Im-SCC-FRET: improved single-cell-based calibration of a FRET system.

We recently developed a SCC-FRET (single-cell-based calibration of a FRET system) method to quantify spectral crosstalk correction parameters (ß and δ) and system calibration parameters (G and k) of a Förster resonance energy transfer (FRET) system by imaging a single cell expressing a standard FRET plasmid with known FRET efficiency (E) and donor-acceptor concentration ratio (RC) (Liu et al., Opt. Express30, 29063 (2022)10.1364/OE.459861). Here we improved the SCC-FRET method (named as Im-SCC-FRET) to simultaneously obtain ß, δ, G, k and the acceptor-to-donor extinction coefficient ratio (ε A ε D), which is a key parameter to calculate the acceptor-centric FRET efficiency (EA), of a FRET system when the range of ß and δ values is set as 0-1. In Im-SCC-FRET, the target function is changed from the sum of absolute values to the sum of squares according to the least squares method, and the initial value of ß and δ estimated by the integral but not the maximum value spectral overlap between fluorophore and filter. Compared with SCC-FRET, the experimental results demonstrate that Im-SCC-FRET can obtain more accurate and stable results for ß, δ, G, and k, and add the ratio ε A ε D, which is necessary for the FRET hybrid assay. Im-SCC-FRET reduces the complexity of experiment preparation and opens up a promising avenue for developing an intelligent FRET correction system.

EHR Data Quality Assessment Tools and Issue Reporting Workflows for the 'All of Us' Research Program Clinical Data Research Network.

The All of Us (AoU) Research Program aggregates electronic health records (EHR) data from 300,00+ participants spanning 50+ distinct data sites. The diversity and size of AoU's data network result in multifaceted obstacles to data integration that may undermine the usability of patient EHR. Consequently, the AoU team implemented data quality tools to regularly evaluate and communicate EHR data quality issues at scale. The use of systematic feedback and educational tools ultimately increased site engagement and led to quantitative improvements in EHR quality as measured by program- and externally-defined metrics. These improvements enabled the AoU team to save time on troubleshooting EHR and focus on the development of alternate mechanisms to improve the quality of future EHR submissions. While this framework has proven effective, further efforts to automate and centralize communication channels are needed to deepen the program's efforts while retaining its scalability.

Feasibility of Linking Area Deprivation Index Data to the OMOP Common Data Model.

Linking Area Deprivation Index (ADI) scores to observational data offers the opportunity to characterize healthcare treatment and outcomes that are driven by socioeconomic deprivation. The current study aims to assess the feasibility of creating an analysis package to link ADI rankings to multiple patient-level EHR datasets transformed into the OMOP CDM. Patients within two cancer cohorts (breast cancer and multiple myeloma) were identified within two OMOP datasets and their records were linked with ADI scores using address information in the OMOP location table. With ADI linked to patient addresses, we generated visualizations showing the geographic distribution of each cohort based on ADI scores. Additionally, further assessment showed that over 89% of patient addresses could successfully be linked with ADI rankings. In conducting this assessment, we have demonstrated that developing a package to link ADI scores with multiple OMOP datasets is feasible.

Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study.

Many patients with bipolar disorder (BD) are initially misdiagnosed with major depressive disorder (MDD) and are treated with antidepressants, whose potential iatrogenic effects are widely discussed. It is unknown whether MDD is a comorbidity of BD or its earlier stage, and no consensus exists on individual conversion predictors, delaying BD's timely recognition and treatment. We aimed to build a predictive model of MDD to BD conversion and to validate it across a multi-national network of patient databases using the standardization afforded by the Observational Medical Outcomes Partnership (OMOP) common data model. Five "training" US databases were retrospectively analyzed: IBM MarketScan CCAE, MDCR, MDCD, Optum EHR, and Optum Claims. Cyclops regularized logistic regression models were developed on one-year MDD-BD conversion with all standard covariates from the HADES PatientLevelPrediction package. Time-to-conversion Kaplan-Meier analysis was performed up to a decade after MDD, stratified by model-estimated risk. External validation of the final prediction model was performed across 9 patient record databases within the Observational Health Data Sciences and Informatics (OHDSI) network internationally. The model's area under the curve (AUC) varied 0.633-0.745 (µ = 0.689) across the five US training databases. Nine variables predicted one-year MDD-BD transition. Factors that increased risk were: younger age, severe depression, psychosis, anxiety, substance misuse, self-harm thoughts/actions, and prior mental disorder. AUCs of the validation datasets ranged 0.570-0.785 (µ = 0.664). An assessment algorithm was built for MDD to BD conversion that allows distinguishing as much as 100-fold risk differences among patients and validates well across multiple international data sources.

Phenotype Concept Set Construction from Concept Pair Likelihoods.

Phenotyping algorithms are essential tools for conducting clinical research on observational data. Manually devel- oped phenotyping algorithms, such as those curated within the eMERGE (electronic Medical Records and Genomics) Network, represent the gold standard but are time consuming to create. In this work, we propose a framework for learning from the structure of eMERGE phenotype concept sets to assist construction of novel phenotype definitions. We use eMERGE phenotypes as a source of reference concept sets and engineer rich features characterizing the con- cept pairs within each set. We treat these pairwise relationships as edges in a concept graph, train models to perform edge prediction, and identify candidate phenotype concept sets as highly connected subgraphs. Candidate concept sets may then be interrogated and composed to construct novel phenotype definitions.