환자 보고 성과 지표를 활용한 한국인 폐암 무병 생존자 생존 예측 모형 개발

학위논문 (박사)-- 서울대학교 대학원 : 의과대학 의과학과, 2018. 2. 윤영호.Introduction: The prediction of lung cancer survival is a crucial factor for successful cancer survivorship and follow-up planning. The principal objective of this study is to construct a novel Korean prognostic model of 5-year survival within lung cancer disease-free survivors using socio-clinical and HRQOL variables and to compare its predictive performance with the prediction model based on the traditional known clinical variables. Diverse techniques such as Cox proportional hazard model and machine learning technologies (MLT) were applied to the modeling process. Methods: Data of 809 survivors, who underwent lung cancer surgery between 1994 and 2002 at two Korean tertiary teaching hospitals, were used. The following variables were selected as independent variables for the prognostic model by using literature reviews and univariate analysis: clinical and socio-demographic variables, including age, sex, stage, metastatic lymph node and incomehealth related quality of life (HRQOL) factors from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30Quality of Life Questionnaire Lung Cancer ModuleHospital Anxiety and Depression Scale, and Post-traumatic Growth Inventory. Survivors body mass index before a surgery and physical activity were also chosen. The three prediction modeling features sets included 1) only clinical and socio-demographic variables, 2) only HRQOL and lifestyle factors, and 3) variables from feature set 1 and 2 considered altogether. For each feature set, three Cox proportional hazard regression model were constructed and compared among each other by evaluating their performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow chi-square statistics. Further, four machine learning algorithms using decision tree (DT), random forest (RF), bagging, and adaptive boosting (AdaBoost) were applied to three feature sets and compared with the performances of one another. The performance of the derived predictive models based on MLTs were internally validated by K-fold cross-validation. Results: In the Cox modeling, Model Cox-3 (based on Feature set 3: HRQOL factors added into clinical and socio-demographic variables) showed the highest area under curve (AUC = 0.809) compared with two other Cox regression (Cox-1, 2). When we applied the modeling methods into all other MLT based models, the most effective models were Model DT-3 from DT, Model RF-3 from RF, Model Bag-3 from Bagging, Model AdaBoost-3 from AdaBoost techniques, showing the highest accuracy for each of MLT. Model RF-3, Model Bag-3, Model AdaBoost-3 showed the highest accuracy even after k-fold cross-validation were conducted. Conclusions: Considering that the HRQOLs were added with clinical and socio-demographic variables, the proposed model proved to be useful based on the Cox model or we can apply MLT algorithms in the prediction of lung cancer survival. Improved accuracy for lung cancer survival prediction model has the potential to help clinicians and survivors make more meaningful decisions about future plans and their support to cancer care.I. INTRODUCTION 1 A. Background 1 1. Lung cancer statistics 1 