323234416,"identity":"85daba77-6704-4406-88d4-4716760aaaa1","order_by":12,"name":"Yizhou Zheng","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences Institute of Hematology and Blood Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yizhou","middleName":"","lastName":"Zheng","suffix":""},{"id":323234417,"identity":"f300bc30-068c-4c75-b0e8-f91a246d926d","order_by":13,"name":"Wenjun Xie","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences Institute of Hematology and Blood Diseases Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-06-12 01:07:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4566671/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4566671/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62126481,"identity":"d93f571e-9909-4551-97e8-9be940682d01","added_by":"auto","created_at":"2024-08-09 14:47:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57283,"visible":true,"origin":"","legend":"\u003cp\u003eThe predicted mean HRQOL for each trajectory group. PF: physical functioning, MH: mental health, RE: role emotional, RP: role physical, GH: general health, SF: social functioning, VT: vitality , and BP: bodily pain.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4566671/v1/ad90e7d9932741a71a2065ed.png"},{"id":62126480,"identity":"f033bf92-9270-493e-8e1f-9d5f1f652503","added_by":"auto","created_at":"2024-08-09 14:47:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56436,"visible":true,"origin":"","legend":"\u003cp\u003eTendencies of the three distinctive HRQOL groups. PF: physical functioning, MH: mental health, RE: role emotional, RP: role physical, GH: general health, SF: social functioning, VT: vitality , and BP: bodily pain.\u003c/p\u003e","description":"","filename":"floatimage222.png","url":"https://assets-eu.researchsquare.com/files/rs-4566671/v1/4e13cb5e31de6e12718cd5cc.png"},{"id":66039533,"identity":"50ce6b3e-c188-43d9-a54e-91ee052d1ebf","added_by":"auto","created_at":"2024-10-07 05:32:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1023184,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4566671/v1/a09ca438-6449-4013-9c70-386d94a38c89.pdf"}],"financialInterests":"","formattedTitle":"A latent profile analysis of health-related quality of life in patients with aplastic anemia","fulltext":[{"header":"Background","content":"\u003cp\u003eAplastic anemia (AA) is a bone marrow failure disorder characterized by peripheral pancytopenia and hypocellular bone marrow caused by many different etiologies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In North America and Europe, the yearly prevalence of AA is estimated to be approximately 2.0 per million people[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], while in China, the prevalence of AA is 7.4 per million people[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. AA may occur in individuals of all age groups, with a notable prevalence among people ages 15 to 25 and 65 to 69, and there is no substantial disparity in the prevalence of AA between males and females[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. AA is characterized by a complex pathophysiology, a challenging therapy process, and a substantial disease burden, particularly for severe AA (SAA) and very severe AA (VSAA), which are characterized by acute onset, rapid progression, and high mortality[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], posing a critical threat to patients\u0026rsquo; lives and health. Researches on AA patients have shown significant improvements in survival rates since the introduction of hematopoietic stem cell transplantation (HSCT) and immunosuppressive therapy (IST) with anti-thymocyte globulin in the 1980s and 1990s[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Studies have shown that SAA patients treated with IST have a 4-year survival rate exceeding 80%[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while those who receive haploidentical HSCT have a 9 year failure-free survival and overall survival rates of 85.4% and 84.0%, respectively[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, the diagnosis of AA can be a traumatic experience for both patients and their families. Patients with AA may endure not only the undesired consequences of the disease itself but also the adverse effects resulting from associated treatments. These effects frequently emerge as pathological problems, including infections, bleeding, anemia and malnutrition[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, patients may face significant psychological pressure due to the financial strain associated with prolonged treatment and the unpredictability of disease prognosis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. By extension, the disease hinders their ability to resume normal family and social roles, potentially leading to psychological distress, including anxiety, depression, and even suicidal[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. As a result, HRQOL for patients is profoundly diminished. Hence, the complete evaluation and enhancement of HRQOL for AA patients has emerged as an important concern.