OneFlorida Clinical Research Consortium Research Articles

Diabetes-related complications, glycemic levels, and healthcare utilization outcomes after therapeutic inertia in type 2 diabetes mellitus

AimsTo assess diabetes-related complications, glycemic levels, and healthcare utilization 12 months after exposure to therapeutic inertia among patients with type 2 diabetes mellitus (T2D). MethodsThis retrospective cohort study analyzed data from the OneFlorida Clinical Research Consortium (electronic health records from Florida practices/clinics). The cohort included adult patients (≥18 years old) with T2D who had an HbA1c≥7.0% (53 mmol/mol) recorded from January 1, 2014-September 30, 2019. Therapeutic inertia (exposed vs. not exposed) was evaluated during the six months following HbA1c≥7.0% (53 mmol/mol). The outcomes assessed during the 12-month follow-up period included diabetes-related complications (continuous Diabetes Complications and Severity Index (DCSI)), glycemic levels (continuous follow-up HbA1c lab), and healthcare utilization counts. We analyzed data using multivariable regression models, adjusting for covariates. ResultsThe cohort included 26,881 patients with T2D (58.94% White race, 49.72% female, and mean age of 58.82 (SD=13.09)). After adjusting for covariates, therapeutic inertia exposure was associated with lower DCSI (estimate=−0.14 (SE=0.03), p < 0.001), higher follow-up HbA1c (estimate=0.14 (SE=0.04), p < 0.001), and lower rates of ambulatory visits (rate ratio=0.79, 95% CI=0.75–0.82). ConclusionsFindings communicate the clinical practice implications and public health implications for combating therapeutic inertia in diabetes care.

Vulnerable Patients with Inflammatory Bowel Disease

Crohn’s Disease and ulcerative colitis, collectively known as Inflammatory Bowel Disease (IBD), are conditions characterized by inflammation and ulceration of the intestines. This work aims to highlight disparities among vulnerable IBD populations using the OneFlorida Clinical Research Consortium, a data network housing claims and electronic health record information for more than 15 million patients in Florida. A retrospective analysis using data collected between 01/01/2012 and 06/30/2020 was performed using OneFlorida, and patients were identified using diagnosis and pharmacy codes for IBD. 10,578 patients met the inclusion criteria. Healthcare utilization (emergency department visits, inpatient stay, medications, procedures), disease severity (c-reactive protein, white blood cell count, hemoglobin, albumin), and outcomes (i.e., abdominal surgery) were assessed for patients of different ages, gender, ethnicities, races, and locations. Notably, adolescent and young adult (AYA) patients (ages 17-25) use significantly more corticosteroids than both the pediatric and older adult groups (p-values 0.005 and 0.0007). Contrary to prior literature, Hispanic IBD patients used more biologic medications compared to non-Hispanic White patients (p&lt;0.0001), whereas non-Hispanic Black patients used more steroids (p=0.0004). These findings illustrate a need for targeted educational interventions to support these vulnerable populations.

Patterns of Cough Medication Prescribing among Patients with Chronic Cough in Florida: 2012-2021.

Among patients with chronic cough (CC) in the 2012-2021 statewide OneFlorida Clinical Research Consortium database, we examined trends in cough medication (CM) prescribing prevalence over time in repeated cross-sectional analyses and identified distinct CM utilization trajectories using group-based trajectory modeling (GBTM) in a retrospective cohort study. Among eligible adults (≥18 years) without cancer/benign respiratory tumor diagnoses, we identified CC patients and non-CC patients with any cough-related diagnosis. In the GBTM analysis, we calculated the number of monthly prescriptions for any CMs (excluding gabapentinoids) during the 12 months from the first qualifying cough event to identify distinct utilization trajectories. From 2012 to 2021, benzonatate (9.6% to 26.1%), dextromethorphan (5.2% to 8.6%), and gabapentinoid (5.3% to 14.4%) use increased among CC patients, while opioid antitussive use increased from 2012 to 2015 and decreased thereafter (8.4% in 2012, 14.7% in 2015, 6.7% in 2021; all p < 0.001). Of 15,566 CC patients and 655,250 non-CC patients identified in the GBTM analysis, CC patients had substantial burdens of respiratory/non-respiratory comorbidities and healthcare service and concomitant medication use compared to non-CC patients. Among CC patients, GBTM identified three distinct CM utilization trajectories: (1) no CM use (n = 11,222; 72.1%); (2) declining CM use (n = 4105; 26.4%); and (3) chronic CM use (n = 239; 1.5%). CC patients in Florida had limited CM use with increasing trends in use of benzonatate, dextromethorphan, and gabapentinoids and a decreasing trend in opioid antitussive use. CC patients, particularly with chronic prescription CM use, experienced substantial disease burden.

