A Prediction Model for the Length of Stay after Single-Port Thoracoscopic surgery of lung cancer:Based on Preoperative Physical Activity Level

A Prediction Model for the Length of Stay after Single-Port Thoracoscopic surgery of lung cancer:Based on Preoperative Physical Activity Level | 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 Prediction Model for the Length of Stay after Single-Port Thoracoscopic surgery of lung cancer:Based on Preoperative Physical Activity Level 苗 刘, 风岩 妈妈, 邱居 张, 文井 黄色, 姚 fu, chen chen, xuan wang, hongzhu wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7153227/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background With 2.2 million new cases annually, lung cancer remains the leading cause of cancer mortality globally. Preoperative physical activity (PA) may optimize postoperative recovery, but its role in predicting Length of stay (LOS) after VATS is understudied. Methods The clinical data of 231 patients with lung cancer who underwent surgery in the Department of Thoracic Surgery of National Cancer Center from August 1, 2023, to April 1, 2024 were prospectively collected. The preoperative physical activity levels were assessed by the International Physical Activity Questionnaire (IPAQ), the residence, operation duration and complications were recorded. Binary logistic regression with bootstrapping (231 resamples) identified predictors of LOS, adjusting for age, sex, and comorbidities. Model performance was assessed via ROC analysis (AUC). Results A total of 231 lung cancer patients were enrolled, including 90 males and 141 females, with an average age of 53.1 years. The median preoperative physical activity value (MET) was 1086 (rang: 0-3312) and the mean LOS was 3.69 (1.58). Preoperative PA ( OR: 7.98, 95% CI: 1.65–38.64 ), urban residence ( OR: 4.01, 95% CI: 1.81–8.89 ), shorter operation time ( OR: 0.40, 95% CI : 0.18–0.91) and complications ( OR: 0.32, 95% CI: 0.12–0.86 ) independently predicted reduced LOS (all P < 0.05 ). The model achieved an AUC of 0.85 ( 95% CI: 0.79–0.91) , the sensitivity was 80.0% and the specificity was 74.9%. Conclusion Preoperative PA is a modifiable predictor of LOSin patients with lung cancer. Integration of PA assessment into prehabilitation programs may optimize resource allocation and recovery pathways. prediction model length of stay lung cancer physical artical Figures Figure 1 Figure 2 Figure 9 Figure 10 1.Introduction Lung cancer is among the most prevalent malignancies worldwide, characterized by persistently high incidence and mortality rates. According to Global Cancer Statistics 2020, an estimated 2.2 million new cases of lung cancer were reported, accounting for 11.4% of all newly diagnosed cancers globally. It remains the leading cause of cancer-related deaths, contributing to 18.0% of total cancer mortality 1 . The five-year survival rate for lung cancer is approximately 20% 2 . In China, lung cancer has the highest incidence and mortality rates among malignancies, imposing a significant burden on public health 3 . Non-small cell lung cancer (NSCLC) comprises more than 80% of lung cancer cases. The standard treatment for NSCLC involves lobectomy combined with lymph node dissection 4 . With the increasing adoption of thoracoscopic techniques, uniportal video-assisted thoracoscopic lobectomy has become the mainstream surgical approach in China. This method offers several advantages, including minimal invasiveness, high efficacy, and safety, effectively reducing postoperative pain and complications while shortening LOS 5 . LOS is a critical indicator for evaluating healthcare resource utilization and patient recovery speed 6 . In surgical treatments, LOS typically refers to the number of postoperative days spent in the hospital. Reducing LOS alleviates the financial burden on patients, lowers hospital operating costs, eases healthcare system pressure, and enhances overall medical efficiency 7 . A study by Li et al. reported that the average LOS for patients undergoing uniportal thoracoscopic surgery was 6.5 days, with a range of 4–7 days 8 . However, some patients experienced longer LOS due to postoperative complications or prolonged surgeries. For lung cancer patients, LOS is influenced by various factors, including the patient’s physical condition, surgical techniques, postoperative complication management 9 , and preoperative prehabilitation measures 10 . Prehabilitation, which has garnered significant attention in recent years, encompasses preoperative assessments of nutritional status, psychological well-being, and physical activity levels 11 . Studies have shown that good preoperative physical activity levels are crucial in reducing postoperative LOS and complications among lung cancer patients 12,13 . As highlighted by Gillis et al. 14 , preoperative physical activity improves tolerance to surgical stress, enhances cardiopulmonary function and immunity, and alleviates psychological stress, thus laying a foundation for successful postoperative recovery. Physical activity has also been associated with reduced inflammatory responses and improved nutritional status, both of which are critical for recovery and LOS 15,16 . Consequently, good preoperative physical activity not only accelerates recovery but also provides patients with more time for subsequent treatments. Despite these findings, current research on LOS has primarily focused on critically ill patients or postoperative factors, with limited exploration of the role of preoperative physical activity. To address this gap, we developed a prediction model for LOS based on preoperative physical activity levels. This model aims to provide scientific evidence for preoperative evaluations, optimize surgical scheduling, and enhance resource allocation. By identifying high-risk patients and implementing targeted preoperative interventions, the model seeks to minimize LOS and improve overall recovery outcomes for lung cancer patients. This approach aligns with modern patient-centered care principles and offers significant potential for the efficient and equitable distribution of healthcare resources. 2.Methods 2.1Study Design and Participants This prospective study was approved bythe Institutional Review Board (IRB) of the Cancer Hospital, Chinese Academy of Medical Sciences (No.24/325–4605), and all patients provided written informed consent.This study follows Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guideline. The study was carried out in a thoracic surgery ward with 40 beds at the National Cancer Center. The subjects of the study were patients who underwent surgery between August 1, 2023 and April 1, 2024. Eligibility was determined through review of the electronic health record. The data were mainly collected through questionnaires on the day when patients were admitted to the hospital. Our database was constructed by extracting the following information: (a) sociodemographic variables, such as age, sex, body mass index (BMI), place of residence and the presence of a caregiver at home; (b) comorbidities at the time of admission; (c) the level of physical activity within one week before the operation; (d) surgical and rehabilitation-related factors, including the duration of the operation, the length of hospital stay after the operation, the surgical team, and postoperative complications (i.e., bleeding, subcutaneous emphysema, arrhythmia) as well as hospitalization expenses. Finally, we used the International Physical Activity Questionnaire - Long Form (IPAQ-Long Form) to calculate the patients' physical activity levels. The IPAQ-Long Form consists of 27 questions in total, among which 25 questions inquire about individuals' physical activity and 2 questions ask about individuals' sedentary behavior. The physical activity is mainly composed of activity types (work, transportation, household gardening, leisure) and activity intensity (walking and moderate, high intensity). It asks individuals about their physical activities related to work, transportation, household gardening and leisure in the past 7 days in turn. MET-min/week were calculated for work, transportation, and leisure domains, categorized as low ( 3,000) per WHO guidelines. 2.2Outcomes The discharge destination (after being judged by clinicians that the patients had met the discharge standards and no transfer to other departments or hospitals occurred) and the length of postoperative hospital stay (defined as the number of days the patients stayed in the general ward after the operation) were extracted from the patients' electronic medical records. 2.3Statistical Analysis Continuous data are expressed as the mean and standard deviation (SD). Categorical data are expressed as the number and percentage and were analyzed using Pearson chi-square test or Fisher exact test as appropriate. The level of significance was set at P value < 0.05. Variables showing statistical significance in Pearson chi-square test were included in the binary logistic regression analysis to find independent perioperative predictors for LOS. Model calibration was assessed via Hosmer-Lemeshow test. Statistical analysis was performed using SPSS v24.0 (IBM, Armonk, NY, USA). 