Large Electronic Health Record Database Research Articles

Racial-ethnic disparities in concurrent rates of peripapillary & macular OCT parameters among a large glaucomatous clinical population.

To compare rates of change in peripapillary retinal nerve fibre layer (pRNFL) and macular ganglion cell-inner plexiform layer (mGCIPL) parameters among different race-ethnicities from a large electronic health record database of subjects with or suspected of glaucoma. In this retrospective cohort study, rates of change were obtained using joint longitudinal linear mixed models for eyes with ≥3 visits and ≥1 year of follow-up, adjusting for age, sex, intraocular pressure, central corneal thickness, and baseline pRNFL and mGCIPL thickness. Best linear unbiased predictor estimates of various parameters were stratified by baseline glaucoma severity and analysed by racial-ethnic group. A total of 21,472 spectral domain optical coherence tomography (OCT) pRNFL scans and 14,431 mGCIPL scans from 2002 eyes were evaluated. A total of 200 (15.6%) and 601 (46.8%) subjects identified as non-Hispanic Black (NHB) and Hispanic, respectively. NHB eyes exhibited faster rates of change in pRNFL among glaucoma suspect (global pRNFL -0.57 ± 0.55 µm/year vs. -0.37 ± 0.62 µm/year among Hispanics, p < 0.001), mild glaucoma (superior pRNFL quadrant -1.20 ± 1.06 µm/year vs. -0.75 ± 1.51 µm/year among non-Hispanic Whites (NHW), p = 0.043), and moderate glaucoma eyes (superior pRNFL quadrant -1.31 ± 1.49 µm/year vs. -0.52 ± 1.26 µm/year among Hispanics, p = 0.003). NHB eyes exhibited faster rates of mGCIPL loss corresponding to pRNFL rates. Global pRNFL and mGCIPL rates were strongly correlated (R2 = 0.70). Adjusted rates of pRNFL and mGCIPL loss significantly differed between racial-ethnic groups when stratified by glaucoma severity, with faster rates among NHB patients. These differences highlight key racial-ethnic disparities in adjusted rates of glaucoma OCT parameters.

Machine learning to identify chronic cough from administrative claims data

Accurate identification of patient populations is an essential component of clinical research, especially for medical conditions such as chronic cough that are inconsistently defined and diagnosed. We aimed to develop and compare machine learning models to identify chronic cough from medical and pharmacy claims data. In this retrospective observational study, we compared 3 machine learning algorithms based on XG Boost, logistic regression, and neural network approaches using a large claims and electronic health record database. Of the 327,423 patients who met the study criteria, 4,818 had chronic cough based on linked claims–electronic health record data. The XG Boost model showed the best performance, achieving a Receiver-Operator Characteristic Area Under the Curve (ROC-AUC) of 0.916. We selected a cutoff that favors a high positive predictive value (PPV) to minimize false positives, resulting in a sensitivity, specificity, PPV, and negative predictive value of 18.0%, 99.6%, 38.7%, and 98.8%, respectively on the held-out testing set (n = 82,262). Logistic regression and neural network models achieved slightly lower ROC-AUCs of 0.907 and 0.838, respectively. The XG Boost and logistic regression models maintained their robust performance in subgroups of individuals with higher rates of chronic cough. Machine learning algorithms are one way of identifying conditions that are not coded in medical records, and can help identify individuals with chronic cough from claims data with a high degree of classification value.

The Use of a Large Electronic Health Records System to Define and Characterize a Thrombotic Thrombocytopenic Purpura Population

