기계학습 및 결측자료 대체를 이용한 IgA 신염 예후 예측

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. Robert Ian McKay.IgA 신염은 IgA 항체가 신장 사구체에 침착되면서 발생하는 염증이다. 이는 가장 흔한 사구체신염으로 우리나라를 비롯한 동아시아에서 특히 높은 유병률을 보인다. IgA 신염 환자는 평균 35세 전후로 젊고 말기신부전에 의해 개인적인 부담 뿐만이 아니라 사회적, 경제적인 부담이 높기 때문에, IgA 신염 환자들을 위험도에 따라 분류하여 그에 따른 적절한 치료 방침을 세우는 것은 중차대한 과제라고 할 수 있다. 이미 IgA 신염의 결과를 예측하는 연구들이 기존에 있지만, 체계적이고 좋은 예측력을 갖는 방법은 부족한 상황이다. 우리는 본 연구에서 기계학습의 적용을 통해 새로운 예측 모형을 구축하는 것을 목표로 한다. 우리는 이를 위해 서울대학교 신경내과에서 1979년부터 2014까지 모은 자료를 기반으로 연구를 진행하였다. 자료에는 1622명의 환자들에 대한 90개 이상의 속성 정보가 들어있다. 우리는 이 중 17개의 속성들을 뽑아 예측 모형의 독립변수로 사용하였다. 하지만 이 속성들에 대해 하나 이상의 결측치를 가진 환자의 정보가 269개였는데, 이는 통계적 검정력의 큰 손실을 가져올 수 있다. 따라서 우리는 결측치 대체 방식을 이용하여 손실된 환자 정보를 복원하였다. 대체 방식의 결정을 위하여 평균값, 최빈값, 임의 대체와 같은 간단한 대체 방식을 기준으로 최근린 핫덱 대체와 연쇄식을 이용한 다변량 대체와 같은 더 복잡한 방식을 검증했다. 결과적으로 분류회귀나무를 이용한 다변량 대체가 가장 좋은 성능을 보였고 이를 적용하여 데이터를 최종 생성하였다. 위 데이터를 바탕으로 우리는 환자의 초기정보를 이용하여 10년 내에 말기신부전으로의 진행 여부를 예측하는 이진분류문제를 다뤘다. 이를 위해 다양한 기계학습법들이 적용되었는데, 의사결정나무, 로지스틱 회귀, 인공 신경망과 같은 단일 학습법을 비롯하여 배깅, 랜덤 포레스트, 부스팅의 앙상블 학습법을 사용하였다. 6가지 방식은 모두 시험 자료에 대해 0.804(의사결정나무)와 0.868(부스팅) 사이의 AUC 값을 가지며 좋은 성능을 보였다. 또한 해석력이 좋은 모형들을 분석함으로써 예후 예측 인자들에 대해 예상했던 결과를 모형 내에서 볼 수 있었고, 더 나아가 인자들 간의 상대적 중요도나 인자 별 좋고 나쁨의 기준이 되는 값들을 확인할 수 있었다. 일부 환자들에 대해서는 예상치 못한 결과를 볼 수 있었는데 이러한 결과들에 대해 후속 연구를 진행함으로써 임상적으로 유의미한 사실을 발견할 수 있을 것으로 기대된다.IgA Nephropathy(IgAN) occurs when IgA, an immune-system protein, deposits in kidney glomerules for unknown reasons. It is the most common glomerulonephritis, and has a high prevalence rate in East Asian nations. Determining appropriate treatment protocols and classifying IgAN patients by risk level are the most pressing issues. IgAN can occur even at a very young age (average age 35), hence the patients suffer from many personal, social and economic problems during the disease course - progression to End-Stage Renal Disease(ESRD). Although a number of approaches for predicting the prognosis of IgAN are available, well-advanced methods and techniques are scarce. In this work, we aimed to build new prediction models through careful application of machine learning methods. Our dataset was collected from 1979 to 2014 by the Division of Nephrology, Seoul National University Hospital. It includes 1622 patients' records, with more than 90 attributes. Among them, we chose 17 independent attributes for building our models. However, 269 records have missing values for at least one of these attributes, which can lead to a substantial loss of statistical prediction power. Hence, we used value imputation techniques to restore the records for our modelling. We used mean, mode and random imputation techniques as our baselines and analysed more sophisticated methods such as nearest neighbour hot deck imputation and Multivariate Imputation by Chained Equation(MICE). MICE with Classification And Regression Trees (CART) showed better performance, and hence we used this technique for the subsequent analysis. With this imputed data, we explored various machine learning methods. We investigated the most popular individual learners namely CART, logistic regression and neural network, and also the ensemble learners such as bagging, random forest and boosting. We treated the problem as a classification problem, of predicting progression to ESRD within the ten years following the initial diagnosis. All six methods yielded good classifiers, with AUC performance between 0.804 (decision tree) and 0.868 (boosting). The results were generally in-line with expectations, with poor kidney performance on presentation, and evident macroscopic and microscopic damages, all associated with poorer prognosis. Further demonstrating the benefits of the application of machine learning models in medical problems. However, a set of unexpected decision rules for a small group of patients arise some interesting questions and urge us for further detailed investigation.Chapter 1 Introduction 1 1.1 Problem Denition 2 1.2 Motivation 2 1.3 Importance 2 1.4 Contribution 3 1.5 Outline of the paper 3 Chapter 2 Background 4 2.1 Immunoglobulin A Nephropathy 4 2.2 Supervised Machine Learning Models 5 2.2.1 Individual Learners 5 2.2.2 Ensemble Learners 6 Chapter 3 Methods 8 3.1 Dataset 8 3.2 Attributes Used for Modelling 9 3.3 Missing Value Imputation 10 3.4 Target Attribute 12 3.5 Setting Prediction Period 13 3.6 Data Partitions 15 3.7 Building Models and Parameter Tuning 15 3.8 Performance Evaluation of Models 16 3.9 Validation and Prediction of Models 17 Chapter 4 Results 18 4.1 Missing Value Imputation 18 4.1.1 Mean, Mode, Random Imputation 18 4.1.2 Nearest Neighbour (NN) Hot Deck Imputation 19 4.1.3 Multivariate Imputation by Chained Equation (MICE) 20 4.1.4 MICE with Supplemented Attributes 21 4.2 Modelling with Individual Learning 22 4.2.1 Classication & Regression Trees (CART) 23 4.2.2 Logistic Regression 25 4.2.3 Neural Networks 29 4.3 Modelling with Ensemble Learning 32 4.3.1 Bagging 32 4.3.2 Random Forest 34 4.3.3 Boosting 36 4.4 Model Assessment: Comparative Study 37 4.4.1 Test Dataset 37 4.4.2 Mixed Dataset 39 Chapter 5 Analysis 40 Chapter 6 Summary and Conclusions 43 6.1 Future Work 44Maste