3.Results 3.1Study Population A total of 231 participants met the inclusion criteria and were enrolled in the prospective validation cohort. The detailed patient selection process is summarized in Fig. 1 . Of the participants, 90 patients (39.0%) were men and 141 (61.0%) were women. The mean (SD) age was 53.1 (9.4) years, and the mean (SD) BMI was 24.0 (9.2) kg/㎡. Regarding marital status, 216 patients (5.4%) were married, while 15 (18.9%) were single, divorced or widowed. Participants resided in either rural or urban areas, with 55 (23.8%) living in villages and 176 (76.2%) in urban. Education levels were categorized as junior high school or below, senior high school or college, and bachelor's degree or above, accounting for 60 (26.0%), 101 (43.7%), and 70 (30.3%), respectively. Employment status showed that 124 patients (53.7%) were retired or unemployed, while 107 (46.3%) were employed. Additionally, 195 patients (84.4%) had cohabitants and 36 (15.6%) lived alone. During hospitalization, 113 patients (48.9%) employed caregivers, while 118 (51.1%) were accompanied by relatives. Preoperative physical pain was scored using the visual analogue scale (VAS), of which 197 patients (85.3%) reported no pain (0 points), 17 (7.3%) experienced mild pain (1–3 points), 12 (5.2%) reported moderate pain (4–6 points) and 5 (2.2%) experienced severe pain (7–10 points). Additionally, 116 (50.2%) patients reported having physical exercise habits before surgery, while 115 (49.8%) patients did not. Only 4 patients (1.7%) require assistance with Activity of Daily Living (ADL). Furthermore, eighty-five (36.8%) patients had OCD other than cancer. Participant demographics and model-specific characteristics are detailed in Table 1 . Table 1 Participant demographic and model-specific characteristics Demographics LOS ≤ 4(n = 191) LOS>4(n = 40) Statistic value P Gender 0.742 a 0.389 Male = 1 72(37.7%) 18(45.0%) Female = 2 119(62.3%) 22(55.0%) Age(years) 52.65 ± 9.45 55.48 ± 8.65 -1.741 b 0.083 BMI(kg/㎡) 24.01 ± 3.13 24.14 ± 3.55 -0.223 b 0.824 Marrige 1.271 a 0.260 YES = 0 177(92.7%) 39(97.5%) NO = 1 14(7.3%) 1(2.5%) Residence 21.951 a < 0.000 Village = 0 34(17.8%) 21(52.5%) Urban = 1 157(82.2%) 19(47.5%) Education 9.279 a 0.010 Junior = 0 42(22.0%) 18(45.0%) Senior = 1 87(45.5%) 14(35.0%) Bachelor = 2 62(32.5%) 8(20.0%) Retire 0.743 a 0.389 Unemployed = 0 105(55.0%) 19(47.5%) Employed = 1 86(45.0%) 21(52.5%) Solitude 0.717 a 0.397 NO = 0 163(85.3) 32(80.0%) YES = 1 28(14.7) 8(20.0%) Caregiver 0.248 a 0.618 Caregiver = 0 92(48.2%) 21(52.5%) Relatives = 1 99(51.8%) 19(47.5%) Pain 0.531 a 0.912 None = 0 164(85.9%) 33(82.5%) Low = 1 13(6.8%) 4(10.0%) Middle = 2 10(5.2%) 2(5.0%) High = 3 4(2.1%) 1(2.5%) Exercise 0.001 a 0.976 NO = 0 95(49.7%) 20(50.0%) YES = 1 96(50.3%) 20(50.0%) Activity 3.037 a 0.219 Self care = 0 189(99.0%) 38(95%) Need help = 1 2(1.0%) 2(5.0%) OCD 0.067 a 0.796 NO = 0 120(62.8%) 26(65.0%) YES = 1 71(37.2%) 14(35.0%) Met 2159.11 ± 2480.99 1167.60 ± 2210.20 2.340 b 0.020 PAL 9.905 a 0.007 Low = 1 74(38.7%) 25(62.5%) Middle = 2 74(38.7%) 13(32.5%) High = 3 43(22.5%) 2(5.0%) Operation Time 14.756 a 0.001 2 hours = 3 42(22.0%) 18(45.0%) Part 0.305 a 0.581 Left = 1 75(39.3%) 14(35.0%) Right = 2 116(60.7%) 26(65.0%) Type 2.984 a 0.225 SqCa = 1 5(2.6%) 3(7.5%) AC = 2 177(92.7%) 34(85.0%) Other = 3 9(4.7%) 3(7.5%) TNM T 15.080 a 0.005 1 172(90.0%) 30(75.0%) 2 16(8.4%) 7(17.5%) 3 3(1.6%) 1(2.5%) 4 0(0.0%) 2(5.0%) N 0.399 a 0.819 0 172(90.1%) 37(92.5) 1 9(4.7%) 1(2.5%) 2 10(5.2%) 2(5.0%) Complication 4.535 a 0.033 NO = 0 168(88.0%) 30(75.0%) YES = 1 23(12.0%) 10(25.0%) Cost (Thousand RMB) 64.85 ± 17.45 79.26 ± 16.97 -4.774 b < 0.000 Abbreviations: BMI, Body Mass Index; OCD, Other Chronic Disease; MET, Metabolic Equivalent of Task; PAL, Physical Activity Level; SqCa, Squamous Carcinoma; AC, Adeno Carcinoma; a Statistic value of t; b Statistic value of X 2 The variablest show a statistically significant ( P < 0.05) Table 2 Binary logistic regression analysis of LOS in patients who underwent B S.E, Wals df Sig. Exp (B) EXP(B) 95% C.I. Min Max Residence 1.389 .406 11.699 1 .001 4.011 1.810 8.891 PAL 8.205 2 .017 Middle vs Low 2.076 .805 6.648 1 .010 7.974 1.645 38.644 High vs Low 1.279 .815 2.461 1 .117 3.593 .727 17.764 Operation Time 4.775 2 .092 1–2 hours vs 2 hours vs <1hour − .913 .418 4.775 1 .029 .401 .177 .910 Complications -1.143 .508 5.060 1 .024 .319 .118 .863 Constants -1.830 .829 4.879 1 .027 .160 3.2LOS and Costs The mean LOS for all patients was 3.69 (SD = 1.58), ranging from 2 to 13 days. Among them, 42 patients (18.2%) had a LOS of 2 days, 87 patients (37.7%) had a LOS of 3 days, and 62 patients (26.8%) had a LOS of 4 days. In this study, the patients were divided into two groups (≤ 4 days and > 4 days), with 191 cases (82.7%) and 40 cases (17.3%), respectively. Among the patients with > 4 days, most frequent LOS was 6 days, accounting for 22 cases (9.5%). Table 1 shows that the average hospitalization expenses of patients in the ≤ 4 days group and > 4 days group were 64.85 ± 17.45 thousand yuan and 79.26 ± 16.97 thousand yuan, respectively. There was a significant difference in hospitalization expenses between the two groups. 3.3Physical Activity The physical activity of all patients was measured by International Physical Activity Questionnaire-long (IPAQ-L), with results expressed in Metabolic Equivalent of Task (MET) with a median value of 1086 (range: 0-3312). Of these, 144 (62.3%) engaged in walking as their primary form of physical activity, making it the most common form of physical activity; 123 (53.2%) engaged in specific exercise, with 116 (50.2%) choosing walking, 11 (4.8%) choosing more intense activities such as ball sports, swimming, and aerobics, and 13 (5.6%) choosing lower-intensity activities such as tai chi. These patients spent from 0.5 to 8 hours sitting still during workdays, with 181 (78.4%) spending ≤ 3 hours sitting still, and 50 (21.6%) spending > 3 hours; on weekends, 79 (34.2%) spent > 3 hours sitting still. Physical activity level was calculated using MET and activity frequency and categorized into low, moderate, and high levels. In this study, 99 (42.9%) had a low physical activity level, 87 (37.7%) had a moderate level, and 55 (23.8%) had a high level. 3.4Model Performance Having identified several demographics parameters as statistically significant between LOS ≤ 4 and LOS>4 groups, a binary logistic regression model with split sampling was employed to predict outcomes based on LOS. The analysis revealed that longer operation time ( P = 0.029) and the presence of comorbidities ( P = 0.024) were significantly associated with a longer LOS, whereas urban residence ( P < 0.001) and a high physical activity level ( P = 0.017) were associated with a shorter LOS (Table.3). The model demonstrated excellent discrimination (AUC: 0.847, 95% CI, 0.789–0.905) and calibration (Hosmer-Lemeshow P = 0.34). Sensitivity (80%) and specificity (75%) were balanced at the optimal cutoff (Youden’s index).(Figure.2.) 4.Discussion 4.1The Significance of Developing a LOS Prediction Model Based on Preoperative Physical Activity Levels in Lung Cancer Patients Preoperative physical activity levels are among the critical factors influencing postoperative recovery in lung cancer patients. Developing a LOS prediction model based on preoperative physical activity holds significant clinical value. Such a model can assist clinicians in more accurately evaluating patients’ postoperative recovery potential and potential risks of complications, enabling the design of personalized treatment and rehabilitation plans. By quantifying preoperative physical activity, the model can predict the recovery pace and estimate the medical resources and support needed after surgery. Additionally, the model provides clearer prognostic information, helping patients and their families prepare psychologically and materially, thereby enhancing treatment confidence and cooperation. Ultimately, the development and application of this model could improve postoperative quality of life and survival rates for lung cancer patients, reduce the wastage of medical resources, and provide scientific evidence for decision-making in lung cancer treatment. 4.2Analysis of Factors Influencing LOS in Lung Cancer Patients Residence This study revealed that residence was a significant factor affecting LOS duration (P < 0.001), with rural patients experiencing significantly longer LOS than urban patients. Several factors may contribute to this disparity. First, studies have shown that rural residents tend to have lower educational levels, limited health knowledge, and insufficient preventive care 17 , leading to delayed disease detection and treatment 18 , thus necessitating longer LOS. Second, geographical barriers in rural areas restrict access to medical resources 19 , making timely medical care more challenging and exacerbating disease progression, which prolongs hospitalization. Additionally, rural patients may have different expectations and needs regarding healthcare services compared to urban patients. They might prefer extended LOS to ensure treatment stability. Addressing the extended LOS of rural patients requires a multifaceted approach, including improving rural patients’ health awareness and medical knowledge, enhancing access to healthcare information, and optimizing the distribution of medical resources. These efforts are crucial for reducing LOS among rural patients and improving overall health outcomes. 