Are polypharmacy side effects predicted by public data still valid in real-world data?

Background and Objective: Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods: We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results: The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions: Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.

LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis.

Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult to sufficiently utilize them. Generative models based on deep learning can solve this problem by creating synthetic data similar to real patient data. However, the data used for training these deep learning models run into the risk of getting leaked because of malicious attacks. This means that traditional deep learning-based generative models cannot completely solve the privacy issues. Therefore, we suggested a method to prevent the leakage of training data by protecting the model from malicious attacks using local differential privacy(LDP). Our method was evaluated in terms of utility and privacy. Experimental results demonstrated that the proposed method can generate medical data with reasonable performance while protecting training data from malicious attacks.

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.

Heterogeneous graph construction and HinSAGE learning from electronic medical records.

Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to address the challenge of implementing a complex and highly heterogeneous dataset, including the following: (1) demonstrating how to build a multi-attributed and multi-relational graph model (2) and applying a downstream disease prediction task of a patient's prognosis using the HinSAGE algorithm. We present a bipartite graph schema and a graph database construction in detail. The first constructed graph database illustrates a query of a predictive network that provides analytical insights using a graph representation of a patient's journey. Moreover, we demonstrate an alternative bipartite model where we apply the model to the HinSAGE to perform the link prediction task for predicting the event occurrence. Consequently, the performance evaluation indicated that our heterogeneous graph model was successfully predicted as a baseline model. Overall, our graph database successfully demonstrated efficient real-time query performance and showed HinSAGE implementation to predict cardiovascular disease event outcomes on supervised link prediction learning.

RIDAB: Electronic medical record-integrated real world data platform for predicting and summarizing interactions in biomedical research from heterogeneous data resources.

BACKGROUND AND OBJECTIVE: With the advent of bioinformatics, biological databases have been constructed to computerize data. Biological systems can be described as interactions and relationships between elements constituting the systems, and they are organized in various biomedical open databases. These open databases have been used in approaches to predict functional interactions such as protein-protein interactions (PPI), drug-drug interactions (DDI) and disease-disease relationships (DDR). However, just combining interaction data has limited effectiveness in predicting the complex relationships occurring in a whole context. Each contributing source contains information on each element in a specific field of knowledge but there is a lack of inter-disciplinary insight in combining them. METHODS: In this study, we propose the RWD Integrated platform for Discovering Associations in Biomedical research (RIDAB) to predict interactions between biomedical entities. RIDAB is established as a graph network to construct a platform that predicts the interactions of target entities. Biomedical open database is combined with EMRs each representing a biomedical network and a real-world data. To integrate databases from different domains to build the platform, mapping of the vocabularies was required. In addition, the appropriate structure of the network and the graph embedding method to be used were needed to be selected to fit the tasks. RESULTS: The feasibility of the platform was evaluated using node similarity and link prediction for drug repositioning task, a commonly used task for biomedical network. In addition, we compared the US Food and Drug Administration (FDA)-approved repositioned drugs with the predicted result. By integrating EMR database with biomedical networks, the platform showed increased f1 score in predicting repositioned drugs, from 45.62% to 57.26%, compared to platforms based on biomedical networks alone. CONCLUSIONS: This study demonstrates that the elements of biomedical research findings can be reflected by integrating EMR data with open-source biomedical networks. In addition, showed the feasibility of using the established platform to represent the integration of biomedical networks and reflected the relationship between real world networks.

Association between social support and postpartum depression.

Postpartum depression is common; however, little is known about its relationship to social support and postpartum depression. This study examined the association between them among South Korean women within one year of childbirth. This study was based on the 2016 Korean Study of Women's Health-Related Issues (K-Stori), a cross-sectional survey employing nationally-representative random sampling. Participants were 1,654 postpartum women within a year of giving birth. Chi-square test and logistic regression analysis were conducted to analyze the associations between social support (and other covariates) and postpartum depression. Among participants, 266 (16.1%) had postpartum depression. Depending on the level of social support, 6.0%, 53.9%, and 40.1% of them had low, moderate, and high social support, respectively. Women with moderate or low social support were more likely to have postpartum depression (OR = 1.78, 95% CI = 1.26-2.53; OR = 2.76, 95% CI = 1.56-4.89). This trend was observed in participants with multiparity, pregnancy loss, obese body image, and employed women. Social support was associated with a decreased likelihood of postpartum depression, indicating the importance of social support, especially for women experiencing multiparity, pregnancy loss, negative body image, as well as for employed women.

