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Objective: Adopting features of the Chronic Care Model may reduce coronary heart disease risk and blood pressure in vulnerable populations. We evaluated a peer and practice team intervention on reduction in 4-year coronary heart disease risk and systolic blood pressure.Design and Subjects: A single blind, randomized, controlled trial in two adjacent urban university-affiliated primary care practices. Two hundred eighty African-American subjects aged 40 to 75 with uncontrolled hypertension.Intervention: Three monthly calls from trained peer patients with well-controlled hypertension and, on alternate months, two practice staff visits to review a personalized 4-year heart disease risk calculator and slide shows about heart disease risks. All subjects received usual physician care and brochures about healthy cooking and heart disease.Main Measures: Change in 4-year coronary heart disease risk (primary) and change in systolic blood pressure, both assessed at 6 months.Key Results: At baseline, the 136 intervention and 144 control subjects' mean 4-year coronary heart disease risk did not differ (intervention=5.8 % and control=6.4 %, P=0.39), and their mean systolic blood pressure was the same (140.5 mmHg, p=0.83). Endpoint data for coronary heart disease were obtained for 69 % of intervention and 82 % of control subjects. After multiple imputation for missing endpoint data, the reduction in risk among all 280 subjects favored the intervention, but was not statistically significant (difference -0.73 %, 95 % confidence interval: -1.54 % to 0.09 %, p=0.08). Among the 247 subjects with a systolic blood pressure endpoint (85 % of intervention and 91 % of control subjects), more intervention than control subjects achieved a >5 mmHg reduction (61 % versus 45 %, respectively, p=0.01). After multiple imputation, the absolute reduction in systolic blood pressure was also greater for the intervention group (difference -6.47 mmHg, 95 % confidence interval: -10.69 to -2.25, P=0.003). One patient died in each study arm.Conclusions: Peer patient and office-based behavioral support for African-American patients with uncontrolled hypertension did not result in a significantly greater reduction in coronary heart disease risk but did significantly reduce systolic blood pressure. [ABSTRACT FROM AUTHOR]

Resistance to extended-spectrum cephalosporins complicates treatment of Pseudomonas aeruginosa infections. To elucidate risk factors for cefepime-resistant P. aeruginosa and determine its association with patient death, we conducted a case-control study in Philadelphia, Pennsylvania. Among 2,529 patients hospitalized during 2001-2006, a total of 213 (8.4%) had cefepime-resistant P. aeruginosa infection. Independent risk factors were prior use of an extended-spectrum cephalosphorin (p<0.001), prior use of an extended-spectrum penicillin (p = 0.005), prior use of a quinolone (p<0.001), and transfer from an outside facility (p = 0.01). Among those hospitalized at least 30 days, mortality rates were higher for those with cefepime-resistant than with cefepime-susceptible P. aeruginosa infection (20.2% vs. 13.2%, p = 0.007). Cefepime-resistant P. aeruginosa was an independent risk factor for death only for patients for whom it could be isolated from blood (p = 0.001). Strategies to counter its emergence should focus on optimizing use of antipseudomonal drugs. [ABSTRACT FROM AUTHOR]

Background: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging., Objectives: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients., Materials and Methods: In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process., Results: Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided., Discussion: AI-enabled Clinical Decision Support (AI-CDS) systems promise to revolutionize healthcare decision-making, necessitating a comprehensive framework for their development, implementation, and regulation that emphasizes trustworthiness, transparency, and safety. This framework encompasses various aspects including model training, explainability, validation, certification, monitoring, and continuous evaluation, while also addressing challenges such as data privacy, fairness, and the need for regulatory oversight to ensure responsible integration of AI into clinical workflow., Conclusions: Achieving responsible AI-CDS systems requires a collective effort from many healthcare stakeholders. This involves implementing robust safety, monitoring, and transparency measures while fostering innovation. Future steps include testing and piloting proposed trust mechanisms, such as safety reporting protocols, and establishing best practice guidelines., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: Studies show metformin use before and during SARS-CoV-2 infection reduces severe COVID-19 and postacute sequelae of SARS-CoV-2 (PASC) in adults. Our objective was to describe the incidence of PASC and possible associations with prevalent metformin use in adults with type 2 diabetes mellitus (T2DM)., Research Design and Methods: This is a retrospective cohort analysis using the National COVID Cohort Collaborative (N3C) and Patient-Centered Clinical Research Network (PCORnet) electronic health record (EHR) databases with an active comparator design that examined metformin-exposed individuals versus nonmetformin-exposed individuals who were taking other diabetes medications. T2DM was defined by HbA1c ≥6.5 or T2DM EHR diagnosis code. The outcome was death or PASC within 6 months, defined by EHR code or computable phenotype., Results: In the N3C, the hazard ratio (HR) for death or PASC with a U09.9 diagnosis code (PASC-U09.0) was 0.79 (95% CI 0.71-0.88; P < 0.001), and for death or N3C computable phenotype PASC (PASC-N3C) was 0.85 (95% CI 0.78-0.92; P < 0.001). In PCORnet, the HR for death or PASC-U09.9 was 0.87 (95% CI 0.66-1.14; P = 0.08), and for death or PCORnet computable phenotype PASC (PASC-PCORnet) was 1.04 (95% CI 0.97-1.11; P = 0.58). Incident PASC by diagnosis code was 1.6% metformin vs. 2.0% comparator in the N3C, and 2.1% metformin vs. 2.5% comparator in PCORnet. By computable phenotype, incidence was 4.8% metformin and 5.2% comparator in the N3C and 24.7% metformin vs. 26.1% comparator in PCORnet., Conclusions: Prevalent metformin use is associated with a slightly lower incidence of death or PASC after SARS-CoV-2 infection. PASC incidence by computable phenotype is higher than by EHR code, especially in PCORnet. These data are consistent with other observational analyses showing prevalent metformin is associated with favorable outcomes after SARS-CoV-2 infection in adults with T2DM., (© 2024 by the American Diabetes Association.)

We characterized comorbidity profiles and cardiometabolic risk factors among older adults with multiple chronic conditions (MCCs) in New York City using an intersectionality approach. Electronic health record data were obtained from the INSIGHT Clinical Research Network on 367,901 New York City residents aged 50 years or older with MCCs. Comorbidity profiles were heterogeneous. The most common profile across sex and racial and ethnic groups was co-occurring hypertension and hyperlipidemia; prevalence of these 2 conditions differed across groups (4.7%-7.3% co-occurrence alone, 65.1%-88.0% with other conditions). Significant sex and racial and ethnic differences were observed, which may reflect accumulated disparities in risk factors and health care access across the life course.