4.3Physical Activity Levels (PAL) Physical activity encompasses a wide range of forms, from daily activities to organized exercise programs 20 , such as work, sports, leisure, transportation (walking, cycling), and household tasks. Both structured and unstructured activities benefit health. Insufficient physical activity increases the risk of noncommunicable diseases (NCDs) and other health issues 20 , as well as negatively impacts mental health 21 . In this study, 144 patients (62.3%) primarily engaged in walking as their main form of physical activity, indicating limited variety. On rest days, 79 patients (34.2%) spent over 3 hours sedentary. Compared to workdays, more patients chose extended sedentary periods on rest days. Among participants, 99 patients (42.9%) had low physical activity levels in the week prior to the study, falling short of WHO’s recommended levels. Insufficient physical activity and prolonged sedentary behavior have become significant risk factors for global mortality 20 . WHO’s Global Action Plan on Physical Activity 2018–2030 emphasizes the importance of regular physical activity for all age groups to maintain and enhance health 22 . The American Cancer Society (ACS) guidelines align with WHO’s recommendations, suggesting cancer survivors and healthy adults engage in 150–300 minutes of moderate-intensity aerobic exercise or an equivalent amount of vigorous activity weekly 23,24 . This study also found that patients with low preoperative physical activity levels had longer LOS. Research suggests that patients with higher preoperative physical activity levels recover faster postoperatively 12,25 . Therefore, it is essential to develop health promotion policies to encourage physical activity, reduce barriers, and foster participation across diverse populations. 4.4Operation Time Enhanced Recovery After Surgery (ERAS) is an innovative perioperative management concept aimed at promoting recovery and reducing LOS 26,27 . Minimally invasive surgery to shorten operation time is a core component of ERAS. Studies indicate that ERAS significantly reduces average LOS compared to traditional surgery 28 . In this study, patients with operation times ≤ 1 hour had LOS ≤ 4 days. Shorter operation times reduce tissue damage, fluid loss, and inflammation, minimizing complications and expediting recovery. Conversely, patients with operation times ≥ 2 hours experienced longer LOS due to the complexity of the surgery and increased postoperative management needs 29,30 . Optimizing surgical procedures and improving efficiency are essential for promoting rapid recovery and reducing LOS. 4.5Complications Postoperative complications, such as lung infections, atelectasis, and prolonged air leaks, are significant risk factors for recovery in lung cancer patients 31,32 . Complications increase the difficulty of recovery, leading to prolonged LOS and higher resource consumption 11,33 . In this study, 198 patients experienced no complications, with 84.8% of them having a LOS (LOS) ≤ 4 days. A significant difference in LOS was observed between patients with and without complications, with those without complications exhibiting shorter LOS, consistent with findings from other studies. These complications increase the difficulty of physiological recovery, leading to prolonged hospitalization. Additionally, research has indicated that the degree of pulmonary fissure completeness can predict postoperative complications and the LOS following video-assisted thoracoscopic surgery (VATS) lobectomy for early-stage non-small-cell lung cancer 34 . Incomplete pulmonary fissure development may result in greater intraoperative trauma and higher rates of postoperative complications, necessitating longer recovery times and extended LOS. Therefore, the prevention, monitoring, and management of postoperative pulmonary complications are critical to improving patient recovery and outcomes. By optimizing perioperative management, reducing the incidence of complications, and shortening LOS, healthcare providers can enhance recovery rates and lower medical costs.Effective prevention, monitoring, and management of complications are vital for improving recovery and reducing LOS. 5.Limitations This study, conducted prospectively at the National Cancer Center of China, faces challenges in sample selection. Although the center attracts a large and diverse patient population from across the country, reflecting a certain level of domestic variability, its samples may not be globally representative. This limits the model’s ability to capture the diverse characteristics of patients from various ethnic, geographical, and healthcare system backgrounds. Future research should consider multi-center collaborations, incorporating patient samples from different countries and regions, to enhance the diversity and generalizability of the data. Additionally, the data collection process primarily relies on patient self-report questionnaires, which are susceptible to recall bias. This can compromise the accuracy of the model’s input data. It is crucial to introduce more objective data collection methods in the future, such as utilizing wearable devices to monitor patients’ daily physical activities and physiological indicators. Integrating these data with electronic medical record systems can provide more accurate clinical information, reducing reliance on patients’ subjective recollections and thereby improving the model’s precision. Moreover, while the current model includes several common clinical indicators and demographic characteristics, it is not yet comprehensive. Many potential factors, such as patients’ psychological states (e.g., anxiety and depression) and their changes, social support systems, detailed long-term lifestyle habits, regional air quality, and living conditions’ convenience, are challenging to quantify and incorporate into the model. Furthermore, the model has not yet been externally validated, and its accuracy and reliability in practical applications require further testing. 6.Conclusion The level of preoperative physical activity in lung cancer patients significantly influences the duration of their postoperative hospital stay. Utilizing binary logistic regression analysis, this study has successfully integrated a range of clinical medical information, including patients’ sociodemographic characteristics, disease features, and preoperative physical activity levels, to develop a predictive model. This model identifies four key predictive factors: place of residence, physical activity status, duration of surgery, and postoperative complications. Through meticulous data analysis, the model has been constructed and demonstrates robust predictive capabilities, offering a valuable tool for forecasting the postoperative hospital stay length of lung cancer patients.However, the study is not without limitations. Future research should consider multi-center collaborations and larger sample sizes to enhance the model’s generalizability. Additionally, incorporating a broader range of clinical factors will allow for more in-depth exploration and continuous refinement of the model’s predictive accuracy and reliability. Such improvements will provide a more solid foundation for the clinical treatment and rehabilitation management of lung cancer patients. Abbreviations NSCLC Non-small cell lung cancer VATS Video-assisted thoracoscopic surgery SEE Self-Efficacy for Exercise TSK Tampa Scale for Kinesiophobia Declarations 6.2Ethics approval and consent to participate This study was conducted in accordance with the ethical principles of the Declaration of Helsinki (as revised in 2013). The study protocol was approved by the Independent Ethics Committee of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, and the National GCP Center for Anticancer Drugs (Approval No. NCC2023C-946, dated October 20, 2023). All participants provided written informed consent prior to inclusion in the study. 6.3 Consent for publication Not applicable. 6. 5 Competing interests The authors declare that they have no competing interests. 6.7 Authors’contributions Miao Liu and Fengyan Ma contributed equally to this work and share first authorship. Miao Liu and Fengyan Ma were responsible for the study design, data collection, and initial drafting of the manuscript. Qiuju Zhang and Wenjing Huang contributed to data analysis and interpretation. Yao Fu, Chen Chen, and Xuan Wang assisted with patient recruitment and data acquisition. Hongzhu Wang and Liting Wang participated in the literature review and manuscript revision. Jiagen Li supervised the study, provided critical revisions, and is the corresponding author. All authors read and approved the final manuscript. 