bluuebunny/crossref_metadata_2025 huggingface dataset:
Reporting Rates for VAERS Death Reports Following COVID-19 Vaccination, December 14, 2020-November 17, 2021
Authors: Day Brendan , Menschik David , Thompson Deborah , Jankosky Christopher , Su John , Moro Pedro , Zinderman Craig , Welsh Kerry , Nair Narayan
Published: May 2022

Background

Despite widely available safety information for the COVID-19 vaccines, vaccine hesitancy remains a challenge. In some cases, vaccine hesitancy may be related to concerns about the number of reports of death to the Vaccine Adverse Event Reporting System (VAERS).

Objective

To provide information and context about reports of death to VAERS following COVID-19 vaccination.

Design

Descriptive study; reporting rates for VAERS death reports.

Setting

United States; December 14, 2020, to November 17, 2021.

Participants

COVID-19 vaccine recipients.

Measurements

Reporting rates for death events per million persons vaccinated; adverse event counts; data mining signals of disproportionate reporting.

Results

9,201 death events were reported for COVID-19 vaccine recipients aged five years and older (or age unknown). Reporting rates for death events increased with increasing age, and males generally had higher reporting rates than females. For death events within seven days and 42 days of vaccination, respectively, observed reporting rates were lower than the expected all-cause death rates. Reporting rates for Ad26.COV2.S vaccine were generally higher than for mRNA COVID-19 vaccines, but still lower than the expected all-cause death rates. Reported adverse events were non-specific or reflected the known leading causes of death.

Limitations

VAERS data are subject to several limitations such as reporting bias (underreporting and stimulated reporting), missing or inaccurate information, and lack of a control group. Reported diagnoses, including deaths, are not causally verified diagnoses.

Conclusion

Reporting rates for death events were lower than the expected all-cause mortality rates. Trends in reporting rates reflected known trends in background mortality rates. These findings do not suggest an association between vaccination and overall increased mortality.

Funding Source

No external sources of funding were used.
Temporal Characterization of Longitudinal Sequelae Including Acute Pain, Physiologic Status, and Toxicity Kinetics in Head and Neck Cancer Patients Receiving Radiotherapy: A Prospective Electronic Health Record Embedded Registry Study
Authors: Salama Vivian , Youssef Sara , Xu Tianlin , Chen Jaime , Rigert Jillian , Wahid Kareem , Reyes-Gibby Cielito C. , Lee Anna , Hutcheson Katherine A. , Gunn Brandon , Rosenthal David I. , Mohammed Abdallah S. R. , van Dijk Lisanne V , Lin Ruitao , Fuller Clifton D. , Moreno Amy C.
Published: May 2022

Background

Pain is a common, debilitating symptom experienced by patients with oral cavity and oropharyngeal cancer (OC/OPC) treated with radiotherapy (RT). Managing acute pain (AP) over 6 - 7 weeks of RT remains a significant challenge, warranting further investigation. Using a modern prospective registry, the objective of this study was to characterize longitudinal AP profiles and temporal changes in vital signs (VS), radiation toxicities, and analgesic prescribing patterns during RT.

Methods

A total of 351 patients with OC (n=120) and OPC (n=228) treated with curative RT from 2013-2021 were included. Baseline cohort characteristics, weekly patient-reported pain descriptors, physician-graded toxicities (CTCAE v5), and analgesic orders during RT were extracted. Temporal changes in AP scores and VS were analyzed using linear mixed effect models. AP trajectories were reduced to single metric area under the curve calculations (AUCpain). Correlations were assessed using Spearman correlation coefficients.

Results

Median age was 60 years, and 70% and 42% received chemotherapy and surgery, respectively. A significant increase in pain, mucositis, dermatitis, and overall treatment toxicity severity were observed by the end of RT. AUCpain was significantly different based on gender, primary tumor site, surgery, drug use history and pre-RT pain. There was a temporal mean weight loss of 7.1% bodyweight (95%CI, 10-8.2; P<0.001), a mean arterial pressure (MAP) decline of 6.8 mmHg (95%CI, −8.8 to −4.7; P<0.001), and increased pulse rate of 11 beats/min (95%CI, 7.6-13.8; P<0.001). AP and pulse rate were positively associated over time (P<0.001) while weight and MAP were negatively associated over time (P<0.001). A temporal increase in analgesics use, mainly opioids, was detected.

Conclusion

This study characterizes longitudinal treatment-related toxicity kinetics using a prospective OC/OPC registry and demonstrates an ongoing need for optimized, timely pain control. Pain AUC metrics preserve temporal information and may be useful for developing algorithmic pain prediction and management models.
Deep learning identified genetic variants associated with COVID-19 related mortality
Authors: Li Zihuan , Dai Wei , Wang Shiying , Yao Yisha , Zhang Heping
Published: May 2022

Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data. We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (OR=1.594, p=5.47×10−9) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050 and rs12540488. We also discover a super variant (OR=1.353, p=2.87×10−8) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G and rs180899355.
Retinal transcriptome and cellular landscape in relation to the progression of diabetic retinopathy
Authors: Wang Jiang-Hui , Wong Raymond C.B. , Liu Guei-Sheung
Published: May 2022

Importance

Previous studies identify putative genes associated with diabetic retinopathy only focusing on specific clinical stage, thus resulting genes are not necessarily reflective of disease progression.