Background: SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection. These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems. Limited studies have investigated the predictability of Long COVID development and its associated risk factors., Methods: In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (~1.4 million patients from New York) and OneFlorida+ (~0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development. Both SARS-CoV-2-infected and non-infected adults were analysed during the period of March 2020 to November 2021. Factors associated with Long COVID risk were identified by removing background associations and correcting for multiple tests., Results: We observed complex association patterns between baseline factors and a variety of Long COVID conditions, and we highlight that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities (e.g., cancer and cirrhosis) are likely associated with increased risk of developing Long COVID. Several Long COVID conditions, e.g., dementia, malnutrition, chronic obstructive pulmonary disease, heart failure, PASC diagnosis U099, and acute kidney failure are well predicted (C-index > 0.8). Moderately predictable conditions include atelectasis, pulmonary embolism, diabetes, pulmonary fibrosis, and thromboembolic disease (C-index 0.7-0.8). Less predictable conditions include fatigue, anxiety, sleep disorders, and depression (C-index around 0.6)., Conclusions: This observational study suggests that association patterns between investigated factors and Long COVID are complex, and the predictability of different Long COVID conditions varies. However, machine learning-based predictive models can help in identifying patients who are at risk of developing a variety of Long COVID conditions., (© 2024. The Author(s).)

Background: Data modernization efforts to strengthen surveillance capacity could help assess trends in use of preventive services and diagnoses of new chronic disease during the COVID-19 pandemic, which broadly disrupted health care access., Methods: This cross-sectional study examined electronic health record data from US adults aged 21 to 79 years in a large national research network (PCORnet), to describe use of 8 preventive health services (N = 30,783,825 patients) and new diagnoses of 9 chronic diseases (N = 31,588,222 patients) during 2018 through 2022. Joinpoint regression assessed significant trends, and health debt was calculated comparing 2020 through 2022 volume to prepandemic (2018 and 2019) levels., Results: From 2018 to 2022, use of some preventive services increased (hemoglobin A 1c and lung computed tomography, both P < .05), others remained consistent (lipid testing, wellness visits, mammograms, Papanicolaou tests or human papillomavirus tests, stool-based screening), and colonoscopies or sigmoidoscopies declined (P < .01). Annual new chronic disease diagnoses were mostly stable (6% hypertension; 4% to 5% cholesterol; 4% diabetes; 1% colonic adenoma; 0.1% colorectal cancer; among women, 0.5% breast cancer), although some declined (lung cancer, cervical intraepithelial neoplasia or carcinoma in situ, cervical cancer, all P < .05). The pandemic resulted in health debt, because use of most preventive services and new diagnoses of chronic disease were less than expected during 2020; these partially rebounded in subsequent years. Colorectal screening and colonic adenoma detection by age group aligned with screening recommendation age changes during this period., Conclusion: Among over 30 million patients receiving care during 2018 through 2022, use of preventive services and new diagnoses of chronic disease declined in 2020 and then rebounded, with some remaining health debt. These data highlight opportunities to augment traditional surveillance with EHR-based data.

Importance: The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant., Objective: To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021., Design: Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021., Setting: Healthcare facilities in New York and Florida., Participants: Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period., Exposure: Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time., Main Outcome(s) and Measure(s): Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons without a COVID-19 test or diagnosis during the 31-180 days after the last negative test., Results: We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those without a COVID-19 test or diagnosis (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons)., Conclusions and Relevance: We documented a substantial relative risk of pulmonary embolism and a large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Varma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Little is known about post-acute sequelae of SARS-CoV-2 infection (PASC) after acquiring SARS-CoV-2 infection during pregnancy. We aimed to evaluate the association between acquiring SARS-CoV-2 during pregnancy compared with acquiring SARS-CoV-2 outside of pregnancy and the development of PASC., Methods: This retrospective cohort study from the Researching COVID to Enhance Recovery (RECOVER) Initiative Patient-Centred Clinical Research Network (PCORnet) used electronic health record (EHR) data from 19 U.S. health systems. Females aged 18-49 years with lab-confirmed SARS-CoV-2 infection from March 2020 through June 2022 were included. Validated algorithms were used to identify pregnancies with a delivery at >20 weeks' gestation. The primary outcome was PASC, as previously defined by computable phenotype in the adult non-pregnant PCORnet EHR dataset, identified 30-180 days post-SARS-CoV-2 infection. Secondary outcomes were the 24 component diagnoses contributing to the PASC phenotype definition. Univariable comparisons were made for baseline characteristics between individuals with SARS-CoV-2 infection acquired during pregnancy compared with outside of pregnancy. Using inverse probability of treatment weighting to adjust for baseline differences, the association between SARS-CoV-2 infection acquired during pregnancy and the selected outcomes was modelled. The incident risk is reported as the adjusted hazard ratio (aHR) with 95% confidence intervals., Findings: In total, 83,915 females with SARS-CoV-2 infection acquired outside of pregnancy and 5397 females with SARS-CoV-2 infection acquired during pregnancy were included in analysis. Non-pregnant females with SARS-CoV-2 infection were more likely to be older and have comorbid health conditions. SARS-CoV-2 infection acquired in pregnancy as compared with acquired outside of pregnancy was associated with a lower incidence of PASC (25.5% vs 33.9%; aHR 0.85, 95% CI 0.80-0.91). SARS-CoV-2 infection acquired in pregnant females was associated with increased risk for some PASC component diagnoses including abnormal heartbeat (aHR 1.67, 95% CI 1.43-1.94), abdominal pain (aHR 1.34, 95% CI 1.16-1.55), and thromboembolism (aHR 1.88, 95% CI 1.17-3.04), but decreased risk for other diagnoses including malaise (aHR 0.35, 95% CI 0.27-0.47), pharyngitis (aHR 0.36, 95% CI 0.26-0.48) and cognitive problems (aHR 0.39, 95% CI 0.27-0.56)., Interpretation: SARS-CoV-2 infection acquired during pregnancy was associated with lower risk of development of PASC at 30-180 days after incident SARS-CoV-2 infection in this nationally representative sample. These findings may be used to counsel pregnant and pregnant capable individuals, and direct future prospective study., Funding: National Institutes of Health (NIH) Other Transaction Agreement (OTA) OT2HL16184., Competing Interests: Dr. Metz is the site Principal Investigator (PI) for a Pfizer RSV vaccination study that is now complete and a Pfizer pharmacokinetic study of Paxlovid for mild to moderate COVID-19 in pregnancy. She also served on the Medical Advisory Board and is a site PI for a Pfizer SARS-CoV-2 vaccination study in pregnancy. Megan Fitzgerald was compensated for work evaluating COVID re-infections funded through the Patient Lead Research Collaborative. This content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Programme, the National Institutes of Health (NIH), or other funders. This study is part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, which seeks to understand, treat, and prevent the post-acute sequelae of SARS-CoV-2 infection (PASC). For more information on RECOVER, visit https://recovercovid.org/., (© 2024 The Author(s).)

