Pulmonary hypertension is common among patients with advanced lung cancer and Khorana score is the predictive indicator

Pulmonary hypertension is common among patients with advanced lung cancer and Khorana score is the predictive indicator | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pulmonary hypertension is common among patients with advanced lung cancer and Khorana score is the predictive indicator Chanyoot Bandidwattanawong, Phanthaphan Sureeyathanaphat, Gorn Vrakornvoravuti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4585295/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 Introduction: Pulmonary hypertension (PHT) has been reported to be prevalent across various stages of lung cancer patients and associated with adverse outcomes. This study was aimed to determine prevalence among patients with advanced lung cancer and its association with dyspnea symptom and survival and. Methods Patients with stage IV lung cancer were recruited. PHT was diagnosed, if mean arterial pulmonary pressure (mPAP) was above 20 mmHg as determined by echocardiography. Baseline demographics including age, sex, smoking status, histologic types, performance status (PS), extent of pulmonary involvement, Khorana score, presenting symptoms, systemic cancer therapy, cancer dyspnea score (CDS) and 1-year survival were collected. Results There were 69 eligible patients. Thirty-four patients (49.28%) had PHT. Only high Khorana risk score was the independent predictive factor of development of PHT at baseline (OR adj = 48.71 (95% C.I. 1.51-1569.17); p = 0.028). History of smoking had a trend towards a predictor (OR adj = 4.36 (95% C.I. 0.83–22.87); p = 0.081). Furthermore, those with PHT had a trend towards shorter survival than those without (1-year OS, 55.23% vs 88.69%; p = 0.003); however, ECOG 2 (HR adj = 6.66 (95% C.I., 1.91–19.82); p = 0.002), non-adenocarcinoma cell types (HR adj = 5.33 (95% C.I., 1.18–24.10); p = 0.03), anemia (HR adj = 4.59 (95% C.I., 1.12–18.74); p = 0.034), and abnormal PT (HR adj = 5.52 (95% C.I., 1.60-19.09); p = 0.007) were the independent prognostic factors of short survival. Higher degree of PHT was also correlated with higher CDS (Pearson correlation, r = 0.458; 95% C.I. 0.25–0.63; p < 0.001). Conclusion In line with the historical reports, PHT is quite prevalent in patients with advanced lung cancer. Due to its co-relation with CDS, any agents which can lessen the degree of PHT should be further investigated for the purpose of improving patients’ symptom burden before the systemic therapy takes its action. pulmonary hypertension lung cancer Khorana score adverse outcomes Figures Figure 1 Figure 2 Introduction Lung cancer is the leading cause of cancer death in both men and women and accounts for nearly 28% of all cancer deaths worldwide, with global incidence increasing by up to 0.5% per year. 1 Around 60–70% of patients with lung cancer have dyspnea at presentation, and 90% report dyspnea subsequently. However, the mechanisms that cause cancer-associated dyspnea are complex and poorly understood. Even though, tumor cells can directly invade, infiltrate and destroy the pulmonary parenchyma, the tumor burden per se has poor correlation with the dyspnea symptom. 2 Dyspnea can be triggered by pulmonary hypertension (PHT) which is a common concomitant condition associated with cancer. Ballout et al. proposed that cancer and its treatments would link to all groups of PHT. In some cases, cancer may directly induce PHT to generate through developing tumor micro-embolism and indirectly through promoting thrombus. In other cases, cancer can cause other health conditions leading to PHT. 3 Pullamsetti et al. 4 reported that among 519 patients who underwent a computed tomography (CT) scan for the diagnosis of lung cancer, 250 had a mean pulmonary artery (mPA) diameter of > 28 mm, representing PHT. By applying another non-invasive indirect diagnosis of PHT, Eul et al. 5 measured pulmonary artery (PA) and ascending aorta (A) diameter (size) from the images acquired from baseline high-resolution computed tomography (HRCT). They revealed that 151 of their cohort of 670 lung cancer patients (22.5%) had aPA/A ratio ≥ 1, their surrogate parameter for PHT. Interestingly, such patients had both dismal progression-free (PFS) and overall survival (OS). Noticeably, a decrease in the median OS of a year among lung cancer patients with PHT was demonstrated. Yang et al . 6 conducted a retrospective analysis of 612 non-small cell lung cancer (NSCLC) patients. By using the echocardiographic measurement of pulmonary artery systolic pressure (PASP) criteria of more than 35 mmHg as the diagnostic criteria, they reported that 19.8% of their patients had PHT. Furthermore, after adjustment for age, symptom, coagulation disorders, lymph node metastasis, distant metastasis, histological type, clinical stage, PASP ≥ 35 mmHg remained a significant associated factor relating to the adverse OS. At a cut-off value of ≥ 45 mmHg, elevated PASP was an independent prognostic predictor for peri-operative death among lung cancer patients with earlier stages. Independent predictive factors of elevated PASP were age, the presence of intrapulmonary metastasis and coagulation disorders. Based on the most recent 6th World Symposium on Pulmonary Hypertension, PHT has been defined as mean pulmonary artery pressure (mPAP) of more than 20 mmHg. 7 Even though assessment of PAP by performing right heart catheterization (RHC) remains the gold standard, it is invasive and too costly. An acceptable correlation has been demonstrated between the RHC and echocardiography. 8 By using the echocardiographic measurement of mPAP of ≥ 20 mmHg as the diagnostic criteria of PHT, this study intended to determine the prevalence of PHT among patients with advanced lung cancer, predictive factors associated with this condition, its correlation with dyspnea symptoms, treatment response and survival. Materials and Methods Patients The investigators prospectively analyzed patients with advanced lung cancer of all histological types who had attended Division of Medical Oncology, Department of Internal Medicine, Faculty of Medicine, Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand from January 15, 2023 to January 9, 2024. At least 1 year of follow-up was required in patients who remained alive after receiving systemic treatment, otherwise from the time of cancer diagnosis to death. Median duration of follow-up for survival was 5.6 months. Inclusion criteria were patients aged more than 18 years with histologically confirmed lung cancer of advanced stage (defined as stage IV based on Eighth Edition of TNM Staging of Lung Cancer 9 in case of non-small cell lung cancer (NSCLC) and extensive disease based on Veterans Administration Lung Study Group and International Association for the Study of Lung Cancer 10 in case of small cell lung cancer (SCLC), Eastern Co-operative Cancer Group (ECOG) performance status from 0–2, and adequate renal, hepatic and bone marrow functions whom a medical oncologist determined suitable for systemic therapy of any kinds. Exclusion criteria were those with pre-existing pulmonary hypertension supposedly unrelated to lung cancer (i.e. valvular heart disease, chronic congestive heart failure, chronic venous thromboembolism, HIV infection, cirrhosis with portal hypertension, connective tissue diseases, auto-immune diseases), patients with obstructive sleep apnea requiring any forms of treatment interventions, patients with COPD GOLD severity grade of 2-4 11 , and patients with any restrictive lung diseases. Baseline demographics included age, sex, histologic type, body weight, body mass index (BMI), ECOG performance status were collected. Smoking status was evaluated and reported as current smoker, past smoker and never smoker. EGFR mutation status would also be determined, if a participant could afford the cost of testing. Opioid use either as a pain killer or a sedative agent to palliate dyspneic symptoms was allowed during the follow-up period. Specific kind of opioids, dosage and frequency of uses were recorded. To calculate the sample size, the investigators calculated based on the results of the study by Yang, et al who reported that the prevalence of PHT in lung cancer patients was 19.8%. 6 Using the formula 12 , N = [Z α/ 2 2 x p(1-p)]/d 2 , the investigators indicated α = 0.05, p = 0.198, margin of error (d) = 0.10 and estimated the drop-out rate of 10%, therefore, the required sample size was at least 70 participants. The study was undertaken in accordance with international guideline on Good Clinical Practice and the Declaration of Helsinki. It was approved by the Ethics Committee of Navamindradhiraj University (COA 077/2566). Informed written consents were acquired from all participating individuals. Echocardiographic Measurements Pulmonary artery systolic pressure (PASP) was measured first by transthoracic echocardiography (TTE) that was performed by one general cardiologist and confirmed by the cardiologist who was the expert in echocardiography (P.S.). The estimation of PASP by echocardiograph derived from the application of the modified Bernoulli equation: PASP = 4 x (peak TRV) 2 + RAP, TRV is the maximum tricuspid regurgitation jet speed and RAP is the right atrium pressure, which is estimated by inferior vena cava diameter. With the assumption that PASP equals to right ventricular systolic pressure (RVSP), it is then converted to mean pulmonary arterial pressure (mPAP) using the following formula mPAP = 0.61 x RVSP + 2 mmHg. 13 Pulmonary hypertension was diagnosed if mPAP value was ≥ 20 mmHg according to the recent 6th World Symposium on Pulmonary Hypertension diagnostic criteria. 7 Arguably, this updated criteria would be subject to over diagnosis and whether this threshold can determine the worse clinical outcomes has still be debatable. The investigators also used the previous criteria that defined PHT as mPAP of more than 25 mmHg as a determining factor of adverse survival. The echocardiographic measurement was performed prior to commencement of a systemic cancer treatment and after 4 cycles of such treatment and assessed in concomitant with treatment response, if a participant was still alive and able to attend follow-up visit. Laboratory tests Besides the routine laboratory parameters including complete blood counts (CBC), renal and hepatic function tests were collected prior to starting a systemic cancer treatment and subsequently as an oncologist’s discretion. Coagulation test including activated partial thromboplastin time (APTT) and prothrombin time (PT) was obtained at initial visit. All measurements were performed according to standard methods. Pre-treatment hemoglobin level, white blood cell counts, platelet counts and a participant’s BMI were then collected to calculate the Khorana score 14 , widely used for prediction of cancer-associated VTE in the ambulatory setting. Diagnostic imaging studies to determine staging included computerized topography (CT) scan of chest and upper abdomen with contrast study and bone scintigraphy. Both were obtained at cancer diagnosis. Cranial CT or magnetic resonance imaging (MRI) was obtained only in case of clinically suspicious of intra-cranial metastasis. The CT scan of chest and upper abdomen with contrast study was repeated after 4 cycles of a systemic cancer treatment had been administered to determine treatment response. Fluorodeoxyglucose (FDG) positron emission tomography (FDG-PET) was optional. The treatment response was determined and categorized according to RECIST v1.1 criteria. 15 Questionnaires The investigators intended to determine the correlation between the degree of dyspnea symptoms and PHT. The Cancer Dyspnea Score (CDS-V) 17 was used to determine the degree of dyspnea symptoms. The CDS-V was queried prior to starting a systemic cancer treatment and subsequently after 4 cycles of such treatment in concomitant with treatment response assessment. Outcomes All participants were followed up at least 1 year after signing the informed consent. Telephone call was also done to obtain information on survival (date and cause of death). The primary outcome was the incidence of PHT among patients with advanced lung cancer eligible for treatment with systemic cancer therapy. The secondary outcomes included the 1) independent predictive factors associated with PHT, 2) correlation between the degree of PHT and dyspnea score, 3) correlation between the degree of PHT and treatment response and 4) association between PHT and 1-year survival. Statistical Methods All statistical analyses were performed using SPSS software, version 28.0 (IBM Corp. Armonk, NY). Continuous variables were reported as means and standard deviation (SD) if the data was normally distributed, and median and interquartile range (IQR), if not. Comparison between the groups was performed using either Student’s t -test or Mann–Whitney U test. Categorical variables were compared using either Chi-squared test or Fisher exact test. The investigators explored the independent factors predicting the occurrence of PHT and factors associated with worse 1-year survival. Uni-variate analysis was performed using logistic regression. Factors associated with PHT in uni-variate Cox analysis at p < 0.05 were included in multi-variate Cox models. The models also included those considered confounders factors. Survival analysis was conducted using the Kaplan‑Meier method, and comparison was performed via log‑rank test. Multi-variate analysis was conducted using Cox proportional hazard regression model. All variables that showed association at p value < 0.05 in multi-variate analyses were determined statistically significant. Overall survival (OS) was defined as the interval since the date of histopathological diagnosis of lung cancer to the date of death, regardless of the causes or the date of data censorship (January 10, 2024), if a participant was still alive. To prove the correlation between the degree of PHT and Cancer Dyspnea Score (CDS), the investigators used Pearson correlation to determine and then reported its coefficient (r). The investigators also compared the echocardiographic parameters and CDS evaluated before (pre-treatment) and after (post-treatment) the commencement of systemic cancer therapy. The timing to repeat the post-treatment evaluations was within one month after 4 cycles of systemic chemotherapy or 4 months of a targeted therapy. Results Figure 1 showed the consort diagram of enrollment of the study participants. Most of the excluded patients were those with underlying conditions supposed to have pre-existing pulmonary hypertension prior to cancer diagnosis, i.e. chronic obstructive pulmonary disease (COPD), congestive heart failure, thromboembolic diseases and connective tissue diseases. Table 1 revealed the baseline demographic data. Overall, there were 69 patients eligible for evaluation. Most were aged more than 60 years old. Thirty seven of them (53.6%) were male and 55 of them (79.71%) had good ECOG PS (0–1). Only 31 participants (44.93%) were smokers. Nearly two thirds of the participants manifested their diseases without any chest symptoms at all. Forty eight (69.57%) of them were categorized into intermediate Khorana Risk. Most (91.30%) of the participants had non-small cell lung cancer with adenocarcinoma histopathology. Forty seven patients (68.12%) received upfront treatment with platinum-doublet chemotherapy. Strikingly, when excluding patients with any concomitant conditions supposed to have pre-existing PHT, the real incidence of PHT in patients with advanced lung cancer was astonishingly high at 49.28%. Table 1 Baseline demographic and clinical characteristics of the participants. Characteristics All patients (n = 69) Median age (years) (IQR) 63 (56.5–71.5) < 60 years – no.