Ability of IMPROVE and IMPROVE-DD scores to predict outcomes in patients with severe COVID-19: a prospective observational study.

In this study we aimed to evaluate the ability of IMPROVE and IMPROVE-DD scores in predicting in-hospital mortality in patients with severe COVID-19. This prospective observational study included adult patients with severe COVID-19 within 12 h from admission. We recorded patients' demographic and laboratory data, Charlson comorbidity index (CCI), SpO2 at room air, acute physiology and chronic health evaluation II (APACHE II), IMPROVE score and IMPROVE-DD score. In-hospital mortality and incidence of clinical worsening (the need for invasive mechanical ventilation, vasopressors, renal replacement therapy) were recorded. Our outcomes included the ability of the IMPROVE and IMPROVE-DD to predict in-hospital mortality and clinical worsening using the area under receiver operating characteristic curve (AUC) analysis. Multivariate analysis was used to detect independent risk factors for the study outcomes. Eighty-nine patients were available for the final analysis. The IMPROVE and IMPROVE-DD score showed the highest ability for predicting in-hospital mortality (AUC [95% confidence intervals {CI}] 0.96 [0.90-0.99] and 0.96 [0.90-0.99], respectively) in comparison to other risk stratification tools (APACHE II, CCI, SpO2). The AUC (95% CI) for IMPROVE and IMPROVE-DD to predict clinical worsening were 0.80 (0.70-0.88) and 0.79 (0.69-0.87), respectively. Using multivariate analysis, IMPROVE-DD and SpO2 were the only predictors for in-hospital mortality and clinical worsening. In patients with severe COVID-19, high IMPROVE and IMOROVE-DD scores showed excellent ability to predict in-hospital mortality and clinical worsening. Independent risk factors for in-hospital mortality and clinical worsening were IMPROVE-DD and SpO2.

The association between RANKL and Osteoprotegerin gene polymorphisms with breast cancer.

Breast cancer is the most common cause of cancer death among women (522,000 deaths in 2012). Imbalance between RANKL and OPG is observed in many cancers, including breast cancer. Consequently, SNPs in the genes of RANKL and OPG may be involved in breast cancer development. This study included 276 subjects. Group I (n = 100) healthy females as a control group, group II (n = 96) breast cancer patients without bone metastases, and group III (n = 80) breast cancer patients with bone metastases. RANKL rs9533156, OPG rs2073618, and OPG rs2073617 SNPs and their serum protein levels were studied for a possible association with breast cancer development. The allele frequency [(OR: 4.832 CI 2.18-10.71, P = 0.001) and genotype distribution (P = 0.001)] of OPG SNP rs2073618 showed a highly significant difference between breast cancer patients and healthy controls. The allele C is more common in breast cancer patients. The allele frequency [(OR: 0.451 CI 0.232-0.879, P = 0.018) and genotype distribution (P = 0.003)] of RANKL SNP rs9533156 differed significantly between breast cancer patients and healthy controls. The allele T is more common in breast cancer patients. The allele frequency [(OR: 0.36 CI 0.184-0.705, P = 0.002) and genotype distribution (P = 0.011)] of OPG SNP rs2073617 differed significantly between breast cancer patients and healthy controls. The allele T is more common in breast cancer patients. The C allele of OPG SNP rs2073618 may be associated with breast cancer development. No association was found between any of the SNPs and the serum protein levels of RANKL and OPG.