Background: An increasing number of studies have described new and persistent symptoms and conditions as potential post-acute sequelae of SARS-CoV-2 infection (PASC). However, it remains unclear whether certain symptoms or conditions occur more frequently among persons with SARS-CoV-2 infection compared with those never infected with SARS-CoV-2. We compared the occurrence of specific COVID-associated symptoms and conditions as potential PASC 31- to 150-day following a SARS-CoV-2 test among adults and children with positive and negative test results., Methods: We conducted a retrospective cohort study using electronic health record (EHR) data from 43 PCORnet sites participating in a national COVID-19 surveillance program. This study included 3,091,580 adults (316,249 SARS-CoV-2 positive; 2,775,331 negative) and 675,643 children (62,131 positive; 613,512 negative) who had a SARS-CoV-2 laboratory test during March 1, 2020-May 31, 2021 documented in their EHR. We used logistic regression to calculate the odds of having a symptom and Cox models to calculate the risk of having a newly diagnosed condition associated with a SARS-CoV-2 positive test., Results: After adjustment for baseline covariates, hospitalized adults and children with a positive test had increased odds of being diagnosed with ≥ 1 symptom (adults: adjusted odds ratio[aOR], 1.17[95% CI, 1.11-1.23]; children: aOR, 1.18[95% CI, 1.08-1.28]) or shortness of breath (adults: aOR, 1.50[95% CI, 1.38-1.63]; children: aOR, 1.40[95% CI, 1.15-1.70]) 31-150 days following a SARS-CoV-2 test compared with hospitalized individuals with a negative test. Hospitalized adults with a positive test also had increased odds of being diagnosed with ≥ 3 symptoms or fatigue compared with those testing negative. The risks of being newly diagnosed with type 1 or type 2 diabetes (adjusted hazard ratio[aHR], 1.25[95% CI, 1.17-1.33]), hematologic disorders (aHR, 1.19[95% CI, 1.11-1.28]), or respiratory disease (aHR, 1.44[95% CI, 1.30-1.60]) were higher among hospitalized adults with a positive test compared with those with a negative test. Non-hospitalized adults with a positive test also had higher odds or increased risk of being diagnosed with certain symptoms or conditions., Conclusions: Patients with SARS-CoV-2 infection, especially those who were hospitalized, were at higher risk of being diagnosed with certain symptoms and conditions after acute infection., (© 2024. The Author(s).)

The evaluation of completeness of real-world data is a particularly challenging component of data quality assessment because the degree of truly versus erroneously absent data is unknown. Among inpatient data sets, while absolute counts of admissions having specific categories of diagnoses in the principal or any position may vary depending on hospital size, we hypothesized that the ratio of these parameters will be preserved across sites, with outliers suggesting the potential for erroneously absent data. For several categories of clinical conditions assigned to inpatient admissions, we analyzed the ratio of their recording as the principal diagnosis versus any diagnosis across several hospitals and compared the ratios against a national benchmark. Our analysis showed ratios that matched clinical expectations, with reasonable preservation of ratios across sites. However, some conditions exhibited more variability in the ratios and some sites had many outliers possibly reflecting data quality issues that warrant further attention., (©2023 AMIA - All rights reserved.)

Objective: Generation of automated clinical notes has been posited as a strategy to mitigate physician burnout. In particular, an automated narrative summary of a patient's hospital stay could supplement the hospital course section of the discharge summary that inpatient physicians document in electronic health record (EHR) systems. In the current study, we developed and evaluated an automated method for summarizing the hospital course section using encoder-decoder sequence-to-sequence transformer models., Materials and Methods: We fine-tuned BERT and BART models and optimized for factuality through constraining beam search, which we trained and tested using EHR data from patients admitted to the neurology unit of an academic medical center., Results: The approach demonstrated good ROUGE scores with an R-2 of 13.76. In a blind evaluation, 2 board-certified physicians rated 62% of the automated summaries as meeting the standard of care, which suggests the method may be useful clinically., Discussion and Conclusion: To our knowledge, this study is among the first to demonstrate an automated method for generating a discharge summary hospital course that approaches a quality level of what a physician would write., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background Hypertension and diabetes are associated with increased COVID-19 severity. The association between level of control of these conditions and COVID-19 severity is less well understood. Methods and Results This retrospective cohort study identified adults with COVID-19, March 2020 to February 2022, in 43 US health systems in the National Patient-Centered Clinical Research Network. Hypertension control was categorized as blood pressure (BP) <130/80, 130 to 139/80 to 89, 140 to 159/90 to 99, or ≥160/100 mm Hg, and diabetes control as glycated hemoglobin <7%, 7% to <9%, ≥9%. Adjusted, pooled logistic regression assessed associations between hypertension and diabetes control and severe COVID-19 outcomes. Among 1 494 837 adults with COVID-19, 43% had hypertension and 12% had diabetes. Among patients with hypertension, the highest baseline BP was associated with greater odds of hospitalization (adjusted odds ratio [aOR], 1.30 [95% CI, 1.23-1.37] for BP ≥160/100 versus BP <130/80), critical care (aOR, 1.30 [95% CI, 1.21-1.40]), and mechanical ventilation (aOR, 1.32 [95% CI, 1.17-1.50]) but not mortality (aOR, 1.08 [95% CI, 0.98-1.12]). Among patients with diabetes, the highest glycated hemoglobin was associated with greater odds of hospitalization (aOR, 1.61 [95% CI, 1.47-1.76] for glycated hemoglobin ≥9% versus <7%), critical care (aOR, 1.42 [95% CI, 1.31-1.54]), mechanical ventilation (aOR, 1.12 [95% CI, 1.02-1.23]), and mortality (aOR, 1.18 [95% CI, 1.09-1.27]). Black and Hispanic adults were more likely than White adults to experience severe COVID-19 outcomes, independent of comorbidity score and control of hypertension or diabetes. Conclusions Among 1.5 million patients with COVID-19, higher BP and glycated hemoglobin were associated with more severe COVID-19 outcomes. Findings suggest that adults with poorest control of hypertension or diabetes might benefit from efforts to prevent and initiate early treatment of COVID-19.

Background: Inequities in health care access leads to suboptimal medication adherence and blood pressure (BP) control. Informatics-based approaches may deliver equitable care and enhance self-management. Patient-reported outcomes (PROs) complement clinical measures to assess the impact of illness on patients' well-being in poststroke care., Objectives: The aim of this study was to determine the feasibility of incorporating PROs into Telehealth After Stroke Care (TASC) and to explore the effect of this team-based remote BP monitoring program on psychological distress and quality of life in an underserved urban setting., Methods: Patients discharged home from a Comprehensive Stroke Center were randomized to TASC or usual care for 3 months. They were provided with a BP monitor and a tablet that wirelessly transmitted data to a cloud-based platform, which were integrated with the electronic health record. Participants who did not complete the tablet surveys were contacted via telephone or e-mail. We collected the Patient-Reported Outcomes Measurement Information System Managing Medications and Treatment (PROMIS-MMT), Patient Activation Measure (PAM), Neuro-QOL (Quality of Life in Neurological Disorders) Cognitive Function, Neuro-QOL Depression, and Patient Health Questionnaire-9 (PHQ-9). T -tests and linear regression were used to evaluate the differences in PRO change between the arms., Results: Of the 50 participants, two-thirds were Hispanic or non-Hispanic Black individuals. Mechanisms of PRO submission for the arms included tablet (62 vs. 47%), phone (24 vs. 37%), tablet with phone coaching (10 vs. 16%), and e-mail (4 vs. 0%). PHQ-9 depressive scores were nominally lower in TASC at 3 months compared with usual care (2.7 ± 3.6 vs. 4.0 ± 4.1; p  = 0.06). No significant differences were observed in PROMIS-MMT, PAM, or Neuro-QoL measures., Conclusion: Findings suggest the feasibility of collecting PROs through an interactive web-based platform. The team-based remote BP monitoring demonstrated a favorable impact on patients' well-being. Patients equipped with appropriate resources can engage in poststroke self-care to mitigate inequities in health outcomes., Competing Interests: None declared., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Study Objectives: Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC)., Methods: We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities., Results: Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis., Conclusions: Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae., (© The Author(s) 2023. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling approach, however, is unclear. In 2,836,569 encounters of 36,641 diabetes patients, deep learning (DL) long short-term memory (LSTM) models predicting unplanned, all-cause, 30-day readmission were developed and compared to several traditional models. Models used EHR data defined by a Common Data Model. The LSTM model Area Under the Receiver Operating Characteristic Curve (AUROC) was significantly greater than that of the next best traditional model [LSTM 0.79 vs Random Forest (RF) 0.72, p<0.0001]. Experiments showed that performance of the LSTM models increased as prior encounter number increased up to 30 encounters. An LSTM model with 16 selected laboratory tests yielded equivalent performance to a model with all 981 laboratory tests. This new DL model may provide the basis for a more useful readmission risk prediction tool for diabetes patients., (©2022 AMIA - All rights reserved.)

