Development and Evaluation of a CDSS-Enabled Early Warning System for Venous Thromboembolism Risk in Advanced Lung Cancer Patients

Development and Evaluation of a CDSS-Enabled Early Warning System for Venous Thromboembolism Risk in Advanced Lung Cancer Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and Evaluation of a CDSS-Enabled Early Warning System for Venous Thromboembolism Risk in Advanced Lung Cancer Patients Jian Fan, Bai Gao, Jiayi Zhao, Xuefeng Gao, Baiqiu Liu, Kai Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5580344/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUNDː This study establishes the clinical practice library of Venous Thromboembolism (VTE) complications in advanced lung cancer, forms a VTE risk warning system for advanced lung cancer patients based on the Clinical Decision Support System (CDSS) and plan to use it in hospitals in the future, and plays a role in preventing VTE complications in advanced lung cancer patients. METHODSː We summarized the VTE risk factors by searching the literature and constructed a knowledge base of advanced lung cancer complication VTE risk factors based on evidence-based medicine; collected real-world clinical data on lung cancer patients and constructed a real-world best-practice library of advanced lung cancer complication VTE by cleaning, quality control and analyzing clinical data; and constructed an equation for the prediction risk coefficient of advanced lung cancer complication VTE by logistic regression analysis. Finally, the three were combined to construct a CDSS-based VTE risk warning system for advanced lung cancer patients. Statistical analysis was performed using R software, with a test level of α = 0.05, and the difference was considered statistically significant at P < 0.05. RESULTS: A total of 12,222 patients were screened through the hospital's electronic medical record system, and 3,320 patients were included after screening in strict accordance with the exclusion criteria. The patients were divided into 67 cases in the group of lung cancer complicating VTE and 3253 cases in the group of uncomplicated VTE. According to the stepwise regression method and the AIC law, the equation of the predicted risk coefficient of advanced lung cancer complicating VTE was constructed. Combining the three basic factor modules of advanced lung cancer complicating VTE and the CDSS system, a CDSS-based risk warning system for advanced lung cancer complicating VTE was established, and the externally validated results showed that the AUC of 0.756 (95% CI: 0.726–0.785) was better than that of the Caprini risk scoring scale AUC of 0.654 (95% CI: 0.638–0.670). CONCLUSIONSː This study established a CDSS-based early warning system for the risk of concurrent VTE in advanced lung cancer. The results of comparative validation showed that this early warning system can improve the clinical diagnosis and treatment efficiency of advanced lung cancer patients with complications of VTE, and at the same time, it can achieve the purpose of real-time monitoring and timely diagnosis and treatment. Biological sciences/Cancer/Lung cancer Health sciences/Cardiology Health sciences/Health care Health sciences/Medical research Health sciences/Oncology Health sciences/Risk factors CDSS lung cancer VTE prediction system risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Lung cancer is the most common malignant tumor in the world, with an incidence rate of 11.4% and a mortality rate of 18% [ 1 ]. Advanced lung cancer generally refers to patients with lung cancer at stage III and stage IV, and they often have a poor prognosis. VTE is one of the common complications of lung cancer, and its incidence is about 2%. It mainly includes two types of Pulmonary Embolism (PE) and Deep Vein Thrombosis (DVT) [ 2 – 3 ]. In patients with cancer and VTE, the risk of death is three times as high as in those without VTE [ 4 ]. Currently, the commonly used clinical methods to predict the risk of lung cancer complicating VTE include the Caprini Risk Assessment Scale, Rogers Assessment Scale, Kucher Risk e-Assessment Scale, and Padua Prediction Scale; however, these procedures are cumbersome and time-consuming [ 5 ]. Guidelines recommend that all hospitalized patients be assessed for VTE risk and that prophylactic measures be taken for high-risk patients [ 6 ]. Studies have shown that lung cancer is prone to complicate VTE, and the mechanism of its occurrence has three main aspects, including the release of procoagulant factors by tumor cells, the procoagulant properties of normal cells induced by tumor cells, and chemotherapy [ 7 ]. With the continuous development of medical informatization means and technology, constructing an intelligent VTE risk prediction system based on real clinical data is increasingly favored. A Clinical Decision Support System (CDSS) is an information-based intelligent application system that improves the quality of healthcare and healthcare services by applying systematic clinical knowledge to enhance the decision-making ability of healthcare-related actions [ 5 ]. CDSSs were being developed internationally as early as 1970 [ 8 ] to simulate the process of disease diagnosis and treatment by senior experts through clinical data collection, cleaning, in-depth analysis, and the establishment of other computer language arithmetic rules for patients beyond the risk value of timely expression of relevant information, such as warning information, condition information, medical advice packages, and information management, as well as the form of relevant data, high-risk patients to whom the first assessment should be provided, early warning, and decision-making support for diagnosis and treatment, which can be used by clinicians to provide treatment advice, diagnosis, and treatment. It can provide decision support for clinical doctors in diagnosis and treatment in terms of treatment suggestions, high-risk reminders, and risk prediction. At present, CDSS is an inevitable trend of combining medicine and artificial intelligence, and its advantages include the assessment of one's own VTE risk at anytime and anywhere on the Internet. CDSS systems have long been introduced at home and abroad for information-based intelligent assistance management in several industries, but there are fewer studies based on CDSS to predict concurrent VTE in advanced lung cancer patients, including small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This study is a cross-sectional study aimed at researching a CDSS-based early warning system for the risk of concurrent VTE in advanced lung cancer based on the real situation in China and providing timely diagnostic and therapeutic measures, which is conducive to the early detection, diagnosis, and treatment of concurrent VTE in advanced lung cancer with the full help of the CDSS system, whether on a hospital ward or living in the community. Materials and methods Study setting and population PubMed was searched for English-language articles from January 2010 through December 2020, using search terms for "Lung cancer"[Mesh] and "venous thromboembolism" [Mesh]. Article inclusion criteria were based on relevance. Clinical data collection (I) Clinical data sources Clinical data sources included Hospital Information System(HIS), Electronic Medical Record(EMR), Laboratory Information System(LIS), Picture Archiving and Communication Systems(PACS), Radiology Information System(RIS), and other systems. We collected lung cancer (Both SCLC and NSCLC) patients with clinical stages III and IV in the respiratory department of a tertiary hospital in Shanghai from January 2010 to December 2020. The inclusion criteria and exclusion criteria were strictly enforced to collect medical record data. (II) Inclusion criteria 1. All advanced lung cancer patients were diagnosed by pathological examination; 2. All complete and searchable clinical medical record data of lung cancer patients; 3. Age ≥18 years. (III) Exclusion criteria 1. Those who were hospitalized once and did not receive any treatment during hospitalization; 2. Those with serious missing laboratory-related data and basic medical record information; 3. Those with serious organ dysfunction; 4. Patients with tumor metastasis to the lungs from other sites; The study followed the Declaration of Helsinki (2000 version) and was approved by the Ethics Committee of Shanghai Changhai Hospital (Approval No. CHEC2021-182). We have applied to the Ethics Committee of Shanghai Changhai Hospital and obtained approval for exemption from informed consent. Development of the CDSS to Predict the VTE discovery risk Data Acquisition This layer is the data source layer, which collects data from the hospital's information system, including HIS, EMR, LIS, PACS, RIS, and other diagnosis- and treatment-related systems. Clinical VTE diagnosis criteria include for DVT, clinical symptoms/signs, compression ultrasonography, and D-dimer testing, and for PE, clinical manifestations, imaging studies like CTPA and V/Q scanning, and blood gas analysis. Other external data and knowledge sources include relevant literature and knowledge bases. Data Processing This layer is the data processing layer, which firstly unifies the data standards and establishes a standardized terminology dictionary, including a basic dictionary library of disease diagnosis, examination, and testing, and basic patient information, which is used to classify the data for the subsequent processing, quality control, and governance processes. Second, data processing is carried out on the collected data, and structured as well as non-standardized unstructured data within the hospital are standardized through NLP, data standardization, and other technologies. At the same time, through the connotative evaluation system, the standardized data are regulated for completeness, accuracy, and standardization. Finally, per the results of data quality control, residual data, poor consistency data, etc. are processed and transformed into high-quality usable scientific research data. Data Model This layer establishes a variety of data models based on the conclusions of the preliminary data, including disease-related basic models, comprehensive models integrating relevant knowledge graphs, patient profiles, and other comprehensive models, as well as models combining knowledge bases, literature, and relevant models based on the conclusions of real-world clinical research, etc., to support the establishment of the system. Data Management This layer adds the management of data security to the results of the previous data processing step, which mainly includes desensitization management to