Objective

To identify genes associated with the severity level of diabetic retinopathy using likelihood-ration test (LRT) and ordinal logistic regression (OLR) model, as well as to profile immune and retinal cell landscape in progressive diabetic retinopathy using a machine learning deconvolution approach.

Design, Setting, and Participants

This study used published transcriptomic dataset (GSE160306) from macular regions of donors with different degrees of diabetic retinopathy (10 healthy controls, 10 cases of diabetes, 9 cases of nonproliferative diabetic retinopathy and 10 cases of proliferative diabetic retinopathy or combined with diabetic macular edema). Transcriptomic dataset was released on April 19, 2021; data were analyzed on 28th March 2022.

Main Outcomes and Measures

Identification of severity-associated genes with LRT and OLR model and proportional changes of immune and retinal cells in progressive diabetic retinopathy.

Results

By controlling for gender and age using LRT and OLR, 50 genes were identified to be significantly increased in expression with the severity of diabetic retinopathy. Functional enrichment analyses suggested these severity-associated genes are related to inflammation and immune responses. CCND1 and FCGR2B are further identified as key regulators to interact with many other severity-associated genes and are crucial to inflammation. Deconvolution analyses demonstrated that the proportions of memory B cells, M2 macrophages and Müller glia were significantly increased with the progression of diabetic retinopathy.

Conclusions and Relevance

These findings demonstrate the deep analyses of transcriptomic data can advance our understanding of progressive ocular diseases, such as DR, by applying LRT and OLR model as well as bulk gene expression deconvolution.

Key Points

Question

Do genes change in expression or immune cells and retinal cells change in proportions in the human retina with the severity of diabetic retinopathy?

Findings

In this in-depth retinal transcriptomic analysis of 39 participants, consisting of 10 heathy controls, 29 cases of diabetic retinopathy at different clinical stages, severity-associated genes were identified, and the estimated proportions of immune cell and retinal cells were profiled, respectively.

Meaning

These findings demonstrate that a group of genes are significantly increased in expression, and the proportions of memory B cells, M2 macrophages and Müller glia were significantly increased with the severity of diabetic retinopathy.
Validation of a transcriptome-based assay for classifying cancers of unknown primary origin
Authors: Michuda Jackson , Breschi Alessandra , Kapilivsky Joshuah , Manghnani Kabir , McCarter Calvin , Hockenberry Adam J , Mineo Brittany , Igartua Catherine , Dudley Joel T , Stumpe Martin C , Beaubier Nike , Shirazi Maryam , Jones Ryan , Morency Elizabeth , Blackwell Kim , Guinney Justin , Beauchamp Kyle A , Taxter Timothy
Published: May 2022

Cancers assume a variety of distinct histologies and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision making based on consensus guidelines such as NCCN is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings—in addition to ambiguous clinical presentations such as recurrence versus new primary—a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for CUP patients, with a median survival of 8-11 months. Here we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-seq-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. We show that the Tempus TO model is 91% accurate when assessed on retrospectively and prospectively held out cohorts of containing 9,210 samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype. Combining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology.
COVID-19 vaccine effectiveness during a prison outbreak when the Omicron was the dominant circulating variant— Zambia, December 2021
Authors: Simwanza John , Hines Jonas Z. , Sinyange Danny , Sinyange Nyambe , Mulenga Chilufya , Hanyinza Sarah , Sakubita Patrick , Langa Nelia , Nowa Haggai , Gardner Priscilla , Saasa Ngonda , Chipeta Gabriel , Simpungwe James , Malambo Warren , Hamainza Busiku , Kapata Nathan , Kapina Muzala , Musonda Kunda , Liwewe Mazyanga , Mwale Consity , Fwoloshi Sombo , Mulenga Lloyd B. , Agolory Simon , Mukonka Victor , Chilengi Roma
Published: May 2022

During a COVID-19 outbreak in a prison in Zambia from 14th to 19th December 2021, a case control study was done to measure vaccine effectiveness (VE) against infection and symptomatic infection, when the Omicron variant was the dominant circulating variant. Among 382 participants, 74.1% were fully vaccinated and the median time since full vaccination was 54 days. There were no hospitalizations or deaths. COVID-19 VE against any SARS-CoV-2 infection was 64.8% and VE against symptomatic SARS-CoV-2 infection was 72.9%. COVID-19 vaccination helped protect incarcerated persons against SARS-CoV-2 infection during an outbreak while Omicron was the dominant variant in Zambia.
PixelPrint: Three-dimensional printing of realistic patient-specific lung phantoms for validation of computed tomography post-processing and inference algorithms
Authors: Shapira Nadav , Donovan Kevin , Mei Kai , Geagan Michael , Roshkovan Leonid , Gang Grace J. , Abed Mohammed , Linna Nathaniel , Cranston Coulter , O’Leary Cathal , Dhanaliwala Ali , Kontos Despina , Litt Harold I. , Stayman J. Webster , Shinohara Russell T. , Noël Peter B.
Published: May 2022