(%) 23 (33.3) ≥ 60 years – no.(%) 46 (66.7) Sex – no.(%) Male 37 (53.6) Female 32 (46.4) Median body weight (kg) (IQR) 56 (50–63) Median BMI (kg/m 2 ) (IQR) 21.16 (19.24–24.11) Median BSA (m 2 ) (IQR) 1.58 (1.49–1.71) ECOG – no.(%) 0 21 (30.4) 1 34 (49.3) 2 14 (20.3) ECOG – no.(%) 0–1 55 (79.7) 2 14 (20.3) Smoking – no.(%) No 38 (55.1) Yes 31 (44.9) Presenting symptoms – no.(%) Dyspnea 22 (31.9) Cough 21 (30.4) Hemoptysis 4 (5.8) Weight loss 7 (10.1) Chest pain 2 (2.9) Others 38 (55.1) Khorana risk – no.(%) Intermediate risk 48 (69.6) High risk 21 (30.4) Histologic type – no.(%) NSCLC Adenocarcinoma 63 (91.3) Squamous 3 (4.3) Adenosquamous 1 (1.4) SCLC 2 (2.9) Extent of metastasis – no.(%) Intrapulmonary metastasis 46 (66.7) Extrapulmonary metastasis 53 (76.8) EGFR mutation status – no.(%) Yes 16 (23.2) No 34 (49.3) Others ROS1 3 (4.3) ALK 1 (1.4) Unknown 16 (23.2) Systemic Therapy – no.(%) Platinum-Doublet Chemotherapy 47 (68.1) Targeted Therapy 15 (21.7) Immunotherapy 1 (1.4) Unknown 6 (8.7) Hemoglobin (g/dL) – median (IQR) 11.4 (10.05–12.95) <10 g/dL – no.(%) 17 (24.6) ≥10 g/dL – no.(%) 52 (75.4) Table 1 (Continued) Characteristics All patients (n = 69) White Blood Cell (cells/mm 3 ) – median (IQR) 8.21 (6.27–11.18) <11,000 cells/mm3 – no.(%) 50 (72.5) ≥11,000 cells/mm3 -no.(%) 19 (27.5) Platelet (/mm 3 ) – median (IQR) 321.0 (234.0-403.0) <350,000 – no.(%) 41 (59.4) ≥350,000 – no.(%) 28 (40.6) Creatinine – median (IQR) 0.73 (0.54–0.90) Creatinine Clearance (ml/min) – median (IQR) 76.30 (55.3–102.2) Coagulation Function – no.(%) PT Normal 47 (68.1) Abnormal 22 (31.9) PTT Normal 58 (84.1) Abnormal 11 (15.9) Opioid use – no.(%) No 28 (40.6) Yes 41 (59.4) Pre-treatment Echocardiographic Parameters – median (IQR) LVEF (%) 64.2 (55.9–69.5) RVSP (mmHg) 29.0 (26.0–37.0) TRVmax (cm/s) 254.0 (228.0-270.0) RAP (mmHg) 3.0 (3.0–8.0) mPAP(mmHg) 19.7 (17.9–25.0) Post-treatment Echocardiographic Parameters – median (IQR) LVEF (%) 64.0 (55.1–65.8) RVSP (mmHg) 27.0 (26.0-30.7) TRVmax (cm/s) 248.4 (239.0-258.0) RAP (mmHg) 3.0 (3.0-6.75) mPAP(mmHg) 18.5 (17.9–20.7) Data are presented as number (%), mean ± SD or median (interquartile range). Table 2 demonstrated clinical and laboratory factors associated with PHT. In uni-variate analysis, male sex, smokers, patients with high Khorana risk score, intra-pulmonary metastases, high white blood counts ( ≥ 11,000/mm 3 ), high platelet counts ( ≥ 350,000/mm 3 ) and abnormal prothrombin time (PT) at baseline were associated with PHT; however, in multi-variate analysis, only high Khorana risk score remained the independent predictive factor (OR adj = 48.71 (95% C.I. 1.51-1569.17); p = 0.028). Interestingly, history of smoking had a trend towards a predictor (OR adj = 4.36 (95% C.I. 0.83–22.87); p = 0.081). Table 2 Uni-variate and multi-variate analyses of factors associated with pulmonary hypertension Factors Univariable analysis Multivariable analysis OR* 95%CI p-value OR** adj 95%CI p-value Age (years) <60 years 1.19 (0.44 - 3.24) 0.734 ≥60 years 1.00 Reference Sex Male 4.06 (1.48 - 11.12) 0.006 1.48 (0.28 - 7.80) 0.646 Table 2 (Continued) Factors Univariable analysis Multivariable analysis OR* 95%CI p-value OR** adj 95%CI p-value Female 1.00 Reference 1.00 Reference ECOG 0-1 1.00 Reference 2 1.49 (0.46 - 4.86) 0.511 Smoking No 1.00 Reference 1.00 Reference Yes 5.30 (1.88 - 14.90) 0.002 4.36 (0.83 - 22.87) 0.081 Symptoms Dyspnea 1.79 (0.64 - 4.99) 0.267 Non-dyspnea 1.00 Reference Khorana risk Intermediate risk 1.00 Reference 1.00 Reference High risk 48.57 (5.93 - 397.67) <0.001 48.71 (1.51- 1569.17) 0.028 Histologic type Adenocarcinoma 1.00 Reference Non-adenocarcinoma 5.86 (0.65 - 53.09) 0.116 Extent of metastasis Intrapulmonary metastasis 4.41 (1.46 - 13.28) 0.008 1.74 (0.41 - 7.32) 0.453 Extrapulmonary metastasis 0.96 (0.32 - 2.95) 0.947 Hemoglobin (g/dL) <10 g/dL 2.31 (0.74 - 7.19) 0.148 ≥10 g/dL 1.00 Reference White Blood Cell (x10 3 cells/mm 3 ) <11,000 cells/mm 3 1.00 Reference 1.00 Reference ≥11,000 cells/mm 3 6.12 (1.77 - 21.19) 0.004 0.93 (0.08 - 10.49) 0.954 Platelet (x10 3 ) <350,000 1.00 Reference 1.00 Reference ≥350,000 4.82 (1.70 - 13.69) 0.003 0.70 (0.11 - 4.49) 0.708 PT Normal 1.00 Reference 1.00 Reference Abnormal 4.30 (1.42 - 13.00) 0.010 1.21 (0.24 - 6.19) 0.818 PTT Normal 1.00 Reference Abnormal 2.01 (0.53 - 7.62) 0.305 Opioid use No 1.00 Reference Yes 1.98 (0.74 - 5.25) 0.172 Abbreviations: OR, Odds Ratio; OR adj , Adjusted Odds Ratio; CI, confident interval; NA, data not applicable. * Crude Odds Ratio was estimated by binary logistic regression. ** Adjusted Odds Ratio was estimated by multiple logistic regression. Table 3 displayed clinical and laboratory factors associated with 1-year survival. In uni-variate analysis, patients with poor performance status (ECOG 2), non-adenocarcinoma cell types, PHT, high Khorana risk score, anemia (Hb 11,000/mm 3 ) and abnormal prothrombin time (PT) at baseline were associated with shorter overall survival; however, only ECOG 2 (HR adj = 6.66 (95% C.I., 1.91-19.82); p = 0.002), non-adenocarcinoma cell types (HR adj = 5.33 (95% C.I., 1.18-24.10); p = 0.03), anemia (HR adj = 4.59 (95% C.I., 1.12-18.74); p = 0.034), and abnormal PT (HR adj = 5.52 (95% C.I., 1.60-19.09); p = 0.007) were the independent prognostic factors of adverse 1-year survival rate. Those with PHT had a trend towards worse 1-year OS than those without (1-year OS, 55.23% vs 88.69%; p = 0.003); however, it was not significantly associated in multi-variate model (HR adj = 3.95 (95% C.I., 0.56-28.01); p = 0.169). The investigators also performed the sensitivity analysis by adjusting the mean pulmonary arterial pressure (mPAP) threshold to either above 25 and above 30 mmHg, the result remained unchanged (Supplement appendix, Table S1). Provocatively, the investigators demonstrated that the degree of pre-treatment PHT somewhat correlated with Cancer Dyspnea Score (CDS) (Pearson correlation, r = 0.458 (95% C.I. 0.24-0.627); p < 0.001) as shown in Figure 2 ; however, the post-treatment mPAP did not (Supplement appendix, Table S2) . The investigators also evaluated the correlation between echocardiographic parameters and CDS before (pre-treatment) and after (post-treatment) administration of systemic cancer therapy. There were 32 patients who could proceed to post-treatment echocardiographic evaluation; while there were 45 patients could respond to post-treatment CDS questionnaires. The investigators noticed that those who had received systemic therapy and could repeat response evaluations, significantly had decreased mPAP and improvement in CDS (Supplement appendix, Table S3) . However, such findings should be interpreted cautiously, those who could repeat the echocardiographic evaluation and respond post-treatment CDS were those who remained alive and could attend the follow-up visits. Table 3 Multi-variate analysis of overall survival (OS) Factors Univariable analysis Multivariable analysis HR † 95%CI p-value HR ‡ adj 95%CI p-value mPAP <20 mmHg 1.00 Referrence 1.00 Referrence ≥20 mmHg 5.33 (1.53 - 18.62) 0.009 3.95 (0.56 - 28.01) 0.169 Age <60 years 1.00 Reference ≥60 years 1.45 (0.51 - 4.12) 0.487 Sex Male 1.31 (0.50 - 3.43) 0.588 Female 1.00 Reference ECOG 0-1 1.00 Reference 1.00 Reference 2 3.58 (1.35 - 9.47) 0.010 6.16 (1.91 - 19.82) 0.002 Smoking No 1.00 Reference Yes 1.78 (0.68 - 4.69) 0.241 Symptoms Non-Dyspnea 1,00 Reference Dyspnea 2.33 (0.90 - 6.04) 0.083 Cough 2.21 (0.85 - 5.73) 0.103 Hemoptysis 1.97 (0.45 - 8.65) 0.368 Weight loss 2.79 (0.91 - 8.58) 0.074 Chest pain 2.85 (0.38 - 21.57) 0.310 Others 0.51 (0.20 - 1.35) 0.178 Khorana risk Intermediate risk 1.00 Reference 1.00 Reference High risk 5.65 (2.06 - 15.51) 0.001 1.14 (0.10 - 12.42) 0.917 Histologic type Adenocarcinoma 1.00 Reference 1.00 Reference Non-adenocarcinoma 4.03 (1.30 - 12.43) 0.015 5.33 (1.18 - 24.10) 0.030 Extent of metastasis Intrapulmonary metastasis 1.66 (0.54 - 5.11) 0.373 Extrapulmonary metastasis 2.51 (0.57 - 10.99) 0.221 Hemoglobin <10 g/dL 6.48 (2.45 - 17.15) <0.001 4.59 (1.12 - 18.74) 0.034 ≥10 g/dL 1.00 Reference 1.00 Reference Table 3 (Continued) Factors Univariable analysis Multivariable analysis HR † 95%CI p-value HR ‡ adj 95%CI p-value White Blood Cell <11,000 cells/mm 3 1.00 Reference 1.00 Reference ≥11,000 cells/mm 3 2.63 (1.01 - 6.84) 0.048 0.46 (0.09 - 2.26) 0.339 Platelet <350,000 1.00 Reference ≥350,000 2.40 (0.91 - 6.34) 0.078 PT Normal 1.00 Reference 1.00 Reference Abnormal 5.87 (2.16 - 15.97) 0.001 5.52 (1.60 - 19.09) 0.007 PTT Normal 1.00 Reference Abnormal 2.69 (0.95 - 7.64) 0.064 Opioid use No 1.00 Reference Yes 2.81 (0.92 - 8.64) 0.071 Abbreviations: NA, data not applicable; HR, Hazard Ratio; HR adj , Adjusted Hazard Ratio; CI, confident interval. †Crude HR was estimated by Cox proportional hazard model. ‡Adjusted HR was estimated by Cox proportional hazard model adjusting for ECOG, Khorana risk, hemoglobin, white blood cell, and PT coagulation function. (Any variables with significant correlation at p < 0.05 in uni-variate analysis were selected to evaluate correlations in multi-variated model.) Discussion Pulmonary hypertension (PHT) in patients with cancer can occur as a result of various factors. Most of the world authorities usually classify PHT into 5 categories. 18 Salamo et al. 19 retrospectively studied cancer patients who underwent right heart catheterization (RHC) and revealed that 133 out of 180 patients had PHT. Half of them had solid malignancies, while most of the rests had hematologic ones. Half of them had PHT in association with left-sided heart disease (group 2), around a quarter of them had pre-existing systemic diseases supposed to be the leading cause of PHT (group 1), around one sixth of them had underlying chronic lung diseases (group 3), the rests had concomitant macro-thromboembolism (due to blood clot or tumor emboli) (group 4) or had a variety of poorly defined causes (group 5). Interestingly, nearly two-thirds of them had recently treated with specific anti-cancer treatment. Currently, cancer-associated PHT (or tumoral PHT) is classified within group 5 to represent the multifaceted etiology. The tumoral PHT is a “microvascular disease” manifesting as the spectrum from pulmonary tumor micro-embolism (PTE) characterized by the occlusion of small pulmonary arteries by cohesive tumor cells to pulmonary tumor thrombotic microangiopathy (PTTM) characterized by the presence of pulmonary vascular tumor micro-embolic nests with evidence for activation of coagulation resulting in obliterative intimal proliferation. Both of these pathologic findings usually co-exist. The PTE usually occur in association with adenocarcinomas, including cholangiocarcinoma, renal, breast, gastric, bladder and choriocarcinoma. While, the PTTM typically relates to a carcinoma, especially an adenocarcinoma, including gastric cancer, breast, lung, bladder, ovarian clear cell, hepatobiliary and choriocarcinoma. 20 Cancer by itself is a hypercoagulable state. Cancer cells can directly activate of coagulation and platelets can occur through their expression of tissue factor (TF), the key initiator of the coagulation cascade, cancer procoagulant (CP) shown to directly activate coagulation cascade by activating Factor X and podoplanin (PDPN) causing platelet activation and aggregation in concert with plasminogen activation inhibitor-1 (PAI-1), a key inhibitor of fibrinolysis. Cancer cells also secrete platelet agonists such as ADP and thrombin, thus further promoting platelet activation. Phosphatidyl serine (PS) expressed on tumor microparticles may also promote coagulation as PS serves as a surface for formation of coagulation complexes. Indirectly, disseminated cancer cells can infiltrate into nearby blood vessels. Moreover, inflammatory cytokines secreted from cancer cells result in platelet activation and promote the procoagulant phenotype in endothelial cells. Cancer-derived factors also induce neutrophils to release neutrophil extracellular traps serving as a scaffold that effectively entrap platelets, or activate platelets through NET-associated histones, ultimately leading to profound platelet activation, fibrin deposition, and entrapment of red blood cells, further exacerbating clot formation. 