Machine Learning-Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study.

BACKGROUND: Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient's hospitalization period may support the making of judicious decisions regarding bed management. OBJECTIVE: First, this study aims to develop a machine learning (ML)-based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services. METHODS: We set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features. RESULTS: We experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes. CONCLUSIONS: In this study, we propose an individual explainer based on an ML-based predictive model, which provides the discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources.

Self-Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study.

BACKGROUND: When using machine learning in the real world, the missing value problem is the first problem encountered. Methods to impute this missing value include statistical methods such as mean, expectation-maximization, and multiple imputations by chained equations (MICE) as well as machine learning methods such as multilayer perceptron, k-nearest neighbor, and decision tree. OBJECTIVE: The objective of this study was to impute numeric medical data such as physical data and laboratory data. We aimed to effectively impute data using a progressive method called self-training in the medical field where training data are scarce. METHODS: In this paper, we propose a self-training method that gradually increases the available data. Models trained with complete data predict the missing values in incomplete data. Among the incomplete data, the data in which the missing value is validly predicted are incorporated into the complete data. Using the predicted value as the actual value is called pseudolabeling. This process is repeated until the condition is satisfied. The most important part of this process is how to evaluate the accuracy of pseudolabels. They can be evaluated by observing the effect of the pseudolabeled data on the performance of the model. RESULTS: In self-training using random forest (RF), mean squared error was up to 12% lower than pure RF, and the Pearson correlation coefficient was 0.1% higher. This difference was confirmed statistically. In the Friedman test performed on MICE and RF, self-training showed a P value between .003 and .02. A Wilcoxon signed-rank test performed on the mean imputation showed the lowest possible P value, 3.05e-5, in all situations. CONCLUSIONS: Self-training showed significant results in comparing the predicted values and actual values, but it needs to be verified in an actual machine learning system. And self-training has the potential to improve performance according to the pseudolabel evaluation method, which will be the main subject of our future research.

Sleep Quality and Associated Factors in Premenopausal, Perimenopausal, and Postmenopausal Women in Korea: Findings from the K-Stori 2016.

PURPOSE: Poor sleep quality is a common problem among middle-aged women. Few studies, however, have assessed differences in sleep quality among premenopausal, perimenopausal, and postmenopausal women and related risk factors in Korea women. The aim of this study was to assess sleep quality and factors associated therewith according to menopausal status in Korean women. PATIENTS AND METHODS: This study was based on the 2016 Korean Study of Women's Health Related Issues (K-Stori), a cross-sectional survey employing nationally representative random sampling. In total, 3000 Korean women aged 45 to 64 years completed the Pittsburgh Sleep Quality Index (PSQI). Comparison of demographic characteristics and sleep quality among pre-, peri-, and postmenopausal women was conducted. RESULTS: Among the participants, 26% suffered from poor sleep quality based on the PSQI. The prevalence of poor sleep quality increased with later menopausal stage (from 18.8% in the premenopausal stage to 29.5% in the postmenopausal stage P <0.001). Multivariate logistic regression analysis showed that peri- and postmenopausal women were 1.50 and 1.73 times more likely to have poor sleep quality in comparison to premenopausal women, respectively. Chronic disease, depression, at-risk drinking, taking dietary supplements, and single women were associated with a higher likelihood of having poor sleep quality. Health status, at-risk drinking, chronic illness, dietary supplementation, and depression were significantly associated with poor sleep quality. CONCLUSION: Poor sleep quality appears to be prevalent in peri- and postmenopausal women in Korea. The management of sleep quality during menopause transition is important, and further research on how sleep disturbances influence the health status of women in menopausal transition is required.

Analgesic effect of ropivacaine with fentanyl in comparison with ropivacaine alone for continuous femoral nerve block after knee replacement arthroplasty: a prospective, randomized, double-blinded study.