desensitize patient privacy and non-diagnosis and treatment-related data. At the same time, system access rights are restricted to ensure data security. For completely random missing data, if the missing proportion is small, deletion methods (such as list deletion or paired deletion) can be used for processing. If the missing proportion is large, multiple imputation can be considered for processing. Establishment of a Risk Influencing Factor Knowledge Base Based on the establishment of the above four layers, for the high-risk group of hospitalized patients, combined with the relevant standards of VTE prevention and treatment guidelines, patient risk assessment, combined with the assessment nodes configured in the main links of our hospital's electronic medical records and medical prescriptions, a knowledge base of VTE risk-influencing factors is established, and risk early-warning rules are set. Establishment of a CDSS-based VTE Early Warning System Based on the above knowledge base of VTE risk-influencing factors, the early warning system provides knowledge support for VTE risk-influencing factors, combines with patients' diagnosis and treatment data, integrates clinical application systems, such as electronic medical records, medical advice, examination, testing, and nursing care, etc., and carries out whole-process, whole-time, whole-domain integrated dynamic monitoring of the patient's whole process of medical consultation to promote medical and nursing cooperation and provide clinical medical care with decision-making support for clinical medical care. See Figure 1. The CDSS uses integrated platform interfaces and front-end interfaces to connect electronic medical record data, and through deep integration with EMR, HIS, LIS, RIS, PACS, and other information systems. All data on admitted patients are processed, and after the construction of data standardization and quality control, the CDSS data processing platform structurally dumps the data into different data models. In data standardization construction, natural language processing (NLP) technology was used for post-structured processing to segment the language randomly entered by doctors into different entity concepts and logical relationships, and terminology coding was performed to form a common computer language that can be used for real-world clinical knowledge base construction and clinical decision support. In data management, sensitive data desensitization management and account privileges are used to protect patient privacy. A VTE risk warning system was developed and designed to improve the quality of VTE prevention and control through artificial intelligence data processing and real-time human-computer interaction, providing a foundation for further clinical decision support and intelligent management applications. Assessments Three basic factor modules based on evidence-based medicine were formed through the literature database: the module of influencing factors of baseline population characteristics, the module of high-risk factors of advanced lung cancer complicating VTE, and the module of VTE diagnosis and treatment. Real-world data were collected and processed through washing, quality control, and analyzing, to construct a library of clinical best practices for real-world advanced lung cancer patients. The real clinical data were divided into experimental groups (70%) and validation groups (30%), and the data of the experimental group were analyzed by logistic regression to construct the prediction risk coefficient equation of advanced lung cancer complicating VTE. Combined with the CDSS medical big data processing system, a CDSS-based risk warning system for advanced lung cancer complicating VTE was constructed. The Caprini risk assessment scale has been validated and widely used in hospitalized patients [9]. The validation data for the external validation were used to compare the prediction performance of the VTE risk warning system with the Caprini risk assessment scale to assess the accuracy of predicting the occurrence of VTE and ultimately to form a CDSS-based VTE risk warning system for advanced lung cancer that can be put into clinical use. See Fig 2. Statistical analysis R language software was used to process the data statistically. Qualitative data (e.g., gender) were described by χ 2 test or Fisher's exact probability method; quantitative data such as those conforming to a normal distribution were described by mean and standard deviation (`X ±S), and those not conforming to a normal distribution were described by median and interquartile range, and independent samples t-test and Mann-Whitney U-test were used for the comparison of the data between groups, respectively. Binary logistic regression analysis was performed for each factor, and the test results were considered statistically significant at P < 0.05. According to the stepwise regression method and Akaike information criterion [10], the prediction risk coefficient equation for advanced lung cancer complicating VTE was established. The comparison between the two prediction models was compared by Z-test, the ROC curves were plotted, the AUC values were compared, and the optimal Cutoff was determined by the Youden index, and the point corresponding to the maximum value of the Youden index (sensitivity + specificity - 1) was the Cutoff value. Model fitness was tested by the Hosmer–Lemeshow test (P > 0.05) which indicates a well-fitted model. Design flow Results Patient characteristics A total of 12,222 patients admitted for lung cancer from January 2010 to December 2020 were collected through the hospital's electronic medical record system, and according to the exclusion criteria, 3,320 lung cancer patients were finally included in this study. Among them, 2,266 (68.25%) were male and 1,054 (31.75%) were female; the median age was 67 years; and there were 257 (7.74%) with a BMI ≥ 28 kg/m 2 . See Table 1 . Table 1 The general data of the first admission of lung cancer in the early stage of the table Indicators Category Population composition ratio(%) Gender Male 2266 68.25 Female 1054 31.75 Age ≤ 65 1611 48.52 >65 1709 51.48 BMI(Kg/m2) <28 3063 92.26 ≥ 28 257 7.74 Smoking yes 94 2.83 no 3226 97.17 Surgery yes 2063 62.14 no 1257 37.86 Malignant tumor yes 2719 81.90 no 601 18.10 Chemotherapy yes 2976 89.64 no 344 10.36 Targeted therapy yes 900 27.11 no 2300 72.89 Radiotherapy yes 292 8.80 no 3028 91.20 Concurrent VTE yes 67 2.01 no 3253 97.99 Univariate Analysis of Complicated VTE in Patients with Advanced Lung Cancer The patients were divided into a group with complicated VTE and a group without VTE, and the results showed significant differences in alanine aminotransferase [Z = 1.981, P = 0.048], the D-dimer value [Z = 2.875, P < 0.001], PT [Z = 2.494, P = 0.013], CA-199 [Z = 2.503, P = 0.012], cytokeratin 19 fragment [Z = 2.628, P = 0.009], FDP [Z = 5.240, P < 0.001], squamous cell carcinoma-associated antigen [Z = 2.266, P = 0.023], albumin [Z = -2.128, P = 0.033], and targeted therapy for lung cancer [χ2 = 7.460, P = 0.010 ], and the differences between the two groups of indicators mentioned above were statistically significant (P < 0.05). See Table 2 . Table 2 Comparison of clinical data between advanced lung cancer patients with VTE and those without VTE Indicators Non-VTE (N = 3253) VTE (N = 67) t P value Age 66.00 (58.00,71.00) 68.00 (58.50,73.00) 1.402 0.161 BMI 23.11 (20.76,25.30) 23.34 (20.86,24.88) -0.067 0.947 Gender 0.210 0.744 Male 2222 (68.31) 44 (65.67) Female 1031 (31.69) 23 (34.33) Atrial fibrillation 9 (0.28) 0 (0.00) 0.186 1.000 Varicose veins in the lower extremities 5 (0.15) 1 (1.49) 6.523 0.115 Myelosuppressive state 109 (3.35) 5 (7.46) 3.348 0.078 Acute exacerbation of COPD 2 (0.06) 0 (0.00) 0.041 1.000 Respiratory failure 5 (0.15) 0 (0.00) 0.103 1.000 Smoking 93 (2.86) 1 (1.49) 0.446 1.000 surgery 2019 (62.07) 44 (65.67) 0.363 0.635 Blood transfusion 26 (0.80) 1 (1.49) 0.532 0.425 Hypertension 991 (30.46) 20 (29.85) 0.012 1.000 Diabetes 383 (11.77) 12 (17.91) 2.359 0.179 Hyperlipemia 73 (2.24) 1 (1.49) 0.170 1.000 Stroke 71 (2.18) 2 (2.99) 0.197 0.659 Malignancy 2662 (81.83) 57 (85.07) 0.466 0.602 Adenocarcinoma of lung 1527 (46.94) 30 (44.78) 0.124 0.820 Chemotherapy for LC 2921 (89.79) 55 (82.09) 4.196 0.065 Targeted therapy for LC 872 (26.81) 28 (41.79) 7.460 0.010 Radiotherapy for LC 287 (8.82) 5 (7.46) 0.151 0.864 Albumin protein 39.00 (37.00,42.00) 39.00 (35.00,41.00) -2.128 0.033 Alanine aminotransferase 20.00 (14.00,30.00) 24.00 (17.50,33.00) 1.981 0.048 White blood cell count 6.14 (4.87,7.79) 6.22 (4.69,8.44) 0.321 0.748 Red blood cell count 4.14 (3.74,4.50) 4.14 (3.43,4.45) 0.043 0.214 D- dimer 0.51 (0.35,0.98) 0.78 (0.44,2.86) 2.875 < 0.001 PT 13.20 (12.80,13.80) 13.50 (12.80,14.20) 2.494 0.013 APTT 37.50 (34.90,40.40) 36.55 (34.28,40.68) -0.490 0.624 Aspartate aminotransferase 20.00 (16.00,26.00) 21.00 (16.50,28.00) 0.968 0.333 Gamma-glutamyl transpeptidase 31.00 (21.00,52.00) 35.00 (21.50,82.00) 1.519 0.129 Lipoprotein 27.00 (12.00,77.00) 23.00 (13.00,50.75) -0.613 0.540 FIB 3.89 (3.19,5.00) 4.14 (3.09,4.90) -0.088 0.930 Platelet distribution width 13.20 (11.00,15.90) 13.10 (11.00,15.85) -0.411 0.681 CEA 4.56 (2.35,14.90) 6.51 (3.42,16.99) 1.638 0.101 CA-199 10.27 (4.97,26.67) 17.61 (6.82,66.77) 2.503 0.012 Cytokeratin 19 fragment 3.15 (1.92,6.30) 4.20 (2.28,8.37) 2.628 0.009 cholesterol 4.65 (4.05,5.36) 4.48 (3.90,5.40) -0.505 0.613 TG 1.25 (0.92,1.79) 1.23 (0.92,1.85) 0.192 0.848 Haemoglobin 130.00 (119.00,140.00) 132.00 (121.00,142.00) 1.035 0.301 FDP 2.70 (1.60,4.70) 7.30 (2.20,19.69) 5.240 < 0.001 Alpha-fetoprotein 2.82 (2.03,3.76) 2.76 (1.81,3.44) -0.869 0.385 CA-125 21.50 (12.10,53.60) 30.80 (11.73,117.92) 1.490 0.136 TT 16.30 (15.50,17.30) 16.40 (15.70,17.40) 0.748 0.454 Squamous cell carcinoma associated antigen 0.90 (0.60,1.40) 1.20 (0.60,2.60) 2.266 0.023 Sodium ion 141.00 (139.00,143.00) 141.00 (140.00,142.50) 0.695 0.487 Creatinine 68.00 (59.00,78.00) 70.00 (57.75,81.25) 0.937 0.349 Multifactorial analysis of VTE complication in patients with advanced lung cancer After logistic multifactorial regression analysis, the results suggested that the differences in variables including targeted therapy, varicose veins of the lower limbs, FDP, and alanine aminotransferase were all statistically significant (P < 0.05), and they were independent risk factors for the complication of VTE in patients with advanced lung cancer. See Table 3 . Table 3 Comparison of clinical data between the advanced lung cancer complicated VTE group and the group without VTE Variable Estimate ProbChiSq OR 95%CI Constant -4.927 < 0.001 Targeted therapy for LC 0.650 