Background: Thrombotic thrombocytopenic purpura (TTP) is a rare life-threatening microangiopathy due to a deficiency of the von Willebrand factor (VWF)-cleaving protease ADAMTS13. Microangiopathic hemolytic anemia, severe thrombocytopenia and ischemic end organ injury due to microvascular thrombi are the defining features of TTP. TTP can be caused by auto-antibodies against ADAMTS13 enzyme (immune TTP [ITTP]) or by inherited mutations of the ADAMTS13 gene (congenital TTP [CTTP]). Therapeutic plasma exchange (TPE) to replenish ADAMTS13, with or without steroids, has been the mainstay of treatment. Due to the auto-immune nature of ITTP the International Society on Thrombosis and Hemostasis (ISTH) made a strong recommendation in 2020 to add corticosteroids to TPE for ITTP and a conditional recommendation to add the CD20 inhibitor rituximab as well the VWF monoclonal antibody caplacizumab. Objective: To define and characterize TTP in a living population from a large electronic health record (EHR) database in terms of disease prevalence, demographic characteristics and therapeutic interventions and procedures in light of recent ISTH guidelines. Methods: We conducted a study of data from TriNetX Analytics Network, which contains EHRs from &amp;gt;140 million individuals from 79 Health Care Organizations in the US. We used ICD-10-CM diagnosis codes, ATC and CPT codes applied in the last 10 years to evaluate TTP disease prevalence, therapeutic interventions and procedural interventions respectively. Eligibility criteria applied to define the TTP cohort were as follows: Inclusion criteria: ICD-10 M31.1 diagnosis of thrombotic microangiopathy (TMA)History of thrombocytopenia (platelet volume ≤150,000 per microliter)ICD-10-PCS code for pheresis of plasma or a CPT code for therapeutic apheresis, for plasma pheresis Exclusion criteria: ICD-10-CM codes linked to etiologies of TMA other than TTP (hemopoietic stem cell-associated TMA [M31.11], hemolytic uremic syndrome [D59.3], disseminated intravascular coagulation [D65], antiphospholipid syndrome [D68.61], malignant hypertension [I10, I15])Deceased Results: As of July 31, 2023, there were a total of 142,046,930 living individuals registered in the database in the last 10 years. Of these, 560 individuals met eligibility criteria to define the TTP population (prevalence 0.0004% or 4 cases per million). Demographic characteristics included mean age at diagnosis (39.3 years [SD=14.3]), gender (73% female, 27% male), race (White = 44%, Black or African American = 33%, Asian = 3%, Other or unknown = 20%) and ethnicity (Not Hispanic or Latino = 73%, Unknown = 16%, Hispanic or Latino = 12%). Therapeutic interventions included systemic glucocorticoids (93% [prednisone 84%, methylprednisolone 68%, hydrocortisone 46%, dexamethasone 38%, triamcinolone 14%, betamethasone 9%, prednisolone 5%]), antithrombotics (86% [heparin group 79%, platelet aggregation inhibitors {excluding heparin} 29%, enzymes 27%, other antithrombotic agents {including caplacizumab} 18%, vitamin K antagonists 7%, direct factor Xa inhibitors 7%, direct thrombin inhibitors 2%]) and CD20 inhibitors (including rituximab) (40%). Conclusion: This this is the first report to define and characterize a TTP population from a large US EHR database using a combination administrative codes and laboratory data. The study found a very low prevalence rate; that closely aligns with previously published estimates from registries of 4-5 cases per million for ITTP and 1-2 cases per million for CTTP. It was not possible to differentiate between CTTP and ITTP cases in this study due to a lack of ADAMTS13 genomic data. Demographic findings were consistent with previous registry data (median age of acute TTP presentation in the third to fourth decade of life, typically affecting females more than males). We found a high level of systemic glucocorticoid use as expected, but also high percentage of patients treated with rituximab in line with recent ISTH guidance. The authors are aware of other potential treatments in early development including gene therapy candidates for CTTP and cell therapy candidates for ITTP. This represents a tantalizing prospect for this population; however, to demonstrate efficacy and safety, these new therapies will require scientifically rigorous and well planned clinical studies including robust long-term follow-up.

An efficient landmark model for prediction of suicide attempts in multiple clinical settings

Growing evidence has shown that applying machine learning models to large clinical data sources may exceed clinician performance in suicide risk stratification. However, many existing prediction models either suffer from “temporal bias” (a bias that stems from using case-control sampling) or require training on all available patient visit data. Here, we adopt a “landmark model” framework that aligns with clinical practice for prediction of suicide-related behaviors (SRBs) using a large electronic health record database. Using the landmark approach, we developed models for SRB prediction (regularized Cox regression and random survival forest) that establish a time-point (e.g., clinical visit) from which predictions are made over user-specified prediction windows using historical information up to that point. We applied this approach to cohorts from three clinical settings: general outpatient, psychiatric emergency department, and psychiatric inpatients, for varying prediction windows and lengths of historical data. Models achieved high discriminative performance (area under the Receiver Operating Characteristic curve 0.74–0.93 for the Cox model) across different prediction windows and settings, even with relatively short periods of historical data. In short, we developed accurate, dynamic SRB risk prediction models with the landmark approach that reduce bias and enhance the reliability and portability of suicide risk prediction models.

