Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

Bayesian safety surveillance with adaptive bias correction.

Postmarket safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by sequential multiple testing and by biases induced by residual confounding in observational data. The current standard approach based on the maximized sequential probability ratio test (MaxSPRT) fails to satisfactorily address these practical challenges and it remains a rigid framework that requires prespecification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both aforementioned challenges using a more flexible framework. To mitigate bias, we jointly analyze a large set of negative control outcomes that are adverse events with no known association with the vaccines in order to inform an empirical bias distribution, which we then incorporate into estimating the effect of vaccine exposure on the adverse event of interest through a Bayesian hierarchical model. To address multiple testing and improve on flexibility, at each analysis timepoint, we update a posterior probability in favor of the alternative hypothesis that vaccination induces higher risks of adverse events, and then use it for sequential detection of safety signals. Through an empirical evaluation using six US observational healthcare databases covering more than 360 million patients, we benchmark the proposed procedure against MaxSPRT on testing errors and estimation accuracy, under two epidemiological designs, the historical comparator and the self-controlled case series. We demonstrate that our procedure substantially reduces Type 1 error rates, maintains high statistical power and fast signal detection, and provides considerably more accurate estimation than MaxSPRT. Given the extensiveness of the empirical study which yields more than 7 million sets of results, we present all results in a public R ShinyApp. As an effort to promote open science, we provide full implementation of our method in the open-source R package EvidenceSynthesis.

Causal fairness assessment of treatment allocation with electronic health records.

OBJECTIVE: Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice. While various fairness metrics have emerged to assess fairness in decision-making processes, a growing focus has been on causality-based fairness concepts due to their capacity to mitigate confounding effects and reason about bias. However, the application of causal fairness notions in evaluating the fairness of clinical decision-making with electronic health record (EHR) data remains an understudied domain. This study aims to address the methodological gap in assessing causal fairness of treatment allocation with electronic health records data. In addition, we investigate the impact of social determinants of health on the assessment of causal fairness of treatment allocation. METHODS: We propose a causal fairness algorithm to assess fairness in clinical decision-making. Our algorithm accounts for the heterogeneity of patient populations and identifies potential unfairness in treatment allocation by conditioning on patients who have the same likelihood to benefit from the treatment. We apply this framework to a patient cohort with coronary artery disease derived from an EHR database to evaluate the fairness of treatment decisions. RESULTS: Our analysis reveals notable disparities in coronary artery bypass grafting (CABG) allocation among different patient groups. Women were found to be 4.4%-7.7% less likely to receive CABG than men in two out of four treatment response strata. Similarly, Black or African American patients were 5.4%-8.7% less likely to receive CABG than others in three out of four response strata. These results were similar when social determinants of health (insurance and area deprivation index) were dropped from the algorithm. These findings highlight the presence of disparities in treatment allocation among similar patients, suggesting potential unfairness in the clinical decision-making process. CONCLUSION: This study introduces a novel approach for assessing the fairness of treatment allocation in healthcare. By incorporating responses to treatment into fairness framework, our method explores the potential of quantifying fairness from a causal perspective using EHR data. Our research advances the methodological development of fairness assessment in healthcare and highlight the importance of causality in determining treatment fairness.

Genomic Disorders in CKD across the Lifespan.

SIGNIFICANCE STATEMENT: Pathogenic structural genetic variants, also known as genomic disorders, have been associated with pediatric CKD. This study extends those results across the lifespan, with genomic disorders enriched in both pediatric and adult patients compared with controls. In the Chronic Renal Insufficiency Cohort study, genomic disorders were also associated with lower serum Mg, lower educational performance, and a higher risk of death. A phenome-wide association study confirmed the link between kidney disease and genomic disorders in an unbiased way. Systematic detection of genomic disorders can provide a molecular diagnosis and refine prediction of risk and prognosis. BACKGROUND: Genomic disorders (GDs) are associated with many comorbid outcomes, including CKD. Identification of GDs has diagnostic utility. METHODS: We examined the prevalence of GDs among participants in the Chronic Kidney Disease in Children (CKiD) cohort II ( n =248), Chronic Renal Insufficiency Cohort (CRIC) study ( n =3375), Columbia University CKD Biobank (CU-CKD; n =1986), and the Family Investigation of Nephropathy and Diabetes (FIND; n =1318) compared with 30,746 controls. We also performed a phenome-wide association analysis (PheWAS) of GDs in the electronic MEdical Records and GEnomics (eMERGE; n =11,146) cohort. RESULTS: We found nine out of 248 (3.6%) CKiD II participants carried a GD, replicating prior findings in pediatric CKD. We also identified GDs in 72 out of 6679 (1.1%) adult patients with CKD in the CRIC, CU-CKD, and FIND cohorts, compared with 199 out of 30,746 (0.65%) GDs in controls (OR, 1.7; 95% CI, 1.3 to 2.2). Among adults with CKD, we found recurrent GDs at the 1q21.1, 16p11.2, 17q12, and 22q11.2 loci. The 17q12 GD (diagnostic of renal cyst and diabetes syndrome) was most frequent, present in 1:252 patients with CKD and diabetes. In the PheWAS, dialysis and neuropsychiatric phenotypes were the top associations with GDs. In CRIC participants, GDs were associated with lower serum magnesium, lower educational achievement, and higher mortality risk. CONCLUSION: Undiagnosed GDs are detected both in children and adults with CKD. Identification of GDs in these patients can enable a precise genetic diagnosis, inform prognosis, and help stratify risk in clinical studies. GDs could also provide a molecular explanation for nephropathy and comorbidities, such as poorer neurocognition for a subset of patients.

