Gemcitabine-cisplatin versus MVAC chemotherapy for urothelial carcinoma: a nationwide cohort study.

This study assessed the trends in methotrexate, vinblastine, doxorubicin, and cisplatin (MVAC) and gemcitabine-cisplatin (GC) regimens in Korean patients with metastatic urothelial carcinoma (UC) and compared the side effects and overall survival (OS) rates of the two regimens using nationwide population-based data. The data of patients diagnosed with UC between 2004 and 2016 were collected using the National Health Insurance Service database. The overall treatment trends were assessed according to the chemotherapy regimens. The MVAC and GC groups were matched by propensity scores. Cox proportional hazard analysis and Kaplan-Meier analysis were performed to assess survival. Of 3108 patients with UC, 2,880 patients were treated with GC and 228 (7.3%) were treated with MVAC. The transfusion rate and volume were similar in both the groups, but the granulocyte colony-stimulating factor (G-CSF) usage rate and number were higher in the MVAC group than in the GC group. Both groups had similar OS. Multivariate analysis revealed that the chemotherapy regimen was not a significant factor for OS. Subgroup analysis revealed that a period of ≥ 3 months from diagnosis to systemic therapy enhanced the prognostic effects of the GC regimen. The GC regimen was widely used as the first-line chemotherapy in more than 90% of our study population with metastatic UC. The MVAC regimen showed similar OS to the GC regimen but needed greater use of G-CSF. The GC regimen could be a suitable treatment option for metastatic UC after ≥ 3 months from diagnosis.

Neoadjuvant versus adjuvant chemotherapy in upper tract urothelial carcinoma: A nationwide cohort study.

PURPOSE: This study aimed to evaluate the trend of adjuvant chemotherapy (AC) and neoadjuvant chemotherapy (NAC) in patients who underwent radical nephroureterectomy with bladder cuff excision (NUx) for upper tract urothelial carcinoma (UTUC) to compare the perioperative outcomes and overall survival (OS) between AC and NAC using nationwide population-based data. MATERIALS AND METHODS: We collected data on patients diagnosed with UTUC and treated with NUx between 2004 and 2016 using the National Health Insurance Service database, and evaluated the overall treatment trends. The AC and NAC groups were propensity score-matched. Cox proportional hazard and Kaplan-Meier analyses were used to assess survival. RESULTS: Of the 8,705 enrolled patients, 6,627 underwent NUx only, 94 underwent NAC, and 1,984 underwent AC. The rate of NUx without perioperative chemotherapy increased from 70.8 to 78.2% (R2 = 0.632; p < 0.001). The rates of dialysis (p = 0.398), TUR-BT (p = 1.000), and radiotherapy (p = 0.497) after NUx were similar. In the Kaplan-Meier curve, the NAC and AC groups showed no significant difference (p = 0.480). In multivariate analysis, treatment with AC or NAC was not associated with OS (hazard ratio 0.83, 95% confidence interval 0.49-1.40, p = 0.477). CONCLUSION: The use of NUx without perioperative chemotherapy has tended to increase in South Korea. Dialysis, TUR-BT, and radiotherapy rates after NUx were similar between the NAC and AC groups. There was no significant difference in OS between the NAC and AC groups. Proper perioperative chemotherapy according to patient and tumor conditions should be determined by obtaining more evidence of UTUC.

Neoadjuvant versus adjuvant chemotherapy in bladder cancer: a nationwide cohort study.