BACKGROUND: The analgesic effect of perineural opioid in clinical practice are still controversial. This randomized controlled trial compared analgesic effect of ropivacaine with fentanyl or ropivacaine alone for continuous femoral nerve block following unilateral total knee arthroplasty. METHODS: Fourty patients of ASA PS Ⅰ or Ⅱ receiving total knee arthroplasty with spinal anesthesia were enlisted and randomly allocated into two groups. Group R; bolus injection of 0.375% ropivacaine, 30 ml and an infusion of 0.2% ropivacaine at 8 ml/h (n = 20). Group RF; 0.375% ropivacaine, 29 ml added with 50 µg of fentanyl as a bolus and an infusion of 0.2% ropivacaine mixed with 1 µg/ml of fentanyl at 8 ml/h (n = 20). Local anesthetic infusion via a femoral nerve catheter was started at the end of operation and continued for 48 h. Intravenous patient-controlled analgesia with hydromorphone (0.15 mg/ml, 0-1-10) were used for adjuvant analgesics. Position of catheter tip and contrast distribution, visual analogue scale of pain, hydromorphone consumption, side effects were recorded for 48 h after operation. Patient satisfaction for the pain control received were noted. RESULTS: The pain visual analogue scale, incidences of side effects and satisfaction were not different between the two groups (P > 0.05), but the hydromorphone usage at 48 h after operation were lower in the Group RF than in the Group R (P = 0.047). CONCLUSIONS: The analgesic effect of ropivacaine with fentanyl for continuous femoral nerve block after knee replacement arthroplasty was not superior to that of the ropivacaine alone.

Views on Precision Medicine among Health Professionals in Korea: A Mixed Methods Study.

This study aimed to investigate awareness, attitudes, and perspectives on precision medicine among health professionals in Korea and to identify issues that need to be addressed before implementing precision medicine. Mixed methods research was applied. For qualitative research, a semi-structured focus group interview was conducted with six health professionals. For quantitative research, a self-reported survey was administered. A total of 542 health professionals participated in the survey, and 526 completed the entire questionnaire. Health professionals showed positive attitudes toward precision medicine. About 95-96% of respondents agreed that precision medicine will be effective in treatment and precise diagnosis, and 69.9% reported that they would participate as study subjects. Meanwhile, they expressed concerns regarding educating patients and health professionals in precision medicine and developing research and data sharing infrastructure. Also, they emphasized the importance of developing precision medicine in an equitable way. Despite varying levels of awareness of precision medicine, the health professionals expressed a willingness to engage in precision medicine research, and recommended that health professionals work closely with policymakers to design precision medicine in a way that can be effectively adopted. Health professionals showed had a positive, but cautious, attitude toward precision medicine. The results of this study suggest areas to be addressed before ushering in precision medicine in Korea.

Weight control behaviors according to body weight status and accuracy of weight perceptions among Korean women: a nationwide population-based survey.

This study aimed to identify associations among self-perceived weight status, accuracy of weight perceptions, and weight control behaviors, including both healthy and unhealthy behaviors, in a large, nationally representative sample from an East Asian country. Data were collected from the 2016 Korean Study of Women's Health Related Issues, a population-based, nationwide survey. Accurate weight perceptions were investigated by comparing body mass index (BMI) categories, based on self-reported height and weight, and weight perceptions. Weight control behaviors over the previous 12 months were additionally surveyed. Odds ratios (ORs) and 95% confidence intervals (CIs) are presented as an index of associations. Among normal weight, overweight, and obese women, 12.8%, 44.3%, and 17.4% under-assessed their weight; 17.9% of normal weight women over-assessed their weight. Both weight status according to BMI category and weight perceptions were strongly associated with having tried to lose weight. Exercise and diet (ate less) were the most commonly applied weight control behaviors. Misperception of weight was related to more unhealthy weight control behaviors and less healthy behaviors: Women who under-assessed their weight showed a lower tendency to engage in dieting (OR = 0.57, 95% CI = 0.43-0.75) and a greater tendency to fast/skip meals (OR = 1.47, 95% CI = 1.07-1.99). Meanwhile, normal weight or overweight women who over-assessed their weight were more likely to have engaged in fasting/skipping meals or using diet pills (OR = 5.72, 95% CI = 2.45-13.56 for fasting/skipping meal in overweight women; OR = 1.62, 95% CI = 1.15-2.29 and OR = 3.16, 95% CI = 1.15-8.23 for using diet pills in normal and overweight women). Inaccuracy of weight perceptions in any direction (over/under) were related to more unhealthy weight control behaviors and less healthy weight control behaviors, especially in normal and overweight women.