Arrhythmia Variant Associations and Reclassifications in the eMERGE-III Sequencing Study.

BACKGROUND: Sequencing Mendelian arrhythmia genes in individuals without an indication for arrhythmia genetic testing can identify carriers of pathogenic or likely pathogenic (P/LP) variants. However, the extent to which these variants are associated with clinically meaningful phenotypes before or after return of variant results is unclear. In addition, the majority of discovered variants are currently classified as variants of uncertain significance, limiting clinical actionability. METHODS: The eMERGE-III study (Electronic Medical Records and Genomics Phase III) is a multicenter prospective cohort that included 21 846 participants without previous indication for cardiac genetic testing. Participants were sequenced for 109 Mendelian disease genes, including 10 linked to arrhythmia syndromes. Variant carriers were assessed with electronic health record-derived phenotypes and follow-up clinical examination. Selected variants of uncertain significance (n=50) were characterized in vitro with automated electrophysiology experiments in HEK293 cells. RESULTS: As previously reported, 3.0% of participants had P/LP variants in the 109 genes. Herein, we report 120 participants (0.6%) with P/LP arrhythmia variants. Compared with noncarriers, arrhythmia P/LP carriers had a significantly higher burden of arrhythmia phenotypes in their electronic health records. Fifty-four participants had variant results returned. Nineteen of these 54 participants had inherited arrhythmia syndrome diagnoses (primarily long-QT syndrome), and 12 of these 19 diagnoses were made only after variant results were returned (0.05%). After in vitro functional evaluation of 50 variants of uncertain significance, we reclassified 11 variants: 3 to likely benign and 8 to P/LP. CONCLUSIONS: Genome sequencing in a large population without indication for arrhythmia genetic testing identified phenotype-positive carriers of variants in congenital arrhythmia syndrome disease genes. As the genomes of large numbers of people are sequenced, the disease risk from rare variants in arrhythmia genes can be assessed by integrating genomic screening, electronic health record phenotypes, and in vitro functional studies. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier; NCT03394859.

Observational Study of Hydroxychloroquine in Hospitalized Patients with Covid-19.

BACKGROUND: Hydroxychloroquine has been widely administered to patients with Covid-19 without robust evidence supporting its use. METHODS: We examined the association between hydroxychloroquine use and intubation or death at a large medical center in New York City. Data were obtained regarding consecutive patients hospitalized with Covid-19, excluding those who were intubated, died, or discharged within 24 hours after presentation to the emergency department (study baseline). The primary end point was a composite of intubation or death in a time-to-event analysis. We compared outcomes in patients who received hydroxychloroquine with those in patients who did not, using a multivariable Cox model with inverse probability weighting according to the propensity score. RESULTS: Of 1446 consecutive patients, 70 patients were intubated, died, or discharged within 24 hours after presentation and were excluded from the analysis. Of the remaining 1376 patients, during a median follow-up of 22.5 days, 811 (58.9%) received hydroxychloroquine (600 mg twice on day 1, then 400 mg daily for a median of 5 days); 45.8% of the patients were treated within 24 hours after presentation to the emergency department, and 85.9% within 48 hours. Hydroxychloroquine-treated patients were more severely ill at baseline than those who did not receive hydroxychloroquine (median ratio of partial pressure of arterial oxygen to the fraction of inspired oxygen, 223 vs. 360). Overall, 346 patients (25.1%) had a primary end-point event (180 patients were intubated, of whom 66 subsequently died, and 166 died without intubation). In the main analysis, there was no significant association between hydroxychloroquine use and intubation or death (hazard ratio, 1.04, 95% confidence interval, 0.82 to 1.32). Results were similar in multiple sensitivity analyses. CONCLUSIONS: In this observational study involving patients with Covid-19 who had been admitted to the hospital, hydroxychloroquine administration was not associated with either a greatly lowered or an increased risk of the composite end point of intubation or death. Randomized, controlled trials of hydroxychloroquine in patients with Covid-19 are needed. (Funded by the National Institutes of Health.).

