Development and validation of a nomogram for predicting cancer-specific survival in small-bowel adenocarcinoma patients using the SEER database.

BACKGROUND: Small bowel adenocarcinoma (SBA) is a rare gastrointestinal malignancy forwhich survival is hampered by late diagnosis, complex responses to treatment, and poor prognosis. Accurate prognostic tools are crucial for optimizing treatment strategies and improving patient outcomes. This study aimed to develop and validate a nomogram based on the Surveillance, Epidemiology, and End Results (SEER) database to predict cancer-specific survival (CSS) in patients with SBA and compare it to traditional American Joint Committee on Cancer (AJCC) staging. METHODS: We analyzed data from 2,064 patients diagnosed with SBA between 2010 and 2020 from the SEER database. Patients were randomly assigned to training and validation cohorts (7:3 ratio). Kaplan‒Meier survival analysis, Cox multivariate regression, and nomograms were constructed for analysis of 3-year and 5-year CSS. The performance of the nomograms was evaluated using Harrell's concordance index (C-index), the area under the receiver operating characteristic (ROC) curve, calibration curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). RESULTS: Multivariate Cox regression identified sex, age at diagnosis, marital status, tumor site, pathological grade, T stage, N stage, M stage, surgery, retrieval of regional lymph nodes (RORLN), and chemotherapy as independent covariates associated with CSS. In both the training and validation cohorts, the developed nomograms demonstrated superior performance to that of the AJCC staging system, with C-indices of 0.764 and 0.759, respectively. The area under the curve (AUC) values obtained by ROC analysis for 3-year and 5-year CSS prediction significantly surpassed those of the AJCC model. The nomograms were validated using calibration and decision curves, confirming their clinical utility and superior predictive accuracy. The NRI and IDI indicated the enhanced predictive capability of the nomogram model. CONCLUSION: The SEER-based nomogram offers a significantly superior ability to predict CSS in SBA patients, supporting its potential application in clinical decision-making and personalized approaches to managing SBA to improve survival outcomes.

A multiple imputation-based sensitivity analysis approach for regression analysis with a missing not at random covariate.

Missing covariate problems are common in biomedical and electrical medical record data studies while evaluating the relationship between a biomarker and certain clinical outcome, when biomarker data are not collected for all subjects. However, missingness mechanism is unverifiable based on observed data. If there is a suspicion of missing not at random (MNAR), researchers often perform sensitivity analysis to evaluate the impact of various missingness mechanisms. Under the selection modeling framework, we propose a sensitivity analysis approach with a standardized sensitivity parameter using a nonparametric multiple imputation strategy. The proposed approach requires fitting two working models to derive two predictive scores: one for predicting missing covariate values and the other for predicting missingness probabilities. For each missing covariate observation, the two predictive scores along with the pre-specified sensitivity parameter are used to define an imputing set. The proposed approach is expected to be robust against mis-specifications of the selection model and the sensitivity parameter since the selection model and the sensitivity parameter are not directly used to impute missing covariate values. A simulation study is conducted to study the performance of the proposed approach when MNAR is induced by Heckman's selection model. Simulation results show the proposed approach can produce plausible regression coefficient estimates. The proposed sensitivity analysis approach is also applied to evaluate the impact of MNAR on the relationship between post-operative outcomes and incomplete pre-operative Hemoglobin A1c level for patients who underwent carotid intervetion for advanced atherosclerotic disease.

Multiple imputation of missing data with skip-pattern covariates: a comparison of alternative strategies.

Multiple imputation (MI) is a widely used approach to address missing data issues in surveys. Variables included in MI can have various distributional forms with different degrees of missingness. However, when variables with missing data contain skip patterns (i.e. questions not applicable to some survey participants are thus skipped), implementation of MI may not be straightforward. In this research, we compare two approaches for MI when skip-pattern covariates with missing values exist. One approach imputes missing values in the skip-pattern variables only among applicable subjects (denoted as imputation among applicable cases (IAAC)). The second approach imputes skip-pattern covariates among all subjects while using different recoding methods on the skip-pattern variables (denoted as imputation with recoded non-applicable cases (IWRNC)). A simulation study is conducted to compare these methods. Both approaches are applied to the 2015 and 2016 Research and Development Survey data from the National Center for Health Statistics.

Isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analysis of mRNA splicing relevant proteins in aging HSPCs.