Background

Radiomics and other modern clinical decision-support algorithms are emerging as the next frontier for diagnostic and prognostic medical imaging. However, heterogeneities in image characteristics due to variations in imaging systems and protocols hamper the advancement of reproducible feature extraction pipelines. There is a growing need for realistic patient-based phantoms that accurately mimic human anatomy and disease manifestations to provide consistent ground-truth targets when comparing different feature extraction or image cohort normalization techniques.

Materials and Methods

PixelPrint was developed for 3D-printing lifelike lung phantoms for computed tomography (CT) by directly translating clinical images into printer instructions that control the density on a voxel-by-voxel basis. CT datasets of three COVID-19 pneumonia patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Linear mixed models were utilized to evaluate effect sizes of evaluating phantom as opposed to patient images. Finally, PixelPrint’s reproducibility was evaluated by producing four phantoms from the same clinical images.

Results

Estimated mean differences between patient and phantom images were small (0.03-0.29, using a 1-5 scale). Effect size assessment with respect to rating variabilities revealed that the effect of having a phantom in the image is within one-third of the inter- and intra-reader variabilities. PixelPrint’s production reproducibility tests showed high correspondence among four phantoms produced using the same patient images, with higher similarity scores between high-dose scans of the different phantoms than those measured between clinical-dose scans of a single phantom.

Conclusions

We demonstrated PixelPrint’s ability to produce lifelike 3D-printed CT lung phantoms reliably. These can provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols, as well as for optimizing scan protocols with realistic patient-based phantoms.
How to improve access to TB care for the nomads? Review of barriers and enablers for Sahel nomadic populations
Authors: Traore Hugues Asken , Chaverondier Roxane , Combary Adjima , Diallo Adama , Ouedraogo Salifou , Kane El hadj , Koita Mohamedou , Mourtala Assao , Adamou Sina , Sarr Marie , Toure Nafissatou , Ba Tacko Aly , Abdelhadi Oumar , Abdelrahim Abderramane , Konaté Bakary , Ballayira Yaya , Camara Fatima , Roij Madelon , Merle Corinne Simone
Published: May 2022

Tuberculosis (TB) control in nomadic populations represents a major public health problem in sub–Saharan Africa.

Barriers and enablers of TB care for nomads were identified through a literature review and survey conducted among the National TB Programs (NTPs) of six Sahelian countries: Burkina Faso, Chad, Niger, Mali, Mauritania, and Senegal. A conceptual framework was developed. Data retrieved from twenty-eight peer-reviewed papers or collected through the survey were regrouped in 5 categories: health system related factors, socioeconomic factors, cultural, political and environmental factors.

The large distance between nomadic camps and health care facilities and the absence of TB-specific programmatic interventions for nomads were the main barriers identified. The establishment of a multi-ministerial national committee in charge of nomadic populations, the mapping of nomadic transhumance roads, the identification of gaps in health service provision and community engagement for defining fit for purpose solutions are key elements to improve TB control in nomadic population.

Some countries in the region successfully implemented interventions to overcome the barriers to TB care. These interventions should be more widely shared to inform other countries for the development of appropriate strategies for which community engagement is essential.
Novel visualization of the spatiotemporal relationship between ictal spiking and LFP supports the involvement of mid-range excitatory circuits during human focal seizures
Authors: Lee Somin , Deshpande Sarita S. , Merricks Edward M. , Schlafly Emily , Goodman Robert , McKhann Guy M. , Eskandar Emad N. , Madsen Joseph R. , Cash Sydney S. , van Putten Michel J.A.M. , Schevon Catherine A. , van Drongelen Wim
Published: May 2022