Variation by race/ethnicity in the utilization and weight loss following metabolic bariatric surgery

BackgroundUnderstanding the disparities in utilization and weight loss outcomes of metabolic and bariatric surgery (MBS) by demographics will inform strategies targeting potential treatment gaps and enhance overall clinical obesity treatment. ObjectiveTo identify factors associated with utilization and longitudinal weight loss after MBS. SettingOneFlorida Clinical Research Consortium Database. MethodsWe performed a retrospective study using data from the OneFlorida Clinical Research Consortium between 2012 and 2018. We used logistic regression with intersectional effects to identify factors associated with utilization of MBS. Mixed-effect models were used to estimate longitudinal percentage total weight loss among those who underwent MBS with up to 18 months of follow-up. ResultsAmong 429,821 patients eligible for MBS, 8290 (1.9%) underwent MBS between 2012 and 2018. Intersectional analysis revealed that non-Hispanic Black patients experienced an inferior utilization of MBS compared with non-Hispanic White and Hispanic counterparts, defined by the interaction between race/ethnicity and demographic factors, including male sex, older age, and insurance coverage. In the longitudinal weight loss assessment, 4016 patients (48.3% Roux-en-Y gastric bypass, 51.7% sleeve gastrectomy) were included. We found that non-Hispanic Black patients experienced significantly less weight loss than non-Hispanic White and Hispanic counterparts. Other factors associated with less weight loss over time included undergoing sleeve gastectomy, male sex, lower preoperative body mass index, and having type 2 diabetes at the time of surgery. ConclusionsOur findings will help to design new strategies focusing on the intersection of race/ethnicity and sociodemographic factors to improve access and effectiveness of MBS.

Prediction of long-term survival among patients with cirrhosis using time-varying models.

Risk prediction among patients with cirrhosis has historically focused on short-term (ie, 90 days) mortality among patients waitlisted for a transplant. Although several models have been developed to predict intermediate and longer term survivals, they have important limitations, namely, including only baseline laboratory and clinical variables to predict survival over a time horizon of years. We developed prediction models using time-varying laboratory and clinical data among patients with cirrhosis in the OneFlorida Clinical Research Consortium. We fit extended Cox models and assessed model discrimination and calibration in complete-case analysis and imputation of missing laboratory data. Among 15,277 patients, 9922 (64.9%) were included in the complete-case analysis. Final models included demographic (age and sex), time-updating laboratory (albumin, alanine transaminase, alkaline phosphatase, bilirubin, platelet, and sodium), and time-updating clinical (ascites, hepatic encephalopathy, spontaneous bacterial peritonitis, and bleeding esophageal varices) variables. Model discrimination was excellent in the complete-case analysis [AUC and concordance-index (C-index) > 0.85] at 1-, 2-, 3-, 4-, and 5-year time points. Model performance was unchanged with the exclusion of race and ethnicity as model predictors. Model discrimination was excellent (C-index >0.8) when imputation was used for patients with 1 or 2 missing laboratory variables. Using data from a statewide sample of patients with cirrhosis, we developed and internally validated a time-updating model to predict survival with excellent discrimination. Based on its measures of discrimination (AUC and c-index), this model matched or exceeded the performance of other published risk models depending on the time horizon. If externally validated, this risk score could improve the care of patients with cirrhosis by improving counseling on intermediate and longer term outcomes to guide clinical decision-making and advanced care planning.

Peripartum women's perspectives on research study participation in the OneFlorida Clinical Research Consortium during COVID-19 pandemic.