BACKGROUND: HSPC (hematopoietic stem/progenitor cell) aging was closely associated with the organism aging, senile diseases and hematopoietic related diseases. Therefore, study on HSPC aging is of great significance to further elucidate the mechanisms of aging and to treat hematopoietic disease resulting from HSPC aging. Little attention had been paid to mRNA splicing as a mechanism underlying HSPC senescence. RESULTS: We used our lab's patented in vitro aging model of HSPCs to analyze mRNA splicing relevant protein alterations with iTRAQ-based proteomic analysis. We found that not only the notable mRNA splicing genes such as SR, hnRNP, WBP11, Sf3b1, Ptbp1 and U2AF1 but also the scarcely reported mRNA splicing relevant genes such as Rbmxl1, Dhx16, Pcbp2, Pabpc1 were significantly down-regulated. We further verified their gene expressions by qRT-PCR. In addition, we reported the effect of Spliceostatin A (SSA), which inhibits mRNA splicing in vivo and in vitro, on HSPC aging. CONCLUSIONS: It was concluded that mRNA splicing emerged as an important factor for the vulnerability of HSPC aging. This study improved our understanding of the role of mRNA splicing in the HSPC aging process.

A multiple imputation-based sensitivity analysis approach for data subject to missing not at random.

Missingness mechanism is in theory unverifiable based only on observed data. If there is a suspicion of missing not at random, researchers often perform a sensitivity analysis to evaluate the impact of various missingness mechanisms. In general, sensitivity analysis approaches require a full specification of the relationship between missing values and missingness probabilities. Such relationship can be specified based on a selection model, a pattern-mixture model or a shared parameter model. Under the selection modeling framework, we propose a sensitivity analysis approach using a nonparametric multiple imputation strategy. The proposed approach only requires specifying the correlation coefficient between missing values and selection (response) probabilities under a selection model. The correlation coefficient is a standardized measure and can be used as a natural sensitivity analysis parameter. The sensitivity analysis involves multiple imputations of missing values, yet the sensitivity parameter is only used to select imputing/donor sets. Hence, the proposed approach might be more robust against misspecifications of the sensitivity parameter. For illustration, the proposed approach is applied to incomplete measurements of level of preoperative Hemoglobin A1c, for patients who had high-grade carotid artery stenosisa and were scheduled for surgery. A simulation study is conducted to evaluate the performance of the proposed approach.

Causal inference of latent classes in complex survey data with the estimating equation framework.

Latent class analysis (LCA) has been effectively used to cluster multiple survey items. However, causal inference with an exposure variable, identified by an LCA model, is challenging because (1) the exposure variable is unobserved and harbors the uncertainty of estimating parameters in the LCA model and (2) confounding bias adjustments need to be done with the unobserved LCA-driven exposure variable. In addition to these challenges, complex survey design features and survey weights must be accounted for if they are present. Our solutions to these issues are to (1) assess point estimates with the expected estimating function approach and (2) modify the survey design weights with LCA-based propensity scores. This paper aims to introduce a statistical procedure to apply the estimating equation approach to assessing the effects of LCA-driven cause in complex survey data using an example of the National Health and Nutrition Examination Survey.

Strategies for imputing missing covariates in accelerated failure time models.

Missing covariates often occur in biomedical studies with survival outcomes. Multiple imputation via chained equations (MICE) is a semi-parametric and flexible approach that imputes multivariate data by a series of conditional models, one for each incomplete variable. When applying MICE, practitioners tend to specify the conditional models in a simple fashion largely dictated by the software, which could lead to suboptimal results. Practical guidelines for specifying appropriate conditional models in MICE are lacking. Motivated by a study of time to hip fractures in the Women's Health Initiative Observational Study using accelerated failure time models, we propose and experiment with some rationales leading to appropriate MICE specifications. This strategy starts with specifying a joint model for the variables involved. We first derive the conditional distribution of each variable under the joint model, then approximate these conditional distributions to the extent which can be characterized by commonly used regression models. We propose to fit separate models to impute incomplete variables by the failure status, which is key to generating appropriate MICE specifications for survival outcomes. The proposed strategy can be conveniently implemented with all available imputation software that uses fully conditional specifications. Our simulation results show that some commonly used simple MICE specifications can produce suboptimal results, while those based on the proposed strategy appear to perform well and be robust toward model misspecifications. Hence, we warn against a mechanical use of MICE and suggest careful modeling of the conditional distributions of variables to ensure proper performance.

A nonparametric multiple imputation approach for missing categorical data.