Prediction of ESRD in IgA Nephropathy Patients from an Asian Cohort: A Random Forest Model

Background/Aims: There is an increasing risk of end-stage renal disease (ESRD) among Asian people with immunoglobulin A nephropathy (IgAN). A computer-aided system for ESRD prediction in Asian IgAN patients has not been well studied. Methods: We retrospectively reviewed biopsy-proven IgAN patients treated at the Department of Nephrology of the Second Xiangya Hospital from January 2009 to November 2013. Demographic and clinicopathological data were obtained within 1 month of renal biopsy. A random forest (RF) model was employed to predict the ESRD status in IgAN patients. All cases were initially trained and validated, taking advantage of the out-of-bagging(OOB) error. Predictors used in the model were selected according to the Gini impurity index in the RF model and verified by logistic regression analysis. The area under the receiver operating characteristic(ROC) curve (AUC) and F-measure were used to evaluate the RF model. Results: A total of 262 IgAN patients were enrolled in this study with a median follow-up time of 4.66 years. The importance rankings of predictors of ESRD in the RF model were first obtained, indicating some of the most important predictors. Logistic regression also showed that these factors were statistically associated with ESRD status. We first trained an initial RF model using gender, age, hypertension, serum creatinine, 24-hour proteinuria and histological grading suggested by the Clinical Decision Support System for IgAN (CDSS, www.IgAN.net). This 6-predictor model achieved a F-measure of 0.8 and an AUC of 92.57%. By adding Oxford-MEST scores, this model outperformed the initial model with an improved AUC (96.1%) and F-measure (0.823). When C3 staining was incorporated, the AUC was 97.29% and F-measure increased to 0.83. Adding the estimated glomerular filtration rate (eGFR) improved the AUC to 95.45%. We also observed improved performance of the model with additional inputs of blood urea nitrogen (BUN), uric acid, hemoglobin and albumin. Conclusion: In addition to the predictors in the CDSS, Oxford-MEST scores, C3 staining and eGFR conveyed additional information for ESRD prediction in Chinese IgAN patients using a RF model

Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy

Background: Urine proteome analysis is rapidly emerging as a tool for diagnosis and prognosis in disease states. For diagnosis of diabetic nephropathy (DN), urinary proteome analysis was successfully applied in a pilot study. The validity of the previously established proteomic biomarkers with respect to the diagnostic and prognostic potential was assessed on a separate set of patients recruited at three different European centers. In this case-control study of 148 Caucasian patients with diabetes mellitus type 2 and duration >= 5 years, cases of DN were defined as albuminuria >300 mg/d and diabetic retinopathy (n = 66). Controls were matched for gender and diabetes duration (n = 82). Methodology/Principal Findings: Proteome analysis was performed blinded using high-resolution capillary electrophoresis coupled with mass spectrometry (CE-MS). Data were evaluated employing the previously developed model for DN. Upon unblinding, the model for DN showed 93.8% sensitivity and 91.4% specificity, with an AUC of 0.948 (95% CI 0.898-0.978). Of 65 previously identified peptides, 60 were significantly different between cases and controls of this study. In <10% of cases and controls classification by proteome analysis not entirely resulted in the expected clinical outcome. Analysis of patient's subsequent clinical course revealed later progression to DN in some of the false positive classified DN control patients. Conclusions: These data provide the first independent confirmation that profiling of the urinary proteome by CE-MS can adequately identify subjects with DN, supporting the generalizability of this approach. The data further establish urinary collagen fragments as biomarkers for diabetes-induced renal damage that may serve as earlier and more specific biomarkers than the currently used urinary albumin