21 Pullamsetti, et al. 4 performed histo-pathologically analysis to determine the changes in microvasculature within human lung cancer tissue obtained from 14 different tissue sections and observed that there were increased vascular remodeling and perivascular accumulation of inflammatory cells within human lung cancer tissue. Additionally, when they co-cultured human lung cancer cells with macrophages and lymphocytes, it resulted in releasing of inflammatory cytokines that promoted tumor migration, apoptosis resistance, as well as phosphodiesterase 5 (PDE5)-mediated upregulation of human lung epithelial cells mimicking the features of PHT. Their findings supported the hypothesis that the interplay between tumor cells and inflammatory cells consequently promotes derangement of intra-tumoral vasculature which is the hallmark of pulmonary hypertension. The true incidence and prevalence of lung cancer-associated PHT have been scarcely reported. Furthermore, most of them had different methodologies in terms of different inclusion criteria, modalities of diagnostic tools and diagnostic criteria. Pullamsetti et al. 4 retrospectively studied 519 patients who underwent a computed tomography scan for the diagnosis of lung cancer, and reported that 250 of them (48.2%) had a mean pulmonary artery (PA) diameter of more than 28 mm, their diagnostic criteria of PHT. Notably, most of their participants had SCLC and had pre-existing COPD. Eul et al. 5 retrospectively analyzed 670 patients with lung cancer and measured their PA/A ratio from the images acquired with baseline HRCT and demonstrated that 51 of them (22.5%) had aPA/A ratio of more than 1, their diagnostic criteria of PHT. Yang et al. 6 conducted a retrospective study on 612 Chinese NSCLC patients regardless of cancer stages at diagnosis and used PASP of more than 35 mmHg measured by echocardiography as their diagnostic criteria of PHT and showed that 19.8% of them had PHT. Notably, more than half of their participants had either stage III or IV and nearly two thirds of them had adenocarcinoma. This study recruited the participants who had advanced lung cancer, regardless of histologic type but on the condition that they were eligible for systemic cancer treatment. On the contrary, this prospective study tried to excluded any pre-existing systemic conditions supposed to be the leading causes of PHT besides their lung cancer per se. According to this study’s result, the incidence of PHT is 49.28%, which was more pronounced than Yang’s. Presumably, according to the inclusion criteria, this study’s participants were all diagnosed at advanced stage, the more extensive tumor burden would explain the higher proportion of participants who had PHT. The investigators intended to investigate among such group of patients who needed urgent specific cancer therapy and required optimal supportive care in particular. To exclude those with earlier stages was essential to eliminate the confounding factors such as radiotherapy and surgery that might affect the study’s outcomes. Regarding the associated predictive factors of PHT, the investigators demonstrated that only high Khorana risk score was the independent factor. While smoking had a trend. In accordance with the report by Yang et al. 6 , extensive intra-pulmonary metastases and abnormal coagulation were also their significant associated predictors, while smoking and abnormal blood counts had the trends towards associating with PHT. This study had its strength in applying more relevant parameter of predicting cancer-associated thromboembolism like Khorana score that was proved their benefit in predicting the chance of developing PHT in addition. The association of Khorana score in predicting the PHT has substantiated the hypothesis that the hypercoagulable state is one of the promoting factor of PHT generation. 20 Based on the fact that exertional dyspnea is the most frequent complaint for which a PHT patient seeks medical attention and its severity of dyspnea on exertion progresses even to dyspnea at rest as the degree of PHT advances. However, among cancer patients who had multifactorial causes of dyspnea, these assumptions are arguable. The investigators performed the Pearson correlation and showed that the degree of PHT was partially in agreement with the degree of dyspneic symptoms as determined by the CDS questionnaire. Furthermore, the investigators also found that those who remained alive after 4 months of proper cancer treatment, there were the hints that the degree of PHT and CDS were alleviated. In comparison with healthy individuals, at a given CO 2 generation during exercise, ventilatory demands in patients with PHT are higher as a result of metabolic acidosis (owing to early reaching their anabolic threshold), hypoxemia, and excessive upward shift of metabolic hyperbola due to abnormal exercise response of dead space to tidal volume ratio. Simultaneously, dynamic hyperinflation and respiratory weakness further reduces the actual ventilation for a given respiratory center activity, creating a demand-to-ventilation dissociation. Consequently, a progression in ventilatory demands and respiratory center activity occurs during exercise. Moreover, the forebrain projection of high respiratory center activity results in exertional dyspnea despite the relatively low ventilation and significant ventilatory reserve. 22 These captivating findings will provide us the opportunities to conduct the clinical trials on strategies to relieve symptoms in lung cancer patients who have concomitant cancer-associated dyspnea and PHT. Besides, supportive measures such as opioids, O 2 supplement, if indicated and appropriate systemic cancer treatment, anti-coagulants, vasodilators, and novel agents 23 should be investigated in well-designed clinical trials specifically in cancer patients with dyspnea. In respect of its association with survival, the investigators demonstrated a trend towards worse 1-year survival rate among participants with PHT. In agreement with Eul et al. 5 when a cutoff PA/A ratio of > 1 was employed to determine the diagnosis of PHT, those with PHT had significant shorter PFS and OS than those without (median PFS, 133 vs 270 days (p = 0.004); median OS, 207 vs 568 days (p 35 mmHg was applied to determine the diagnosis of PHT, the 3- and 5-year OS rates of NSCLC were 57.1% vs 49.5% and 91.3% vs 84.4% in patients with and without PHT, respectively. After adjustment for age, symptom, coagulation disorders, lymph node metastasis, distant metastasis, histological type, clinical stage, PHT remained a significantly associated factor adverse survival (p = 0.028). This study may not have enough power to demonstrate the co-relation with survival. In line with the conventional prognostic factors like male sex, poorer PS, non-adenocarcinoma cell types (squamous cell carcinoma and small cell lung cancer) 24 , the investigators disclosed that anemia and abnormal PT were also the independent prognostic factors of shorter survival. Caro et al. carried out the comprehensive literature review on the effect of anemia on survival outcome among patients with various cancers and showed that the relative risk of death increased by 19% (95% C.I., 10 − 29%), 47% (21–78%), 75% (37–123%), 67% (30 − 113%) in anemic patients with lung carcinoma, head and neck carcinoma, prostate carcinoma, and lymphoma, respectively. The overall estimate increase in risk of death was 65% (54 − 77%). 25 In accordance with the results reported by recent individual studies, anemia was the independent prognostic factor of worse survival. Chen et al. 26 revealed that patients with advanced NSCLC with anemia of grade 3 to 4 had the shortest OS. Even among those harboring EGFR mutation in particular, patients with anemia of grade 2 or better had independently longer median OS. 27 Advanced cancer is a systemic inflammatory disease. Its biologic effects, especially through cytokine interference related to high tumor burden would affect the red blood cell production. Provocatively, abnormal PT was also among the unconventional prognostic factor of survival demonstrated by this study. Bayleyegn et al. 28 performed a meta-analysis to determine the correlation between basic coagulation abnormalities in patients with lung cancer and found that comparing with the control, lung cancer patients had higher prothrombin time (PT), D-dimer level, fibrinogen level and higher platelet counts. This evidence supports the hypercoagulability in patients with lung cancer. Tas et al. 29 explored the prognostic value of blood coagulation tests among 110 patients with lung cancer (both small and non-small cell lung cancer). Pre-treatment blood coagulation tests including PT, aPTT, PTA, INR, D-dimer, fibrinogen levels and platelet counts were evaluated. Only elevation of PT and INR were associated with significantly associated with adverse survival. Li et al. 30 retrospectively collected the coagulation data from 604 histologically confirmed NSCLC patients and found that among basic coagulation parameters i.e. PT, INR, activated partial thromboplastin time (aPTT), D-dimer, fibrinogen level, and platelet counts, only PT and INR were the independent prognostic factors of survival. Abbass et al. 31 also retrospective evaluated 216 patients with advanced NSCLC who had received first-line combination platinum-based chemotherapy with or without an anti-angiogenic agent and found that abnormal PT and higher fibrinogen and D-dimer levels were associated with shorter survival. The investigators speculate that besides the usual systemic cancer treatment whether lung cancer patients with obvious evidence of hypercoagulable state should receive thromboprophylaxis is a research question to be elucidated. Strengths This study was conducted in prospective fashion; therefore, the crucial parameters were collected systemically. The widely acceptable clinical tool to predict cancer-associated thromboembolism like Khorana score was also included. In order to eliminate the confounding factors existed in previous study, the investigators excluded patients with earlier stages who were more suitable for locoregional therapy and those who had pre-existing conditions supposed to relate with PHT, unrelated to their cancers per se. Limitations Due to limited budget and time, the investigators conducted the clinical study in small group of patients. Larger sample size would result in more extensive clinical findings and stronger evidences. Nevertheless, the investigators disclosed some provocative data that previous study had not mentioned or omitted. Conclusion This study demonstrated that nearly half of the patients with advanced lung cancer had PHT. Khorana risk score would be a clinical tool to predict this condition and select patients for further investigations. Its occurrence co-related with the degree of dyspneic symptoms. The investigators suggested that those with complaint of dyspneic symptoms, it should be compulsory to exclude concomitant pre-existing cardiopulmonary morbidities. Echocardiography would be useful to evaluate cardiac condition and determine the co-existing PHT. Besides symptomatic and supportive care in combination with appropriate anti-cancer therapy, the well-designed randomized trials to investigate the values of novel agents conducted particularly in cancer patients are warranted. Besides the conventional prognostic factors like ECOG performance status, histological subtype, the hypercoagulable state would be an emerging clinical prognostic factor. Abbreviations APTT Activated partial thromboplastin time CDS Cancer Dyspnea Score mPA Mean pulmonary artery mPAP Mean pulmonary artery pressure PASP Pulmonary artery systolic pressure PHT Pulmonary hypertension PT Prothrombin time Declarations Acknowledgements The investigators thank the patients and family members for their trust and contribution to this study. Yotsawaj Runglodvatana, M.D., Apisada Sutepvarnon, M.D., Gorawich Kerkarchachai, M.D. and Lucksika Wanichtanom, M.D. for the assistance in patient recruitment. Nurses and medical personnel at the Division of Medical Oncology and Division of Cardiology, Department of Internal Medicine, Vajira Hospital, Navamindradhiraj University for follow-up assistance during study period. Mr. Anucha Kamsom for statistical consultation and clinical outcomes assessment. Authors’contributions C. Bandidwattanawong was responsible for conceptualization, methodology, supervision, data analysis, writing the original draft and editing. P. Sureeyathanaphat was responsible for performing echocardiography and validating data results. G.Vrakornvoravuti was responsible for data collection and analysis. Funding This study was supported under the approval by Navamindradhiraj University Research Management System committees. Availability of data and materials The raw data and statistical analytic models that support the findings of this study are available upon request from the corresponding authors. Ethics approval and consent to participate The study proposal was approved by the Ethics Committee of Navamindradhiraj University (COA 077/2566). Informed written consents were acquired from all participating individuals. References Barta JA, Powell CA, Wisnivesky JP. Global Epidemiology of Lung Cancer. Ann Glob Health. 