6.6 Funding This study was supported by the CAMS Innovation Fund for Medical Sciences (CIFMS), Grant number: 2023-I2M-C&T-B-087. Author Contribution Miao Liu, Fengyan Ma and Wenjing Huang wrote the main manuscript text; Qiuju Zhang, Yao Fu, Ermei Cui, Chang Zhan, Wenqi Li and Ziwei Fu prepared all figures and tables, all authors reviewed the manuscript. 6.8 Acknowledgements The authors would like to thank all patients who participated in this study and the clinical staff who supported the data collection process. 6.4 Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021; 71 (3): 209-49. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin 2022; 72 (1): 7-33. Xia C, Dong X, Li H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. 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Perioperative Nutritional Support: A Review of Current Literature. Nutrients 2022; 14 (8). de Groot PM, Shroff GS, Carter BW, et al. Lung Cancer: Postoperative Imaging and Complications. J Thorac Imaging 2017; 32 (5): 276-87. Motono N, Ishikawa M, Iwai S, Iijima Y, Usuda K, Uramoto H. Individualization of risk factors for postoperative complication after lung cancer surgery: a retrospective study. BMC Surg 2021; 21 (1): 311. Li RD, Joung RH, Chung JW, Holl J, Bilimoria KY, Merkow RP. Divergent Trends in Postoperative LOS and Postdischarge Complications over Time. Jt Comm J Qual Patient Saf 2024; 50 (9): 630-7. Li S, Zhou K, Wang M, Lin R, Fan J, Che G. Degree of pulmonary fissure completeness can predict postoperative cardiopulmonary complications and length of LOS in patients undergoing video-assisted thoracoscopic lobectomy for early-stage lung cancer. Interact Cardiovasc Thorac Surg 2018; 26 (1): 25-33. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 07 Oct, 2025 Editor invited by journal 04 Sep, 2025 Editor assigned by journal 23 Jul, 2025 Submission checks completed at journal 22 Jul, 2025 First submitted to journal 22 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7153227","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531798115,"identity":"12afd594-da8b-4795-8cfd-7d1645f7410b","order_by":0,"name":"苗 刘","email":"","orcid":"","institution":"Cancer Hospital of Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"苗","middleName":"","lastName":"刘","suffix":""},{"id":531798117,"identity":"d01b9fc5-388a-4326-b72c-62abc69a084c","order_by":1,"name":"风岩 妈妈","email":"","orcid":"","institution":"Cancer Hospital of Chinese Academy of Medical 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1","display":"","copyAsset":false,"role":"figure","size":46203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient selection flow chart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7153227/v1/9670e19fe6c8b9abd251bfea.png"},{"id":93977608,"identity":"78c07f20-a19b-474b-8d79-b57d8d307200","added_by":"auto","created_at":"2025-10-21 01:30:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13409,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eArea Under the Receiver Operating Characteristics Curve for the LOS of Lung Cancer\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7153227/v1/bee2bf1484502c94f2a92cc9.png"},{"id":93977609,"identity":"8f899a58-a860-4d44-b518-25b388be6198","added_by":"auto","created_at":"2025-10-21 01:30:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":46203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient selection flow chart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7153227/v1/b959aa4ef71f521a07d07b19.png"},{"id":93976778,"identity":"ed85211d-2a7a-4520-a8ca-f3bb9e80969a","added_by":"auto","created_at":"2025-10-21 01:22:01","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":13409,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eArea Under the Receiver Operating Characteristics Curve for the LOS of Lung Cancer\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7153227/v1/079c618b24d8f4314ee6fdfc.png"},{"id":93978774,"identity":"1864e9d7-9e57-4659-af29-8c4ce37f167b","added_by":"auto","created_at":"2025-10-21 01:54:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1294379,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7153227/v1/538f4fc3-8e35-4316-982d-594b1cadbd25.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eA Prediction Model for the Length of Stay after Single-Port Thoracoscopic surgery of lung cancer:Based on Preoperative Physical Activity Level\u003c/p\u003e","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eLung cancer is among the most prevalent malignancies worldwide, characterized by persistently high incidence and mortality rates. According to Global Cancer Statistics 2020, an estimated 2.2\u0026nbsp;million new cases of lung cancer were reported, accounting for 11.4% of all newly diagnosed cancers globally. It remains the leading cause of cancer-related deaths, contributing to 18.0% of total cancer mortality\u003csup\u003e1\u003c/sup\u003e. The five-year survival rate for lung cancer is approximately 20%\u003csup\u003e2\u003c/sup\u003e. In China, lung cancer has the highest incidence and mortality rates among malignancies, imposing a significant burden on public health\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNon-small cell lung cancer (NSCLC) comprises more than 80% of lung cancer cases. The standard treatment for NSCLC involves lobectomy combined with lymph node dissection\u003csup\u003e4\u003c/sup\u003e. With the increasing adoption of thoracoscopic techniques, uniportal video-assisted thoracoscopic lobectomy has become the mainstream surgical approach in China. This method offers several advantages, including minimal invasiveness, high efficacy, and safety, effectively reducing postoperative pain and complications while shortening LOS\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLOS is a critical indicator for evaluating healthcare resource utilization and patient recovery speed\u003csup\u003e6\u003c/sup\u003e. In surgical treatments, LOS typically refers to the number of postoperative days spent in the hospital. Reducing LOS alleviates the financial burden on patients, lowers hospital operating costs, eases healthcare system pressure, and enhances overall medical efficiency\u003csup\u003e7\u003c/sup\u003e. A study by Li et al. reported that the average LOS for patients undergoing uniportal thoracoscopic surgery was 6.5 days, with a range of 4\u0026ndash;7 days\u003csup\u003e8\u003c/sup\u003e. However, some patients experienced longer LOS due to postoperative complications or prolonged surgeries.\u003c/p\u003e\u003cp\u003eFor lung cancer patients, LOS is influenced by various factors, including the patient\u0026rsquo;s physical condition, surgical techniques, postoperative complication management\u003csup\u003e9\u003c/sup\u003e, and preoperative prehabilitation measures\u003csup\u003e10\u003c/sup\u003e. Prehabilitation, which has garnered significant attention in recent years, encompasses preoperative assessments of nutritional status, psychological well-being, and physical activity levels\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eStudies have shown that good preoperative physical activity levels are crucial in reducing postoperative LOS and complications among lung cancer patients\u003csup\u003e12,13\u003c/sup\u003e. As highlighted by Gillis et al.\u003csup\u003e14\u003c/sup\u003e, preoperative physical activity improves tolerance to surgical stress, enhances cardiopulmonary function and immunity, and alleviates psychological stress, thus laying a foundation for successful postoperative recovery. Physical activity has also been associated with reduced inflammatory responses and improved nutritional status, both of which are critical for recovery and LOS\u003csup\u003e15,16\u003c/sup\u003e. Consequently, good preoperative physical activity not only accelerates recovery but also provides patients with more time for subsequent treatments.\u003c/p\u003e\u003cp\u003eDespite these findings, current research on LOS has primarily focused on critically ill patients or postoperative factors, with limited exploration of the role of preoperative physical activity. To address this gap, we developed a prediction model for LOS based on preoperative physical activity levels. This model aims to provide scientific evidence for preoperative evaluations, optimize surgical scheduling, and enhance resource allocation. By identifying high-risk patients and implementing targeted preoperative interventions, the model seeks to minimize LOS and improve overall recovery outcomes for lung cancer patients. This approach aligns with modern patient-centered care principles and offers significant potential for the efficient and equitable distribution of healthcare resources.\u003c/p\u003e"},{"header":"2.Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1Study Design and Participants\u003c/h2\u003e\u003cp\u003e This prospective study was approved bythe Institutional Review Board (IRB) of the Cancer Hospital, Chinese Academy of Medical Sciences (No.24/325\u0026ndash;4605), and all patients provided written informed consent.This study follows Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guideline.