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations., (© 2023. The Author(s).)

Background: Compared to white individuals, Black and Hispanic individuals have higher rates of COVID-19 hospitalization and death. Less is known about racial/ethnic differences in post-acute sequelae of SARS-CoV-2 infection (PASC)., Objective: Examine racial/ethnic differences in potential PASC symptoms and conditions among hospitalized and non-hospitalized COVID-19 patients., Design: Retrospective cohort study using data from electronic health records., Participants: 62,339 patients with COVID-19 and 247,881 patients without COVID-19 in New York City between March 2020 and October 2021., Main Measures: New symptoms and conditions 31-180 days after COVID-19 diagnosis., Key Results: The final study population included 29,331 white patients (47.1%), 12,638 Black patients (20.3%), and 20,370 Hispanic patients (32.7%) diagnosed with COVID-19. After adjusting for confounders, significant racial/ethnic differences in incident symptoms and conditions existed among both hospitalized and non-hospitalized patients. For example, 31-180 days after a positive SARS-CoV-2 test, hospitalized Black patients had higher odds of being diagnosed with diabetes (adjusted odds ratio [OR]: 1.96, 95% confidence interval [CI]: 1.50-2.56, q<0.001) and headaches (OR: 1.52, 95% CI: 1.11-2.08, q=0.02), compared to hospitalized white patients. Hospitalized Hispanic patients had higher odds of headaches (OR: 1.62, 95% CI: 1.21-2.17, q=0.003) and dyspnea (OR: 1.22, 95% CI: 1.05-1.42, q=0.02), compared to hospitalized white patients. Among non-hospitalized patients, Black patients had higher odds of being diagnosed with pulmonary embolism (OR: 1.68, 95% CI: 1.20-2.36, q=0.009) and diabetes (OR: 2.13, 95% CI: 1.75-2.58, q<0.001), but lower odds of encephalopathy (OR: 0.58, 95% CI: 0.45-0.75, q<0.001), compared to white patients. Hispanic patients had higher odds of being diagnosed with headaches (OR: 1.41, 95% CI: 1.24-1.60, q<0.001) and chest pain (OR: 1.50, 95% CI: 1.35-1.67, q < 0.001), but lower odds of encephalopathy (OR: 0.64, 95% CI: 0.51-0.80, q<0.001)., Conclusions: Compared to white patients, patients from racial/ethnic minority groups had significantly different odds of developing potential PASC symptoms and conditions. Future research should examine the reasons for these differences., (© 2023. The Author(s).)

Post-acute sequelae of SARS-CoV-2 infection (PASC) affects a wide range of organ systems among a large proportion of patients with SARS-CoV-2 infection. Although studies have identified a broad set of patient-level risk factors for PASC, little is known about the association between "exposome"-the totality of environmental exposures and the risk of PASC. Using electronic health data of patients with COVID-19 from two large clinical research networks in New York City and Florida, we identified environmental risk factors for 23 PASC symptoms and conditions from nearly 200 exposome factors. The three domains of exposome include natural environment, built environment, and social environment. We conducted a two-phase environment-wide association study. In Phase 1, we ran a mixed effects logistic regression with 5-digit ZIP Code tabulation area (ZCTA5) random intercepts for each PASC outcome and each exposome factor, adjusting for a comprehensive set of patient-level confounders. In Phase 2, we ran a mixed effects logistic regression for each PASC outcome including all significant (false positive discovery adjusted p-value < 0.05) exposome characteristics identified from Phase I and adjusting for confounders. We identified air toxicants (e.g., methyl methacrylate), particulate matter (PM 2.5 ) compositions (e.g., ammonium), neighborhood deprivation, and built environment (e.g., food access) that were associated with increased risk of PASC conditions related to nervous, blood, circulatory, endocrine, and other organ systems. Specific environmental risk factors for each PASC condition and symptom were different across the New York City area and Florida. Future research is warranted to extend the analyses to other regions and examine more granular exposome characteristics to inform public health efforts to help patients recover from SARS-CoV-2 infection., Competing Interests: We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions., (© 2022. The Author(s).)

The COVID-19 pandemic has magnified longstanding health care and social inequities, resulting in disproportionately high COVID-19-associated illness and death among members of racial and ethnic minority groups (1). Equitable use of effective medications (2) could reduce disparities in these severe outcomes (3). Monoclonal antibody (mAb) therapies against SARS-CoV-2, the virus that causes COVID-19, initially received Emergency Use Authorization (EUA) from the Food and Drug Administration (FDA) in November 2020. mAbs are typically administered in an outpatient setting via intravenous infusion or subcutaneous injection and can prevent progression of COVID-19 if given after a positive SARS-CoV-2 test result or for postexposure prophylaxis in patients at high risk for severe illness. † Dexamethasone, a commonly used steroid, and remdesivir, an antiviral drug that received EUA from FDA in May 2020, are used in inpatient settings and help prevent COVID-19 progression § (2). No large-scale studies have yet examined the use of mAb by race and ethnicity. Using COVID-19 patient electronic health record data from 41 U.S. health care systems that participated in the PCORnet, the National Patient-Centered Clinical Research Network, ¶ this study assessed receipt of medications for COVID-19 treatment by race (White, Black, Asian, and Other races [including American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, and multiple or Other races]) and ethnicity (Hispanic or non-Hispanic). Relative disparities in mAb** treatment among all patients †† (805,276) with a positive SARS-CoV-2 test result and in dexamethasone and remdesivir treatment among inpatients §§ (120,204) with a positive SARS-CoV-2 test result were calculated. Among all patients with positive SARS-CoV-2 test results, the overall use of mAb was infrequent, with mean monthly use at 4% or less for all racial and ethnic groups. Hispanic patients received mAb 58% less often than did non-Hispanic patients, and Black, Asian, or Other race patients received mAb 22%, 48%, and 47% less often, respectively, than did White patients during November 2020-August 2021. Among inpatients, disparities were different and of lesser magnitude: Hispanic inpatients received dexamethasone 6% less often than did non-Hispanic inpatients, and Black inpatients received remdesivir 9% more often than did White inpatients. Vaccines and preventive measures are the best defense against infection; use of COVID-19 medications postexposure or postinfection can reduce morbidity and mortality and relieve strain on hospitals but are not a substitute for COVID-19 vaccination. Public health policies and programs centered around the specific needs of communities can promote health equity (4). Equitable receipt of outpatient treatments, such as mAb and antiviral medications, and implementation of prevention practices are essential to reducing existing racial and ethnic inequities in severe COVID-19-associated illness and death., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Amy K. Feehan receives research support from the Louisiana Public Health Institute. Joshua L. Denson reports grants from the American Diabetes Association, research funding from the Gordon and Betty Moore Foundation, the Society of Critical Care Medicine, and the National Institutes of Health (NIH); and personal fees from Astrazeneca, GlaxoSmithKline, and Guidepoint Global, outside the current work. Jason P. Block reports a grant from the National Institute of Diabetes and Digestive and Kidney Diseases, NIH and receipt of honoraria for participating in a panel discussion for the Kenner Foundation on “Early Detection of Pancreatic Cancer.” No other potential conflicts of interest were disclosed.

Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence., Competing Interests: None of the authors have any conflicts of interest to report nor received any funding in support of the manuscript development., (© 2021 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic., (© 2021. The Author(s).)

From early March through mid-May 2020, the COVID-19 pandemic overwhelmed hospitals in New York City. In anticipation of ventilator shortages and limited ICU bed capacity, hospital operations prioritized the development of prognostic tools to predict clinical deterioration. However, early experience from frontline physicians observed that some patients developed unanticipated deterioration after having relatively stable periods, attesting to the uncertainty of clinical trajectories among hospitalized patients with COVID-19. Prediction tools that incorporate clinical variables at one time-point, usually on hospital presentation, are suboptimal for patients with dynamic changes and evolving clinical trajectories. Therefore, our study team developed a machine-learning algorithm to predict clinical deterioration among hospitalized COVID-19 patients by extracting clinically meaningful features from complex longitudinal laboratory and vital sign values during the early period of hospitalization with an emphasis on informative missing-ness. To incorporate the evolution of the disease and clinical practice over the course of the pandemic, we utilized a time-dependent cross-validation strategy for model development. Finally, we validated our prediction model on an external validation cohort of COVID-19 patients served in a demographically distinct population from the training cohort. The main finding of our study is the identification of risk profiles of early, late and no clinical deterioration during the course of hospitalization. While risk prediction models that include simple predictors at ED presentation and clinical judgement are able to identify any deterioration vs. no deterioration, our methodology is able to isolate a particular risk group that remain stable initially but deteriorate at a later stage of the course of hospitalization. We demonstrate the superior predictive performance with the utilization of laboratory and vital sign data during the early period of hospitalization compared to the utilization of data at presentation alone. Our results will allow efficient hospital resource allocation and will motivate research in understanding the late deterioration risk group., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Web-based interventions have shown promise for chronic disease management but have not been widely applied to populations with stroke. Existing barriers may inhibit the adoption of web-based interventions among stroke survivors and necessitate the involvement of informal caregivers. However, limited information is available on internet accessibility and usability among stroke survivors and their caregivers., Objective: This study aims to investigate internet access and usage in a cohort of stroke survivors and their caregivers., Methods: A cross-sectional survey was conducted with 375 participants (248 stroke survivors and 127 caregivers). Descriptive statistics were generated using cross-tabulation. Comparisons with categorical data were conducted using the chi-square test, whereas the Mann-Whitney U test was used for comparisons involving ordinal variables., Results: Overall, 86.1% (323/375) of the participants reported having internet access. Caregivers were more likely than stroke survivors to access the internet (N=375, χ 2 1 =18.5, P<.001) and used text messaging (n=321, χ 2 1 =14.7, P<.001). Stroke survivors and caregivers with internet access were younger than stroke survivors and caregivers without internet access. The highest number of participants who reported internet access were non-Hispanic White. Smartphones were the most common devices used to access the internet. Email was the most common type of internet usage reported. Patients who survived for >12 months after a stroke reported higher internet access than those who survived <3 months (P<.001). The number of hours per week spent using the internet was higher for caregivers than for stroke survivors (P<.001)., Conclusions: Future feasibility and acceptability studies should consider the role of the informal caregiver, participant age, race and ethnicity, the use of smartphone apps, email and text correspondence, and the amount of time elapsed since the stroke event in the design and implementation of web-based interventions for populations with stroke., (©Imama Ali Naqvi, Tahani Casameni Montiel, Yazan Bittar, Norma Hunter, Munachi Okpala, Constance Johnson, Mark G Weiner, Sean Savitz, Anjail Sharrief, Jennifer Elizabeth Sanner Beauchamp. Originally published in JMIR Formative Research (http://formative.jmir.org), 08.03.2021.)

Purpose: Our aim was to develop a novel approach for lung cancer screening among a diverse population that integrates the Centers for Medicare and Medicaid Services (CMS) recommended components including shared decision making (SDM), low-dose CT (LDCT), reporting of results in a standardized format, smoking cessation, and arrangement of follow-up care., Methods: Between October of 2015 and March of 2018, we enrolled patients, gathered data on demographics, delivery of SDM, reporting of LDCT results using Lung-RADS, discussion of results, and smoking cessation counseling. We measured adherence to follow-up care, cancer diagnosis, cancer treatment, and smoking cessation at 2 years after initial LDCT., Results: We enrolled 505 patients who were 57% African American, 30% Caucasian, 13% Hispanic, < 1% Asian, and 61% were active smokers. All participants participated in SDM, 88.1% used a decision aid, and 96.1% proceeded with LDCT. Of 496 completing LDCT, all received a discussion about results and follow-up recommendations. Overall, 12.9% had Lung-RADS 3 or 4, and 3.2% were diagnosed with lung cancer resulting in a false-positive rate of 10.7%. All 48 patients with positive screens but no cancer diagnosis adhered to follow-up care at 1 year, but only 35.4% adhered to recommended follow-up care at 2 years. The annual follow-up for patients with negative lung cancer screening results (Lung-RADS 1 and 2) was only 23.7% after one year and 2.8% after 2 years. All active smokers received smoking cessation counseling, but only 11% quit smoking., Conclusion: The findings show that an integrated lung cancer screening program can be safely implemented in a diverse population, but adherence to annual screening is poor.