\u003c/p\u003e \u003cp\u003eSeveral studies have been conducted to investigate the HRQOL of AA patients, with a predominant focus on specific facets of HRQOL (such as fatigue[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]) or the independent analysis of subdomains with HRQOL instruments through means-comparing tests or regression analysis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the utilization of these methodologies may ignore a crucial aspect of HRQOL among AA patients: the vast heterogeneity of HRQOL among AA patients[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This heterogeneity can be attributed to individual differences in self-management and treatment adherence. For example, some AA patients might retain optimal physical performance following effective therapy but may suffer from social dysfunction, whereas others might experience poor sleep quality or hypertrichosis. The heterogeneity among AA patients is not captured by the overall score and cannot be accurately modeled when subscales are analyzed independently, as information regarding the relationship between these subscales would be lost[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, it is crucial to assess the heterogeneity of HRQOL in AA patients. Recently, many studies have used latent profile analysis (LPA) to analyze the heterogeneity of HRQOL in the field of oncology[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. LPA is a statistical methodology that employs subject values on many variables to discover homogenous subgroups or classes of individuals within a heterogeneous population[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Unfortunately, the degree to which heterogeneity impacts our understanding of HRQOL among AA patients is still unknown. Therefore, the important aim of this study was to utilize LPA to explore potential classifications of HRQOL among AA patients. Then, we analyzed the sociodemographic and disease-related variables linked to different subgroups.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis cross-sectional study enrolled AA patients from the Institute of Hematology \u0026amp; Blood Diseases Hospital, Chinese Academy of Medical Sciences \u0026amp; Peking Union Medical College from May 2022 to March 2023 through convenience sampling. Participants were eligible if they were aged over 18 years old with a diagnosis of AA according to the \u0026ldquo;Guidelines for the Diagnosis and Management of Aplastic Anemia in China (2022)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u0026rdquo;. Individuals with profound consciousness impairments or psychiatric conditions were excluded.\u003c/p\u003e \u003cp\u003eWhen conducting multivariate logistic regression analysis, it was recommended that the sample size should be at least 5\u0026ndash;10 times greater than the number of independent variables[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, 21 independent variables were included. To ensure the accuracy and efficacy of the model, as well as to account for a 10% loss-to-follow-up rate, a total of 258 patients were recruited.\u003c/p\u003e \u003cp\u003e All patients provided written informed consent, and the study was approved by the Ethics Committee of the Institute of Hematology, Chinese Academy of Medical Sciences and Peking Union Medical College in accordance with the guidelines of the Declaration of Helsinki (No.QTJC2022035-EC-1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eTo maintain the quality of the questionnaire results, the interviewers received training provided by the researchers to ensure a comprehensive understanding of the questionnaire content and scoring method. Interviewers were instructed to adhere to the established inclusion and exclusion criteria when recruiting participants. The purpose and methods of the survey were explained to patients before the survey, and only those who agreed to participate were enrolle\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ed.\u003c/span\u003e All participants completed the questionnaires independently.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eHRQOL\u003c/h2\u003e \u003cp\u003eHRQOL was measured using the Medical Outcomes Study 36-Item Short Form Health Survey version 2.0 (SF-36 v2), originally developed by the Boston Health Research Institute in 1988[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and subsequently revised in 1996 to improve response accuracy[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The SF-36 v2, a 36-item questionnaire that individuals complete to assess their HRQOL, is a globally acknowledged instrument for evaluating QOL. The instrument consists of 8 subscales that assess several aspects of HRQOL, including physical functioning (PF), mental health (MH), role emotional (RE), role physical (RP), general health (GH), social functioning (SF), vitality (VT), and bodily pain (BP). Conversion computations are required to derive the subscale scores. The subscales conversion score can be calculated using the following formula: (raw score - lowest possible score) / (highest possible score - lowest possible score) \u0026times; 100. The raw score is obtained by summing the scores of the subscale items[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Researchers from China (Li et al.) have successfully translated the SF-36 v2 into the Chinese language and culturally adapted it, ensuring its accuracy and appropriateness. They have also validated its reliability and validity, making it extensive utilization of the Chinese version of the SF-36 v2 in various domains[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eSociodemographic information\u003c/h2\u003e \u003cp\u003eThe sociodemographic information included gender (male, female), age, marital status (married, single/divorced/widowed), education level (senior or lower, college or higher), annual household income (the total income earned by all family members over a year), having children or not, healthcare payment status (insured, uninsured), employment status (employed, unemployed), smoking history (yes or no), and drinking history (yes or no).