The relationship between action potentials and the associated local field potential (LFP) in neural recordings is typically studied only in the temporal domain using the spike-triggered average (STA). In this study, we present a novel approach, termed the spatiotemporal spike-centered average (st-SCA), that allows for visualization of the spike-LFP relationship in both the temporal and spatial domains. In this method, a 3D spatiotemporal topography of spike-associated LFP is calculated from a 2D spatial average of the LFP centered around the time and location of individual spikes. We applied this method to 25 microelectrode array (MEA) recordings obtained from seven patients with pharmacoresistant focal epilepsy during ictal and interictal states. Five patients in this dataset had MEA implants in recruited cortex, and two had implants in unrecruited cortex. Of the five patients with arrays implanted in recruited territory, three showed STAs that resembled sine cardinal (sinc) functions, and two showed non-sinc functions. Using the st-SCA, we found that the patients who showed a sinc-function pattern in the temporal domain showed a donut-shaped ring of LFP activity in the spatial domain. This observation was corroborated by a theoretical model describing an ictal spike as measured by a macroelectrode. The model also revealed a special symmetry wherein temporal component of the st-SCA predicts the spatial component when they both approximate sinc-functions. Supporting this theoretical derivation, a radial cut of the donut-shaped st-SCA showed a spatial pattern consistent with a sinc-function. This spatial sinc-function had peaks separated by ∼2.5mm—a measurement that supports the role of mid-range excitatory connections during ictal activity. In sum these findings suggest that patients whose seizures engage mid-range connections may be identifiable by the spatiotemporal features of ictal spike-associated LFP activity.
Covid-19 vaccine effectiveness against general SARS-CoV-2 infection from the omicron variant: A retrospective cohort study
Authors: Rennert Lior , Ma Zichen , McMahan Christopher S. , Dean Delphine
Published: May 2022

Objective

To estimate the effectiveness of 2-dose and 3-dose mRNA vaccination (BNT162b2 and mRNA-1273) against general SARS-CoV-2 infection (asymptomatic or symptomatic) caused by the omicron variant.

Design

Propensity-score matched retrospective Cohort Study.

Setting

Large public university undergoing weekly Covid-19 testing in South Carolina, USA.

Participants

Population consists of 24,145 university students and employees undergoing weekly Covid-19 testing between January 3rd and January 31st, 2022. The analytic sample was constructed via propensity score matching on vaccination status: Unvaccinated, completion of 2-dose mRNA series within previous 5 months, and receipt of mRNA booster dose within previous 5 months. The resulting analytic sample consists of 1,944 university students and 658 university employees.

Intervention

Vaccination with a two dose or 3 dose regimen of the BNT162b2 or mRNA-1273 vaccine.

Results

Booster protection against any SARS-CoV-2 infection was 66.4% among employees (95% CI: 46.1-79.0%; P<.001) and 45.4% among students (95% CI: 30.0-57.4%; P<.001). Compared to the 2-dose mRNA series, estimated increase in protection from the booster dose was 40.8% among employees (P=.024) and 37.7% among students (P=.001). We did not have enough evidence to conclude a statistically significant protective effect of the 2-dose mRNA vaccination series, nor did we have enough evidence to conclude that protection waned in the 5-month period after receipt of the 2nd or 3rd mRNA dose. Furthermore, we did not find evidence that protection varied by manufacturer.

Conclusions

Covid-19 mRNA booster doses offer moderate protection against any SARS-CoV-2 infection caused by the omicron variant and provide a substantial increase in protection relative to the 2-dose mRNA vaccination series.
Clinical and Virological Features of patients hospitalized with different types of COVID-19 vaccination in Mexico City
Authors: Hernández-Terán Alejandra , GarciaDiego Pamela , Villanueva-Reza Marco , Boukadida Celia , Taboada Blanca , Porras Eduardo , Ahumada-Topete Victor , Diaz Kathia Tapia , Matias-Florentino Margarita , Pérez-García Marissa , Ávila-Rios Santiago , Mejía-Nepomuceno Fidencio , Serna-Muñoz Ricardo , Juárez-Hernández Fortunato , Jiménez-Corona Maria Eugenia , Becerril-Vargas Eduardo , Barreto Omar , Jose Arturo Martínez-Orozco , Pérez-Padilla Rogelio , Arias Carlos F. , Vázquez-Pérez Joel Armando
Published: May 2022

Coronavirus disease 2019 (COVID-19) vaccines are very effective at protecting against severe disease and death. However, the impact of the vaccine used, viral variants, and host factors on disease severity remain poorly understood. Here we compared COVID-19 clinical presentations and outcomes in vaccinated and unvaccinated patients in Mexico City. From March to September 2021, clinical and demographic characteristics were obtained from 1,014 individuals with a documented SARS-CoV-2 infection, and viral variants were identified in a subset of 386 patients. We compared unvaccinated, partially vaccinated, and fully vaccinated patients, stratifying by age groups. We fitted multivariate statistical models to evaluate the impact of vaccination status, SARS-CoV-2 lineages, vaccine types, and clinical parameters. Most hospitalized patients were unvaccinated. In patients over 61 years old, mortality was significantly higher in unvaccinated compared to fully vaccinated individuals. In patients aged 31 to 60 years, vaccinated patients were more likely to be outpatients (46%) than unvaccinated individuals (6.1%). We found immune disease and age above 61 years old as risk factors. While fully vaccination was found as the most protective factor against in-hospital death. This study suggests that vaccination is essential to reduce mortality in a comorbid population such as that of Mexico.
Estimating Counterfactual Placebo HIV Incidence in HIV Prevention Trials Without Placebo Arms Based on Markers of HIV Exposure
Authors: Zhu Yifan , Gao Fei , Glidden David , Donnell Deborah , Janes Holly
Published: May 2022