\u003c/p\u003e\u003cp\u003eThe study was carried out in a thoracic surgery ward with 40 beds at the National Cancer Center. The subjects of the study were patients who underwent surgery between August 1, 2023 and April 1, 2024. Eligibility was determined through review of the electronic health record.\u003c/p\u003e\u003cp\u003eThe data were mainly collected through questionnaires on the day when patients were admitted to the hospital. Our database was constructed by extracting the following information: (a) sociodemographic variables, such as age, sex, body mass index (BMI), place of residence and the presence of a caregiver at home; (b) comorbidities at the time of admission; (c) the level of physical activity within one week before the operation; (d) surgical and rehabilitation-related factors, including the duration of the operation, the length of hospital stay after the operation, the surgical team, and postoperative complications (i.e., bleeding, subcutaneous emphysema, arrhythmia) as well as hospitalization expenses. Finally, we used the International Physical Activity Questionnaire - Long Form (IPAQ-Long Form) to calculate the patients' physical activity levels. The IPAQ-Long Form consists of 27 questions in total, among which 25 questions inquire about individuals' physical activity and 2 questions ask about individuals' sedentary behavior. The physical activity is mainly composed of activity types (work, transportation, household gardening, leisure) and activity intensity (walking and moderate, high intensity). It asks individuals about their physical activities related to work, transportation, household gardening and leisure in the past 7 days in turn. MET-min/week were calculated for work, transportation, and leisure domains, categorized as low (\u0026lt;\u0026thinsp;600), moderate (600\u0026ndash;3,000), or high (\u0026gt;\u0026thinsp;3,000) per WHO guidelines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2Outcomes\u003c/h2\u003e\u003cp\u003eThe discharge destination (after being judged by clinicians that the patients had met the discharge standards and no transfer to other departments or hospitals occurred) and the length of postoperative hospital stay (defined as the number of days the patients stayed in the general ward after the operation) were extracted from the patients' electronic medical records.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3Statistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous data are expressed as the mean and standard deviation (SD). Categorical data are expressed as the number and percentage and were analyzed using Pearson chi-square test or Fisher exact test as appropriate. The level of significance was set at \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Variables showing statistical significance in Pearson chi-square test were included in the binary logistic regression analysis to find independent perioperative predictors for LOS. Model calibration was assessed via Hosmer-Lemeshow test. Statistical analysis was performed using SPSS v24.0 (IBM, Armonk, NY, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"3.Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1Study Population\u003c/h2\u003e\u003cp\u003eA total of 231 participants met the inclusion criteria and were enrolled in the prospective validation cohort. The detailed patient selection process is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the participants, 90 patients (39.0%) were men and 141 (61.0%) were women. The mean (SD) age was 53.1 (9.4) years, and the mean (SD) BMI was 24.0 (9.2) kg/㎡. Regarding marital status, 216 patients (5.4%) were married, while 15 (18.9%) were single, divorced or widowed. Participants resided in either rural or urban areas, with 55 (23.8%) living in villages and 176 (76.2%) in urban. Education levels were categorized as junior high school or below, senior high school or college, and bachelor's degree or above, accounting for 60 (26.0%), 101 (43.7%), and 70 (30.3%), respectively. Employment status showed that 124 patients (53.7%) were retired or unemployed, while 107 (46.3%) were employed. Additionally, 195 patients (84.4%) had cohabitants and 36 (15.6%) lived alone. During hospitalization, 113 patients (48.9%) employed caregivers, while 118 (51.1%) were accompanied by relatives. Preoperative physical pain was scored using the visual analogue scale (VAS), of which 197 patients (85.3%) reported no pain (0 points), 17 (7.3%) experienced mild pain (1\u0026ndash;3 points), 12 (5.2%) reported moderate pain (4\u0026ndash;6 points) and 5 (2.2%) experienced severe pain (7\u0026ndash;10 points). Additionally, 116 (50.2%) patients reported having physical exercise habits before surgery, while 115 (49.8%) patients did not. Only 4 patients (1.7%) require assistance with Activity of Daily Living (ADL). Furthermore, eighty-five (36.8%) patients had OCD other than cancer. Participant demographics and model-specific characteristics are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\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\u003eParticipant demographic and model-specific characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOS\u0026thinsp;\u0026le;\u0026thinsp;4(n\u0026thinsp;=\u0026thinsp;191)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLOS\u0026gt;4(n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStatistic value\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\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\u003cp\u003e0.742 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72(37.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(45.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119(62.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(55.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.65\u0026thinsp;\u0026plusmn;\u0026thinsp;9.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.48\u0026thinsp;\u0026plusmn;\u0026thinsp;8.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.741\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/㎡)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.223 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarrige\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\u003cp\u003e1.271 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177(92.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39(97.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14(7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence\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\u003cp\u003e21.951 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVillage\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34(17.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(52.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157(82.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19(47.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003cp\u003e9.279 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42(22.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(45.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87(45.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(35.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62(32.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetire\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\u003cp\u003e0.743 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105(55.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19(47.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86(45.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(52.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolitude\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\u003cp\u003e0.717 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163(85.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32(80.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28(14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8(20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaregiver\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\u003cp\u003e0.248 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaregiver\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92(48.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(52.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelatives\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99(51.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19(47.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePain\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\u003cp\u003e0.531 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164(85.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33(82.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13(6.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10(5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExercise\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\u003cp\u003e0.001 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95(49.