BACKGROUND: Incomplete categorical variables with more than two categories are common in public health data. However, most of the existing missing-data methods do not use the information from nonresponse (missingness) probabilities. METHODS: We propose a nearest-neighbour multiple imputation approach to impute a missing at random categorical outcome and to estimate the proportion of each category. The donor set for imputation is formed by measuring distances between each missing value with other non-missing values. The distance function is calculated based on a predictive score, which is derived from two working models: one fits a multinomial logistic regression for predicting the missing categorical outcome (the outcome model) and the other fits a logistic regression for predicting missingness probabilities (the missingness model). A weighting scheme is used to accommodate contributions from two working models when generating the predictive score. A missing value is imputed by randomly selecting one of the non-missing values with the smallest distances. We conduct a simulation to evaluate the performance of the proposed method and compare it with several alternative methods. A real-data application is also presented. RESULTS: The simulation study suggests that the proposed method performs well when missingness probabilities are not extreme under some misspecifications of the working models. However, the calibration estimator, which is also based on two working models, can be highly unstable when missingness probabilities for some observations are extremely high. In this scenario, the proposed method produces more stable and better estimates. In addition, proper weights need to be chosen to balance the contributions from the two working models and achieve optimal results for the proposed method. CONCLUSIONS: We conclude that the proposed multiple imputation method is a reasonable approach to dealing with missing categorical outcome data with more than two levels for assessing the distribution of the outcome. In terms of the choices for the working models, we suggest a multinomial logistic regression for predicting the missing outcome and a binary logistic regression for predicting the missingness probability.

Doubly robust multiple imputation using kernel-based techniques.

We consider the problem of estimating the marginal mean of an incompletely observed variable and develop a multiple imputation approach. Using fully observed predictors, we first establish two working models: one predicts the missing outcome variable, and the other predicts the probability of missingness. The predictive scores from the two models are used to measure the similarity between the incomplete and observed cases. Based on the predictive scores, we construct a set of kernel weights for the observed cases, with higher weights indicating more similarity. Missing data are imputed by sampling from the observed cases with probability proportional to their kernel weights. The proposed approach can produce reasonable estimates for the marginal mean and has a double robustness property, provided that one of the two working models is correctly specified. It also shows some robustness against misspecification of both models. We demonstrate these patterns in a simulation study. In a real-data example, we analyze the total helicopter response time from injury in the Arizona emergency medical service data.

Do Patients at Sites With High RCT Enrollment Propensity Have Better Outcomes?

BACKGROUND: Concerns about randomized controlled trial (RCT) generalizability typically center on characteristics of RCT patient participants. Possibly there are RCT site characteristics that distinguish RCT outcomes from those that can be expected in non-RCT settings. OBJECTIVES: To examine whether site propensity toward RCT enrollment is associated with recovery outcomes for patients and whether the association differs between patients who participate in a RCT compared with those who remain in an observational (OBS) treatment environment. DATA: Study participants with acute bipolar depression from The Systematic Treatment Enhancing Program for Bipolar Disorder acute depression pharmacotherapy RCT (N=337) and OBS treatment arm (N=1581). METHODS: A longitudinal OBS study comparing the likelihood of recovery in the RCT to the OBS arm, allowing effect modification by site high RCT enrollment propensity (defined as >the median) and other predictors over a 6-month follow-up period. RESULTS: Non-RCT participants who received care in sites with high RCT enrollment propensity had a higher probability of recovering from their bipolar-depression episode compared with participants from low propensity sites [odds ratio (95% confidence interval)=2.13 (1.28-3.55)]. RCT enrollment propensity was not associated with recovery outcomes for RCT participants [1.03 (0.35-3.03)]. CONCLUSIONS: Sites with high propensity to enroll patients in RCTs appear to have unobserved characteristics, which play a significant role in outcomes for non-RCT patients. For RCT participants in low-enrollment sites, possibly RCT protocols, which proscribe care delivery and monitoring, attenuate this effect. These results have implications for future research to improve outcomes in nonresearch care settings.

Outcomes after multivessel or culprit-Vessel intervention for ST-elevation myocardial infarction in patients with multivessel coronary disease: a Bayesian cross-design meta-analysis.

