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.

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.

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.

Identifying risk and prognostic factors for synchronous liver metastasis in small bowel adenocarcinoma: a predictive analysis using the SEER database.

Background: Small bowel adenocarcinoma (SBA) is a rare gastrointestinal malignancy with an increasing incidence and a high propensity for liver metastasis (LM). This study aimed to investigate the risk factors for synchronous LM and prognostic factors in patients with LM. Methods: Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, this study analyzed data from 2,064 patients diagnosed with SBA between 2010 and 2020. Logistic regression was used to determine risk factors for synchronous LM. A nomogram was developed to predict the risk of LM in SBA patients, and its predictive performance was assessed through receiver operating characteristic (ROC) curves and calibration curves. Kaplan-Meier and Cox regression analyses were conducted to evaluate survival outcomes for SBA patients with LM. Results: Synchronous LM was present in 13.4% of SBA patients (n = 276). Six independent predictive factors for LM were identified, including tumor location, T stage, N stage, surgical intervention, retrieval of regional lymph nodes (RORLN), and chemotherapy. The nomogram demonstrated good discriminative ability, with an area under the curve (AUC) of 83.8%. Patients with LM had significantly lower survival rates than those without LM (P < 0.001). Survival analysis revealed that advanced age, tumor location in the duodenum, surgery, RORLN and chemotherapy were associated with cancer-specific survival (CSS) in patients with LM originating from SBA. Conclusions: This study highlights the significant impact of LM on the survival of SBA patients and identifies key risk factors for its occurrence. The developed nomogram aids in targeted screening and personalized treatment planning.

Epidemiological characteristics of first-time SARS-CoV-2 Omicron infection among hospital staff in Chengdu, China.

BACKGROUND: After China ended its 'dynamic zero-COVID policy' on 7 December 2022, a large-scale outbreak of SARS-CoV-2 Omicron infections emerged across the country. We conducted a hospital-wide prospective study to document the epidemiological characteristics of the outbreak among healthcare workers in a hospital of Chengdu, where no previous staff SARS-CoV-2 infections were detected. METHODS: All hospital staff members were invited to complete an online questionnaire on COVID-19 in January 2023, and SARS-CoV-2 infection cases were followed up by telephone in June 2023 to collect data on long COVID. Univariable and multivariable logistic regression analyses were performed to evaluate factors associated with SARS-CoV-2 infection. RESULTS: A total of 2,899 hospital staff (93.5%) completed the online questionnaire, and 86.4% were infected with SARS-CoV-2 Omicron. The clinical manifestations of these patients were characterized by a high incidence of systemic symptoms. Cough (83.4%), fatigue (79.8%) and fever (74.3%) were the most frequently reported symptoms. Multivariable logistic analysis revealed that females [adjusted odds ratio (aOR): 1.42, 95% confidence interval (CI): 1.07-1.88] and clinical practitioners (aOR: 10.32, 95% CI: 6.57-16.20) were associated with an increased risk of SARS-CoV-2 infection, whereas advanced age ≥ 60 years (aOR: 0.30, 95% CI: 0.19-0.49) and a three-dose COVID-19 vaccination with the most recent dose administered within 3 months before 7 December 2022 (aOR: 0.44, 95% CI: 0.23-0.87 for within 1 month; aOR: 0.46, 95% CI: 0.22-0.97 for within 1-3 months) were associated with reduced risk. Among the cases, 4.27% experienced long COVID of fatigue, brain fog or both, with the majority reporting minor symptoms. CONCLUSION: Our findings provide a snapshot of the epidemiological situation of SARS-CoV-2 infection among healthcare workers in Chengdu after China's deregulation of COVID-19 control. Data in the study can aid in the development and implementation of effective measures to protect healthcare workers and maintain the integrity of healthcare systems during challenging times such as a rapid and widespread Omicron outbreak.

Developing Sampling Weights for Statistical Analysis of Parent-Child Pair Data From the National Health Interview Survey.

The National Health Interview Survey (NHIS), conducted by the National Center for Health Statistics since 1957, is the principal source of information on the health of the U.S. civilian noninstitutionalized population. NHIS selects one adult (Sample Adult) and, when applicable, one child (Sample Child) randomly within a family (through 2018) or a household (2019 and forward). Sampling weights for the separate analysis of data from Sample Adults and Sample Children are provided annually by the National Center for Health Statistics. A growing interest in analysis of parent-child pair data using NHIS has been observed, which necessitated the development of appropriate analytic weights. Objective This report explains how dyad weights were created such that data users can analyze NHIS data from both Sample Children and their mothers or fathers, respectively. Methods Using data from the 2019 NHIS, adult-child pair-level sampling weights were developed by combining each pair's conditional selection probability with their household-level sampling weight. The calculated pair weights were then adjusted for pair-level nonresponse, and large sampling weights were trimmed at the 99th percentile of the derived sampling weights. Examples of analyzing parent-child pair data by means of domain estimation methods (that is, statistical analysis for subpopulations or subgroups) are included in this report. Conclusions The National Center for Health Statistics has created dyad or pair weights that can be used for studies using parent-child pairs in NHIS. This method could potentially be adapted to other surveys with similar sampling design and statistical needs.

Untangling the effects of multiple exposures with a common reference group in an epidemiologic study: A practical revisit.

When assessing multiple exposures in epidemiologic studies, epidemiologists often use multivariable regression models with main effects only to control for confounding. This method can mask the true effects of individual exposures, potentially leading to wrong conclusions. We revisited a simple, practical, and often overlooked approach to untangle effects of the exposures of interest, in which the combinations of all levels of the exposures of interest are recoded into a single, multicategory variable. One category, usually the absence of all exposures of interest, is selected as the common reference group (CRG). All other categories representing individual and joint exposures are then compared to the CRG using indicator variables in a regression model or in a 2×2 contingency table analysis. Using real data examples, we showed that using the CRG analysis results in estimates of individual and joint effects that are mutually comparable and free of each other's confounding effects, yielding a clear, accurate, intuitive, and simple summarization of epidemiologic study findings involving multiple exposures of interest.

A SEMIPARAMETRIC MULTIPLE IMPUTATION APPROACH TO FULLY SYNTHETIC DATA FOR COMPLEX SURVEYS.

Data synthesis is an effective statistical approach for reducing data disclosure risk. Generating fully synthetic data might minimize such risk, but its modeling and application can be difficult for data from large, complex surveys. This article extended the two-stage imputation to simultaneously impute item missing values and generate fully synthetic data. A new combining rule for making inferences using data generated in this manner was developed. Two semiparametric missing data imputation models were adapted to generate fully synthetic data for skewed continuous variable and sparse binary variable, respectively. The proposed approach was evaluated using simulated data and real longitudinal data from the Health and Retirement Study. The proposed approach was also compared with two existing synthesis approaches: (1) parametric regressions models as implemented in IVEware; and (2) nonparametric Classification and Regression Trees as implemented in synthpop package for R using real data. The results show that high data utility is maintained for a wide variety of descriptive and model-based statistics using the proposed strategy. The proposed strategy also performs better than existing methods for sophisticated analyses such as factor analysis.