Abstract
Patients with gastric cancer (GC) are at higher risk of thromboembolism when compared to other solid tumors. We aim to determine the predictive performance of current venous thromboembolism (VTE) predictive tools and their variability and validity after first treatment. Single institution cohort of GC-treated patients (2010*15). We abstracted predictive tools, validated for VTE prediction in patient with cancer; including the Khorana Score (KRS), platelet to lymphocyte ratio (PLR), and neutrophil to lymphocyte ratio (NLR). The primary outcome was CAT prediction. We included 112 patients who were predominantly men (66%), 58 (51-64)-year-olds, with adenocarcinoma (84%) and advanced disease (59%). The median follow-up was 21.3 months (9.5-42.6). The VTE occurrence was 12%. The median time from diagnosis to VTE occurrence was 59 days (36-258). In our cohort, performance status (PS; hazard ratio [HR], 8.02; 95% confidence interval [CI], 2.37-27.14; P < .01) was an independent predictor of VTE whereas KRS (univariate HR, 2.3; 95% CI, 0.7-7.4; P = .17), PLR (univariate HR, 0.8; 95% CI, 0.2-3.1; P = .8), and NLR (univariate HR, 0.8; 95% CI, 0.3-2.5; P = .8) at baseline were not associated with VTE risk. The posttreatment KRS was an independent predictor of VTE (HR, 3.69; 95% CI, 1.17-11.65; P = .25) along with PS (HR, 7.58; 95% CI, 2.27-25.33; P = .01). Posttreatment KRS appears as a valid tool to identify patients with GC at high risk of VTE after first cancer treatment.
Keywords: gastric cancer, Khorana Score, cancer-associated thrombosis, risk assessment model
Introduction
Venous thromboembolism (VTE) is a largely preventable disease and represents a leading cause of morbidity and mortality among patients with cancer.1 Patients with gastric cancer (GC) are at higher risk of VTE2,3 compared to other solid tumors. Accounting for its prevalence, GC may be responsible for one of the highest incidences of cancer-associated thrombosis (CAT).4 Patients with cancer have a 4- to 7-fold increased risk of VTE.5–8 In addition, chemotherapy is associated with a 2 to 6-fold increase in likelihood of VTE compared with the general population.7–10 Although primary prevention against CAT is proven effective,11 using current risk assessment tool has not yet selected the population who will clearly benefit from this strategy.12
The pro-coagulant state in patients with cancer is in part secondary to its complex interaction with hematopoietic cells and activation of the coagulation system.13 In this context, Khorana et al derived a simple calculated model which weighs 5 clinical/biochemical risk factors to create an aggregate risk score that correlates with CAT in chemotherapy.14 The Khorana Score (KRS) is the most widely validated risk assessment model.15,16 In addition, platelet to lymphocyte ratio (PLR) and neutrophil to lymphocyte ratio (NLR) have been proposed as surrogate markers of systemic inflammation able to predict the occurrence of thrombotic events in patients with cancer.17,18
Nonetheless, there is a paucity of data analyzing the VTE prediction rate of these risk assessment tools in patients with GC. Moreover, the variability after cancer treatment may impact these prediction strategies. Therefore, we aimed to determine their predictive performance, and posttreatment reclassification in patients with GC.
Material and Methods
Database
We retrospectively abstracted the records of patients with the diagnosis of GC treated in the Department of Oncology at John Stroger Hospital in Chicago-Illinois between the years of 2010 and 2015. Faculties of the Department of Internal Medicine settled and maintained the database. The Institutional review board approved this study.
Patients
We included patients 18 years or older; we only included patients with histological confirmation, and all were followed for at least 5 weeks. We excluded patient on chronic anticoagulation and those who started cancer treatment in a different institution.