0.011 1.91 (1.15–3.15) Varicose veins in the lower extremities 2.770 0.015 15.96 (1.79–31.31) FDP 1.434 < 0.001 4.20 (2.56–6.94) Alanine aminotransferase 0.008 < 0.001 1.01 (1.00-1.02) Construction of risk prediction model equations and column-line diagrams for concurrent VTE in patients with advanced lung cancer. Considering the interference caused by the self-effects of variables and the confounding effects of other variables, we included all factors in establishing a risk prediction model (including variables with P < 0.05 in univariate analysis). The equation for the prediction risk coefficient of VTE complication in advanced lung cancer was established as In (P/1-P) = -4.911 + 2.779 * varicose veins of the lower extremities + 0.686 * targeted therapy for lung cancer + 0.109 * D-dimer + 0.385 * CA-199 + 1.175 * FDP. See Table 4 . Table 4 Results of multifactorial analysis affecting the complication of VTE in lung cancer patients Variable Estimate ProbChiSq OR 95%CI Constant -4.911 < 0.001 Varicose veins in the lower extremities 2.779 0.015 16.10 (1.79–31.97) Targeted therapy for lung cancer 0.686 0.008 1.99 (1.19–3.28) D- dimer 0.109 < 0.001 1.12 (1.05–1.18) CA-199 0.385 0.143 1.47 (0.87–2.44) FDP 1.175 < 0.001 3.24 (1.92–5.49) Table 4 . Results of multifactorial analysis affecting the complication of VTE in lung cancer patients Description of using the line chart: the scores of each index were obtained according to the patient's clinical data, the scores of each index were added, and the corresponding value on the total score line was the probability of advanced lung cancer complicated with VTE. Because the actual incidence of advanced lung cancer complicated with VTE is low, the maximum incidence of VTE is set at 0.5 in the statistical prediction model. According to the Youden index, the best cut-off value is 0.336. When the P value is ≥ 0.336, the patient has a high risk of advanced lung cancer complicated with VTE and needs timely intervention (See Fig. 3 ). The predictive performance of the above early warning system was analyzed by ROC with the same validation set. As shown in Fig. 4 , the AUC of this early warning system was statistically significant at AUC = 0.756 (95% CI: 0.726–0.785), indicating some clinical predictive effect. To verify the performance of this early warning system, the Hosmer-Lemeshow test showed P = 0.998 > 0.05, indicating that the model was well calibrated (Table 5 ). The early warning system and Caprini risk assessment scale were compared to the validation set data, and the results showed that the early warning system (AUC = 0.756) was non-inferior to the Caprini risk assessment scale (AUC = 0.654) in predicting the occurrence of VTE complicated by advanced lung cancer. Table 5 Statistical test results of Model A and Model B Sensitivity Specificity Positive predictive value Negative predictive value AUC 95%CI cutoff Z value P Hosmer-Lemeshow's chi-square value Hosmer-Lemeshow's p-value ModelA 0.688 0.693 0.042 0.991 0.756 (0.726–0.785) 0.336 2.534 0.011 1.030 0.998 ModelB 0.672 0.637 0.036 0.990 0.654 (0.638–0.670) Discussion There are fewer reports on the application of a CDSS system to the early warning system for advanced lung cancer complicating VTE [ 11 ]. A CDSS system is an artificial intelligence system suitable for clinical use that consists of a computerized automated program combining data screening, a knowledge algorithm, and logical reasoning for a number of clinical problems with structural and procedural aspects [ 11 ]. With the help of CDSS, an intelligent system can be developed to implement ready monitoring of the occurrence of new infectious diseases and propose timely and appropriate preventive measures to face the sudden outbreak of new coronary pneumonia [ 12 ]. Clinical staff's mastery of VTE-related expertise is low, and some clinical workers believe that the current clinical use of the Caprini scale will increase the time of clinical work, considerations are more complicated, and multiple tertiary hospitals are unable to dynamically track and assess changes in the patient's condition, and the prevention and control of VTE in hospitals that use the Caprini scale is not ideal [ 13 ]. The research team collected real-world clinical data with the help of an in-hospital information system, combined with the CDSS system for standardized construction, quality control, and processing of data to form a real-world prediction system model. At the same time, the background of the system can be networked online to obtain the clinical data on existing patients, dynamically observe the VTE risk indicators of patients, and obtain the assessment results of patients' VTE. In addition, on the basis of clinical application, it is proposed that further intelligent applets be made according to the risk coefficient of VTE complication in advanced lung cancer, which will help advanced lung cancer patients with self-testing, self-observation, and self-protection and remind patients to consult a doctor in time if their condition evolves. Therefore, it is necessary to screen for risk factors of VTE according to the actual situation of each hospital or department to obtain predictive factors with high specificity and sensitivity and make full use of the advantages of the CDSS system to develop an early warning system for VTE complication in advanced lung cancer and put it into clinical use. There are studies indicating that CA-199 is associated with VTE in advanced lung cancer [ 14 ]. Based on professional knowledge and previous literature results, we conducted a comprehensive analysis of CA-199. In univariate analysis, it may not have statistical significance due to mutual interference between independent variables. Therefore, we still need to include it in multivariate analysis and establish a risk prediction model to obtain statistical significance. In this study, we can conclude that the risk factors of advanced lung cancer complicating VTE include varicose veins of the lower extremities, targeted therapy for lung cancer, D-dimer, Ca-199, and FDP. Varicose veins of the lower extremities are a common disease in middle-aged and senior people and are most common among those who have been engaged in long-term physical labor or standing workers [ 15 ]. Research has shown a correlation between lower limb varicose veins and the occurrence of VTE [ 16 ]. Perhaps due to insufficient sample size of collected clinical data or interference from related factors, univariate analysis has no statistical significance, while multivariate analysis results have statistical significance. In addition, varicose veins are morphologically altered, hemodynamics are affected, and blood turbulence tends to cause blood stasis thereby inducing VTE formation [ 17 ]. D-dimer is regarded as an important predictive factor; when the D-dimer test is negative, acute VTE can be largely excluded, and if the D-dimer test is positive, further imaging is recommended to confirm the occurrence of VTE [ 18 ]. This study is the same as the results of other studies [ 19 ], which concluded that D-dimer is an independent risk factor for the complication of VTE in patients with lung cancer, which is significant in unifactorial analysis but not in multifactorial analysis, and it is possibly due to interference between multifactorial factors. Considering the five risk factors mentioned above, targeted prediction and evaluation is conducive to improving the accuracy of treatment, and the addition of the CDSS system improves the efficiency of diagnosis and treatment. Due to the limitations of manpower, material resources, and technology, this study could not carry out a multicenter survey, and the number of sampling cases was relatively small, which could not avoid causing selective bias and affecting the final results to a certain extent. The next step is to evaluate the actual situation of improving the treatment effect of patients by modifying the model, as well as the degree of acceptance by hospital staff or patients. Conclusions Combining the three major aspects of VTE risk factors of evidence-based medicine, statistical analysis of real data, and CDSS data processing platform, a CDSS-based early warning system for the risk of concurrent VTE in advanced lung cancer was initially established. Compared with the Caprinin Risk Assessment Scale, the results show that this early warning system presents non-inferiority in the risk prediction performance of VTE complications in advanced lung cancer, which can effectively assess the risk magnitude of VTE complications in patients with lung cancer and realize early diagnosis and early treatment. Declarations Ethical approval statement The study followed the Declaration of Helsinki (2000 version) and was approved by the Ethics Committee of Shanghai Changhai Hospital (Approval No. CHEC2021-182). Funding This research was funded by a grant from Shanghai "Rising Stars of Medical Talent" Youth Development Program（Youth Medical Talents – General Practitioner Program）； National Natural Science Foundation of China (82170033)；Natural Science Foundation of Shanghai (21ZR1479200)；2022 Shanghai Health Management Research Fund project (2022KJCX007); "Community Medicine and Health management research Project Research" special fund of Shanghai (2023SQ01);The Medical Research Project Plan of Shanghai Hongkou District Municipal Health Committee (2302-43); The Medical Research Project Plan of Shanghai Hongkou District Municipal Health Committee (2403-18). Authors’ contributions JF , BG and JYZ have given substantial contributions to the conception or the design of the manuscript. XFG,BQL,KH and CHZ to acquisition, analysis and interpretation of the data. All authors have participated to drafting the manuscript, YPH and YS revised it critically. All authors read and approved the final version of the manuscript. Acknowledge JF , BG and JYZ Contributed equally to this article. Conflicts of interest There is no conflict of interest in this article. Data availability statement The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/Caac.21660. Almodaimegh H, Alfehaid L, Alsuhebany N,et al. Awareness of venous thromboembolism and thromboprophylaxis among hospitalized patients:a cross-sectional study J . Thromb J,2017,19(15):19. Silverstein MD,Heit JA,Mohr DN,et al.Trends in the incidence of deep vein thrombosis and pulmonary embolism: a 25-year population-based study J .Arch Intern Med,1998,158(6):585-593. Wang P, Zhao H, Zhao Q, Ren F, Shi R, Liu X, Liu J, Liu H, Chen G, Chen J. Risk Factors and Clinical Significance of D-Dimer in the Development of Postoperative Venous Thrombosis in Patients with Lung Tumor. Cancer Manag Res. 2020 Jun 30;12:5169-5179. doi: 10.2147/CMAR.S256484. PMID: 32636679; PMCID: PMC7335272. Wang Chen, Liu Changqing, An Jingjing, et al. Research progress of risk assessment tools for venous thromboembolism J . Nursing Research.,2020,34(23):4211-4217. doi:10.12102/j.issn.1009-6493.2020.23.020. Kahn SR, Lim W, Dunn AS,et al.Prevention of VTE in nonsurgical patients: anti-thrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines J .Chest,2012,141:e195S-e226S.DOI: 10.1378/chest.11-2296. Tan Y, Pan X T. Research progress of venous thromboembolism associated with malignant hematologic tumors J . Clinical medical research and practice, 2020, 5 (28) : 195-198. The DOI: 10.19347 / j.carol carroll nki. 2096-1413.202028073. Reed T.Sutton.etc.An overview of clinical decision support systems: benefits, risks,and strategies for success.Digital Medicine (2020)3-17. Caprini JA. Thrombosis risk assessment as a guide to quality patient care. Dis Mon 2005;51:70-8. DOI:10.1016/j.disamonth.2005.02.003 Akaike Hirotugu. Proceedings of the Second International Symposium on Information Theory. 