University Of Michigan Health System Research Articles

Changing sustainable diet behaviours during the COVID-19 Pandemic: inequitable outcomes across a sociodemographically diverse sample of adults.

The objective of this study was to describe changes in sustainable dietary behaviours (those that support environmental, economic, and physical health) among a sample of US adults during the COVID-19 pandemic and to examine differences in changes by individuals' race/ethnicity and socioeconomic status. Therefore, a cross-sectional online survey study was conducted in April 2021 (N = 1,488, mean age = 42.7 (SD = 12.6)) receiving outpatient care from Michigan Medicine, the University of Michigan health system. Enrolment quotas were established to ensure a diverse sample-one-third of participants identified as African American/Black, one-third Hispanic/Latino, one-third White, and one-third low-income. Participants reported engaging in more behaviours that are supportive of a sustainable diet one year into the COVID-19 pandemic compared to before. This is particularly true regarding ecologically and economically sustaining behaviours such as taking fewer trips to the grocery store, increased use of home grocery delivery, increased cooking at home, and greater consumption of healthy foods. Not all behaviour changes promoted sustainable food systems; namely, the use of farmer's markets and Community Supported Agriculture (CSAs) declined. White and high-income participants were more likely than African American/Black, Hispanic/Latino, and low-income individuals to engage in ecologically and economically sustainable dietary behaviours during the pandemic. Meanwhile, African American/Black participants reported large increases in physical health sustainable dietary behaviours. To support the continuation of greater engagement with sustainable diets, policies that increase access to public transportation, limit the frequency with which consumers have groceries delivered, increase work-from-home options, and improve access for low-income populations should be prioritised.

Multiple sclerosis and seizures: A retrospective observational study in a multiple sclerosis autoimmunity center of excellence

PurposeThe prevalence of epilepsy in patients with multiple sclerosis (MS) is three to six times the prevalence in the general population. Mechanisms resulting in increased seizure risk are not fully understood. Our objective is to characterize patients with MS and epilepsy regarding timing of diagnoses, MS and seizure (SZ) type, EEG findings suggesting cortical dysfunction, frequency of status epilepticus (SE), and seizure freedom. MethodsThis was a single center retrospective study. Cases were obtained via DataDirect via the University of Michigan electronic medical record from January 1, 2006 through October, 12, 2016. The University of Michigan Health System is a large academic institute with a tertiary referral center and an Autoimmunity Center of Excellence. Patients were included if chart listed one or more of the top 62 epilepsy, and one or more of the top 2 MS, most frequently entered ICD9 and ICD10 codes. Patients with alternative epilepsy etiology were excluded. 74 of 361 patients were included. We collected information regarding demographics, MS and SZ type, age at diagnosis, imaging, EEG, seizure freedom, medications, and SE. ResultsWe found a high percentage of patients with SE. Most patients with imaging had multiple lesions at seizure onset. 27/54 of patients with EEG data showed electrographic evidence of cortical dysfunction. 6/8 of EEGs in PPMS showed features consistent with cortical dysfunction, followed by 9/17 in SPMS and 11/23 in RRMS. 7/8 of patients with PPMS showed EEG evidence of temporal lobe dysfunction. ConclusionTime of seizure onset relative to MS diagnosis varied with MS type suggesting distinct pathophysiology. EEG results correspond with reports of increased cortical damage and temporal dysfunction in PPMS, but are unique as a functional modality (EEG) as indicator of gray matter dysfunction. EEG findings differed in RRMS and progressive MS suggesting possibility of supportive diagnostic marker. Our data suggests higher risk of SE in progressive MS and diminished rate of seizure freedom for MS patients with SE. We conclude that early treatment with antiseizure medication would be beneficial for MS patients with SE and with progressive MS forms and SZ, in agreement with previous studies.

Associations between metabolic hyperferritinaemia, fibrosis-promoting alleles and clinical outcomes in steatotic liver disease.