Impact of concomitant cardiovascular medications on overall survival in patients with liver cirrhosis.

OBJECTIVES OF THE ARTICLE: Liver cirrhosis is the end-stage liver disease associated with poor prognosis and cardiovascular comorbidity could significantly impact mortality of cirrhotic patients. We conducted a large, retrospective study to investigate the survival impact of cardiovascular co-medications in patients with liver cirrhosis. MATERIALS AND METHODS: A study-specific R package was processed on the local databases of partner institutions within the Observational Health Data Sciences and Informatics consortium, namely Columbia University, New York City (NYC), USA and Ajou University School of Medicine (AUSOM), South Korea. Patients with cirrhosis diagnosed between 2000 and 2020 were included. Final analysis of the anonymous survival data was performed at Medical Faculty Mannheim, Heidelberg University. RESULTS: We investigated a total of 32,366 patients with liver cirrhosis. Our data showed that administration of antiarrhythmics amiodarone or digoxin presented as a negative prognostic indicator (p = 0.000 in both cohorts). Improved survival was associated with angiotensin-converting enzyme inhibitor ramipril (p = 0.005 in NYC cohort, p = 0.075 in AUSOM cohort) and angiotensin II receptor blocker losartan (p = 0.000 in NYC cohort, p = 0.005 in AUSOM cohort). Non-selective beta blocker carvedilol was associated with a survival advantage in the NYC (p = 0.000) cohort but not in the AUSOM cohort (p = 0.142). Patients who took platelet inhibitor clopidogrel had a prolonged overall survival compared to those without (p = 0.000 in NYC cohort, p = 0.003 in AUSOM cohort). CONCLUSION: Concomitant cardiovascular medications are associated with distinct survival difference in cirrhotic patients. Multidisciplinary management is needed for a judicious choice of proper cardiovascular co-medications in cirrhotic patients.

Efficacy of Co-Medications in Patients with Alcoholic Liver Disease.

BACKGROUND: Alcoholic liver disease (ALD) is still increasing and leads to acute liver injury but also liver cirrhosis and subsequent complications such as liver failure or hepatocellular carcinoma (HCC). As most patients fail to achieve alcohol abstinence, it is essential to identify alternative treatment options in order to improve the outcome of ALD patients. METHODS: Evaluating two large cohorts of patients with ALD from the USA and Korea with a total of 12,006 patients, we investigated the effect on survival of aspirin, metformin, metoprolol, dopamine, and dobutamine drugs in patients with ALD between 2000 and 2020. Patient data were obtained through the "The Observational Health Data Sciences and Informatics consortium," an open-source, multi-stakeholder, and interdisciplinary collaborative effort. RESULTS: The use of aspirin (p = 0.000, p = 0.000), metoprolol (p = 0.002, p = 0.000), and metformin (p = 0.000, p = 0.000) confers a survival benefit for both AUSOM- and NY-treated cohorts. Need of catecholamines dobutamine (p = 0.000, p = 0.000) and dopamine (p = 0.000, p = 0.000) was strongly indicative of poor survival. ß-Blocker treatment with metoprolol (p = 0.128, p = 0.196) or carvedilol (p = 0.520, p = 0.679) was not shown to be protective in any of the female subgroups. CONCLUSION: Overall, our data fill a large gap in long-term, real-world data on patients with ALD, confirming an impact of metformin, acetylsalicylic acid, and ß-blockers on ALD patient's survival. However, gender and ethnic background lead to diverse efficacy in those patients.

Interpretable physiological forecasting in the ICU using constrained data assimilation and electronic health record data.

OBJECTIVE: Prediction of physiological mechanics are important in medical practice because interventions are guided by predicted impacts of interventions. But prediction is difficult in medicine because medicine is complex and difficult to understand from data alone, and the data are sparse relative to the complexity of the generating processes. Computational methods can increase prediction accuracy, but prediction with clinical data is difficult because the data are sparse, noisy and nonstationary. This paper focuses on predicting physiological processes given sparse, non-stationary, electronic health record data in the intensive care unit using data assimilation (DA), a broad collection of methods that pair mechanistic models with inference methods. METHODS: A methodological pipeline embedding a glucose-insulin model into a new DA framework, the constrained ensemble Kalman filter (CEnKF) to forecast blood glucose was developed. The data include tube-fed patients whose nutrition, blood glucose, administered insulins and medications were extracted by hand due to their complexity and to ensure accuracy. The model was estimated using an individual's data as if they arrived in real-time, and the estimated model was run forward producing a forecast. Both constrained and unconstrained ensemble Kalman filters were estimated to compare the impact of constraints. Constraint boundaries, model parameter sets estimated, and data used to estimate the models were varied to investigate their influence on forecasting accuracy. Forecasting accuracy was evaluated according to mean squared error between the model-forecasted glucose and the measurements and by comparing distributions of measured glucose and forecast ensemble means. RESULTS: The novel CEnKF produced substantial gains in robustness and accuracy while minimizing the data requirements compared to the unconstrained ensemble Kalman filters. Administered insulin and tube-nutrition were important for accurate forecasting, but including glucose in IV medication delivery did not increase forecast accuracy. Model flexibility, controlled by constraint boundaries and estimated parameters, did influence forecasting accuracy. CONCLUSION: Accurate and robust physiological forecasting with sparse clinical data is possible with DA. Introducing constrained inference, particularly on unmeasured states and parameters, reduced forecast error and data requirements. The results are not particularly sensitive to model flexibility such as constraint boundaries, but over or under constraining increased forecasting errors.

Hypothesis-driven modeling of the human lung-ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research.

Mechanical ventilation is an essential tool in the management of Acute Respiratory Distress Syndrome (ARDS), but it exposes patients to the risk of ventilator-induced lung injury (VILI). The human lung-ventilator system (LVS) involves the interaction of complex anatomy with a mechanical apparatus, which limits the ability of process-based models to provide individualized clinical support. This work proposes a hypothesis-driven strategy for LVS modeling in which robust personalization is achieved using a pre-defined parameter basis in a non-physiological model. Model inversion, here via windowed data assimilation, forges observed waveforms into interpretable parameter values that characterize the data rather than quantifying physiological processes. Accurate, model-based inference on human-ventilator data indicates model flexibility and utility over a variety of breath types, including those from dyssynchronous LVSs. Estimated parameters generate static characterizations of the data that are 50%-70% more accurate than breath-wise single-compartment model estimates. They also retain sufficient information to distinguish between the types of breath they represent. However, the fidelity and interpretability of model characterizations are tied to parameter definitions and model resolution. These additional factors must be considered in conjunction with the objectives of specific applications, such as identifying and tracking the development of human VILI.

Padé approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes.

OBJECTIVE: We developed and evaluated a novel one-shot distributed algorithm for evidence synthesis in distributed research networks with rare outcomes. MATERIALS AND METHODS: Fed-Padé, motivated by a classic mathematical tool, Padé approximants, reconstructs the multi-site data likelihood via Padé approximant whose key parameters can be computed distributively. Thanks to the simplicity of [2,2] Padé approximant, Fed-Padé requests an extremely simple task and low communication cost for data partners. Specifically, each data partner only needs to compute and share the log-likelihood and its first 4 gradients evaluated at an initial estimator. We evaluated the performance of our algorithm with extensive simulation studies and four observational healthcare databases. RESULTS: Our simulation studies revealed that a [2,2]-Padé approximant can well reconstruct the multi-site likelihood so that Fed-Padé produces nearly identical estimates to the pooled analysis. Across all simulation scenarios considered, the median of relative bias and rate of instability of our Fed-Padé are both <0.1%, whereas meta-analysis estimates have bias up to 50% and instability up to 75%. Furthermore, the confidence intervals derived from the Fed-Padé algorithm showed better coverage of the truth than confidence intervals based on the meta-analysis. In real data analysis, the Fed-Padé has a relative bias of <1% for all three comparisons for risks of acute liver injury and decreased libido, whereas the meta-analysis estimates have a substantially higher bias (around 10%). CONCLUSION: The Fed-Padé algorithm is nearly lossless, stable, communication-efficient, and easy to implement for models with rare outcomes. It provides an extremely suitable and convenient approach for synthesizing evidence in distributed research networks with rare outcomes.

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.

OBJECTIVE: Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). METHODS: A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. RESULTS: The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. CONCLUSION: The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

Representing and utilizing clinical textual data for real world studies: An OHDSI approach.

Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.

Characteristics and outcomes of COVID-19 patients with and without asthma from the United States, South Korea, and Europe.

Objective: Large international comparisons describing the clinical characteristics of patients with COVID-19 are limited. The aim of the study was to perform a large-scale descriptive characterization of COVID-19 patients with asthma.Methods: We included nine databases contributing data from January to June 2020 from the US, South Korea (KR), Spain, UK and the Netherlands. We defined two cohorts of COVID-19 patients ('diagnosed' and 'hospitalized') based on COVID-19 disease codes. We followed patients from COVID-19 index date to 30 days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes in people with asthma defined by codes and prescriptions.Results: The diagnosed and hospitalized cohorts contained 666,933 and 159,552 COVID-19 patients respectively. Exacerbation in people with asthma was recorded in 1.6-8.6% of patients at presentation. Asthma prevalence ranged from 6.2% (95% CI 5.7-6.8) to 18.5% (95% CI 18.2-18.8) in the diagnosed cohort and 5.2% (95% CI 4.0-6.8) to 20.5% (95% CI 18.6-22.6) in the hospitalized cohort. Asthma patients with COVID-19 had high prevalence of comorbidity including hypertension, heart disease, diabetes and obesity. Mortality ranged from 2.1% (95% CI 1.8-2.4) to 16.9% (95% CI 13.8-20.5) and similar or lower compared to COVID-19 patients without asthma. Acute respiratory distress syndrome occurred in 15-30% of hospitalized COVID-19 asthma patients.Conclusion: The prevalence of asthma among COVID-19 patients varies internationally. Asthma patients with COVID-19 have high comorbidity. The prevalence of asthma exacerbation at presentation was low. Whilst mortality was similar among COVID-19 patients with and without asthma, this could be confounded by differences in clinical characteristics. Further research could help identify high-risk asthma patients.[Box: see text]Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2025392 .

Identification of patients with drug-resistant epilepsy in electronic medical record data using the Observational Medical Outcomes Partnership Common Data Model.

OBJECTIVE: More than one third of appropriately treated patients with epilepsy have continued seizures despite two or more medication trials, meeting criteria for drug-resistant epilepsy (DRE). Accurate and reliable identification of patients with DRE in observational data would enable large-scale, real-world comparative effectiveness research and improve access to specialized epilepsy care. In the present study, we aim to develop and compare the performance of computable phenotypes for DRE using the Observational Medical Outcomes Partnership (OMOP) Common Data Model. METHODS: We randomly sampled 600 patients from our academic medical center's electronic health record (EHR)-derived OMOP database meeting previously validated criteria for epilepsy (January 2015-August 2021). Two reviewers manually classified patients as having DRE, drug-responsive epilepsy, undefined drug responsiveness, or no epilepsy as of the last EHR encounter in the study period based on consensus definitions. Demographic characteristics and codes for diagnoses, antiseizure medications (ASMs), and procedures were tested for association with DRE. Algorithms combining permutations of these factors were applied to calculate sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for DRE. The F1 score was used to compare overall performance. RESULTS: Among 412 patients with source record-confirmed epilepsy, 62 (15.0%) had DRE, 163 (39.6%) had drug-responsive epilepsy, 124 (30.0%) had undefined drug responsiveness, and 63 (15.3%) had insufficient records. The best performing phenotype for DRE in terms of the F1 score was the presence of ≥1 intractable epilepsy code and ≥2 unique non-gabapentinoid ASM exposures each with ≥90-day drug era (sensitivity = .661, specificity = .937, PPV = .594, NPV = .952, F1 score = .626). Several phenotypes achieved higher sensitivity at the expense of specificity and vice versa. SIGNIFICANCE: OMOP algorithms can identify DRE in EHR-derived data with varying tradeoffs between sensitivity and specificity. These computable phenotypes can be applied across the largest international network of standardized clinical databases for further validation, reproducible observational research, and improving access to appropriate care.

Correction: Personalized glucose forecasting for type 2 diabetes using data assimilation.

[This corrects the article DOI: 10.1371/journal.pcbi.1005232.].

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Patient characteristics and antiseizure medication pathways in newly diagnosed epilepsy: Feasibility and pilot results using the common data model in a single-center electronic medical record database.

INTRODUCTION: Efforts to characterize variability in epilepsy treatment pathways are limited by the large number of possible antiseizure medication (ASM) regimens and sequences, heterogeneity of patients, and challenges of measuring confounding variables and outcomes across institutions. The Observational Health Data Science and Informatics (OHDSI) collaborative is an international data network representing over 1 billion patient records using common data standards. However, few studies have applied OHDSI's Common Data Model (CDM) to the population with epilepsy and none have validated relevant concepts. The goals of this study were to demonstrate the feasibility of characterizing adult patients with epilepsy and ASM treatment pathways using the CDM in an electronic health record (EHR)-derived database. METHODS: We validated a phenotype algorithm for epilepsy in adults using the CDM in an EHR-derived database (2001-2020) against source records and a prospectively maintained database of patients with confirmed epilepsy. We obtained the frequency of all antecedent conditions and procedures for patients meeting the epilepsy phenotype criteria and characterized ASM exposure sequences over time and by age and sex. RESULTS: The phenotype algorithm identified epilepsy with 73.0-85.0% positive predictive value and 86.3% sensitivity. Many patients had neurologic conditions and diagnoses antecedent to meeting epilepsy criteria. Levetiracetam incrementally replaced phenytoin as the most common first-line agent, but significant heterogeneity remained, particularly in second-line and subsequent agents. Drug sequences included up to 8 unique ingredients and a total of 1,235 unique pathways were observed. CONCLUSIONS: Despite the availability of additional ASMs in the last 2 decades and accumulated guidelines and evidence, ASM use varies significantly in practice, particularly for second-line and subsequent agents. Multi-center OHDSI studies have the potential to better characterize the full extent of variability and support observational comparative effectiveness research, but additional work is needed to validate covariates and outcomes.

Adjusting for indirectly measured confounding using large-scale propensity score.

Confounding remains one of the major challenges to causal inference with observational data. This problem is paramount in medicine, where we would like to answer causal questions from large observational datasets like electronic health records (EHRs) and administrative claims. Modern medical data typically contain tens of thousands of covariates. Such a large set carries hope that many of the confounders are directly measured, and further hope that others are indirectly measured through their correlation with measured covariates. How can we exploit these large sets of covariates for causal inference? To help answer this question, this paper examines the performance of the large-scale propensity score (LSPS) approach on causal analysis of medical data. We demonstrate that LSPS may adjust for indirectly measured confounders by including tens of thousands of covariates that may be correlated with them. We present conditions under which LSPS removes bias due to indirectly measured confounders, and we show that LSPS may avoid bias when inadvertently adjusting for variables (like colliders) that otherwise can induce bias. We demonstrate the performance of LSPS with both simulated medical data and real medical data.

Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria's impact on patient count and safety.

OBJECTIVE: To present an approach on using electronic health record (EHR) data that assesses how different eligibility criteria, either individually or in combination, can impact patient count and safety (exemplified by all-cause hospitalization risk) and further assist with criteria selection for prospective clinical trials. MATERIALS AND METHODS: Trials in three disease domains - relapsed/refractory (r/r) lymphoma/leukemia; hepatitis C virus (HCV); stages 3 and 4 chronic kidney disease (CKD) - were analyzed as case studies for this approach. For each disease domain, criteria were identified and all criteria combinations were used to create EHR cohorts. Per combination, two values were derived: (1) number of eligible patients meeting the selected criteria; (2) hospitalization risk, measured as the hazard ratio between those that qualified and those that did not. From these values, k-means clustering was applied to derive which criteria combinations maximized patient counts but minimized hospitalization risk. RESULTS: Criteria combinations that reduced hospitalization risk without substantial reductions on patient counts were as follows: for r/r lymphoma/leukemia (23 trials; 9 criteria; 623 patients), applying no infection and adequate absolute neutrophil count while forgoing no prior malignancy; for HCV (15; 7; 751), applying no human immunodeficiency virus and no hepatocellular carcinoma while forgoing no decompensated liver disease/cirrhosis; for CKD (10; 9; 23893), applying no congestive heart failure. CONCLUSIONS: Within each disease domain, the more drastic effects were generally driven by a few criteria. Similar criteria across different disease domains introduce different changes. Although results are contingent on the trial sample and the EHR data used, this approach demonstrates how EHR data can inform the impact on safety and available patients when exploring different criteria combinations for designing clinical trials.