PURPOSE: Radical cystectomy is the standard of care for muscle-invasive bladder cancer. However, the 5-year survival rate is only about 50%. Therefore, additional treatments are needed. We compared the perioperative outcomes, overall survival, and treatment trends in patients with bladder cancer who underwent radical cystectomy and either neoadjuvant or adjuvant chemotherapy using nationwide population-based data. MATERIALS AND METHODS: We collected the data of patients diagnosed with bladder cancer treated with radical cystectomy between 2004 and 2016 using the National Health Insurance Service database. We evaluated overall treatment trends. The neoadjuvant chemotherapy and adjuvant chemotherapy groups were matched by propensity score. Cox proportional hazard analysis and Kaplan-Meier analysis were used to assess survival. RESULTS: Of 6134 patients, 1379 underwent adjuvant chemotherapy and 389 underwent neoadjuvant chemotherapy. The utilization rate of neoadjuvant chemotherapy increased from 6.4 to 12.2% from 2004 to 2016 (p = 0.018). The administration rate and number of granulocyte colony-stimulating factor cycles were lower in the neoadjuvant chemotherapy group than in the adjuvant chemotherapy group (p < 0.001 and p = 0.027, respectively). After propensity score matching, the neoadjuvant chemotherapy group had significantly better overall survival than the adjuvant chemotherapy group (p = 0.004). In multivariate analysis, neoadjuvant chemotherapy was associated with better overall survival (hazard ratio 0.77, 95% confidence interval 0.65-0.92, p = 0.003). CONCLUSIONS: Neoadjuvant chemotherapy was associated with lower granulocyte colony-stimulating factor administration and better overall survival than adjuvant chemotherapy. Neoadjuvant chemotherapy should be considered for patients with bladder cancer who undergo radical cystectomy.

Chronic Disease Prediction Using the Common Data Model: Development Study.

BACKGROUND: Chronic disease management is a major health issue worldwide. With the paradigm shift to preventive medicine, disease prediction modeling using machine learning is gaining importance for precise and accurate medical judgement. OBJECTIVE: This study aimed to develop high-performance prediction models for 4 chronic diseases using the common data model (CDM) and machine learning and to confirm the possibility for the extension of the proposed models. METHODS: In this study, 4 major chronic diseases-namely, diabetes, hypertension, hyperlipidemia, and cardiovascular disease-were selected, and a model for predicting their occurrence within 10 years was developed. For model development, the Atlas analysis tool was used to define the chronic disease to be predicted, and data were extracted from the CDM according to the defined conditions. A model for predicting each disease was built with 4 algorithms verified in previous studies, and the performance was compared after applying a grid search. RESULTS: For the prediction of each disease, we applied 4 algorithms (logistic regression, gradient boosting, random forest, and extreme gradient boosting), and all models show greater than 80% accuracy. As compared to the optimized model's performance, extreme gradient boosting presented the highest predictive performance for the 4 diseases (diabetes, hypertension, hyperlipidemia, and cardiovascular disease) with 80% or greater and from 0.84 to 0.93 in area under the curve standards. CONCLUSIONS: This study demonstrates the possibility for the preemptive management of chronic diseases by predicting the occurrence of chronic diseases using the CDM and machine learning. With these models, the risk of developing major chronic diseases within 10 years can be demonstrated by identifying health risk factors using our chronic disease prediction machine learning model developed with the real-world data-based CDM and National Health Insurance Corporation examination data that individuals can easily obtain.

Prediction Algorithms for Blood Pressure Based on Pulse Wave Velocity Using Health Checkup Data in Healthy Korean Men: Algorithm Development and Validation.

BACKGROUND: Pulse transit time and pulse wave velocity (PWV) are related to blood pressure (BP), and there were continuous attempts to use these to predict BP through wearable devices. However, previous studies were conducted on a small scale and could not confirm the relative importance of each variable in predicting BP. OBJECTIVE: This study aims to predict systolic blood pressure and diastolic blood pressure based on PWV and to evaluate the relative importance of each clinical variable used in BP prediction models. METHODS: This study was conducted on 1362 healthy men older than 18 years who visited the Samsung Medical Center. The systolic blood pressure and diastolic blood pressure were estimated using the multiple linear regression method. Models were divided into two groups based on age: younger than 60 years and 60 years or older; 200 seeds were repeated in consideration of partition bias. Mean of error, absolute error, and root mean square error were used as performance metrics. RESULTS: The model divided into two age groups (younger than 60 years and 60 years and older) performed better than the model without division. The performance difference between the model using only three variables (PWV, BMI, age) and the model using 17 variables was not significant. Our final model using PWV, BMI, and age met the criteria presented by the American Association for the Advancement of Medical Instrumentation. The prediction errors were within the range of about 9 to 12 mmHg that can occur with a gold standard mercury sphygmomanometer. CONCLUSIONS: Dividing age based on the age of 60 years showed better BP prediction performance, and it could show good performance even if only PWV, BMI, and age variables were included. Our final model with the minimal number of variables (PWB, BMI, age) would be efficient and feasible for predicting BP.