Rare Kidney Diseases

Rare kidney diseases comprise a large group of different life-threatening or chronically debilitating disorders that affect very small numbers of people (<1 in 2000 individuals in Europe and <200,000 in USA) with local or systemic manifestations. For several years, the research and development of treatments in this field have been neglected in favor of more common diseases. The main reasons for the lack of interest in rare kidney diseases seem to be the small numbers of patients and limited epidemiological data on the natural history of many of these diseases. Rare diseases can affect people differently. Even patients with the same condition can exhibit very different signs and symptoms, or there may be many subtypes of the same condition. This diversity constitutes a significant challenge to healthcare practitioners and scientists alike, in terms of being able to acquire sufficient experience for the most appropriate and timely definition, diagnosis, and management. Fortunately, in the last ten years, concerted efforts have led to a marked improvement in the understanding of these disorders. In particular, an important step forward has been taken with the employment of innovative technologies (including next-generation sequencing), in order to replace obsolete phenotypic classifications and to discover new useful diagnostic biomarkers. These new tools are, in fact, becoming part of routine clinical practice, increasing diagnostic accuracy and facilitating genetic counseling. Moreover, biomedical research, providing insights into the pathologies of these rare diseases and elucidating their underlying mechanisms, is revealing new therapeutic avenues and driving the industry to develop safer and more effective orphan drugs. Finally, in this field, it is desirable that, in the future, the crosstalk between basic scientists and clinicians could achieve a great clinical benefit by improving the quality of life of these patients as well. This Special Issue welcomes scientific contributions and critical reviews describing new pathogenetic insights, reporting novel and specific disease biomarkers, and underlying new pharmacological targets or therapies for rare diseases of the kidney and urinary tract

Pediatric and Adolescent Nephrology Facing the Future: Diagnostic Advances and Prognostic Biomarkers in Everyday Practice

The Special Issue entitled “Pediatric and adolescent nephrology facing the future: diagnostic advances and prognostic biomarkers in everyday practice” contains articles written in the era when COVID-19 had not yet been a major clinical problem in children. Now that we know its multifaceted clinical course, complications concerning the kidneys, and childhood-specific post-COVID pediatric inflammatory multisystem syndrome (PIMS), the value of diagnostic and prognostic biomarkers in the pediatric area should be appreciated, and their importance ought to increase

Next-Generation Sequencing for New Gene Identification and for Diagnosis in Steroid-Resistant Nephrotic Syndrome

Nephrotic syndrome is clinically characterized by massive proteinuria, and hypoalbuminemia. It represents a heterogeneous group of glomerular disorders characterized by distinct causes and histopathologic lesions. The present thesis evaluates the contribution of genetics in conditions commonly associated with NS: the immune complex-mediated membranoproliferative glomerulonephritis (IC-MPGN), the C3 glomerulopathy (C3G) and the podocytopathies, usually characterized by steroid-resistant nephrotic syndrome (SRNS). The first part of the thesis focused on IC-MPGN and C3G. We found likely pathogenic (LP) variants in complement alternative pathway genes in 18% of patients and describe, for the first time, thrombomodulin rare pathogenic variants in C3G patients. Interestingly, mutations alone did not increase the risk of developing IC-MPGN or C3G, but they did so when combined with common susceptibility variants. The prevalence of alternative pathway abnormalities (mutations and/or C3 Nephritic Factors, C3NeFs) was similar in IC-MPGN (53-56%) and C3G (64-65%). To investigate for more homogeneous subgroups within IC-MPGN/C3G, an operator-independent approach was applied using genetic, biochemical, histological and clinical data. Four different groups emerged showing distinct pathogenesis, and histological and clinical features. In the second part, the genetic causes of podocytopathies were investigated. LP variants in podocytopathy-associated genes are found in 23% of SRNS patients, prevalently in COL4A3-5 genes. Moreover, 8% of patients carried LP variants in Congenital Anomalies of the Kidney and the Urinary Tract (CAKUT)-associated genes, which were not previously associated with podocytopathies. LP variants in CAKUT-associated and podocytopathy-associated genes frequently combined together. Finally, possibly pathogenic variants in EPB41L45, a candidate gene for podocytopathies, were identified in 3 unrelated patients. In conclusion, this thesis contributes to understand the complex genetic basis of both IC-MPGN/C3G and podocytopathies. It introduces new players in the pathogenesis of these diseases. Finally, it provides evidence of the presence of subgroups within IC-MPGN/C3G with distinct underlying mechanisms, and clinical and histopathologic features