South Indian Children's Neurodevelopmental Outcomes After Group B Streptococcus Invasive Disease: A Matched-Cohort Study.

BACKGROUND: This study is part of a multicountry matched-cohort study designed to estimate the risk of long-term neurodevelopmental impairment (NDI) of children exposed to invasive group B Streptococcus (iGBS). The specific objective of this paper is to compare NDI across domains of iGBS survivors with a matched non iGBS group in our population. METHODS: Survivors of iGBS in a South Indian hospital were identified and recruited between January 2020 and April 2021. Cases were compared with age- and gender-matched non iGBS children. Participants were assessed using Bayley Scales of Infant and Toddler Development-3rd edition (BSID-III), Wechsler Preschool and Primary Scale of Intelligence-4th edition (WPPSI-IV), Wechsler Intelligence Scale for Children-5th edition (WISC-V), Child Behavior Checklist (CBCL), and Bruininks-Oseretsky Test of Motor Proficiency, 2nd edition (BOT-2), depending on age. RESULTS: Our cohort comprised 35 GBS-exposed and 65 matched non iGBS children, aged 1-14 years. The iGBS-exposed group had 17 (48.6%) children with impairment in ≥1 domain compared to 25 (38%) in the non iGBS group (unadjusted OR, 1.51; 95% CI, .65-3.46), 9 (26%) children with "multi-domain impairment" compared to 10 (15.4%) in the non iGBS group (unadjusted OR, 1.90; 95% CI, .69-5.24), and 1 (2.9%) child with moderate to severe impairment compared to 3 (4.6%) in the non iGBS group (unadjusted OR, .60; 95% CI, .06-6.07). In the iGBS group, more children had motor impairments compared with the non iGBS group (unadjusted OR, 10.7; 95% CI, 1.19-95.69; P = .034). CONCLUSIONS: Children with iGBS seem at higher risk of developing motor impairments compared with a non iGBS group

Evaluation einer SARS-CoV-2-Teststrategie zu Beginn der COVID-19-Pandemie in einem südwestdeutschen Universitätsklinikum

Background!#!At the beginning of the COVID-19 pandemic, the German Robert Koch Institute (RKI) published several guidelines addressing the medical health services helping to detect SARS CoV‑2. Needing an available and specific test strategy regarding SARS-CoV‑2, our own test strategy strictly followed these testing criteria.!##!Materials and methods!#!Using a retrospective analysis, we verified if such a test strategy was an effective tool in the context of infection prevention control and as reliable SARS-CoV‑2 detection. Therefore, we analysed our own test results of suspected SARS-CoV‑2 cases between 26 February and 6 April 2020. Additionally, we used a geovisualisation tool to visualise test frequencies and positive test results within different districts of Mannheim based on people's addresses.!##!Results!#!There were on average 7% positive test results of SARS-CoV‑2 within a population with typical symptoms of COVID-19 (n = 2808). There was no positive test result within an asymptomatic population (n = 448). However, one positive test result turned out to be a nosocomial infection. Finally, geovisualisation highlighted a shift of test frequencies and local positive rates for SARS-CoV‑2 from one district of Mannheim to another.!##!Discussion!#!In conclusion, our test strategy strictly based on testing criteria suggested by the Robert Koch Institute resulted in a steady rate of positive tests and allowed us to increase test capacity without causing numbers of nosocomial infections of COVID-19. Geovisualisation tools can offer support in analysing an ongoing spread of transmissible diseases. In the future, they could be used as helpful tools for infection prevention control, for example in the context of vaccination programs

Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review

BackgroundVisual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns. ObjectiveThis review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques. MethodsThis scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. ResultsOf the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively. ConclusionsThe articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space

Triptychon: Usability evaluation and implementation of a web-based application for patients’ lab and vital parameters