A systems approach points to a therapeutic role for retinoids in asparaginase-associated pancreatitis.

Among drug-induced adverse events, pancreatitis is life-threatening and results in substantial morbidity. A prototype example is the pancreatitis caused by asparaginase, a crucial drug used to treat acute lymphoblastic leukemia (ALL). Here, we used a systems approach to identify the factors affecting asparaginase-associated pancreatitis (AAP). Connectivity Map analysis of the transcriptomic data showed that asparaginase-induced gene signatures were potentially reversed by retinoids (vitamin A and its analogs). Analysis of a large electronic health record database (TriNetX) and the U.S. Federal Drug Administration Adverse Events Reporting System demonstrated a reduction in AAP risk with concomitant exposure to vitamin A.Furthermore, we performed a global metabolomic screening of plasma samples from 24 individuals with ALL who developed pancreatitis (cases) and 26 individuals with ALL who did not develop pancreatitis (controls), before and after a single exposure to asparaginase. Screening from this discovery cohort revealed that plasma carotenoids were lower in the cases than in controls. This finding was validated in a larger external cohort. A 30-day dietary recall showed that the cases received less dietary vitamin A than the controls did. In mice, asparaginase administration alone was sufficient to reduce circulating and hepatic retinol. Based on these data, we propose that circulating retinoids protect against pancreatic inflammation and that asparaginase reduces circulating retinoids. Moreover, we show that AAP is more likely to develop with reduced dietary vitamin A intake. The systems approach taken for AAP provides an impetus to examine the role of dietary vitamin A supplementation in preventing or treating AAP.

106. High Maternal Tdap Vaccine Uptake During Early Part of Vaccination Window: Implications for Future Maternal Vaccines

Abstract Background Maternal vaccines to prevent respiratory syncytial virus (RSV) among infants are in development. Uptake of existing maternal vaccines can be used to predict uptake of future maternal RSV vaccines and may be used to inform vaccine policy decisions. Previous reports of maternal vaccination rates do not estimate vaccine uptake by gestational week (wGA) of pregnancy, which is needed for precise estimation of vaccine impact. This study estimated the uptake of maternal Tdap vaccination overall and by wGA in a large electronic health records (EHR) database representing both privately and publicly insured patients over a recent 5-year period. Methods We identified pregnant women aged 15 – 44 years who had a live birth delivery between 01/01/2017 – 9/29/2021 in the Optum EHR database. Continuous activity for 6 months pre-conception through 1 day after delivery were required. Patients with &amp;gt;1 type of pregnancy outcome within 7 days and/or unidentifiable wGA were excluded. We utilized recently published gestational age algorithms to estimate the uptake of maternal Tdap vaccination overall and by wGA of pregnancy. Results were reported by year. Results The population included 1,056,488 live births among 919,510 pregnant women during the study period. The average age at delivery was 29.7 years (SD: 5.6), 72% were white, 82% were non-Hispanic; 58% had private insurance, and 38% had Medicaid. Overall, 56% of the pregnancies included a Tdap vaccine during their pregnancy. Among vaccinated pregnancies, the majority (68%) of Tdap vaccines were administered during the first 6 weeks of the recommended 10-week vaccination window (CDC recommends Tdap vaccination from 27-36 wGA) (Table). Table.Timing of Maternal Tdap Vaccination among Pregnant Women, by Year Conclusion In this analysis using a large EHR database, the overall uptake of maternal Tdap vaccination was consistent with previously published estimates. Notably, the majority of Tdap vaccination occurred during the earliest weeks of the recommended vaccination period. These results may have important implications for estimating potential impact of future maternal vaccines. Disclosures Amy W. Law, PharmD, Pfizer: Employment|Pfizer: Stocks/Bonds Jennifer Judy, MS, PhD, Pfizer Inc: Employee|Pfizer Inc: Stocks/Bonds Sarah J. Willis, PhD, MPH, Pfizer: Pfizer supported research at Harvard Pilgrim Health Care Institute (paid to Institute)|Pfizer: Employment Kimberly M. Shea, Ph.D., M.P.H., Pfizer: Employee|Pfizer: Stocks/Bonds.