Summary

Given recent advances in HIV prevention, future trials of many experimental interventions are likely to be “active-controlled” designs, whereby HIV negative individuals are randomized to the experimental intervention or an active control known to be effective based on a historical trial. The efficacy of the experimental intervention to prevent HIV infection relative to placebo cannot be evaluated directly based on the trial data alone. One approach that has been proposed is to leverage an HIV exposure marker, such as incident rectal gonorrhea which is highly correlated with HIV infection in populations of men who have sex with men (MSM). Assuming we can fit a model associating HIV incidence and incidence of the exposure marker, based on data from multiple historical studies, incidence of the marker in the active-controlled trial population can be used to infer the HIV incidence that would have been observed had a placebo arm been included, i.e. a “counterfactual placebo”, and to evaluate efficacy of the experimental intervention relative to this counterfactual placebo. We formalize this approach and articulate the underlying assumptions, develop an estimation approach and evaluate its performance in finite samples, and discuss the implications of our findings for future development and application of the approach in HIV prevention. Improved HIV exposure markers and careful assessment of assumptions and study of their violation are needed before the approach is applied in practice.
ArcticAI: A Deep Learning Platform for Rapid and Accurate Histological Assessment of Intraoperative Tumor Margins
Authors: Levy Joshua , Davis Matthew , Chacko Rachael , Davis Michael , Fu Lucy , Goel Tarushii , Pamal Akash , Nafi Irfan , Angirekula Abhinav , Christensen Brock , Hayden Matthew , Vaickus Louis , LeBoeuf Matthew
Published: May 2022

Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Radial sectioning of the resected tumor and surrounding tissue is the most common form of intra-operative and post-operative margin assessment. However, this technique samples only a tiny fraction of the available tissue and therefore may result in incomplete excision of the tumor, increasing the risk of recurrence and distant metastasis and decreasing survival. Repeat procedures, chemotherapy, and other resulting treatments pose significant morbidity, mortality, and fiscal costs for our healthcare system. Mohs Micrographic Surgery (MMS) is used for the removal of basal cell and squamous cell carcinoma utilizing frozen sections for real-time margin assessment while assessing 100% of the peripheral and deep margins, resulting in a recurrence rate of less than one percent. Real-time assessment in many tumor types is constrained by tissue size and complexity and the time to process tissue and evaluate slides while a patient is under general anesthesia. In this study, we developed an artificial intelligence (AI) platform, ArcticAI, which augments the surgical workflow to improve efficiency by reducing rate-limiting steps in tissue preprocessing and histological assessment through automated mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma (BCC) as a model system, the results demonstrate that ArcticAI can provide effective grossing recommendations, accurately identify tumor on histological sections, map tumor back onto the surgical resection map, and automate pathology report generation resulting in seamless communication between the surgical pathology laboratory and surgeon. AI-augmented-surgical excision workflows may make real-time margin assessment for the excision of more complex and challenging tumor types more accessible, leading to more streamlined and accurate tumor removal while increasing healthcare delivery efficiency.
Informative Missingness: What can we learn from patterns in missing laboratory data in the electronic health record?
Authors: Tan Amelia L.M. , Getzen Emily J. , Hutch Meghan R. , Strasser Zachary H. , Gutiérrez-Sacristán Alba , Le Trang T. , Dagliati Arianna , Morris Michele , Hanauer David A. , Moal Bertrand , Bonzel Clara-Lea , Yuan William , Chiudinelli Lorenzo , Das Priam , Zhang Harrison G. , Aronow Bruce J , Avilllach Paul , Brat Gabriel. A. , Cai Tianxi , Hong Chuan , Cava William G. La , Will Loh He Hooi , Luo Yuan , Murphy Shawn N. , Hgiam Kee Yuan , Omenn Gilbert S. , Patel Lav P. , Samayamuthu Malarkodi Jebathilagam , Shriver Emily R. , Hossein Abad Zahra Shakeri , Tan Byorn W.L. , Visweswaran Shyam , Wang Xuan , Weber Griffin M , Xia Zongqi , Verdy Bertrand ,   The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) (Collaborative Group/Consortium), Long Qi , Mowery Danielle L , Holmes John H.
Published: May 2022

Background

In electronic health records, patterns of missing laboratory test results could capture patients’ course of disease as well as reflect clinician’s concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to characterize the patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients.

Methods

We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern.

Results

With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors.