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: Life expectancy of the U.S. population will continue to rise, increasing the workforce demands in the treatment of musculoskeletal pathologies. With a declining primary care physician workforce, physical therapists (PT) may be in a unique situation to help absorb this demand., Objective: Our primary objective was to compare physical function between two primary care groups, a "physical therapy-led pathway" versus "physician-led pathway" in the management of recent-onset musculoskeletal pain at 1-month follow-up. Our secondary objectives were to compare other patient-important outcomes between the groups at 1 month and health utilization at 1 month and 1 year., Methods: A pragmatic randomized controlled trial with a 1-year follow-up was conducted, enrolling 150 university employees with recent onset musculoskeletal pain. Participants were randomized at first contact to either a PT-led pathway or physician-led pathway. The primary outcome was change in Patient-Reported Outcomes Measurement Information System (PROMIS) ShortForm v1-Physical Function 10a at 1 month; groups were compared with repeated measurement of analysis of variance and chi-square for both primary and secondary outcomes, except for satisfaction score at 1 month, which was cross-sectionally compared by independent two-sample t-test. At 1 year, healthcare utilization was assessed through medical record extraction, and healthcare utilization was converted to total episodic standard cost. Utilization was compared between groups using chi-square and Wilcoxon rank-sum tests., Results: Both groups demonstrated improvement at 1-month follow-up but showed no significant between-group difference in mean PROMIS scores at 1 month (PT-led pathway vs physician-led pathway, 2.04, [95% CI -0.28 to 4.36]; P = .082). At 1 year, there was no difference in healthcare utilization or cost between groups. No harm or misdiagnosis was found, filed, or reported by participants at 1 year., Conclusion: This study shows equivalence in outcomes for two low-cost musculoskeletal care pathways with no risk of harm. These results reinforce other findings in the literature that support PTs as safe and effective initial providers for individuals with musculoskeletal disorders., (© 2020 American Academy of Physical Medicine and Rehabilitation.)

Opportunistic osteoporosis screening involves measuring the attenuation of L1 vertebrae on abdominal computed tomography (CT), which correlates with DXA T-score. We found that this approach is useful for detecting low bone mass in patients with diabetes and propose L1 attenuation ≤ 135 Hounsfield units (HU) as a threshold for which DXA should be strongly considered., Introduction: Attenuation of the L1 vertebrae on computer tomography (CT) images done for other reasons ("Opportunistic Osteoporosis Screening") has been found to correlate well with DXA-derived T-score. However, the method and the thresholds have never been tested specifically in those with diabetes mellitus (DM), in whom the fracture risk is greater than explained by BMD., Methods: In a retrospective study of subjects with DM who had both abdominal CT and DXA within 6 months of each other, we compared L1 attenuation and DXA T-score to define the sensitivity and specificity of thresholds previously established in the general population., Results: There were 313 subjects among whom 18 (5.8%) had prior major osteoporotic fracture (MOF). Subjects with MOF had lower T-scores (- 2.3 ± 1.4 vs. - 0.9 ± 1.4, p < 0.001) and L1 attenuation (104 HU ± 46 vs. 149 HU ± 47, p < 0.001) than non-fracture subjects. L1 attenuation ≤ 160 HU was 91% sensitive for osteoporosis, while ≤ 110 HU was 80% specific. For a higher T-score of ≤ - 1.5, L1 attenuation ≤ 135 HU showed balanced sensitivity and specificity (65% and 69%, respectively)., Conclusion: Opportunistic osteoporosis screening with abdominal CT is useful in determining the need for DXA screening in subjects with diabetes. We propose L1 attenuation ≤ 135 HU as a reasonable threshold for detecting the T-score of ≤ - 1.5, which is likely associated with increased fragility in DM.

Background: Many large-scale cardiovascular clinical trials are plagued with escalating costs and low enrollment. Implementing a computable phenotype, which is a set of executable algorithms, to identify a group of clinical characteristics derivable from electronic health records or administrative claims records, is essential to successful recruitment in large-scale pragmatic clinical trials. This methods paper provides an overview of the development and implementation of a computable phenotype in ADAPTABLE (Aspirin Dosing: a Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness)-a pragmatic, randomized, open-label clinical trial testing the optimal dose of aspirin for secondary prevention of atherosclerotic cardiovascular disease events., Methods and Results: A multidisciplinary team developed and tested the computable phenotype to identify adults ≥18 years of age with a history of atherosclerotic cardiovascular disease without safety concerns around using aspirin and meeting trial eligibility criteria. Using the computable phenotype, investigators identified over 650 000 potentially eligible patients from the 40 participating sites from Patient-Centered Outcomes Research Network-a network of Clinical Data Research Networks, Patient-Powered Research Networks, and Health Plan Research Networks. Leveraging diverse recruitment methods, sites enrolled 15 076 participants from April 2016 to June 2019. During the process of developing and implementing the ADAPTABLE computable phenotype, several key lessons were learned. The accuracy and utility of a computable phenotype are dependent on the quality of the source data, which can be variable even with a common data model. Local validation and modification were required based on site factors, such as recruitment strategies, data quality, and local coding patterns. Sustained collaboration among a diverse team of researchers is needed during computable phenotype development and implementation., Conclusions: The ADAPTABLE computable phenotype served as an efficient method to recruit patients in a multisite pragmatic clinical trial. This process of development and implementation will be informative for future large-scale, pragmatic clinical trials. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02697916.

Context: Diabetes mellitus (DM) is associated with an increased risk of fracture, but it is not clear which diabetes and nondiabetes risk factors may be most important., Objective: The aim of the study was to evaluate risk factors for incident major osteoporotic fractures (MOFs) of the hip, wrist, and humerus in African American (AA), Hispanic (HIS), and Caucasian (CA) subjects with DM., Methods: This was a retrospective cohort study of 18 210 subjects with DM (7298 CA, 7009 AA and 3903 HIS) at least 40 years of age, being followed at a large healthcare system in Philadelphia, Pennsylvania., Results: In a global model in CA with DM, MOF were associated with dementia (HR 4.16; 95% CI, 2.13-8.12), OSA (HR 3.35; 95% CI, 1.78-6.29), COPD (HR 2.43; 95% CI, 1.51-3.92), and diabetic neuropathy (HR 2.52; 95% CI, 1.41-4.50). In AA, MOF were associated with prior MOF (HR 13.67; 95% CI, 5.48-34.1), dementia (HR 3.10; 95% CI, 1.07-8.98), glomerular filtration rate (GFR) less than 45 (HR 2.05; 95% CI, 1.11-3.79), thiazide use (HR 0.54; 95% CI, 0.31-0.93), metformin use (HR 0.59; 95% CI, 0.36-0.97), and chronic steroid use (HR 5.03; 95% CI, 1.51-16.7). In HIS, liver disease (HR 3.06; 95% CI, 1.38-6.79) and insulin use (HR 2.93; 95% CI, 1.76-4.87) were associated with MOF., Conclusion: In patients with diabetes, the risk of fracture is related to both diabetes-specific variables and comorbid conditions, but these relationships vary by race/ethnicity., (© Endocrine Society 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Context: Diabetes mellitus (DM) has been associated with a 60% to 90% increased risk of fracture but few studies have been performed in African American and Hispanic subjects., Objective: The aim of the present study was to quantify the risk of incident major osteoporotic fractures (MOFs) of the hip, wrist, and humerus in African Americans, Hispanics, and Caucasians with DM compared with those with hypertension (HTN)., Methods: We performed a retrospective cohort study of 19,153 subjects with DM (7618 Caucasians, 7456 African Americans, and 4079 Hispanics) and 26,217 with HTN (15,138 Caucasians, 8301 African Americans, and 2778 Hispanics) aged ≥40 years, treated at a large health care system in Philadelphia, Pennsylvania. All information about the subjects was obtained from electronic health records., Results: The unadjusted MOF rates for each race/ethnicity were similar among those with DM and those with HTN (Caucasians, 1.85% vs 1.84%; African Americans, 1.07% vs 1.29%; and Hispanics, 1.69% vs 1.33%; P = NS for all). However, the MOF rates were higher for Caucasians and Hispanics with DM than for African Americans with DM (P < 0.01). In a multivariable model controlled for age, body mass index, sex, and previous MOF, DM was a statistically significant predictor of MOFs only for Caucasians and Hispanics [hazard ratio (HR), 1.23; 95% CI, 1.02 to 1.48; P = 0.026] but not for African Americans (HR, 0.92; 95% CI, 0.68 to 1.23; P = 0.56)., Conclusions: Hispanics had a DM-related fracture risk similar to that of Caucasians, but AAs did not have an additional fracture risk conferred by DM., (Copyright © 2019 Endocrine Society.)

