An electronic health record (EHR)-based risk calculator can predict fractures comparably to FRAX: a proof-of-concept study.

Information in the electronic health record (EHR), such as diagnoses, vital signs, utilization, medications, and laboratory values, may predict fractures well without the need to verbally ascertain risk factors. In our study, as a proof of concept, we developed and internally validated a fracture risk calculator using only information in the EHR. PURPOSE: Fracture risk calculators, such as the Fracture Risk Assessment Tool, or FRAX, typically lie outside the clinician workflow. Conversely, the electronic health record (EHR) is at the center of the clinical workflow, and many variables in the EHR could predict fractures without having to verbally ascertain FRAX risk factors. We sought to evaluate the utility of EHR variables to predict fractures and, as a proof of concept, to create an EHR-based fracture risk model. METHODS: Routine clinical data from 24,189 subjects presenting to primary care from 2010 to 2018 was utilized. Major osteoporotic fractures (MOFs) were captured by physician diagnosis codes. Data was split into training (n = 18,141) and test sets (n = 6048). We fit Cox regression models for candidate risk factors in the training set, and then created a global model using a backward stepwise approach. We then applied the model to the test set and compared the discrimination and calibration to FRAX. RESULTS: We found variables related to vital signs, utilization, diagnoses, medications, and laboratory values to be associated with incident MOF. Our final model included 19 variables, including age, BMI, Parkinson's disease, chronic kidney disease, and albumin levels. When applied to the test set, we found the discrimination (AUC 0.73 vs. 0.70, p = 0.08) and calibration were comparable to FRAX. CONCLUSION: Routinely collected data in EHR systems can generate adequate fracture predictions without the need to verbally ascertain fracture risk factors. In the future, this could allow for automated fracture prediction at the point of care to improve osteoporosis screening and treatment rates.

Association between SARS-CoV-2 infection and select symptoms and conditions 31 to 150 days after testing among children and adults.

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.

Descriptive Epidemiology of New York City Older Adult Patients With Multiple Chronic Conditions.

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.

Preventive Service Usage and New Chronic Disease Diagnoses: Using PCORnet Data to Identify Emerging Trends, United States, 2018-2022.

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 A1c 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.

Electronic Health Records (EHRs) Can Identify Patients at High Risk of Fracture but Require Substantial Race Adjustments to Currently Available Fracture Risk Calculators.

BACKGROUND: Osteoporotic fracture prediction calculators are poorly utilized in primary care, leading to underdiagnosis and undertreatment of those at risk for fracture. The use of these calculators could be improved if predictions were automated using the electronic health record (EHR). However, this approach is not well validated in multi-ethnic populations, and it is not clear if the adjustments for race or ethnicity made by calculators are appropriate. OBJECTIVE: To investigate EHR-generated fracture predictions in a multi-ethnic population. DESIGN: Retrospective cohort study using data from the EHR. SETTING: An urban, academic medical center in Philadelphia, PA. PARTICIPANTS: 12,758 White, 7,844 Black, and 3,587 Hispanic patients seeking routine care from 2010 to 2018 with mean 3.8 years follow-up. INTERVENTIONS: None. MEASUREMENTS: FRAX and QFracture, two of the most used fracture prediction tools, were studied. Risk for major osteoporotic fracture (MOF) and hip fracture were calculated using data from the EHR at baseline and compared to the number of fractures that occurred during follow-up. RESULTS: MOF rates varied from 3.2 per 1000 patient-years in Black men to 7.6 in White women. FRAX and QFracture had similar discrimination for MOF prediction (area under the curve, AUC, 0.69 vs. 0.70, p=0.08) and for hip fracture prediction (AUC 0.77 vs 0.79, p=0.21) and were similar by race or ethnicity. FRAX had superior calibration than QFracture (calibration-in-the-large for FRAX 0.97 versus QFracture 2.02). The adjustment factors used in MOF prediction were generally accurate in Black women, but underestimated risk in Black men, Hispanic women, and Hispanic men. LIMITATIONS: Single center design. CONCLUSIONS: Fracture predictions using only EHR inputs can discriminate between high and low risk patients, even in Black and Hispanic patients, and could help primary care physicians identify patients who need screening or treatment. However, further refinements to the calculators may better adjust for race-ethnicity.

Racial/Ethnic Disparities in Post-acute Sequelae of SARS-CoV-2 Infection in New York: an EHR-Based Cohort Study from the RECOVER Program.

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.

Racial and Ethnic Disparities in Receipt of Medications for Treatment of COVID-19 - United States, March 2020-August 2021.

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.

Adherence to annual lung cancer screening with low-dose CT scan in a diverse population.

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.

A predictive model of clinical deterioration among hospitalized COVID-19 patients by harnessing hospital course trajectories.

