Promoting Learning Health System Cycles by Optimizing EHR Data Clinical Concept Encoding Processes.

Electronic health records (EHRs) and other real-world data (RWD) are critical to accelerating and scaling care improvement and transformation. To efficiently leverage it for secondary uses, EHR/RWD should be optimally managed and mapped to industry standard concepts (ISCs). Inherent challenges in concept encoding usually result in inefficient and costly workflows and resultant metadata representation structures outside the EHR. Using three related projects to map data to ISCs, we describe the development of standard, repeatable processes for precisely and unambiguously representing EHR data using appropriate ISCs within the EHR platform lifecycle and mappings specific to SNOMED-CT for Demographics, Specialty and Services. Mappings in these 3 areas resulted in ISC mappings of 779 data elements requiring 90 new concept requests to SNOMED-CT and 738 new ISCs mapped into the workflow within an accessible, enterprise-wide EHR resource with supporting processes.

Multimaterial fiber as a physical simulator of a capillary instability.

Capillary breakup of cores is an exclusive approach to fabricating fiber-integrated optoelectronics and photonics. A physical understanding of this fluid-dynamic process is necessary for yielding the desired solid-state fiber-embedded multimaterial architectures by design rather than by exploratory search. We discover that the nonlinearly complex and, at times, even chaotic capillary breakup of multimaterial fiber cores becomes predictable when the fiber is exposed to the spatiotemporal temperature profile, imposing a viscosity modulation comparable to the breakup wavelength. The profile acts as a notch filter, allowing only a single wavelength out of the continuous spectrum to develop predictably, following Euler-Lagrange dynamics. We argue that this understanding not only enables designing the outcomes of the breakup necessary for turning it into a technology for materializing fiber-embedded functional systems but also positions a multimaterial fiber as a universal physical simulator of capillary instability in viscous threads.

Transcending shift-invariance in the paraxial regime via end-to-end inverse design of freeform nanophotonics.

Traditional optical elements and conventional metasurfaces obey shift-invariance in the paraxial regime. For imaging systems obeying paraxial shift-invariance, a small shift in input angle causes a corresponding shift in the sensor image. Shift-invariance has deep implications for the design and functionality of optical devices, such as the necessity of free space between components (as in compound objectives made of several curved surfaces). We present a method for nanophotonic inverse design of compact imaging systems whose resolution is not constrained by paraxial shift-invariance. Our method is end-to-end, in that it integrates density-based full-Maxwell topology optimization with a fully iterative elastic-net reconstruction algorithm. By the design of nanophotonic structures that scatter light in a non-shift-invariant manner, our optimized nanophotonic imaging system overcomes the limitations of paraxial shift-invariance, achieving accurate, noise-robust image reconstruction beyond shift-invariant resolution.

Does COVID-19 Infection Increase the Risk of Diabetes? Current Evidence.

PURPOSE OF REVIEW: Multiple studies report an increased incidence of diabetes following SARS-CoV-2 infection. Given the potential increased global burden of diabetes, understanding the effect of SARS-CoV-2 in the epidemiology of diabetes is important. Our aim was to review the evidence pertaining to the risk of incident diabetes after COVID-19 infection. RECENT FINDINGS: Incident diabetes risk increased by approximately 60% compared to patients without SARS-CoV-2 infection. Risk also increased compared to non-COVID-19 respiratory infections, suggesting SARS-CoV-2-mediated mechanisms rather than general morbidity after respiratory illness. Evidence is mixed regarding the association between SARS-CoV-2 infection and T1D. SARS-CoV-2 infection is associated with an elevated risk of T2D, but it is unclear whether the incident diabetes is persistent over time or differs in severity over time. SARS-CoV-2 infection is associated with an increased risk of incident diabetes. Future studies should evaluate vaccination, viral variant, and patient- and treatment-related factors that influence risk.

Are fewer cases of diabetes mellitus diagnosed in the months after SARS-CoV-2 infection? A population-level view in the EHR-based RECOVER program.