The COVID-19 pandemic created an unprecedented need for population-level clinical trials focused on the discovery of life-saving therapies and treatments. However, there is limited information on perception of research participation among perinatal populations, a population of particular interest during the pandemic. Eligible respondents were 18 years or older, were currently pregnant or had an infant (≤12 months old), and lived in Florida within 50 miles of sites participating in the OneFlorida Clinical Research Consortium. Respondents were recruited via Qualtrics panels between April and September 2020. Respondents completed survey items about barriers and facilitators to participation and answered sociodemographic questions. Of 533 respondents, most were between 25 and 34 years of age (n = 259, 49%) and identified as White (n = 303, 47%) and non-Hispanic (n = 344, 65%). Facebook was the most popular social media platform among our respondents. The most common barriers to research participation included poor explanation of study goals, discomforts to the infant, and time commitment. Recruitment through healthcare providers was perceived as the best way to learn about clinical research studies. When considering research participation, "myself" had the greatest influence, followed by familial ties. Noninvasive biological samples were highly acceptable. Hispanics had higher positive perspectives on willingness to participate in a randomized study (p = 0.009). Education (p = 0.007) had significant effects on willingness to release personal health information. When recruiting women during the pregnancy and postpartum periods for perinatal studies, investigators should consider protocols that account for common barriers and preferred study information sources. Social media-based recruitment is worthy of adoption.

S1285 Association Between Distance to a Radiology Center and HCC Surveillance Among Patients With Cirrhosis in Florida

Introduction: Hepatocellular carcinoma (HCC) is a leading cause of morbidity and mortality in patients with cirrhosis, and HCC survival is directly correlated with stage at diagnosis. Current guidelines recommend HCC surveillance with an abdominal ultrasound every 6 months, but unfortunately many patients do not meet this threshold.1 Our aim was to determine whether distance to a radiology center, measured in travel time, was associated with HCC surveillance rates. Methods: We included data on adult patients with cirrhosis within the OneFlorida Clinical Research Consortium from October 1, 2015- December 31, 2019. The primary outcome was a continuous measure of the percentage of time up to date with HCC surveillance (PTUDS) based on abdominal ultrasound (US), triple phase CT, and/or MRI with contrast. Travel time was calculated using ArcGIS geomodelling software as the estimated minimum travel time between the geographic centroid of patient’s zip code and the nearest American College of Radiology-accredited center for US, CT, and/or MRI. Linear regression models were fit with PTUDS as the outcome; all covariates with a p< 0.05 were included in the final multivariable model. (Figure) Results: Among 25,299 patients with cirrhosis (median follow-up=4.1 years), the median PTUDS was 10.0% (interquartile range 0-29.9%). Variables found to have a statistically significant association with PTUDS are displayed in Table. Travel time, hepatic encephalopathy at baseline, and ascites at baseline were associated with increased PTUDS. Patients with alcohol-related liver disease, nonalcoholic steatohepatitis, and cryptogenic cirrhosis had lower PTUDS compared with patients with HCV. Conclusion: Travel time to the nearest radiology center is not associated with lower HCC surveillance rates while race, etiology of liver disease, and disease severity do appear to variably influence surveillance. By establishing factors associated with currently suboptimal surveillance rates, we can create targeted interventions to improve surveillance and, ultimately, patient outcomes.Figure 1.: Median PTUDS by County Table 1. - Multivariable Linear Regression Model of Factors Associated with HCC Surveillance Variable Beta Coefficient, 95% CI P-Value Travel Time 0.0016 (0.0012-0.0020) <0.001 Liver Disease Etiology Hepatitis C Virus Reference - Hepatitis B Virus 0.0246 (0.0019-0.0473) 0.034 Wilson's Disease 0.0137 (-0.571-0.0845) 0.704 Hemochromatosis 0.0067 (-0.0220-0.0354) 0.647 a1-Antitrypsin Deficiency 0.0673 (0.0200-0.1145) 0.005 Alcohol-Related Liver Disease -0.0720 (-0.0801- -0.0639) <0.001 Primary Biliary Cholangitis -0.0037 (-0.0261- 0.0186) 0.745 Autoimmune Hepatitis -0.0193 (-0.0406- 0.0019) 0.075 Primary Sclerosing Cholangitis 0.0123 (-0.0462- 0.0708) 0.680 Nonalcoholic Steatohepatitis -0.0835 (-0.0917- -0.0752) <0.001 Unknown/Cryptogenic -0.01566 (-0.1664- -0.1467) <0.001 Race White/Caucasian Reference - American Indian/Alaskan -0.0207 (-0.0871- 0.0457) 0.541 Asian 0.0531 (0.0258- 0.0805) <0.001 Black 0.0171 (0.0082- 0.0260) <0.001 Native Hawaiian/Pacific Islander -0.0075 (-0.1109- 0.0959) 0.887 Multiple Race -0.0474 (-0.0781- -0.0168) 0.002 Refuse to answer 0.0904 (0.0212- 0.1596) 0.010 No information 0.0575 (0.0283- 0.0866) <0.001 Other 0.0050 (-0.0048- 0.0148) 0.318 Unknown -0.0591 (-0.0837- -0.0346) <0.001 Disease Severity Hepatic Encephalopathy at Baseline 0.0431 (0.0328- 0.0534) <0.001 Ascites at Baseline 0.0638 (0.0551- 0.0724) <0.001