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96(50.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActivity\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\u003cp\u003e3.037 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf care\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e189(99.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38(95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeed help\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOCD\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\u003cp\u003e0.067 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120(62.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26(65.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71(37.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(35.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2159.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2480.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1167.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2210.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.340 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAL\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\u003cp\u003e9.905 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74(38.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25(62.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74(38.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13(32.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43(22.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperation Time\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\u003cp\u003e14.756 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;1hour\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38(19.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2 hours\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111(58.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(55.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;2 hours\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42(22.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(45.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePart\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\u003cp\u003e0.305 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75(39.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14(35.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116(60.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26(65.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\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\u003cp\u003e2.984 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSqCa\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5(2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177(92.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34(85.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(4.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\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\u003cp\u003e15.080 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e172(90.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30(75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16(8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7(17.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3(1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0(0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\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\u003cp\u003e0.399 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e172(90.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37(92.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(4.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10(5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplication\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\u003cp\u003e4.535 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168(88.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30(75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYES\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23(12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10(25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCost (Thousand RMB)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.85\u0026thinsp;\u0026plusmn;\u0026thinsp;17.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.26\u0026thinsp;\u0026plusmn;\u0026thinsp;16.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.774 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: BMI, Body Mass Index; OCD, Other Chronic Disease; MET, Metabolic Equivalent of Task; PAL, Physical Activity Level; SqCa, Squamous Carcinoma; AC, Adeno Carcinoma; a Statistic value of t; b Statistic value of X\u003csup\u003e2\u003c/sup\u003e The variablest show a statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\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\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\u003eBinary logistic regression analysis of LOS in patients who underwent\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eS.E,\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWals\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExp (B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eEXP(B) 95% C.I.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.891\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAL\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\u003cp\u003e8.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle vs Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38.644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh vs Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17.764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperation Time\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\u003cp\u003e4.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2 hours vs \u0026lt;1hour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-20.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6198.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;2 hours vs \u0026lt;1hour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.910\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComplications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.863\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2LOS and Costs\u003c/h2\u003e\u003cp\u003eThe mean LOS for all patients was 3.69 (SD\u0026thinsp;=\u0026thinsp;1.58), ranging from 2 to 13 days. Among them, 42 patients (18.2%) had a LOS of 2 days, 87 patients (37.7%) had a LOS of 3 days, and 62 patients (26.8%) had a LOS of 4 days. In this study, the patients were divided into two groups (\u0026le;\u0026thinsp;4 days and \u0026gt;\u0026thinsp;4 days), with 191 cases (82.7%) and 40 cases (17.3%), respectively. Among the patients with \u0026gt;\u0026thinsp;4 days, most frequent LOS was 6 days, accounting for 22 cases (9.5%). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the average hospitalization expenses of patients in the \u0026le;\u0026thinsp;4 days group and \u0026gt;\u0026thinsp;4 days group were 64.85\u0026thinsp;\u0026plusmn;\u0026thinsp;17.45 thousand yuan and 79.26\u0026thinsp;\u0026plusmn;\u0026thinsp;16.97 thousand yuan, respectively. There was a significant difference in hospitalization expenses between the two groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3Physical Activity\u003c/h2\u003e\u003cp\u003eThe physical activity of all patients was measured by International Physical Activity Questionnaire-long (IPAQ-L), with results expressed in Metabolic Equivalent of Task (MET) with a median value of 1086 (range: 0-3312). Of these, 144 (62.3%) engaged in walking as their primary form of physical activity, making it the most common form of physical activity; 123 (53.2%) engaged in specific exercise, with 116 (50.2%) choosing walking, 11 (4.8%) choosing more intense activities such as ball sports, swimming, and aerobics, and 13 (5.6%) choosing lower-intensity activities such as tai chi. These patients spent from 0.5 to 8 hours sitting still during workdays, with 181 (78.4%) spending\u0026thinsp;\u0026le;\u0026thinsp;3 hours sitting still, and 50 (21.6%) spending\u0026thinsp;\u0026gt;\u0026thinsp;3 hours; on weekends, 79 (34.2%) spent\u0026thinsp;\u0026gt;\u0026thinsp;3 hours sitting still. Physical activity level was calculated using MET and activity frequency and categorized into low, moderate, and high levels. In this study, 99 (42.9%) had a low physical activity level, 87 (37.7%) had a moderate level, and 55 (23.8%) had a high level.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4Model Performance\u003c/h2\u003e\u003cp\u003eHaving identified several demographics parameters as statistically significant between LOS\u0026thinsp;\u0026le;\u0026thinsp;4 and LOS\u0026gt;4 groups, a binary logistic regression model with split sampling was employed to predict outcomes based on LOS. The analysis revealed that longer operation time (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) and the presence of comorbidities (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) were significantly associated with a longer LOS, whereas urban residence (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a high physical activity level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) were associated with a shorter LOS (Table.3). The model demonstrated excellent discrimination (AUC: 0.847, 95% CI, 0.789\u0026ndash;0.905) and calibration (Hosmer-Lemeshow P\u0026thinsp;=\u0026thinsp;0.34). Sensitivity (80%) and specificity (75%) were balanced at the optimal cutoff (Youden\u0026rsquo;s index).