Electronic Health Record Network Research Articles

Abstract P507: Adherence to Pediatric Screening Guidelines for Cardiovascular Risk Factors in a Diverse Cohort of Children in Chicago

Background: Preventive screenings in children and adolescents encourage maintenance of optimal cardiovascular health (CVH) early in life, but there may be a gap between recommendations and clinical practice. We evaluated adherence to pediatric guidelines for universal age-based and risk-based screening for body mass index (BMI), blood pressure (BP), lipids, and blood glucose. Methods: We used 2010-2018 ambulatory visit data from children aged 2-12y within CAPriCORN, an electronic health record network from major healthcare systems in Chicago. For each child who attended a well-child visit and met age and/or risk-based criteria, receipt of BMI, BP, lipids, and/or HbA1c or fasting blood glucose measurements were assessed. For each metric, proportion adherence was calculated via generalized estimating equations with an exchangeable correlation structure and was stratified by age, sex, race/ethnicity, and insurance status. Results: This study included 87,549 children who attended 197,559 well-child encounters. Across all encounters, children were 51.5% male and mean (SD) age 6.4 (3.3) years. Universal age-based screening prevalence (95% CI) per 100 eligible children was 77.1 (76.8, 77.3) for BMI, 33.4 (33.1, 33.7) for BP, and 9.6 (9.3, 9.9) for lipids. Risk-based screening prevalence (95% CI) per 100 eligible children was 13.9 (12.2, 15.9) for BP, 6.9 (6.4, 7.5) for lipids, and 13.3 (12.6, 14.1) for blood glucose. Conclusions: Early screening of CVH risk factors could lead to earlier interventions, which could alter CVH trajectories across the lifetime. Low levels of adherence to universal age-based and risk-based CVH screening highlight the gap between recommendations and clinical practice, emphasizing the need to understand and address barriers to screening in pediatric populations.

(197) The Impact of Connective Tissue Disorders on Rates of Penile Fracture and Peyronie’s Disease

Abstract Introduction Ehlers Danlos Syndrome (EDS) and collagen-related hypermobility disorders are well known to cause frequent joint dislocations, easy bruising, and poor wound healing. Marfan syndrome is caused by a mutation in the protein fibrillin, a key component of collagen and elastin connective tissue, whose dysfunction causes the marfanoid phenotype. Currently, little is known regarding the impact of connective tissue disorders on men’s sexual health. As a first step in exploring this association, we investigated two physical phenomena that may be impacted by collagen disorders, penile fractures and Peyronie’s disease. Objective This study aims to investigate rates of penile fracture and Peyronie’s disease in EDS and Marfan syndrome patients. Methods A multi-center, international, electronic health record network (TriNetX) was queried to identify adult male patients with or without EDS between 1993 and 2023 using ICD-10 codes Q79.6, Q79.60, and Q79.62 specific for EDS and EDS related hypermobility syndrome. TriNetX was separately queried for Marfan syndrome using ICD-10 code Q87.4. Incidence and prevalence of penile fractures and Peyronie’s disease were compared between patients with and without the diseases of interest. Penile fractures were identified using ICD-10 codes S39.840, S39.84A, S39.84S, S39.84D. Peyronie’s disease was defined using ICD-10 code N48.6. Odds ratios were generated with 95% confidence intervals. All statistical analyses were conducted within the TriNetX platform. Results Data was queried from 82 healthcare organizations to yield 11,937 EDS patients, and from 101 healthcare organizations to yield 13,705 Marfan syndrome patients. 109 healthcare organizations contributed data to yield a control group of 63,790,628 men. Men with EDS had a 25 times higher rate of penile fractures (0.09%) compared to men without EDS (OR 25; 95% CI [13.8-47.8]). Men with Marfan’s syndrome had a 23 times higher rate of penile fractures (0.08%) compared to men without Marfan’s syndrome (OR 23; 95% CI [12.4-43.1]). Men with Marfan’s syndrome also had a significantly higher incidence of Peyronie’s disease compared to men without Marfan’s syndrome (OR 1.9; 95% CI [1.2-3]). The difference in rates of Peyronie’s disease was not significantly higher for men with EDS (OR 1.4; 95% CI [0.8 – 2.5]). Conclusions Patients with EDS and Marfan syndrome are at an over 20 times higher risk for penile fracture. These increased risks may be due to EDS’s and Marfan syndrome’s effects on collagen and fibrillin/elastic fibers, respectively, which are significant structural components of the corpora cavernosa. Patients with these syndromes should be aware of this increased risk and further research is needed to establish how to council these patients about taking appropriate precautions. Disclosure No.