Variables
Demographic variables and cancer-specific variables including performance status (PS), stage, histological type, and number of metastatic sites at diagnosis were extracted after review of the electronic health records. We defined cancer treatment as radiation, first cycle of chemotherapy, and surgery for palliative and/or curative purposes. Advanced disease was considered as stages III and IV and limited disease less than stage III to make groups comparable. We obtained clinical and biochemical data corresponding to each risk assessment model before and after any cancer treatment. Pretreatment predictors were quantified within 1 month of diagnosis and prior starting any type of cancer treatment. Posttreatment KRS, PLR, and NLR were estimated based on clinical/biochemical data extracted after 1 week of first cancer treatment. We calculated the pre and posttreatment KRS as follows: 2 points for GC and 1 point for each of the following body mass index (BMI) >35 kg/m2, hemoglobin <10 g dL−1 or erythropoietin stimulating agent, white blood cells ≥11.000 and platelets ≥350.000. Subsequently, we dichotomized the KRS into intermediate (1-2 points) and high (≥3 points) based on the original description.14 We used proposed values for NLR (>3)17,18 and PLR (>260)17 to dichotomize patients into high (above cutoffs) and low risk (below cutoffs) for VTE.
Outcomes
The primary outcome measure was VTE, including symptomatic or incidentally found deep vein thrombosis (DVT) of the upper and lower extremities, pulmonary embolism (PE), and splanchnic vein thrombosis (SVT). The VTE was radiologically diagnosed, by Doppler ultrasonography, computerized tomography (CT), CT angiography, and/or ventilation/perfusion scans. Outcomes were blindly adjudicated by 3 abstractors and in case of discrepancy we adjudicated by consensus. We censored patients at time of death and at time of last follow-up.
Statistical Analysis
We calculated descriptive statistics to summarize the distribution of baseline parameters. Continuous variables are expressed by the mean and the standard deviation; or by the median and interquartile range (IQR) as appropriate. Categorical variables are expressed by the absolute numbers and percentages. We used previously published cutoffs for PLR (>260)17 and NLR (>3).17,18 We used Pearson chi-square (with Yates continuity correction), Fisher exact test, independent t test, and Mann-Whitney U test to ascertain differences between VTE and non-VTE groups accordingly. We used the Kaplan-Meier method to estimate the cumulative incidence of VTE.
We created independent models for each construct. Then, we used Cox proportional hazard regression with forward modeling to explore the association between pre/posttreatment predictors and CAT. We did not include BMI in the multivariate analysis in conjunction with the Khorana model to avoid singularity. The variability of each risk assessment model (RAM) after first treatment was examined by McNemar test. We also addressed the variability of each component of the VTE predicting tools using paired sample t test, McNemar test, and Wilcoxon signed rank test as appropriate according to normality and type of variable. Furthermore, we adopted the approach proposed by Pencina et al19 to calculate the net reclassification improvement (NRI) to quantify the degree of correct reclassification of patients after cancer treatment, accounting for the prevalence in this study.20 We only calculated the NRI in those predictive tools associated with CAT to avoid misinterpretation.21
We used the receiver operating characteristic (ROC) methodology to analyze the discriminatory capacity of PLR and NLR. We evaluated the prediction increment of these ratios considering the improvement in the area under the curve (AUC) after treatment (ΔAUC). We considered P < .05 to be statistically significant, and all tests were 2-sided. For all statistical computations, we used SPSS, version 23 (IBM Corp, Armonk, New York).
Results
Study Population
From a total of 127 patients with GC, we included 112 patients in the analysis, as 12 received treatments outside our institution and no pretreatment blood counts were available, 1 had chronic VTE on anticoagulation, and 2 did not follow up after diagnosis. The patients in this study were predominantly men (66%), 58 years old (IQR: 51-64) with adenocarcinoma (84%) and advanced disease (58.9%). From the eligible patients, 35 (38.5%) received radiotherapy, 89 (69.2%) received chemotherapy, and 68 (46.2%) had surgery (Tables 1 and 2). None of the patients included used erythropoietin. The median follow-up was 21.3 months (IQR: 9.5-42.6) and at the end of the follow-up period 59 (52.7%) patients died, 20 (17.86%) patients became hospice, 17 (15.17%) lost follow-up, and 16 were alive.
Table 1.