1973. Information theory and an extension of the maximum likelihood principle; pp. 267–281 Li Li, Wang Peng, Left Front, Wang Hongqian, Wang Fei. Design and application of hospital clinical Decision support system J . Journal of Medical Informatics,2019,40(2):22-24. (in Chinese) He Haifeng, Zhang Xu, Wang Lihua. Monitoring and early warning function design of Clinical Decision Support System for novel infectious diseases J . J Clinical and Experimental Medicine,20,19(10):1026-1028. (in Chinese) DOI:10.3969/j.issn.1671-4695.2020.010.006. Zhang Min, Wang Yong, Huang Jun, et al. Investigation and countermeasure analysis of prevention and treatment of venous thromboembolism in seven general hospitals in Beijing J . Chinese Journal of Hospital Administration, 2018, 34 (6): 482-486. DOI: 10.3760 / cma.j.issn.1000-6672.2018.06.011. Ye Wei, Chen Yuexin, Li Ming, et al The clinical significance of serum tumor marker screening in patients with unexplained venous thromboembolism [J] Chinese Medical Journal, 2014, 94 (15): 1143-1146 DOI:10.3760/cma.j.issn.0376-2491.2014.15.007 Mallick R，Raju A，Campbell C，et aI．Treatment patterns and outcomes in patients with varicose veins．Am Health Drug Benefits，2016，9(8)：455-465． Lobastov K, Dubar E, Schastlivtsev I, Bargandzhiya A. A systematic review and meta-analysis for the association between duration of anticoagulation therapy and the risk of venous thromboembolism in patients with lower limb superficial venous thrombosis. J Vasc Surg Venous Lymphat Disord. 2024;12(2):101726. doi:10.1016/j.jvsv.2023.101726 Kemp N．A synopsis of current international guidelines and new modalities for the treatment of varicose veins．Aust Fam Physician，2017，46(4)：229-233． Pulmonary Embolism and pulmonary Vascular Disease Group, Chinese Medical Association Respiratory Society, Pulmonary Embolism and Pulmonary Vascular Disease Working Committee, Chinese Medical Doctor Association Respiratory Physician Branch, National Collaborative Group on Pulmonary embolism and pulmonary Vascular disease Prevention. Guidelines for diagnosis, treatment and prevention of pulmonary thromboembolism. Chinese Journal of Medicine, 2018, 98(10): 1060-1087. Zhang Yaodong, Gao Pan, Deng Wei. Diagnostic value of D-dimer combined with coagulation factor in patients with early deep vein thrombosis J . Journal of Thrombosis and Hemostasis, 201,27(1):114-115. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5580344","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":391478889,"identity":"ff17627d-b47e-44da-b9f6-b91a4c06d9ac","order_by":0,"name":"Jian Fan","email":"","orcid":"","institution":"The First Hospital Affiliated of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Fan","suffix":""},{"id":391478891,"identity":"a22c1441-e1c8-45e8-a3f0-49487619e645","order_by":1,"name":"Bai Gao","email":"","orcid":"","institution":"The First Hospital Affiliated of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bai","middleName":"","lastName":"Gao","suffix":""},{"id":391478894,"identity":"52ba576c-a50a-4128-90d8-7419d9d0eb44","order_by":2,"name":"Jiayi Zhao","email":"","orcid":"","institution":"411 Hospital, Shanghai University","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Zhao","suffix":""},{"id":391478896,"identity":"47d5144c-f094-4d52-9d26-eec47bef15bb","order_by":3,"name":"Xuefeng Gao","email":"","orcid":"","institution":"The First Hospital Affiliated of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuefeng","middleName":"","lastName":"Gao","suffix":""},{"id":391478899,"identity":"5f5f869c-62bf-44ee-b9b0-2d917847965e","order_by":4,"name":"Baiqiu Liu","email":"","orcid":"","institution":"The First Hospital Affiliated of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Baiqiu","middleName":"","lastName":"Liu","suffix":""},{"id":391478901,"identity":"d705170a-21b9-4807-b335-906f54328dd5","order_by":5,"name":"Kai Huang","email":"","orcid":"","institution":"The First Hospital Affiliated of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Huang","suffix":""},{"id":391478903,"identity":"2be9da8b-8123-4956-8ee9-052e62ee85af","order_by":6,"name":"Yan Shang","email":"","orcid":"","institution":"The First Hospital Affiliated of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Shang","suffix":""},{"id":391478904,"identity":"0f13b018-a9d9-4d3c-8f4c-ad7605d3fab6","order_by":7,"name":"Yiping Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYFACHiCukODhZ29sfPiBeC1nbOQkew43G0sQrYWxLc3YYEZ6mwAPMRoMbvce/FzAdjhxg+TDNgYJBjs53QZCWu6cS5aewXM4cbt0YtuDAoZkY7MDhLTcyDGQ5pE4nLhzdmK7gQTDgcRtRGgx/s1jAHTYzYNtEjxEajGT5kkAev8GI5FaJO+cMbPmOQAK5ERgIBsQ4Re+2z3Gt3n/gaLy+MOHHyrs5AhqUbiB6k4CykFAfgYRikbBKBgFo2CEAwDoV0V4vzG12AAAAABJRU5ErkJggg==","orcid":"","institution":"The First Hospital Affiliated of Naval Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yiping","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-12-04 13:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5580344/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5580344/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72283665,"identity":"b6898392-001c-4a1e-b47b-d893c8495090","added_by":"auto","created_at":"2024-12-24 16:43:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":287660,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of CDSS system for processing real clinical data\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5580344/v1/5fcd30d54beeb197dbb9a4bb.png"},{"id":72283662,"identity":"f2437d21-e268-497f-aaf9-10fb517149ad","added_by":"auto","created_at":"2024-12-24 16:43:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25498,"visible":true,"origin":"","legend":"\u003cp\u003eResearch technology route\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5580344/v1/5f72a79f322c2e34a05dfba9.png"},{"id":72283669,"identity":"9b941cf8-e1f7-428d-93cc-1fa549e35a4d","added_by":"auto","created_at":"2024-12-24 16:43:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29610,"visible":true,"origin":"","legend":"\u003cp\u003eThe line chart. The score of each index was obtained according to the clinical data of the patient.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5580344/v1/d7871007732702291fd9212b.png"},{"id":72285031,"identity":"5d3933a1-4561-413f-94ca-50553f051205","added_by":"auto","created_at":"2024-12-24 16:51:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22551,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of ROC curves between CDSS-based VTE risk warning system for advanced lung cancer and Caprini scale\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5580344/v1/0855e0d5ee657c53ac016430.png"},{"id":79889488,"identity":"dec0b509-dc5c-404f-9f2f-fd37db4f6cd7","added_by":"auto","created_at":"2025-04-04 07:02:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1096719,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5580344/v1/db40cc9e-6af6-4af6-9084-9b07b62620ca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Evaluation of a CDSS-Enabled Early Warning System for Venous Thromboembolism Risk in Advanced Lung Cancer Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is the most common malignant tumor in the world, with an incidence rate of 11.4% and a mortality rate of 18% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Advanced lung cancer generally refers to patients with lung cancer at stage III and stage IV, and they often have a poor prognosis. VTE is one of the common complications of lung cancer, and its incidence is about 2%. It mainly includes two types of Pulmonary Embolism (PE) and Deep Vein Thrombosis (DVT) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In patients with cancer and VTE, the risk of death is three times as high as in those without VTE [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Currently, the commonly used clinical methods to predict the risk of lung cancer complicating VTE include the Caprini Risk Assessment Scale, Rogers Assessment Scale, Kucher Risk e-Assessment Scale, and Padua Prediction Scale; however, these procedures are cumbersome and time-consuming [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Guidelines recommend that all hospitalized patients be assessed for VTE risk and that prophylactic measures be taken for high-risk patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies have shown that lung cancer is prone to complicate VTE, and the mechanism of its occurrence has three main aspects, including the release of procoagulant factors by tumor cells, the procoagulant properties of normal cells induced by tumor cells, and chemotherapy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. With the continuous development of medical informatization means and technology, constructing an intelligent VTE risk prediction system based on real clinical data is increasingly favored.\u003c/p\u003e \u003cp\u003eA Clinical Decision Support System (CDSS) is an information-based intelligent application system that improves the quality of healthcare and healthcare services by applying systematic clinical knowledge to enhance the decision-making ability of healthcare-related actions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. CDSSs were being developed internationally as early as 1970 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] to simulate the process of disease diagnosis and treatment by senior experts through clinical data collection, cleaning, in-depth analysis, and the establishment of other computer language arithmetic rules for patients beyond the risk value of timely expression of relevant information, such as warning information, condition information, medical advice packages, and information management, as well as the form of relevant data, high-risk patients to whom the first assessment should be provided, early warning, and decision-making support for diagnosis and treatment, which can be used by clinicians to provide treatment advice, diagnosis, and treatment. It can provide decision support for clinical doctors in diagnosis and treatment in terms of treatment suggestions, high-risk reminders, and risk prediction.