Association of LDL cholesterol levels and all cause mortality in a real world analysis using data from an international network

Abstract Background Current cholesterol guidelines have recommended low low-density lipoprotein cholesterol (LDL-C) treatment targets for secondary prevention of cardiovascular disease. However, recent studies have suggested that very low or very high levels of LDL-C may be associated with increased mortality and other adverse outcomes. The association between LDL-C levels and mortality remains to be determined. Methods and Results This cohort study included data from a large multinational electronic health record network (TriNetX) of patients aged 18 years or older with coronary artery disease. LDL-C levels were divided into 4 categories: <30 (cohort 1), 30-50 (cohort 2), 50-70 (cohort 3), and 70-100 (cohort 4). Deaths were ascertained by linkage to death records in the last 10 years before the analysis. Five-year Kaplan-Meier curves and hazard ratios were calculated. We identified 1,136,600 patients, of whom 36,060 had LDL-C <30 mg/dL, 282,500 had LDL-C <50 mg/dL, 255,964 had LDL-C 50-70 mg/dL and 596,136 had LDL-C 70-100 mg/dL. Comparative analyzes were performed on the cohorts (cohort 1 vs. cohort 2, cohort 2 vs. cohort 3, and cohort 3 vs. cohort 4) after propensity score matching. After adjustment, individuals of cohort 2 (n = 36,004), compared with those of cohort 1 (n = 36,004), had higher all-cause mortality (HR 1.086; CI 95% 1.064-1.109, p <0.0001), individuals of cohort 3 (n = 244,908), compared to those of cohort 2 (n = 270,359), had higher risk of all-cause mortality (HR 1.063; 95% CI 1.055-1.1071, p <0.0001), and induviduals of cohort 4 (n = 254,465), compared with those of cohort 3 (n = 254,465), had lower risk of all-cause mortality (HR 0.937; CI 95% 0.929-0.944, p <0.0001). Conclusions Lower LDL-C levels are associated with a reduced risk of all-cause mortality in patients with coronary artery disease.

Machine Learning Approach to Estimating ECOG PS for a Multiple-Myeloma Cohort from Real World Data

Background: ECOG Performance Status Scale (ECOG PS) is a measure of patient's ability to function, and an important clinical attribute used in treatment decisions and trial recruitment. In the real world, therapies might be used for patients with more advanced disease, lower functional status, and a higher dependency on others for daily activities, and this population is often excluded from registry trials.This is particularly important for multiple myeloma (MM) in which patients often present with advanced disease and require new therapies despite their high ECOG scores.Of note, in real-world data sources (RWD), ECOG PS is often not recorded by physicians in either structured or unstructured clinical notes. To better understand patient and physician preferences amongst the new treatments available and to generate insights into distinct clinical subgroups, we developed a machine learning (ML) method to estimate ECOG PS for a multiple myeloma cohort using a curated RWD data set. Methods: A curated RWD data set of 126K patients diagnosed with MM was derived from a federated Electronic Health Record network of 57 Healthcare Organizations in the US. Natural Language Processing (NLP) methods were used to extract ECOG scores from clinical notes for 4,799 patients and used as ground truth for model building. Based on input from clinical experts, we identified 96 features including MM specific features such as disease characteristics including, ISS Staging, CRAB values, etc. as well as features indicative of patient performance such as use of orthotic device, stroke/brain injury, days in inpatient settings, etc. Our initial experiments suggested that non-MM specific variables may contribute significantly to the performance of the model. For our final model building we included 38 non-MM specific features after subtracting features of low variance and collinearity. Dataset was divided into training, testing and validation sets with a split of 60, 20, 20 respectively. We used an XGBoost algorithm and tuned hyper-parameters for the classifier. We tested both multiclass (ECOG PS 0-4) and binary (0-1, 2-4) classifiers. The resulting model was used to estimate ECOG scores longitudinally, first at time of diagnosis and subsequently on a date which meets all of the following: at least one year after diagnosis, had key features available and prior to discontinuation of MM treatments. Succeeding ECOG scores were estimated at least 365 days apart to ensure patients were monitored along their treatment. Results: For multiclass classification, our model achieved ROC-AUC (receiver operating characteristic area under the curve) of 0.91, F1 scores of 0.76, precision and recall scores of 0.87 and 0.70 respectively. For binary classification F1 was 0.86 and precision and recall scores were 0.96 and 0.8 respectively. Body mass index (BMI), age, the number of outpatient visits and total amount of days spent in an inpatient setting were found to be the most important features in predicting ECOG PS, with next-most important being co-morbid diagnoses of a stroke/brain injury and difficulty walking. Using the model, we estimated ECOG scores for an additional 30K patients resulting in a total of 58K longitudinal ECOG scores. Median time between estimation dates was 394 days to match how MM patients were monitored prior to discontinuation of any MM treatments. Conclusions: ECOG PS can have wide variance including inter-operator discordance (19%-56%, Datta SS et al. Ecancermedicalscience, 2019; 13:913) and discordance between physician and patient reported scores (57%, Schnadig ID et al. Cancer. 2008). In this context, our multiclass and binary classifiers achieved positive predictive values 0.87 and 0.96 respectively using non-MM clinical features. Given the real-world variability in ECOG estimation between physicians and between patients and physicians, performance characteristics of our model are appropriate and acceptable in deriving clinical insights using estimated ECOG PS. To further validate our model, work to characterize and compare outcomes in our cohort using NLP and ML derived ECOG scores will be carried out. In addition, since our model uses non-oncology features to estimate ECOG PS, we will continue to test validity in additional liquid and solid tumors.