A machine learning approach to identifying patients with pulmonary hypertension using real-world electronic health records

BackgroundThis study aimed to develop a machine learning (ML) model to identify patients who are likely to have pulmonary hypertension (PH), using a large patient-level US-based electronic health record (EHR) database. MethodsA gradient boosting model, XGBoost, was developed using data from Optum's US-based de-identified EHR dataset (2007–2019). PH and disease control adult patients were identified using diagnostic, treatment and procedure codes and were randomly split into the training (90%) or test set (10%). Model features included patient demographics, physician visits, diagnoses, procedures, prescriptions, and laboratory test results. SHapley Additive exPlanations values were used to determine feature importance. ResultsWe identified 11,279,478 control and 115,822 PH patients (mean age, respectively: 62 and 68 years, both 53% female). The final model used 165 features, with the most important predictive features including diagnosis of heart failure, shortness of breath and atrial fibrillation. The model predicted PH with an area under the receiver operating characteristic curve (AUROC) of 0.92. AUROC remained above 0.80 for the prediction of PH up to and beyond 18 months before diagnosis. Among the PH patients, we also identified 955 pulmonary arterial hypertension (PAH) and 1432 chronic thromboembolic pulmonary hypertension (CTEPH) patients, and the range of AUROCs obtained for these cohorts was 0.79–0.90 and 0.87–0.96, respectively. ConclusionsThis model to detect PH based on patients’ EHR records is viable and performs well in subgroups of PAH and CTEPH patients. This approach has the potential to improve patient outcomes by reducing diagnostic delay in PH.

Testosterone Therapy in Men with Klinefelter Syndrome: Analysis of a Global Federated Research Network.

The objective of this study was to determine the rates of hypogonadism and prescription of testosterone replacement therapy (TRT) in men with Klinefelter syndrome (KS). We hypothesized that men with KS are under-treated for testosterone deficiency with TRT due to a combination of factors, including a poor understanding of hypogonadism in this population and neurocognitive issues leading to delay in seeking of treatment for hypogonadism. We queried TriNetX, a large multicenter electronic health record database, to identify all men with a diagnosis of KS (ICD-10-CM Q98.4). Prevalence of testosterone deficiency was determined as defined by testosterone level < 300 ng/dL. The primary outcome of the study was prescription of any of the following forms of TRT on the day of diagnosis or later. There were in total 5437 men with diagnosis of KS. A total of 1581 men with KS received laboratory measurement of testosterone level, 1113 (70.4%) of whom were hypogonadal. Mean testosterone level in this group was 354 ng/dL [50-658]. Of the 1113 men found to be hypogonadal, only 657 (59.0%) men were given prescription for TRT. This is the first study to evaluate TRT prescribing habits in men with KS. In this large retrospective study, TRT was underprescribed in men with KS. Further studies are needed to corroborate these findings and to evaluate barriers to receiving care in this population.

A data preparation framework for cleaning electronic health records and assessing cleaning outcomes for secondary analysis

Even though data preparation constitutes a large proportion of the total effort involved in electronic health record (EHR) based secondary analysis, guidelines for EHR data preparation are still insufficient to date. This study proposes a data preparation framework that can guide and validate the cleaning of EHRs for secondary analysis. The developed framework consists of three core themes—workflow, assessment and cleaning methods, and cleaning evaluation scheme. To illustrate the viability of the proposed framework, we applied it to a hip-fracture readmission scenario using the underlying data extracted from a large EHR database. The case study demonstrated the effectiveness of the proposed framework in organizing and standardizing phases and processes within an EHR data preparation workflow. Furthermore, the cleaning evaluation scheme was found to be effective in validating EHR cleaning methods, especially for those used to handle complex issues that usually appear in patient demographics, longitudinal attributes of EHRs, and the application of filtering and imputation cleaning methods.

Sex differences in comorbidities and COVID-19 mortality-Report from the real-world data.

BackgroundThe differential effect of comorbidities on COVID-19 severe outcomes by sex has not been fully evaluated.ObjectiveTo examine the association of major comorbidities and COVID-19 mortality in men and women separately.MethodsWe performed a retrospective cohort analysis using a large electronic health record (EHR) database in the U.S. We included adult patients with a clinical diagnosis of COVID-19 who also had necessary information on demographics and comorbidities from January 1, 2016 to October 31, 2021. We defined comorbidities by the Charlson Comorbidity Index (CCI) using ICD-10 codes at or before the COVID-19 diagnosis. We conducted logistic regressions to compare the risk of death associated with comorbidities stratifying by sex.ResultsA total of 121,342 patients were included in the final analysis. We found significant sex differences in the association between comorbidities and COVID-19 death. Specifically, moderate/severe liver disease, dementia, metastatic solid tumor, and heart failure and the increased number of comorbidities appeared to confer a greater magnitude of mortality risk in women compared to men.ConclusionsOur study suggests sex differences in the effect of comorbidities on COVID-19 mortality and highlights the importance of implementing sex-specific preventive or treatment approaches in patients with COVID-19.

