Looking beyond the numbers: quality assurance procedures in the Global Network for Women's and Children's Health Research Maternal Newborn Health Registry.

BACKGROUND: Quality assurance (QA) is a process that should be an integral part of research to protect the rights and safety of study participants and to reduce the likelihood that the results are affected by bias in data collection. Most QA plans include processes related to study preparation and regulatory compliance, data collection, data analysis and publication of study results. However, little detailed information is available on the specific procedures associated with QA processes to ensure high-quality data in multi-site studies. METHODS: The Global Network for Women's and Children's Health Maternal Newborn Health Registy (MNHR) is a prospective population-based registry of pregnancies and deliveries that is carried out in 8 international sites. Since its inception, QA procedures have been utilized to ensure the quality of the data. More recently, a training and certification process was developed to ensure that standardized, scientifically accurate clinical definitions are used consistently across sites. Staff complete a web-based training module that reviews the MNHR study protocol, study forms and clinical definitions developed by MNHR investigators and are certified through a multiple choice examination prior to initiating study activities and every six months thereafter. A standardized procedure for supervision and evaluation of field staff is carried out to ensure that research activites are conducted according to the protocol across all the MNHR sites. CONCLUSIONS: We developed standardized QA processes for training, certification and supervision of the MNHR, a multisite research registry. It is expected that these activities, together with ongoing QA processes, will help to further optimize data quality for this protocol.

CT-based pathological lung opacities volume as a predictor of critical illness and inflammatory response severity in patients with COVID-19.

Objective: The aim of the study was to assess the impact of CT-based lung pathological opacities volume on critical illness and inflammatory response severity of patients with COVID-19. Methods: A retrospective, single center, single arm study was performed over a 30-day period. In total, 138 patients (85.2%) met inclusion criteria. All patients were evaluated with non-contrast enhanced chest CT scan at hospital admission. CT-based lung segmentation was performed to calculate pathological lung opacities volume (LOV). At baseline, complete blood count (CBC) and inflammation response biomarkers were obtained. The primary endpoint of the study was the occurrence of critical illness, as defined as, the need of mechanical ventilation and/or ICU admission. Mann-Whitney U test was performed for univariate analysis. Logistic regression analysis was performed to determine independent predictors of critical illness. Spearman analysis was performed to assess the correlation between inflammatory response biomarkers serum concentrations and LOV. Results: Median LOV was 28.64% (interquartile range [IQR], 6.33-47.22%). Correlation analysis demonstrated that LOV was correlated with higher levels of D-dimer (r = 0.51, p < 0.01), procalcitonin (r = 0.47, p < 0.01) and IL6 (r = 0.48, p < 0.01). Critical illness occurred in 51 patients (37%). Univariate analysis demonstrated that inflammatory response biomarkers and LOV were associated with critical illness (p < 0.05). However, multivariate analysis demonstrated that only D-dimer and LOV were independent predictors of critical illness. Furthermore, a ROC analysis demonstrated that a LOV equal or greater than 60% had a sensitivity of 82.1% and specificity of 70.2% to determine critical illness with an odds ratio of 19.4 (95% CI, 4.2-88.9). Conclusion: Critical illness may occur in up to 37% of the patients with COVID-19. Among patients with critical illness, higher levels of inflammatory response biomarkers with larger LOVs were observed. Furthermore, multivariate analysis demonstrated that pathological lung opacities volume was an independent predictor of critical illness. In fact, patients with a pathological lung opacities volume equal or greater than 60% had 19.4-fold increased risk of critical illness.

Computed Tomography-based Lung Residual Volume and Mortality of Patients With Coronavirus Disease-19 (COVID-19).

RATIONALE AND OBJECTIVES: To assess the effect of computed tomography (CT)-based residual lung volume (RLV) on mortality of patients with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: A single-center, retrospective study of a prospectively maintained database was performed. In total, 138 patients with COVID-19 were enrolled. Baseline chest CT scan was performed in all patients. CT-based automated and semi-automated lung segmentation was performed using the Alma Medical workstation to calculate normal lung volume, lung opacities volume, total lung volume, and RLV. The primary end point of the study was mortality. Univariate and multivariate analyses were performed to determine independent predictors of mortality. RESULTS: Overall, 84 men (61%) and 54 women (39%) with a mean age of 47.3 years (±14.3 y) were included in the study. Overall mortality rate was 21% (29 patients) at a median time of 7 days (interquartile range, 4 to 11 d). Univariate analysis demonstrated that age, hypertension, and diabetes were associated with death (P<0.01). Similarly, patients who died had lower normal lung volume and RLV than patients who survived (P<0.01). Multivariate analysis demonstrated that low RLV was the only independent predictor of death (odds ratio, 1.042; 95% confidence interval, 10.2-10.65). Furthermore, receiver operating characteristic curve analysis demonstrated that a RLV ≤64% significantly increased the risk of death (odds ratio, 4.8; 95% confidence interval, 1.9-11.7). CONCLUSION: Overall mortality of patients with COVID-19 may reach 21%. Univariate and multivariate analyses demonstrated that reduced RLV was the principal independent predictor of death. Furthermore, RLV ≤64% is associated with a 4-fold increase on the risk of death.

Predictive biomarkers candidates for patients with metastatic colorectal cancer treated with bevacizumab-containing regimen.

Bevacizumab was the first molecular-targeted antiangiogenic therapy approved for the treatment of metastatic colorectal cancer. Until now, there are no predictive biomarkers available to decide the prescription of bevacizumab in patients with colorectal cancer. The purposes of this review were to provide a critical appraisal of the evidence and to identify possible predictive genetic biomarkers. A literature search was performed to identify studies that determine different levels of treatment response between patients stratified according to defined biomarkers. Interesting findings were reported between patients stratified according to rs3025039 and rs833061 polymorphisms of the gene VEGFA, with statistically and clinically significant differences for progression-free survival and overall survival. However, another study conducted in a larger sample does not confirm these previous findings, suggesting that well-designed prospective studies are still needed to achieve conclusive results. FLT1 (or VEGFR1) rs9513070 seems to be an interesting candidate as a predictive biomarker, with differences of more than 10 months in OS between different patients groups. In our opinion, possible interesting biomarker candidates for future research could be the polymorphisms rs833061 and rs3025039 of VEGF-A, rs9513070 or haplotype analysis of FLT1, rs2661280 of RGS5, rs444903 and rs6220 of EGF and Ang-2 or LDH plasma levels.

Equity in access to high cost drugs in Uruguay.

OBJECTIVE: The aim of this study was to determine the equity in access to high cost oncology medicines reimbursed by the Uruguayan National Health System. Also, were determined the causes of access inequities. METHODS: Different levels of access were determined by crossing epidemiological and reimbursement data with geographical distribution and number of Health System users. Possible causes of inequities were determined and weighted by Delphi technique. RESULTS: Access of patients assisted in the public sector to bevacizumab for metastatic colorectal cancer, rituximab for the treatment of non-Hodgkin lymphoma and trastuzumab for advanced HER2+ breast cancer, appears to be lower compared to patients assisted in private sector.Regarding rituximab for the treatment of non-Hodgkin lymphoma and trastuzumab for advanced HER2+ breast cancer, the results seem to show less access for patients residing outside the south region compared to those living in the south region.The main barriers to get reimbursement for patients living outside southern region are the access to pathological anatomy studies, imaging and other clinical analysis. Late diagnosis appears to be the main hurdles in access to these anti-cancer drugs, for patients assisted in the public sector. CONCLUSIONS: Equitable access to high cost drugs reimbursed by the National Health System requires policy decisions to address this issue.