Cardiovascular health trajectories from age 2–12: a pediatric electronic health record study

PurposeMany children have non-ideal cardiovascular health (CVH), but little is known about the course of CVH in early childhood. We identified CVH trajectories in children and assess the generalizability of these trajectories in an external sample. MethodsWe used data spanning 2010–2018 from children aged 2–12 years within the Chicago Area Patient-Centered Outcomes Research Network—an electronic health record network. Four clinical systems comprised the derivation sample and a fifth the validation sample. Body mass index, blood pressure, cholesterol, and blood glucose were categorized as ideal, intermediate, and poor using clinical measurements, laboratory readings, and International Classification of Diseases diagnosis codes and summed for an overall CVH score. Group-based trajectory modeling was used to create CVH score trajectories which were assessed for classification accuracy in the validation sample. ResultsUsing data from 122,363 children (47% female, 47% non-Hispanic White) three trajectories were identified: 59.5% maintained high levels of clinical CVH, 23.4% had high levels of CVH that declined, and 17.1% had intermediate levels of CVH that further declined with age. A similar classification emerged when the trajectories were fitted in the validation sample. ConclusionsStratification of CVH was present by age 2, implicating the need for early life and preconception prevention strategies.

Percutaneous coronary intervention or medical therapy in patients with severe aortic stenosis and coronary artery disease undergoing transcatheter aortic valve implantation: a real-world analysis using data from an international network.

Patients undergoing transcatheter aortic valve implantation (TAVI) due to severe aortic stenosis have a high prevalence of coronary artery disease (CAD). As many of them have high surgical risk, CAD treatment in this group has typically been carried out with optimal medical treatment or paired with percutaneous coronary intervention (PCI). However, the best approach in this scenario is not well established. We aimed to evaluate 5-year cardiovascular outcomes in patients with aortic stenosis and chronic CAD treated with medical treatment alone compared to PCI coupled with medical therapy before or during TAVI. We used data from a large multinational electronic health record network (TriNetX). Patients aged 18 years or older with severe aortic stenosis and CAD who underwent TAVI in the last 10 years before the analysis were considered eligible. Five-year Kaplan-Meier curves and hazard ratios were calculated. We identified 19 058 patients undergoing isolated TAVI and 2277 patients undergoing TAVI and PCI. Using propensity matching scores, 2277 patients in each group were compared. The 5-year cumulative incidence of MACE was 22.92% in the isolated TAVI group, vs. 25.91% in the PCI-TAVI group. The probability of the composite primary outcome was not significantly different between the isolated TAVI group vs. the PCI-TAVI group [53.1 vs. 47.6%, adjusted hazard ratio (HR) 0.92, 95% confidence interval (CI), 0.80-1.05]. In a real-world study of patients with CAD and severe aortic stenosis, the 5-year probability of death, acute coronary syndrome and ischemic stroke did not differ between patients undergoing isolated TAVI compared to patients undergoing PCI before or during TAVI.

COVID-19 Increases Risk of Psychiatric Disorders

COVID-19 Increases Risk of Psychiatric Disorders

Robust Watermarking Method for Secure Transmission of Medical Images in EHR Systems

Confidentiality of Electronic Health Record (EHR) and privacy are two important security requirements for healthcare systems. Many devices on the EHR network utilize little or no encryption, which makes data in transit vulnerable to exploitative attacks, such as Man-in-the-Middle and other filtration methods. Recently, watermarking algorithms as an efficient response to these requirements is in the underline. In this paper, we present a robust watermarking method conceived as part of an Electronic Health Record platform. In this method a chaotic encryption and blind medical image watermarking technique was incorporated into the system as an authorization mechanism to ensure confidentiality and integrity of electronic health information. We present a hybrid watermarking method based on a combination of discrete wavelet transform (DWT), hessenberg Decomposition (HD), Singular value decomposition (SVD) and an original chaos crypto system based on the Arnold Transform (AT) of Singular Value Decomposition. In order to spread the robustness of our algorithm and provide additional security, an improved SVD-AT embedding and extraction procedure has been used to scramble the EHR data in the preprocessing step of the proposed method. In the process of watermark embedding, an R-level discrete wavelet transform was applied to the host image. The low frequency wavelet coefficients are selected to carry this scrambled-watermark. In extraction process, the stored used plain image is obtained from the trusted authority server to complete the verification process. The receiver should compare the unsigned watermark with the extracted watermark again. The verification can be done before clinical procedures and diagnosis. The proposed watermarking method endures entirety attacks and rightly extracts the hidden watermark without significant degradation in the image quality, thus, when the Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) performance of the proposed algorithm is performed.