Baseline Demographics in the VTE and Non-VTE Group Among Patients With Gastric Cancer.
| Variables | Cohort | Non-VTE | VTE | ||
|---|---|---|---|---|---|
| N | 112 | 99 | 13 | P Value | |
| Patient-related variables | |||||
| Age | <65 years | 84 (75) | 74 (74.7) | 10 (76.9) | 1.000a |
| Gender | Men | 74 (66.1) | 64 (64.6) | 10 (76.9) | 0.537a |
| Race | African American | 49 (43.8) | 41 (41.4) | 8 (61.5) | 0.578a |
| White | 29 (25.9) | 27 (27.3) | 2 (15.4) | ||
| Hispanic | 26 (23.2) | 24 (24.2) | 2 (15.4) | ||
| Asian | 8 (7.1) | 7 (7.1) | 1 (7.7) | ||
| BMI | ≤30 | 90 (80.4) | 82 (82.8) | 8 (61.5) | 0.128a |
| Smoker | Yes | 64 (57.1) | 56 (56.6) | 8 (61.5) | 0.966b |
| ECOG | 0-1 | 78 (69.6) | 74 (74.7) | 4 (30.7) | 0.003b |
| Cancer-related variables | |||||
| Stage | Advanced | 66 (58.9) | 56 (56.6) | 10 (76.9) | 0.270b |
| Histology | Adenocarcinoma | 95 (83.9) | 82 (82.8) | 13 (100) | 0.212a |
| Metastasis at diagnosis | One site | 55 (49.1) | 47 (83.9) | 8 (80) | 1.000b |
| Treatment-related variables | |||||
| Radiation | 35 (31.3) | 30 (30.3) | 5 (38.5) | 0.540a | |
| Surgery | Total | 68 (60.7) | 62 (62.6) | 6 (46.2) | 0.400b |
| Palliative intent | 25 (36) | 24 (38.7) | 1 (16.7) | 0.402a | |
| Curative intent | 43 (64) | 38 (61.3) | 5 (83.3) | ||
| Chemotherapy | Total | 89 (79.5) | 80 (80.8) | 9 (69.2) | 0.463a |
| First line | 58 (65.2) | 50 (66.3) | 8 (88.9) | 0.714a | |
| ≥Second line | 31 (34.8) | 30 (33.7) | 1 (11.1) | ||
| Pretreatment risk assessment tools | |||||
| KRS | High risk | 59 (52.7) | 50 (50.5) | 9 (69.2) | 0.329b |
| NLR | >3 | 57 (50.9) | 51 (51.5) | 6 (46.2) | 0.917b |
| PLR | >260 | 34 (30.4) | 31 (31.3) | 3 (23.1) | 0.751a |
| Posttreatment risk assessment tools | |||||
| KRS | Intermediate risk | 86 (76.8) | 78 (78.8) | 8 (61.5) | 0.176a |
| NLR | ≤3 | 84 (75) | 75 (75.8) | 9 (69.2) | 0.917b |
| PLR | ≤260 | 93 (83.1) | 81 (81.8) | 12 (92.3) | 0.303a |
| Biomarkers | |||||
| Prechemotherapy hemoglobin, mean (SD) | 11.2 (2.03) | 11.2 (1.9) | 10.6 (2.7) | 0.408c | |
| Prechemotherapy neutrophils, median (IQR) | 4.4 (3.8) | 4.2 (3.7) | 5.1 (4.4) | 0.239d | |
| Prechemotherapy lymphocytes, median (IQR) | 1.6 (1.1) | 1.5 (1) | 1.8 (1.25) | 0.057d | |
| Prechemotherapy platelets, median (IQR) | 266 (144) | 264.0 (120) | 328.0 (220) | 0.421d | |
Abbreviations: BMI, body mass index; ECOG, Eastern Cooperative Oncology Group; IQR, interquartile range; KRS, Khorana score; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; SD, standard deviation; VTE, venous thromboembolism.
aFisher exact test.
bChi-square.
ct Test.
dMann-Whitney U test.
Table 2.