Multidimensional characteristics of complex chronic patients in emergency services in primary care

<h2>ABSTRACT</h2><h3>Background</h3> In Spain, people between 65 and 74 years old have an average of 2.8 chronic problems or diseases, rising to 3.2 among people aged 75 and older. <h3>Aims</h3> We aimed to describe the multidimensional characteristics of complex chronic patients who sought urgent care. <h3>Design</h3> We conducted a descriptive, cross-sectional, retrospective study, drawing on a large electronic health record database in primary care in Barcelona, Spain. <h3>Methods</h3> We used health record data about subjects' clinical state, functional state, mental health state, social state, nutritional state, and movement. A descriptive analysis was carried out to determine percentages and means, given a standard deviation. <h3>Finding</h3> In 2019, 3,732 complex chronic patients sought urgent care at the centres under study. Subjects had a mean age of 82.5 ± 9.8 years, and 58.7% (n = 2,189) were women. Frailty was present in 69.3% (n = 2,586), and 81.7% (n = 3,050) were adherent to therapy. There were 2,470 visits to hospital emergency rooms (66.2%) and 1,651 hospital admissions (44.2%). Malnutrition was evident in 46.5% (n = 1,623) and 27.9% (n = 1,042) had low risk of social exclusion. <h3>Discussion</h3> Having such descriptive information can help health services from all areas and levels of care to use comprehensive, collaborative practices to care for complex chronic patients and their non-institutional caregivers. <h3>Conclusion</h3> We noted a high proportion of emergency room visits and hospital admissions among complex chronic patients. Low-level depression and malnutrition were detected. Proactive multidisciplinary interventions could improve the situation of these patients.

Disparities Exist in Physical Therapy Utilization and Time to Utilization Between Black and White Patients With Musculoskeletal Pain.

Black patients are less likely than White patients to receive physical therapy for musculoskeletal pain conditions. Current evidence, however, is limited to self-reported conditions and health services use. The purpose of this study was to use a large electronic health record database to determine whether a race disparity existed in use of physical therapy within 90days of a new musculoskeletal diagnosis. Eligible patients (n = 52,384) were sampled from an Optum deidentified electronic health record database of 5 million adults distributed throughout the United States. In this database, patients were designated as "Black" and "White." Patients were eligible if they had a new diagnosis for musculoskeletal neck, shoulder, back, or knee pain between January 1, 2012, and December 31, 2017. Logistic regression and Cox proportional hazard models were computed before and after adjusting for covariates to estimate the association between race and receipt of physical therapy services within 90days of musculoskeletal pain diagnoses. Patients were on average 47.5 (SD = 14.9) years of age, 12.8% were Black, 87.2% were White, and 52.7% were female. Ten percent of Black patients and 15.5% of White patients received physical therapy services within 90days of musculoskeletal pain diagnoses. After adjusting for covariates, White patients were 57% more likely (odds ratio = 1.57; 95% CI = 1.44-1.71) to receive physical therapy compared with Black patients and had significantly shorter time to physical therapy than Black patients (hazard ratio = 1.53; 95% CI = 1.42-1.66). In a nationally distributed cohort, Black patients were less likely than White patients to utilize physical therapy and had a longer time to utilization of physical therapy for musculoskeletal pain. These findings highlight the need to determine the mechanisms underlying the observed disparities and how these disparities influence health outcomes.

Trends in COVID-19 patient characteristics in a large electronic health record database in the United States: A cohort study.