(Figure.2.)\u003c/p\u003e\u003c/div\u003e"},{"header":"4.Discussion","content":"\u003cp\u003e\u003cb\u003e4.1The Significance of Developing a LOS Prediction Model Based on Preoperative Physical Activity Levels in Lung Cancer Patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePreoperative physical activity levels are among the critical factors influencing postoperative recovery in lung cancer patients. Developing a LOS prediction model based on preoperative physical activity holds significant clinical value. Such a model can assist clinicians in more accurately evaluating patients\u0026rsquo; postoperative recovery potential and potential risks of complications, enabling the design of personalized treatment and rehabilitation plans. By quantifying preoperative physical activity, the model can predict the recovery pace and estimate the medical resources and support needed after surgery. Additionally, the model provides clearer prognostic information, helping patients and their families prepare psychologically and materially, thereby enhancing treatment confidence and cooperation. Ultimately, the development and application of this model could improve postoperative quality of life and survival rates for lung cancer patients, reduce the wastage of medical resources, and provide scientific evidence for decision-making in lung cancer treatment.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.2Analysis of Factors Influencing LOS in Lung Cancer Patients Residence\u003c/h2\u003e\u003cp\u003eThis study revealed that residence was a significant factor affecting LOS duration (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with rural patients experiencing significantly longer LOS than urban patients. Several factors may contribute to this disparity. First, studies have shown that rural residents tend to have lower educational levels, limited health knowledge, and insufficient preventive care \u003csup\u003e17\u003c/sup\u003e, leading to delayed disease detection and treatment \u003csup\u003e18\u003c/sup\u003e, thus necessitating longer LOS. Second, geographical barriers in rural areas restrict access to medical resources \u003csup\u003e19\u003c/sup\u003e, making timely medical care more challenging and exacerbating disease progression, which prolongs hospitalization. Additionally, rural patients may have different expectations and needs regarding healthcare services compared to urban patients. They might prefer extended LOS to ensure treatment stability. Addressing the extended LOS of rural patients requires a multifaceted approach, including improving rural patients\u0026rsquo; health awareness and medical knowledge, enhancing access to healthcare information, and optimizing the distribution of medical resources. These efforts are crucial for reducing LOS among rural patients and improving overall health outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.3Physical Activity Levels (PAL)\u003c/h2\u003e\u003cp\u003ePhysical activity encompasses a wide range of forms, from daily activities to organized exercise programs\u003csup\u003e20\u003c/sup\u003e, such as work, sports, leisure, transportation (walking, cycling), and household tasks. Both structured and unstructured activities benefit health. Insufficient physical activity increases the risk of noncommunicable diseases (NCDs) and other health issues\u003csup\u003e20\u003c/sup\u003e, as well as negatively impacts mental health\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, 144 patients (62.3%) primarily engaged in walking as their main form of physical activity, indicating limited variety. On rest days, 79 patients (34.2%) spent over 3 hours sedentary. Compared to workdays, more patients chose extended sedentary periods on rest days. Among participants, 99 patients (42.9%) had low physical activity levels in the week prior to the study, falling short of WHO\u0026rsquo;s recommended levels. Insufficient physical activity and prolonged sedentary behavior have become significant risk factors for global mortality\u003csup\u003e20\u003c/sup\u003e. WHO\u0026rsquo;s Global Action Plan on Physical Activity 2018\u0026ndash;2030 emphasizes the importance of regular physical activity for all age groups to maintain and enhance health\u003csup\u003e22\u003c/sup\u003e. The American Cancer Society (ACS) guidelines align with WHO\u0026rsquo;s recommendations, suggesting cancer survivors and healthy adults engage in 150\u0026ndash;300 minutes of moderate-intensity aerobic exercise or an equivalent amount of vigorous activity weekly\u003csup\u003e23,24\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study also found that patients with low preoperative physical activity levels had longer LOS. Research suggests that patients with higher preoperative physical activity levels recover faster postoperatively\u003csup\u003e12,25\u003c/sup\u003e. Therefore, it is essential to develop health promotion policies to encourage physical activity, reduce barriers, and foster participation across diverse populations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.4Operation Time\u003c/h2\u003e\u003cp\u003eEnhanced Recovery After Surgery (ERAS) is an innovative perioperative management concept aimed at promoting recovery and reducing LOS\u003csup\u003e26,27\u003c/sup\u003e. Minimally invasive surgery to shorten operation time is a core component of ERAS. Studies indicate that ERAS significantly reduces average LOS compared to traditional surgery\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, patients with operation times\u0026thinsp;\u0026le;\u0026thinsp;1 hour had LOS\u0026thinsp;\u0026le;\u0026thinsp;4 days. Shorter operation times reduce tissue damage, fluid loss, and inflammation, minimizing complications and expediting recovery. Conversely, patients with operation times\u0026thinsp;\u0026ge;\u0026thinsp;2 hours experienced longer LOS due to the complexity of the surgery and increased postoperative management needs\u003csup\u003e29,30\u003c/sup\u003e. Optimizing surgical procedures and improving efficiency are essential for promoting rapid recovery and reducing LOS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.5Complications\u003c/h2\u003e\u003cp\u003ePostoperative complications, such as lung infections, atelectasis, and prolonged air leaks, are significant risk factors for recovery in lung cancer patients\u003csup\u003e31,32\u003c/sup\u003e. Complications increase the difficulty of recovery, leading to prolonged LOS and higher resource consumption\u003csup\u003e11,33\u003c/sup\u003e. In this study, 198 patients experienced no complications, with 84.8% of them having a LOS (LOS)\u0026thinsp;\u0026le;\u0026thinsp;4 days. A significant difference in LOS was observed between patients with and without complications, with those without complications exhibiting shorter LOS, consistent with findings from other studies. These complications increase the difficulty of physiological recovery, leading to prolonged hospitalization.\u003c/p\u003e\u003cp\u003eAdditionally, research has indicated that the degree of pulmonary fissure completeness can predict postoperative complications and the LOS following video-assisted thoracoscopic surgery (VATS) lobectomy for early-stage non-small-cell lung cancer\u003csup\u003e34\u003c/sup\u003e. Incomplete pulmonary fissure development may result in greater intraoperative trauma and higher rates of postoperative complications, necessitating longer recovery times and extended LOS. Therefore, the prevention, monitoring, and management of postoperative pulmonary complications are critical to improving patient recovery and outcomes. By optimizing perioperative management, reducing the incidence of complications, and shortening LOS, healthcare providers can enhance recovery rates and lower medical costs.Effective prevention, monitoring, and management of complications are vital for improving recovery and reducing LOS.\u003c/p\u003e\u003c/div\u003e"},{"header":"5.Limitations","content":"\u003cp\u003eThis study, conducted prospectively at the National Cancer Center of China, faces challenges in sample selection. Although the center attracts a large and diverse patient population from across the country, reflecting a certain level of domestic variability, its samples may not be globally representative. This limits the model\u0026rsquo;s ability to capture the diverse characteristics of patients from various ethnic, geographical, and healthcare system backgrounds. Future research should consider multi-center collaborations, incorporating patient samples from different countries and regions, to enhance the diversity and generalizability of the data.