HIV-associated malignancies among children attending the Baylor Clinic in Kampala, Uganda

Introduction: HIV-associated malignancies such as Kaposi’s sarcoma and Non-Hodgkin’s lymphoma occur in children and usually lead to significant morbidity and mortality. No studies have been done to establish prevalence and outcome of these malignancies in children in a hospital setting in Uganda. ^ Research question: What proportion of children attending the Baylor-Uganda COE present with HIV-associated malignancies and what are the characteristics and outcome of these malignancies? The objective was to determine the prevalence, associated factors and outcome of HIV-associated malignancies among children attending the Baylor-Uganda Clinic in Kampala, Uganda. Study Design: This was a retrospective case series involving records review of patients who presented to the Baylor-Clinic between January 2004 and December 2008. Study Setting: The Baylor-Uganda Clinic, where I worked as a physician before coming to Houston, is a well funded, well staffed; Pediatric HIV clinic located in Mulago Hospital, Kampala, Uganda and is affiliated to Makerere University Medical School. Study Participants: Medical charts of patients aged 6 weeks to 18 years who enrolled for care at the clinic during the years 2004 to 2008 were retrieved for data abstraction. Selection Criteria: Study participants had to be patients of Baylor-Uganda seen during the study period; they had to be aged 6 weeks to 18 years; and had to be HIV positive. Patients with incomplete data or whose malignancies were not confirmed by histology were excluded. Study Variables: Data on patient’s age, sex, diagnosis, type of malignancy, anatomic location of the malignancy; pathology report, baseline laboratory results and outcome of treatment, were abstracted. Data Analysis: Cross tabulation to determine associations between variables using Pearson’s chi square at 95% level of significance was done. Proportions of malignancies among different groups were determined. In addition, Kaplan Meier survival analysis and comparison of survival distributions using the log-rank test was done. Change in CD4 percentages from baseline was assessed with the Wilcoxon signed rank test. Results: The proportion of children with malignancies during the study period was found to be 1.65%. Only 2 malignancies: Kaposi’s sarcoma and Non-Hodgkin’s lymphoma were found. 90% of the malignancies were Kaposi’s sarcoma. Lymph node involvement in children with Kaposi’s sarcoma was common, but the worst prognosis was seen with visceral involvement. Deaths during follow-up were seen in the first few weeks to months. Upon starting treatment the CD4 cell percentage increased significantly from a baseline median of 6% to 14% at 6 months and 15.8% at 12 months of follow-up.^

Using incomplete citation data for MEDLINE results ranking.

Information overload is a significant problem for modern medicine. Searching MEDLINE for common topics often retrieves more relevant documents than users can review. Therefore, we must identify documents that are not only relevant, but also important. Our system ranks articles using citation counts and the PageRank algorithm, incorporating data from the Science Citation Index. However, citation data is usually incomplete. Therefore, we explore the relationship between the quantity of citation information available to the system and the quality of the result ranking. Specifically, we test the ability of citation count and PageRank to identify "important articles" as defined by experts from large result sets with decreasing citation information. We found that PageRank performs better than simple citation counts, but both algorithms are surprisingly robust to information loss. We conclude that even an incomplete citation database is likely to be effective for importance ranking.

Using incomplete citation data for MEDLINE results ranking.

Information overload is a significant problem for modern medicine. Searching MEDLINE for common topics often retrieves more relevant documents than users can review. Therefore, we must identify documents that are not only relevant, but also important. Our system ranks articles using citation counts and the PageRank algorithm, incorporating data from the Science Citation Index. However, citation data is usually incomplete. Therefore, we explore the relationship between the quantity of citation information available to the system and the quality of the result ranking. Specifically, we test the ability of citation count and PageRank to identify "important articles" as defined by experts from large result sets with decreasing citation information. We found that PageRank performs better than simple citation counts, but both algorithms are surprisingly robust to information loss. We conclude that even an incomplete citation database is likely to be effective for importance ranking.

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Estimating parameters in Markov models for longitudinal studies with missing data or surrogate outcomes

The discrete-time Markov chain is commonly used in describing changes of health states for chronic diseases in a longitudinal study. Statistical inferences on comparing treatment effects or on finding determinants of disease progression usually require estimation of transition probabilities. In many situations when the outcome data have some missing observations or the variable of interest (called a latent variable) can not be measured directly, the estimation of transition probabilities becomes more complicated. In the latter case, a surrogate variable that is easier to access and can gauge the characteristics of the latent one is usually used for data analysis. ^ This dissertation research proposes methods to analyze longitudinal data (1) that have categorical outcome with missing observations or (2) that use complete or incomplete surrogate observations to analyze the categorical latent outcome. For (1), different missing mechanisms were considered for empirical studies using methods that include EM algorithm, Monte Carlo EM and a procedure that is not a data augmentation method. For (2), the hidden Markov model with the forward-backward procedure was applied for parameter estimation. This method was also extended to cover the computation of standard errors. The proposed methods were demonstrated by the Schizophrenia example. The relevance of public health, the strength and limitations, and possible future research were also discussed. ^