Pediatric and adult asthma clinical phenotypes: a real world, big data study based on acute exacerbations

Introduction Asthma is a heterogeneous disease with a range of observable phenotypes. To date, the characterization of asthma phenotypes is mostly limited to allergic versus non-allergic disease. Therefore, the aim of this big data study was to computationally derive asthma subtypes from the OneFlorida Clinical Research Consortium Methods We obtained data from 2012-2020 from the OneFlorida Clinical Research Consortium. Longitudinal data for patients greater than two years of age who met inclusion criteria for an asthma exacerbation based on International Classification of Diseases codes. We used matrix factorization to extract information and K-means clustering to derive subtypes. The distributions of demographics, comorbidities, and medications were compared using Chi-square statistics. Results A total of 39,807 pediatric patients and 23,883 adult patients met inclusion criteria. We identified five distinct pediatric subtypes and four distinct adult subtypes. Pediatric subtype P1 had the highest proportion of black patients, but the lowest use of inhaled corticosteroids and allergy medications. Subtype P2 had a predominance of patients with gastroesophageal reflux disease, whereas P3 had a predominance of patients with allergic disorders. Adult subtype A2 was the most severe and all patients were on biologic agents. Most of subtype A3 patients were not taking controller medications, whereas most patients (>90%) in subtypes A2 and A4 were taking corticosteroids and allergy medications. Conclusion We found five distinct pediatric asthma subtypes and four distinct adult asthma subtypes. Future work should externally validate these subtypes and characterize response to treatment by subtype to better guide clinical treatment of asthma.

Study protocol for a type III hybrid effectiveness-implementation trial to evaluate scaling interoperable clinical decision support for patient-centered chronic pain management in primary care

BackgroundThe US continues to face public health crises related to both chronic pain and opioid overdoses. Thirty percent of Americans suffer from chronic noncancer pain at an estimated yearly cost of over $600 billion. Most patients with chronic pain turn to primary care clinicians who must choose from myriad treatment options based on relative risks and benefits, patient history, available resources, symptoms, and goals. Recently, with attention to opioid-related risks, prescribing has declined. However, clinical experts have countered with concerns that some patients for whom opioid-related benefits outweigh risks may be inappropriately discontinued from opioids. Unfortunately, primary care clinicians lack usable tools to help them partner with their patients in choosing pain treatment options that best balance risks and benefits in the context of patient history, resources, symptoms, and goals. Thus, primary care clinicians and patients would benefit from patient-centered clinical decision support (CDS) for this shared decision-making process.MethodsThe objective of this 3-year project is to study the adaptation and implementation of an existing interoperable CDS tool for pain treatment shared decision making, with tailored implementation support, in new clinical settings in the OneFlorida Clinical Research Consortium. Our central hypothesis is that tailored implementation support will increase CDS adoption and shared decision making. We further hypothesize that increases in shared decision making will lead to improved patient outcomes, specifically pain and physical function. The CDS implementation will be guided by the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. The evaluation will be organized by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. We will adapt and tailor PainManager, an open source interoperable CDS tool, for implementation in primary care clinics affiliated with the OneFlorida Clinical Research Consortium. We will evaluate the effect of tailored implementation support on PainManager’s adoption for pain treatment shared decision making. This evaluation will establish the feasibility and obtain preliminary data in preparation for a multi-site pragmatic trial targeting the effectiveness of PainManager and tailored implementation support on shared decision making and patient-reported pain and physical function.DiscussionThis research will generate evidence on strategies for implementing interoperable CDS in new clinical settings across different types of electronic health records (EHRs). The study will also inform tailored implementation strategies to be further tested in a subsequent hybrid effectiveness-implementation trial. Together, these efforts will lead to important new technology and evidence that patients, clinicians, and health systems can use to improve care for millions of Americans who suffer from pain and other chronic conditions.Trial registrationClinicalTrials.gov, NCT05256394, Registered 25 February 2022.

