i2b2 to Optimize Patients Enrollment.

i2b2 data-warehouse could be a useful tool to support the enrollment phase of clinical studies. The aim of this work is to evaluate its performance on two clinical trials. We developed also an i2b2 extension to help in suggesting eligible patients for a study. The work showed good results in terms of ability to implement inclusion/exclusion criteria, but also in terms of identified patients actually enrolled and high number of patients suggested as potentially enrollable

Contribution of MUTYH variants to male breast cancer risk: results from a multicenter study in Italy

Inherited mutations in BRCA1, and, mainly, BRCA2 genes are associated with increased risk of male breast cancer (MBC). Mutations in PALB2 and CHEK2 genes may also increase MBC risk. Overall, these genes are functionally linked to DNA repair pathways, highlighting the central role of genome maintenance in MBC genetic predisposition. MUTYH is a DNA repair gene whose biallelic germline variants cause MUTYH-associated polyposis (MAP) syndrome. Monoallelic MUTYH variants have been reported in families with both colorectal and breast cancer and there is some evidence on increased breast cancer risk in women with monoallelic variants. In this study, we aimed to investigate whether MUTYH germline variants may contribute to MBC susceptibility. To this aim, we screened the entire coding region of MUTYH in 503 BRCA1/2 mutation negative MBC cases by multigene panel analysis. Moreover, we genotyped selected variants, including p.Tyr179Cys, p.Gly396Asp, p.Arg245His, p.Gly264Trpfs*7, and p.Gln338His, in a total of 560 MBC cases and 1,540 male controls. Biallelic MUTYH pathogenic variants (p.Tyr179Cys/p.Arg241Trp) were identified in one MBC patient with phenotypic manifestation of adenomatous polyposis. Monoallelic pathogenic variants were identified in 14 (2.5%) MBC patients, in particular, p.Tyr179Cys was detected in seven cases, p.Gly396Asp in five cases, p.Arg245His and p.Gly264Trpfs*7 in one case each. The majority of MBC cases with MUTYH pathogenic variants had family history of cancer including breast, colorectal, and gastric cancers. In the case-control study, an association between the variant p.Tyr179Cys and increased MBC risk emerged by multivariate analysis [odds ratio (OR) = 4.54; 95% confidence interval (CI): 1.17-17.58; p = 0.028]. Overall, our study suggests that MUTYH pathogenic variants may have a role in MBC and, in particular, the p.Tyr179Cys variant may be a low/moderate penetrance risk allele for MBC. Moreover, our results suggest that MBC may be part of the tumor spectrum associated with MAP syndrome, with implication in the clinical management of patients and their relatives. Large-scale collaborative studies are needed to validate these findings

From EHR to EDC - The Experience at the Policlinico Hospital in Milan.

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SCOR: A secure international informatics infrastructure to investigate COVID-19

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale

Beyond Cohort Selection: An Analytics-Enabled i2b2

The i2b2 software is a widely adopted solution for secondary use of clinical data for clinical research, specifically designed for cohort identification. i2b2 is still lacking functionalities for data analysis. The aim of this work is to empower the i2b2 framework enabling clinical researchers to perform statistical analyses for accelerating the process of hypothesis testing. To this aim we have developed a flexible extension of i2b2 able to exploit different statistical engines. We have implemented some first applications for basic statistics and survival analyses, exploiting this extension and accessible through suitable user interfaces designed with a special consideration for usability

A data gathering framework to collect Type 2 diabetes patients data

In this work, we present a framework implemented within the EU project MOSAIC, funded under the FP7 framework, to gather Type 2 Diabetes (T2D) patients' data coming from three European hospitals and a local health care agency. A subset of the MOSAIC activities is centered on the development of Temporal Data Mining models to identify relevant clinical pathways in patients' histories and will in particular benefit from the data coming from the medical centers involved in the project. To best exploit this repository, the need for creating a common and sharable data model becomes immediately apparent. This model is the main subject of this paper. The proposed approach relies on the Informatics for Integrating Biology and the Bedside (i2b2) and the Shared Health Research Information Network (SHRINE) open source software tools. It provides an integrated research setting to merge clinical and environmental data that will enable obtaining a broader vision of individual patients' histories, which will be then mined with multivariate models to identify relevant clinical pathways

International electronic health record-derived post-acute sequelae profiles of COVID-19 patients

10.1038/s41746-022-00623-8NPJ DIGITAL MEDICINE5

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

International audienceBackground Many countries have experienced 2 predominant waves of COVID-19–related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. Objective In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. Methods Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. Results Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. Conclusions Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve

International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study

To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. Retrospective cohort study. The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measures: Patients were categorized as ″ever-severe″ or ″never-severe″ using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models