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical information\u003c/h2\u003e \u003cp\u003eThe clinical information included Eastern Cooperative Oncology Group performance status (ECOG-PS), comorbidity (heart failure, diabetes, hypertension, etc.), transfusion-dependence (transfusion-dependence was defined as at least one transfusion of platelets or red blood cells every 8 weeks, on average, for a duration of 4 months[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]), AA-related symptoms (fever, bleeding, fatigue, etc.), medication adherence status (we asked, \u0026ldquo;How often have you taken the prescription given by your doctor in the past three months?\u0026rdquo; The potential responses were nearly all of the time (\u0026gt;\u0026thinsp;90%, classified as \u0026ldquo;excellent\u0026rdquo;), most of the time (75%, classified as \u0026ldquo;good\u0026rdquo;), approximately half the time (50%, classified as \u0026ldquo;fair\u0026rdquo;), or less than half the time (\u0026lt;\u0026thinsp;50%, classified as \u0026ldquo;poor\u0026rdquo;)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], hemoglobin (HGB), neutrophil (N), platelet (PLT), disease severity (non-severe AA (NSAA), SAA, VSAA), time since diagnosis (years), and regular follow-up status (yes or no).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eStatistics were analyzed using SPSS 26.0 and Mplus 7.0. Continuous variables that followed a normal distribution were presented as the mean with standard deviations and were compared using one-way ANOVA. Conversely, non-normally distributed variables were compared using the Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test and were reported as medians with inter-quartile range (IQR). The Spearman correlation test was used to analyze the correlation. Categorical variables were depicted as frequency counts and percentages, with Chi-square tests or Fisher tests employed for group comparisons.\u003c/p\u003e \u003cp\u003eLPA was used to analyze the latent categories of HRQOL levels among AA patients based on the eight subscales scores of the SF-36 v2. The iterative procedure began with a one-class model and progressively expanded the number of categories until optimal fit indices were achieved.\u003c/p\u003e \u003cp\u003eThe indicators for model fitting included the following: (1) the model fit improved as the akaike information criterion (AIC), bayesian information criterion (BIC), and sample size-adjusted BIC (aBIC) decreased; (2) The \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for the lo-mendell-rubin likelihood ratio test (LMR) and bootstrapped likelihood ratio test (BLRT) indicated that the current model fit (N\u0026thinsp;=\u0026thinsp;k) was better than that of the former model (N\u0026thinsp;=\u0026thinsp;k-1); and (3) entropy (which ranges between 0 and 1) was greater than 0.8, indicating a good model fit[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The final classification of the model was determined based on model fit metrics and clinical significance[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Afterwards, we used multivariable logistic regression analysis to examine how sociodemographic and clinical variables influenced various categories of HRQOL.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic and clinical characteristics of the participants\u003c/h2\u003e \u003cp\u003eAmong 258 eligible participants, 29 individuals were excluded due to incomplete responses, resulting in a final 229 (88.76%) individuals who completed the survey. The median age of the participants was 30 (IQR 22.00\u0026ndash;40.50) years, with over half being male (55.45%), married (54.15%), childless (55.46%), unemployed (54.59%), had no history of smoking (51.97%), free of comorbidities (52.40%), and without AA-related symptoms (52.84%). The majority of participants were diagnosed with NSAA (79.91%), reported a history of drinking alcohol (69.87%), good/excellent compliance with medication (78.17%), and regular follow-up (75.11%). More than one-third of participants (41.05%) possessed a college education or higher, and a smaller percentage (18.34%) were uninsured. The median time since diagnosis was 6.00 (IQR 4.00-10.80) years, and the household annual income of the patients was 7.12 (IQR 5.00-7.12) million Chinese yuan.\u003c/p\u003e \u003cp\u003eThe PF domain exhibited the highest median score (85.00, IQR 70.00,95.00), while the GH domain displayed the lowest median score (60.00, IQR 45.00,77.00), and slight variation in the median scores among the eight dimensions. According to the correlation analysis, there were strong, positive correlations between PF and RP (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.545, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001 ), RE and RP (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.612, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001 ), GH and VT (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.505, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001 ), and MH and VT (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.623, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001 ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSelection and naming of the models\u003c/h2\u003e \u003cp\u003eWe