Department of Biostatistics

Introduction: We performed a systematic review and meta-analysis to identify predictors of serious clinical outcomes after an acute-care evaluation for syncope.

Methods: We identified studies that assessed for predictors of short-term (≤30 days) serious clinical events after an emergency department (ED) visit for syncope. We performed a MEDLINE search (January 1, 1990 - July 1, 2017) and reviewed reference lists of retrieved articles. The primary outcome was the occurrence of a serious clinical event (composite of mortality, arrhythmia, ischemic or structural heart disease, major bleed, or neurovascular event) within 30 days. We estimated the sensitivity, specificity, and likelihood ratio of findings for the primary outcome. We created summary estimates of association on a variable-by-variable basis using a Bayesian random-effects model.

Results: We reviewed 2,773 unique articles; 17 met inclusion criteria. The clinical findings most predictive of a short-term, serious event were the following: 1) An elevated blood urea nitrogen level (positive likelihood ratio [LR+]: 2.86, 95% confidence interval [CI] [1.15, 5.42]); 2); history of congestive heart failure (LR+: 2.65, 95%CI [1.69, 3.91]); 3) initial low blood pressure in the ED (LR+: 2.62, 95%CI [1.12, 4.9]); 4) history of arrhythmia (LR+: 2.32, 95%CI [1.31, 3.62]); and 5) an abnormal troponin value (LR+: 2.49, 95%CI [1.36, 4.1]). Younger age was associated with lower risk (LR-: 0.44, 95%CI [0.25, 0.68]). An abnormal electrocardiogram was mildly predictive of increased risk (LR+ 1.79, 95%CI [1.14, 2.63]).

Conclusion: We identified specific risk factors that may aid clinical judgment and that should be considered in the development of future risk-prediction tools for serious clinical events after an ED visit for syncope.

Introduction: We sought to compare three hospital cost estimation models for patients undergoing evaluation for unexplained syncope with hospital cost data. Developing such a model would allow researchers to assess the value of novel clinical algorithms for syncope management.

Methods: Complete health services data, including disposition, testing, and length of stay (LOS), were collected on 67 adult patients (age 60 years and older) who presented to the Emergency Department (ED) with syncope at a single hospital. Patients were excluded if a serious medical condition was identified. Three hospital cost estimation models were created to estimate facility costs: V1, unadjusted Medicare payments for observation and/or hospital admission, V2: modified Medicare payment, prorated by LOS in calendar days, and, V3: modified Medicare payment, prorated by LOS in hours. Total hospital costs included unadjusted Medicare payments for diagnostic testing and estimated facility costs. These estimates were plotted against actual cost data from the hospital finance department. Correlation and regression analyses were performed.

Results: Of the three models, V3 consistently outperformed the others with regard to correlation and goodness of fit. The Pearson correlation coefficient for V3 was 0.88 (95% Confidence Interval 0.81, 0.92) with an R-square value of 0.77 and a linear regression coefficient of 0.87 (95% Confidence Interval 0.76, 0.99).

Conclusion: Using basic health services data, it is possible to accurately estimate hospital costs for older adults undergoing a hospital-based evaluation for unexplained syncope. This methodology could help assess the potential economic impact of implementing novel clinical algorithms for ED syncope. 

Introduction: The emergency department (ED) is an inherently high-risk setting. Risk scores can help practitioners understand the risk of ED patients for developing poor outcomes after discharge. Our objective was to develop two risk scores that predict either general inpatient admission or death/intensive care unit (ICU) admission within seven days of ED discharge.

Methods: We conducted a retrospective cohort study of patients age > 65 years using clinical data from a regional, integrated health system for years 2009-2010 to create risk scores to predict two outcomes, a general inpatient admission or death/ICU admission. We used logistic regression to predict the two outcomes based on age, body mass index, vital signs, Charlson comorbidity index (CCI), ED length of stay (LOS), and prior inpatient admission.

Results: Of 104,025 ED visit discharges, 4,638 (4.5%) experienced a general inpatient admission and 531 (0.5%) death or ICU admission within seven days of discharge. Risk factors with the greatest point value for either outcome were high CCI score and a prolonged ED LOS. The C-statistic was 0.68 and 0.76 for the two models.