Coronary angioplasty or medical treatment in patients with aortic stenosis and coronary artery disease undergoing transcatheter aortic valve implantation: a real world analysis

Abstract Backgroud Patients undergoing transcatheter aortic valve implantation (TAVI) due to severe aortic stenosis have a high prevalence of coronary artery disease (CAD). As many of them have high surgical risk, CAD treatment in this group usually has been carried out with optimal medical treatment alone or associated with percutaneous coronary intervention (PCI). However, the best approach in this scenario is not well established. Purpose We aimed to compare the incidences of major adverse cardiovascular outcomes in patients with aortic stenosis and chronic CAD treated with medical treatment alone or medical treatment plus PCI before or during TAVI, using data from a large multinational electronic health record network (TriNetX). Methods Patients aged ≥18 years with severe aortic stenosis and CAD who underwent TAVI in the last 10 years before the analysis were considered eligible. Patients who underwent PCI before or during TAVI were named the PCI-TAVI group and no coronary intervention patients were named isolated TAVI group. Patients with a past history of acute coronary syndrome, stroke or coronary artery bypass graft before TAVI were excluded from the analysis. The primary outcome was major adverse cardiovascular events (MACE) including the composite of death, unstable angina or myocardial infarction and ischemic stroke. We used propensity score matching to balance the cohorts. The 5-year cumulative incidences for MACE, Kaplan Meier curves and hazard ratios were calculated. Results We identified 13,171 patients undergoing isolated TAVI and 1,884 patients undergoing TAVI and PCI. Using propensity score matching, 1,884 patients in each group were compared. The 5-year incidence of MACE was 23.1% in the isolated TAVI group vs 24.7% in the PCI-TAVI group. The probability of survival without MACE was not significantly different between the isolated TAVI group vs PCI-TAVI group (47.4% vs 50.3%, Log-rank test p=0.718) (Figure 1). The hazard ratio (HR) between the 2 groups was also not significant (HR 1.027, CI 95% 0.89–1.18). Conclusions In a real-world cohort of patients with severe aortic stenosis and chronic coronary artery disease, the 5-year probability of major adverse cardiovascular events was not different between patients undergoing isolated TAVI versus patients undergoing PCI before or during TAVI. Funding Acknowledgement Type of funding sources: None.

MP39-08 IMPACT OF SARS-COV-2 PANDEMIC ON THE DIAGNOSIS AND SURGICAL INTERVENTION OF COMMON UROLOGICAL CONDITIONS: RESULTS FROM A MULTI-INSTITUTIONAL DATABASE FROM THE UNITED STATES