2019;85(1):8. McKenzie E, Hwang MK, Chan S, Zhang L, Zaki P, Tsao M, et al. Predictors of dyspnea in patients with advanced cancer. Ann Palliat Med. 2018;7(4):427–36. Ballout FA, Manshad AS, Okwuosa TM. Pulmonary Hypertension and Cancer: Etiology, Diagnosis, and Management. Curr Treat Options Cardiovasc Med. 2017;19(6):44. Pullamsetti SS, Kojonazarov B, Storn S, Gall H, Salazar Y, Wolf J, et al. Lung cancer-associated pulmonary hypertension: Role of microenvironmental inflammation based on tumor cell-immune cell cross-talk. Sci Transl Med. 2017;9(416):eaai9048. Eul B, Cekay M, Pullamsetti SS, Tello K, Wilhelm J, Gattenlöhner S, et al. Noninvasive Surrogate Markers of Pulmonary Hypertension Are Associated with Poor Survival in Patients with Lung Cancer. Am J Respir Crit Care Med. 2021;203(10):1316–9. Yang X, Wang L, Lin L, Liu X. Elevated Pulmonary Artery Systolic Pressure is Associated with Poor Survival of Patients with Non-Small Cell Lung Cancer. Cancer Manag Res. 2020;12:6363–71. Galiè N, McLaughlin VV, Rubin LJ, Simonneau G. An overview of the 6th World Symposium on Pulmonary Hypertension. Eur Respir J. 2019;53(1):1802148. Simonneau G, Montani D, Celermajer DS, Denton CP, Gatzoulis MA, Krowka M, et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. Eur Respir J. 2019;53(1):1801913. Lababede O, Meziane MA. The Eighth Edition of TNM Staging of Lung Cancer: Reference Chart and Diagrams. Oncologist. 2018;23(7):844–8. Micke P, Faldum A, Metz T, Beeh KM, Bittinger F, Hengstler JG, et al. Staging small cell lung cancer: Veterans Administration Lung Study Group versus International Association for the Study of Lung Cancer–what limits limited disease? Lung Cancer. 2002;37(3):271–6. Singh D, Agusti A, Anzueto A, Barnes PJ, Bourbeau J, Celli BR, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease: the GOLD science committee report 2019. Eur Respir J. 2019;53(5):1900164. Daniel WW. (1999). Biostatistics: A Foundation for Analysis in the Health Sciences. 7th edition. New York: John Wiley & Sons. Jang AY, Shin MS. Echocardiographic Screening Methods for Pulmonary Hypertension: A Practical Review. J Cardiovasc Imaging. 2020;28(1):1–9. Khorana AA, Kuderer NM, Culakova E, Lyman GH, Francis CW. Development and validation of a predictive model for chemotherapy-associated thrombosis. Blood. 2008;111(10):4902–7. Schwartz LH, Litière S, de Vries E, Ford R, Gwyther S, Mandrekar S, et al. RECIST 1.1-Update and clarification: From the RECIST committee. Eur J Cancer. 2016;62:132–7. Williams MT, Lewthwaite H, Paquet C, Johnston K, Olsson M, Belo LF, et al. Dyspnoea-12 and Multidimensional Dyspnea Profile: Systematic Review of Use and Properties. J Pain Symptom Manage. 2022;63(1):e75–87. Tanaka K, Akechi T, Okuyama T, Nishiwaki Y, Uchitomi Y. Development and validation of the Cancer Dyspnoea Scale: a multidimensional, brief, self-rating scale. Br J Cancer. 2000;82(4):800. Humbert M, Kovacs G, Hoeper MM, Badagliacca R, Berger RMF, Brida M, ESC/ERS Scientific Document Group, et al. 2022 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J. 2022;43(38):3618–731. Salamo O, Lopez-Mettei J, Sargsyan L, Kaous M, Iliescu C, Palaskas N et al. Pulmonary Hypertension in Patients with Cancer. Chest. 2020 October;158(4):Suppl.A2230. Price LC, Seckl MJ, Dorfmüller P, Wort SJ. Tumoral pulmonary hypertension. Eur Respir Rev. 2019;28(151):180065. Abdol Razak NB, Jones G, Bhandari M, Berndt MC, Metharom P. Cancer-Associated Thrombosis: An Overview of Mechanisms, Risk Factors, and Treatment. Cancers (Basel). 2018;10(10):380. Mitrouska I, Bolaki M, Vaporidi K, Georgopoulos D. Respiratory system as the main determinant of dyspnea in patients with pulmonary hypertension. Pulm Circ. 2022;12(1):e12060. Alamri AK, Ma CL, Ryan JJ. Novel Drugs for the Treatment of Pulmonary Arterial Hypertension. Where Are We Going? Drugs. 2023;83(7):577–85. Alexander M, Wolfe R, Ball D, Conron M, Stirling RG, Solomon B, et al. Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer. Br J Cancer. 2017;117(5):744–51. Caro JJ, Salas M, Ward A, Goss G. Anemia as an independent prognostic factor for survival in patients with cancer: a systemic, quantitative review. Cancer. 2001;91(12):2214–21. Chen C, Song Z, Wang W, Zhou J. Baseline anemia and anemia grade are independent prognostic factors for stage IV non-small cell lung cancer. Mol Clin Oncol. 2021;14(3):59. Wei J, Xiang J, Hao Y, Si J, Wang W, Li F, Song Z. Baseline anemia predicts a poor prognosis in patients with non-small cell lung cancer with epidermal growth factor receptor mutations: a retrospective study. BMC Pulm Med. 2022;22(1):381. Bayleyegn B, Adane T, Getawa S, Aynalem M, Kifle ZD. Coagulation parameters in lung cancer patients: A systematic review and meta-analysis. J Clin Lab Anal. 2022;36(7):e24550. Tas F, Kilic L, Serilmez M, Keskin S, Sen F, Duranyildiz D. Clinical and prognostic significance of coagulation assays in lung cancer. Respir Med. 2013;107(3):451–7. Li Y, Wei S, Wang J, Hong L, Cui L, Wang C. [Analysis of the factors associated with abnormal coagulation and prognosis in patients with non-small cell lung cancer]. Zhongguo Fei Ai Za Zhi. 2014;17(11):789–96. Abbas M, Kassim SA, Wang ZC, Shi M, Hu Y, Zhu HL. Clinical evaluation of plasma coagulation parameters in patients with advanced-stage non-small cell lung cancer treated with palliative chemotherapy in China. Int J Clin Pract. 2020;74(12):e13619. 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-4585295","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":316276416,"identity":"f4ce2a39-748f-4c95-aff6-1e195178a68a","order_by":0,"name":"Chanyoot Bandidwattanawong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACCTgr+QCIK0O0FiCVlgCieUjRkmMAYhDWwj+7+dljngqbOt32nM+vbtRY8DCwHz66Aa8ld46ZG/OcSZMwO/N2m3XOMaDDeNLSbuDTYiCRYCbN23ZYwuxG7jbjHDagFgkeMwJa0r9BteQ8M875R5SWHJgtOcyPc9uI0CJxI6dMcs6ZNMltZ56ZMef2SfCwEfIL/4z0bRJvKmz4zY4nP/6c861Ojp/98DG8WkCACRoXbOA4YiOkHAQYf0Bo5g/EqB4Fo2AUjIKRBwB3D0SWs5p6zgAAAABJRU5ErkJggg==","orcid":"","institution":"Navamindradhiraj University","correspondingAuthor":true,"prefix":"","firstName":"Chanyoot","middleName":"","lastName":"Bandidwattanawong","suffix":""},{"id":316276420,"identity":"b784e4e7-abc4-44b8-8044-c13180986305","order_by":1,"name":"Phanthaphan Sureeyathanaphat","email":"","orcid":"","institution":"Navamindradhiraj University","correspondingAuthor":false,"prefix":"","firstName":"Phanthaphan","middleName":"","lastName":"Sureeyathanaphat","suffix":""},{"id":316276421,"identity":"9285fa36-e1f8-4d57-b052-b99e07c7f027","order_by":2,"name":"Gorn Vrakornvoravuti","email":"","orcid":"","institution":"Navamindradhiraj University","correspondingAuthor":false,"prefix":"","firstName":"Gorn","middleName":"","lastName":"Vrakornvoravuti","suffix":""}],"badges":[],"createdAt":"2024-06-15 07:12:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4585295/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4585295/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60353683,"identity":"1913ba54-9f01-42a9-b4d0-736a12c6b01b","added_by":"auto","created_at":"2024-07-15 23:42:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87561,"visible":true,"origin":"","legend":"\u003cp\u003eConsort Diagram\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4585295/v1/d80fb979c13af6462ca1b6a3.png"},{"id":60353960,"identity":"09847fd8-9bc7-4cf8-9051-394761998375","added_by":"auto","created_at":"2024-07-15 23:50:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72436,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Between Pre-treatment Mean Pulmonary Arterial Pressure (mPAP) and Cancer Dyspnea Score (CDS)\u003c/p\u003e\n\u003cp\u003eAbbreviations: r, Pearson correlation coefficient; C.I., confident interval; CDS, Cancer Dyspnea Score; mPAP, mean pulmonary arterial pressure.\u003c/p\u003e\n\u003cp\u003e* Significant at p-value \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4585295/v1/dc508c5634830c6bc6b6f6b3.png"},{"id":79228929,"identity":"00991ae0-2952-4a90-b632-34ae5e77b939","added_by":"auto","created_at":"2025-03-26 02:01:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1379734,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4585295/v1/0347b83c-3d48-453b-a008-7ca142bf4976.pdf"},{"id":60353684,"identity":"39428eab-6848-45f6-89c2-d045c0f09b93","added_by":"auto","created_at":"2024-07-15 23:42:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":428079,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4585295/v1/1d8bf37617e839c5d76e1277.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pulmonary hypertension is common among patients with advanced lung cancer and Khorana score is the predictive indicator","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is the leading cause of cancer death in both men and women and accounts for nearly 28% of all cancer deaths worldwide, with global incidence increasing by up to 0.5% per year.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Around 60\u0026ndash;70% of patients with lung cancer have dyspnea at presentation, and 90% report dyspnea subsequently. However, the mechanisms that cause cancer-associated dyspnea are complex and poorly understood. Even though, tumor cells can directly invade, infiltrate and destroy the pulmonary parenchyma, the tumor burden per se has poor correlation with the dyspnea symptom.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Dyspnea can be triggered by pulmonary hypertension (PHT) which is a common concomitant condition associated with cancer. \u003cem\u003eBallout et al.\u003c/em\u003e proposed that cancer and its treatments would link to all groups of PHT. In some cases, cancer may directly induce PHT to generate through developing tumor micro-embolism and indirectly through promoting thrombus. In other cases, cancer can cause other health conditions leading to PHT.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e \u003cem\u003ePullamsetti et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e reported that among 519 patients who underwent a computed tomography (CT) scan for the diagnosis of lung cancer, 250 had a mean pulmonary artery (mPA) diameter of \u0026gt;\u0026thinsp;28 mm, representing PHT. By applying another non-invasive indirect diagnosis of PHT, \u003cem\u003eEul et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e measured pulmonary artery (PA) and ascending aorta (A) diameter (size) from the images acquired from baseline high-resolution computed tomography (HRCT). They revealed that 151 of their cohort of 670 lung cancer patients (22.5%) had aPA/A ratio\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;1, their surrogate parameter for PHT. Interestingly, such patients had both dismal progression-free (PFS) and overall survival (OS). Noticeably, a decrease in the median OS of a year among lung cancer patients with PHT was demonstrated. \u003cem\u003eYang et al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e conducted a retrospective analysis of 612 non-small cell lung cancer (NSCLC) patients. By using the echocardiographic measurement of pulmonary artery systolic pressure (PASP) criteria of more than 35 mmHg as the diagnostic criteria, they reported that 19.8% of their patients had PHT. Furthermore, after adjustment for age, symptom, coagulation disorders, lymph node metastasis, distant metastasis, histological type, clinical stage, PASP\u0026thinsp;\u0026ge;\u0026thinsp;35 mmHg remained a significant associated factor relating to the adverse OS. At a cut-off value of \u0026ge;\u0026thinsp;45 mmHg, elevated PASP was an independent prognostic predictor for peri-operative death among lung cancer patients with earlier stages. Independent predictive factors of elevated PASP were age, the presence of intrapulmonary metastasis and coagulation disorders.\u003c/p\u003e \u003cp\u003eBased on the most recent 6th World Symposium on Pulmonary Hypertension, PHT has been defined as mean pulmonary artery pressure (mPAP) of more than 20 mmHg.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Even though assessment of PAP by performing right heart catheterization (RHC) remains the gold standard, it is invasive and too costly. An acceptable correlation has been demonstrated between the RHC and echocardiography.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e By using the echocardiographic measurement of mPAP of \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;20 mmHg as the diagnostic criteria of PHT, this study intended to determine the prevalence of PHT among patients with advanced lung cancer, predictive factors associated with this condition, its correlation with dyspnea symptoms, treatment response and survival.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThe investigators prospectively analyzed patients with advanced lung cancer of all histological types who had attended Division of Medical Oncology, Department of Internal Medicine, Faculty of Medicine, Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand from January 15, 2023 to January 9, 2024. At least 1 year of follow-up was required in patients who remained alive after receiving systemic treatment, otherwise from the time of cancer diagnosis to death. Median duration of follow-up for survival was 5.6 months. Inclusion criteria were patients aged more than 18 years with histologically confirmed lung cancer of advanced stage (defined as stage IV based on Eighth Edition of TNM Staging of Lung Cancer\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e in case of non-small cell lung cancer (NSCLC) and extensive disease based on Veterans Administration Lung Study Group and International Association for the Study of Lung Cancer\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003ein case of small cell lung cancer (SCLC), Eastern Co-operative Cancer Group (ECOG) performance status from 0\u0026ndash;2, and adequate renal, hepatic and bone marrow functions whom a medical oncologist determined suitable for systemic therapy of any kinds. Exclusion criteria were those with pre-existing pulmonary hypertension supposedly unrelated to lung cancer (i.e. valvular heart disease, chronic congestive heart failure, chronic venous thromboembolism, HIV infection, cirrhosis with portal hypertension, connective tissue diseases, auto-immune diseases), patients with obstructive sleep apnea requiring any forms of treatment interventions, patients with COPD GOLD severity grade of 2-4\u003csup\u003e11\u003c/sup\u003e, and patients with any restrictive lung diseases. Baseline demographics included age, sex, histologic type, body weight, body mass index (BMI), ECOG performance status were collected. Smoking status was evaluated and reported as current smoker, past smoker and never smoker. \u003cem\u003eEGFR\u003c/em\u003e mutation status would also be determined, if a participant could afford the cost of testing. Opioid use either as a pain killer or a sedative agent to palliate dyspneic symptoms was allowed during the follow-up period. Specific kind of opioids, dosage and frequency of uses were recorded.