BackgroundElectronic health record (EHR) databases provide an opportunity to facilitate characterization and trends in patients with COVID-19.MethodsPatients with COVID-19 were identified based on an ICD-10 diagnosis code for COVID-19 (U07.1) and/or a positive SARS-CoV-2 viral lab result from January 2020 to November 2020. Patients were characterized in terms of demographics, healthcare utilization, clinical comorbidities, therapies, laboratory results, and procedures/care received, including critical care, intubation/ventilation, and occurrence of death were described, overall and by month.ResultsThere were 393,773 patients with COVID-19 and 56,996 with a COVID-19 associated hospitalization. A greater percentage of patients hospitalized with COVID-19 relative to all COVID-19 cases were older, male, African American, and lived in the Northeast and South. The most common comorbidities before admission/infection date were hypertension (40.8%), diabetes (29.5%), and obesity (23.8%), and the most common diagnoses during hospitalization were pneumonia (59.6%), acute respiratory failure (44.8%), and dyspnea (28.0%). A total of 85.7% of patients hospitalized with COVID-19 had CRP values > 10 mg/L, 75.5% had fibrinogen values > 400 mg/dL, and 76.8% had D-dimer values > 250 ng/mL. Median values for platelets, CRP, lactate dehydrogenase, D-dimer, and fibrinogen tended to decrease from January-March to November. The use of chloroquine/hydroxychloroquine during hospitalization peaked by March (71.2%) and was used rarely by May (5.1%) and less than 1% afterwards, while the use of remdesivir had increased by May (10.0%) followed by dexamethasone by June (27.7%). All-cause mortality was 3.2% overall and 15.0% among those hospitalized; 21.0% received critical care and 16.0% received intubation/ventilation/ECMO.ConclusionsThis study characterizes US patients with COVID-19 and their management during hospitalization over the first eleven months of this disease pandemic.

Maternal Postnatal Depression and Completion of Infant Immunizations: A UK Cohort Study of 196,329 Mother-Infant Pairs, 2006-2015.

Objective: To examine the relationship between maternal postnatal depression and completion of infant vaccinations.Methods: We conducted a cohort study using data from The Health Improvement Network (THIN), a large UK primary care electronic health record database. We identified 196,329 mother-infant pairs in which the infant was born between 2006 and 2015. Postnatal depression was identified through antidepressant prescriptions or diagnoses or symptoms of depression in first year after childbirth. Primary outcome was completion of three 5-in-1 vaccination doses in infants before 1 year of age; this vaccine protects against diphtheria, tetanus, whooping cough, polio, and Haemophilus influenzae type b. We used Poisson regression models to compare likelihood of infant 5-in-1 vaccine uptake among children of women with a record of postnatal depression to likelihood among those without.Results: Of the 196,329 women, 20,802 (10.6%) had a record of postnatal depression and/or antidepressant prescription. There was no difference in infants' 5-in-1 vaccination completion between those of mothers with a record and those of mothers' without (adjusted incidence rate ratio [IRR] = 1.01; 95% CI, 0.99-1.02). Those from more socially deprived areas were less likely to complete infant vaccinations compared to those from the least deprived areas (IRR = 0.92; 95% CI, 0.90-0.93). Likelihood of completing infant vaccination decreased over time, comparing 2014-2015 to 2006-2007 (IRR = 0.90; 95% CI, 0.89-0.92).Conclusions: Among mothers who engage with primary care, maternal postnatal depression is not associated with lower rates of infant vaccination, though more research is needed to conclude if either more severe depression or unrecognized depression is associated with lower completion rates.

Demographics of children with feeding difficulties from a large electronic health record database.

Feeding difficulties are among the most common concerns expressed by parents in younger children. However, few studies have reported on the characteristics of patients with clinically significant feeding diagnoses. The aim of the current study is to describe the characteristics of patients diagnosed with feeding difficulties including concurrent conditions, age, and sex, sampled nationwide utilizing the Cerner Health Facts Database. We identified patients with a diagnosis of feeding difficulties (ICD-9 783.3 or ICD-10 R63.3), age 7 months to 17 years, with an outpatient visit between 2010 and 2017. The demographics and complex clinical conditions of this population were categorized. The cohort was then collapsed into a matrix defining recognized phenotype codes for ICD-9 and ICD-10 diagnoses to identify associated conditions. We identified 39,674 patients (0.95%) representing 101,684 encounters from 68 health systems across the United States; 43% of patients were female. Gastrointestinal conditions were the most common, followed by malnutrition, developmental and behavioral diagnoses, and neurologic conditions. This study is one of the most robust studies defining the prevalence, demographic characteristics, and phenotypic profiling of patients with feeding difficulties. Our observations have implications on screening and resource allocation to recognize and manage this poorly understood population.