Hepatitis C virus (HCV) seroprevalence, RNA detection, and genotype distribution across Florida, 2015–2018

Chronic hepatitis C virus (HCV) infection is a leading cause of hepatocellular carcinoma (HCC) in the U.S. Due to high rates of HCV among baby boomers (born 1945–1965), it was recommended they receive universal screening. This was expanded to all U.S. adults in 2020 due to evidence of increasing rates of chronic HCV in younger adults. An assessment of HCV burden across demographics is crucial to understand the future burden of HCC and target under-screened adults for HCV. Using the OneFlorida Clinical Research Consortium, of more than one million individuals in Florida, all HCV antibody and viral RNA tests completed from 2015 to 2018 were identified. HCV seroprevalence, HCV viral load (active infection), and HCV genotype distribution by risk groups were assessed. Overall, HCV seroprevalence and active infection were highest among White non-Hispanic individuals, males, and baby boomers. However, odds of a positive HCV antibody test were higher among Black non-Hispanic individuals born before 1945 (aOR: 2.74; 95% CI: 1.98–3.78) or 1945–1965 (aOR: 1.46; 95% CI: 1.36–1.56) compared to White non-Hispanic individuals. In contrast, among individuals born after 1965, Black non-Hispanics were less likely than White non-Hispanics to test HCV antibody positive (aOR of 0.5–0.28). A similar age/race pattern was observed for active HCV infection. There was a higher prevalence of genotype 1A and 3 and lower prevalence of 1B in younger adults. Patterns of HCV seroprevalence and active HCV infection identified in our study support the recent shift from age and risk-based screening guidelines to universal adult screening.

Multisite learning of high-dimensional heterogeneous data with applications to opioid use disorder study of 15,000 patients across 5 clinical sites

Integrating data across institutions can improve learning efficiency. To integrate data efficiently while protecting privacy, we propose A one-shot, summary-statistics-based, Distributed Algorithm for fitting Penalized (ADAP) regression models across multiple datasets. ADAP utilizes patient-level data from a lead site and incorporates the first-order (ADAP1) and second-order gradients (ADAP2) of the objective function from collaborating sites to construct a surrogate objective function at the lead site, where model fitting is then completed with proper regularizations applied. We evaluate the performance of the proposed method using both simulation and a real-world application to study risk factors for opioid use disorder (OUD) using 15,000 patient data from the OneFlorida Clinical Research Consortium. Our results show that ADAP performs nearly the same as the pooled estimator but achieves higher estimation accuracy and better variable selection than the local and average estimators. Moreover, ADAP2 successfully handles heterogeneity in covariate distributions.

Abstract 2265: HCV seroprevalence and RNA detection rates across Florida, 2015-2018

