No description available.

The detection of subtypes of complex diseases has important implications for diagnosis and treatment. Numerous prior studies have used data-driven approaches to identify clusters of similar patients, but it is not yet clear how to best specify what constitutes a clinically meaningful phenotype. This study explored disease subtyping on the basis of temporal development patterns. In particular, we attempted to differentiate infants with autism spectrum disorder into more fine-grained classes with distinctive patterns of early skill development. We modeled the progression of autism explicitly using a continuous-time hidden Markov model. Subsequently, we compared subjects on the basis of their trajectories through the model state space. Two approaches to subtyping were utilized, one based on time-series clustering with a custom distance function and one based on tensor factorization. A web application was also developed to facilitate the visual exploration of our results. Results suggested the presence of 3 developmental subgroups in the ASD outcome group. The two subtyping approaches are contrasted and possible future directions for research are discussed.

No description available.

Master of Science / Department of Statistics / Wei-Wen Hsu / BACKGROUND AND PURPOSE: Alzheimer's disease is currently a neurodegenerative diseases without any effective treatments to slow or reverse the progression. To develop any potential treatments, the need of a good statistical model to assess the progression of Alzheimer's disease is becoming increasingly urgent. This study proposed a latent transition model to monitor the progression of Alzheimer's disease which can help the development of a given proposed treatment. METHOD: A latent transition model was used to assess the progression of Alzheimer's disease. The volume of Hippocampus and fluorodeoxyglucose-PET (FDG) were employed as biomarkers in this model. These two biomarkers are very sensitive to the pathological signs of the Alzheimer's disease. The proposed latent transition model was performed with real data from Alzheimer's Disease Neuroimaging Initiative (ADNI), which contain 2,126 participants from 2005 to 2014. RESULTS/FINDINGS: The latent transition model suggested six states of disease progression and two different pathological profiles. One progression profile was mainly determined by the biomarker of FDG and the other by the volume of Hippocampus. CONCLUSION: The results revealed the existence of various progression profiles of Alzheimer's disease, suggesting a new way to evaluate the disease progression.

Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modeling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This thesis describes the application of intelligent data analysis techniques for extracting information from time series generated by different diseases. The aim of this thesis is to identify intermediate stages in a disease process and sub-categories of the disease exhibiting subtly different symptoms. It explores the use of a bootstrap technique that fits trajectories through the data generating “pseudo time-series”. It addresses issues including: how clinical variables interact as a disease progresses along the trajectories in the data; and how to automatically identify different disease states along these trajectories, as well as the transitions between them. The thesis documents how reliable time-series models can be created from large amounts of historical cross-sectional data and a novel relabling/latent variable approach has enabled the exploration of the temporal nature of disease progression. The proposed algorithms are tested extensively on simulated data and on three real clinical datasets. Finally, a study is carried out to explore whether we can “calibrate” pseudo time-series models with real longitudinal data in order to improve them. Plausible directions for future research are discussed at the end of the thesis.

Among those subjects admitted with AECOPD and concomitant pneumonia, a total of 118 patients (91 males) with 150 episodes were identified. Haemophilus influenzae was the commonest organism found in sputum (26.0%), followed by Pseudomonas aeruginosa (5.5%), Streptococcus pneumoniae (3.4%), and Moraxella catarrhalis (3.4%). In contrast to most studies from other countries reporting Streptococcus pneumoniae as the most likely bacterial pathogen, Haemophilus influenzae was the commonest bacterium isolated in sputum in this cohort of patients with AECOPD and concomitant pneumonia. / Chronic obstructive pulmonary disease (COPD) is a disease state characterized by airflow limitation that is not fully reversible. / Exhaled breath condensate (EBC) analysis is a novel tool which has been developed in recent years and the technique is believed to reflect the lower airway lining fluid. My previous work has demonstrated the repeatability of certain inflammatory markers in the EBC of stable asthma and COPD patients. / Hypothesis 1: Bacterial pathogens are the major cause of AECOPD with and without concomitant pneumonia in patients requiring hospitalization. In the one-year retrospective bacteriology study, there were 329 patients with 418 episodes of AECOPD without concomitant pneumonia. These result noted that H. influenzae was the commonest bacterium isolated in sputum in patients with AECOPD without concomitant pneumonia. In areas endemic of tuberculosis, it is advisable to use fluoroquinolones for AECOPD with caution in view of the positive sputum culture of mycobacterium tuberculosis in some patients. / Hypothesis 2: Viral pathogens are an important cause of AECOPD in patients hospitalized with AECOPD. For the prospective infectious aetiology study, there were 643 episodes of AECOPD among 373 patients (307 males). Severe airflow obstruction (stable state spirometry) was associated with a higher chance of positive sputum culture (28.2% for FEV1 ≥30% vs. 40.4% for FEV1 <30% predicted normal, p=0.006). In this study, Haemophilus influenzae and influenza A were the commonest aetiological agents in patients hospitalized with AECOPD. More severe airflow obstruction was associated with a higher chance of positive sputum culture. / Hypothesis 3: The rates of hospital admissions due to AECOPD are associated with indices of air pollution in Hong Kong. Concerning the effect of air pollutants on AECOPD, significant associations were found between hospital admissions for COPD with all 5 air pollutants. Adverse effects of ambient concentrations of air pollutants on hospitalization rates for COPD are evident, especially during the winter season in Hong Kong. / Hypothesis 4: During the course of AECOPD, it is possible to assess inflammation in the airway by measuring biomarkers non-invasively using the method of EBC collection. To explore the course of inflammation in the airway during AECOPD, 26 patients (22 male) with AECOPD (mean percentage predicted FEV1, 44.8 +/- 14.3), 11 stable COPD and 14 age and sex-matched healthy controls were studied. Repeatability measurements of TNFalpha and LTB4 in 6 stable COPD patients were satisfactory. EBC TNFalpha level was low in patients receiving systemic steroid and antibiotic therapy for AECOPD whereas EBC TNFalpha level was also lower in stable patients receiving ICS post AECOPD than those who were not. These findings suggest a potential role for serial EBC TNFalpha for non-invasive monitoring of disease activity. / Summary. The above studies have shown that bacterial pathogens are the major cause of AECOPD with and without concomitant pneumonia in patients requiring hospitalization and the commonest bacterium found in the sputum of the patients was Haemophilus influenzae. Viral pathogens are also an important cause of AECOPD in patients hospitalized with AECOPD in Hong Kong and the commonest virus identified in the NPA of the patients was influenza A. Concerning the effect of air pollutants on AECOPD, significant associations were found between hospital admissions for AECOPD with the air pollutants of SO2, NO3, O3, PM10 and PM2.5. Finally, TNFalpha could be measured in the EBC of patients during the course of AECOPD and its level was low in patients receiving systemic steroid and antibiotic therapy for AECOPD. The results suggest that it is possible to assess inflammation in the airway by measuring biomarkers non-invasively using the method of EBC collection. (Abstract shortened by UMI.) / Ko, Wai-san Fanny. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 0926. / Thesis (M.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 207-250). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / School code: 1307.