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.

Sciatic Nerve Variants in Patients Diagnosed With Sciatica: Is There a Correlation?

PURPOSE: Compression of the sciatic nerve in its path along the piriformis muscle can produce sciatica-like symptoms. There are 6 predominant types of sciatic nerve variations with type 1 being the most common (84.2%), followed by type 2 (13.9%). However, there is scarce literature on the prevalence of sciatic nerve variation in those diagnosed with sciatica. MATERIALS AND METHODS: The charts of 95 patients clinically diagnosed with sciatica who had a magnetic resonance imaging of the pelvis/hip were retrospectively studied. All patients had T1-weighted axial, coronal, and sagittal images. Magnetic resonance imagings were interpreted separately by 2 board-certified fellowship-trained musculoskeletal radiologists to identify the sciatic nerve variant. RESULTS: Seven cases were excluded because of inadequate imaging. Of the remaining 88 patients, 5 had bilateral sciatica resulting in a sample size of 93 limbs. Fifty-two (55.9%) had type 1 sciatic nerve anatomy, 39 (41.9%) had type 2, and 2 (2.2%) had type 3. The proportions of type 1 and 2 variations were significantly different from the normal distribution (P < 0.001), whereas type 3, 4, 5, and 6 variants were not (P = 1.00). CONCLUSIONS: There is strong statistical significance regarding the relationship between sciatic nerve variation and the clinical diagnosis of sciatica. Preoperative magnetic resonance imaging can be considered in sciatica patients to prevent iatrogenic injury in pelvic surgery.

Effect of Financial Incentives to Physicians, Patients, or Both on Lipid Levels: A Randomized Clinical Trial.

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.

Endocarditis is a common stroke mechanism in hemodialysis patients.

BACKGROUND AND PURPOSE: Hemodialysis patients are at high risk for ischemic stroke, and previous studies have noted a high rate of cardioembolism in this population. The aim of this study was to determine ischemic stroke causes among hemodialysis patients and elucidate specific cardioembolic stroke mechanisms. METHODS: This study is a retrospective cross-sectional study of hemodialysis patients admitted with acute stroke to the University of Pennsylvania Health System between 2003 and 2010. Strokes were classified using modified Trial of Org 10,172 in Acute Stroke Treatment (TOAST) criteria as large vessel, cardioembolism, small vessel, atypical, multiple causes, or cryptogenic. Cardioembolic strokes were further characterized for specific mechanism. RESULTS: We identified 52 patients hospitalized with acute stroke while receiving hemodialysis. Mean age was 64±13 years, 56% were female, and 67% were black. Stroke subtypes included 3 (6%) large vessel, 20 (38%) cardioembolism, 6 (11%) small vessel, 3 (6%) other, 4 (8%) with multiple causes, and 16 (31%) were unknown. Among patients who had an echocardiogram performed, 5 of 52 (10%; 95% confidence interval, 1%-18%) had a patent foramen ovale. Cardioembolic stroke mechanisms included 6 with infective endocarditis (accounting for 12% of all strokes). CONCLUSIONS: Cardioembolism and cryptogenic stroke are the predominant stroke mechanisms among hemodialysis patients. Infective endocarditis was identified frequently relative to other stroke cohorts, and a raised index of suspicion is warranted in the hemodialysis population.

Electronic clinical laboratory test results data tables: lessons from Mini-Sentinel.

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.

Association between acquiring SARS-CoV-2 during pregnancy and post-acute sequelae of SARS-CoV-2 infection: RECOVER electronic health record cohort analysis.

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.

Real-World Effectiveness of Nirmatrelvir in Protecting Long COVID for Outpatient Adult Patients - A Large-Scale Observational Cohort Study from the RECOVER Initiative.

Paxlovid has been approved for use in patients who are at high risk for severe acute COVID-19 illness. Evidence regarding whether Paxlovid protects against Post-Acute Sequelae of SARS-CoV-2 infection (PASC), or Long COVID, is mixed in high-risk patients and lacking in low-risk patients. With a target trial emulation framework, we evaluated the association of Paxlovid treatment within 5 days of SARS-CoV-2 infection with incident Long COVID and hospitalization or death from any cause in the post-acute period (30-180 days after infection) using electronic health records from the Patient-Centered Clinical Research Networks (PCORnet) RECOVER repository. The study population included 497,499 SARS-CoV-2 positive patients between March 1, 2022, to February 1, 2023, and among which 165,256 were treated with Paxlovid within 5 days since infection and 307,922 were not treated with Paxlovid or other COVID-19 treatments. Compared with the non-treated group, Paxlovid treatment was associated with reduced risk of Long COVID with a Hazard Ratio (HR) of 0.88 (95% CI, 0.87 to 0.89) and absolute risk reduction of 2.99 events per 100 persons (95% CI, 2.65 to 3.32). Paxlovid treatment was associated with reduced risk of all-cause death (HR, 0.53, 95% CI 0.46 to 0.60; risk reduction 0.23 events per 100 persons, 95% CI 0.19 to 0.28) and hospitalization (HR, 0.70, 95% CI 0.68 to 0.73; risk reduction 2.37 events per 100 persons, 95% CI 2.19 to 2.56) in the post-acute phase. For those without documented risk factors, the associations (HR, 1.03, 95% CI 0.95 to 1.11; risk increase 0.80 events per 100 persons, 95% CI -0.84 to 2.45) were inconclusive. Overall, high-risk, nonhospitalized adult patients with COVID-19 who were treated with Paxlovid within 5 days of SARS-CoV-2 infection had a lower risk of Long COVID and all-cause hospitalization or death in the post-acute period. However, Long COVID risk reduction with Paxlovid was not observed in low-risk patients.