examined potential profile models ranging from 1 to 6, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The AIC, BIC, and aBIC showed a decrease as the number of class profiles increased. The entropy values for model 1 to model 6 all exceeded than 0.8. However, the LMR of Model 2 and Model 6 did not significantly differ. Model 3 and Model 5, which had entropy values closest to 1, exhibited statistically significant results for both LMR (Model 3: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; Model 5: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) and BLRT (Model 3: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Model 5: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, they met the requirement of having at least 3% of cases in each potential category[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, for Model 5, the minimum number of samples was 14, which may considerably restrict its representativeness. After comparing the model fit indices of each model, Model 3 was selected as the best-fitting model. The probabilities of correct attribution for a patient with AA to each category were 0.972, 0.956, and 0.996, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results indicated that the optimal model derived from the potential profile analysis in this study exhibited reliability and a robust capacity for differentiation across categories.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of model fit evaluation results for the different groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eaBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClass probability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLRT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16380.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16435.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" 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\u003cp\u003e0.245/0.188/0.236/0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15641.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15819.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15655.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.114/0.197/0.170/0.061/0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15593.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15803.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15610.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.114/0.061/0.197/0.170/0.135/0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAIC: the Akaike Information Criterion\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eBIC: Bayesian Information Criterion\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eaBIC: sample size-adjusted BIC\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eLMR: Lo-Mendell-Rubin likelihood ratio test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eBLRT: Bootstrapped likelihood ratio test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification probabilities for the most likely latent group membership (Column) by latent group (Row)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStatistically significant differences were observed in the PF, RP, BP, GH, VT, SF, RE, and MH scores for Model 3. The study analyzed the characteristics of three potential groups of HRQOL in AA patients by plotting line graphs of the eight subscale scores of the SF-36 v2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These groups were named based on the fluctuations in the mean values of the subscale items. 54 (23.58%) patients were in Group 1, exhibiting lower HRQOL scores compared to the other two groups. Additionally, this group was classified as the \"poor HRQOL with RE limitations\" group showed a bipartition trend in the RE subscale compared to the other two groups. There were 56 patients (24.45%) in Group 2 whose HRQOL scores fell between Group 1 and Group 3, and significantly lower score on RP. As a result, these participants were classified into the \"moderate HRQOL with RP limitation\" group. Group 3, consisting 119 patients (51.97%), exhibited higher HRQOL scores compared to the other two groups. Therefore, this group was named the \"good HRQOL\" group. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the trends of HRQOL curves for all three groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of HRQOL groups\u003c/h2\u003e \u003cp\u003eThe findings of the intergroup comparison analysis demonstrated statistically disparities (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the three groups of HRQOL among AA patients in terms of household annual income, having children or not, ECOG-PS score, comorbidity, transfusion-dependence, AA-related symptoms, and time since diagnosis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of sociodemographic characteristics among the different HRQOL groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epoor HRQOL with RE limitation\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emoderate HRQOL with RP limitation\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003egood HRQOL\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;119)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFisher/χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/H\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.00\u003c/p\u003e \u003cp\u003e(23.75, 40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.50\u003c/p\u003e \u003cp\u003e(23.25, 39.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.00\u003c/p\u003e \u003cp\u003e(20.00, 39.