\u003c/p\u003e \u003cp\u003eAt present, CDSS is an inevitable trend of combining medicine and artificial intelligence, and its advantages include the assessment of one's own VTE risk at anytime and anywhere on the Internet. CDSS systems have long been introduced at home and abroad for information-based intelligent assistance management in several industries, but there are fewer studies based on CDSS to predict concurrent VTE in advanced lung cancer patients, including small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This study is a cross-sectional study aimed at researching a CDSS-based early warning system for the risk of concurrent VTE in advanced lung cancer based on the real situation in China and providing timely diagnostic and therapeutic measures, which is conducive to the early detection, diagnosis, and treatment of concurrent VTE in advanced lung cancer with the full help of the CDSS system, whether on a hospital ward or living in the community.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy setting and population\u003c/p\u003e\n\u003cp\u003ePubMed was searched for English-language articles from January 2010 through December 2020, using search terms for \"Lung cancer\"[Mesh] and \"venous thromboembolism\" [Mesh]. Article inclusion criteria were based on relevance.\u003c/p\u003e\n\u003cp\u003eClinical data collection\u003c/p\u003e\n\u003cp\u003e(I) Clinical data sources\u003c/p\u003e\n\u003cp\u003eClinical data sources included Hospital Information System(HIS), Electronic Medical Record(EMR), Laboratory Information System(LIS), Picture Archiving and Communication Systems(PACS), Radiology Information System(RIS), and other systems. We collected lung cancer (Both SCLC and NSCLC) patients with clinical stages III and IV in the respiratory department of a tertiary hospital in Shanghai from January 2010 to December 2020. The inclusion criteria and exclusion criteria were strictly enforced to collect medical record data.\u003c/p\u003e\n\u003cp\u003e(II) Inclusion criteria\u003c/p\u003e\n\u003cp\u003e1. All advanced lung cancer patients were diagnosed by pathological examination;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. All complete and searchable clinical medical record data of lung cancer patients;\u003c/p\u003e\n\u003cp\u003e3. Age ≥18 years.\u003c/p\u003e\n\u003cp\u003e(III) Exclusion criteria\u003c/p\u003e\n\u003cp\u003e1. Those who were hospitalized once and did not receive any treatment during hospitalization;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. Those with serious missing laboratory-related data and basic medical record information;\u003c/p\u003e\n\u003cp\u003e3. Those with serious organ dysfunction;\u003c/p\u003e\n\u003cp\u003e4. Patients with tumor metastasis to the lungs from other sites;\u003c/p\u003e\n\u003cp\u003eThe study followed the Declaration of Helsinki (2000 version) and was approved by the Ethics Committee of Shanghai Changhai Hospital (Approval No. CHEC2021-182). We have applied to the Ethics Committee of Shanghai Changhai Hospital and obtained approval for exemption from informed consent.\u003c/p\u003e\n\u003cp\u003eDevelopment of the CDSS to Predict the VTE discovery risk\u003c/p\u003e\n\u003cp\u003eData Acquisition\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis layer is the data source layer, which collects data from the hospital's information system, including HIS, EMR, LIS, PACS, RIS, and other diagnosis- and treatment-related systems. Clinical VTE diagnosis criteria include for DVT, clinical symptoms/signs, compression ultrasonography, and D-dimer testing, and for PE, clinical manifestations, imaging studies like CTPA and V/Q scanning, and blood gas analysis. Other external data and knowledge sources include relevant literature and knowledge bases.\u003c/p\u003e\n\u003cp\u003eData Processing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis layer is the data processing layer, which firstly unifies the data standards and establishes a standardized terminology dictionary, including a basic dictionary library of disease diagnosis, examination, and testing, and basic patient information, which is used to classify the data for the subsequent processing, quality control, and governance processes. Second, data processing is carried out on the collected data, and structured as well as non-standardized unstructured data within the hospital are standardized through NLP, data standardization, and other technologies. At the same time, through the connotative evaluation system, the standardized data are regulated for completeness, accuracy, and standardization. Finally, per the results of data quality control, residual data, poor consistency data, etc. are processed and transformed into high-quality usable scientific research data.\u003c/p\u003e\n\u003cp\u003eData Model \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis layer establishes a variety of data models based on the conclusions of the preliminary data, including disease-related basic models, comprehensive models integrating relevant knowledge graphs, patient profiles, and other comprehensive models, as well as models combining knowledge bases, literature, and relevant models based on the conclusions of real-world clinical research, etc., to support the establishment of the system.\u003c/p\u003e\n\u003cp\u003eData Management \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis layer adds the management of data security to the results of the previous data processing step, which mainly includes desensitization management to desensitize patient privacy and non-diagnosis and treatment-related data. At the same time, system access rights are restricted to ensure data security. For completely random missing data, if the missing proportion is small, deletion methods (such as list deletion or paired deletion) can be used for processing. If the missing proportion is large, multiple imputation can be considered for processing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEstablishment of a Risk Influencing Factor Knowledge Base \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the establishment of the above four layers, for the high-risk group of hospitalized patients, combined with the relevant standards of VTE prevention and treatment guidelines, patient risk assessment, combined with the assessment nodes configured in the main links of our hospital's electronic medical records and medical prescriptions, a knowledge base of VTE risk-influencing factors is established, and risk early-warning rules are set.\u003c/p\u003e\n\u003cp\u003eEstablishment of a CDSS-based VTE Early Warning System\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the above knowledge base of VTE risk-influencing factors, the early warning system provides knowledge support for VTE risk-influencing factors, combines with patients' diagnosis and treatment data, integrates clinical application systems, such as electronic medical records, medical advice, examination, testing, and nursing care, etc., and carries out whole-process, whole-time, whole-domain integrated dynamic monitoring of the patient's whole process of medical consultation to promote medical and nursing cooperation and provide clinical medical care with decision-making support for clinical medical care. See Figure 1.\u003c/p\u003e\n\u003cp\u003eThe CDSS uses integrated platform interfaces and front-end interfaces to connect electronic medical record data, and through deep integration with EMR, HIS, LIS, RIS, PACS, and other information systems. All data on admitted patients are processed, and after the construction of data standardization and quality control, the CDSS data processing platform structurally dumps the data into different data models. In data standardization construction, natural language processing (NLP) technology was used for post-structured processing to segment the language randomly entered by doctors into different entity concepts and logical relationships, and terminology coding was performed to form a common computer language that can be used for real-world clinical knowledge base construction and clinical decision support. In data management, sensitive data desensitization management and account privileges are used to protect patient privacy. A VTE risk warning system was developed and designed to improve the quality of VTE prevention and control through artificial intelligence data processing and real-time human-computer interaction, providing a foundation for further clinical decision support and intelligent management applications.\u003c/p\u003e\n\u003cp\u003eAssessments\u003c/p\u003e\n\u003cp\u003eThree basic factor modules based on evidence-based medicine were formed through the literature database: the module of influencing factors of baseline population characteristics, the module of high-risk factors of advanced lung cancer complicating VTE, and the module of VTE diagnosis and treatment. Real-world data were collected and processed through washing, quality control, and analyzing, to construct a library of clinical best practices for real-world advanced lung cancer patients. The real clinical data were divided into experimental groups (70%) and validation groups (30%), and the data of the experimental group were analyzed by logistic regression to construct the prediction risk coefficient equation of advanced lung cancer complicating VTE. Combined with the CDSS medical big data processing system, a CDSS-based risk warning system for advanced lung cancer complicating VTE was constructed. The Caprini risk assessment scale has been validated and widely used in hospitalized patients [9]. The validation data for the external validation were used to compare the prediction performance of the VTE risk warning system with the Caprini risk assessment scale to assess the accuracy of predicting the occurrence of VTE and ultimately to form a CDSS-based VTE risk warning system for advanced lung cancer that can be put into clinical use. See Fig 2.