You have accessJournal of UrologyCME1 May 2022MP39-08 IMPACT OF SARS-COV-2 PANDEMIC ON THE DIAGNOSIS AND SURGICAL INTERVENTION OF COMMON UROLOGICAL CONDITIONS: RESULTS FROM A MULTI-INSTITUTIONAL DATABASE FROM THE UNITED STATES Maxwell Towe, Sirpi Nackeeran, Mohammad Hout, Abhishek Bhat, Ranjith Ramasamy, and Hemendra Shah Maxwell ToweMaxwell Towe More articles by this author , Sirpi NackeeranSirpi Nackeeran More articles by this author , Mohammad HoutMohammad Hout More articles by this author , Abhishek BhatAbhishek Bhat More articles by this author , Ranjith RamasamyRanjith Ramasamy More articles by this author , and Hemendra ShahHemendra Shah More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002599.08AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The SARS-CoV-2 (COVID) pandemic threatened access to healthcare, raising concerns that patients were going underdiagnosed and undertreated. The aim of our study was to understand the impact of the COVID pandemic on diagnosis and surgical management of common urological conditions. METHODS: Using a large multi-center electronic health record network (TRINETx) consisting of 46 healthcare organizations, we conducted an epidemiological study investigating the number of patients newly diagnosed with common urological conditions and those undergoing urologic surgeries at yearly intervals from March 1st, 2016 to March 1st, 2021. Relevant international classification of diseases (ICD) codes used to identify urologic conditions are elaborated on in Table 1. Current procedural terminology (CPT) codes used to identify surgeries are detailed in Figure 1. We then determined the percentage of newly diagnosed patients who underwent surgery for each specific year. RESULTS: We saw a decrease in number of all urologic surgeries being performed during the initial year of the pandemic (Figure 1). From March 2020-2021, there was a >20% decrease in surgical case load for benign prostatic hyperplasia procedures (−29.5%), prostate biopsies (−30.1%), incontinence procedures (−33.6%), and vasectomies (−22.8%), compared to the preceding year. Radical cystectomies and orchiectomies saw the lowest decrease, −5.9% and −8.6%, respectively. A similar trend was seen in the number of individuals newly diagnosed with urologic conditions and percentage of patients undergoing surgical intervention. The lowest drops were seen with ureteral stent placements (−5.0%) and prostate biopsies (−3.1%). CONCLUSIONS: The number of people receiving urologic diagnoses and surgical case load for urologic procedures significantly reduced during the first year of the COVID pandemic. Providers should be aware of this healthcare disparity, and greater efforts made to identify these “missed” patients moving forward. Source of Funding: Research was supported by Acerus Pharmaceuticals in the form of an educational grant used to access the TriNetX database. Acerus Pharmaceuticals was not involved in the planning, design, writing or any other aspect of this project.This work was also supported by National Institutes of Health Grant R01 DK130991 and Clinician Scientist Development Grant from the American Cancer Society to Ranjith Ramasamy © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e667 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Maxwell Towe More articles by this author Sirpi Nackeeran More articles by this author Mohammad Hout More articles by this author Abhishek Bhat More articles by this author Ranjith Ramasamy More articles by this author Hemendra Shah More articles by this author Expand All Advertisement PDF DownloadLoading ...

MP38-19 PREVALENCE OF TESTOSTERONE DEFICIENCY AND SECONDARY POLYCYTHEMIA IN MEN WITH OBSTRUCTIVE SLEEP APNEA

You have accessJournal of UrologyCME1 May 2022MP38-19 PREVALENCE OF TESTOSTERONE DEFICIENCY AND SECONDARY POLYCYTHEMIA IN MEN WITH OBSTRUCTIVE SLEEP APNEA Chase Carto, Sirpi Nackeeran, and Ranjith Ramasamy Chase CartoChase Carto More articles by this author , Sirpi NackeeranSirpi Nackeeran More articles by this author , and Ranjith RamasamyRanjith Ramasamy More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002592.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Secondary polycythemia is a well-known complication of obstructive sleep apnea (OSA), a disorder that increases in incidence with age. Similarly, increased recognition of low testosterone and its associated symptoms in aging men has led to increased administration of testosterone replacement therapy (TRT), which has also been associated with secondary polycythemia. We hypothesized that given the common association of aging with OSA and testosterone deficiency, a significant percentage of men with OSA would carry concurrent diagnoses of testosterone deficiency and secondary polycythemia. The objective of this study was to investigate the epidemiology of men with OSA, secondary polycythemia, and testosterone deficiency in men using a large electronic health record network. METHODS: We performed a real-time search for adult men (18+) with a diagnosis of obstructive sleep apnea (ICD-10-CM G47.33) in the TriNetX network, which provides health record data for more than 75 million patients from 48 healthcare organizations. This cohort was used to separately identify men with testosterone deficiency, as defined by those with a diagnosis of testicular dysfunction or hypofunction (ICD-10-CM E29, E29.1), and secondary polycythemia (ICD-10-CM D75.1). Lastly, we queried the network for men with all three diagnoses: OSA, testosterone deficiency, and secondary polycythemia. All men with diagnosis of polycythemia vera or familial erythrocytosis were removed from the analysis to account for men with concurrent primary polycythemia. RESULTS: A total of 1,166,640 men with a diagnosis of OSA were identified in the network. Mean age of men with OSA was 61 [46 – 76]. Among men with OSA, 6.0% of men had testosterone deficiency (TT <300ng/dL), and 1.3% of men had associated polycythemia (Hct >52%). Interestingly, only 3,562 (0.3%) of men in the network had all three diagnoses of obstructive sleep apnea, testosterone deficiency, and secondary polycythemia. Strengths of this study include use of a large and heterogenous study population and adjustment for other causes of polycythemia. Limitations include lack of data on chronology of diagnoses, inconsistent data recording, and missing data. CONCLUSIONS: In a large, cross-sectional, multicenter electronic health record study, we found that 6.0% of men with OSA will have testosterone deficiency. Reassuringly, only 0.3% of men carry all diagnoses of obstructive sleep apnea, testosterone deficiency, and secondary polycythemia. Nevertheless, men with OSA who receive TRT warrant close follow-up for potentially dangerous increases in hematocrit when considering treatment for testosterone deficiency. Source of Funding: This work was supported by National Institutes of Health Grant R01 DK130991 and the Clinician Scientist Development Grant from the American Cancer Society to Ranjith Ramasamy © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e625 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Chase Carto More articles by this author Sirpi Nackeeran More articles by this author Ranjith Ramasamy More articles by this author Expand All Advertisement PDF DownloadLoading ...