Determinants of undergoing thyroid cancer screening in Korean women: a cross-sectional analysis from the K-Stori 2016.

OBJECTIVES: Thyroid cancer is the most common cancer among Korean women. Studies suggest that the incidence of thyroid cancer might be associated with overdiagnosis resulting from thyroid cancer screening. The objective of this study was to identify the determinants of participation in thyroid cancer screening in Korean women. METHODS: Data were obtained from the 2016 Korean Study of Women's Health-Related Issues, a nationwide cross-sectional survey of women according to the reproductive life cycle. A total of 8697 cancer-free women of ages between 20 and 79 years were included for analysis. Multivariable logistic regression analysis was applied to analyse factors associated with adherence to thyroid cancer screening based on Andersen's health behavioural model. RESULTS: Over the last 2 years, the rate of thyroid cancer screening was 39.2%. In multivariable models, older age, higher household income, high school education level and higher perceived risk of cancer were positively associated with thyroid cancer screening participation. Moreover, women who underwent cervical cancer screening (adjusted OR [aOR] 3.67; 95% CI 2.90 to 4.64) and breast cancer screening (aOR 10.91; 95% CI 8.41 to 14.14) had higher odds of attending thyroid cancer screening than women who did not attend cancer screening. CONCLUSIONS: These findings highlight the need to increase awareness of different recommendations on screening for various cancers to improve cost-effectiveness and to prevent unnecessary treatments.

Socioeconomic inequalities in obesity among Korean women aged 19-79 years: the 2016 Korean Study of Women's Health-Related Issues.

OBJECTIVES: While the prevalence of obesity in Asian women has remained stagnant, studies of socioeconomic inequalities in obesity among Asian women are scarce. This study aimed to examine the recent prevalence of obesity in Korean women aged between 19 years and 79 years and to analyze socioeconomic inequalities in obesity. METHODS: Data were derived from the 2016 Korean Study of Women's Health-Related Issues. The chi-square test and logistic regression analysis were used to analyze the associations between socioeconomic factors and obesity using Asian standard body mass index (BMI) categories: low (<18.5 kg/m2 ), normal (18.5-22.9 kg/m2 ), overweight (23.0-24.9 kg/m2 ), and obese (≥25.0 kg/ m2 ). As inequality-specific indicators, the slope index of inequality (SII) and relative index of inequality (RII) were calculated, with adjustment for age and self-reported health status. RESULTS: Korean women were classified into the following BMI categories: underweight (5.3%), normal weight (59.1%), overweight (21.2%), and obese (14.4%). The SII and RII revealed substantial inequalities in obesity in favor of more urbanized women (SII, 4.5; RII, 1.4) and against of women who were highly educated (SII, -16.7; RII, 0.3). Subgroup analysis revealed inequalities in obesity according to household income among younger women and according to urbanization among women aged 65-79 years. CONCLUSIONS: Clear educational inequalities in obesity existed in Korean women. Reverse inequalities in urbanization were also apparent in older women. Developing strategies to address the multiple observed inequalities in obesity among Korean women may prove essential for effectively reducing the burden of this disease.

Self-perceptions of body weight status according to age-groups among Korean women: A nationwide population-based survey.

While numerous studies have investigated body image, including body weight perception, most of which have focused on adolescents or young women, few studies have attempted to evaluate body weight perceptions in adult women according to age groups. This study was conducted to investigate the accuracy of self-perceived weight and actual body mass index (BMI) values among adult Korean women according to age. We used data from the 2016 Korean Study of Women's Health Related Issues, a population-based, nationwide, cross-sectional survey. BMI was calculated from self-reported weight and height. Participants were asked to describe their body image by choosing one of the following descriptions: very underweight, underweight, about right, overweight, or obese. The proportions of women aged 20-29, 30-39, 40-49, 50-59, 60-69, and 70-79 years who underestimated their body weight relative to their actual BMI category were 12.6%, 15.1%, 22.2%, 34.0%, 45.6%, and 50.7%, respectively; those who overestimated their body weight comprised 18.7%, 17.8%, 14.3%, 10.8%, and 7.4%. In all BMI categories, the proportion of those who overestimated their weight status increased as age decreased, while those who underestimated their weight status increased as age increased. After adjusting for possible covariates, age was strongly associated with both underestimation and overestimation. The odds ratio for underestimating one's weight status among women aged 70-79 yeas was 2.96 (95% CI: 2.10-4.18), and that for overestimation was 0.52 (95% CI: 0.35-0.79), compared to women aged 20-29 years. Age is the most important factor associated with weight perceptions among Korean women, affecting both underestimation and overestimation of weight status.

Stages of Adoption for Fecal Occult Blood Test and Colonoscopy Tests for Colorectal Cancer Screening in Korea.

PURPOSE: While colorectal cancer (CRC) is common in Asian countries, screening for CRC is not. Moreover, CRC screening behaviors in Asian populations remain largely unknown. The present study aimed to investigate the stages of adopting CRC screening in Korea according to screening modality. MATERIALS AND METHODS: Data were obtained from the 2014 Korean National Cancer Screening Survey, a cross-sectional survey that utilized nationally representative random sampling to investigate cancer screening rates. A total of 2,066 participants aged 50-74 years were included in this study. Chi-square test and multinomial logistic regressionwere applied to determine stages of adoption for fecal occult blood test (FOBT) and colonoscopy and factors associated with each stage. RESULTS: Of 1,593 participants included in an analysis of stage of adoption for FOBT, 36% were in action/maintenance stages, while 18%, 40%, and 6% were in precontemplation, contemplation, and relapse/relapse risk stages, respectively. Of 1,371 subjects included in an analysis of stage of adoption for colonoscopy, 48% were in action/maintenance stages, with 21% in precontemplation, 21% in contemplation, and 11% in relapse/relapse risk stages. Multinomial logistic regression highlighted sex, household income, place of residency, family history of cancer, having private cancer insurance, smoking status, alcohol use, and regular exercise as being associated with stages of adoption for FOBT and colonoscopy. CONCLUSION: This study outlines the distributions of stages of adoption for CRC screening by screening modality. Interventions to improve screening rates should be tailored to individuals in particular stages of adoption for CRC screening by modality.

Initial titration with 200 µg fentanyl buccal tablets: a retrospective safety analysis in Korean cancer patients.

BACKGROUND/AIMS: Managing breakthrough pain (BTP) is important for many cancer patients because of the rapid onset and unpredictable nature of the pain episodes. Fentanyl buccal tablets (FBTs) are a rapid-onset opioid indicated for BTP management. However, FBT titration is needed to optimize BTP management. In this study, we aimed to evaluate the safety and efficacy of initiating 200 µg FBTs in Korean cancer patients. METHODS: A retrospective analysis of medical records was performed on all advanced cancer patients treated with FBTs for BTP between October 2014 and July 2015. Patients who received initial doses of 200 µg FBTs for at least 3 days and cases in which FBT was available at doses of 200, 400, and 800 µg were included. RESULTS: A total of 56 patients with a median age of 62 years (range, 32 to 80) were analyzed, 61% of whom were male. The median and mean values of morphine equivalent daily doses were 60 mg/day (range, 15 to 540) and 114.8 ± 124.8 mg/day, respectively. The most frequent effective doses of FBT were 200 µg (41 patients, 74%) and 400 µg (12 patients, 21%). Three patients (5%) could not tolerate 200 µg of FBT and discontinued treatment. Nausea, vomiting, somnolence, and dizziness were the most frequent treatment-related adverse events (AEs), and all AEs were grade 1 (mild) or 2 (moderate). CONCLUSIONS: FBT at the initial 200 µg dosage was well-tolerated and effective as a BTP management strategy in Korean cancer patients. Further prospective studies are needed to determine appropriate initiating doses of FBT in Korean patients with opioid tolerance.