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Abstract A significant minority of individuals develop trauma- and stressor-related disorders (TSRD) after surviving sepsis, a life-threatening immune response to infections. Accurate prediction of risk for TSRD can facilitate targeted early intervention strategies, but many existing models rely on research measures that are impractical to incorporate to standard emergency department workflows. To increase the feasibility of implementation, we developed models that predict TSRD in the year after survival from sepsis using only electronic health records from the hospitalization (n = 217,122 hospitalizations from 2012-2015). The optimal model was evaluated in a temporally independent prospective test sample (n = 128,783 hospitalizations from 2016-2017), where patients in the highest-risk decile accounted for nearly one-third of TSRD cases. Our approach demonstrates that risk for TSRD after sepsis can be stratified without additional assessment burden on clinicians and patients, which increases the likelihood of model implementation in hospital settings.

Objectives: To assess whether escalating to high-dose corticosteroids or anakinra compared with continuing low-dose corticosteroids reduced mortality in patients with severe COVID-19 whose respiratory function deteriorated while receiving dexamethasone 6 mg daily. Methods: We conducted a retrospective cohort study between March 1 to December 31, 2020, of hospitalized patients with confirmed COVID-19 pneumonia. In-hospital death was analyzed using logistic regression with inverse probability of treatment weighting of receiving anakinra, high-dose corticosteroid (dexamethasone >10 mg daily), or remaining on low-dose corticosteroids on the day of first respiratory deterioration. Results: We analyzed 6671 patients whose respiratory status deteriorated while receiving dexamethasone 6 mg daily for COVID-19 pneumonia, of whom 6265 stayed on low-dose corticosteroids, 232 were escalated to high-dose corticosteroids, and 174 to anakinra in addition to corticosteroids. The propensity score-adjusted odds of death were higher in the anakinra (odds ratio [OR] 1.76; 95% CI 1.13-2.72) and high-dose corticosteroid groups (OR 1.53; 95% CI 1.14-2.07) compared with those who continued low-dose corticosteroids on the day of respiratory deterioration. The odds of hospital-acquired infections were also higher in the anakinra (OR 2.00; 95% CI 1.28-3.11) and high-dose corticosteroid groups (OR 1.43; 95% CI 1.00-2.04) compared with low-dose corticosteroid group. Conclusion: Our findings do not support escalating patients with COVID-19 pneumonia who deteriorate on low-dose corticosteroids to high-dose corticosteroids or anakinra.

Objectives: To assess whether high- compared with low-dose corticosteroids started upon hospitalization reduce mortality in patients with severe COVID-19 pneumonia or in subgroups stratified by severity of respiratory impairment on admission. Methods: We conducted a retrospective cohort study of patients with confirmed SARS-CoV-2 infection who required oxygen supplementation upon hospitalization between March 1 and December 31, 2020. In-hospital death was analyzed using logistic regression with inverse probability of treatment weighting of receiving low- or high-dose corticosteroid (dexamethasone 6-10 mg daily or >10-20 mg daily or other corticosteroid equivalents). Results: We analyzed 13,366 patients who received low-dose and 948 who received high-dose corticosteroids, of whom 31.3% and 40.4% had severe respiratory impairment (>15 l/min of oxygen or mechanical ventilation) upon admission, respectively. There were no differences in the propensity score-adjusted odds of death (odds ratio 1.17, 95% CI 0.72-1.90) or infections (odds ratio 0.70, 95% CI 0.44-1.11) for patients who received high-dose compared with low-dose corticosteroids, beginning on the day of admission. No significant differences in subgroups stratified by severity of respiratory impairment were found. Conclusion: Initiating high-dose compared with low-dose corticosteroids among newly hospitalized patients with COVID-19 pneumonia did not improve survival. However, benefit of high-dose corticosteroids in specific subgroups cannot be excluded.