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle/Divorced/Widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecollege or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual household income \u003cem\u003e(million Chinese Yuan)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003cp\u003e(3.00, 7.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003cp\u003e(5.25, 7.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003cp\u003e(5.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare payment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUninsured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG-PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Continues)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003epoor HRQOL with RE limitation\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;54)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003emoderate HRQOL with RP limitation\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;56)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003egood HRQOL\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;119)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFisher/χ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/H\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransfusion-dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA-related symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication adherence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003cp\u003e(60.00, 132.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.50\u003c/p\u003e \u003cp\u003e(64.55, 122.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.00\u003c/p\u003e \u003cp\u003e(64.00, 126.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.00\u003c/p\u003e \u003cp\u003e(25.50, 108.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.00\u003c/p\u003e \u003cp\u003e(26.00, 186.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.00\u003c/p\u003e \u003cp\u003e(28.00, 166.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003cp\u003e(0.99, 5.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003cp\u003e(1.26, 6.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003cp\u003e(1.08, 4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVSAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime since diagnosis \u003cem\u003e(years)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003cp\u003e(3.00, 10.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003cp\u003e(4.00, 7.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003cp\u003e(4.10, 11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular follow-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this study, we utilized these statistically significant variables as independent variables and potential profile categories of HRQOL as dependent variables in an unordered multicategorical logistic regression analysis, with the \u0026ldquo;poor HRQOL with RE limitation\u0026rdquo; group served as the reference group.\u003c/p\u003e \u003cp\u003eThe results presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, indicated that patients without AA-related symptoms or with a higher annual household income were more likely to be classified in the \"moderate HRQOL with RP limitation\" group. Conversely, patients with higher ECOG-PS scores were more likely to be categorized in the \"poor HRQOL with RE limitation\" group, and these findings were observed when comparing the \"poor HRQOL with RE limitation\" with the \"moderate HRQOL with RP limitation\" subgroups. Patients in the \"good HRQOL\" group, compared to those in the \"poor HRQOL with RE limitation\" group, were more likely to be childless, transfusion-independent, and had no comorbidities, AA-related symptoms and have a higher annual household income.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of categories of HRQOL trajectories in AA patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWald χ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003epoor HRQOL with RE limitation vs moderate HRQOL with RP limitation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren (ref\u0026thinsp;=\u0026thinsp;With)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.839\u0026thinsp;~\u0026thinsp;4.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity (ref\u0026thinsp;=\u0026thinsp;With)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.905\u0026thinsp;~\u0026thinsp;5.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTransfusion-dependent (ref\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.775\u0026thinsp;~\u0026thinsp;4.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAA-related symptoms (ref\u0026thinsp;=\u0026thinsp;With)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.470\u0026thinsp;~\u0026thinsp;8.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnua household income\u003c/p\u003e \u003cp\u003e(\u003cem\u003emillion Chinese yuan\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.043\u0026thinsp;~\u0026thinsp;1.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime since diagnosis (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.879\u0026thinsp;~\u0026thinsp;1.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG-PS (ref\u0026thinsp;\u0026ge;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.917\u0026thinsp;~\u0026thinsp;8.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003epoor HRQOL with RE limitation vs good HRQOL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChildren (ref\u0026thinsp;=\u0026thinsp;With)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.312\u0026thinsp;~\u0026thinsp;6.