\u003c/p\u003e\u003cp\u003eAdditionally, the data collection process primarily relies on patient self-report questionnaires, which are susceptible to recall bias. This can compromise the accuracy of the model\u0026rsquo;s input data. It is crucial to introduce more objective data collection methods in the future, such as utilizing wearable devices to monitor patients\u0026rsquo; daily physical activities and physiological indicators. Integrating these data with electronic medical record systems can provide more accurate clinical information, reducing reliance on patients\u0026rsquo; subjective recollections and thereby improving the model\u0026rsquo;s precision.\u003c/p\u003e\u003cp\u003eMoreover, while the current model includes several common clinical indicators and demographic characteristics, it is not yet comprehensive. Many potential factors, such as patients\u0026rsquo; psychological states (e.g., anxiety and depression) and their changes, social support systems, detailed long-term lifestyle habits, regional air quality, and living conditions\u0026rsquo; convenience, are challenging to quantify and incorporate into the model. Furthermore, the model has not yet been externally validated, and its accuracy and reliability in practical applications require further testing.\u003c/p\u003e"},{"header":"6.Conclusion","content":"\u003cp\u003eThe level of preoperative physical activity in lung cancer patients significantly influences the duration of their postoperative hospital stay. Utilizing binary logistic regression analysis, this study has successfully integrated a range of clinical medical information, including patients\u0026rsquo; sociodemographic characteristics, disease features, and preoperative physical activity levels, to develop a predictive model. This model identifies four key predictive factors: place of residence, physical activity status, duration of surgery, and postoperative complications. Through meticulous data analysis, the model has been constructed and demonstrates robust predictive capabilities, offering a valuable tool for forecasting the postoperative hospital stay length of lung cancer patients.However, the study is not without limitations. Future research should consider multi-center collaborations and larger sample sizes to enhance the model\u0026rsquo;s generalizability. Additionally, incorporating a broader range of clinical factors will allow for more in-depth exploration and continuous refinement of the model\u0026rsquo;s predictive accuracy and reliability. Such improvements will provide a more solid foundation for the clinical treatment and rehabilitation management of lung cancer patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNon-small cell lung cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVATS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVideo-assisted thoracoscopic surgery\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSEE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSelf-Efficacy for Exercise\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTSK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTampa Scale for Kinesiophobia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003e6.2Ethics approval and consent to participate\u003c/h2\u003e\u003cp\u003e This study was conducted in accordance with the ethical principles of the Declaration of Helsinki (as revised in 2013). The study protocol was approved by the Independent Ethics Committee of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, and the National GCP Center for Anticancer Drugs (Approval No. NCC2023C-946, dated October 20, 2023). All participants provided written informed consent prior to inclusion in the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003e6.3 Consent for publication\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003e6. 5 Competing interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003e6.7 Authors\u0026rsquo;contributions\u003c/h2\u003e\u003cp\u003eMiao Liu and Fengyan Ma contributed equally to this work and share first authorship. Miao Liu and Fengyan Ma were responsible for the study design, data collection, and initial drafting of the manuscript. Qiuju Zhang and Wenjing Huang contributed to data analysis and interpretation. Yao Fu, Chen Chen, and Xuan Wang assisted with patient recruitment and data acquisition. Hongzhu Wang and Liting Wang participated in the literature review and manuscript revision. Jiagen Li supervised the study, provided critical revisions, and is the corresponding author. All authors read and approved the final manuscript.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003e6.6 Funding\u003c/h2\u003e\u003cp\u003eThis study was supported by the CAMS Innovation Fund for Medical Sciences (CIFMS), Grant number: 2023-I2M-C\u0026amp;T-B-087.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMiao Liu, Fengyan Ma and Wenjing Huang wrote the main manuscript text; Qiuju Zhang, Yao Fu, Ermei Cui, Chang Zhan, Wenqi Li and Ziwei Fu prepared all figures and tables, all authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003e6.8 Acknowledgements\u003c/h2\u003e\u003cp\u003eThe authors would like to thank all patients who participated in this study and the clinical staff who supported the data collection process.\u003c/p\u003e\u003ch2\u003e6.4 Availability of data and materials\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al. 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Degree of pulmonary fissure completeness can predict postoperative cardiopulmonary complications and length of LOS in patients undergoing video-assisted thoracoscopic lobectomy for early-stage lung cancer. \u003cem\u003eInteract Cardiovasc Thorac Surg\u003c/em\u003e 2018; \u003cstrong\u003e26\u003c/strong\u003e(1): 25-33.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"prediction model, length of stay, lung cancer, physical artical","lastPublishedDoi":"10.21203/rs.3.rs-7153227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7153227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWith 2.2\u0026nbsp;million new cases annually, lung cancer remains the leading cause of cancer mortality globally. Preoperative physical activity (PA) may optimize postoperative recovery, but its role in predicting Length of stay (LOS) after VATS is understudied.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe clinical data of 231 patients with lung cancer who underwent surgery in the Department of Thoracic Surgery of National Cancer Center from August 1, 2023, to April 1, 2024 were prospectively collected. The preoperative physical activity levels were assessed by the International Physical Activity Questionnaire (IPAQ), the residence, operation duration and complications were recorded. Binary logistic regression with bootstrapping (231 resamples) identified predictors of LOS, adjusting for age, sex, and comorbidities. Model performance was assessed via ROC analysis (AUC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 231 lung cancer patients were enrolled, including 90 males and 141 females, with an average age of 53.1 years. The median preoperative physical activity value (MET) was 1086 (rang: 0-3312) and the mean LOS was 3.69 (1.58). Preoperative PA (\u003cem\u003eOR: 7.98, 95% CI: 1.65\u0026ndash;38.64\u003c/em\u003e), urban residence (\u003cem\u003eOR: 4.01, 95% CI: 1.81\u0026ndash;8.89\u003c/em\u003e), shorter operation time (\u003cem\u003eOR: 0.40, 95% CI\u003c/em\u003e: 0.18\u0026ndash;0.91) and complications (\u003cem\u003eOR: 0.32, 95% CI: 0.12\u0026ndash;0.86\u003c/em\u003e) independently predicted reduced LOS (all \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). The model achieved an AUC of 0.85 (\u003cem\u003e95% CI: 0.79\u0026ndash;0.91)\u003c/em\u003e, the sensitivity was 80.0% and the specificity was 74.9%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePreoperative PA is a modifiable predictor of LOSin patients with lung cancer. Integration of PA assessment into prehabilitation programs may optimize resource allocation and recovery pathways.\u003c/p\u003e","manuscriptTitle":"A Prediction Model for the Length of Stay after Single-Port Thoracoscopic surgery of lung cancer:Based on Preoperative Physical Activity Level","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 01:21:56","doi":"10.21203/rs.3.rs-7153227/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-07T09:24:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T11:53:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-23T13:32:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-23T02:14:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-07-23T02:11:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9babc9f6-f060-41c8-9214-d63fd3faaf37","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T01:21:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 01:21:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7153227","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7153227","identity":"rs-7153227","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}