Long-term sequelae of severe acute respiratory coronavirus-2 (SARS-CoV-2) infection may include increased incidence of diabetes. Here we describe the temporal relationship between new type 2 diabetes and SARS-CoV-2 infection in a nationwide database. We found that while the proportion of newly diagnosed type 2 diabetes increased during the acute period of SARS-CoV-2 infection, the mean proportion of new diabetes cases in the 6 months post-infection was about 83% lower than the 6 months preinfection. These results underscore the need for further investigation to understand the timing of new diabetes after COVID-19, etiology, screening, and treatment strategies.

Designing structures that maximize spatially averaged surface-enhanced Raman spectra.

We present a general framework for inverse design of nanopatterned surfaces that maximize spatially averaged surface-enhanced Raman (SERS) spectra from molecules distributed randomly throughout a material or fluid, building upon a recently proposed trace formulation for optimizing incoherent emission. This leads to radically different designs than optimizing SERS emission at a single known location, as we illustrate using several 2D design problems addressing effects of hot-spot density, angular selectivity, and nonlinear damage. We obtain optimized structures that perform about 4 × better than coating with optimized spheres or bowtie structures and about 20 × better when the nonlinear damage effects are included.

Module-Level Polaritonic Thermophotovoltaic Emitters via Hierarchical Sequential Learning.

Thermophotovoltaic (TPV) generators provide continuous and high-efficiency power output by utilizing local thermal emitters to convert energy from various sources to thermal radiation matching the bandgaps of photovoltaic cells. Lack of effective guidelines for thermal emission control at high temperatures, poor thermal stability, and limited fabrication scalability are the three key challenges for the practical deployment of TPV devices. Here we develop a hierarchical sequential-learning optimization framework and experimentally realize a 6″ module-scale polaritonic thermal emitter with bandwidth-controlled thermal emission as well as excellent thermal stability at 1473 K. The 300 nm bandwidth thermal emission is realized by a complex photon polariton based on the superposition of Tamm plasmon polariton and surface plasmon polariton. We experimentally achieve a spectral efficiency of 65.6% (wavelength range of 0.4-8 µm) with statistical deviation less than 4% over the 6″ emitter, demonstrating industrial-level reliability for module-scale TPV applications.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Are fewer cases of diabetes mellitus diagnosed in the months after SARS-CoV-2 infection?

Long-term sequelae of severe acute respiratory coronavirus-2 (SARS-CoV-2) infection may include an increased incidence of diabetes. Our objective was to describe the temporal relationship between new diagnoses of diabetes mellitus and SARS-CoV-2 infection in a nationally representative database. There appears to be a sharp increase in diabetes diagnoses in the 30 days surrounding SARS-CoV-2 infection, followed by a decrease in new diagnoses in the post-acute period, up to 360 days after infection. These results underscore the need for further investigation, as understanding the timing of new diabetes onset after COVID-19 has implications regarding potential etiology and screening and treatment strategies.

Diabetes medications and associations with Covid-19 outcomes in the N3C database: A national retrospective cohort study.

BACKGROUND: While vaccination is the most important way to combat the SARS-CoV-2 pandemic, there may still be a need for early outpatient treatment that is safe, inexpensive, and currently widely available in parts of the world that do not have access to the vaccine. There are in-silico, in-vitro, and in-tissue data suggesting that metformin inhibits the viral life cycle, as well as observational data suggesting that metformin use before infection with SARS-CoV2 is associated with less severe COVID-19. Previous observational analyses from single-center cohorts have been limited by size. METHODS: Conducted a retrospective cohort analysis in adults with type 2 diabetes (T2DM) for associations between metformin use and COVID-19 outcomes with an active comparator design of prevalent users of therapeutically equivalent diabetes monotherapy: metformin versus dipeptidyl-peptidase-4-inhibitors (DPP4i) and sulfonylureas (SU). This took place in the National COVID Cohort Collaborative (N3C) longitudinal U.S. cohort of adults with +SARS-CoV-2 result between January 1 2020 to June 1 2021. Findings included hospitalization or ventilation or mortality from COVID-19. Back pain was assessed as a negative control outcome. RESULTS: 6,626 adults with T2DM and +SARS-CoV-2 from 36 sites. Mean age was 60.7 +/- 12.0 years; 48.7% male; 56.7% White, 21.9% Black, 3.5% Asian, and 16.7% Latinx. Mean BMI was 34.1 +/- 7.8kg/m2. Overall 14.5% of the sample was hospitalized; 1.5% received mechanical ventilation; and 1.8% died. In adjusted outcomes, compared to DPP4i, metformin had non-significant associations with reduced need for ventilation (RR 0.68, 0.32-1.44), and mortality (RR 0.82, 0.41-1.64). Compared to SU, metformin was associated with a lower risk of ventilation (RR 0.5, 95% CI 0.28-0.98, p = 0.044) and mortality (RR 0.56, 95%CI 0.33-0.97, p = 0.037). There was no difference in unadjusted or adjusted results of the negative control. CONCLUSIONS: There were clinically significant associations between metformin use and less severe COVID-19 compared to SU, but not compared to DPP4i. New-user studies and randomized trials are needed to assess early outpatient treatment and post-exposure prophylaxis with therapeutics that are safe in adults, children, pregnancy and available worldwide.

