Predicting Colon Cancer-Specific Survival for the Asian Population Using National Cancer Registry Data from Taiwan.

PURPOSE: Colon cancer is the third most incident and life-threatening cancer in Taiwan. A comprehensive survival prediction system would greatly benefit clinical practice in this area. This study was designed to develop an accurate prognostic model for colon cancer patients by using clinicopathological variables obtained from the Taiwan Cancer Registry database. METHODS: We analyzed 20,218 colon cancer patients from the Taiwan Cancer Registry database, who were diagnosed between 2007 and 2015, were followed up until December 31, 2017, and had undergone curative surgery. We proposed two prognostic models, with different combinations of predictors. The first model used only traditional clinical features. The second model included several colon cancer site-specific factors (circumferential resection margin, perineural invasion, obstruction, and perforation), in addition to the traditional features. Both prediction models were developed by using a Cox proportional hazards model. Furthermore, we investigated whether race is a significant predictor of survival in colon cancer patients by using Model 1 on the Surveillance, Epidemiology, and End Results (SEER) cancer registry dataset. RESULTS: The proposed models displayed a robust prediction performance (all Harrell's c-index >0.8). For both the calibration and validation steps, the differences between the predicted and observed mortality were mostly less than 5%. CONCLUSIONS: The prediction model (Model 1) is an effective predictor of survival regardless of the ethnic background of patients and can potentially help to provide better prediction of colon cancer-specific survival outcomes, thus allowing physicians to improve treatment plans.

Polygenic approaches to detect gene-environment interactions when external information is unavailable.

The exploration of 'gene-environment interactions' (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our 'adaptive combination of Bayes factors method' (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.

Development of a prediction model for breast cancer based on the national cancer registry in Taiwan.

BACKGROUND: This study aimed to develop a prognostic model to predict the breast cancer-specific survival and overall survival for breast cancer patients in Asia and to demonstrate a significant difference in clinical outcomes between Asian and non-Asian patients. METHODS: We developed our prognostic models by applying a multivariate Cox proportional hazards model to Taiwan Cancer Registry (TCR) data. A data-splitting strategy was used for internal validation, and a multivariable fractional polynomial approach was adopted for prognostic continuous variables. Subjects who were Asian, black, or white in the US-based Surveillance, Epidemiology, and End Results (SEER) database were analyzed for external validation. Model discrimination and calibration were evaluated in both internal and external datasets. RESULTS: In the internal validation, both training data and testing data calibrated well and generated good area under the ROC curves (AUC; 0.865 in training data and 0.846 in testing data). In the external validation, although the AUC values were larger than 0.85 in all populations, a lack of model calibration in non-Asian groups revealed that racial differences had a significant impact on the prediction of breast cancer mortality. For the calibration of breast cancer-specific mortality, P values < 0.001 at 1 year and 0.018 at 4 years in whites, and P values ≤ 0.001 at 1 and 2 years and 0.032 at 3 years in blacks, indicated that there were significant differences (P value < 0.05) between the predicted mortality and the observed mortality. Our model generally underestimated the mortality of the black population. In the white population, our model underestimated mortality at 1 year and overestimated it at 4 years. And in the Asian population, all P values > 0.05, indicating predicted mortality and actual mortality at 1 to 4 years were consistent. CONCLUSIONS: We developed and validated a pioneering prognostic model that especially benefits breast cancer patients in Asia. This study can serve as an important reference for breast cancer prediction in the future.

Efficacy of a sepsis clinical decision support system in identifying patients with sepsis in the emergency department.