INTRODUCTION: During primary percutaneous coronary intervention (PCI), patients with ST-elevation myocardial infarction (STEMI) and multivessel coronary disease can undergo either multivessel intervention (MVI) or culprit-vessel intervention (CVI) only. BACKGROUND: Randomized controlled trials (RCTs) support the use of MVI, but cohort studies support the use of CVI. METHODS: We developed Bayesian models that incorporated parameters for study type and study outcome after MVI or CVI. RESULTS: A total of 18 studies (4 RCTs, 3 matched cohort studies, and 11 unmatched observational studies) enrolled 48,398 patients with STEMI and multivessel CAD and reported outcomes after MVI or CVI-only at the time of primary PCI. Using a Bayesian hierarchical model, we found that the point estimates replicated previously reported trends, but the wide Bayesian credible intervals (BCI) excluded any plausible mortality difference between MVI versus CVI in all three study types: RCTs (odds ratio [OR] 0.60, 95% BCI 0.31-1.20), matched cohort studies (OR 1.37, 95% BCI 0.86-2.24), or unmatched cohort studies (OR 1.16, 95% BCI 0.70-1.89). Both the global summary (OR 1.10, 95% BCI 0.74-1.51) and a sensitivity analysis that weighted the RCTs 1-5 times as much as observational studies revealed no credible advantage of one PCI strategy over the other (OR 1.05, 95% BCI 0.64-1.48). CONCLUSIONS: Bayesian approaches contextualize the comparison of different strategies by study type and suggest that neither MVI nor CVI emerges as a preferred strategy in an analysis that accounts mortality differences.

Bayesian methods affirm the use of percutaneous coronary intervention to improve survival in patients with unprotected left main coronary artery disease.

BACKGROUND: Several randomized clinical trials support the use of coronary artery bypass grafting (CABG) for patients with unprotected left main coronary artery disease. Studies suggesting the equivalence of percutaneous coronary intervention (PCI) with CABG for this indication indirectly support the 2011 American College of Cardiology Foundation/American Heart Association Class IIa recommendation for PCI to improve survival in patients with unprotected left main coronary artery disease. We tested whether bayesian approaches uphold the new recommendation. METHODS AND RESULTS: We performed a bayesian cross-design and network meta-analysis of 12 studies (4 randomized clinical trials and 8 observational studies) comparing CABG with PCI (n=4574 patients) and of 7 studies (2 randomized clinical trials and 5 observational studies) comparing CABG with medical therapy (n=3224 patients). The odds ratios of 1-year mortality after PCI compared with CABG using bayesian cross-design meta-analysis were not different among randomized clinical trials (odds ratio, 0.99; 95% bayesian credible interval, 0.67-1.43), matched cohort studies (odds ratio, 1.10; 95% bayesian credible interval, 0.76-1.73), and other types of cohort studies (odds ratio, 0.93; 95% bayesian credible interval, 0.58-1.35). A network meta-analysis suggested that medical therapy is associated with higher 1-year mortality than the use of PCI for patients with unprotected left main coronary artery disease (odds ratio, 3.22; 95% bayesian credible interval, 1.96-5.30). CONCLUSIONS: Bayesian methods support the current guidelines, which were based on traditional statistical methods and have proposed that PCI, like CABG, improves survival for patients with unprotected left main coronary artery disease compared with medical therapy. An integrated approach using both direct and indirect evidence may yield new insights to enhance the translation of clinical trial data into practice.

Pain experiences among a population-based cohort of current, former, and never regular smokers with lung and colorectal cancer.

BACKGROUND: Smoking and pain are prevalent and comorbid among patients with cancer. Limited work has compared pain experiences among current, former, and never (regular) smokers with lung and colorectal cancer. METHODS: We studied pain experiences of patients with lung (n = 2390) and colorectal (n = 2993) cancer participating in the multi-regional Cancer Care Outcomes Research and Surveillance study. We examined reports of pain, pain treatment, pain severity, and pain-related interference within each cancer group by smoking status, adjusting for demographic, psychosocial, and cancer characteristics. RESULTS: Among lung cancer patients, current smokers reported pain and receiving pain treatment more often than former smokers. Never smokers did not differ from current and former smokers on endorsement of pain; however, they reported pain treatment less often than their counterparts. Current smokers reported greater pain severity than former smokers after adjusting for other contributing factors; however, no differences were detected between current and never smokers. There were no differences in pain-related interference. Among colorectal cancer patients, current smokers reported pain and pain treatment more often than former and never smokers; however, the latter 2 groups did not differ. Current smokers also reported greater pain severity than never smokers after adjustments; however, no differences were detected between current and former smokers. An identical pattern of findings was observed for pain-related interference. CONCLUSIONS: Many smokers with lung and colorectal cancer experience pain following a cancer diagnosis. Future work should assess if comprehensive smoking cessation treatments that address pain can reduce pain and facilitate smoking cessation among patients with cancer.