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eR language software was used to process the data statistically. Qualitative data (e.g., gender) were described by χ\u003csup\u003e2\u003c/sup\u003e test or Fisher's exact probability method; quantitative data such as those conforming to a normal distribution were described by mean and standard deviation (`X ±S), and those not conforming to a normal distribution were described by median and interquartile range, and independent samples t-test and Mann-Whitney U-test were used for the comparison of the data between groups, respectively. Binary logistic regression analysis was performed for each factor, and the test results were considered statistically significant at P \u0026lt; 0.05. According to the stepwise regression method and Akaike information criterion [10], the prediction risk coefficient equation for advanced lung cancer complicating VTE was established. The comparison between the two prediction models was compared by Z-test, the ROC curves were plotted, the AUC values were compared, and the optimal Cutoff was determined by the Youden index, and the point corresponding to the maximum value of the Youden index (sensitivity + specificity - 1) was the Cutoff value. Model fitness was tested by the Hosmer–Lemeshow test (P \u0026gt; 0.05) which indicates a well-fitted model.\u003c/p\u003e\n\u003cp\u003eDesign flow\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePatient characteristics\u003c/p\u003e \u003cp\u003eA total of 12,222 patients admitted for lung cancer from January 2010 to December 2020 were collected through the hospital's electronic medical record system, and according to the exclusion criteria, 3,320 lung cancer patients were finally included in this study. Among them, 2,266 (68.25%) were male and 1,054 (31.75%) were female; the median age was 67 years; and there were 257 (7.74%) with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e. See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eThe general data of the first admission of lung cancer in the early stage of the table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ecomposition ratio(%)\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 \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e2266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026gt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(Kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcurrent VTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.99\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\u003eUnivariate Analysis of Complicated VTE in Patients with Advanced Lung Cancer\u003c/p\u003e \u003cp\u003eThe patients were divided into a group with complicated VTE and a group without VTE, and the results showed significant differences in alanine aminotransferase [Z\u0026thinsp;=\u0026thinsp;1.981, P\u0026thinsp;=\u0026thinsp;0.048], the D-dimer value [Z\u0026thinsp;=\u0026thinsp;2.875, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001], PT [Z\u0026thinsp;=\u0026thinsp;2.494, P\u0026thinsp;=\u0026thinsp;0.013], CA-199 [Z\u0026thinsp;=\u0026thinsp;2.503, P\u0026thinsp;=\u0026thinsp;0.012], cytokeratin 19 fragment [Z\u0026thinsp;=\u0026thinsp;2.628, P\u0026thinsp;=\u0026thinsp;0.009], FDP [Z\u0026thinsp;=\u0026thinsp;5.240, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001], squamous cell carcinoma-associated antigen [Z\u0026thinsp;=\u0026thinsp;2.266, P\u0026thinsp;=\u0026thinsp;0.023], albumin [Z = -2.128, P\u0026thinsp;=\u0026thinsp;0.033], and targeted therapy for lung cancer [χ2\u0026thinsp;=\u0026thinsp;7.460, P\u0026thinsp;=\u0026thinsp;0.010 ], and the differences between the two groups of indicators mentioned above were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eComparison of clinical data between advanced lung cancer patients with VTE and those without VTE\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\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-VTE (N\u0026thinsp;=\u0026thinsp;3253)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVTE (N\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.00 (58.00,71.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.00 (58.50,73.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.11 (20.76,25.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.34 (20.86,24.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\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\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2222 (68.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (65.67)\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1031 (31.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (34.33)\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\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaricose veins in the\u003c/p\u003e \u003cp\u003elower extremities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyelosuppressive state\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (7.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute exacerbation of COPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019 (62.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (65.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e991 (30.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (29.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e383 (11.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (17.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2662 (81.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (85.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma of lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1527 (46.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (44.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy for LC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2921 (89.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (82.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted therapy for LC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e872 (26.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (41.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.460\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\u003eRadiotherapy for LC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e287 (8.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (7.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.00 (37.00,42.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.00 (35.00,41.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.128\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\u003eAlanine aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00 (14.00,30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.00 (17.50,33.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.14 (4.87,7.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.22 (4.69,8.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed blood cell count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.14 (3.74,4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.14 (3.43,4.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD- dimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.35,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78 (0.44,2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.20 (12.80,13.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.50 (12.80,14.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.50 (34.90,40.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.55 (34.28,40.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00 (16.00,26.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.00 (16.50,28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGamma-glutamyl transpeptidase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.00 (21.00,52.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.00 (21.50,82.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipoprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.00 (12.00,77.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.00 (13.00,50.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.89 (3.19,5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.14 (3.09,4.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet distribution width\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.20 (11.00,15.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.10 (11.00,15.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.56 (2.35,14.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.51 (3.42,16.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA-199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.27 (4.97,26.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.61 (6.82,66.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytokeratin 19 fragment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.15 (1.92,6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.20 (2.28,8.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echolesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.65 (4.05,5.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.48 (3.90,5.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25 (0.92,1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 (0.92,1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130.00 (119.00,140.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132.00 (121.00,142.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.70 (1.60,4.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.30 (2.20,19.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha-fetoprotein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.82 (2.03,3.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.76 (1.81,3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA-125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.50 (12.10,53.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.80 (11.73,117.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.30 (15.50,17.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.40 (15.70,17.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous cell carcinoma associated antigen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.60,1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (0.60,2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium ion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141.00 (139.00,143.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.00 (140.00,142.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.00 (59.00,78.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.00 (57.75,81.