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity (ref\u0026thinsp;=\u0026thinsp;With)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.213\u0026thinsp;~\u0026thinsp;11.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTransfusion-dependent (ref\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.424\u0026thinsp;~\u0026thinsp;7.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAA-related symptoms (ref\u0026thinsp;=\u0026thinsp;With)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.168\u0026thinsp;~\u0026thinsp;11.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual household income\u003c/p\u003e \u003cp\u003e(\u003cem\u003emillion Chinese yuan\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.079\u0026thinsp;~\u0026thinsp;1.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime since diagnosis (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.989\u0026thinsp;~\u0026thinsp;1.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG-PS (ref\u0026thinsp;\u0026ge;\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.037\u0026thinsp;~\u0026thinsp;13.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we explored the HRQOL of patients with AA by utilizing LPA to investigate the heterogeneity of their HRQOL and identify the factors contributing to the various latent groups. To our knowledge, this is the first study to use LPA to explore the heterogeneity of HRQOL among AA patients. Our study revealed significant individual differences of HRQOL among AA patients, who were classified into three groups: the poor HRQOL with RE limitation, the moderate HRQOL with RP limitation, and the good HRQOL subgroups, respectively. These characteristics used to define these groups were similar to those utilized by Lidia et al. to define the potential categories for exploring HRQOL among older age groups through LPA[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The HRQOL for the three groups, as determined by LPA, showed statistically significant, which indicated that the categorization results were reasonable to some extent. Our findings confirmed that household annual income, have children or not, comorbidities, transfusion-dependence and AA-related symptoms were all significant factors associated with the identified HRQOL groups.\u003c/p\u003e \u003cp\u003eGroup 1, which had members of all the diagnostic groupings, was the smallest group (23.58%). Focusing on each subscale of the SF-36 v2, Group 1 had the lowest RP and RE scores. Moreover, the RE subscale scores exhibited the greatest discrepancy with those of Group 2 and Group 3, indicating a polarized state. This implies that compared to PF, MH might be the primary factor influencing the lowest level of HRQOL for individuals with AA. AA patients experience psychological symptoms that impede their ability to engage in work and daily activities, potentially leading to reduced treatment adherence, poorer prognosis and lower HRQOL. Martin et al.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] emphasized the unique impacts of both physical and mental illnesses on mortality and disability, argued that the lack of MH is equivalent to the absence of PF. Thus, it is imperative for medical staff in clinical practice to focus not only on physical symptoms of AA patients but also their psychological problems, and to provide tailored interventions to patients in need of help.\u003c/p\u003e \u003cp\u003eThe composition of Group 2 was very interesting. Despite displaying a positive psychological state, these patients still encountered notable limitation in RP, such as limited mobility and decreased independence, due to physical health issues. Compared with those in the other two groups, more than 50% of the patients in Group 2 exhibited a higher prevalence of comorbidities, transfusion-dependence, and had the shortest time since diagnosis. Notably, RP was strongly correlated with RE in AA patients in this study. Thus, it may be necessary for health professional to tailor individualized interventions for patients in this group in order to decrease the probability of progression to Group 1. Julius et al.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], stated that PF can influence MH through lifestyle choices and social capital. In clinical practice, healthcare professional can enhance patients' health through health investments (such as health education) and social interactions (such as patients\u0026rsquo; families participate in decision-making). Furthermore, the composition of Group 2 highlighted the advantages of using LPA to analyze HRQOL, and identifying AA patients with this specific level of HRQOL solely based on the characteristics of the SF-36 v2 can pose a significant challenge.\u003c/p\u003e \u003cp\u003eGroup 3 was characterized by high levels of HRQOL, even exceeding the average HRQOL level of the general population (matched for age and sex[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]). Patients within this group demonstrated the highest scores across all subscales of the SF-36 v2 scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, Group 3 also have the most members of patients (51.97%), encompassing individuals from various diagnostic group, underscoring the prognostic significance of HRQOL for a substantial portion of AA patients. Response shift has been extensively documented in HRQOL researches[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], indicating that the experience of a condition such as cancer can alter survivors\u0026rsquo; perceptions of their HRQOL[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. AA patients who possess a redefined perception of HRQOL and adjusted expectations during the period from diagnosis to treatment may demonstrated unexpectedly elevated levels of HRQOL in this study. Furthermore, these patients exhibited favorable prognosis following prompt initiation of treatment, which may also explain the high proportion of patients in this group.