\u003c/p\u003e \u003cp\u003eTo calculate the sample size, the investigators calculated based on the results of the study by \u003cem\u003eYang, et al\u003c/em\u003e who reported that the prevalence of PHT in lung cancer patients was 19.8%.\u003csup\u003e6\u003c/sup\u003e Using the formula\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, N = [Z\u003cem\u003eα/\u003c/em\u003e2\u003csup\u003e2\u003c/sup\u003e x p(1-p)]/d\u003csup\u003e2\u003c/sup\u003e, the investigators indicated \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, p\u0026thinsp;=\u0026thinsp;0.198, margin of error (d)\u0026thinsp;=\u0026thinsp;0.10 and estimated the drop-out rate of 10%, therefore, the required sample size was at least 70 participants.\u003c/p\u003e \u003cp\u003e The study was undertaken in accordance with international guideline on Good Clinical Practice and the Declaration of Helsinki. It was approved by the Ethics Committee of Navamindradhiraj University (COA 077/2566). Informed written consents were acquired from all participating individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEchocardiographic Measurements\u003c/h2\u003e \u003cp\u003ePulmonary artery systolic pressure (PASP) was measured first by transthoracic echocardiography (TTE) that was performed by one general cardiologist and confirmed by the cardiologist who was the expert in echocardiography (P.S.). The estimation of PASP by echocardiograph derived from the application of the modified Bernoulli equation: PASP\u0026thinsp;=\u0026thinsp;4 x (peak TRV)\u003csup\u003e2\u003c/sup\u003e + RAP, TRV is the maximum tricuspid regurgitation jet speed and RAP is the right atrium pressure, which is estimated by inferior vena cava diameter. With the assumption that PASP equals to right ventricular systolic pressure (RVSP), it is then converted to mean pulmonary arterial pressure (mPAP) using the following formula mPAP\u0026thinsp;=\u0026thinsp;0.61 x RVSP\u0026thinsp;+\u0026thinsp;2 mmHg.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Pulmonary hypertension was diagnosed if mPAP value was \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;20 mmHg according to the recent 6th World Symposium on Pulmonary Hypertension diagnostic criteria.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Arguably, this updated criteria would be subject to over diagnosis and whether this threshold can determine the worse clinical outcomes has still be debatable. The investigators also used the previous criteria that defined PHT as mPAP of more than 25 mmHg as a determining factor of adverse survival. The echocardiographic measurement was performed prior to commencement of a systemic cancer treatment and after 4 cycles of such treatment and assessed in concomitant with treatment response, if a participant was still alive and able to attend follow-up visit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory tests\u003c/h2\u003e \u003cp\u003eBesides the routine laboratory parameters including complete blood counts (CBC), renal and hepatic function tests were collected prior to starting a systemic cancer treatment and subsequently as an oncologist\u0026rsquo;s discretion. Coagulation test including activated partial thromboplastin time (APTT) and prothrombin time (PT) was obtained at initial visit. All measurements were performed according to standard methods. Pre-treatment hemoglobin level, white blood cell counts, platelet counts and a participant\u0026rsquo;s BMI were then collected to calculate the Khorana score\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, widely used for prediction of cancer-associated VTE in the ambulatory setting. Diagnostic imaging studies to determine staging included computerized topography (CT) scan of chest and upper abdomen with contrast study and bone scintigraphy. Both were obtained at cancer diagnosis. Cranial CT or magnetic resonance imaging (MRI) was obtained only in case of clinically suspicious of intra-cranial metastasis. The CT scan of chest and upper abdomen with contrast study was repeated after 4 cycles of a systemic cancer treatment had been administered to determine treatment response. Fluorodeoxyglucose (FDG) positron emission tomography (FDG-PET) was optional. The treatment response was determined and categorized according to RECIST v1.1 criteria.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQuestionnaires\u003c/h2\u003e \u003cp\u003eThe investigators intended to determine the correlation between the degree of dyspnea symptoms and PHT. The Cancer Dyspnea Score (CDS-V)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e was used to determine the degree of dyspnea symptoms. The CDS-V was queried prior to starting a systemic cancer treatment and subsequently after 4 cycles of such treatment in concomitant with treatment response assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003e All participants were followed up at least 1 year after signing the informed consent. Telephone call was also done to obtain information on survival (date and cause of death). The primary outcome was the incidence of PHT among patients with advanced lung cancer eligible for treatment with systemic cancer therapy. The secondary outcomes included the 1) independent predictive factors associated with PHT, 2) correlation between the degree of PHT and dyspnea score, 3) correlation between the degree of PHT and treatment response and 4) association between PHT and 1-year survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS software, version 28.0 (IBM Corp. Armonk, NY). Continuous variables were reported as means and standard deviation (SD) if the data was normally distributed, and median and interquartile range (IQR), if not. Comparison between the groups was performed using either Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or Mann\u0026ndash;Whitney U test. Categorical variables were compared using either Chi-squared test or Fisher exact test. The investigators explored the independent factors predicting the occurrence of PHT and factors associated with worse 1-year survival. Uni-variate analysis was performed using logistic regression. Factors associated with PHT in uni-variate Cox analysis at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included in multi-variate Cox models. The models also included those considered confounders factors. Survival analysis was conducted using the Kaplan‑Meier method, and comparison was performed via log‑rank test. Multi-variate analysis was conducted using Cox proportional hazard regression model. All variables that showed association at p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in multi-variate analyses were determined statistically significant. Overall survival (OS) was defined as the interval since the date of histopathological diagnosis of lung cancer to the date of death, regardless of the causes or the date of data censorship (January 10, 2024), if a participant was still alive. To prove the correlation between the degree of PHT and Cancer Dyspnea Score (CDS), the investigators used Pearson correlation to determine and then reported its coefficient (r). The investigators also compared the echocardiographic parameters and CDS evaluated before (pre-treatment) and after (post-treatment) the commencement of systemic cancer therapy. The timing to repeat the post-treatment evaluations was within one month after 4 cycles of systemic chemotherapy or 4 months of a targeted therapy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the consort diagram of enrollment of the study participants. Most of the excluded patients were those with underlying conditions supposed to have pre-existing pulmonary hypertension prior to cancer diagnosis, i.e. chronic obstructive pulmonary disease (COPD), congestive heart failure, thromboembolic diseases and connective tissue diseases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e revealed the baseline demographic data. Overall, there were 69 patients eligible for evaluation. Most were aged more than 60 years old. Thirty seven of them (53.6%) were male and 55 of them (79.71%) had good ECOG PS (0\u0026ndash;1). Only 31 participants (44.93%) were smokers. Nearly two thirds of the participants manifested their diseases without any chest symptoms at all. Forty eight (69.57%) of them were categorized into intermediate Khorana Risk. Most (91.30%) of the participants had non-small cell lung cancer with adenocarcinoma histopathology. Forty seven patients (68.12%) received upfront treatment with platinum-doublet chemotherapy. Strikingly, when excluding patients with any concomitant conditions supposed to have pre-existing PHT, the real incidence of PHT in patients with advanced lung cancer was astonishingly high at 49.28%.\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\u003eBaseline demographic and clinical characteristics of the participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age (years) (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (56.5\u0026ndash;71.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60 years \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (33.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60 years \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (66.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (53.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (46.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian body weight (kg) (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (50\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian BMI (kg/m\u003csup\u003e2\u003c/sup\u003e ) (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.16 (19.24\u0026ndash;24.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian BSA (m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e ) (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.58 (1.49\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (30.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (49.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (20.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (79.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (20.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (55.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (44.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresenting symptoms \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (31.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (30.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoptysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (10.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (55.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKhorana risk \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (69.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (30.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (91.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenosquamous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtent of metastasis \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrapulmonary metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (66.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtrapulmonary metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (76.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e mutation status \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (23.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (49.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eROS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eALK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (23.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystemic Therapy \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatinum-Doublet Chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (68.1)\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\" colname=\"c2\"\u003e \u003cp\u003e15 (21.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (8.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL) \u0026ndash; median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4 (10.05\u0026ndash;12.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;10 g/dL \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (24.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;10 g/dL \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (75.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e(Continued)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Blood Cell (cells/mm\u003csup\u003e3\u003c/sup\u003e) \u0026ndash; median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.21 (6.27\u0026ndash;11.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;11,000 cells/mm3 \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (72.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;11,000 cells/mm3 -no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (27.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet (/mm\u003csup\u003e3\u003c/sup\u003e) \u0026ndash; median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e321.0 (234.0-403.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;350,000 \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (59.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;350,000 \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (40.