Novel statistical approaches for missing values in truncated high-dimensional metabolomics data with a detection threshold.

Despite considerable advances in high throughput technology over the last decade, new challenges have emerged related to the analysis, interpretation, and integration of high-dimensional data. The arrival of omics datasets has contributed to the rapid improvement of systems biology, which seeks the understanding of complex biological systems. Metabolomics is an emerging omics field, where mass spectrometry technologies generate high dimensional datasets. As advances in this area are progressing, the need for better analysis methods to provide correct and adequate results are required. While in other omics sectors such as genomics or proteomics there has and continues to be critical understanding and concern in developing appropriate methods to handle missing values, handling of missing values in metabolomics has been an undervalued step. Missing data are a common issue in all types of medical research and handling missing data has always been a challenge. Since many downstream analyses such as classification methods, clustering methods, and dimension reduction methods require complete datasets, imputation of missing data is a critical and crucial step. The standard approach used is to remove features with one or more missing values or to substitute them with a value such as mean or half minimum substitution. One of the major issues from the missing data in metabolomics is due to a limit of detection, and thus sophisticated methods are needed to incorporate different origins of missingness. This dissertation contributes to the knowledge of missing value imputation methods with three separate but related research projects. The first project consists of a novel missing value imputation method based on a modification of the k nearest neighbor method which accounts for truncation at the minimum value/limit of detection. The approach assumes that the data follows a truncated normal distribution with the truncation point at the detection limit. The aim of the second project arises from the limitation in the first project. While the novel approach is useful, estimation of the truncated mean and standard deviation is problematic in small sample sizes (N \u3c 10). In this project, we develop a Bayesian model for imputing missing values with small sample sizes. The Bayesian paradigm has generally been utilized in the omics field as it exploits the data accessible from related components to acquire data to stabilize parameter estimation. The third project is based on the motivation to determine the impact of missing value imputation on down-stream analyses and whether ranking of imputation methods correlates well with the biological implications of the imputation

An adaptive ensemble learner function via bagging and rank aggregation with applications to high dimensional data.

An ensemble consists of a set of individual predictors whose predictions are combined. Generally, different classification and regression models tend to work well for different types of data and also, it is usually not know which algorithm will be optimal in any given application. In this thesis an ensemble regression function is presented which is adapted from Datta et al. 2010. The ensemble function is constructed by combining bagging and rank aggregation that is capable of changing its performance depending on the type of data that is being used. In the classification approach, the results can be optimized with respect to performance measures such as accuracy, sensitivity, specificity and area under the curve (AUC) whereas in the regression approach, it can be optimized with respect to measures such as mean square error and mean absolute error. The ensemble classifier and ensemble regressor performs at the level of the best individual classifier or regression model. For complex high-dimensional datasets, it may be advisable to combine a number of classification algorithms or regression algorithms rather than using one specific algorithm

The burden and characteristics of HIV-infected COVID-19 patients at a tertiary care hospital in sub-Saharan Africa—A retrospective cohort study

Background: After the first case of COVID-19 caused by the novel SARS-CoV-2 virus was discovered in Wuhan, China, in December 2019, the disease spread viciously throughout the world. Little is known about the impact of HIV infection on the clinical outcomes of patients co-infected with SARS-CoV-2. Studying the characteristics and outcomes of COVID-19 among HIV-positive patients is key to characterising the risk of morbidity and mortality of HIV-positive patients from COVID-19. Methods: In this retrospective cohort study, we included patients admitted to Aga Khan University Hospital, Nairobi, with laboratory-confirmed COVID-19 infection and who had consented to HIV screening. We compared the prevalence and characteristics of HIV patients with those of non-HIV patients and described the results for both groups. Results: In our sample of 582 patients, the mean age was 49.2 years (SD = 15.2), with 68% of the sample being men. The cumulative HIV prevalence was 3.7%, and the most common symptoms were cough (58.1%), fever (45.2%), difficulty in breathing (36.8%) and general body malaise (23.9%). The most common comorbidities included hypertension (28.5%), diabetes mellitus (26.1%), and heart disease (4.1%). Most participants (228 or 49.5%) had mild COVID-19, and the mortality rate was 5%. Overall, there were no statistically significant differences in demographic characteristics, clinical characteristics, and outcomes between HIV-positive and HIV-negative patients. Conclusions: There was a 3.7% prevalence of HIV in COVID-19 positive patients. Demographic characteristics and clinical outcomes were similar between the two groups. Future studies should seek to achieve larger samples, include multiple study sites and conduct subgroup analyses based on the immunologic status of HIV-positive patients