PENILE PROSTHESIS IS PROTECTIVE AGAINST MAJOR DEPRESSIVE DISORDER IN MEN WITH ERECTILE DYSFUNCTION: A MULTICENTER RESEARCH NETWORK STUDY

There is a known link between erectile dysfunction (ED) and major depressive disorder (MDD). Previous studies have shown that pharmacologic management of ED is effective in reducing depressive symptoms. What is not known is whether placement of penile prosthesis may improve depressive symptoms in men with ED. We investigated whether penile prosthesis is protective against MDD in men with ED by querying a large, multicenter, electronic health record network (TriNetX), which contains data on roughly 60 hospitals and 66 million patients in the United States.

Incidence of neurodegenerative and cerebrovascular diseases associated with antihypertensive drug classes.

Antihypertensive drugs (AHTs) are associated with lowered risks of neurodegenerative diseases and stroke. However, the relative risks associated with different AHT classes are unclear. Using an electronic health record network with 34 million eligible patients, we compared rates of these disorders over a 2-year period, in propensity score-matched cohorts of people taking calcium channel blockers (CCBs) compared with those taking other AHT classes. CCBs were associated with a higher incidence of all disorders compared with renin-angiotensin system agents, and a higher incidence of dementia and cerebrovascular disease compared with diuretics. CCBs were associated with a lower incidence of movement disorders and cerebrovascular disease compared with beta-blockers. The data show that AHT classes confer differential risks of neurodegenerative and cerebrovascular diagnoses.

Temporal trends in severe COVID-19 outcomes in patients with rheumatic disease: a cohort study.

BackgroundAs the COVID-19 pandemic continues worldwide, severe COVID-19 outcomes remain a major concern for patients with rheumatic and musculoskeletal diseases. We aimed to investigate temporal trends in COVID-19 outcomes in patients with rheumatic and musculoskeletal diseases over the course of the pandemic.MethodsUsing a large, multicentre, electronic health record network (TriNetX), we did a comparative cohort study of patients with rheumatic and musculoskeletal diseases who were diagnosed with COVID-19 (by International Classification of Diseases, Tenth Revision code or positive PCR test) during the first 90 days of the pandemic (early cohort) compared with the second 90 days of the pandemic (late cohort), matched (1:1) for demographics, comorbidities, laboratory results, glucocorticoid use, and previous hospitalisations using an exposure score method. Outcomes were assessed within 30 days of COVID-19 diagnosis, including hospitalisation, intensive care unit admission, invasive mechanical ventilation, renal failure, and death. We did a subgroup analysis among patients with rheumatic and musculoskeletal diseases who were hospitalised with COVID-19.FindingsWe identified 8540 patients with rheumatic and musculoskeletal diseases who were diagnosed with COVID-19 during the 6-month study period, including 2811 in the early cohort and 5729 in the late cohort. In the exposure score matched analysis, the risk of hospitalisation was lower in the late cohort than in the early cohort (874 [32·4%] of 2701 patients vs 1227 [45·4%] of 2701 patients; relative risk [RR] 0·71, 95% CI 0·67–0·76). The risks of intensive care unit admission (214 [7·9%] vs 385 [14·3%]; RR 0·56, 95% CI 0·47–0·65), mechanical ventilation (96 [3·6%] vs 247 [9·1%]; 0·39, 0·31–0·49), acute kidney injury (372 [13·8%] vs 560 [20·7%]; 0·66, 0·59–0·75), renal replacement therapy (17 [0·6%] vs 32 [1·2%]; 0·53, 0·30–0·96), and death (122 [4·5%] vs 252 [9·3%]; 0·48, 0·39–0·60) were lower in the late cohort compared with the early cohort. Among the hospitalised subgroup, the risk of the composite outcome of intensive care unit admission, mechanical ventilation, and death was lower in the late cohort than in the early cohort (334 [30·7%] of 1089 patients vs 450 [41·3%] of 1089 patients; RR 0·74, 95% CI 0·67–0·83).InterpretationThe risks of severe COVID-19 outcomes have improved over time in patients with rheumatic and musculoskeletal disease but remain substantial. These findings might reflect ascertainment of milder cases in the later cohort and improvements in treatment and supportive care.FundingNone.

Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62 354 COVID-19 cases in the USA

BackgroundAdverse mental health consequences of COVID-19, including anxiety and depression, have been widely predicted but not yet accurately measured. There are a range of physical health risk factors for COVID-19, but it is not known if there are also psychiatric risk factors. In this electronic health record network cohort study using data from 69 million individuals, 62 354 of whom had a diagnosis of COVID-19, we assessed whether a diagnosis of COVID-19 (compared with other health events) was associated with increased rates of subsequent psychiatric diagnoses, and whether patients with a history of psychiatric illness are at a higher risk of being diagnosed with COVID-19.MethodsWe used the TriNetX Analytics Network, a global federated network that captures anonymised data from electronic health records in 54 health-care organisations in the USA, totalling 69·8 million patients. TriNetX included 62 354 patients diagnosed with COVID-19 between Jan 20, and Aug 1, 2020. We created cohorts of patients who had been diagnosed with COVID-19 or a range of other health events. We used propensity score matching to control for confounding by risk factors for COVID-19 and for severity of illness. We measured the incidence of and hazard ratios (HRs) for psychiatric disorders, dementia, and insomnia, during the first 14 to 90 days after a diagnosis of COVID-19.FindingsIn patients with no previous psychiatric history, a diagnosis of COVID-19 was associated with increased incidence of a first psychiatric diagnosis in the following 14 to 90 days compared with six other health events (HR 2·1, 95% CI 1·8–2·5 vs influenza; 1·7, 1·5–1·9 vs other respiratory tract infections; 1·6, 1·4–1·9 vs skin infection; 1·6, 1·3–1·9 vs cholelithiasis; 2·2, 1·9–2·6 vs urolithiasis, and 2·1, 1·9–2·5 vs fracture of a large bone; all p<0·0001). The HR was greatest for anxiety disorders, insomnia, and dementia. We observed similar findings, although with smaller HRs, when relapses and new diagnoses were measured. The incidence of any psychiatric diagnosis in the 14 to 90 days after COVID-19 diagnosis was 18·1% (95% CI 17·6–18·6), including 5·8% (5·2–6·4) that were a first diagnosis. The incidence of a first diagnosis of dementia in the 14 to 90 days after COVID-19 diagnosis was 1·6% (95% CI 1·2–2·1) in people older than 65 years. A psychiatric diagnosis in the previous year was associated with a higher incidence of COVID-19 diagnosis (relative risk 1·65, 95% CI 1·59–1·71; p<0·0001). This risk was independent of known physical health risk factors for COVID-19, but we cannot exclude possible residual confounding by socioeconomic factors.InterpretationSurvivors of COVID-19 appear to be at increased risk of psychiatric sequelae, and a psychiatric diagnosis might be an independent risk factor for COVID-19. Although preliminary, our findings have implications for clinical services, and prospective cohort studies are warranted.FundingNational Institute for Health Research.

Modeling Care Team Structures in the Neonatal Intensive Care Unit through Network Analysis of EHR Audit Logs.

In the neonatal intensive care unit (NICU), predefined acuity-based team care models are restricted to core roles and neglect interactions with providers outside of the team, such as interactions that transpire via electronic health record (EHR) systems. These unaccounted interactions may be related to the efficiency of resource allocation, information flow, communication, and thus impact patient outcomes. This study applied network analysis methods to EHR audit logs to model the interactions of providers beyond their core roles to better understand the interaction network patterns of acuity-based teams and relationships of the network structures with postsurgical length of stay (PSLOS). The study used the EHR log data of surgical neonates from a large academic medical center. The study included 104 surgical neonates, for whom 9,206 unique actions were performed by 457 providers in their EHRs. We applied network analysis methods to model EHR provider interaction networks of acuity-based teams in NICU postoperative care. We partitioned each EHR network into three subnetworks based on interaction types: (1) interactions between known core providers who were documented in scheduling records (core subnetwork); (2) interactions between core and noncore providers (extended subnetwork); and (3) interactions between noncore providers (extended subnetwork). For each core subnetwork, we assessed its capability to replicate predefined core-provider relations as documented in scheduling records. We further compared each EHR network, as well as its subnetworks, using standard network measures to determine its differences in network topologies. We conducted a case study to learn provider interaction networks taking care of 15 neonates who underwent gastrostomy tube placement surgery from EHR log data and measure the effectiveness of the interaction networks on PSLOS by the proportional-odds model. The provider networks of four acuity-based teams (two high and two low acuity), along with their subnetworks, were discovered. We found that beyond capturing the predefined core-provider relations, EHR audit logs can also learn a large number of relations between core and noncore providers or among noncore providers. Providers in the core subnetwork exhibited a greater number of connections with each other than with providers in the extended subnetworks. Many more providers in the core subnetwork serve as a hub than those in the other types of subnetworks. We also found that high-acuity teams exhibited more complex network structures than low-acuity teams, with high-acuity team generating 6,416 interactions between 407 providers compared with 931 interactions between 124 providers, respectively. In addition, we discovered that high-acuity and low-acuity teams shared more than 33 and 25% of providers with each other, respectively, but exhibited different collaborative structures demonstrating that NICU providers shift across different acuity teams and exhibit different network characteristics. Results of case study show that providers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those who treated higher PSLOS patients (p = 0.03). Network analysis can be applied to EHR log data to model acuity-based NICU teams capturing interactions between providers within the predesigned core team as well as those outside of the core team. In the NICU, dissemination of information may be linked to reduced PSLOS. EHR log data provide an efficient, accessible, and research-friendly way to study provider interaction networks. Findings should guide improvements in the EHR system design to facilitate effective interactions between providers.