Objective: Clinical implementation of predictive analytics that assess risk of high-cost outcomes are presumed to save money because they help focus interventions designed to avert those outcomes on a subset patients who are most likely to benefit from the intervention. This premise may not always be true. A cost-benefit analysis is necessary to show if a strategy of applying the predictive algorithm is truly favorable to alternative strategies. Methods: We designed and implemented an interactive web-based cost-benefit calculator, enabling specification of accuracy parameters for the predictive model and other clinical and financial factors related to the occurrence of an undesirable outcome. We use the web tool, populated with real-world data to illustrate a cost-benefit analysis of a strategy of applying predictive analytics to select a cohort of high-risk patients to receive interventions to avert readmissions for Congestive Heart Failure (CHF). Results: Application of predictive analytics in clinical care may not always be a cost-saving strategy compared with intervening on all patients. Improving the accuracy of a predictive model may lower costs, but other factors such as the prevalence and cost of the outcome, and the cost and effectiveness of the intervention designed to avert the outcome may be more influential in determining the favored strategy. Conclusion: An interactive cost-benefit analyses provides insights regarding the financial implications of a clinical strategy that implements predictive analytics.

Importance: Financial incentives to physicians or patients are increasingly used, but their effectiveness is not well established., Objective: To determine whether physician financial incentives, patient incentives, or shared physician and patient incentives are more effective than control in reducing levels of low-density lipoprotein cholesterol (LDL-C) among patients with high cardiovascular risk., Design, Setting, and Participants: Four-group, multicenter, cluster randomized clinical trial with a 12-month intervention conducted from 2011 to 2014 in 3 primary care practices in the northeastern United States. Three hundred forty eligible primary care physicians (PCPs) were enrolled from a pool of 421. Of 25,627 potentially eligible patients of those PCPs, 1503 enrolled. Patients aged 18 to 80 years were eligible if they had a 10-year Framingham Risk Score (FRS) of 20% or greater, had coronary artery disease equivalents with LDL-C levels of 120 mg/dL or greater, or had an FRS of 10% to 20% with LDL-C levels of 140 mg/dL or greater. Investigators were blinded to study group, but participants were not., Interventions: Primary care physicians were randomly assigned to control, physician incentives, patient incentives, or shared physician-patient incentives. Physicians in the physician incentives group were eligible to receive up to $1024 per enrolled patient meeting LDL-C goals. Patients in the patient incentives group were eligible for the same amount, distributed through daily lotteries tied to medication adherence. Physicians and patients in the shared incentives group shared these incentives. Physicians and patients in the control group received no incentives tied to outcomes, but all patient participants received up to $355 each for trial participation., Main Outcomes and Measures: Change in LDL-C level at 12 months., Results: Patients in the shared physician-patient incentives group achieved a mean reduction in LDL-C of 33.6 mg/dL (95% CI, 30.1-37.1; baseline, 160.1 mg/dL; 12 months, 126.4 mg/dL); those in physician incentives achieved a mean reduction of 27.9 mg/dL (95% CI, 24.9-31.0; baseline, 159.9 mg/dL; 12 months, 132.0 mg/dL); those in patient incentives achieved a mean reduction of 25.1 mg/dL (95% CI, 21.6-28.5; baseline, 160.6 mg/dL; 12 months, 135.5 mg/dL); and those in the control group achieved a mean reduction of 25.1 mg/dL (95% CI, 21.7-28.5; baseline, 161.5 mg/dL; 12 months, 136.4 mg/dL; P < .001 for comparison of all 4 groups). Only patients in the shared physician-patient incentives group achieved reductions in LDL-C levels statistically different from those in the control group (8.5 mg/dL; 95% CI, 3.8-13.3; P = .002)., Conclusions and Relevance: In primary care practices, shared financial incentives for physicians and patients, but not incentives to physicians or patients alone, resulted in a statistically significant difference in reduction of LDL-C levels at 12 months. This reduction was modest, however, and further information is needed to understand whether this approach represents good value., Trial Registration: clinicaltrials.gov Identifier: NCT01346189.

Purpose: Developing electronic clinical data into a common data model posed substantial challenges unique from those encountered with administrative data. We present here the design, implementation, and use of the Mini-Sentinel Distributed Database laboratory results table (LRT)., Methods: We developed the LRT and guided Mini-Sentinel data partners (DPs) in populating it from their source data. Data sources included electronic health records and internal and contracted clinical laboratory systems databases. We employed the Logical Observation Identifiers, Names, and Codes (LOINC®) results reporting standards. We evaluated transformed results data using data checks and an iterative, ongoing characterization and harmonization process., Results: Key LRT variables included test name, subcategory, specimen source, LOINC, patient location, specimen date and time, result unit, and unique person identifier. Selected blood and urine chemistry, hematology, coagulation, and influenza tests were included. Twelve DPs with outpatient test results participated; four also contributed inpatient test results. As of September 2013, the LRT included 385,516,239 laboratory test results; data are refreshed at least quarterly. LOINC availability and use varied across DP. Multiple data quality and content issues were identified and addressed., Conclusion: Developing the LRT brought together disparate data sources with no common coding structure. Clinical laboratory test results obtained during routine healthcare delivery are neither uniformly coded nor documented in a standardized manner. Applying a systematic approach with data harmonization efforts and ongoing oversight and management is necessary for a clinical laboratory results data table to remain valid and useful., (Copyright © 2014 John Wiley & Sons, Ltd.)

Sepsis is a major cause of hospital admissions and mortality. Nevertheless, there are significant gaps in our knowledge of the epidemiology of sepsis in obese people, who now represent more than one-third of the population in the United States. The objective of this study was to measure the association between obesity and mortality from presumed sepsis. A retrospective cohort study was used of 1,779 adult inpatients with presumed sepsis at a Tertiary Care Academic Institution from March 1, 2007 to June 30, 2011. Cases of sepsis were identified using a standardized algorithm for sepsis antibiotic treatment. Exposure (i.e., obesity) was defined as a body mass index ≥30 kg/m(2). Multivariable logistic regression was used to assess the adjusted association between obesity and mortality. Patients with presumed sepsis were of a median age of 60.9 years (interquartile range 49.7-71) and 41.1 % were women. A total of 393 patients died, resulting in a 28-day in-hospital mortality of 22.1 %. In adjusted analysis, obesity was not significantly associated with increased mortality (odds ratio 1.11, 95 % CI 0.85-1.41, P = 0.47). There was also no difference in the in-hospital length of stay (P = 0.45) or maximum percent change in serum creatinine (P = 0.32) between obese and non-obese patients. Finally, there was no difference in the proportion of initial inadequate vancomycin levels (P = 0.1) after presumed sepsis. Obesity was not associated with increased mortality in patients with presumed sepsis. Further research is needed to determine how excess adiposity modulates inflammation from sepsis.