Conclusion: Risk scores were successfully created for both outcomes from an integrated health system, inpatient admission or death/ICU admission. Patients who accrued the highest number of points and greatest risk present to the ED with a high number of comorbidities and require prolonged ED evaluations.

Tracking the spread of emerging pathogens is critical to the design of timely and effective public health responses. Policymakers face the challenge of allocating finite resources for testing and surveillance across locations, with the goal of maximizing the information obtained about the underlying trends in prevalence and incidence. We model this decision-making process as an iterative node classification problem on an undirected and unweighted graph, in which nodes represent locations and edges represent movement of infectious agents among them. To begin, a single node is randomly selected for testing and determined to be either infected or uninfected. Test feedback is then used to update estimates of the probability of unobserved nodes being infected and to inform the selection of nodes for testing at the next iterations, until certain test budget is exhausted. Using this framework, we evaluate and compare the performance of previously developed active learning policies for node selection, including Node Entropy and Bayesian Active Learning by Disagreement. We explore the performance of these policies under different outbreak scenarios using simulated outbreaks on both synthetic and empirical networks. Further, we propose a policy that considers the distance-weighted average entropy of infection predictions among neighbors of each candidate node. Our proposed policy outperforms existing ones in most outbreak scenarios given small test budgets, highlighting the need to consider an exploration-exploitation trade-off in policy design. Our findings could inform the design of cost-effective surveillance strategies for emerging and endemic pathogens and reduce uncertainties associated with early risk assessments in resource-constrained situations.

HIV and substance abuse are common among young men, associated with a cluster of risk behaviors. Yet, most services addressing these challenges are delivered in setting underutilized by men and are often inconsistent with male identity. This cluster randomized controlled trial aimed to reduce multiple risk behaviors found among young men township areas on the outskirts of Cape Town, South Africa. Young men aged 18-29 years (N = 1193) across 27 neighborhoods were randomized by area to receive HIV-related skills training during either: (1) a 12-month soccer league (SL) intervention; (2) 6-month SL followed by 6 months of vocational training (VT) intervention (SL/VT, n = 9); or 3) a control condition (CC). Bayesian longitudinal mixture models were used to evaluate behaviors over time. Because we targeted multiple outcomes as our primary outcome, we analyzed if the number of significantly different outcomes between conditions exceeded chance for 13 measures over 18 months (with 83%, 76%, and 61% follow-up). Only if there were three significant benefits favoring the SL/VT over the SL would benefits be significant. Outcome measures included substance use, HIV-testing, protective sexual behaviors, violence, community engagement and mental health. Consistent participation in the SL was typically around 45% over time across conditions, however, only 17% of men completed SL/VT. There were no significant differences between conditions over time based on the number of study outcomes. These structural interventions were ineffective in addressing young men's substance abuse and risk for HIV.Clinical Trial Registration: This trial was prospectively registered on 24 November 2014 with ClinicalTrials.gov (NCT02358226).

MOTIVATION: Bayesian phylogeographic analyses are pivotal in reconstructing the spatio-temporal dispersal histories of pathogens. However, interpreting the complex outcomes of phylogeographic reconstructions requires sophisticated visualization tools. RESULTS: To meet this challenge, we developed spread.gl, an open-source, feature-rich browser application offering a smooth and intuitive visualization tool for both discrete and continuous phylogeographic inferences, including the animation of pathogen geographic dispersal through time. Spread.gl can render and combine the visualization of multiple layers that contain information extracted from the input phylogeny and diverse environmental data layers, enabling researchers to explore which environmental factors may have impacted pathogen dispersal patterns before conducting formal testing. We showcase the visualization features of spread.gl with representative examples, including the smooth animation of a phylogeographic reconstruction based on >17 000 SARS-CoV-2 genomic sequences. AVAILABILITY AND IMPLEMENTATION: Source code, installation instructions, example input data, and outputs of spread.gl are accessible at https://github.com/GuyBaele/SpreadGL.