Linkage of 1986-2009 National Health Interview Survey with 1981-2010 Florida Cancer Data System.

BACKGROUND: National survey data linked with state cancer registry data has the potential to create a valuable tool for cancer prevention and control research. A pilot project-developed in a collaboration of the Centers for Disease Control and Prevention's National Center for Health Statistics (NCHS) and the Florida Cancer Data System (FCDS) at the University of Miami -links the records of the 1986-2009 National Health Interview Survey (NHIS) and the 1981-2010 FCDS. The project assesses the feasibility of performing a record linkage between NCHS survey data and a state-based cancer registry, as well as the value of the data produced. The linked NHIS-FCDS data allow researchers to follow NHIS survey participants longitudinally to examine factors associated with future cancer diagnosis, and to assess the characteristics and quality of life among cancer survivors. METHODS: This report provides a preliminary evaluation of the linked national and state cancer data and examines both analytic issues and complications presented by the linkage. CONCLUSIONS: Residential mobility and the number of years of data linked in this project create some analytic challenges and limitations for the types of analyses that can be conducted. However, the linked data set offers the ability to conduct analyses not possible with either data set alone.

Health care spending and quality in year 1 of the alternative quality contract.

BACKGROUND: In 2009, Blue Cross Blue Shield of Massachusetts (BCBS) implemented a global payment system called the Alternative Quality Contract (AQC). Provider groups in the AQC system assume accountability for spending, similar to accountable care organizations that bear financial risk. Moreover, groups are eligible to receive bonuses for quality. METHODS: Seven provider organizations began 5-year contracts as part of the AQC system in 2009. We analyzed 2006-2009 claims for 380,142 enrollees whose primary care physicians (PCPs) were in the AQC system (intervention group) and for 1,351,446 enrollees whose PCPs were not in the system (control group). We used a propensity-weighted difference-in-differences approach, adjusting for age, sex, health status, and secular trends to isolate the treatment effect of the AQC in comparisons of spending and quality between the intervention group and the control group. RESULTS: Average spending increased for enrollees in both the intervention and control groups in 2009, but the increase was smaller for enrollees in the intervention group--$15.51 (1.9%) less per quarter (P=0.007). Savings derived largely from shifts in outpatient care toward facilities with lower fees; from lower expenditures for procedures, imaging, and testing; and from a reduction in spending for enrollees with the highest expected spending. The AQC system was associated with an improvement in performance on measures of the quality of the management of chronic conditions in adults (P<0.001) and of pediatric care (P=0.001), but not of adult preventive care. All AQC groups met 2009 budget targets and earned surpluses. Total BCBS payments to AQC groups, including bonuses for quality, are likely to have exceeded the estimated savings in year 1. CONCLUSIONS: The AQC system was associated with a modest slowing of spending growth and improved quality of care in 2009. Savings were achieved through changes in referral patterns rather than through changes in utilization. The long-term effect of the AQC system on spending growth depends on future budget targets and providers' ability to further improve efficiencies in practice. (Funded by the Commonwealth Fund and others.).

Elderly breast and colorectal cancer patients' clinical course: patient and contextual influences.

BACKGROUND: The social and medical environments that surround people are each independently associated with their cancer course. The extent to which these characteristics may together mediate patients' cancer care and outcomes is not known. METHODS: Using multilevel methods and data, we studied elderly breast and colorectal cancer patients (level I) within urban social (level II: ZIP code tabulation area) and health care (level III: hospital service area) contexts. We sought to determine (1) which, if any, observable social and medical contextual attributes were associated with patient cancer outcomes after controlling for observable patient attributes, and (2) the magnitude of residual variation in patient cancer outcomes at each level. RESULTS: Numerous patient attributes and social area attributes, including poverty, were associated with unfavorable patient cancer outcomes across the full clinical cancer continuum for both cancers. Health care area attributes were not associated with patient cancer outcomes. After controlling for observable covariates at all 3 levels, there was substantial residual variation in patient cancer outcomes at all levels. CONCLUSIONS: After controlling for patient attributes known to confer risk of poor cancer outcomes, we find that neighborhood socioeconomic disadvantage exerts an independent and deleterious effect on residents' cancer outcomes, but the area supply of the specific types of health care studied do not. Multilevel interventions targeted at cancer patients and their social areas may be useful. We also show substantial residual variation in patient outcomes across social and health care areas, a finding potentially relevant to traditional small area variation research methods.