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine \u0026ndash; median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.54\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine Clearance (ml/min) \u0026ndash; median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.30 (55.3\u0026ndash;102.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoagulation Function \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (68.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (31.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58 (84.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (15.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpioid use \u0026ndash; no.(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (40.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (59.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-treatment Echocardiographic Parameters \u0026ndash; median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.2 (55.9\u0026ndash;69.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRVSP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.0 (26.0\u0026ndash;37.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRVmax (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e254.0 (228.0-270.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0 (3.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emPAP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.7 (17.9\u0026ndash;25.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-treatment Echocardiographic Parameters \u0026ndash; median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.0 (55.1\u0026ndash;65.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRVSP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.0 (26.0-30.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRVmax (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e248.4 (239.0-258.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.0 (3.0-6.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emPAP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.5 (17.9\u0026ndash;20.7)\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\u003eData are presented as number (%), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (interquartile range).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrated clinical and laboratory factors associated with PHT. In uni-variate analysis, male sex, smokers, patients with high Khorana risk score, intra-pulmonary metastases, high white blood counts (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;11,000/mm\u003csup\u003e3\u003c/sup\u003e), high platelet counts (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;350,000/mm\u003csup\u003e3\u003c/sup\u003e) and abnormal prothrombin time (PT) at baseline were associated with PHT; however, in multi-variate analysis, only high Khorana risk score remained the independent predictive factor (OR\u003csub\u003eadj\u003c/sub\u003e = 48.71 (95% C.I. 1.51-1569.17); p\u0026thinsp;=\u0026thinsp;0.028). Interestingly, history of smoking had a trend towards a predictor (OR\u003csub\u003eadj\u003c/sub\u003e = 4.36 (95% C.I. 0.83\u0026ndash;22.87); p\u0026thinsp;=\u0026thinsp;0.081).\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u0026nbsp; Uni-variate and multi-variate analyses of factors associated with pulmonary hypertension\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.76923076923077%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.846153846153847%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR**\u003csub\u003eadj\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.846153846153847%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u0026lt;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e(0.44 - 3.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u0026ge;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.98969072164948%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.216494845360825%\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e(1.48 - 11.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e(0.28 - 7.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u0026nbsp; (Continued)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.632653061224488%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.76923076923077%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.846153846153847%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.846153846153847%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR**\u003csub\u003eadj\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.615384615384617%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.846153846153847%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eECOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003e0-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(0.46 - 4.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eSmoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(1.88 - 14.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e(0.83 - 22.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eSymptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eDyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(0.64 - 4.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eNon-dyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eKhorana risk\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eIntermediate risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eHigh risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e48.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(5.93 - 397.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e48.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e(1.51- 1569.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eHistologic type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eNon-adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(0.65 - 53.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eExtent of metastasis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eIntrapulmonary metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(1.46 - 13.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e(0.41 - 7.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eExtrapulmonary metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(0.32 - 2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003e\u0026lt;10 g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(0.74 - 7.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003e\u0026ge;10 g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eWhite Blood Cell (x10\u003csup\u003e3\u0026nbsp;\u003c/sup\u003ecells/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003e\u0026lt;11,000 cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003e\u0026ge;11,000 cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(1.77 - 21.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e(0.08 - 10.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003ePlatelet (x10\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003e\u0026lt;350,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003e\u0026ge;350,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(1.70 - 13.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e(0.11 - 4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(1.42 - 13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e(0.24 - 6.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003ePTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(0.53 - 7.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eOpioid use\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.291666666666664%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e(0.74 - 5.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: OR, Odds Ratio; OR\u003csub\u003eadj\u003c/sub\u003e, Adjusted Odds Ratio; CI, confident interval; NA, data not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eCrude Odds Ratio was estimated by binary logistic regression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e**\u003c/sup\u003eAdjusted Odds Ratio was estimated by multiple logistic regression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e displayed clinical and laboratory factors associated with 1-year survival. In uni-variate analysis, patients with poor performance status (ECOG 2), non-adenocarcinoma cell types, PHT, high Khorana risk score, anemia (Hb \u0026lt; 10 g/dL), high white blood cell counts (\u003cu\u003e\u0026gt;\u003c/u\u003e 11,000/mm\u003csup\u003e3\u003c/sup\u003e) and abnormal prothrombin time (PT) at baseline were associated with shorter overall survival; however, only ECOG 2 (HR\u003csub\u003eadj\u003c/sub\u003e = 6.66 (95% C.I., 1.91-19.82); p = 0.002), non-adenocarcinoma cell types (HR\u003csub\u003eadj\u003c/sub\u003e = 5.33 (95% C.I., 1.18-24.10); p = 0.03), anemia (HR\u003csub\u003eadj\u003c/sub\u003e = 4.59 (95% C.I., 1.12-18.74); p = 0.034), and abnormal PT (HR\u003csub\u003eadj\u003c/sub\u003e = 5.52 (95% C.I., 1.60-19.09); p = 0.007) were the independent prognostic factors of adverse 1-year survival rate. Those with PHT had a trend towards worse 1-year OS than those without (1-year OS, 55.23% \u003cem\u003evs\u003c/em\u003e 88.69%; p = 0.003); however, it was not significantly associated in multi-variate model (HR\u003csub\u003eadj\u003c/sub\u003e = 3.95 (95% C.I., 0.56-28.01); p = 0.169). \u0026nbsp;The investigators also performed the sensitivity analysis by adjusting the mean pulmonary arterial pressure (mPAP) threshold to either above 25 and above 30 mmHg, the result remained unchanged \u003cstrong\u003e(Supplement appendix, Table S1).\u003c/strong\u003e Provocatively, the investigators demonstrated that the degree of pre-treatment PHT somewhat correlated with Cancer Dyspnea Score (CDS) (Pearson correlation, \u003cem\u003er\u003c/em\u003e = 0.458 (95% C.I. 0.24-0.627); p \u0026lt; 0.001) as shown in \u003cstrong\u003eFigure 2\u003c/strong\u003e; however, the post-treatment mPAP did not \u003cstrong\u003e(Supplement appendix, Table S2)\u003c/strong\u003e. The investigators also evaluated the correlation between echocardiographic parameters and CDS before (pre-treatment) and after (post-treatment) administration of systemic cancer therapy. There were 32 patients who could proceed to post-treatment echocardiographic evaluation; while there were 45 patients could respond to post-treatment CDS questionnaires. The investigators noticed that those who had received systemic therapy and could repeat response evaluations, significantly had decreased mPAP and improvement in CDS \u003cstrong\u003e(Supplement appendix, Table S3)\u003c/strong\u003e. However, such findings should be interpreted cautiously, those who could repeat the echocardiographic evaluation and respond post-treatment CDS were those who remained alive and could attend the follow-up visits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u0026nbsp; Multi-variate analysis of overall survival (OS)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.580645161290324%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.516129032258064%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003cstrong\u003e\u003csub\u003eadj\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.580645161290324%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.516129032258064%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003emPAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e\u0026lt;20 mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReferrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReferrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e\u0026ge;20 mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(1.53 - 18.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.56 - 28.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e\u0026lt;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e\u0026ge;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.51 - 4.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.50 - 3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eECOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e0-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(1.35 - 9.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(1.91 - 19.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eSmoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.68 - 4.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eSymptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eNon-Dyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1,00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eDyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.90 - 6.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eCough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.85 - 5.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eHemoptysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.45 - 8.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eWeight loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.91 - 8.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eChest pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.38 - 21.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.20 - 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eKhorana risk\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eIntermediate risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eHigh risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(2.06 - 15.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.10 - 12.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eHistologic type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eNon-adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(1.30 - 12.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(1.18 - 24.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eExtent of metastasis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eIntrapulmonary metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.54 - 5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eExtrapulmonary metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.57 - 10.