Human mobility is strongly associated with the spread of SARS-CoV-2 via air travel on an international scale and with population mixing and the number of people moving between locations on a local scale. However, these conclusions are drawn mostly from observations in the context of the global north where international and domestic connectivity is heavily influenced by the air travel network; scenarios where land-based mobility can also dominate viral spread remain understudied. Furthermore, research on the effects of nonpharmaceutical interventions (NPIs) has mostly focused on national- or regional-scale implementations, leaving gaps in our understanding of the potential benefits of implementing NPIs at higher granularity. Here, we use Chile as a model to explore the role of human mobility on disease spread within the global south; the country implemented a systematic genomic surveillance program and NPIs at a very high spatial granularity. We combine viral genomic data, anonymized human mobility data from mobile phones and official records of international travelers entering the country to characterize the routes of importation of different variants, the relative contributions of airport and land border importations, and the real-time impact of the countrys mobility network on the diffusion of SARS-CoV-2. The introduction of variants which are dominant in neighboring countries (and not detected through airport genomic surveillance) is predicted by land border crossings and not by air travelers, and the strength of connectivity between comunas (Chiles lowest administrative divisions) predicts the time of arrival of imported lineages to new locations. A higher stringency of local NPIs was also associated with fewer domestic viral importations. Our analysis sheds light on the drivers of emerging respiratory infectious disease spread outside of air travel and on the consequences of disrupting regular movement patterns at lower spatial scales.

We investigate the emergence, mutation profile, and dissemination of SARS-CoV-2 lineage B.1.214.2, first identified in Belgium in January 2021. This variant, featuring a 3-amino acid insertion in the spike protein similar to the Omicron variant, was speculated to enhance transmissibility or immune evasion. Initially detected in international travelers, it substantially transmitted in Central Africa, Belgium, Switzerland, and France, peaking in April 2021. Our travel-aware phylogeographic analysis, incorporating travel history, estimated the origin to the Republic of the Congo, with primary European entry through France and Belgium, and multiple smaller introductions during the epidemic. We correlate its spread with human travel patterns and air passenger data. Further, upon reviewing national reports of SARS-CoV-2 outbreaks in Belgian nursing homes, we found this strain caused moderately severe outcomes (8.7% case fatality ratio). A distinct nasopharyngeal immune response was observed in elderly patients, characterized by 80% unique signatures, higher B- and T-cell activation, increased type I IFN signaling, and reduced NK, Th17, and complement system activation, compared to similar outbreaks. This unique immune response may explain the variants epidemiological behavior and underscores the need for nasal vaccine strategies against emerging variants.

This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally introduced in the Bayesian imaging literature, ProxMCMC employs the Moreau-Yosida envelope for a smooth approximation of the total-variation regularization term, fixes variance and regularization strength parameters as constants, and uses the Langevin algorithm for the posterior sampling. We extend ProxMCMC to be fully Bayesian by providing data-adaptive estimation of all parameters including the regularization strength parameter. More powerful sampling algorithms such as Hamiltonian Monte Carlo are employed to scale ProxMCMC to high-dimensional problems. Analogous to the proximal algorithms in optimization, ProxMCMC offers a versatile and modularized procedure for conducting statistical inference on constrained and regularized problems. The power of ProxMCMC is illustrated on various statistical estimation and machine learning tasks, the inference of which is traditionally considered difficult from both frequentist and Bayesian perspectives.

Feature selection in Knowledge Graphs (KGs) is increasingly utilized in diverse domains, including biomedical research, Natural Language Processing (NLP), and personalized recommendation systems. This paper delves into the methodologies for feature selection (FS) within KGs, emphasizing their roles in enhancing machine learning (ML) model efficacy, hypothesis generation, and interpretability. Through this comprehensive review, we aim to catalyze further innovation in FS for KGs, paving the way for more insightful, efficient, and interpretable analytical models across various domains. Our exploration reveals the critical importance of scalability, accuracy, and interpretability in FS techniques, advocating for the integration of domain knowledge to refine the selection process. We highlight the burgeoning potential of multi-objective optimization and interdisciplinary collaboration in advancing KG FS, underscoring the transformative impact of such methodologies on precision medicine, among other fields. The paper concludes by charting future directions, including the development of scalable, dynamic FS algorithms and the integration of explainable AI principles to foster transparency and trust in KG-driven models.