Abstract Chronic hepatitis C virus (HCV) infection is a leading cause of hepatocellular carcinoma in the U.S. While there is no vaccine to prevent HCV, current treatment can cure &amp;gt;90% of infections. About 3 million US residents have been exposed to HCV and 65%-75% remain chronically infected. In response to increasing incidence of HCV infection in younger ages, the US Preventive Services Task Force and the Centers for Disease Control and Prevention (CDC) broadened HCV screening recommendations from baby boomers (born 1945-1965) to all adults aged 18-79 in 2020. Florida has the third highest number of individuals living with HCV in the U.S. (n=151,000). Therefore, our study aimed to identify current high-risk groups that may require advanced HCV screening and targeted HCV management using the OneFlorida Clinical Research Consortium, a state-wide health database representing a diverse set of more than one million individuals for the years 2015-2018. All HCV antibody and viral RNA tests ordered and completed during the study period were used to describe HCV seroprevalence (evidence of current/past HCV infection) and HCV viral load (active infection). Logistic regression was used to determine the odds of HCV seroprevalence or active infection. Analyses were adjusted for sex, race/ethnicity, birth cohort, and HIV status, then stratified by birth cohort and HIV status. Overall, HCV seroprevalence and active infection rates were highest among white/non-Hispanics, males, and baby boomers. However, stratified results revealed distinct risk groups across year and race/ethnicity. Odds of a positive HCV antibody test were higher among Black/non-Hispanic individuals born before 1945 (aOR: 2.74; 95% CI: 1.98-3.78) and 1945-1965 (aOR: 1.46; 95% CI: 1.36-1.56) when compared to white/non-Hispanics. However, in individuals born after 1966, Black/non-Hispanics had lower odds of a positive HCV antibody test (1966-1985, aOR: 0.28; 95% CI: 0.25-0.32; after 1985, aOR: 0.15; 95% CI: 0.13-0.18), with white/non-Hispanics having the highest HCV seroprevalence in younger birth cohorts. The same was observed for HCV active infection. When stratified by HIV status, odds of HCV seroprevalence were highest among baby boomers, regardless of HIV status. However, odds of an active HCV infection across birth cohorts differed by HIV status. Among people living with HIV, active infection was lower in people born after 1985 compared to baby boomers (aOR: 0.45; 95% CI: 0.24-0.86). In contrast, active infection was higher among individuals born after 1985 (aOR: 1.48; 95% CI: 1.28-1.70) for HIV-uninfected individuals in Florida. Patterns of seroprevalence and active HCV infection identified in our study support the recent shift form age and risk based screening guidelines to universal screening. This study provides further guidance to support a strategy for early detection and treatment of HCV to prevent hepatocellular carcinoma among diverse populations. Citation Format: Brittney L. Dickey, Anna E. Coghill, Richard R. Reich, Julie Rathwell, Susan T. Vadaparampil, Shannon M. Christy, Richard Roetzheim, Elizabeth Shenkman, Anna R. Giuliano. HCV seroprevalence and RNA detection rates across Florida, 2015-2018 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2265.

Distributed Quasi-Poisson regression algorithm for modeling multi-site count outcomes in distributed data networks

BackgroundObservational studies incorporating real-world data from multiple institutions facilitate study of rare outcomes or exposures and improve generalizability of results. Due to privacy concerns surrounding patient-level data sharing across institutions, methods for performing regression analyses distributively are desirable. Meta-analysis of institution-specific estimates is commonly used, but has been shown to produce biased estimates in certain settings. While distributed regression methods are increasingly available, methods for analyzing count outcomes are currently limited. Count data in practice are commonly subject to overdispersion, exhibiting greater variability than expected under a given statistical model. ObjectiveWe propose a novel computational method, a one-shot distributed algorithm for quasi-Poisson regression (ODAP), to distributively model count outcomes while accounting for overdispersion. MethodsODAP incorporates a surrogate likelihood approach to perform distributed quasi-Poisson regression without requiring patient-level data sharing, only requiring sharing of aggregate data from each participating institution. ODAP requires at most three rounds of non-iterative communication among institutions to generate coefficient estimates and corresponding standard errors. In simulations, we evaluate ODAP under several data scenarios possible in multi-site analyses, comparing ODAP and meta-analysis estimates in terms of error relative to pooled regression estimates, considered the gold standard. In a proof-of-concept real-world data analysis, we similarly compare ODAP and meta-analysis in terms of relative error to pooled estimatation using data from the OneFlorida Clinical Research Consortium, modeling length of stay in COVID-19 patients as a function of various patient characteristics. In a second proof-of-concept analysis, using the same outcome and covariates, we incorporate data from the UnitedHealth Group Clinical Discovery Database together with the OneFlorida data in a distributed analysis to compare estimates produced by ODAP and meta-analysis. ResultsIn simulations, ODAP exhibited negligible error relative to pooled regression estimates across all settings explored. Meta-analysis estimates, while largely unbiased, were increasingly variable as heterogeneity in the outcome increased across institutions. When baseline expected count was 0.2, relative error for meta-analysis was above 5% in 25% of iterations (250/1000), while the largest relative error for ODAP in any iteration was 3.59%. In our proof-of-concept analysis using only OneFlorida data, ODAP estimates were closer to pooled regression estimates than those produced by meta-analysis for all 15 covariates. In our distributed analysis incorporating data from both OneFlorida and the UnitedHealth Group Clinical Discovery Database, ODAP and meta-analysis estimates were largely similar, while some differences in estimates (as large as 13.8%) could be indicative of bias in meta-analytic estimates. ConclusionsODAP performs privacy-preserving, communication-efficient distributed quasi-Poisson regression to analyze count outcomes using data stored within multiple institutions. Our method produces estimates nearly matching pooled regression estimates and sometimes more accurate than meta-analysis estimates, most notably in settings with relatively low counts and high outcome heterogeneity across institutions.