Background A major challenge in healthcare is the interpretation of the constantly increasing amount of clinical data of interest to inpatients for diagnosis and therapy. It is vital to accurately structure and represent data from different sources to help clinicians make informed decisions. Objective We evaluated the usability of our tool ‘Triptychon’ – a three-part visualisation dashboard of essential patients’ medical data provided by a direct overview of their hospitalisation information, laboratory, and vital parameters over time. Methods The study followed a cohort of 20 participants using the mixed-methods approach, including interviews and the usability questionnaires, Health Information Technology Usability Evaluation Scale (Health-ITUES), and User Experience Questionnaire (UEQ). The participant's interactions with the dashboard were also observed. A thematic analysis approach was applied to analyse qualitative data and the quantitative data's task completion time and success rates. Results The usability evaluation of the visualisation dashboard revealed issues relating to the terminology used in the user interface and colour coding in its left and middle panels. The Health-ITUES score was 3.72 (standard deviation (SD) = 1.0), and the UEQ score was 1.6 (SD = 0.74). The study demonstrated improvements in intuitive dashboard use and overall satisfaction with using the dashboard daily. Conclusion The Triptychon dashboard is a promising new tool for medical data presentation. We identified design and layout issues of the dashboard for improving its usability in routine clinical practice. According to users’ feedback, the three panels on the dashboard provided a holistic view of a patient's hospital stay

Neurodevelopmental and growth outcomes after invasive Group B Streptococcus in early infancy: A multi-country matched cohort study in South Africa, Mozambique, India, Kenya, and Argentina

BACKGROUND: Data are limited regarding long-term consequences of invasive GBS (iGBS) disease in early infancy, especially from low- and middle-income countries (LMIC) where most cases occur. We aimed to estimate risk of neurodevelopmental impairment (NDI) in children with a history of iGBS disease. METHODS: A multi-country matched cohort study was undertaken in South Africa, India, Mozambique, Kenya, and Argentina from October 2019 to April 2021. The exposure of interest was defined as a history of iGBS disease (sepsis or meningitis) before 90 days of age, amongst children now aged 1.5–18 years. Age and sex-matched, children without history of GBSwere also recruited. Age-appropriate, culturally-adapted assessments were used to define NDI across multiple domains (cognitive, motor, hearing, vision, emotional-behaviour, growth). Pooled NDI risk was meta-analysed across sites. Association of iGBS exposure and NDI outcome was estimated using modified Poisson regression with robust variance estimator. FINDINGS: Amongst 138 iGBS survivors and 390 non-iGBS children, 38.1% (95% confidence interval [CI]: 30.0% – 46.6%) of iGBS children had any NDI, compared to 21.7% (95% CI: 17.7% - 26.0%) of non- iGBS children, with notable between-site heterogeneity. Risk of moderate/severe NDI was 15.0% (95% CI: 3.4% - 30.8%) among GBS-meningitis, 5.6% (95% CI: 1.5% - 13.7%) for GBS-sepsis survivors. The adjusted risk ratio (aRR) for moderate/severe NDI among iGBS survivors was 1.27 (95% CI: 0.65, 2.45), when compared to non-GBS children. Mild impairment was more frequent in iGBS (27.6% (95% CI: 20.3 – 35.5%)) compared to non-GBS children (12.9% (95% CI: 9.7% - 16.4%)). The risk of emotional-behavioural problems was similar irrespective of iGBS exposure (aRR=0.98 (95% CI: 0.55, 1.77)). INTERPRETATION: Our findings suggest that iGBS disease is on average associated with a higher risk of moderate/severe NDI, however substantial variation in risk was observed between sites and data are consistent with a wide range of values. Our study underlines the importance of long-term follow-up for at-risk neonates and more feasible, standardised assessments to facilitate diagnosis in research and clinical practice. FUNDING: This work was supported by a grant (INV-009018) from the Bill & Melinda Gates Foundation to the London School of Hygiene &Tropical Medicine

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

International audienceAbstract Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach