Characteristics of Pregnancy-related Health Events Across Care Settings Nationwide in PCORnet®

Medical Care · 2026-01-08 · 1 citations

The maternal mortality rate in the United States is higher than peer countries throughout the world. There is a critical need to implement and evaluate the effectiveness of interventions to address factors that contribute to maternal mortality and morbidity (MMM). Legislation passed by the US Congress in 2019 reauthorized funding for the Patient-Centered Outcomes Research Institute (PCORI) and identified maternal morbidity and mortality as a research priority. PCORnet® is a large, distributed "network of networks" funded by PCORI to improve the nation's capacity to efficiently conduct definitive health research. PCORnet® Network Partners convened a workgroup of experts in topics related to MMM-including patient stakeholders-and developed an exploratory query to identify and characterize the cohort of patients with pregnancy-related health events served by health systems participating in PCORnet. This article presents query results for 1.1 million pregnancies resulting in delivery or interruption that occurred between July 28, 2021, and July 28, 2023 among patients receiving care at 72 sites participating in PCORnet. Three percent of patients experienced severe maternal morbidity, and 357 cases of mortality were recorded. The results also include occurrence of mental and physical comorbidities in the prenatal, peripartum, and postpartum periods. These data are intended to support use of the PCORnet research infrastructure to produce evidence that matters to patients, caregivers, and the broader public health and health care communities. We also discuss ways to enhance the PCORnet infrastructure to accelerate maternal health research, including work that is currently underway to augment data pertinent to studying MMM.

Association between subway iron particulate matter exposure and respiratory disease in New York City

PLOS Global Public Health · 2026-01-07

Particulate matter exposure is linked to increased morbidity and mortality. Iron-rich particulate matter (PM2.5), common in rapid transit systems, is a potential but understudied contributor to respiratory illness. Using electronic health records (EHR) from 452,272 patients in the INSIGHT Clinical Research Network in New York City (2020-2023), we examined whether local iron exposure is associated with asthma, chronic obstructive pulmonary disease (COPD), breathing difficulties, or respiratory inhaler use. Iron exposure was estimated using particulate matter measurements from New York City (NYC) subway stations, linked to each patients residential census block group. To account for potential non-linear relationships, we applied linear probability models and an adjacent block group estimator with paired fixed effects to assess respiratory outcomes across deciles of iron exposure. We found that the relative risk of developing asthma, COPD, or breathing difficulties increased by 6-15% between the lowest two exposure deciles. Beyond this range, there was no significant association between iron exposure and respiratory disease. This suggests that iron exposure from rapid transit is associated with respiratory disease primarily at lower exposure levels, with limited health benefits from marginal reductions in iron exposure at already high exposure levels.

Toward responsible AI governance: Balancing multi-stakeholder perspectives on AI in healthcare

International Journal of Medical Informatics · 2025-06-19 · 14 citations

Towards responsible artificial intelligence in healthcare—getting real about real-world data and evidence

Journal of the American Medical Informatics Association · 2025-07-26 · 6 citations

BACKGROUND: The use of real-world data (RWD) in artificial intelligence (AI) applications for healthcare offers unique opportunities but also poses complex challenges related to interpretability, transparency, safety, efficacy, bias, equity, privacy, ethics, accountability, and stakeholder engagement. METHODS: A multi-stakeholder expert panel comprising healthcare professionals, AI developers, policymakers, and other stakeholders was assembled. Their task was to identify critical issues and formulate consensus recommendations, focusing on the responsible use of RWD in healthcare AI. The panel's work involved an in-person conference and workshop and extensive deliberations over several months. RESULTS: The panel's findings revealed several critical challenges, including the necessity for data literacy and documentation, the identification and mitigation of bias, privacy and ethics considerations, and the absence of an accountability structure for stakeholder management. To address these, the panel proposed a series of recommendations, such as the adoption of metadata standards for RWD sources, the development of transparency frameworks and instructional labels likened to "nutrition labels" for AI applications, the provision of cross-disciplinary training materials, the implementation of bias detection and mitigation strategies, and the establishment of ongoing monitoring and update processes. CONCLUSION: Guidelines and resources focused on the responsible use of RWD in healthcare AI are essential for developing safe, effective, equitable, and trustworthy applications. The proposed recommendations provide a foundation for a comprehensive framework addressing the entire lifecycle of healthcare AI, emphasizing the importance of documentation, training, transparency, accountability, and multi-stakeholder engagement.