Context & Rationale: Individuals with similar CD4 cell counts and RNA levels can vary considerably with regards to clinical progression. This variation is likely the result of a complex interplay between viral, host and environmental factors. This study aimed to characterize and identify predictors associated with disease progression to AIDS or death in Saskatoon, Saskatchewan. Methods: This is a retrospective cohort study of 343 seroprevalent HIV positive patients diagnosed from Jan 2005 to Dec 2010. Of these, 73 had an estimated seroconversion date. Data was extracted from medical charts at two clinics specialized in HIV/AIDS care. Disease progression was measured as time from HIV diagnosis (or seroconversion) to immunological AIDS and death. The Cox hazard model was used. Results: The 3-year and 5-year immunological AIDS free probability was 53% and 33%, respectively. The 3-year and 5-year survival probability was 89% and 77%, respectively. Among the seroconversion cohort, the 3-year immunological AIDS free probability was 76%. Due to multicollinearity, separate models were built for IDU, hepatitis C and ethnicity. A history of IDU (HR, 3.0; 95%CI, 1.2-7.1), hepatitis C coinfection (HR, 2.9; 95%CI, 1.2-6.9), baseline CD4 counts (HR, 0.95; 95%CI, 0.92-0.98, per ever 10 unit increase), ever on ART, and year of diagnosis were significant predictors of progression to immunological AIDS among the seroprevalent cohort. Age at diagnosis, sex and ethnicity were not. For survival, only treatment use was a significant predictor (HR, 0.34; 95%CI, 0.1-0.8). Hepatitis C coinfection was marginally significant (p=0.067), while a history of IDU, ethnicity, gender, age at diagnosis, and year of diagnosis were not. Among the seroconversion cohort, no predictors of progression to immunological AIDS were identified. Ethnicity, hepatitis C coinfection and history of IDU could not be assessed. Conclusion: Our study found that IDU, HCV coinfections, baseline CD4 counts, and ART use were significant predictors of disease progression. This highlights the need for increased testing and early detection and for targeted interventions for these particularly vulnerable populations to slow disease progression.

Context & Rationale: Individuals with similar CD4 cell counts and RNA levels can vary considerably with regards to clinical progression. This variation is likely the result of a complex interplay between viral, host and environmental factors. This study aimed to characterize and identify predictors associated with disease progression to AIDS or death in Saskatoon, Saskatchewan. Methods: This is a retrospective cohort study of 343 seroprevalent HIV positive patients diagnosed from Jan 2005 to Dec 2010. Of these, 73 had an estimated seroconversion date. Data was extracted from medical charts at two clinics specialized in HIV/AIDS care. Disease progression was measured as time from HIV diagnosis (or seroconversion) to immunological AIDS and death. The Cox hazard model was used. Results: The 3-year and 5-year immunological AIDS free probability was 53% and 33%, respectively. The 3-year and 5-year survival probability was 89% and 77%, respectively. Among the seroconversion cohort, the 3-year immunological AIDS free probability was 76%. Due to multicollinearity, separate models were built for IDU, hepatitis C and ethnicity. A history of IDU (HR, 3.0; 95%CI, 1.2-7.1), hepatitis C coinfection (HR, 2.9; 95%CI, 1.2-6.9), baseline CD4 counts (HR, 0.95; 95%CI, 0.92-0.98, per ever 10 unit increase), ever on ART, and year of diagnosis were significant predictors of progression to immunological AIDS among the seroprevalent cohort. Age at diagnosis, sex and ethnicity were not. For survival, only treatment use was a significant predictor (HR, 0.34; 95%CI, 0.1-0.8). Hepatitis C coinfection was marginally significant (p=0.067), while a history of IDU, ethnicity, gender, age at diagnosis, and year of diagnosis were not. Among the seroconversion cohort, no predictors of progression to immunological AIDS were identified. Ethnicity, hepatitis C coinfection and history of IDU could not be assessed. Conclusion: Our study found that IDU, HCV coinfections, baseline CD4 counts, and ART use were significant predictors of disease progression. This highlights the need for increased testing and early detection and for targeted interventions for these particularly vulnerable populations to slow disease progression.

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 117-143).

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 77-89).