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e\u0026lt;10 g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(2.45 - 17.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(1.12 - 18.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e\u0026ge;10 g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u0026nbsp; (Continued)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.580645161290324%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.516129032258064%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.903225806451612%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003cstrong\u003e\u003csub\u003eadj\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.580645161290324%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.516129032258064%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eWhite Blood Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e\u0026lt;11,000 cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e\u0026ge;11,000 cells/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(1.01 - 6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.09 - 2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003ePlatelet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e\u0026lt;350,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003e\u0026ge;350,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.91 - 6.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(2.16 - 15.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(1.60 - 19.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003ePTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.95 - 7.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eOpioid use\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.73684210526316%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e(0.92 - 8.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: NA, data not applicable; HR, Hazard Ratio; HR\u003csub\u003eadj\u003c/sub\u003e, Adjusted Hazard Ratio; CI, confident interval.\u003c/p\u003e\n\u003cp\u003e\u0026dagger;Crude HR was estimated by Cox proportional hazard model.\u003c/p\u003e\n\u003cp\u003e\u0026Dagger;Adjusted HR was estimated by Cox proportional hazard model adjusting for ECOG, Khorana risk, hemoglobin, white blood cell, and PT coagulation function. (Any variables with significant correlation at p \u0026lt; 0.05 in uni-variate analysis were selected to evaluate correlations in multi-variated model.)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePulmonary hypertension (PHT) in patients with cancer can occur as a result of various factors. Most of the world authorities usually classify PHT into 5 categories.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eSalamo et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e retrospectively studied cancer patients who underwent right heart catheterization (RHC) and revealed that 133 out of 180 patients had PHT. Half of them had solid malignancies, while most of the rests had hematologic ones. Half of them had PHT in association with left-sided heart disease (group 2), around a quarter of them had pre-existing systemic diseases supposed to be the leading cause of PHT (group 1), around one sixth of them had underlying chronic lung diseases (group 3), the rests had concomitant macro-thromboembolism (due to blood clot or tumor emboli) (group 4) or had a variety of poorly defined causes (group 5). Interestingly, nearly two-thirds of them had recently treated with specific anti-cancer treatment. Currently, cancer-associated PHT (or tumoral PHT) is classified within group 5 to represent the multifaceted etiology. The tumoral PHT is a \u0026ldquo;microvascular disease\u0026rdquo; manifesting as the spectrum from \u003cem\u003epulmonary tumor micro-embolism (PTE)\u003c/em\u003e characterized by the occlusion of small pulmonary arteries by cohesive tumor cells to \u003cem\u003epulmonary tumor thrombotic microangiopathy (PTTM)\u003c/em\u003e characterized by the presence of pulmonary vascular tumor micro-embolic nests with evidence for activation of coagulation resulting in obliterative intimal proliferation. Both of these pathologic findings usually co-exist. The PTE usually occur in association with adenocarcinomas, including cholangiocarcinoma, renal, breast, gastric, bladder and choriocarcinoma. While, the PTTM typically relates to a carcinoma, especially an adenocarcinoma, including gastric cancer, breast, lung, bladder, ovarian clear cell, hepatobiliary and choriocarcinoma.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Cancer by itself is a hypercoagulable state. Cancer cells can directly activate of coagulation and platelets can occur through their expression of tissue factor (TF), the key initiator of the coagulation cascade, cancer procoagulant (CP) shown to directly activate coagulation cascade by activating Factor X and podoplanin (PDPN) causing platelet activation and aggregation in concert with plasminogen activation inhibitor-1 (PAI-1), a key inhibitor of fibrinolysis. Cancer cells also secrete platelet agonists such as ADP and thrombin, thus further promoting platelet activation. Phosphatidyl serine (PS) expressed on tumor microparticles may also promote coagulation as PS serves as a surface for formation of coagulation complexes. Indirectly, disseminated cancer cells can infiltrate into nearby blood vessels. Moreover, inflammatory cytokines secreted from cancer cells result in platelet activation and promote the procoagulant phenotype in endothelial cells. Cancer-derived factors also induce neutrophils to release neutrophil extracellular traps serving as a scaffold that effectively entrap platelets, or activate platelets through NET-associated histones, ultimately leading to profound platelet activation, fibrin deposition, and entrapment of red blood cells, further exacerbating clot formation.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e \u003cem\u003ePullamsetti, et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e performed histo-pathologically analysis to determine the changes in microvasculature within human lung cancer tissue obtained from 14 different tissue sections and observed that there were increased vascular remodeling and perivascular accumulation of inflammatory cells within human lung cancer tissue. Additionally, when they co-cultured human lung cancer cells with macrophages and lymphocytes, it resulted in releasing of inflammatory cytokines that promoted tumor migration, apoptosis resistance, as well as phosphodiesterase 5 (PDE5)-mediated upregulation of human lung epithelial cells mimicking the features of PHT. Their findings supported the hypothesis that the interplay between tumor cells and inflammatory cells consequently promotes derangement of intra-tumoral vasculature which is the hallmark of pulmonary hypertension.\u003c/p\u003e \u003cp\u003eThe true incidence and prevalence of lung cancer-associated PHT have been scarcely reported. Furthermore, most of them had different methodologies in terms of different inclusion criteria, modalities of diagnostic tools and diagnostic criteria. \u003cem\u003ePullamsetti et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e retrospectively studied 519 patients who underwent a computed tomography scan for the diagnosis of lung cancer, and reported that 250 of them (48.2%) had a mean pulmonary artery (PA) diameter of more than 28 mm, their diagnostic criteria of PHT. Notably, most of their participants had SCLC and had pre-existing COPD. \u003cem\u003eEul et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e retrospectively analyzed 670 patients with lung cancer and measured their PA/A ratio from the images acquired with baseline HRCT and demonstrated that 51 of them (22.5%) had aPA/A ratio of more than 1, their diagnostic criteria of PHT. \u003cem\u003eYang et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e conducted a retrospective study on 612 Chinese NSCLC patients regardless of cancer stages at diagnosis and used PASP of more than 35 mmHg measured by echocardiography as their diagnostic criteria of PHT and showed that 19.8% of them had PHT. Notably, more than half of their participants had either stage III or IV and nearly two thirds of them had adenocarcinoma. This study recruited the participants who had advanced lung cancer, regardless of histologic type but on the condition that they were eligible for systemic cancer treatment. On the contrary, this prospective study tried to excluded any pre-existing systemic conditions supposed to be the leading causes of PHT besides their lung cancer per se. According to this study\u0026rsquo;s result, the incidence of PHT is 49.28%, which was more pronounced than Yang\u0026rsquo;s. Presumably, according to the inclusion criteria, this study\u0026rsquo;s participants were all diagnosed at advanced stage, the more extensive tumor burden would explain the higher proportion of participants who had PHT. The investigators intended to investigate among such group of patients who needed urgent specific cancer therapy and required optimal supportive care in particular. To exclude those with earlier stages was essential to eliminate the confounding factors such as radiotherapy and surgery that might affect the study\u0026rsquo;s outcomes.\u003c/p\u003e \u003cp\u003eRegarding the associated predictive factors of PHT, the investigators demonstrated that only high Khorana risk score was the independent factor. While smoking had a trend. In accordance with the report by \u003cem\u003eYang et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, extensive intra-pulmonary metastases and abnormal coagulation were also their significant associated predictors, while smoking and abnormal blood counts had the trends towards associating with PHT. This study had its strength in applying more relevant parameter of predicting cancer-associated thromboembolism like Khorana score that was proved their benefit in predicting the chance of developing PHT in addition. The association of Khorana score in predicting the PHT has substantiated the hypothesis that the hypercoagulable state is one of the promoting factor of PHT generation.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBased on the fact that exertional dyspnea is the most frequent complaint for which a PHT patient seeks medical attention and its severity of dyspnea on exertion progresses even to dyspnea at rest as the degree of PHT advances. However, among cancer patients who had multifactorial causes of dyspnea, these assumptions are arguable. The investigators performed the Pearson correlation and showed that the degree of PHT was partially in agreement with the degree of dyspneic symptoms as determined by the CDS questionnaire. Furthermore, the investigators also found that those who remained alive after 4 months of proper cancer treatment, there were the hints that the degree of PHT and CDS were alleviated. In comparison with healthy individuals, at a given CO\u003csub\u003e2\u003c/sub\u003e generation during exercise, ventilatory demands in patients with PHT are higher as a result of metabolic acidosis (owing to early reaching their anabolic threshold), hypoxemia, and excessive upward shift of metabolic hyperbola due to abnormal exercise response of dead space to tidal volume ratio. Simultaneously, dynamic hyperinflation and respiratory weakness further reduces the actual ventilation for a given respiratory center activity, creating a demand-to-ventilation dissociation. Consequently, a progression in ventilatory demands and respiratory center activity occurs during exercise. Moreover, the forebrain projection of high respiratory center activity results in exertional dyspnea despite the relatively low ventilation and significant ventilatory reserve.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e These captivating findings will provide us the opportunities to conduct the clinical trials on strategies to relieve symptoms in lung cancer patients who have concomitant cancer-associated dyspnea and PHT. Besides, supportive measures such as opioids, O\u003csub\u003e2\u003c/sub\u003e supplement, if indicated and appropriate systemic cancer treatment, anti-coagulants, vasodilators, and novel agents\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e should be investigated in well-designed clinical trials specifically in cancer patients with dyspnea.\u003c/p\u003e \u003cp\u003eIn respect of its association with survival, the investigators demonstrated a trend towards worse 1-year survival rate among participants with PHT. In agreement with \u003cem\u003eEul et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e when a cutoff PA/A ratio of \u0026gt;\u0026thinsp;1 was employed to determine the diagnosis of PHT, those with PHT had significant shorter PFS and OS than those without (median PFS, 133 vs 270 days (p\u0026thinsp;=\u0026thinsp;0.004); median OS, 207 vs 568 days (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)). Their statistical analysis was adjusted for sex, age, BMI, cancer type, cancer stage, and PO\u003csub\u003e2\u003c/sub\u003e. Also in accordance with \u003cem\u003eYang et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e ,when a cutoff PASP\u0026thinsp;\u0026gt;\u0026thinsp;35 mmHg was applied to determine the diagnosis of PHT, the 3- and 5-year OS rates of NSCLC were 57.1% vs 49.5% and 91.3% vs 84.4% in patients with and without PHT, respectively. After adjustment for age, symptom, coagulation disorders, lymph node metastasis, distant metastasis, histological type, clinical stage, PHT remained a significantly associated factor adverse survival (p\u0026thinsp;=\u0026thinsp;0.028). This study may not have enough power to demonstrate the co-relation with survival.