A descriptive study of abdominal complications in patients with mild COVID-19 presenting to the emergency department: a single-center experience in Japan during the omicron variant phase - BMC Gastroenterology

In Japan, 1.7 million patients were infected with COVID-19 from the first to the fifth wave . Among the more highly infectious strains, omicron variants BA1, BA2 and BA5 caused widespread infection, and there were up to 20 million patients with COVID-19 during the sixth and seventh waves in Japan. Although previous reports have addressed abdominal complications with COVID-19, these have not been adequately elucidated for the omicron variant, the latest strain.

As the infection spread in Japan, many patients with mild COVID-19 presented to emergency departments with extra-pulmonary symptoms. Thus, the aim of our study was to clarify the diagnosis of concomitant abdominal diseases in patients with mild COVID-19 who presented to the hospital with abdominal symptoms during the sixth and seventh waves of the pandemic in Japan.This study was a retrospective, single-center, descriptive study.

Doesn’t sound “mild” at all. JFC

Do y'all know what 'mild' means? 🤨

So mild

mild ™

Everyone should be taking probiotics on a regular basis to mitigate the incidence of such events. Gotta avoid infection as well but doing this is key as well.

Since 2/2020 Various politicians & even the CDC did not not want to acknowledge that COVID exists or can cause severe illness….

You mean people get mild acute hemorrhagic colitis, right?

None of that sounds mild to me!!!

How is it a “mild case” if it’s causing acute internal bleeding? Is this a parody account?

What qualifies as a severe case if GI bleeding is out?

Hello, my mum have this bleeding after her covid Infektion. What can she do? To help in that case?

mikethemadbiol That sounds bad.

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Coding long COVID: characterizing a new disease through an ICD-10 lens - BMC MedicineBackground Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for “Post COVID-19 condition, unspecified.” Methods We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. Results We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. Conclusions This work offers insight into potential subtypes and current practice patterns around long COVID and spe
Source: BioMedCentral - 🏆 22. / 71 Read more »

Personality traits and hardiness as risk- and protective factors for mental distress during the COVID-19 pandemic: a Norwegian two-wave study - BMC PsychiatryBackground Several risk factors for anxious-depressive symptomatology during the COVID-19 pandemic have been established. However, few studies have examined the relationship between personality traits, hardiness, and such symptomatology during the pandemic. These constructs might serve as risk- and/or protective factors for such mental distress through the pandemic. Methods A sample of 5783 Norwegians responded to a survey at two time points within the first year of the pandemic. The first data collection was in April 2020 (T1) and the second in December 2020 (T2). Measures included the Ten-Item Personality-Inventory, the Revised Norwegian Dispositional Resilience Scale, and the Patient Health Questionnaire Anxiety and Depression Scale. Analyses were performed using Pearson’s correlations, multiple linear regression, and a moderation analysis. Results Anxious-depressive symptomatology in early phases (T1) of the pandemic was the strongest predictor for the presence of such symptomatology 9 months after the outbreak (T2). Personality and hardiness correlated significantly with mental distress at T1 and T2. Personality traits explained 5% variance in symptoms when controlling for age, gender, solitary living, negative economic impact, and mental distress at baseline. Higher neuroticism predicted higher mental distress, whereas higher conscientiousness and extraversion predicted less mental distress. Hardiness did not explain variance in outcome beyond personality traits. Hardiness did not significantly moderate the relationship between neuroticism and mental distress. Conclusion Individuals with high levels of neuroticism had greater difficulties adapting to the circumstances of the COVID-19 pandemic and were more prone to mental distress. Contrastingly, higher conscientiousness and extraversion may have served as protective factors for mental distress during the pandemic. The current findings might aid identification of vulnerable individuals and groups. Consequently
Source: medical_xpress - 🏆 101. / 51 Read more »

Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learningThere have been over 621 million cases of COVID-19 worldwide with over 6.5 million deaths. Despite the high secondary attack rate of COVID-19 in shared households, some exposed individuals do not contract the virus. In addition, little is known about whether the occurrence of COVID-19 resistance differs among people by health characteristics as stored in the electronic health records (EHR). In this retrospective analysis, we develop a statistical model to predict COVID-19 resistance in 8,536 individuals with prior COVID-19 exposure using demographics, diagnostic codes, outpatient medication orders, and count of Elixhauser comorbidities in EHR data from the COVID-19 Precision Medicine Platform Registry. Cluster analyses identified 5 patterns of diagnostic codes that distinguished resistant from non-resistant patients in our study population. In addition, our models showed modest performance in predicting COVID-19 resistance (best performing model AUROC=0.61). Monte Carlo simulations conducted indicated that the AUROC results are statistically significant (p | 0.001) for the testing set. We hope to validate the features found to be associated with resistance/non-resistance through more advanced association studies.
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