Abstract Background We reviewed the epidemiology, risk factors, pathophysiology, and clinical presentations of coronavirus disease 2019 (COVID-19)-associated mucormycosis (CAM), then discussed the importance of rapid diagnosis and treatment facilitated by multidisciplinary approach. Main body India has reported world’s highest number of CAM cases where Rhizopus arrhizus was the most predominant etiology. CAM caused by Rhizopus microsporus was the most common from the rest of the world. Multiple risk factors for CAM were identified including diabetes mellitus, inappropriate corticosteroid use, COVID-19-related hypoxia, and lung damage. Rhino-orbito-cerebral mucormycosis (ROCM) accounted for almost 90% of CAM in India while 64% of global cases were ROCM. Less than 10% of CAM from India were pulmonary while the rest of the world reported 21% of pulmonary CAM. CAM is diagnosed by confirmed SARS-CoV2 infection along with clinical, radiological, histopathological, and/or microbiological evidence of mucormycosis. In patients with risks of CAM and associated symptoms, CT or MRI are recommended. If ROCM is suspected, endoscopy and biopsy are recommended. If pulmonary CAM is suspected, tissue biopsies, nasal samples, or bronchoalveolar lavage is recommended with histopathological exams. Early diagnosis, surgical, and pharmaceutical interventions are key to treat mucormycosis. Upon diagnosis, antifungal therapy with liposomal amphotericin B (IV) is considered first-line of therapy. Alternatively, posaconazole (PO/IV) or isavuconazole (PO/IV) can be used. Conclusion Treating CAM requires a multidisciplinary approach for early diagnosis and prompt initiation of interventions to maximize patient’s chance of survival.

Improvements in soil moisture observations and modeling play a vital role in drought, water resources, flooding, and landslide management and forecasting. However, the lack of multisensor products that integrate different spatial scales (i.e., from 1 m2 to 102 km2) is a pressing need in the management and forecasting chain. Up to date, surface soil moisture estimates could be obtained through three primary approaches: (1) in situ measurements and their interpolations, (2) remote sensing observations, and (3) land surface model (LSM) outputs. Each source of soil moisture has its own spatiotemporal resolution, strengths, and weaknesses. Therefore, their correct interpretation and application require an in-depth understanding of their accuracy and appropriateness. In this study, we explore the utility of the triple collocation (TC) method for an independent assessment of three soil moisture products to characterize their uncertainty structures and make recommendations toward a potential product merge. The state of Oklahoma is an ideal domain to test the hypotheses of this work because of the presence of marked west-to-east gradients in climate, vegetation, and soils. The three target soil moisture products include (1) the remotely sensed microwave soil moisture active passive (SMAP) L3_SM_P_E (9 km, daily), (2) the physically based LSM estimates from NLDAS_NOAH0125_H (1/8°, hourly; Noah), and (3) the Oklahoma Mesonet ground sensor network (point, 30 min). The product assessment was conducted from April 2015 to July 2019. The results indicate that, in general, Mesonet and Noah are the most reliable products, although their performance varies geographically and by land cover type, reflecting the main spatiotemporal characteristics and scope of each product. Specifically, Mesonet provides the best estimates of volumetric soil moisture with a mean Pearson correlation coefficient of 0.805, followed by Noah with 0.747. However, Noah represents the true soil moisture variation better than the interpolated Mesonet product on the mesoscale, with an averaged RMSE of 0.026 m3⁄m3. Over different land cover types, Mesonet had the best performance in shrub/scrub, herbaceous, hay/pasture, and cultivated crops with an average correlation coefficient of 0.79, while Noah achieved the best performance in evergreen, mixed, and deciduous forests, with an average correlation coefficient of 0.74. The period-integrated TC intercomparison results over nine climate divisions indicated that Noah outperformed in the central, northeast, and east-central regions. TC provides not only a new perspective for comparatively assessing multisource soil moisture products but also a basis for objective data merging to capitalize on the strengths of multisensor, multiplatform soil moisture products.

The global pandemic of the novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has presented newfound challenges to the oncology community regarding management of disease progression in immunocompromised and cancer patients. Further, the large influx of COVID-19 patients has overwhelmed healthcare facilities, limited access to intensive care unit beds and ventilators, and canceled elective surgeries causing disruptions to the cancer care continuum and re-organization of oncological care. While it is known that the potential threat of infection is greatest in elderly patients (>60 years of age) and patients with underlying comorbidities, there is still insufficient data to determine the risk of COVID-19 in cancer patients. Given the immunosuppressive status in cancer patients arising from chemotherapy and other comorbidities, management of COVID-19 in this patient population carries a unique set of challenges. We report three cases of COVID-19 in immunocompromised cancer patients and discuss the challenges in preventing, diagnosing, and treating this vulnerable group.