Effects of Background Colors, Flashes, and Exposure Values on the Accuracy of a Smartphone-Based Pill Recognition System Using a Deep Convolutional Neural Network: Deep Learning and Experimental Approach.

BACKGROUND: Pill image recognition systems are difficult to develop due to differences in pill color, which are influenced by external factors such as the illumination from and the presence of a flash. OBJECTIVE: In this study, the differences in color between reference images and real-world images were measured to determine the accuracy of a pill recognition system under 12 real-world conditions (ie, different background colors, the presence and absence of a flash, and different exposure values [EVs]). METHODS: We analyzed 19 medications with different features (ie, different colors, shapes, and dosages). The average color difference was calculated based on the color distance between a reference image and a real-world image. RESULTS: For images with black backgrounds, as the EV decreased, the top-1 and top-5 accuracies increased independently of the presence of a flash. The top-5 accuracy for images with black backgrounds increased from 26.8% to 72.6% when the flash was on and increased from 29.5% to 76.8% when the flash was off as the EV decreased. However, the top-5 accuracy increased from 62.1% to 78.4% for images with white backgrounds when the flash was on. The best top-1 accuracy was 51.1% (white background; flash on; EV of +2.0). The best top-5 accuracy was 78.4% (white background; flash on; EV of 0). CONCLUSIONS: The accuracy generally increased as the color difference decreased, except for images with black backgrounds and an EV of -2.0. This study revealed that background colors, the presence of a flash, and EVs in real-world conditions are important factors that affect the performance of a pill recognition model.

Application of Efficient Data Cleaning Using Text Clustering for Semistructured Medical Reports to Large-Scale Stool Examination Reports: Methodology Study.

BACKGROUND: Since medical research based on big data has become more common, the community's interest and effort to analyze a large amount of semistructured or unstructured text data, such as examination reports, have rapidly increased. However, these large-scale text data are often not readily applicable to analysis owing to typographical errors, inconsistencies, or data entry problems. Therefore, an efficient data cleaning process is required to ensure the veracity of such data. OBJECTIVE: In this paper, we proposed an efficient data cleaning process for large-scale medical text data, which employs text clustering methods and value-converting technique, and evaluated its performance with medical examination text data. METHODS: The proposed data cleaning process consists of text clustering and value-merging. In the text clustering step, we suggested the use of key collision and nearest neighbor methods in a complementary manner. Words (called values) in the same cluster would be expected as a correct value and its wrong representations. In the value-converting step, wrong values for each identified cluster would be converted into their correct value. We applied these data cleaning process to 574,266 stool examination reports produced for parasite analysis at Samsung Medical Center from 1995 to 2015. The performance of the proposed process was examined and compared with data cleaning processes based on a single clustering method. We used OpenRefine 2.7, an open source application that provides various text clustering methods and an efficient user interface for value-converting with common-value suggestion. RESULTS: A total of 1,167,104 words in stool examination reports were surveyed. In the data cleaning process, we discovered 30 correct words and 45 patterns of typographical errors and duplicates. We observed high correction rates for words with typographical errors (98.61%) and typographical error patterns (97.78%). The resulting data accuracy was nearly 100% based on the number of total words. CONCLUSIONS: Our data cleaning process based on the combinatorial use of key collision and nearest neighbor methods provides an efficient cleaning of large-scale text data and hence improves data accuracy.