BACKGROUND: Early prediction of sepsis onset is crucial for reducing mortality and the overall cost burden of sepsis treatment. Currently, few effective and accurate prediction tools are available for sepsis. Hence, in this study, we developed an effective sepsis clinical decision support system (S-CDSS) to assist emergency physicians to predict sepsis. METHODS: This study included patients who had visited the emergency department (ED) of Taipei Tzu Chi Hospital, Taiwan, between January 1, 2020, and June 31, 2022. The patients were divided into a derivation cohort (n = 70,758) and a validation cohort (n = 27,545). The derivation cohort was subjected to sixfold stratified cross-validation, reserving 20% of the data (n = 11,793) for model testing. The primary study outcome was a sepsis prediction (International Classification of Diseases, Tenth Revision, Clinical Modification) before discharge from the ED. The S-CDSS incorporated the LightGBM algorithm to ensure timely and accurate prediction of sepsis. The validation cohort was subjected to multivariate logistic regression to identify the associations of S-CDSS-based high- and medium-risk alerts with clinical outcomes in the overall patient cohort. For each clinical outcome in high- and medium-risk patients, we calculated the sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and accuracy of S-CDSS-based predictions. RESULTS: The S-CDSS was integrated into our hospital information system. The system featured three risk warning labels (red, yellow, and white, indicating high, medium, and low risks, respectively) to alert emergency physicians. The sensitivity and specificity of the S-CDSS in the derivation cohort were 86.9% and 92.5%, respectively. In the validation cohort, high- and medium-risk alerts were significantly associated with all clinical outcomes, exhibiting high prediction specificity for intubation, general ward admission, intensive care unit admission, ED mortality, and in-hospital mortality (93.29%, 97.32%, 94.03%, 93.04%, and 93.97%, respectively). CONCLUSION: Our findings suggest that the S-CDSS can effectively identify patients with suspected sepsis in the ED. Furthermore, S-CDSS-based predictions appear to be strongly associated with clinical outcomes in patients with sepsis.

Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests.

The identification of gene-environment interactions (G × E) may eventually guide health-related choices and medical interventions for complex diseases. More powerful methods must be developed to identify G × E. The "adaptive combination of Bayes factors method" (ADABF) has been proposed as a powerful genome-wide polygenic approach to detect G × E. In this work, we evaluate its performance when serving as a gene-based G × E test. We compare ADABF with six tests including the "Set-Based gene-EnviRonment InterAction test" (SBERIA), "gene-environment set association test" (GESAT), etc. With extensive simulations, SBERIA and ADABF are found to be more powerful than other G × E tests. However, SBERIA suffers from a power loss when 50% SNP main effects are in the same direction with the SNP × E interaction effects while 50% are in the opposite direction. We further applied these seven G × E methods to the Taiwan Biobank data to explore gene× alcohol interactions on blood pressure levels. The ADAMTS7P1 gene at chromosome 15q25.2 was detected to interact with alcohol consumption on diastolic blood pressure (p = 9.5 × 10-7, according to the GESAT test). At this gene, the P-values provided by other six tests all reached the suggestive significance level (p < 5 × 10-5). Regarding the computation time required for a genome-wide G × E analysis, SBERIA is the fastest method, followed by ADABF. Considering the validity, power performance, robustness, and computation time, ADABF is recommended for genome-wide G × E analyses.

Distribution of micromotion in implants and alveolar bone with different thread profiles in immediate loading: a finite element study.

PURPOSE: To detect the differences in the distribution of micromotion within implants and alveolar bone with different implant thread designs during immediate loading. MATERIALS AND METHODS: A three-dimensional finite element model with contact elements was used to simulate the contact behavior between the implant and alveolar bone. Implants with four different thread designs were created: Acme (trapezoidal) thread (AT), buttress thread (BT), square thread (ST), and a standard V-thread (VT). To simulate immediate loading, the model was designed without osseointegration between the implant and alveolar bone. A load of 300 N was applied axially to the model, and the micromovements were measured. RESULTS: The maximum micromotion values of the ST, AT, VT, and BT models were 8.53, 9.57, 11.00, and 15.00 µm, respectively. All micromotion was located near the interface of cortical and cancellous bone. Different thread designs showed different distribution of micromotion during loading. This indicates that initial stability in immediate loading may be affected by thread design. CONCLUSION: The ST profile showed the most favorable result in the study. An implant with an ST profile might provide the best primary stability in an immediate loading situation.

Rapid analysis of L-dopa in urine samples using gold nanoelectrode ensembles.

This work describes the application of a three-dimensional gold nanoelectrode ensembles (GNEE) for monitoring L-dopa in standards and human urine samples using flow injection analysis (FIA) with amperometric detection. Analytical results reveal that the GNEE exhibited better electrocatalytic activity than a gold disk or glassy carbon electrode. Under optimal conditions of L-dopa analysis at GNEE, the calibration plot has a linear range of 5-300 ng/mL with a coefficient of variation (CV) of 3.1% in pH 7.0 phosphate buffer saline (PBS, pH 7.0). The detection limit was 3.0 ng/mL for FIA. The high precision and sensitivity of GNEE provides a feasible means of directly determining l-dopa in urine samples.