Anesthesia Information Management Systems: Evolution of the Paper Anesthetic Record to a Multisystem Electronic Medical Record Network That Streamlines Perioperative Care

Initially devised in the 1890s, the traditional anesthetic record comprises physiological changes, crucial anesthetic or surgical events, and medications administered during the perioperative period. The timely collection of quality data facilitates situational awareness and point-of-care clinical decision making. The burgeoning volume and complexity of data in conjunction with financial incentives and the push for improved clinical documentation by regulatory bodies have prompted the transition away from paper records. Anesthesia Information Management Systems (AIMS) are specialized electronic health record networks that allow the anesthesia record to interface with hospital clinical data repositories, resulting in improvements in quality of care, patient safety, operations management, reimbursement, and translational research. Like most new technological advances, adoption was slow at first due to the challenges of integrating complex systems into daily clinical practice, questions about return on investment, and medicolegal liability. Recent technological advances, coupled with government incentives, have allowed AIMS adoption to reach an acceleration phase among US academic medical centers; widespread utilization of AIMS by 84% of US academic medical centers is expected by 2018-2020. Adoption among nonacademic US and European medical centers still remains low; information concerning Asian countries is limited to literature describing only single–hospital center experiences.

Use of a National Electronic Health Record Network to Describe Characteristics and Healing Patterns of Sickle Cell Ulcers.

Objective: Sickle cell ulcers affect as many as 15% of patients with sickle cell disease in the United States and severely impact quality of life. An understanding of baseline healing patterns is important to inform study design for future trials that test therapies for this disease. Approach: In this study, an electronic wound management system was leveraged to analyze retrospective data on 133 unique sickle cell patients who were treated across 114 wound healing centers, and to describe their characteristics and healing patterns as compared with those of venous ulcer patients. The data included 198 care episodes for 427 wounds. Results: Patients with sickle cell ulcers were younger and had fewer comorbid diseases than those with venous ulcers. Larger size and longer duration were predictors of poor healing. Between the first and fourth assessments, mean change in area for sickle cell ulcers showed a 58% increase, compared with a 13% decrease for venous ulcers. Kaplan-Meier curves showed poorer healing in sickle cell ulcers than in venous ulcers across all categories of size and duration. Patients with sickle cell ulcers had longer care episodes and were more likely to re-present for care. Innovation: This study reports on the largest data set of sickle cell ulcer patients analyzed to date in the published literature to provide a more detailed understanding of wound healing patterns of this disease. Conclusion: A national network of electronic health records can effectively identify a large number of patients with sickle cell ulcers to support analysis of epidemiology, healing patterns, and health care utilization.

A new era of quality measurement in rheumatology: electronic clinical quality measures and national registries.

This article reviews the evolution of quality measurement in rheumatology, highlighting new health-information technology infrastructure and standards that are enabling unprecedented innovation in this field. Spurred by landmark legislation that ties physician payment to value, the widespread use of electronic health records, and standards such as the Quality Data Model, quality measurement in rheumatology is rapidly evolving. Rather than relying on retrospective assessments of care gathered through administrative claims or manual chart abstraction, new electronic clinical quality measures (eCQMs) allow automated data capture from electronic health records. At the same time, qualified clinical data registries, like the American College of Rheumatology's Rheumatology Informatics System for Effectiveness registry, are enabling large-scale implementation of eCQMs across national electronic health record networks with real-time performance feedback to clinicians. Although successful examples of eCQM development and implementation in rheumatology and other fields exist, there also remain challenges, such as lack of health system data interoperability and problems with measure accuracy. Quality measurement and improvement is increasingly an essential component of rheumatology practice. Advances in health information technology are likely to continue to make implementation of eCQMs easier and measurement more clinically meaningful and accurate in coming years.

National Electronic Health Record Network Regulation and Synchronization of National and State Privacy Laws Needed to Increase Efficiency and Reduce Costs in Healthcare

In 2005, a Research and Development Corporation study found that if electronic health records (EHRs) were implemented nationally, medical expenditures could be reduced by over 160 billion dollars.1...