COVID-19 and mental well-being of nurses in a tertiary facility in Kenya

Background: The 2019 coronavirus disease (COVID-19) epidemic is a global health emergency which has been shown to pose a great challenge to mental health, well-being and resilience of healthcare workers, especially nurses. Little is known on the impact of COVID-19 among nurses in sub-Saharan Africa. Methods: A cross sectional study was carried out between August and November 2020 among nurses recruited from the Aga Khan University Hospital, Nairobi. The survey questionnaire consisted of six components- demographic and work title characteristics, information regarding care of COVID-19 patients, symptoms of depression, anxiety, insomnia, distress and burnout, measured using standardized questionnaires. Multivariable logistic regression analysis was performed to identify factors associated with mental health disorders. Results: Of 255 nurses, 171 (67.1%) consented to complete the survey. The median age of the participants was 33.47 years, 70.2% were females and 60.8% were married. More than half, 64.9% were frontline workers directly engaged in COVID-19 care. Only 1.8% reported a prior history or diagnosis of any mental health disorder. Depression, anxiety, insomnia, distress, and burnout were reported in 45.9%, 48.2%, 37.0%, 28.8% and 47.9% of all nurses. Frontline nurses reported experiencing more moderate to severe symptoms of depression, distress and burnout. Furthermore, females reported more burnout as compared to males. Multivariate logistic regression analysis showed that after adjustment, working in the frontlines was an independent risk variable for depression and burnout. Conclusion: This is one of the few studies looking at mental health outcomes among nurses during the COVID-19 pandemic in Kenya. Similar to other studies from around the world, nurses directly involved with COVID-19 patients reported higher rates of mental health symptoms. Burnout threatens to exacerbate the pre-existing severe nursing workforce shortage in low-resource settings. Cost-effective and feasible mitigating strategies, geared to low-middle income countries, are urgently needed to help cope with mental health symptoms during such a pandemic

Qualitative Approach to Understanding Barriers to Delivering Difficult News in Sub Saharan Africa

Communication, especially delivery of difficult news (DDN), remains a key part of clinical practice. Despite its importance, many medical providers lack the skill and ability to effectively DDN to their patients. Due to lack of data specific to sub-Saharan Africa and to help us develop an appropriate training tool for this geographical area, we sought to explore what challenges and barriers residents at our institution faced when they deliver difficult news to their patients

HIV Prevalence and Characteristics Among Patients With AIDS-Defining and Non–AIDS-Defining Cancers in a Tertiary Hospital in Kenya