\u003c/p\u003e \u003cp\u003ePrevious studies[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] have validated the clinical significance of the ECOG-PS score, indicating an association between a worse prognosis and higher scores, which was consistent with the findings of this study. Patients with higher ECOG-PS scores were more likely to be classified into the \"poor HRQOL with RE limitation\" group. This relationship can be attributed to the decline in physical condition, activity endurance, and overall PF associated as the ECOG-PS scores increased, which heightens the likelihood of experiencing severe somatic symptoms. On the other hand, the ECOG-PS can also indirectly impact the HRQOL of patients by affecting negative emotions[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The deterioration in physical health may lead to changes in patients\u0026rsquo; original life status and negative emotions due to unfulfilled social roles, ultimately leading to a diminished HRQOL[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Due to its 5-level scale, the ECOG-PS score is simple and allows medical staff to evaluate a patient's physical status quickly[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. It could be an effective tool for assessing and examining a patient's HRQOL in clinical practice.\u003c/p\u003e \u003cp\u003eConsistent with previous studies, AA patients with higher annual household incomes tend to report higher levels of HRQOL[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, our study diverges from prior studies by demonstrating that AA patients without children exhibited a higher HRQOL[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This discrepancy may be attributed to the reduced financial burden of patients free of child-rearing expenses. AA patients without financial pressure could receive a standardized treatment and free of negative emotions, resulting in greater HRQOL. Nevertheless, a study by Hurmuz et al.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] indicated that intimate interpersonal relationships can provide emotional support, maintain and enhance patients' physiological functioning, and increase motivation for recovery. Thus, medical staff should prioritize the evaluation of AA patients\u0026rsquo; financial capacity, promptly identify patients facing financial difficulties and increase the reasonable utilization of healthcare. Moreover, medical staff should encourage patients\u0026rsquo; families to participate in patient care as much as possible to ensure that patients receive more positive social support[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study findings revealed that the likelihood of transfusion-independent patients being attributed to \"good HRQOL\" group was 3.174 times greater than that of patients being attributed to \u0026ldquo;poor HRQOL with RE limitation\u0026rdquo; group. Vaht et al.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] discovered that the lifespan of AA patients with transfusion dependence was considerably shorter than that of those without. Frequent blood transfusions during treatment may result in complications such as transfusion-related infections, iron overload, and inefficient transfusions, which can significantly impact the survival and HRQOL of these patients[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Clinical staff should pay close attention to the HRQOL of AA patients who are transfusion-dependent. Furthermore, AA patients who are transfusion-dependent tend to have more severe disease and experience more clinical symptoms than those who are not[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The results of our study indicated that patients with AA-related symptoms were more likely to be in the \"poor HRQOL with RE limitation\" group. AA patients with clinical symptoms tend to be more concerned and excessively worried about any changes in their bodies[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], potentially exacerbating disease burden, resulting in poor HRQOL. Thus, health education should be improved in order to enhance patients\u0026rsquo; awareness of disease understanding and provide tailor interventions for AA-related symptoms to prevent and minimize complications, alleviate disease burden, and improve patients\u0026rsquo; HRQOL.