Post Covid-19 Acute Invasive Fungal Rhinosinusitis (AIFR): A Study of Histopathological Findings After FESS in Radiologically Diagnosed AIFR.

COVID-19 pandemic has led to a concerning surge of post-COVID-19 AIFR. Mucormycosis (BLACK fungus) is a rare but severe and life-threatening fungal infection occurred by mucormycetes, a family of moulds. More than 49,000 cases of AIFR were reported in three months in India. It primarily affects diabetics and spreads from the nasal cavity and paranasal sinuses (PNS). It also involves eye, palate, or brain. It is diagnosed clinically followed by radiological and pathological findings. We aimed to compare and analyse the pre-operative imaging with postoperative histopathological findings. The study was conducted in ENT department of tertiary care hospital, Rajkot. 200 patients were randomly selected who were presented to ENT OPD with clinically suspected Post COVID-19 AIFR. All patients underwent detailed ENT examination and radiological modality like MRI PNS, Brain, and Orbit. After proper pre-op evaluation, all patients underwent Functional Endoscopic Sinus Surgery (FESS). MRI findings were confirmed with that of histopathological findings done on KOH mount. All the patients were showing AIFR on MRI findings whereas 49% of patients had mucormycosis on Histopathology. Various other fungal infections like aspergillosis (7%), candidiasis (1.5%) were also found on HPE. 9% of patients showed combined infection with mucor and aspergillus species. Rest of the patients showed non-fungal rhinosinusitis. Inflow of the epidemic, plenty of patients were shown invasive fungal sinusitis in MRI patterns whereas many of them were HPE negative. Thus this study was done to know the efficacy of radiological features with pathological diagnosis. We have considered both procedures standard in our study.

A centennial review of discoveries and advances in diabetes: Children and youth.

Diabetes is an increasingly common chronic metabolic disorder in children worldwide. The discovery of insulin in 1921 resulted in unprecedented advancements that improved the lives of children and youth with diabetes. The purpose of this article is to review the history of diabetes in children and youth over the last century and its implications for future developments in the field. We identified 68 relevant events between 1921 and 2021 through literature review and survey of pediatric endocrinologists. Basic research milestones led to the discovery of insulin and other regulatory hormones, established the normal physiology of carbohydrate metabolism and pathophysiology of diabetes, and provided insight into strategies for diabetes prevention. While landmark clinical studies were initially focused on adult diabetes populations, later studies assessed etiologic factors in birth cohort studies, evaluated technology use among children with diabetes, and investigated pharmacologic management of youth type 2 diabetes. Technological innovations culminated in the introduction of continuous glucose monitoring that enabled semi-automated insulin delivery systems. Finally, professional organizations collaborated with patient groups to advocate for the needs of children with diabetes and their families. Together, these advances transformed type 1 diabetes from a terminal illness to a manageable disease with near-normal life expectancy and increased our knowledge of type 2 diabetes and other forms of diabetes in the pediatric population. However, disparities in access to insulin, diabetes technology, education, and care support remain and disproportionately impact minority youth and communities with less resources. The overarching goal of diabetes management remains promoting a high quality of life and improving glycemic management without undermining the psychological health of children and youth living with diabetes.

A case control investigation of COVID-19 associated mucormycosis in India.

BACKGROUND: Increased occurrence of mucormycosis during the second wave of COVID-19 pandemic in early 2021 in India prompted us to undertake a multi-site case-control investigation. The objectives were to examine the monthly trend of COVID-19 Associated Mucormycosis (CAM) cases among in-patients and to identify factors associated with development of CAM. METHODS: Eleven study sites were involved across India; archived records since 1st January 2021 till 30th September 2021 were used for trend analysis. The cases and controls were enrolled during 15th June 2021 to 30th September 2021. Data were collected using a semi-structured questionnaire. Among 1211 enrolled participants, 336 were CAM cases and 875 were COVID-19 positive non-mucormycosis controls. RESULTS: CAM-case admissions reached their peak in May 2021 like a satellite epidemic after a month of in-patient admission peak recorded due to COVID-19. The odds of developing CAM increased with the history of working in a dusty environment (adjusted odds ratio; aOR 3.24, 95% CI 1.34, 7.82), diabetes mellitus (aOR: 31.83, 95% CI 13.96, 72.63), longer duration of hospital stay (aOR: 1.06, 95% CI 1.02, 1.11) and use of methylprednisolone (aOR: 2.71, 95% CI 1.37, 5.37) following adjustment for age, gender, occupation, education, type of houses used for living, requirement of ventilatory support and route of steroid administration. Higher proportion of CAM cases required supplemental oxygen compared to the controls; use of non-rebreather mask (NRBM) was associated as a protective factor against mucormycosis compared to face masks (aOR: 0.18, 95% CI 0.08, 0.41). Genomic sequencing of archived respiratory samples revealed similar occurrences of Delta and Delta derivates of SARS-CoV-2 infection in both cases and controls. CONCLUSIONS: Appropriate management of hyperglycemia, judicious use of steroids and use of NRBM during oxygen supplementation among COVID-19 patients have the potential to reduce the risk of occurrence of mucormycosis. Avoiding exposure to dusty environment would add to such prevention efforts.