Long COVID after SARS-CoV-2 during pregnancy in the United States

Nature Communications · 2025-04-01 · 11 citations

Pregnancy alters immune responses and clinical manifestations of COVID-19, but its impact on Long COVID remains uncertain. This study investigated Long COVID risk in individuals with SARS-CoV-2 infection during pregnancy compared to reproductive-age females infected outside of pregnancy. A retrospective analysis of two U.S. databases, the National Patient-Centered Clinical Research Network (PCORnet) and the National COVID Cohort Collaborative (N3C), identified 29,975 pregnant individuals (aged 18-50) with SARS-CoV-2 infection in pregnancy from PCORnet and 42,176 from N3C between March 2020 and June 2023. At 180 days after infection, estimated Long COVID risks for those infected during pregnancy were 16.47 per 100 persons (95% CI, 16.00-16.95) in PCORnet using the PCORnet computational phenotype (CP) model and 4.37 per 100 persons (95% CI, 4.18-4.57) in N3C using the N3C CP model. Compared to matched non-pregnant individuals, the adjusted hazard ratios for Long COVID were 0.86 (95% CI, 0.83-0.90) in PCORnet and 0.70 (95% CI, 0.66-0.74) in N3C. The observed risk factors for Long COVID included Black race/ethnicity, advanced maternal age, first- and second-trimester infection, obesity, and comorbid conditions. While the findings suggest a high incidence of Long COVID among pregnant individuals, their risk was lower than that of matched non-pregnant females.

Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative

UNC Libraries · 2025-03-13

The benefits and challenges of electronic health records (EHRs) as data sources for clinical and epidemiologic research have been well described. However, several factors are important to consider when using EHR data to study novel, emerging, and multifaceted conditions such as postacute sequelae of SARS-CoV-2 infection or long COVID. In this article, we present opportunities and challenges of using EHR data to improve our understanding of long COVID, based on lessons learned from the National Institutes of Health (NIH)-funded RECOVER (REsearching COVID to Enhance Recovery) Initiative, and suggest steps to maximize the usefulness of EHR data when performing long COVID research.

Severity of acute SARS-CoV-2 infection and risk of new-onset autoimmune disease: A RECOVER initiative study in nationwide U.S. cohorts

PLoS ONE · 2025-06-04 · 8 citations

SARS-CoV-2 infection has been associated with increased autoimmune disease risk. Past studies have not aligned regarding the most prevalent autoimmune diseases after infection, however. Furthermore, the relationship between infection severity and new autoimmune disease risk has not been well examined. We used RECOVER's electronic health record (EHR) networks, N3C, PCORnet, and PEDSnet, to estimate types and frequency of autoimmune diseases arising after SARS-CoV-2 infection and assessed how infection severity related to autoimmune disease risk. We identified patients of any age with SARS-CoV-2 infection between April 1, 2020 and April 1, 2021, and assigned them to a World Health Organization COVID-19 severity category for adults or the PEDSnet acute COVID-19 illness severity classification system for children (<age 21). We collected baseline covariates from the EHR in the year pre-index infection date and followed patients for 2 years for new autoimmune disease, defined as ≥ 2 new ICD-9, ICD-10, or SNOMED codes in the same concept set, starting >30 days after SARS-CoV-2 infection index date and occurring ≥1 day apart. We calculated overall and infection severity-stratified incidence ratesper 1000 person-years for all autoimmune diseases. With least severe COVID-19 severity as reference, survival analyses examined incident autoimmune disease risk. The most common new-onset autoimmune diseases in all networks were thyroid disease, psoriasis/psoriatic arthritis, and inflammatory bowel disease. Among adults, inflammatory arthritis was the most common, and Sjögren's disease also had high incidence. Incident type 1 diabetes and hematological autoimmune diseases were specifically found in children. Across networks, after adjustment, patients with highest COVID-19 severity had highest risk for new autoimmune disease vs. those with least severe disease (N3C: adjusted Hazard Ratio, (aHR) 1.47 (95%CI 1.33-1.66); PCORnet aHR 1.14 (95%CI 1.02-1.26); PEDSnet: aHR 3.14 (95%CI 2.42-4.07)]. Overall, severe acute COVID-19 was most strongly associated with autoimmune disease risk in three EHR networks.

COVID-Related Healthcare Disruptions and Impacts on Chronic Disease Management Among Patients of the New York City Safety-Net System

Journal of General Internal Medicine · 2025-12-19

BACKGROUND: The COVID-19 pandemic had a significant impact on healthcare delivery. Older adults with multimorbidities were at risk of healthcare disruptions for the management of their chronic conditions. OBJECTIVE: To characterize healthcare disruptions during the COVID-19 healthcare shutdown and recovery period (March 7, 2020-October 6, 2020) and their effects on disease management among older adults with multimorbidities who were patients of NYC Health + Hospitals (H + H), the largest municipal safety-net system in the United States. DESIGN: Observational. PATIENTS: Patients aged 50 + with hypertension or diabetes and at least one other comorbidity, at least one H + H ambulatory visit in the six months before COVID-19 pandemic onset (March 6, 2020), and at least one visit in the post-acute shutdown period (October 7, 2020 to December 31, 2023). MAIN MEASURES: We characterized disruption in care (defined as no ambulatory or telehealth visits during the acute shutdown) and estimated the effect of disruption on blood pressure control, hemoglobin A1c (HbA1c), and low-density lipoprotein (LDL) cholesterol using difference-in-differences models. KEY RESULTS: Out of 73,889 individuals in the study population, 12.5% (n = 9,202) received no ambulatory or telehealth care at H + H during the acute shutdown. Low pre-pandemic healthcare utilization, Medicaid insurance, and self-pay were independent predictors of care disruption. In adjusted analyses, the disruption group had a 3.0-percentage point (95% CI: 1.2-4.8) greater decrease in blood pressure control compared to those who received care. Disruption did not have a significant impact on mean HbA1c or LDL. CONCLUSIONS: Care disruption was associated with declines in blood pressure control, which while clinically modest, could impact risk of cardiovascular outcomes if sustained. Disruption did not affect HbA1c or LDL. Telehealth mitigated impacts of the pandemic on care disruption and subsequent disease management. Targeted outreach to those at risk of care disruption is needed during future crises.