Accurate long-term prediction of death for patients with cirrhosis.

Cirrhosis is a major cause of death and is associated with extensive health care use. Patients with cirrhosis have complex treatment choices due to risks of morbidity and mortality. To optimally counsel and treat patients with cirrhosis requires tools to predict their longer-term liver-related survival. We sought to develop and validate a risk score to predict longer-term survival of patients with cirrhosis. We conducted a retrospective cohort study of adults with cirrhosis with no major life-limiting comorbidities. Adults with cirrhosis within the Veterans Health Administration were used for model training and internal validation, and external validation used the OneFlorida Clinical Research Consortium. We used four model-building approaches including variables predictive of cirrhosis-related mortality, focused on discrimination at key time points (1, 3, 5, and 10 years). Among 30,263 patients with cirrhosis ≤75 years old without major life-limiting comorbidities and complete laboratory data during the baseline period, the boosted survival tree models had the highest discrimination, with 1-year, 3-year, 5-year, and 10-year survival rates of 0.77, 0.81, 0.84, and 0.88, respectively. The 1-year, 3-year, and 5-year discrimination was nearly identical in external validation. Secondary analyses with imputation of missing data and subgroups by etiology of liver disease had similar results to the primary model. We developed and validated (internally and externally) a risk score to predict longer-term survival of patients with cirrhosis. This score would transform management of patients with cirrhosis in terms of referral to specialty care and treatment decision-making for non-liver-related care.

Prevalence of Alzheimer's and Related Dementia Diseases and Risk Factors Among Transgender Adults, Florida, 2012‒2020.

Objectives. To estimate the prevalence rates of Alzheimer's disease and related dementias (ADRD) and their risk factors in the transgender population and compare the rates to those in cisgender adults. Methods. We identified 1784 transgender adults in the linked electronic health records and claims data between 2012 and 2020 from the OneFlorida Clinical Research Consortium. We calculated the prevalence of ADRD and ADRD risk factors for the transgender and matched cisgender control adults. Results. The prevalence of ADRD was higher in the transgender adults compared with the cisgender control adults. Overall, the prevalence of ADRD risk factors was significantly higher in the transgender adults than the cisgender controls for 11 out of the 13 risk factors, with the only exceptions being traumatic brain injury and visual impairment. Conclusions. Transgender adults are at significantly higher risk for ADRD than cisgender adults. Our study highlights the urgent need for more research on the unique ADRD risks among the aging transgender and larger sexual- and gender-minority populations. (Am J Public Health. 2022;112(5):754-757. https://doi.org/10.2105/AJPH.2022.306720).

Peripartum women’s perspectives on research study participation in the OneFlorida Clinical Research Consortium

Peripartum women’s perspectives on research study participation in the OneFlorida Clinical Research Consortium

Cognitive functioning in Florida’s oldest old: A successful aging analysis of individuals living over the age of 90

AbstractBackgroundAs our population ages, many individuals are surpassing the age of 90 and living independent lives free of major diseases. It is critical to identify healthy lifestyle and genetic factors that contribute to “Successful Aging” (SA) in the population’s oldest‐old. This pilot study utilizes a unique dataset, the OneFlorida Clinical Research Consortium, to identify and recruit ‘Successfully Aged’ nonagenarians using electronic health records (EHR).MethodA consensus panel of experts created SA criterion, while taking EHR bioinformatics into consideration, to identify individuals 90 years and older who would be considered “Successful Agers”. To validate this cohort, researchers conducted a three‐phase recruitment period that included two contact letters and a third phone call attempt. Interested participants were screened by telephone and administered the Telephone Interview for Cognitive Status (TICS‐M) and the Geriatric Depression Scale (GDS).ResultUsing OneFlorida and EHR data from the University of Florida Health Systems, 1,901 individuals were identified as a virtual SA cohort. Of the 1,901 identified virtual cohort, 103 participants consented to the study (Mean Age = 92.11; 47.57% Female; 91.26% White). Of these, 18 individuals were excluded due to health conditions not captured in the EHR and thus were not administered the TICS‐M or GDS. Of the 85 participants who were administered the TICS‐M (Mean TICS‐M = 31.45, SD = 5.09), 16 participants scored below the cut‐off of ≤27/50 indicating cognitive impairment, 26 participants fell within the MCI range (28‐31/50), and 43 participants scored in the cognitively normal range (≥32/50). Of the 77 participants who were administered the GDS, 2 participant endorsed significant symptoms of depression (Mean GDS = 1.36, SD = 1.42). Together, this resulted in 69 (67%) participants who completed the full assessment battery and were classified as Successfully Aged.ConclusionUtilizing this three‐phase recruitment process, researchers were able to further investigate the demographic characteristic of our “Over 90s” population from the OneFlorida identified cohort. Future research is underway to conduct whole‐genome sequencing and identify a new Over 85 cohort. This study uniquely utilized an EHR registry to identify and assess a population of over 90‐year‐old persons considered SA, living independently and free of cognitive impairment.

Pulmonary nontuberculous mycobacterial disease in Florida and association with large-scale natural disasters

BackgroundLittle is known about the impact of the ecosystem disruption and its contribution on the non-tuberculosis mycobacteria (NTM) diseases (cases) rate in Florida (FL), a state with a high prevalence of NTM in the United States. We aimed to evaluate the epidemiological distribution of NTM in FL and identify its association with extreme weather events.MethodsWe used OneFlorida Clinical Research Consortium dataset and extracted data on NTM cases using ICD codes 9- CM 031.0 and ICD-10 A31 during 2012–2018. The number of hurricanes during the study period which affected FL were extracted data from the National Hurricane Center (NHC) and the National Oceanic and Atmospheric Administration (NOAA).ResultsPrevalence of NTM gradually increased during the study period. The rate was 2012: 14.3/100,000, 2015; 20.1/100,000 and 2018; 22.6/100,00 except in 2014 where there was an 8% decrease. The incidences were 2012; 6.5/100,00, 2015; 4.9/100,000 and in 2015; 5.4/100,000. Geographical analysis demonstrated a gradual expansion of the NTM cases in Alachua, and Marion Counties throughout the study period. Notably, the 2018 heat map showed higher prevalence of NTM in the northwestern, panhandle region of FL which had been absent in the heat maps for years 2012–2018. High number of the hurricanes was associated with the higher number of the new cases of NTM infection for years 2012, 2016–2018, while the lower number of the hurricanes was associated with the lower number of the new cases of NTM infection for years 2014–2015.ConclusionThe current study found the prevalence rates of NTM disease in FL rose from 2012 to 2018. A higher prevalence was seen following the hurricanes.

The OneFlorida Data Trust: a centralized, translational research data infrastructure of statewide scope.

The OneFlorida Data Trust is a centralized research patient data repository created and managed by the OneFlorida Clinical Research Consortium (“OneFlorida”). It comprises structured electronic health record (EHR), administrative claims, tumor registry, death, and other data on 17.2 million individuals who received healthcare in Florida between January 2012 and the present. Ten healthcare systems in Miami, Orlando, Tampa, Jacksonville, Tallahassee, Gainesville, and rural areas of Florida contribute EHR data, covering the major metropolitan regions in Florida. Deduplication of patients is accomplished via privacy-preserving entity resolution (precision 0.97–0.99, recall 0.75), thereby linking patients’ EHR, claims, and death data. Another unique feature is the establishment of mother-baby relationships via Florida vital statistics data. Research usage has been significant, including major studies launched in the National Patient-Centered Clinical Research Network (“PCORnet”), where OneFlorida is 1 of 9 clinical research networks. The Data Trust’s robust, centralized, statewide data are a valuable and relatively unique research resource.

P09.02 A Clinical Evaluation Algorithm to Define Clinical Utility of Lung Nodule Diagnosis in a Multi-Collaborator Setting Using Real World Data

New biomarkers for the diagnosis of indeterminate lung nodules (IPN) suspicious for lung cancer are being developed regularly. Clinically useful new diagnostic tests for IPN requires improvement in post-test risk classification to reduce subsequent testing or time to diagnosis. Therefore, measuring the activity to a diagnosis is needed. In a collaboration between Lahey Hospital and Medical Center (LHMC), representing a large screening only population, Vanderbilt University Medical Center (VUMC), a tertiary referral center with incidental and screening discovered nodules, and OneFlorida Clinical Research Consortium, a statewide network with incidental nodules, we defined an algorithm to combine real world data (RWD) from electronic health records (EHR) to measure the clinical activity necessary to diagnose an IPN.