On the accuracy of classifying hospitals on their performance measures.

The evaluation, comparison, and public report of health care provider performance is essential to improving the quality of health care. Hospitals, as one type of provider, are often classified into quality tiers (e.g., top or suboptimal) based on their performance data for various purposes. However, potential misclassification might lead to detrimental effects for both consumers and payers. Although such risk has been highlighted by applied health services researchers, a systematic investigation of statistical approaches has been lacking. We assess and compare the expected accuracy of several commonly used classification methods: unadjusted hospital-level averages, shrinkage estimators under a random-effects model accommodating between-hospital variation, and two others based on posterior probabilities. Assuming that performance data follow a classic one-way random-effects model with unequal sample size per hospital, we derive accuracy formulae for these classification approaches and gain insight into how the misclassification might be affected by various factors such as reliability of the data, hospital-level sample size distribution, and cutoff values between quality tiers. The case of binary performance data is also explored using Monte Carlo simulation strategies. We apply the methods to real data and discuss the practical implications.

Combining information from two data sources with misreporting and incompleteness to assess hospice-use among cancer patients: a multiple imputation approach.

Combining information from multiple data sources can enhance estimates of health-related measures by using one source to supply information that is lacking in another, assuming the former has accurate and complete data. However, there is little research conducted on combining methods when each source might be imperfect, for example, subject to measurement errors and/or missing data. In a multisite study of hospice-use by late-stage cancer patients, this variable was available from patients' abstracted medical records, which may be considerably underreported because of incomplete acquisition of these records. Therefore, data for Medicare-eligible patients were supplemented with their Medicare claims that contained information on hospice-use, which may also be subject to underreporting yet to a lesser degree. In addition, both sources suffered from missing data because of unit nonresponse from medical record abstraction and sample undercoverage for Medicare claims. We treat the true hospice-use status from these patients as a latent variable and propose to multiply impute it using information from both data sources, borrowing the strength from each. We characterize the complete-data model as a product of an 'outcome' model for the probability of hospice-use and a 'reporting' model for the probability of underreporting from both sources, adjusting for other covariates. Assuming the reports of hospice-use from both sources are missing at random and the underreporting are conditionally independent, we develop a Bayesian multiple imputation algorithm and conduct multiple imputation analyses of patient hospice-use in demographic and clinical subgroups. The proposed approach yields more sensible results than alternative methods in our example. Our model is also related to dual system estimation in population censuses and dual exposure assessment in epidemiology.

Combining KRAS gene status with preoperative D­dimer levels as a predictive marker of venous thromboembolism risk in patients with resectable colorectal cancer: A prospective cohort study.

Colorectal cancer (CRC), one of the most prevalent types of cancer, is accompanied by a notably high incidence of thrombotic complications. The present study aimed to elucidate the association between KRAS mutations and hypercoagulability in operable CRC. The prognostic value of preoperative D-dimer levels was also investigated, thus providing novel insights into the development of therapeutic strategies to enhance patient survival and diminish morbidity. Therefore, a prospective analysis of 333 CRC cases post-surgery at Yan'an Hospital Affiliated to Kunming Medical University, between May 2019 and October 2022 was performed. Data on demographics, tumor characteristics and D-dimer levels were compiled from the electronic health records. Venous thromboembolism (VTE) was diagnosed by doppler or computed tomography angiography, with D-dimer thresholds set at 550 and 1,650 µg/l. KRAS mutations at codons 12 and 13 were assessed in a subset of 56 cases. Subsequently, the factors affecting the hypercoagulable state in these patients were prospectively analyzed, focusing on the pivotal role of KRAS. The results showed that KRAS mutations were associated with elevated preoperative D-dimer levels, with 1,076 µg/l compared with 485 µg/l in the wild-type cohort, indicative of a hypercoagulable state. Increased D-dimer levels were also associated with vascular invasion, distant metastases and a heightened risk of postoperative VTE. Furthermore, multivariate analyses identified KRAS mutations, distant metastases and vascular invasion as independent predictors of elevated D-dimer levels, with relative risk values of 2.912, 1.884 and 1.525, respectively. Conversely, sex, age, tumor location, differentiation grade, Ki67 index and tumor stage could not significantly affect D-dimer levels, thus indicating a complex interplay between tumor genetics and coagulation dysfunction in CRC. The current study suggested that the KRAS mutation status, distant metastasis and vascular invasion could be considered as independent risk factors of blood hypercoagulability in patients with CRC, potentially serving as prognostic factors for VTE risk.