Purpose: Antiretroviral therapy (ART) has resulted in a higher life expectancy of persons living with HIV. This has led to an aging population at risk for both non–AIDS-defining cancers (NADCs) and AIDS-defining cancers (ADCs). HIV testing among patients with cancer in Kenya is not routinely performed, making its prevalence undefined. The aim of our study was to determine the prevalence of HIV and the spectrum of malignancies among HIV-positive and HIV-negative patients with cancer attending a tertiary hospital in Nairobi, Kenya. Materials and Methods: We conducted a cross-sectional study between February 2021 and September 2021. Patients with a histologic cancer diagnosis were enrolled. Demographic data and HIV- and cancer-related clinical variables were obtained. HIV pretest counseling and consent were done, and testing was performed using a fourth-generation assay. Positive results were confirmed using a third-generation assay. Results: We enrolled 301 patients with cancer; 67.8% (204 of 301) were female; the mean age was 50.7 ± 12.5 years. From our cohort, 10.6% (95% CI, 7.4 to 14.7, n = 32 of 301) of patients were HIV-positive with the prevalence of a new HIV diagnosis of 0.7% (n = 2 of 301). Of the HIV-positive patients, 59.4% (19 of 32) had a NADC. The commonest NADC was breast cancer (18.8%; 6 of 32), whereas non-Hodgkin lymphoma (18.8%; 6 of 32) and cervical cancer (18.8%; 6 of 32) were the most prevalent ADCs among HIV-positive patients. Conclusion: The prevalence of HIV infection among patients with cancer was twice the Kenya national HIV prevalence. NADCs comprised a larger percentage of the cancer burden. Universal opt-out HIV testing of patients attending for cancer care regardless of cancer type may facilitate early recognition of HIV-infected patients and aid in appropriate selection of ART and cancer therapies and preventive strategies

Association between dialysate sodium concentration and interdialytic weight gain in patients undergoing twice weekly haemodialysis

Abstract Background Chronic kidney disease is highly prevalent across the globe with more than 2 million people worldwide requiring renal replacement therapy. Interdialytic weight gain is the change in body weight between two sessions of haemodialysis. Higher interdialytic weight gain has been associated with an increase in mortality and adverse cardiovascular outcomes. It has long been questioned whether using a lower dialysate sodium concentration during dialysis would reduce the interdialytic weight gain and hence prevent these adverse outcomes. Methods This study was a single blinded cross-over study of patients undergoing twice weekly haemodialysis at the Aga Khan University Hospital, Nairobi and Parklands Kidney Centre. It was conducted over a twelve-week period and patients were divided into two groups: dialysate sodium concentration of 137 meq/l and 140 meq/l. These groups switched over after a six-week period without a washout period. Univariate analysis was conducted using Fisher’s exact test for categorical data and Mann Whitney test for continuous data. Results Forty-one patients were included in the analysis. The mean age was 61.37 years, and 73% were males. The mean duration for dialysis was 2.53 years. The interdialytic weight gain was not significantly different between the two groups (2.14 for the 137 meq/l group and 2.35 for the 140 meq/l group, p = 0.970). Mean blood pressures were as follows: pre-dialysis: DNa 137 meq/l: systolic 152.14 ± 19.99, diastolic 78.99 ± 12.20, DNa 140 meq/l: systolic 156.95 ± 26.45, diastolic 79.75 ± 11.25 (p = 0.379, 0.629 respectively). Post-dialysis: DNa 137 meq/l: systolic 147.29 ± 22.22, diastolic 77.85 ± 12.82 DNa 140 meq/l: systolic 151.48 ± 25.65, diastolic 79.66 ± 15.78 (p = 0.569, 0.621 respectively). Conclusion There was no significant difference in the interdialytic weight gain as well as pre dialysis and post dialysis systolic and diastolic blood pressures between the two groups. Therefore, using a lower dialysate sodium concentration does not appear useful in altering the interdialytic weight gain or blood pressure although further studies are warranted with a larger sample size, taking into account residual renal function and longer duration for impact on blood pressures

An electronic health record system implementation in a resource limited country—lessons learned

Electronic health records have revolutionized the medical world by improving medical care, refining provider documentation, standardizing care, and minimizing sentinel events. Successful implementation of electronic health records remains a daunting task and requires careful strategic planning and buy-in from key stakeholders. Much has been published in resource-rich settings and high-income countries about implementations of electronic health records. However, little is known about the experience in resource-limited settings where challenges remain unique and distinct from other parts of the world. Our intention is to share lessons learned during implementation of a web-based electronic health record at a tertiary care center in Kenya