Public Attitudes toward Pharmacogenomic Testing and Establishing a Statewide Pharmacogenomics Database in the State of Minnesota.

The clinical adoption and implementation of pharmacogenomics (PGx) beyond academic medical centers remains slow, restricting the general population from benefitting from this important component of personalized medicine. As an initial step in the statewide initiative of PGx implementation in Minnesota, we engaged community members and assessed attitudes towards PGx testing and acceptability of establishing a secure statewide PGx database for clinical and research use among Minnesota residents. Data was collected from 808 adult attendees at the 2021 Minnesota State Fair through an electronic survey. Eighty-four percent of respondents felt comfortable getting a PGx test for clinical care. Most respondents trusted health professionals (78.2%) and researchers (73.0%) to keep their PGx data private. The majority expressed their support and interest in participating in a statewide PGx database for clinical and research use (64-72%). Higher acceptability of the statewide PGx database was associated with younger age, higher education, higher health literacy, having health insurance, and prior genetic testing. The study sample representing Minnesota residents expressed high acceptability of receiving PGx testing and willingness to participate in PGx data sharing for clinical and research use. Community support and engagement are needed to advance PGx implementation and research on the state scale.

End-to-end metasurface inverse design for single-shot multi-channel imaging.

We introduce end-to-end inverse design for multi-channel imaging, in which a nanophotonic frontend is optimized in conjunction with an image-processing backend to extract depth, spectral and polarization channels from a single monochrome image. Unlike diffractive optics, we show that subwavelength-scale "metasurface" designs can easily distinguish similar wavelength and polarization inputs. The proposed technique integrates a single-layer metasurface frontend with an efficient Tikhonov reconstruction backend, without any additional optics except a grayscale sensor. Our method yields multi-channel imaging by spontaneous demultiplexing: the metaoptics front-end separates different channels into distinct spatial domains whose locations on the sensor are optimally discovered by the inverse-design algorithm. We present large-area metasurface designs, compatible with standard lithography, for multi-spectral imaging, depth-spectral imaging, and "all-in-one" spectro-polarimetric-depth imaging with robust reconstruction performance (≲ 10% error with 1% detector noise). In contrast to neural networks, our framework is physically interpretable and does not require large training sets. It can be used to reconstruct arbitrary three-dimensional scenes with full multi-wavelength spectra and polarization textures.

Effect of SARS-CoV-2 Infection and Infection Severity on Longer-Term Glycemic Control and Weight in People With Type 2 Diabetes.

OBJECTIVE: To evaluate the association of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severity of infection with longer-term glycemic control and weight in people with type 2 diabetes (T2D) in the U.S. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study using longitudinal electronic health record data of patients with SARS-CoV-2 infection from the National COVID Cohort Collaborative (N3C). Patients were ≥18 years old with an ICD-10 diagnosis of T2D and at least one HbA1c and weight measurement prior to and after an index date of their first coronavirus disease 2019 (COVID-19) diagnosis or negative SARS-CoV-2 test. We used propensity scores to identify a matched cohort balanced on demographic characteristics, comorbidities, and medications used to treat diabetes. The primary outcome was the postindex average HbA1c and postindex average weight over a 1 year time period beginning 90 days after the index date among patients who did and did not have SARS-CoV-2 infection. Secondary outcomes were postindex average HbA1c and weight in patients who required hospitalization or mechanical ventilation. RESULTS: There was no significant difference in the postindex average HbA1c or weight in patients who had SARS-CoV-2 infection compared with control subjects. Mechanical ventilation was associated with a decrease in average HbA1c after COVID-19. CONCLUSIONS: In a multicenter cohort of patients in the U.S. with preexisting T2D, there was no significant change in longer-term average HbA1c or weight among patients who had COVID-19. Mechanical ventilation was associated with a decrease in HbA1c after COVID-19.

Inverse design enables large-scale high-performance meta-optics reshaping virtual reality.

Meta-optics has achieved major breakthroughs in the past decade; however, conventional forward design faces challenges as functionality complexity and device size scale up. Inverse design aims at optimizing meta-optics design but has been currently limited by expensive brute-force numerical solvers to small devices, which are also difficult to realize experimentally. Here, we present a general inverse-design framework for aperiodic large-scale (20k × 20k λ2) complex meta-optics in three dimensions, which alleviates computational cost for both simulation and optimization via a fast approximate solver and an adjoint method, respectively. Our framework naturally accounts for fabrication constraints via a surrogate model. In experiments, we demonstrate aberration-corrected metalenses working in the visible with high numerical aperture, poly-chromatic focusing, and large diameter up to the centimeter scale. Such large-scale meta-optics opens a new paradigm for applications, and we demonstrate its potential for future virtual-reality platforms by using a meta-eyepiece and a laser back-illuminated micro-Liquid Crystal Display.

Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset.

OBJECTIVE: The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. MATERIALS AND METHODS: The National COVID Cohort Collaborative (N3C) table of laboratory measurement data-over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test. RESULTS: Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors' records lacked units). DISCUSSION: The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference. CONCLUSION: The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.

Trends in COVID-19 Vaccine Administration and Effectiveness Through October 2021.

Importance: COVID-19 vaccines are effective, but inequities in vaccine administration and waning immunity may limit vaccine effectiveness. Objectives: To report statewide trends in vaccine administration and vaccine effectiveness in Minnesota. Design, Setting, and Participants: This cohort study used COVID-19 vaccine data from the Minnesota Immunization Information Connection from October 25, 2020, through October 30, 2021 that were linked with electronic health record (EHR) data from health systems collaborating as part of the Minnesota EHR Consortium (MNEHRC). Participants included individuals who were seen at a participating health system in Minnesota. Exposures: Individuals were considered fully vaccinated in the second week after receipt of a second dose of a BNT162b2 or mRNA-1273 vaccine or a single dose of an Ad26.COV.2.S vaccine. Main Outcomes and Measures: A completed vaccination series and vaccine breakthrough, defined as either a positive SARS-CoV-2 polymerase chain reaction (PCR) test or a hospital admission the same week or within the 3 weeks following a positive SARS-CoV-2 PCR test. A test-negative design and incident rate ratio were used to evaluate COVID-19 vaccine effectiveness separately for the BNT162b2, mRNA-1273, and Ad26.COV.2.S vaccines. Rurality and social vulnerability index were assessed at the area level. Results: This study included 4 431 190 unique individuals at participating health systems, and 3 013 704 (68%) of the individuals were fully vaccinated. Vaccination rates were lowest among Minnesotans who identified as Hispanic (116 422 of 217 019 [54%]), multiracial (30 066 of 57 412 [52%]), American Indian or Alaska Native (22 190 of 41 437 [54%]), and Black or African American (158 860 of 326 595 [49%]) compared with Minnesotans who identified as Asian or Pacific Islander (159 999 of 210 994 [76%]) or White (2 402 928 of 3 391 747 [71%]). Among individuals aged 19 to 64 years, vaccination rates were lower in rural areas (196 479 of 308 047 [64%]) compared with urban areas (151 541 of 1 951 265 [77%]) and areas with high social vulnerability (544 433 of 774 952 [70%]) compared with areas with low social vulnerability (571 613 of 724 369 [79%]). In the 9 weeks ending October 30, 2021, vaccine effectiveness as assessed by a test-negative design was 33% (95% CI, 30%-37%) for Ad26.COV.2.S; 53% (95% CI, 52%-54%) for BNT162b2; and 66% (95% CI, 65%-67%) for mRNA-1273. For SARS-CoV-2-related hospitalizations, vaccine effectiveness in the 9 weeks ending October 30, 2021, was 78% (95% CI, 75%-81%) for Ad26.COV.2.S; 81% (95% CI, 79%-82%) for BNT162b2; and 81% (95% CI, 79%-82%) for mRNA-1273. Conclusions and Relevance: This cohort study of data from a Minnesota statewide consortium suggests disparities in vaccine administration and effectiveness. Vaccine effectiveness against infection was lower for Ad26.COV.2.S and BNT162b2 but was associated with protection against SARS-CoV-2-related hospitalizations despite the increased prevalence of the Delta variant in Minnesota.

A framework for scintillation in nanophotonics.

Bombardment of materials by high-energy particles often leads to light emission in a process known as scintillation. Scintillation has widespread applications in medical imaging, x-ray nondestructive inspection, electron microscopy, and high-energy particle detectors. Most research focuses on finding materials with brighter, faster, and more controlled scintillation. We developed a unified theory of nanophotonic scintillators that accounts for the key aspects of scintillation: energy loss by high-energy particles, and light emission by non-equilibrium electrons in nanostructured optical systems. We then devised an approach based on integrating nanophotonic structures into scintillators to enhance their emission, obtaining nearly an order-of-magnitude enhancement in both electron-induced and x-ray-induced scintillation. Our framework should enable the development of a new class of brighter, faster, and higher-resolution scintillators with tailored and optimized performance.

Effectiveness of BNT162b2 and mRNA-1273 Second Doses and Boosters for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and SARS-CoV-2-Related Hospitalizations: A Statewide Report From the Minnesota Electronic Health Record Consortium.

Using vaccine data combined with electronic health records, we report that mRNA boosters provide greater protection than a 2-dose regimen against SARS-CoV-2 infection and related hospitalizations. The benefit of a booster was more evident in the elderly and those with comorbidities.

High-performance hybrid time/frequency-domain topology optimization for large-scale photonics inverse design.

We present a photonics topology optimization (TO) package capable of addressing a wide range of practical photonics design problems, incorporating robustness and manufacturing constraints, which can scale to large devices and massive parallelism. We employ a hybrid algorithm that builds on a mature time-domain (FDTD) package Meep to simultaneously solve multiple frequency-domain TO problems over a broad bandwidth. This time/frequency-domain approach is enhanced by new filter-design sources for the gradient calculation and new material-interpolation methods for optimizing dispersive media, as well as by multiple forms of computational parallelism. The package is available as free/open-source software with extensive tutorials and multi-platform support.

Minnesota Electronic Health Record Consortium COVID-19 Project: Informing Pandemic Response Through Statewide Collaboration Using Observational Data.

OBJECTIVE: Robust disease and syndromic surveillance tools are underdeveloped in the United States, as evidenced by limitations and heterogeneity in sociodemographic data collection throughout the COVID-19 pandemic. To monitor the COVID-19 pandemic in Minnesota, we developed a federated data network in March 2020 using electronic health record (EHR) data from 8 multispecialty health systems. MATERIALS AND METHODS: In this serial cross-sectional study, we examined patients of all ages who received a COVID-19 polymerase chain reaction test, had symptoms of a viral illness, or received an influenza test from January 3, 2016, through November 7, 2020. We evaluated COVID-19 testing rates among patients with symptoms of viral illness and percentage positivity among all patients tested, in aggregate and by zip code. We stratified results by patient and area-level characteristics. RESULTS: Cumulative COVID-19 positivity rates were similar for people aged 12-64 years (range, 15.1%-17.6%) but lower for adults aged ≥65 years (range, 9.3%-10.7%). We found notable racial and ethnic disparities in positivity rates early in the pandemic, whereas COVID-19 positivity was similarly elevated across most racial and ethnic groups by the end of 2020. Positivity rates remained substantially higher among Hispanic patients compared with other racial and ethnic groups throughout the study period. We found similar trends across area-level income and rurality, with disparities early in the pandemic converging over time. PRACTICE IMPLICATIONS: We rapidly developed a distributed data network across Minnesota to monitor the COVID-19 pandemic. Our findings highlight the utility of using EHR data to monitor the current pandemic as well as future public health priorities. Building partnerships with public health agencies can help ensure data streams are flexible and tailored to meet the changing needs of decision makers.