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.349\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\u003eMultifactorial analysis of VTE complication in patients with advanced lung cancer\u003c/p\u003e \u003cp\u003eAfter logistic multifactorial regression analysis, the results suggested that the differences in variables including targeted therapy, varicose veins of the lower limbs, FDP, and alanine aminotransferase were all statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and they were independent risk factors for the complication of VTE in patients with advanced lung cancer. See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical data between the advanced lung cancer complicated VTE group and the group without VTE\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProbChiSq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eTargeted therapy for LC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.15\u0026ndash;3.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaricose veins in the lower extremities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.79\u0026ndash;31.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.56\u0026ndash;6.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.00-1.02)\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\u003eConstruction of risk prediction model equations and column-line diagrams for concurrent VTE in patients with advanced lung cancer.\u003c/p\u003e \u003cp\u003eConsidering the interference caused by the self-effects of variables and the confounding effects of other variables, we included all factors in establishing a risk prediction model (including variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis). The equation for the prediction risk coefficient of VTE complication in advanced lung cancer was established as In (P/1-P) = -4.911\u0026thinsp;+\u0026thinsp;2.779 * varicose veins of the lower extremities\u0026thinsp;+\u0026thinsp;0.686 * targeted therapy for lung cancer\u0026thinsp;+\u0026thinsp;0.109 * D-dimer\u0026thinsp;+\u0026thinsp;0.385 * CA-199\u0026thinsp;+\u0026thinsp;1.175 * FDP. See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of multifactorial analysis affecting the complication of VTE in lung cancer patients\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProbChiSq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eVaricose veins in the lower extremities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.79\u0026ndash;31.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted therapy for lung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.19\u0026ndash;3.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD- dimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.05\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA-199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.87\u0026ndash;2.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.92\u0026ndash;5.49)\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Results of multifactorial analysis affecting the complication of VTE in lung cancer patients\u003c/p\u003e \u003cp\u003eDescription of using the line chart: the scores of each index were obtained according to the patient's clinical data, the scores of each index were added, and the corresponding value on the total score line was the probability of advanced lung cancer complicated with VTE. Because the actual incidence of advanced lung cancer complicated with VTE is low, the maximum incidence of VTE is set at 0.5 in the statistical prediction model. According to the Youden index, the best cut-off value is 0.336. When the P value is \u0026ge;\u0026thinsp;0.336, the patient has a high risk of advanced lung cancer complicated with VTE and needs timely intervention (See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe predictive performance of the above early warning system was analyzed by ROC with the same validation set. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the AUC of this early warning system was statistically significant at AUC\u0026thinsp;=\u0026thinsp;0.756 (95% CI: 0.726\u0026ndash;0.785), indicating some clinical predictive effect. To verify the performance of this early warning system, the Hosmer-Lemeshow test showed P\u0026thinsp;=\u0026thinsp;0.998\u0026thinsp;\u0026gt;\u0026thinsp;0.05, indicating that the model was well calibrated (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The early warning system and Caprini risk assessment scale were compared to the validation set data, and the results showed that the early warning system (AUC\u0026thinsp;=\u0026thinsp;0.756) was non-inferior to the Caprini risk assessment scale (AUC\u0026thinsp;=\u0026thinsp;0.654) in predicting the occurrence of VTE complicated by advanced lung cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical test results of Model A and Model B\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive predictive value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative predictive value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eZ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHosmer-Lemeshow's chi-square value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHosmer-Lemeshow's p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModelA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.726\u0026ndash;0.785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModelB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.638\u0026ndash;0.670)\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 \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere are fewer reports on the application of a CDSS system to the early warning system for advanced lung cancer complicating VTE [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A CDSS system is an artificial intelligence system suitable for clinical use that consists of a computerized automated program combining data screening, a knowledge algorithm, and logical reasoning for a number of clinical problems with structural and procedural aspects [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. With the help of CDSS, an intelligent system can be developed to implement ready monitoring of the occurrence of new infectious diseases and propose timely and appropriate preventive measures to face the sudden outbreak of new coronary pneumonia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClinical staff's mastery of VTE-related expertise is low, and some clinical workers believe that the current clinical use of the Caprini scale will increase the time of clinical work, considerations are more complicated, and multiple tertiary hospitals are unable to dynamically track and assess changes in the patient's condition, and the prevention and control of VTE in hospitals that use the Caprini scale is not ideal [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe research team collected real-world clinical data with the help of an in-hospital information system, combined with the CDSS system for standardized construction, quality control, and processing of data to form a real-world prediction system model. At the same time, the background of the system can be networked online to obtain the clinical data on existing patients, dynamically observe the VTE risk indicators of patients, and obtain the assessment results of patients' VTE. In addition, on the basis of clinical application, it is proposed that further intelligent applets be made according to the risk coefficient of VTE complication in advanced lung cancer, which will help advanced lung cancer patients with self-testing, self-observation, and self-protection and remind patients to consult a doctor in time if their condition evolves. Therefore, it is necessary to screen for risk factors of VTE according to the actual situation of each hospital or department to obtain predictive factors with high specificity and sensitivity and make full use of the advantages of the CDSS system to develop an early warning system for VTE complication in advanced lung cancer and put it into clinical use.\u003c/p\u003e \u003cp\u003eThere are studies indicating that CA-199 is associated with VTE in advanced lung cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Based on professional knowledge and previous literature results, we conducted a comprehensive analysis of CA-199. In univariate analysis, it may not have statistical significance due to mutual interference between independent variables. Therefore, we still need to include it in multivariate analysis and establish a risk prediction model to obtain statistical significance. In this study, we can conclude that the risk factors of advanced lung cancer complicating VTE include varicose veins of the lower extremities, targeted therapy for lung cancer, D-dimer, Ca-199, and FDP. Varicose veins of the lower extremities are a common disease in middle-aged and senior people and are most common among those who have been engaged in long-term physical labor or standing workers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Research has shown a correlation between lower limb varicose veins and the occurrence of VTE [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Perhaps due to insufficient sample size of collected clinical data or interference from related factors, univariate analysis has no statistical significance, while multivariate analysis results have statistical significance. In addition, varicose veins are morphologically altered, hemodynamics are affected, and blood turbulence tends to cause blood stasis thereby inducing VTE formation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eD-dimer is regarded as an important predictive factor; when the D-dimer test is negative, acute VTE can be largely excluded, and if the D-dimer test is positive, further imaging is recommended to confirm the occurrence of VTE [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This study is the same as the results of other studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which concluded that D-dimer is an independent risk factor for the complication of VTE in patients with lung cancer, which is significant in unifactorial analysis but not in multifactorial analysis, and it is possibly due to interference between multifactorial factors.\u003c/p\u003e \u003cp\u003eConsidering the five risk factors mentioned above, targeted prediction and evaluation is conducive to improving the accuracy of treatment, and the addition of the CDSS system improves the efficiency of diagnosis and treatment.\u003c/p\u003e \u003cp\u003eDue to the limitations of manpower, material resources, and technology, this study could not carry out a multicenter survey, and the number of sampling cases was relatively small, which could not avoid causing selective bias and affecting the final results to a certain extent. The next step is to evaluate the actual situation of improving the treatment effect of patients by modifying the model, as well as the degree of acceptance by hospital staff or patients.