Conclusion

This work elucidates how missing data patterns in EHRs can be leveraged to identify quality control issues and relationships between laboratory measurements. Missing data patterns will allow sites to attain better quality data for subsequent analyses and help researchers identify which sites are better poised to study particular questions. Our results could also provide insight into some of the biological relationships between labs in EHR data for COVID-19 patients.
Cluster Analysis of Allergic Poly-Sensitizations in Urban Adults with Asthma
Authors: Patchett Brian J. , Nriagu Bede N. , Mavraj Granit , Patel Ruchi R. , MacLellan Christopher , Thakur Tushar , Schulman Edward S.
Published: May 2022

Introduction

While reliable, quantitativein vitrotesting for sensitivity to aeroallergens has been available for decades, if and how asthma severity markers might be predictably expressed in clusters matched for comparable multiple sensitizations is unknown.

Objective

Our aim is to use machine learning techniques to explore how allergic poly-sensitization (APS) clusters may serve as precision markers in adult urban patients with moderate to severe asthma.

Methods

We constructed a database of sensitizations to the 25 aeroallergens in the Zone 1 Northeastern US ImmunoCAP® assay. We used the Scikit-Learn® machine learning library to perform model-based clustering to identify APS clusters. Clusters were compared for differences in common clinical markers of asthma.

Results

The database consisted of 509 patients. Unbiased mixture modeling identified ten clusters of increasing APS of varying size (n = 1 to 339) characterized by significant increases in mean serum immunoglobulin E (p<.001), peripheral blood eosinophil count (p<.001), and DLCO(p=.02). There was a significant decline in mean age at presentation (p<.001), FEV1/FVC (p=.01), and FEF25-75(p=.002), but not FEV1(p=.29), nor RV/TLC (p=.14) with increasing APS by simple linear regression. Finally, we identified two divergent paths for the poly-atopic march, one driven by perennial and the other by seasonal allergens.

Conclusion

We conducted a pilot study for a novel machine learning understanding and approach to the classification of APS and potential influences if included in asthma cluster analyses. The methods used here can be easily applied to other geographic regions with different allergens
Mining for Equitable Health: Assessing the Impact of Missing Data in Electronic Health Records
Authors: Getzen Emily , Ungar Lyle , Mowery Danielle , Jiang Xiaoqian , Long Qi
Published: May 2022

Electronic health records (EHRs) are collected as a routine part of healthcare delivery, and have great potential to be utilized to improve patient health outcomes. They contain multiple years of health information to be leveraged for risk prediction, disease detection, and treatment evaluation. However, they do not have standardized formatting, and can present significant analytical challenges– they contain multi-scale data from heterogeneous domains and include both structured and unstructured data. Data for individual patients are collected at irregular time intervals and with varying frequencies. In addition to the analytical challenges, EHRs can reflect inequity– patients belonging to different groups will have differing amounts of data in their health records. Many of these issues can contribute to biased data collection. The consequence is that the data for marginalized groups may be less informative due to more fragmented care, which can be viewed as a type of missing data problem. For EHRs data in this complex form, there is currently no framework for introducing missing values. There has also been little to no work in assessing the impact of missing data in EHRs. In this work, we simulate realistic missing data scenarios in EHRs to adequately assess their impact on predictive modeling. We incorporate the use of a medical knowledge graph to capture dependencies between medical events to create a more realistic missing data framework. In an intensive care unit setting, we found that missing data have greater negative impact on the performance of disease prediction models in groups that tend to have less access to healthcare, or seek less healthcare. We also found that the impact of missing data on disease prediction models is stronger when using the knowledge graph framework to introduce realistic missing values as opposed to random event removal.
Manual sub-segmentation of the cererbellum
Authors: Faber Jennifer , Heinz Lea-Sophie , Timmann Dagmar , Ernst Thomas M. , Deike-Hofmann Katerina , Klockgether Thomas
Published: May 2022

1

Background

1.1

The protocol was developed in the context of generating manual sub-segmentations of the cerebellum to use as ground truth for training, validation and testing of a neural network. In this manuscript we provide methodological considerations and results of our manual sub-segmentation as well as a detailed description for manual sub-segmentation of the cerebellum into 10 hemispheric lobules and 5 vermal sub-segments as well as two cerebellar white matter labels based on a T1 weighted (T1w) MRI to foster reproducibility.
Comparative study between first and second wave of COVID-19 deaths in India - a single center study
Authors: Tendulkar Prakash , Pandey Pragya , Panda Prasan Kumar , Bhadoria Ajeet Singh , Kulshreshtha Poorvi , Mishra Mayank , Saxena Gaurika
Published: May 2022

Background

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving and many mutant variants of the virus are circulating in the world. Recurrent waves of COVID-19 have caused enormous mortality all over the world. It is of utmost importance for a health expert to understand the demographic and clinical attributes between the first and second waves of COVID-19 induced deaths.

Method

This was a hospital record based comparative study of baseline demographic, clinical and laboratory parameters of the first and second wave of COVID-19 in a tertiary care hospital in Uttarakhand, India. The study included all deceased patients admitted to the hospital during the first and second wave of COVID-19, i.e., between March 2020 to January 2021 and between March 2021 to June 2021, respectively.