2. The importance of suggesting survival prediction model to cancer survivors 4 3. HRQOL and lifestyle measurement as important predictors for lung cancer survival 5 4. Traditional survival analysis versus machine learning techniques (MLTs) 7 B. Hypothesis and objectives 10 1. Hypothesis 10 2. Objectives 10 II. MATERIALS AND METHODS 12 A. Study subjects 12 1. Subject selection 12 2. Data collection 13 2.1. Socio-demographic and clinical variables 15 2.2. Patient lifestyle characteristics 17 3. Study process 20 B. Prognostic variables selection and data preprocessing 22 1. Prognostic variables selection 22 1.1. Literature review for the selection of candidate predictors 22 1.2. Grading the evidence and mapping into the conceptual framework 25 1.3. Examination of prognosis variables selection from statistical analyses 28 2. Data preprocessing 29 2.1. Data cleaning, missing imputation 29 2.2. Test of multi-collinearity 29 2.3. Decisions of cut-off points 30 2.4. Data sampling for data balancing, SMOTE 31 2.5. Data splitting (holdout strategy) 32 C. Model development 33 1. Cox model development 34 3. Random forest model 38 4. Bagging (bootstrap aggregating) 40 5. Adaptive boosting (AdaBoost) 42 D. Model validation 44 1. Model validation for Cox model 44 1.1. Discrimination for Cox model 44 1.2. Calibration for Cox model 44 2. Model validation of other MLTs 45 3. K-fold Cross Validation for MLT based prediction models to avoid over-fitting 46 III. RESULTS 48 A. Literature review for selection of candidate predictors 48 1. Selection of candidate prognostic factors with literature review 48 2. Model constructing feature sets with selecting prognostic factors 51 B. Baseline characteristics 52 1. Demographics of participants characteristics and survival data 52 2. Candidate selection from statistical analyses 54 2.1. Univariate analysis of HRQOL mean scores between non-event and event groups 54 2.2. Univariate analysis of BMI, weight change, and MET of lung cancer survivors 58 3. Final candidate variable selection for phased modeling 60 4. Result of data preprocessing 62 4.1. Missing imputation 62 C. Model development 64 1. Cox model development 65 1.1. Prediction model based on Cox regression analysis 67 1.2. Final prediction model equation for Cox models 71 2. Decision tree model development 72 2.1. Assessment of the relative importance and model developing 72 2.2. Selecting CP value for decision tree pruning using rpart packages 74 3. Random forest model development 76 4. Bagged decision tree model development 78 5. AdaBoost model development 79 6. Developed models applied with MLTs 81 D. Model validation and performance 88 1. Cox proportional hazard ratio model internal validation 88 1.1. Discrimination 88 1.2. Calibration 91 2. Comparison model performance of Cox model and other MLTs 96 IV. DISCUSSION 106 A. Literature review for selection of candidate predictors 107 B. Model development using Cox and other MLTs 109 C. Model validation of Cox regression model and application of the predictive models to other MLT based models 112 D. Clinical and practical implications 114 E. Strengths and limitations of this study 117 CONCLUSION 119 REFERENCES 120 국문 초록 133 APPENDIX 135Docto

Leveraging Text Mining Approach to Identify What People Want to Know About Mental Disorders From Online Inquiry Platforms

Online inquiry platforms, which is where a person can anonymously ask questions, have become an important information source for those who are concerned about social stigma and discrimination that follow mental disorders. Therefore, examining what people inquire about regarding mental disorders would be useful when designing educational programs for communities. The present study aimed to examine the contents of the queries regarding mental disorders that were posted on online inquiry platforms. A total of 4,714 relevant queries from the two major online inquiry platforms were collected. We computed word frequencies, centralities, and latent Dirichlet allocation (LDA) topic modeling. The words like symptom, hospital and treatment ranked as the most frequently used words, and the word my appeared to have the highest centrality. LDA identified four latent topics: (1) the understanding of general symptoms, (2) a disability grading system and welfare entitlement, (3) stressful life events, and (4) social adaptation with mental disorders. People are interested in practical information concerning mental disorders, such as social benefits, social adaptation, more general information about the symptoms and the treatments. Our findings suggest that instructions encompassing different scopes of information are needed when developing educational programs

Stronger association of perceived health with socio-economic inequality during COVID-19 pandemic than pre-pandemic era

Abstract Objective The COVID-19 pandemic has changed peoples routine of daily living and posed major risks to global health and economy. Few studies have examined differential impacts of economic factors on health during pandemic compared to pre-pandemic. We aimed to compare the strength of associations between perceived health and socioeconomic position (household income, educational attainment, and employment) estimated before and during the pandemic. Methods Two waves of nationwide survey [on 2018(T1;n= 1200) and 2021(T2;n= 1000)] were done for 2200 community adults. A balanced distribution of confounders (demographics and socioeconomic position) were achieved across the T2 and T1 by use of the inverse probability of treatment weighting. Distributions of perceived health [= (excellent or very good)/(bad, fair, or good)] for physical-mental-social-spiritual subdomains were compared between T1 and T2. Odds of bad/fair/good health for demographics and socioeconomic position were obtained by univariate logistic regression. Adjusted odds (aOR) of bad/fair/good health in lower household income(< 3000 U.S. dollars/month) were retrieved using the multiple hierarchical logistic regression models of T1 and T2. Results Perceived health of excellent/very good at T2 was higher than T1 for physical(T1 = 36.05%, T2 = 39.13%; P= 0.04), but were lower for mental(T1 = 38.71%, T2 = 35.17%; P= 0.01) and social(T1 = 42.48%, T2 = 35.17%; P 0.05). AORs of bad/fair/good health in lower household income were stronger in T2 than T1, for mental [aOR (95% CI) = 2.15(1.68–2.77) in T2, 1.33(1.06–1.68) in T1; aOR difference = 0.82(P< 0.001)], physical [aOR (95% CI) = 2.64(2.05–3.41) in T2, 1.50(1.18–1.90) in T1; aOR difference = 1.14(P< 0.001)] and social [aOR (95% CI) = 2.15(1.68–2.77) in T2, 1.33(1.06–1.68) in T1; aOR difference = 0.35(P= 0.049)] subdomains. Conclusions Risks of perceived health worsening for mental and social subdomains in people with lower monthly household income or lower educational attainment became stronger during the COVID-19 pandemic compared to pre-pandemic era. In consideration of the prolonged pandemic as of mid-2022, policies aiming not only to sustain the monthly household income and compulsory education but also to actively enhance the perceived mental-social health status have to be executed and maintained

Involvement of Src Family Tyrosine Kinase in Apoptosis of Human Neutrophils Induced by Protozoan Parasite Entamoeba histolytica

Tyrosine kinases are one of the most important regulators for intracellular signal transduction related to inflammatory responses. However, there are no reports describing the effects of tyrosine kinases on neutrophil apoptosis induced by Entamoeba histolytica. In this study, isolated human neutrophils from peripheral blood were incubated with live trophozoites in the presence or absence of tyrosine kinase inhibitors. Entamoeba-induced receptor shedding of CD16 and PS externalization in neutrophils were inhibited by pre-incubation of neutrophils with the broad-spectrum tyrosine kinase inhibitor genistein or the Src family kinase inhibitor PP2. Entamoeba-induced ROS production was also inhibited by genistein or PP2. Moreover, genistein and PP2 blocked the phosphorylation of ERK and p38 MAPK in neutrophils induced by E. histolytica. These results suggest that Src tyrosine kinases may participate in the signaling event for ROS-dependent activation of MAPKs during neutrophil apoptosis induced by E. histolytica

Epidemiological and Genetic Characterization of Methicillin-Resistant Staphylococcus aureus Isolates from the Ear Discharge of Outpatients with Chronic Otitis Media

The origin of methicillin-resistant Staphylococcus aureus (MRSA) strains from otolaryngology outpatients has not been evaluated yet in Korea. We analyzed epidemiologic and genetic characteristics of MRSA isolates from the ear discharge of 64 outpatients with chronic otitis media in a Korean University Hospital during 2004. MRSA strains were grouped as either from the initial visit (n=33) or the follow-up visit (n=31) based on the timing of isolation. Healthcare-associated risk factors were frequently present among patients of the initial visit group, especially prior visit to primary clinic (79%) and antibiotic use (73%). SCCmec typing and multilocus sequence typing results showed that two genotypes, ST5-MRSA-II and ST239-MRSA-III, were prevalent in both the initial visit (73% vs. 24%) and the follow-up visit (55% vs. 42%). Pulsed-field gel electrophoresis identified eight types, including two major types shared by both groups. We conclude that majority of MRSA strains from ear discharge of chronic otitis media belonged to nosocomial clones that might be circulating in the community. This is the first report of the genetic analysis of MRSA strains from otolaryngology practices in Korea

Efficacy and safety of rapid intermittent bolus compared with slow continuous infusion in patients with severe hypernatremia (SALSA II trial): a study protocol for a randomized controlled trial

Background Hypernatremia is a common electrolyte disorder in children and elderly people and has high short-term mortality. However, no high-quality studies have examined the correction rate of hypernatremia and the amount of fluid required for correction. Therefore, in this study, we will compare the efficacy and safety of rapid intermittent bolus (RIB) and slow continuous infusion (SCI) of electrolyte-free solution in hypernatremia treatment. Methods This is a prospective, investigator-initiated, multicenter, open-label, randomized controlled study with two experimental groups. A total of 166 participants with severe hypernatremia will be enrolled and divided into two randomized groups; both the RIB and SCI groups will be managed with electrolyte-free water. We plan to infuse the same amount of fluid to both groups, for 1 hour in the RIB group and continuously in the SCI group. The primary outcome is a rapid decrease in serum sodium levels within 24 hours. The secondary outcomes will further compare the efficacy and safety of the two treatment protocols. Conclusion This is the first randomized controlled trial to evaluate the efficacy and safety of RIB correction compared with SCI in adult patients with severe hypernatremia

Prognostic value of quality of life score in disease-free survivors of surgically-treated lung cancer

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Abstract Background We aimed to evaluate the prognostic value of quality of life (QOL) for predicting survival among disease-free survivors of surgically-treated lung cancer after the completion of cancer treatment. Methods We administered the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30), the Quality of Life Questionnaire Lung Cancer Module (QLQ-LC13), Hospital Anxiety and Depression Scale (HADS), and Posttraumatic Growth Inventory (PTGI) to 809 survivors who were surgically-treated for lung cancer at two hospitals from 2001 through 2006. We gathered mortality data by linkage to the National Statistical Office through December 2011. We used Cox proportional hazard models to compute adjusted hazard ratios (aHRs) and 95 % confidence intervals (CIs) to estimate the relationship between QOL and survival. Results Analyses of QOL items adjusted for age, sex, stage, body mass index, and physical activity showed that scores for poor physical functioning, dyspnea, anorexia, diarrhea, cough, personal strength, anxiety, and depression were associated with poor survival. With adjustment for the independent indicators of survival, final multiple proportional hazard regression analyses of QOL show that physical functioning (aHR, 2.39; 95 % CI, 1.13–5.07), dyspnea (aHR, 1.56; 95 % CI, 1.01–2.40), personal strength (aHR, 2.36; 95 % CI, 1.31–4.27), and anxiety (aHR, 2.13; 95 % CI, 1.38–3.30) retained their independent prognostic power of survival. Conclusion This study suggests that patient-reported QOL outcomes in disease-free survivors of surgically-treated lung cancer after the completion of active treatment has independent prognostic value for long-term survival

A randomized controlled trial of physical activity, dietary habit, and distress management with the Leadership and Coaching for Health (LEACH) program for disease-free cancer survivors

Background We aimed to evaluate the potential benefits of the Leadership and Coaching for Health (LEACH) program on physical activity (PA), dietary habits, and distress management in cancer survivors. Methods We randomly assigned 248 cancer survivors with an allocation ratio of two-to-one to the LEACH program (LP) group, coached by long-term survivors, or the usual care (UC) group. At baseline, 3, 6, and 12 months, we used PA scores, the intake of vegetables and fruits (VF), and the Post Traumatic Growth Inventory (PTGI) as primary outcomes and, for secondary outcomes, the Ten Rules for Highly Effective Health Behavior adhered to and quality of life (QOL), the Hospital Anxiety and Depression Scale (HADS), and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). Results For primary outcomes, the two groups did not significantly differ in PA scores or VF intake but differed marginally in PTGI. For secondary outcomes, the LP group showed a significantly greater improvement in the HADS anxiety score, the social functioning score, and the appetite loss and financial difficulties scores of the EORTC QLQ-C30 scales from baseline to 3 months. From baseline to 12 months, the LP group showed a significantly greater decrease in the EORTC QLQ-C30 fatigue score and a significantly greater increase in the number of the Ten Rules for Highly Effective Health Behavior. Conclusion Our findings indicate that the LEACH program, coached by long-term survivors, can provide effective management of the QOL of cancer survivors but not of their PA or dietary habits. Trial registration Clinical trial information can be found for the following: NCT01527409 (the date when the trial was registered: February 2012)