Decline in use of high-risk agents for tight glucose control among older adults with diabetes in New York City: 2017-2022.

BACKGROUND: This study aimed to examine the prevalence of inappropriate tight glycemic control in older adults with type 2 diabetes and other chronic conditions in New York City, and to identify factors associated with this practice. METHODS: We conducted a retrospective cohort study using the INSIGHT Clinical Research Network. The study population included 11,728 and 15,196 older adults in New York City (age ≥ 75 years) with a diagnosis of type 2 diabetes, and at least one other chronic medical condition, in 2017 and 2022, respectively. The main outcome of interest was inappropriate tight glycemic control, defined as HbA1c <7.0% (<53 mmol/mol) with prescription of at least one high-risk agent (insulin or insulin secretagogue). RESULTS: The proportion of older adults with inappropriate tight glycemic control decreased by nearly 19% over a five-year period (19.4% in 2017 to 15.8% in 2022). There was a significant decrease in insulin (27.8% in 2017; 24.3% in 2022) and sulfonylurea (29.4% in 2017; 21.7% in 2022) medication prescription, and increase in use of GLP-1 agonists (1.8% in 2017; 11.4% in 2022) and SGLT-2 inhibitors (5.8% in 2017; 25.1% in 2022), among the total population. Factors associated with inappropriate tight glycemic control in 2022 included history of heart failure (adjusted odds ratio [aOR] 1.38), chronic kidney disease ([aOR] 1.93), colorectal cancer ([aOR] 1.38), acute myocardial infarction ([aOR] 1.28), "other" ([aOR] 0.72) or "unknown" ([aOR] 0.72) race, and a point increase in BMI ([aOR] 0.98). CONCLUSIONS: We found an encouraging trend toward less use of high-risk medication strategies for older adults with type 2 diabetes and multiple chronic conditions. However, one in six patients in 2022 still had inappropriate tight glycemic control, indicating a need for continued efforts to optimize diabetes management in this population.

Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER program.

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.

Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts.

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.

Most people who develop COVID-19 make a full recovery, but some go on to develop post-acute sequelae of SARS-CoV-2 infection, commonly known as Long COVID. Up to now, we did not know why some people are affected by Long COVID whilst others are not. We conducted a study to identify risk factors for Long COVID and developed a mathematical modeling approach to predict those at risk. We find that Long COVID is associated with some factors such as experiencing severe acute COVID-19, being underweight, and having conditions including cancer or cirrhosis. Due to the wide variety of symptoms defined as Long COVID, it may be challenging to come up with a set of risk factors that can predict the whole spectrum of Long COVID. However, our approach could be used to predict a variety of Long COVID conditions.

Prevalent Metformin Use in Adults With Diabetes and the Incidence of Long COVID: An EHR-Based Cohort Study From the RECOVER Program.

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.

Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs.

Personal and family history of suicidal thoughts and behaviors (PSH and FSH, respectively) are significant risk factors associated with suicides. Research is limited in automatic identification of such data from clinical notes in Electronic Health Records. This study developed deep learning (DL) tools utilizing transformer models (Bio_ClinicalBERT and GatorTron) to detect PSH and FSH in clinical notes derived from three academic medical centers, and compared their performance with a rule-based natural language processing tool. For detecting PSH, the rule-based approach obtained an F1-score of 0.75 ± 0.07, while the Bio_ClinicalBERT and GatorTron DL tools scored 0.83 ± 0.09 and 0.84 ± 0.07, respectively. For detecting FSH, the rule-based approach achieved an F1-score of 0.69 ± 0.11, compared to 0.89 ± 0.10 for Bio_ClinicalBERT and 0.92 ± 0.07 for GatorTron. Across sites, the DL tools identified more than 80% of patients at elevated risk for suicide who remain undiagnosed and untreated.