Impact of Immune Checkpoint Inhibitors on COVID-19 Severity in Patients with Cancer.

BackgroundAmid continued uncertainty about the management of cancer patients during the pandemic, this study sought to obtain real-world data on the use of immune checkpoint inhibitors (ICIs) before COVID-19 diagnosis and its association with severity and survival outcomes in cancer patients who contracted COVID-19.MethodsCancer patients diagnosed with COVID-19 were identified from a large electronic health record database; those treated with ICIs before COVID-19+ diagnosis were matched in a 1:2 ratio to those not treated with ICIs, using a 2-step matching procedure. A descriptive analysis examined the difference in COVID-19 mortality (30-day and overall) and severity outcomes between the 2 cohorts, and overall survival was compared.ResultsAmong 17 545 adults ≥18 years with cancer who tested positive for COVID-19 between February 20, 2020, and January 28, 2021, in the US, 228 ICI-treated patients were matched to 456 non-ICI-treated patients, comprising the 2 study cohorts. Clinical characteristics differed significantly between the 2 cohorts before matching, with metastatic disease, lung cancer, a history of smoking, and the presence of pulmonary comorbidities being more common in the ICI-treated cohort; after matching, the 2 cohorts were similar. There were no significant differences between the ICI-treated and non-ICI-treated cohorts for 30-day mortality (12.7% vs. 14.9%, P = .235), overall mortality (22.4% vs. 22.4%, P = 1.000), hospitalization (38.6% vs. 39.0%, P = .912), or emergency department visits (16.7% vs. 14.7%, P = .500). Overall survival was similar between the 2 cohorts.ConclusionThis analysis adds to the clinical evidence base that use of ICIs before SARS-CoV-2 infection does not affect COVID-19 severity or survival outcomes, supporting the continued use of ICIs in cancer patients during the pandemic.

Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database.

A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes-related symptoms using a large nationwide electronic health record (EHR) database. We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (n = 1,136,301 patients) was identified using a rule-based phenotype method. A multistep procedure was then used to identify type 2 diabetes-related symptoms based on International Classification of Diseases, 9th and 10th revisions, diagnosis codes. Type 2 diabetes-related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21-60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies. To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes-related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified.

POSA420 Trends in Thrombotic and Cardiovascular Events Among Hospitalized Patients with COVID-19 in the US: Findings from a Large Electronic Health Records Database

Patients with the coronavirus disease 2019 (COVID-19) have an increased risk of thrombotic and cardiac events. We assessed the incidence and trends in selected thrombotic and cardiac events among hospitalized COVID-19 patients from a large, geographically diverse US electronic health records (EHR) database. This retrospective study analyzed Optum® de-identified COVID-19 EHR dataset to identify patients hospitalized between 03/01/2020 and 10/31/2020 with a COVID-19 diagnosis code. Patients were members of an integrated delivery network with ≥1 encounter during the 12 months prior to admission. Events of interest, including acute coronary syndrome (ACS), venous thromboembolism (VTE), stroke and myocarditis, were identified by diagnosis codes during hospitalization. Patients with any such events in their 12-month baseline were excluded. A composite event was defined by the occurrence of any of these events. Events were reported as proportions, in total as well as by month of admission and age group. Among 25,574 hospitalized COVID-19 patients, the median age was 60 years (IQR: 44-72), and 54.5% were female. The composite event occurred in 3,052 (11.9%) patients (ACS=6.4%, VTE=4.1%, stroke=2.4%, myocarditis=0.6%) and its incidence increased with age (≤19=4.6%, 20-49=6.2%, 50-59=11.7%, 60-69=13.4%, ≥70=17.3%). In the youngest age group, myocarditis (2.2%) and VTE (1.7%) contributed to the majority of events, while in the oldest, ACS (10.9%), VTE (4.2%) and stroke (4.1%) were observed most often. Patients admitted during March had the highest incidence (20.6%), which decreased to 9.1% by July and remained steady through October (9.6%). The percentage decline in the composite event incidence during the study period was higher in younger versus older patients (≤19=77.0% 20-49=61.9%, 50-59=66.0%, 60-69=58.6%, ≥70=44.1%). The incidence of the composite event was highest among patients hospitalized early in the pandemic. The composite event occurred in almost 5% patients ≤19 years old and this group experienced the largest decline during the study period.