COVID-related healthcare disruptions among older adults with multiple chronic conditions in New York City

BMC Health Services Research · 2025-03-05 · 5 citations

BACKGROUND: Results from national surveys indicate that many older adults reported delayed medical care during the acute phase of the COVID-19 pandemic, yet few studies have used objective data to characterize healthcare utilization among vulnerable older adults in that period. In this study, we characterized healthcare utilization during the acute pandemic phase (March 7-October 6, 2020) and examined risk factors for total disruption of care among older adults with multiple chronic conditions (MCC) in New York City. METHODS: This retrospective cohort study used electronic health record data from NYC patients aged ≥ 50 years with a diagnosis of either hypertension or diabetes and at least one other chronic condition seen within six months prior to pandemic onset and after the acute pandemic period at one of several major academic medical centers contributing to the NYC INSIGHT clinical research network (n=276,383). We characterized patients by baseline (pre-pandemic) health status using cutoffs of systolic blood pressure (SBP) < 140mmHg and hemoglobin A1C (HbA1c) < 8.0% as: controlled (below both cutoffs), moderately uncontrolled (below one), or poorly controlled (above both, SBP > 160, HbA1C > 9.0%). Patients were then assessed for total disruption versus some care during shutdown using recommended care schedules per baseline health status. We identified independent predictors for total disruption using logistic regression, including age, sex, race/ethnicity, baseline health status, neighborhood poverty, COVID infection, number of chronic conditions, and quartile of prior healthcare visits. RESULTS: Among patients, 52.9% were categorized as controlled at baseline, 31.4% moderately uncontrolled, and 15.7% poorly controlled. Patients with poor baseline control were more likely to be older, female, non-white and from higher poverty neighborhoods than controlled patients (P < 0.001). Having fewer pre-pandemic healthcare visits was associated with total disruption during the acute pandemic period (adjusted odds ratio [aOR], 8.61, 95% Confidence Interval [CI], 8.30-8.93, comparing lowest to highest quartile). Other predictors of total disruption included self-reported Asian race, and older age. CONCLUSIONS: This study identified patient groups at elevated risk for care disruption. Targeted outreach strategies during crises using prior healthcare utilization patterns and disease management measures from disease registries may improve care continuity.

Developing a Computable Phenotype for Identifying Children, Adolescents, and Young Adults With Diabetes Using Electronic Health Records in the DiCAYA Network

Diabetes Care · 2025-03-31 · 7 citations

OBJECTIVE: The Diabetes in Children, Adolescents, and Young Adults (DiCAYA) network seeks to create a nationwide electronic health record (EHR)-based diabetes surveillance system. This study aimed to develop a DiCAYA-wide EHR-based computable phenotype (CP) to identify prevalent cases of diabetes. RESEARCH DESIGN AND METHODS: We conducted network-wide chart reviews of 2,134 youth (aged <18 years) and 2,466 young adults (aged 18 to <45 years) among people with possible diabetes. Within this population, we compared the performance of three alternative CPs, using diabetes diagnoses determined by chart review as the gold standard. CPs were evaluated based on their accuracy in identifying diabetes and its subtype. RESULTS: The final DiCAYA CP requires at least one diabetes diagnosis code from clinical encounters. Subsequently, diabetes type classification was based on the ratio of type 1 diabetes (T1D) or type 2 diabetes (T2D) diagnosis codes in the EHR. For both youth and young adults, the sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively) in finding diabetes cases were >90%, except for the specificity and NPV in young adults, which were slightly lower at 83.8% and 80.6%, respectively. The final DiCAYA CP achieved >90% sensitivity, specificity, PPV, and NPV in classifying T1D, and demonstrated lower but robust performance in identifying T2D, consistently maintaining >80% across metrics. CONCLUSIONS: The DiCAYA CP effectively identifies overall diabetes and T1D in youth and young adults, though T2D misclassification in youth highlights areas for refinement. The simplicity of the DiCAYA CP enables broad deployment across diverse EHR systems for diabetes surveillance.