Result

The study showed that there were more casualties in the second wave compared to the first, 475 (19.8%) and 424 (24.1%) respectively. There was no significant difference in terms of age. A male preponderance of mortality was evident in both the waves. The median duration of hospital stay was 5 (3-10) days in the second wave, which is significantly different from the corresponding duration in first wave (p<o.ooo). The most common clinical manifestation among the deceased were dyspnoea in both the waves, followed by fever and cough, the difference was statistically significant for cough (p< 0.000) The most prevalent comorbidity was diabetes mellitus (DM), followed by hypertension (HTN), with significant difference for HTN (p<0.003). The most frequently deranged lab parameter was lymphopenia with a significant difference across both the waves (p<0.000).

Conclusion

In both the first and second COVID-19 waves, older males (>45 years) with comorbidities like HTN and DM were most susceptible for COVID-19 related mortality. The study also demonstrated that most of the baseline demographic and clinical characteristics which are attributed to the mortality were more common during the second wave of COVID-19.
Comparison of pandemic excess mortality in 2020-2021 across different empirical calculations
Authors: Levitt Michael , Zonta Francesco , Ioannidis John P.A.
Published: May 2022

Different modeling approaches can be used to calculate excess deaths for the COVID-19 pandemic period. We compared 6 calculations of excess deaths (4 previously published and two new ones that we performed with and without age-adjustment) for 2020-2021. With each approach, we calculated excess deaths metrics and the ratio R of excess deaths over recorded COVID-19 deaths. The main analysis focused on 33 high-income countries with weekly deaths in the Human Mortality Database (HMD at mortality.org) and reliable death registration. Secondary analyses compared calculations for other countries, whenever available. Across the 33 high-income countries, excess deaths were 2.0-2.8 million without age-adjustment, and 1.6-2.1 million with age-adjustment with large differences across countries. In our analyses after age-adjustment, 8 of 33 countries had no overall excess deaths; there was a death deficit in children; and 0.478 million (29.7%) of the excess deaths were in people <65 years old. In countries like France, Germany, Italy, and Spain excess death estimates differed 2 to 4-fold between highest and lowest figures. The R values’ range exceeded 0.3 in all 33 countries. In 16 of 33 countries, the range of R exceeded 1. In 25 of 33 countries some calculations suggest R>1 (excess deaths exceeding COVID-19 deaths) while others suggest R<1 (excess deaths smaller than COVID-19 deaths). Inferred data from 4 evaluations for 42 countries and from 3 evaluations for another 98 countries are very tenuous Estimates of excess deaths are analysis-dependent and age-adjustment is important to consider. Excess deaths may be lower than previously calculated.

SIGNIFICANCE STATEMENT

Excess deaths are a key metric for assessing the impact of a pandemic. They reflect the composite impact of deaths from infection, from indirect pandemic effects, and from the measures taken. Different modeling approaches can be used to calculate excess deaths for the COVID-19 pandemic. Here, we compare four previous calculations of excess deaths and two new ones that we performed with and without adjusting for changing age structure in the estimation. Proper age-adjustment results in substantial reduction in estimates of excess deaths for 2020-2021. While results from different calculation methods are correlated, the absolute differences in estimated excess deaths are very high in most countries. Extrapolations to countries without reliable death registration is extremely tenuous.
Predicting psychological symptoms when Facebook’s digital well-being features are used: A cross-sectional survey
Authors: Barsova Tamara , Cheong Zi Gi , Mak Ann R , Liu Jean CJ
Published: May 2022

Background

Prior research has linked social media usage to poorer mental health. To address these concerns, social media platforms have introduced digital well-being tools to help users monitor their engagement. Nonetheless, little is known about the effectiveness of these tools.

Objective

In this study, we focused on Facebook to assess users’ awareness and usage of 6 Facebook well-being tools (‘Unfollow’, ‘Snooze’, ‘Off-Facebook Activity’, ‘Your Time on Facebook’, ‘Set Daily Reminders’, and ‘Notification Settings’). Additionally, we examined whether use of the tools was associated with better mental health outcomes.

Methods

We conducted a cross-sectional survey of 608 Facebook users. The survey comprised questions about: (i) baseline Facebook use; (ii) adoption of Facebook’s digital well-being tools; and (iii) participant demographics. These were used to predict the primary outcome measure – scores on the Depression, Anxiety and Stress Scale (DASS-21).

Results

Most participants (97%) knew about Facebook’s digital well-being tools, but each tool was used by only 17 to 55% of participants. In turn, use of two tools was associated with better well-being. Namely, although participants who spent more time on Facebook reported higher levels of depression, anxiety, and stress, those who managed their feed content or notifications (using ‘Unfollow’ or ‘Notification settings’) had lower scores on each of these measures. However, use of the ‘Snooze’, ‘Off-Facebook Activity’, ‘Your Time on Facebook’, or ‘Set Time Reminder’ features was not associated with lower depression, anxiety, or stress scores.