Post Covid-19 Acute Invasive Fungal Rhinosinusitis (AIFR): A Study of Histopathological Findings After FESS in Radiologically Diagnosed AIFR

COVID-19 pandemic has led to a concerning surge of post-COVID-19 AIFR. Mucormycosis (BLACK fungus) is a rare but severe and life-threatening fungal infection occurred by mucormycetes, a family of moulds. More than 49,000 cases of AIFR were reported in three months in India. It primarily affects diabetics and spreads from the nasal cavity and paranasal sinuses (PNS). It also involves eye, palate, or brain. It is diagnosed clinically followed by radiological and pathological findings. We aimed to compare and analyse the pre-operative imaging with postoperative histopathological findings. The study was conducted in ENT department of tertiary care hospital, Rajkot. 200 patients were randomly selected who were presented to ENT OPD with clinically suspected Post COVID-19 AIFR. All patients underwent detailed ENT examination and radiological modality like MRI PNS, Brain, and Orbit. After proper pre-op evaluation, all patients underwent Functional Endoscopic Sinus Surgery (FESS). MRI findings were confirmed with that of histopathological findings done on KOH mount. All the patients were showing AIFR on MRI findings whereas 49% of patients had mucormycosis on Histopathology. Various other fungal infections like aspergillosis (7%), candidiasis (1.5%) were also found on HPE. 9% of patients showed combined infection with mucor and aspergillus species. Rest of the patients showed non-fungal rhinosinusitis. Inflow of the epidemic, plenty of patients were shown invasive fungal sinusitis in MRI patterns whereas many of them were HPE negative. Thus this study was done to know the efficacy of radiological features with pathological diagnosis. We have considered both procedures standard in our study.

Decreased access to therapeutic services for children with disabilities during COVID-19 stay-at-home orders in Western Pennsylvania.

PURPOSE: Assess the effects of stay-at-home orders on access to services utilized by families of children with disabilities (CWD). METHODS: Cross-sectional weekly surveys were fielded over four weeks, during which western Pennsylvania was under stay-at-home orders. Respondents were divided into families of CWD (N = 233) or without CWD (N = 1582). Survey questions included measures of socio-economic status, and families of CWD answered questions regarding access to services pre and post-initiation of stay-at-home orders. Differences between families with and without CWD were analyzed using chi-square tests. RESULTS: Among families of CWD that had used services previously, 76.6% of survey respondents stated that they had decreased access, with the greatest percentage experiencing loss among those previously utilizing early intervention (75.5%), outpatient therapies (69.1%), or school-based therapies (80.7%). Compared to families without CWD, families of CWD were more likely to report lower pre-COVID-19 annual incomes (p < 0.001), job or income loss related to COVID-19 (p < 0.001), and higher levels of perceived stress (p < 0.001). CONCLUSION: CWD experienced loss of services during stay-at-home orders implemented as COVID-19 mitigation measures. Due to decreased access to needed services, CWD may be at risk of medical complications and loss of developmental progress.

Erratum to: Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering.

[This corrects the article DOI: 10.1093/jamiaopen/ooab063.].

Characterizing Temporal Patterns in Glucose Dysregulation Following SARS-CoV-2 Infection