Systemic Therapy.a
| Chemotherapy combinations used | |
| 1. 5-Fluorouracil and leucovorin | |
| 2. 5-Fluorouracil, leucovorin, and oxaliplatin (FOLFOX) | |
| 3. 5-Fluorouracil, leucovorin, and irinotecan (FOLFIRI) | |
| 4. Cisplatin and 5-fluorouracil | |
| 5. Epirubicin, cisplatin, and 5-fluorouracil (ECF) | |
| 6. Epirubicin, cisplatin, and capecitabine (ECX) | |
| 7. Docetaxel, cisplatin, and 5-fluorouracil (DCF) | |
| 8. Capecitabine and oxaliplatin (XELOX) | |
| 9. Etoposide, leucovorin, and 5-fluorouracil (ELF) | |
| 10. Etoposide, adriamycin, and cisplatinun (EAP) | |
| 11. Epirubicin, oxaliplatin, and capecitabine (EOX) | |
| 12. Capecitabine and irinotecan (XELIRI) | |
| 13. Paclitaxel and carboplatin | |
| 14. S-1 | |
| Monoclonal antibodies | |
| 1. Trastuzumab | |
| 2. Ramicirumab | |
| 3. Bevacizumab | |
| 4. Imatinib | |
| First-line chemotherapy used | (n) |
| 1. 5-Fluorouracil, leucovorin, and oxaliplatin | 25 |
| 2. 5-Fluorouracil, leucovorin, oxaliplatin and trastuzumab | 4 |
| 3. 5-Fluorouracil, leucovorin, oxaliplatin and bevacizumab | 1 |
| 4. 5-Fluorouracil and leucovorin | 11 |
| 5. Cisplatin, 5-fluorouracil and trastuzumab | 1 |
| 6. Cisplatin and paclitaxel | 1 |
| 7. Paclitaxel and carboplatin | 4 |
| 8. Docetaxel, cisplatin and 5-fluorouracil | 4 |
| 9. Epirubicin, cisplatin and 5-fluorouracil | 4 |
| 10. Etoposide, leucovorin and 5-fluorouraci | 1 |
| 11. Imatinib | 14 |
Abbreviation: N, number of patients.
aSystemic chemotherapy including all chemotherapy combinations, monoclonal antibodies, and first-line chemotherapy used.
Thromboembolic Events
The VTE occurred in 13 (11.6%) patients: 4 patients had extremity DVT, 4 had SVT, and 5 patients had PE (3 unilateral and 2 bilateral). The median time from the diagnosis of cancer to VTE was 59 days (IQR: 36-258). All the 13 patients with VTE were diagnosed with VTE after diagnosis of cancer and 8 (61.5%) died. Of the 13 patients with VTE, 5 (38.5%) received radiotherapy, 9 (69.2%) received chemotherapy, and 6 (46.2%) had surgery. The cumulative incidence at 1 and 2 years was 9%, at 3 years was 13%, and at the end at the end of the follow-up period was 23%.
Pretreatment Khorana Score and Risk of CAT
The KRS classified 59 patients (52.7%) as high risk from which 9 (15%) experienced an episode of VTE. Patients stratified as high risk by KRS were not at a higher risk of developing VTE compared to patients with intermediate risk (univariate hazard ratio [HR], 2.3; 95% confidence interval [CI], 0.7-7.4; P = .17). In multivariate Cox regression analysis, including KRS, PS, chemotherapy, and stage, only PS was predictive of VTE (multivariate HR, 6.2; 95% CI, 1.9-20.2; P = .002). The overall score had a sensitivity of 69%, specificity of 49.5%, positive predictive value (PPV) of 15.3, and negative predictive value (NPV) of 88.4%.
Pretreatment Platelet to Lymphocyte Ratio, Neutrophil to Lymphocyte Ratio, and Risk of CAT
The PLR stratified 34 (30.4%) patients as high risk for VTE. Overall, PLR had a sensitivity of 23.1%, specificity of 67.7%, PPV of 8.8%, and NPV of 87.0%. These patients were not associated with an increased risk of VTE (univariate HR, 0.8; 95% CI, 0.2-3.1; P = .8).
The NLR identified 57 (50.9%) patients as high risk of VTE. In general, NLR had a sensitivity of 46.2%, specificity of 47.5%, PPV of 10.5%, and NPV of 87.0%. In univariate analysis, PLR was not associated with an increased risk of VTE (univariate HR, 0.8; 95% CI, 0.3-2.5; P = .8). In fact, in our cohort, PLR (AUC, 0.4; 95% CI, 0.24 to 0.59) and NLR (AUC, 0.47; 95% CI, 0.31 to 0.62) did not show any VTE predictive value.
Variability of RAM After Cancer Treatment and Risk of VTE
We found a significant variability between the pre and postchemotherapy KRS (P < .001), PLR (P < .001), and NLR (P < .001). Among the components of these predictors, the absolute number of leukocytes (P < .001), neutrophils (P < .001), and lymphocytes (P < .001) changed between pre and posttreatment levels.
