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.

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.

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.

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.

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.

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.

Caveats for the use of operational electronic health record data in comparative effectiveness research.

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.

A new "Comparative Effectiveness" assessment strategy using the THIN database: comparison of the cardiac complications of pioglitazone and rosiglitazone.

PURPOSE: Examine feasibility of a new strategy to perform Electronic Medical Record database valid Comparative Effectiveness Research (CER), using determination of whether rosiglitazone (ROS) treatment increases Acute myocardial infarction (MI) in comparison to pioglitazone (PIO) as a model question. METHODS: Using the UK The Health Improvement Network Database, a retrospective cohort design replicated the proactive RCT of diabetics with ischemic cardiovascular disease (CVD). Replication studies using PIO or ROS, as well as expanded studies of subjects not requiring CVD, were performed. MI assessment used multiple analytics comparing ROS and PIO exposed patients including: unexposed subjects, the proactive RCT, and directly between ROS to PIO exposed cohorts. RESULTS: PIO replication studies did not affect MI [HR 0.88 (0.49 to 1.42)], but ROS increased MI, with prior event rate ratio (PERR) adjusted HR (which overcomes unmeasured confounding) results of: [HR 1.31 (0.94 to 1.74)] versus proactive RCT [HR 0.83 (0.65 to 1.06)] (p = 0.02). Direct ROS to PIO exposed cohort comparisons yielded PERR adj HR of 1.55 (0.98 to 2.65). By contrast, expanded studies showed no differences between ROS and PIO exposure. CONCLUSIONS: These results provide new insight regarding the effects of ROS and PIO on MI. In a population with established ischemic CVD, ROS increased MI in contrast to PIO; whereas in an unselected population, ROS and PIO have reasonably comparable effects. Most importantly, this study demonstrates the feasibility and advantages of a new strategy to perform reliable "CER" using an EMR database.

Validation of a coding algorithm to identify patients with hepatocellular carcinoma in an administrative database.

PURPOSE: International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM)-based algorithms to identify patients with hepatocellular carcinoma (HCC) have not been developed outside of the Veterans Affairs healthcare setting. The development and validation of such algorithms are necessary for the conduct of population-based studies evaluating the epidemiology and comparative effectiveness and safety of therapies for HCC. METHODS: We queried electronic medical records at two tertiary care hospitals to identify patients with two ICD-9-CM diagnosis codes for a chronic liver disease and/or cirrhosis plus two ICD-9-CM codes for HCC. We determined the positive predictive value (PPV) of this algorithm by comparing it to diagnoses of HCC confirmed by expert medical record review. RESULTS: Among 101 patients meeting the algorithm, 88 (PPV: 87.1%; 95% CI: 79.0-93.0%) had confirmed HCC. The algorithm's sensitivity was 91.7% among patients with confirmed HCC, and its specificity was 98.7% among chronic liver disease patients without HCC. Excluding patients who received systemic chemotherapy in the 12 months prior to or 6 months after the initial ICD-9-CM code in the algorithm, the PPV increased to 91.6% (87/95; 95% CI: 84.1-96.3%). CONCLUSIONS: The presence of at least two ICD-9-CM codes for a chronic liver disease and/or cirrhosis plus two ICD-9-CM codes for HCC has a high PPV for identifying HCC cases. This simple, claims-based algorithm can be used in future epidemiologic studies to examine risk factors for HCC and evaluate outcomes and adverse events of medical therapies prescribed for HCC patients.

Outliers in diagnosis ratios: A clue toward possibly absent data.

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.

A method to automate the discharge summary hospital course for neurology patients.

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.

Improving Patient-Reported Outcomes in Stroke Care using Remote Blood Pressure Monitoring and Telehealth.

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.

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 Outcomes and Measures: 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 with only negative tests 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 with a negative test, (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 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.

Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program.

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 (PM2.5) 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.

Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes.

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.

Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative.

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.