\u003c/p\u003e \u003cp\u003eIn line with the conventional prognostic factors like male sex, poorer PS, non-adenocarcinoma cell types (squamous cell carcinoma and small cell lung cancer)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, the investigators disclosed that anemia and abnormal PT were also the independent prognostic factors of shorter survival. \u003cem\u003eCaro et al.\u003c/em\u003e carried out the comprehensive literature review on the effect of anemia on survival outcome among patients with various cancers and showed that the relative risk of death increased by 19% (95% C.I., 10 \u0026minus;\u0026thinsp;29%), 47% (21\u0026ndash;78%), 75% (37\u0026ndash;123%), 67% (30 \u0026minus;\u0026thinsp;113%) in anemic patients with lung carcinoma, head and neck carcinoma, prostate carcinoma, and lymphoma, respectively. The overall estimate increase in risk of death was 65% (54 \u0026minus;\u0026thinsp;77%).\u003csup\u003e25\u003c/sup\u003e In accordance with the results reported by recent individual studies, anemia was the independent prognostic factor of worse survival. \u003cem\u003eChen et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e revealed that patients with advanced NSCLC with anemia of grade 3 to 4 had the shortest OS. Even among those harboring \u003cem\u003eEGFR\u003c/em\u003e mutation in particular, patients with anemia of grade 2 or better had independently longer median OS.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Advanced cancer is a systemic inflammatory disease. Its biologic effects, especially through cytokine interference related to high tumor burden would affect the red blood cell production. Provocatively, abnormal PT was also among the unconventional prognostic factor of survival demonstrated by this study. \u003cem\u003eBayleyegn et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003eperformed a meta-analysis to determine the correlation between basic coagulation abnormalities in patients with lung cancer and found that comparing with the control, lung cancer patients had higher prothrombin time (PT), D-dimer level, fibrinogen level and higher platelet counts. This evidence supports the hypercoagulability in patients with lung cancer. \u003cem\u003eTas et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e explored the prognostic value of blood coagulation tests among 110 patients with lung cancer (both small and non-small cell lung cancer). Pre-treatment blood coagulation tests including PT, aPTT, PTA, INR, D-dimer, fibrinogen levels and platelet counts were evaluated. Only elevation of PT and INR were associated with significantly associated with adverse survival. \u003cem\u003eLi et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e retrospectively collected the coagulation data from 604 histologically confirmed NSCLC patients and found that among basic coagulation parameters i.e. PT, INR, activated partial thromboplastin time (aPTT), D-dimer, fibrinogen level, and platelet counts, only PT and INR were the independent prognostic factors of survival. \u003cem\u003eAbbass et al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e also retrospective evaluated 216 patients with advanced NSCLC who had received first-line combination platinum-based chemotherapy with or without an anti-angiogenic agent and found that abnormal PT and higher fibrinogen and D-dimer levels were associated with shorter survival. The investigators speculate that besides the usual systemic cancer treatment whether lung cancer patients with obvious evidence of hypercoagulable state should receive thromboprophylaxis is a research question to be elucidated.\u003c/p\u003e\n\u003cp\u003e \u003cstrong\u003eStrengths\u003c/strong\u003e \u003cp\u003eThis study was conducted in prospective fashion; therefore, the crucial parameters were collected systemically. The widely acceptable clinical tool to predict cancer-associated thromboembolism like Khorana score was also included. In order to eliminate the confounding factors existed in previous study, the investigators excluded patients with earlier stages who were more suitable for locoregional therapy and those who had pre-existing conditions supposed to relate with PHT, unrelated to their cancers per se.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimitations\u003c/strong\u003e \u003cp\u003eDue to limited budget and time, the investigators conducted the clinical study in small group of patients. Larger sample size would result in more extensive clinical findings and stronger evidences. Nevertheless, the investigators disclosed some provocative data that previous study had not mentioned or omitted.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that nearly half of the patients with advanced lung cancer had PHT. Khorana risk score would be a clinical tool to predict this condition and select patients for further investigations. Its occurrence co-related with the degree of dyspneic symptoms. The investigators suggested that those with complaint of dyspneic symptoms, it should be compulsory to exclude concomitant pre-existing cardiopulmonary morbidities. Echocardiography would be useful to evaluate cardiac condition and determine the co-existing PHT. Besides symptomatic and supportive care in combination with appropriate anti-cancer therapy, the well-designed randomized trials to investigate the values of novel agents conducted particularly in cancer patients are warranted. Besides the conventional prognostic factors like ECOG performance status, histological subtype, the hypercoagulable state would be an emerging clinical prognostic factor.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAPTT Activated partial thromboplastin time\u003c/p\u003e \u003cp\u003eCDS Cancer Dyspnea Score\u003c/p\u003e \u003cp\u003emPA Mean pulmonary artery\u003c/p\u003e \u003cp\u003emPAP Mean pulmonary artery pressure\u003c/p\u003e \u003cp\u003ePASP Pulmonary artery systolic pressure\u003c/p\u003e \u003cp\u003ePHT Pulmonary hypertension\u003c/p\u003e \u003cp\u003ePT Prothrombin time\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe investigators thank the patients and family members for their trust and contribution to this study. Yotsawaj Runglodvatana, M.D., Apisada Sutepvarnon, M.D., Gorawich Kerkarchachai, M.D. and Lucksika Wanichtanom, M.D. for the assistance in patient recruitment. Nurses and medical personnel at the Division of Medical Oncology and Division of Cardiology, Department of Internal Medicine, Vajira Hospital, Navamindradhiraj University for follow-up assistance during study period. Mr. Anucha \u0026nbsp;Kamsom for statistical consultation and clinical outcomes assessment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC. Bandidwattanawong was responsible for conceptualization, methodology, supervision, data analysis, writing the original draft and editing. P. Sureeyathanaphat was responsible for performing echocardiography and validating data results. G.Vrakornvoravuti was responsible for data collection and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported under the approval by Navamindradhiraj University Research Management System committees.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data and statistical analytic models that support the findings of this study are available upon request from the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study proposal was approved by the Ethics Committee of Navamindradhiraj University (COA 077/2566). Informed written consents were acquired from all participating individuals.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarta JA, Powell CA, Wisnivesky JP. Global Epidemiology of Lung Cancer. Ann Glob Health. 2019;85(1):8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKenzie E, Hwang MK, Chan S, Zhang L, Zaki P, Tsao M, et al. Predictors of dyspnea in patients with advanced cancer. Ann Palliat Med. 2018;7(4):427\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBallout FA, Manshad AS, Okwuosa TM. Pulmonary Hypertension and Cancer: Etiology, Diagnosis, and Management. Curr Treat Options Cardiovasc Med. 2017;19(6):44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePullamsetti SS, Kojonazarov B, Storn S, Gall H, Salazar Y, Wolf J, et al. Lung cancer-associated pulmonary hypertension: Role of microenvironmental inflammation based on tumor cell-immune cell cross-talk. 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Pulm Circ. 2022;12(1):e12060.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlamri AK, Ma CL, Ryan JJ. Novel Drugs for the Treatment of Pulmonary Arterial Hypertension. Where Are We Going? Drugs. 2023;83(7):577\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander M, Wolfe R, Ball D, Conron M, Stirling RG, Solomon B, et al. Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer. Br J Cancer. 2017;117(5):744\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaro JJ, Salas M, Ward A, Goss G. Anemia as an independent prognostic factor for survival in patients with cancer: a systemic, quantitative review. Cancer. 2001;91(12):2214\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen C, Song Z, Wang W, Zhou J. Baseline anemia and anemia grade are independent prognostic factors for stage IV non-small cell lung cancer. Mol Clin Oncol. 2021;14(3):59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei J, Xiang J, Hao Y, Si J, Wang W, Li F, Song Z. Baseline anemia predicts a poor prognosis in patients with non-small cell lung cancer with epidermal growth factor receptor mutations: a retrospective study. BMC Pulm Med. 2022;22(1):381.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBayleyegn B, Adane T, Getawa S, Aynalem M, Kifle ZD. Coagulation parameters in lung cancer patients: A systematic review and meta-analysis. J Clin Lab Anal. 2022;36(7):e24550.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTas F, Kilic L, Serilmez M, Keskin S, Sen F, Duranyildiz D. Clinical and prognostic significance of coagulation assays in lung cancer. Respir Med. 2013;107(3):451\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Wei S, Wang J, Hong L, Cui L, Wang C. [Analysis of the factors associated with abnormal coagulation and prognosis in patients with non-small cell lung cancer]. Zhongguo Fei Ai Za Zhi. 2014;17(11):789\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbbas M, Kassim SA, Wang ZC, Shi M, Hu Y, Zhu HL. Clinical evaluation of plasma coagulation parameters in patients with advanced-stage non-small cell lung cancer treated with palliative chemotherapy in China. Int J Clin Pract. 2020;74(12):e13619.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"pulmonary hypertension, lung cancer, Khorana score, adverse outcomes","lastPublishedDoi":"10.21203/rs.3.rs-4585295/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4585295/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003ePulmonary hypertension (PHT) has been reported to be prevalent across various stages of lung cancer patients and associated with adverse outcomes. This study was aimed to determine prevalence among patients with advanced lung cancer and its association with dyspnea symptom and survival and.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatients with stage IV lung cancer were recruited. PHT was diagnosed, if mean arterial pulmonary pressure (mPAP) was above 20 mmHg as determined by echocardiography. Baseline demographics including age, sex, smoking status, histologic types, performance status (PS), extent of pulmonary involvement, Khorana score, presenting symptoms, systemic cancer therapy, cancer dyspnea score (CDS) and 1-year survival were collected.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were 69 eligible patients. Thirty-four patients (49.28%) had PHT. Only high Khorana risk score was the independent predictive factor of development of PHT at baseline (OR\u003csub\u003eadj\u003c/sub\u003e = 48.71 (95% C.I. 1.51-1569.17); p\u0026thinsp;=\u0026thinsp;0.028). History of smoking had a trend towards a predictor (OR\u003csub\u003eadj\u003c/sub\u003e = 4.36 (95% C.I. 0.83\u0026ndash;22.87); p\u0026thinsp;=\u0026thinsp;0.081). Furthermore, those with PHT had a trend towards shorter survival than those without (1-year OS, 55.23% \u003cem\u003evs\u003c/em\u003e 88.69%; p\u0026thinsp;=\u0026thinsp;0.003); however, ECOG 2 (HR\u003csub\u003eadj\u003c/sub\u003e = 6.66 (95% C.I., 1.91\u0026ndash;19.82); p\u0026thinsp;=\u0026thinsp;0.002), non-adenocarcinoma cell types (HR\u003csub\u003eadj\u003c/sub\u003e = 5.33 (95% C.I., 1.18\u0026ndash;24.10); p\u0026thinsp;=\u0026thinsp;0.03), anemia (HR\u003csub\u003eadj\u003c/sub\u003e = 4.59 (95% C.I., 1.12\u0026ndash;18.74); p\u0026thinsp;=\u0026thinsp;0.034), and abnormal PT (HR\u003csub\u003eadj\u003c/sub\u003e = 5.52 (95% C.I., 1.60-19.09); p\u0026thinsp;=\u0026thinsp;0.007) were the independent prognostic factors of short survival. Higher degree of PHT was also correlated with higher CDS (Pearson correlation, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.458; 95% C.I. 0.25\u0026ndash;0.63; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn line with the historical reports, PHT is quite prevalent in patients with advanced lung cancer. Due to its co-relation with CDS, any agents which can lessen the degree of PHT should be further investigated for the purpose of improving patients\u0026rsquo; symptom burden before the systemic therapy takes its action.\u003c/p\u003e","manuscriptTitle":"Pulmonary hypertension is common among patients with advanced lung cancer and Khorana score is the predictive indicator","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 23:42:24","doi":"10.21203/rs.3.rs-4585295/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":"a5bb5545-a164-4b52-9a6d-159c67a9ec03","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-26T01:53:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-15 23:42:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4585295","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4585295","identity":"rs-4585295","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}