IMPACT: Understanding the longitudinal glucose changes following SARS-CoV-2 infection can inform point-of-care guidelines and elucidate the viral hypothesis of diabetes mellitus pathogenesis. OBJECTIVES/GOALS: Hyperglycemia has emerged as an important manifestation of SARS-CoV-2 infection in both diabetic and non-diabetic patients. Whether clinically-detectable glycemic changes persist following SARS-CoV-2 infection remain to be elucidated. This work aims to characterize temporal patterns in glucose dysregulation following SARS-CoV-2 infection. METHODS/STUDY POPULATION: Electronic health records of patients with a diagnosis of COVID-19, positive laboratory test for SARS-CoV-2, and negative history of Diabetes Mellitus prior to infection were extracted from the TriNetX database. 7,502 patients with at least one blood glucose value 2 years to 2 weeks before, 2 weeks before to 2 weeks after, and 2 weeks after to 1 year after COVID-19 diagnosis were used for analysis. Temporal patterns are characterized by training state-of-the-art clustering algorithms, including fuzzy short time-series clustering, k-means for longitudinal data, and spectral clustering. Clustering performance is evaluated using internal evaluation metrics of the Silhouette coefficient, Calinski-Harabasz score, and Davies Bouldin index. RESULTS/ANTICIPATED RESULTS: Based on the success of prior clustering methods with random blood glucose measurements, we anticipate that the proposed time-series clustering algorithms will appropriately characterize temporal patterns of glycemic dysregulation. The best performing algorithm based on interval evaluation metrics will be selected for further analysis. Associations between blood glucose values and cluster membership will be evaluated using Kruskal-Wallis one-way ANOVA and effect size will be calculated using unbiased Cohen’s d. Clinical phenotypes for each cluster will be characterized in terms of current diagnoses, prior medication use, pertinent laboratory tests, and vital signs. DISCUSSION/SIGNIFICANCE OF FINDINGS: A clearer understanding of the longitudinal glucose changes following SARS-CoV-2 infection can elucidate clinically-detectable patterns of glycemic dysregulation, identify sub-phenotypes of patients who are more susceptive to glycemic dysregulation, and inform appropriate point-of-care guidelines.

Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering.

OBJECTIVE: Hyperglycemia has emerged as an important clinical manifestation of coronavirus disease 2019 (COVID-19) in diabetic and nondiabetic patients. Whether these glycemic changes are specific to a subgroup of patients and persist following COVID-19 resolution remains to be elucidated. This work aimed to characterize longitudinal random blood glucose in a large cohort of nondiabetic patients diagnosed with COVID-19. MATERIALS AND METHODS: De-identified electronic medical records of 7502 patients diagnosed with COVID-19 without prior diagnosis of diabetes between January 1, 2020, and November 18, 2020, were accessed through the TriNetX Research Network. Glucose measurements, diagnostic codes, medication codes, laboratory values, vital signs, and demographics were extracted before, during, and after COVID-19 diagnosis. Unsupervised time-series clustering algorithms were trained to identify distinct clusters of glucose trajectories. Cluster associations were tested for demographic variables, COVID-19 severity, glucose-altering medications, glucose values, and new-onset diabetes diagnoses. RESULTS: Time-series clustering identified a low-complexity model with 3 clusters and a high-complexity model with 19 clusters as the best-performing models. In both models, cluster membership differed significantly by death status, COVID-19 severity, and glucose levels. Clusters membership in the 19 cluster model also differed significantly by age, sex, and new-onset diabetes mellitus. DISCUSSION AND CONCLUSION: This work identified distinct longitudinal blood glucose changes associated with subclinical glucose dysfunction in the low-complexity model and increased new-onset diabetes incidence in the high-complexity model. Together, these findings highlight the utility of data-driven techniques to elucidate longitudinal glycemic dysfunction in patients with COVID-19 and provide clinical evidence for further evaluation of the role of COVID-19 in diabetes pathogenesis.

Rapid-Cycle Community Assessment of Health-Related Social Needs of Children and Families During Coronavirus Disease 2019.

OBJECTIVE: To identify unmet health and social resource needs during a county-wide coronavirus disease 19 (COVID-19) stay-at-home order and phased re-opening in Western Pennsylvania. METHODS: With public health, social service, and community partners connected through an ongoing academic-community collaborative, we developed and fielded a weekly repeated cross-sectional electronic survey assessing usage of and unmet need for health and social service resources. Using 10 weeks of surveys (April 3-June 11, 2020) by Allegheny County residents, we examined variation in responses by week and by sociodemographic characteristics using chi-square tests. We shared written reports weekly and discussed emerging trends with community partners. RESULTS: Participants ranged from 229 to 1001 per week. Unmet need for at least 1 health or health-related social need resource varied by week, ranging from 55% (95% confidence interval [CI] 50%-59%) of participants in week 2 to 43% (95% CI 37%-49%) of participants in week 9 (P = .006). Increased use of at least 1 resource ranged from 53% (95% CI 47%-58%) of participants in week 3 to 36% (95% CI 31%-42%) in week 9 (P < .001). Unmet need for food and financial assistance peaked early during the stay-at-home order, while unmet need for mental health care rose later. Unmet need for food assistance varied significantly by race and ethnicity and by household prepandemic income. CONCLUSIONS: Over half of families with children reported unmet health or social service needs during the first month of a county-wide COVID-19 stay-at-home order. Unmet needs varied with race, ethnicity, and income and with duration of the stay-at-home order.