The posttreatment KRS classified 26 patients (23.2%) as high risk from which 5 (38.5%) experienced an episode of VTE. Patients stratified as high risk by KRS had a significant elevated risk of having VTE (univariate HR, 2.6; 95% CI, 0.86-8.1; P = .09). After adjusting for stage, chemotherapy, surgery, and PS in the multivariate Cox regression analysis, PS and posttreatment KRS remained predictive of VTE. In this model, the strongest predictor of VTE was PS (HR, 7.6; 95% CI, 2.3-25.3; P = .001) followed by posttreatment KRS (HR, 3.7; 95% CI, 1.2-11.6; P = .03). In a subgroup analysis in patients with adenocarcinoma only, PS (HR, 8.2; 95% CI, 2.4-27.6; P = .001) and BMI (HR, 4.2; 95% CI, 1.2-13.5; P = .02) remained the only predictors of CAT. We observed similar results when we excluded the 20 patients who received monoclonal antibodies as first line chemotherapy. Overall, the posttreatment KRS had a sensitivity of 38.5%, specificity of 78.8%, PPV of 19.2%, NPV of 90.7%, and an accuracy of 74.11%.
After treatment, the NLR stratified 28 patients (75%) as high, whereas the PLR identified 19 patients (17%) as high risk. However, patients classified as high risk by NLR (univariate HR, 1.809; 95% CI, 0.556-5.889; P = .325) and by PLR (univariate HR, 0.604; 95% CI, 0.077-4.715; P = .630) were not associated with a higher risk of VTE occurrence.
The weighted NRI for posttreatment KRS was 0.22 (95% CI, 0.15-0.31). The weighted NRI for VTE events was −0.03 and for non-VTE events was 0.25. After cancer treatment, the KRS reclassified VTE risk in 22.3% of the patients (Table 3). After treatment, there was a significant improvement in the AUC for PLR (ΔAUC, 0.26; 95% CI, 0.02-0.50; P = .03), but not for NLR (ΔAUC, 0.07; 95% CI, −0.17-0.31; P = .56).
Table 3.
Reclassification Table Among Patients With and Without VTE After First Cancer Treatment in Patients With Gastric Cancer.
| Pretreatment KRS | Posttreatment KRS | ||
|---|---|---|---|
| Frequency n (%) | Intermediate | High | Total |
| Patients with VTE | |||
| Intermediate | 4 (100.0) | 0 (0.0) | 4 |
| High | 4 (44.4) | 5 (55.6) | 9 |
| 8 | 5 | 13 | |
| Patients without VTE | |||
| Intermediate | 49 (100.0) | 0 (0.0) | 49 |
| High | 29 (58.0) | 21 (42.0) | 50 |
| 78 | 21 | 99 | |
| 112 | |||
Abbreviations: VTE, venous thromboembolism; KRS, Khorana score; N, number of patients.
Discussion
In this study, none of the risk predicting tools studied at baseline were predictive of VTE. In addition, the posttreatment KRS was associated with a higher risk of VTE occurrence, but misclassified almost two-thirds of the patients who developed a VTE event as intermediate risk. The incidence of VTE in our study was 11.6%, which is similar to previous reports (5.3%-25.5%).22–25 Moreover, most of the events occurred within 2 months of cancer diagnosis.
At diagnosis, risk stratification using KRS was not predictive of VTE in our cohort. Comparable to the original validation study, the KRS in our cohort had a good NPV and poor PPV which arguably point out that the model performs better at identifying patients at low risk. Possible explanations for the poor performance of the KRS include that the population studied were predominantly nonobese (80%) with higher levels of hemoglobin (mean: 11.15, standard deviation: 2.03) and a relative low-normal platelet levels (median: 266, IQR: 144), which makes at least half of the Khorana model less accurate to the population in this study.
