Advancing collaborations in health research and clinical trials in Sub-Saharan Africa: development and implementation of a biostatistical collaboration module in the Masters in Biostatistics Program at Stellenbosch University

Background Sub-Saharan Africa continues to carry a high burden of communicable diseases such as TB and HIV and non-communicable diseases such as hypertension and other cardiovascular conditions. Although investment in research has led to advances in improvements in outcomes, a lot still remains to be done to build research capacity in health. Like many other regions in the world, Sub-Saharan Africa suffers from a critical shortage of biostatisticians and clinical trial methodologists. Methods Funded through a Fogarty Global Health Training Program grant, the Faculty of Medicine and Health Sciences at Stellenbosch University in South Africa established a new Masters Program in Biostatistics which was launched in January 2017. In this paper, we describe the development of a biostatistical and clinical trials collaboration Module, adapted from a similar course offered in the Health Research Methodology program at McMaster University. Discussion Guided by three core principles (experiential learning; multi-/inter-disciplinary approach; and formal mentorship), the Module aims to advance biostatistical collaboration skills of the trainees by facilitating learning in how to systematically apply fundamental statistical and trial methodological knowledge in practice while strengthening some soft skills which are necessary for effective collaborations with other healthcare researchers to solve health problems. We also share some preliminary findings from the first four cohorts that took the Module in January–November 2018 to 2021. We expect that this Module can provide an example of how to improve biostatistical and clinical trial collaborations and accelerate research capacity building in low-resource settings. Funding source Fogarty International Center of the National Institutes of Health.

Predicting Vancomycin-Resistant Enterococci (VRE) and Carbapenem-Resistant Organism (CRO) Colonization in the Intensive Care Unit

Background: Rapidly identifying patients colonized with multidrug-resistant organisms (MDROs) upon ICU admission is critical to control and prevent the spread of these pathogens in healthcare facilities. Electronic health records (EHR) provide a rich source of data to predict the likelihood of MDRO colonization at admission, whereas surveillance methods are resource intensive and results are not immediately available. Our objectives were (1) to predict VRE and CRO colonization at ICU admission and (2) to identify patient subpopulations at higher risk for colonization with these MDROs. Methods: We conducted a retrospective analysis of patients aged ≥16 years admitted to any of 6 medical or surgical intensive care units (ICU) in the Johns Hopkins Hospital from July 1, 2016, through June 30, 2018. Perirectal swabs were collected at ICU unit admission and were tested for VRE and CRO. Patient demographic data, prior hospitalizations, and preadmission clinical data, including prior medication administration, prior diagnoses, and prior procedures, were extracted to develop prediction models. We employed the machine-learning algorithms logistic regression (LR), random forest (RF), and XGBoost (XG). The sum of sensitivity and specificity (ie, Youden’s index) was selected as the performance metric. Results: In total, 5,033 separate ICU visits from 3,385 patients were included, where 555 (11%) and 373 (7%) admissions tested positive for VRE and CRO, respectively. The sensitivity and specificity of our models for VRE were 78% and 80% with LR, 80% and 82% with RF, and 77% and 87% with XG. Predictions for CRO were not as precise, with LR at 73% and 53%, RF at 81% and 48%, and XG at 69% and 61%. The XG algorithm was the best-performing algorithm for both VRE and CRO. Prior VRE colonization, recent (<180 days) long-term care facility stay, and prior hospitalization >60 days were the key predictors for VRE, whereas the primary predictor for CRO colonization was prior carbapenem use. Conclusions: We demonstrated that EHR data can be used to predict >75% of VRE positive cases with a <15% false-positive rate and ~70% of CRO cases with a <40% false-positive rate. Future studies using larger sample sizes may improve the prediction accuracy and inform model generalizability across sites and thus reduce the risk of transmission of MDROs by rapidly identifying MDRO-colonized patients.Funding: This work was funded by the Centers for Disease Control and Prevention (CDC) Epicenters Program (Grant Number 1U54CK000447) and the CDC MInD-Healthcare Program (Grant Number 1U01CK000536).Disclosures: Aaron Milstone, BD (consulting)

Data Driven Prediction of Seismic Ground Response under Low Level Excitation

&lt;p&gt;It is an important task to model and predict seismic ground response; the results of ground response analysis are, in turn, used to assess liquefaction and integrity of undergound and upper structures. There has been numerious research and development on modelling of seismic ground response, but often there are quite large difference between prediction and measurement. In this study, it is attempted to train the input and output ground excitation data and make prediction based on it. To initiate this work, the deep learning network was trained for low level excitation data; the results showed reasonable match with actual measurements.&lt;/p&gt;&lt;p&gt;ACKNOWLEDGEMENT : The authors would like to thank the Ministry of Land, Infrastructure, and Transport of Korean government for the grant from Technology Advancement Research Program (grant no. 20CTAP-C152100-02) and Basic Science Research Program (grant no. 2017R1D1A3B03034563) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education.&lt;/p&gt;

Experimental Evaluation of Equivalent Permeability for Permeable Interlocking Concrete Paver (Soil-Block) Composite System

&lt;p&gt;Increase in impermeable area and frequency of intense rainfall cause flooding damages in urban areas. Permeable Interlocking Concrete Paver (PICP) system, which is a composite system comprised of soils and blocks, is considered as one of the solutions to improve the urban water environment, and its applications are increasing rapidly worldwide. It is important to evaluate the initial permeability and its reduction due to clogging. In this study, the permeability and effect of clogging were evaluated based on experimental methods developed. The equivalent permeability and its degradation of PICP systems were successfully evaluated using the prodecure developed, and the equation for equivalent permeability presented quite a good agreement with the experimental results.&lt;/p&gt;&lt;p&gt;ACKNOWLEDGEMENT : The authors would like to thank the Ministry of Land, Infrastructure, and Transport of Korean government for the grant from Technology Advancement Research Program (grant no. 20CTAP-C152124-02) and Basic Science Research Program (grant no. 2017R1D1A3B03034563) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education.&lt;/p&gt;

Pragmatic neuroscience for clinical psychiatry

SummaryMental health and substance use disorders are the leading cause of long-term disability and a cause of significant mortality, worldwide. However, it is widely recognised that clinical practice in psychiatry has not fundamentally changed for over half a century. The Royal College of Psychiatrists is reviewing its trainee curriculum to identify neuroscience that relates to psychiatric practice. To date though, neuroscience has had very little impact on routine clinical practice. We discuss how a pragmatic approach to neuroscience can address this problem together with a route to implementation in National Health Service care. This has implications for altered funding priorities and training future psychiatrists. Five training recommendations for psychiatrists are identified.Declaration of interestJ.D.S. receives direct funding from MRC Program Grant MR/S010351/1 aimed at developing machine learning-based methods for routinely acquired NHS data and indirect funding from the Wellcome Trust STRADL study. M.P.P. receives payments for an UpToDate chapter on methamphetamine and is principal investigator on the following grants: NIGMS P20GM121312 and NIDA U01 DA041089 and receives support from the William K. Warren Foundation.