Validation of knee osteoarthritis case identification algorithms in a large electronic health record database

PurposeTo facilitate studies of knee osteoarthritis (OA) in large databases, case finding algorithms with high levels of diagnostic performance are needed. MethodsFrom a UK general practitioner (GP) practice derived database, we selected adults ages 40–90 years meeting algorithms that included various combinations of codes for knee OA or knee pain and imaging. The GP for each patient was mailed a questionnaire to assess the cause of knee pain and provide knee x-ray and/or MRI findings. We considered knee pain with x-ray and/or MRI findings consistent with OA the gold standard. We calculated positive predictive values (PPV) and sensitivity for case identification algorithms. ResultsOf 100 questionnaires sent, 93 were returned; we excluded 8 subjects who had other rheumatic disorders or total knee replacements. Among those with one code for OA, the PPV was 64% (95% CI ​= ​49%–79%) and it increased to 92% (95% CI ​= ​76%–100%) when two or more OA codes over six months were required. The increase in PPV was accompanied by a drop in sensitivity from 44% (95% CI ​= ​31%–57%) to 19% (95% CI ​= ​9%–30%). Use of one pain code yielded similar results to use of one OA code. Requiring two or more knee pain codes over six months yielded a PPV of 68% (95% CI ​= ​49%–88%) and sensitivity of 26% (95% CI ​= ​15%–38%). DiscussionA case identification algorithm requiring two or more knee OA codes yielded the highest PPV at the cost of reduced sensitivity. Tradeoffs between PPV and sensitivity will need to be weighed alongside study goals when selecting a case identification algorithm.

Mortality Risk Among Patients With COVID-19 Prescribed Selective Serotonin Reuptake Inhibitor Antidepressants

Antidepressant use may be associated with reduced levels of several proinflammatory cytokines suggested to be involved with the development of severe COVID-19. An association between the use of selective serotonin reuptake inhibitors (SSRIs)-specifically fluoxetine hydrochloride and fluvoxamine maleate-with decreased mortality among patients with COVID-19 has been reported in recent studies; however, these studies had limited power due to their small size. To investigate the association of SSRIs with outcomes in patients with COVID-19 by analyzing electronic health records (EHRs). This retrospective cohort study used propensity score matching by demographic characteristics, comorbidities, and medication indication to compare SSRI-treated patients with matched control patients not treated with SSRIs within a large EHR database representing a diverse population of 83 584 patients diagnosed with COVID-19 from January to September 2020 and with a duration of follow-up of as long as 8 months in 87 health care centers across the US. Selective serotonin reuptake inhibitors and specifically (1) fluoxetine, (2) fluoxetine or fluvoxamine, and (3) other SSRIs (ie, not fluoxetine or fluvoxamine). Death. A total of 3401 adult patients with COVID-19 prescribed SSRIs (2033 women [59.8%]; mean [SD] age, 63.8 [18.1] years) were identified, with 470 receiving fluoxetine only (280 women [59.6%]; mean [SD] age, 58.5 [18.1] years), 481 receiving fluoxetine or fluvoxamine (285 women [59.3%]; mean [SD] age, 58.7 [18.0] years), and 2898 receiving other SSRIs (1733 women [59.8%]; mean [SD] age, 64.7 [18.0] years) within a defined time frame. When compared with matched untreated control patients, relative risk (RR) of mortality was reduced among patients prescribed any SSRI (497 of 3401 [14.6%] vs 1130 of 6802 [16.6%]; RR, 0.92 [95% CI, 0.85-0.99]; adjusted P = .03); fluoxetine (46 of 470 [9.8%] vs 937 of 7050 [13.3%]; RR, 0.72 [95% CI, 0.54-0.97]; adjusted P = .03); and fluoxetine or fluvoxamine (48 of 481 [10.0%] vs 956 of 7215 [13.3%]; RR, 0.74 [95% CI, 0.55-0.99]; adjusted P = .04). The association between receiving any SSRI that is not fluoxetine or fluvoxamine and risk of death was not statistically significant (447 of 2898 [15.4%] vs 1474 of 8694 [17.0%]; RR, 0.92 [95% CI, 0.84-1.00]; adjusted P = .06). These results support evidence that SSRIs may be associated with reduced severity of COVID-19 reflected in the reduced RR of mortality. Further research and randomized clinical trials are needed to elucidate the effect of SSRIs generally, or more specifically of fluoxetine and fluvoxamine, on the severity of COVID-19 outcomes.