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur study was designed initially as cross-sectional, preventing the determination of changes in HRQOL groups for AA patients over time and whether the predictors of their group status had changed. Second, the participants in our study were all recruited from a single hospital, which may restrict the generalizability of the results. Multicentre, large-sample studies are needed to further validate the present results. Last but not least, in our study, we only explored the effects of sociodemographic and disease factors on different groups of HRQOL in AA patients. However, other significant factors, such as social support and psychological resilience, weren\u0026rsquo;t evaluated in this study and warrant further investigation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study could make a contribution to the awareness of HRQOL among AA patients. Our study revealed that LPA offers notable benefits in addressing the complexity and diversity of data related to HRQOL measures among AA patients. Utilizing this model, we successfully categorized the HRQOL of AA patients into three subgroups: Group 1, poor HRQOL with role emotional limitation; Group 2, moderate HRQOL with role physical limitation, and Group 3, good HRQOL, respectively. Factors such as AA-related symptoms, household annual income, ECOG-PS score, children, comorbidities, and transfusion-dependence were found to significantly impact the patients\u0026rsquo; HRQOL. The findings of our study also provided a better understanding of the heterogeneity in HRQOL among AA patients, it could be a useful to guide clinicians to timely identify patients at heightened risk for diminished HRQOL in the context of limited healthcare resources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe extend our sincere gratitude to all participants who generously contributed their time and participation to this study.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eGW, XL, YZ and WX designed the work. GW, XZ, TL, LS, MH, ZK, XL, LX and QZ collected the data. XL and GW analyzed and interpreted the data. XL, GW, XR, JH, YZ and WX drafted the manuscript, YZ and WX revised it. All authors read and approved the manuscript and declared no potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was funded by Chinese Nursing Association Research Project (ZHKY202115).\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe data supporting the findings of this study can be obtained from the corresponding author [YZ and WX], upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eAll patients provided written informed consent, and the study was approved by the Ethics Committees of the Institute of Hematology, Chinese Academy of Medical Sciences and Peking Union Medical College according to the guidelines of the Declaration of Helsinki.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors affirm that the research was carried out without any commercial or financial affiliations that might present a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFurlong E, Carter T. 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Avoidable Blood Loss in Critical Care and Patient Blood Management: Scoping Review of Diagnostic Blood Loss. J Clin Med. 2022;11(2):320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jcm11020320\u003c/span\u003e\u003cspan address=\"10.3390/jcm11020320\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"Anemia aplastic, Health-related quality of life, Latent profile analysis","lastPublishedDoi":"10.21203/rs.3.rs-4566671/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4566671/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eConcerns over health-related quality of life (HRQOL) in patients with aplastic anemia (AA) have been increasing worldwide. However, most researches on HRQOL in AA patients have ignored individual-level variability. Thus, our study was designed to explore practical classification of HRQOL and related variables among AA patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted from May 2022 to March 2023, utilizing convenience sampling to enroll AA patients. Data of HRQOL, sociodemographic characteristics, and clinical variables were collected. Latent profile analysis (LPA) was used to analyze the latent categories of HRQOL in AA patients, utilizing scores from eight subscales of the Medical Outcomes Study 36-Item Short Form Health Survey version 2.0.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 229 patients completed the survey and were included in the analysis. The LPA results showed significantly individual differences and identified three subgroups of HRQOL: Group 1, poor HRQOL with role emotional limitation (n\u0026thinsp;=\u0026thinsp;54, 23.58%); Group 2, moderate HRQOL with role physical limitation (n\u0026thinsp;=\u0026thinsp;56, 24.45%), and Group 3, good HRQOL (n\u0026thinsp;=\u0026thinsp;119, 51.97%), respectively among AA patients. Childless, no comorbidities, transfusion independence, no AA-related symptoms, and higher annual household income were associated with Group 3, whereas higher Eastern Cooperative Oncology Group performance status scores were associated with Group 1.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings of our study revealed significant heterogeneity in HRQOL among AA patients, providing valuable information for tailoring interventions to meet individual needs, especially for those in the poor HRQOL with role emotional limitation group.\u003c/p\u003e","manuscriptTitle":"A latent profile analysis of health-related quality of life in patients with aplastic anemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 14:47:54","doi":"10.21203/rs.3.rs-4566671/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":"ff85e212-1496-4c7d-b682-56a4e72cdfcd","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-07T05:16:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 14:47:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4566671","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4566671","identity":"rs-4566671","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}