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCombining the three major aspects of VTE risk factors of evidence-based medicine, statistical analysis of real data, and CDSS data processing platform, a CDSS-based early warning system for the risk of concurrent VTE in advanced lung cancer was initially established. Compared with the Caprinin Risk Assessment Scale, the results show that this early warning system presents non-inferiority in the risk prediction performance of VTE complications in advanced lung cancer, which can effectively assess the risk magnitude of VTE complications in patients with lung cancer and realize early diagnosis and early treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study followed the Declaration of Helsinki (2000 version) and was approved by the Ethics Committee of Shanghai Changhai Hospital (Approval No. CHEC2021-182).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by a grant from Shanghai \u0026quot;Rising Stars of Medical Talent\u0026quot; Youth Development Program（Youth Medical Talents \u0026ndash; General Practitioner Program）；\u0026nbsp;National Natural Science Foundation of China (82170033)；Natural Science Foundation of Shanghai (21ZR1479200)；2022 Shanghai Health Management Research Fund project (2022KJCX007); \u0026quot;Community Medicine and Health management research Project Research\u0026quot; special fund of Shanghai \u0026nbsp;(2023SQ01);The Medical Research Project Plan of Shanghai Hongkou District Municipal Health Committee (2302-43); The Medical Research Project Plan of Shanghai Hongkou District Municipal Health Committee (2403-18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJF , BG and JYZ have given substantial contributions to the conception or the design of the manuscript. XFG,BQL,KH and CHZ to acquisition, analysis and interpretation of the data. All authors have participated to drafting the manuscript, YPH and YS revised it critically. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJF , BG and JYZ Contributed equally to this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict of interest in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/Caac.21660. \u003c/li\u003e\n\u003cli\u003eAlmodaimegh H, Alfehaid L, Alsuhebany N,et al. Awareness of venous thromboembolism and thromboprophylaxis among hospitalized patients:a cross-sectional study J . Thromb J,2017,19(15):19.\u003c/li\u003e\n\u003cli\u003eSilverstein MD,Heit JA,Mohr DN,et al.Trends in the incidence of deep vein thrombosis and pulmonary embolism: a 25-year population-based study J .Arch Intern Med,1998,158(6):585-593.\u003c/li\u003e\n\u003cli\u003eWang P, Zhao H, Zhao Q, Ren F, Shi R, Liu X, Liu J, Liu H, Chen G, Chen J. Risk Factors and Clinical Significance of D-Dimer in the Development of Postoperative Venous Thrombosis in Patients with Lung Tumor. Cancer Manag Res. 2020 Jun 30;12:5169-5179. doi: 10.2147/CMAR.S256484. PMID: 32636679; PMCID: PMC7335272.\u003c/li\u003e\n\u003cli\u003eWang Chen, Liu Changqing, An Jingjing, et al. Research progress of risk assessment tools for venous thromboembolism J . Nursing Research.,2020,34(23):4211-4217. doi:10.12102/j.issn.1009-6493.2020.23.020.\u003c/li\u003e\n\u003cli\u003eKahn SR, Lim W, Dunn AS,et al.Prevention of VTE in nonsurgical patients: anti-thrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines J .Chest,2012,141:e195S-e226S.DOI: 10.1378/chest.11-2296.\u003c/li\u003e\n\u003cli\u003eTan Y, Pan X T. Research progress of venous thromboembolism associated with malignant hematologic tumors J . Clinical medical research and practice, 2020, 5 (28) : 195-198. The DOI: 10.19347 / j.carol carroll nki. 2096-1413.202028073.\u003c/li\u003e\n\u003cli\u003eReed T.Sutton.etc.An overview of clinical decision support systems: benefits, risks,and strategies for success.Digital Medicine (2020)3-17.\u003c/li\u003e\n\u003cli\u003eCaprini JA. Thrombosis risk assessment as a guide to quality patient care. Dis Mon 2005;51:70-8. DOI:10.1016/j.disamonth.2005.02.003\u003c/li\u003e\n\u003cli\u003eAkaike Hirotugu. Proceedings of the Second International Symposium on Information Theory. 1973. Information theory and an extension of the maximum likelihood principle; pp. 267\u0026ndash;281\u003c/li\u003e\n\u003cli\u003eLi Li, Wang Peng, Left Front, Wang Hongqian, Wang Fei. Design and application of hospital clinical Decision support system J . Journal of Medical Informatics,2019,40(2):22-24. (in Chinese)\u003c/li\u003e\n\u003cli\u003eHe Haifeng, Zhang Xu, Wang Lihua. Monitoring and early warning function design of Clinical Decision Support System for novel infectious diseases J . J Clinical and Experimental Medicine,20,19(10):1026-1028. (in Chinese) DOI:10.3969/j.issn.1671-4695.2020.010.006.\u003c/li\u003e\n\u003cli\u003eZhang Min, Wang Yong, Huang Jun, et al. Investigation and countermeasure analysis of prevention and treatment of venous thromboembolism in seven general hospitals in Beijing J . Chinese Journal of Hospital Administration, 2018, 34 (6): 482-486. DOI: 10.3760 / cma.j.issn.1000-6672.2018.06.011.\u003c/li\u003e\n\u003cli\u003eYe Wei, Chen Yuexin, Li Ming, et al The clinical significance of serum tumor marker screening in patients with unexplained venous thromboembolism [J] Chinese Medical Journal, 2014, 94 (15): 1143-1146 DOI:10.3760/cma.j.issn.0376-2491.2014.15.007\u003c/li\u003e\n\u003cli\u003eMallick R，Raju A，Campbell C，et aI．Treatment patterns and outcomes in patients with varicose veins．Am Health Drug Benefits，2016，9(8)：455-465．\u003c/li\u003e\n\u003cli\u003eLobastov K, Dubar E, Schastlivtsev I, Bargandzhiya A. A systematic review and meta-analysis for the association between duration of anticoagulation therapy and the risk of venous thromboembolism in patients with lower limb superficial venous thrombosis. J Vasc Surg Venous Lymphat Disord. 2024;12(2):101726. doi:10.1016/j.jvsv.2023.101726\u003c/li\u003e\n\u003cli\u003eKemp N．A synopsis of current international guidelines and new modalities for the treatment of varicose veins．Aust Fam Physician，2017，46(4)：229-233．\u003c/li\u003e\n\u003cli\u003ePulmonary Embolism and pulmonary Vascular Disease Group, Chinese Medical Association Respiratory Society, Pulmonary Embolism and Pulmonary Vascular Disease Working Committee, Chinese Medical Doctor Association Respiratory Physician Branch, National Collaborative Group on Pulmonary embolism and pulmonary Vascular disease Prevention. Guidelines for diagnosis, treatment and prevention of pulmonary thromboembolism. Chinese Journal of Medicine, 2018, 98(10): 1060-1087.\u003c/li\u003e\n\u003cli\u003eZhang Yaodong, Gao Pan, Deng Wei. Diagnostic value of D-dimer combined with coagulation factor in patients with early deep vein thrombosis J . Journal of Thrombosis and Hemostasis, 201,27(1):114-115.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CDSS, lung cancer, VTE, prediction system, risk factors","lastPublishedDoi":"10.21203/rs.3.rs-5580344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5580344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBACKGROUNDː This study establishes the clinical practice library of Venous Thromboembolism (VTE) complications in advanced lung cancer, forms a VTE risk warning system for advanced lung cancer patients based on the Clinical Decision Support System (CDSS) and plan to use it in hospitals in the future, and plays a role in preventing VTE complications in advanced lung cancer patients.\u003c/p\u003e \u003cp\u003eMETHODSː We summarized the VTE risk factors by searching the literature and constructed a knowledge base of advanced lung cancer complication VTE risk factors based on evidence-based medicine; collected real-world clinical data on lung cancer patients and constructed a real-world best-practice library of advanced lung cancer complication VTE by cleaning, quality control and analyzing clinical data; and constructed an equation for the prediction risk coefficient of advanced lung cancer complication VTE by logistic regression analysis. Finally, the three were combined to construct a CDSS-based VTE risk warning system for advanced lung cancer patients. Statistical analysis was performed using R software, with a test level of α\u0026thinsp;=\u0026thinsp;0.05, and the difference was considered statistically significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e RESULTS: A total of 12,222 patients were screened through the hospital's electronic medical record system, and 3,320 patients were included after screening in strict accordance with the exclusion criteria. The patients were divided into 67 cases in the group of lung cancer complicating VTE and 3253 cases in the group of uncomplicated VTE. According to the stepwise regression method and the AIC law, the equation of the predicted risk coefficient of advanced lung cancer complicating VTE was constructed. Combining the three basic factor modules of advanced lung cancer complicating VTE and the CDSS system, a CDSS-based risk warning system for advanced lung cancer complicating VTE was established, and the externally validated results showed that the AUC of 0.756 (95% CI: 0.726\u0026ndash;0.785) was better than that of the Caprini risk scoring scale AUC of 0.654 (95% CI: 0.638\u0026ndash;0.670).\u003c/p\u003e \u003cp\u003eCONCLUSIONSː This study established a CDSS-based early warning system for the risk of concurrent VTE in advanced lung cancer. The results of comparative validation showed that this early warning system can improve the clinical diagnosis and treatment efficiency of advanced lung cancer patients with complications of VTE, and at the same time, it can achieve the purpose of real-time monitoring and timely diagnosis and treatment.\u003c/p\u003e","manuscriptTitle":"Development and Evaluation of a CDSS-Enabled Early Warning System for Venous Thromboembolism Risk in Advanced Lung Cancer Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 16:43:10","doi":"10.21203/rs.3.rs-5580344/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"041fcf11-bfc7-48c1-b508-43f615920cc1","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41726297,"name":"Biological sciences/Cancer/Lung cancer"},{"id":41726298,"name":"Health sciences/Cardiology"},{"id":41726299,"name":"Health sciences/Health care"},{"id":41726300,"name":"Health sciences/Medical research"},{"id":41726301,"name":"Health sciences/Oncology"},{"id":41726302,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-04-04T06:54:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-24 16:43:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5580344","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5580344","identity":"rs-5580344","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}