Nicolau syndrome following Intramuscular Diclofenac Injection: a case report and review of the literature.

Nicolau syndrome (NS), also referred as embolia cutis medicamentosa and livedo-like dermatitis, is an uncommon complication followed by drugs administered intramuscularly, intraarticularly or subcutaneously. In this case report we present a case of a 65-year-old lady who had a single dose of diclofenac sodium as an intramuscular injection in her left buttock due to back pain that led to developing what known as NS. She was treated with surgical debridement, drain insertion and skin approximation with antibiotics for 2 weeks with daily sterile dressing. The wound healed completely with scarring. NS is a preventable outcome, thus, proper procedures and precautions should be taken during intramuscular medication administration. Healthcare providers should avoid unnecessary injections, be familiar with the complication and consider it as a potential diagnosis for severe localized pain after any injection

Errors in Aerosol Inhaler Use and Their Effects on Maternal and Fetal Outcomes among Pregnant Asthmatic Women (Subanalysis from QAKCOP Study)

Data on inhaler technique and its effects on maternal and fetal outcomes during pregnancy are seldom reported. The primary objective of this study was to evaluate inhaler technique and identify errors in inhaler use among pregnant women with asthma. Secondary objectives were to identify factors associated with poor inhaler technique and study the association between inhaler technique and maternal and fetal outcomes. This was a cross-sectional, face-to-face, prospective study of 80 pregnant women with physician-diagnosed asthma. Seventy-three and 41 asthmatic pregnant women reported using pressurized metered dose inhalers (pMDIs) and dry powder inhalers (DPIs), respectively. Overall, wrong inhaler technique was observed in 47 (64.4%) subjects. Among pMDI users, correct inhaler use was observed in only 26/73 (35.6%) of the patients, with lack of coordination between inhalation and generation of the aerosol and failure to breathe out gently before using the inhaler, being the most common errors. Among DPI users, 21 (51.2%) demonstrated correct inhaler use, with failure to perform a breath-hold for 10 seconds after inhaling the powder and to exhale gently before using the inhaler being the most common errors. Significant associations between inhaler technique and patient’s understanding of asthma medications and the kind of follow-up clinic (respiratory versus nonrespiratory clinic) were found. No significant associations between inhaler technique and various maternal and fetal outcomes or asthma control were found. In conclusion, improper inhalation technique is significantly prevalent in pregnant asthmatic women, particularly among those being followed in nonspecialized respiratory clinics. The lack of significant association between the inhaler technique and asthma control (and hence maternal and fetal outcomes) may simply reflect the high prevalence of uncontrolled asthma and significant contribution of other barriers to poor asthma control in the current patient’s cohort. Multidisciplinary management of asthma during pregnancy with particular emphasis on patient’s education is imperative

Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management