Objectives: We examined the cost-effectiveness of an intervention to reduce coronary heart disease (CHD) risk and blood pressure in African Americans., Study Design: Stochastic cost-effectiveness analysis alongside a clinical trial, augmented by a Markov model of lifetime cost-effectiveness., Methods: In 2 urban academic primary care practices, we randomized African American patients with uncontrolled hypertension to a 6-month intervention of office practice and peer coach behavioral support (N = 136) or informational brochures about CHD risk factors (N = 144). Costs were estimated from the perspective of the provider. Outcomes included estimated CHD events avoided over 6 months and reduction in systolic blood pressure (SBP) (mm Hg). Subgroup analysis was performed for compliers who received an "effective" dose of the peer coach and office staff visits. Long-term cost-effectiveness was estimated by applying the clinical trial cost and effectiveness into a Markov model of CHD risk., Results: The average cost for the behavioral support intervention group was $435.36 compared with $74.39 for the brochure control group. The incremental cost-effectiveness ratio (ICER) was $47 per mm Hg reduction in SBP and $453,419 per CHD event avoided in 6 months. Modeled over a 10-year horizon, the intervention had an ICER only as high as $3998 per incremental quality-adjusted life-year., Conclusions: A community-primary care practice behavioral intervention to reduce hypertension in African Americans with sustained uncontrolled hypertension does not appear to be cost-effective in the first 6 months. If intervention results are sustained over the long term, the program may be cost-effective over the patient's lifetime.

Introduction: International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes capture comorbidities that can be used to risk adjust nonrandom patient groups. We explored the accuracy of capturing comorbidities associated with one risk adjustment method, the Elixhauser Comorbidity Measure (ECM), in patients with chronic heart failure (CHF) at one Veterans Affairs (VA) medical center. We explored potential reasons for the differences found between the original codes assigned and conditions found through retrospective review., Methods: This descriptive, retrospective study used a cohort of patients discharged with a principal diagnosis coded as CHF from one VA medical center in 2003. One admission per patient was used in the study; with multiple admissions, only the first admission was analyzed. We compared the assignment of original codes assigned to conditions found in a retrospective, manual review of the medical record conducted by an investigator with coding expertise as well as by physicians. Members of the team experienced with assigning ICD-9-CM codes and VA coding processes developed themes related to systemic reasons why chronic conditions were not coded in VA records using applied thematic techniques., Results: In the 181-patient cohort, 388 comorbid conditions were identified; 305 of these were chronic conditions, originally coded at the time of discharge with an average of 1.7 comorbidities related to the ECM per patient. The review by an investigator with coding expertise revealed a total of 937 comorbidities resulting in 618 chronic comorbid conditions with an average of 3.4 per patient; physician review found 872 total comorbidities with 562 chronic conditions (average 3.1 per patient). The agreement between the original and the retrospective coding review was 88 percent. The kappa statistic for the original and the retrospective coding review was 0.375 with a 95 percent confidence interval (CI) of 0.352 to 0.398. The kappa statistic for the retrospective coding review and physician review was 0.849 (CI, 0.823-0.875). The kappa statistic for the original coding and the physician review was 0.340 (CI, 0.316-0.364). Several systemic factors were identified, including familiarity with inpatient VA and non-VA guidelines, the quality of documentation, and operational requirements to complete the coding process within short time frames and to identify the reasons for movement within a given facility., Conclusion: Comorbidities within the ECM representing chronic conditions were significantly underrepresented in the original code assignment. Contributing factors potentially include prioritization of codes related to acute conditions over chronic conditions; coders' professional training, educational level, and experience; and the limited number of codes allowed in initial coding software. This study highlights the need to evaluate systemic causes of underrepresentation of chronic conditions to improve the accuracy of risk adjustment used for health services research, resource allocation, and performance measurement.

The growing amount of data in operational electronic health record systems provides unprecedented opportunity for its reuse for many tasks, including comparative effectiveness research. However, there are many caveats to the use of such data. Electronic health record data from clinical settings may be inaccurate, incomplete, transformed in ways that undermine their meaning, unrecoverable for research, of unknown provenance, of insufficient granularity, and incompatible with research protocols. However, the quantity and real-world nature of these data provide impetus for their use, and we develop a list of caveats to inform would-be users of such data as well as provide an informatics roadmap that aims to insure this opportunity to augment comparative effectiveness research can be best leveraged.

Quality measurement is an important issue for the United States Department of Veterans Affairs (VA). In this study, we piloted the use of an informatics tool, the Multithreaded Clinical Vocabulary Server (MCVS), which extracted automatically whether the VA Office of Quality and Performance measures of quality of care were met for the completion of discharge instructions for inpatients with congestive heart failure. We used a single document, the discharge instructions, from one section of the medical records for 152 patients and developed a reference standard using two independent reviewers to assess performance. When evaluated against the reference standard, MCVS achieved a sensitivity of 0.87, a specificity of 0.86, and a positive predictive value of 0.90. The automated process using the discharge instruction document worked effectively. The use of the MCVS tool for concept-based indexing resulted in mostly accurate data capture regarding quality measurement, but improvements are needed to further increase the accuracy of data extraction., (© 2012 National Association for Healthcare Quality.)

Background: Patients with Crohn's disease (CD) are frequently exposed to diagnostic radiation in emergency departments (EDs). We aimed to examine clinical predictors of urgent abdominopelvic computed tomography (APCT) findings in this population., Methods: A retrospective cross-sectional study was performed among adults with CD presenting to 2 emergency departments with a gastrointestinal chief complaint. The outcome, APON (abscess, perforation, obstruction, new or worsening non-CD-related findings), included APCTs with new or worsening CD-related or non-CD-related urgent findings. Variables with P < 0.05 in bivariate analyses were included in a multivariable logistic regression model, which was also used to develop a risk score for APON., Results: A total of 481 APCTs were performed and 166 (34.5%) identified APON. Variables retained in the final model were history of intestinal obstruction (odds ratio [OR]: 3.78, 95% confidence interval [CI]: 2.27-6.28), history of intraabdominal abscess (OR: 2.64, 95% CI: 1.43 to 4.88), current hematochezia (OR: 0.38, 95% CI: 0.21 to 0.68), and white blood cell count >12,000/μL (OR: 2.49, 95% CI: 1.63 to 3.84). The c-statistic was 0.72. The risk score subtracts 1 point for hematochezia, and adds 1 point for each of the other variables. Among patients with a risk score of -1, the predicted and observed risk for APON was 9% and 6%, respectively. Any score greater than -1 had a predicted and observed risk of 19.8% and higher., Conclusions: An APON risk score of -1 is associated with a low risk of urgent APCT findings in patients with CD in the emergency department. Implementation of such a tool may support clinical decision-making in the ED setting.