Although the KRS is the most widely validated RAM15,16 and it is suggested by current guidelines for VTE risk stratification in patients with cancer,26 this model had a low sensitivity (40%) and PPV (7%) among high-risk patients in the validation study.14 Also, the original validation study included only 54 (2%) patients with GC in the derivation cohort and 19 (1.4%) patients in the validation cohort which makes less reproducible in patients with GC.14 Additionally, BMI ≥35 kg/m2, which is part of this scoring system, is not common in patients with GC. In fact, weight loss is part of the initial presentation in up to 62% of cases,27 limiting the utility of BMI to a small proportion of patients with GC. Moreover, during the course of the disease, a significant subset of these patients may require a palliative intervention to allow proper calorie intake and an adequate body weight. In our cohort, only 9 (8%) patients had a BMI >35 kg/m2 from which only 1 (0.9%) developed CAT. Similarly, overt bleeding is observed in less than 20% of patients with GC27 which did not always result in a hemoglobin level less than 10 dL−1, limiting even further the applicability of the Khorana model in this population. Surgical treatment is more often required in GC than other tumor types, thus exposing these patients to a higher number of surgical complications predisposing to thrombosis. In our cohort, only 2 patients developed severe sepsis, resulting in a prolonged hospitalization. Nonetheless, we adjusted for surgery in our analysis and our results did not change. Lastly, since its publications, the performance of the KRS has yielded mixed results in cancer-specific databases,28–31 thus it is plausible that GC is yet one more group of patients in which discovery of disease-specific CAT predictors is warranted.
The PLR and NLR were not associated with a higher risk of VTE which could be explained but the relative normal cell counts in our cohort. Although, platelets, lymphocytes, neutrophils, and their ratios have been proposed as surrogate markers of inflammation linked to thrombosis,17,18 normal blood cell counts can underestimate the VTE risk in patients with GC. In fact, Osada et al found multiple molecules associated with platelet activation in patients with GC despite normal platelet counts.32 The VTE risk stratification with biomarkers reflecting the complex platelet–lymphocyte interaction33 such as P-selectin34 and tissue factor-bearing microparticles35 appears a more reliable approach than absolute blood counts and ratios in this population.
All the risk assessment tools changed after first cancer treatment arguably due to disease progression, and cumulative bone marrow suppression secondary to chemotherapy and/or radiation therapy. However, only the posttreatment KRS was predictive of CAT in univariate and multivariate Cox regression analyses even after adjusting for chemotherapy. Indeed, after cancer treatment, the Khorana model reclassified VTE risk in 22.3% of the patients. However, this effect is mainly driven by the non-VTE NRI, highlighting this model’s property of correctly decreasing risk estimates in patients without VTE and thus is useful in avoiding overtreatment rather than identifying patients at high risk. This pattern of VTE risk stratification is also supported by the improvement in specificity from 49% to 78% with the concomitant drop in sensitivity from 69% to 38.5% after first cancer treatment. In addition, the posttreatment KRS misclassified as intermediate risk, two-thirds of the patients who had VTE. Among the patients who had a VTE event, we noticed that nearly 30% of those who were stratified as intermediate risk received additional radiation therapy. It is conceivable that chemoradiation enhanced bone marrow suppression, thus resulting in an inaccurate stratification by the postchemotherapy KRS. Like other type of cancers,36,37 PS in our cohort was an independent predictor of VTE regardless of the treatment status.
The main strength of our study is that it is the first addressing the performance of the Khorana model in patients with GC and the variability of this model after first cancer treatment. Also, we studied a homogenous cohort where all VTE events were objective confirmed by imaging to ensure validity of outcomes. However, this was a single institution cohort with a relative small sample size. And like any other retrospective study, we did not perform a formal power calculation. Therefore, our study might be underpowered to draw firm conclusions about the association between VTE risk tools and risk of VTE. Nonetheless, the VTE incidence in this study is comparable to larger reports. Although we did not perform a stratified analysis in patients treated with chemotherapy only, we adjusted for this covariate in multivariate analysis and the results remained unchanged. Given the small sample size, we were not able to limit our analysis to nonsplanchnic thrombosis. We excluded patients who started treatment at a different institution due to the lack of biochemical data to quantify pretreatment risk assessment tools. Therefore, we conducted a complete-case analysis.
Conclusions
In this study, PS and BMI were independent predictors of VTE regardless of cancer treatment in patients with GC. After further validation, the posttreatment KRS might represent a valid tool to identify patients with GC at a high risk of VTE after first cancer treatment. Although predictive in other cancer databases, pretreatment NLR, PLR, and KRS were not predictive of VTE in our cohort.
Footnotes
Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
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