Conclusions

Of six of Facebook’s digital well-being tools, only two were associated with better user mental health. This underscores the complexity of designing social media platforms to promote user welfare. Consequently, we urge further research to understand the efficacy of various digital well-being tools.

Trial Registration

ClinicalTrials.govNCT04967846

Study registration

NCT04967846: Social media effects on mental health (https://clinicaltrials.gov/ct2/show/study/NCT04967846)
Lack of ownership of mobile phones could hinder the rollout of mHealth interventions in Africa
Authors: Okano Justin T. , Ponce Joan , Krönke Matthias , Blower Sally
Published: May 2022

Background

Mobile Health interventions, which require ownership of mobile phones, are being investigated throughout Africa. We estimate the percentage of individuals who own mobile phones in 33 African countries, identify a relationship between ownership and proximity to a health clinic (HC), and quantify inequities in ownership. We investigate basic mobile phone (BPs) and smartphones (SPs): SPs can connect to the internet, BPs cannot.

Methods

We use nationally representative data collected in 2017—2018 from 45,823 individuals in Round 7 of the Afrobarometer surveys. We use Bayesian Multilevel Logistic regression models for our analyses.

Results

82% of individuals in the 33 countries own mobile phones: 42% BPs, 40% SPs. Individuals who live close to an HC have higher odds of ownership than those who do not (adjusted odds ratio [aOR]: 1.31, Bayesian 95% Highest Posterior Density [HPD] region: 1.24— 1.39). Men, compared with women, have over twice the odds of ownership (aOR: 2.37, 95% HPD region: 1.96—2.84). Urban residents, compared with rural residents, have almost three times the odds (aOR: 2.66, 95% HPD region: 2.22—3.18) and, amongst mobile phone owners, nearly three times the odds of owning an SP (aOR: 2.67, 95% HPD region: 2.33—3.10). Ownership increases with age, peaks in 26—40 year olds, then decreases. Individuals under 30 are more likely to own an SP than a BP, older individuals more likely to own a BP than an SP. Probability of ownership decreases with the Lived Poverty Index; however, some of the poorest individuals own SPs.

Conclusions

If the digital devices needed for mHealth interventions are not equally available within the population (which we have found is the current situation), rolling out mHealth interventions in Africa is likely to propagate already existing inequities in access to healthcare.

Funding

National Institute of Allergy and Infectious Diseases
Machine learning assisted analysis on TCR profiling data from COVID-19-convalescent and healthy individuals unveils cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and other diseases
Authors: Georgakilas Georgios K. , Galanopoulos Achilleas P. , Tsinaris Zafeiris , Kyritsi Maria , Mouchtouri Varvara , Speletas Matthaios , Hadjichristodoulou Christos
Published: May 2022

During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community’s focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS© study, provides thousands of TCRs that can specifically recognize SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML) assisted approach for analysing TCR-Seq data from the antigens’ point of view, with the ability to accurately distinguish between COVID-19-convalescent and healthy individuals in the case of MIRA dataset. Most SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was validated by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding the cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases.
Too much to mask: determinants of sustained adherence to COVID-19 preventive measures among older Syrian refugees in Lebanon
Authors: Salti Nisreen , McCall Stephen J. , Zeid Berthe Abi , Salibi Noura El , Alawieh Marwan , Ramadan Zeinab , Ghattas Hala , Abdulrahim Sawsan
Published: May 2022

Lebanon has battled the COVID-19 pandemic in the midst of an economic crisis. The evolution of the pandemic and a fragile health system have meant that public health policy has had to rely heavily on non-pharmaceutical interventions for disease control. However, changes in disease dynamics and pandemic fatigue have meant that disease control policies need to be updated. Identifying variables associated with adherence to non-pharmaceutical preventive practices, particularly for vulnerable groups, can therefore help inform and refine interventions in the face of pandemic fatigue and changing disease dynamics. Using recent and timely data on older (50 years and above) Syrian refugees in Lebanon, this paper explores the determinants of adherence to two non-pharmaceutical COVID-19 prevention measures (wearing a mask and avoiding social gatherings) among this high-risk subgroup in a vulnerable population. Among respondents who report adhering to these measures, the paper also identifies the determinants of sustained adherence over a period of 6 months. The findings suggest that older refugees and those less educated are less likely to wear a mask, and refugees living in informal tented settlements are more likely to relent on preventive practices within 6 months. Individuals with chronic diseases who initially report avoiding social gatherings are also likelier to desist than those without chronic illness.

The lower continued adherence to mask wearing among residents of informal tented settlements points to factors beyond pandemic fatigue and that should be taken into consideration in devising measures for disease control: the potential for community-based norms to determine individual-level behavior. Recognizing the pivotal effect of community-based norms in settings such as informal tented settlements is essential in adapting current policy and designing future interventions.