Ferritin has been investigated as a biomarker for liver fibrosis and iron in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). However, whether metabolic hyperferritinaemia predicts progression of liver disease remains unknown. In this study, we sought to understand associations between hyperferritinaemia and (1) adverse clinical outcomes and (2) common genetic variants related to iron metabolism and liver fibrosis. This was a retrospective analysis of adults with MASLD seen at the University of Michigan Health System, where MASLD was defined by hepatic steatosis on imaging, biopsy or vibration-controlled transient elastography, plus metabolic risk factors in the absence of chronic liver diseases other than hemochromatosis. The primary predictor was serum ferritin level, which was dichotomized based on a cut-off of 300 or 450 mcg/L for women or men. Primary outcomes included (1) incident cirrhosis, liver-related events, congestive heart failure (CHF), and mortality and (2) distribution of common genetic variants associated with hepatic fibrosis and hereditary hemochromatosis. Of 7333 patients with MASLD, 1468 (20%) had elevated ferritin. In multivariate analysis, ferritinaemia was associated with increased mortality (HR 1.68 [1.35-2.09], p < .001) and incident liver-related events (HR 1.92 [1.11-3.32], p = .019). Furthermore, elevated ferritin was associated with carriage of cirrhosis-promoting alleles including PNPLA3-rs738409-G allele (p = .0068) and TM6SF2-rs58542926-T allele (p = 0.0083) but not with common HFE mutations. In MASLD patients, metabolic hyperferritinaemia was associated with increased mortality and higher incidence of liver-related events, and cirrhosis-promoting alleles but not with iron overload-promoting HFE mutations.

Reversing Synchronized Brain Circuits Using Targeted Auditory-Somatosensory Stimulation to Treat Phantom Percepts

Animal models have shown altered dorsal cochlear nucleus circuitry in animals that develop tinnitus; however, precise treatment using bisensory (auditory and somatosensory) stimuli can reverse altered neural patterns and lessen tinnitus. To confirm and extend the findings of a pilot study, which suggested an increased efficacy of bisensory stimulation, to a clinical trial with a greater duration and greater number of participants. This double-blind, crossover, single-center randomized clinical trial was conducted from March 2019, with a 3-month follow-up per participant ending in July 2022. Eligible adults were recruited from the University of Michigan Health System in Ann Arbor, Michigan. Eligibility criteria included bothersome tinnitus (Tinnitus Functional Index [TFI] score, ≥17 points), somatic tinnitus, normal to moderate hearing loss, and no other tinnitus treatments in the 6 months prior to the trial. Included participants were randomized to either treatment group 1, which received active (bisensory) treatment, or group 2, which received the control (auditory-only) treatment. Results were analyzed using intent-to-treat (ITT) and per protocol (PP) populations. Precisely timed bisensory (combined auditory and somatosensory) treatment was delivered through a portable, custom, take-home device that was provided to each participant for daily, at-home treatments. Group 1 participants received 30 minutes per day of the bisensory treatment for 6 weeks, followed by a 6-week washout phase, and then 30 minutes per day of the auditory-only treatment followed by a second 6-week washout phase. Group 2 participants received the auditory-only treatment first, followed by a washout phase, and then the bisensory treatment followed by a second washout phase. Primary end points were changes in TFI score and tinnitus loudness level from baseline through week 6 and week 12. Of 337 screened individuals, 99 (mean [SD] age, 47 [12.7] years; 59 males [60%]; 85 with non-Hispanic White [86%] race and ethnicity) were enrolled into the study and randomized to treatment group 1 (n = 49) or group 2 (n = 50). The active but not the control treatment resulted in clinically significant decreases in TFI scores at week 6 of phase 1 (ITT population: -12.0 [95% CI, -16.9 to -7.9] points; P < .001; PP population: -13.2 [95% CI, -16.0 to -10.5] points; P < .001). Decreases in tinnitus loudness level were greater than 6 dB sensation level (SL; >half as loud) at week 6 for the bisensory treatment group, with little effect for the auditory-only treatment control group at week 6 of phase 1 (ITT population: -5.8 [95% CI, -9.5 to -2.2] dB; P = .08; PP population: -7.2 [95% CI, -11.4 to -3.1] dB; P = .03), and up to 11 dB SL at week 12 of phase 2 (ITT population: -10.9 [95% CI, -15.2 to -6.5] dB; P = .001; PP population: -14.1 [95% CI, -18.4 to -9.8] dB; P < .001). Decreased tinnitus loudness level and TFI scores extended into the washout phase, indicating a prolonged treatment effect. This trial found that precisely timed bisensory treatment using stimuli and timing developed in a validated animal model was effective for adults with somatic tinnitus. Prolonged reduction in tinnitus symptoms can result from using an extended treatment duration. ClinicalTrials.gov Identifier: NCT03621735.

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Department of Family Medicine, University of Michigan Health System, Ann Arbor, MI Address for correspondence: Robert Kiningham, MD, FACSM, University of Michigan Health System, Department of Family Medicine, M7300 Medical Science, 1150 W. Medical Center, Dr. SPC 5625, Ann Arbor, MI 48109; E-mail: [email protected].

Using electronic medical records to develop a predictive model of 30‐day hospital readmission for people with ADRD

AbstractBackgroundHospitals are insufficiently equipped for Alzheimer’s disease and related dementia (ADRD) patients. Thus, readmission incidence is much higher and costlier among ADRD patients than the general population. Hospital discharges often occur without adequate preparation for the complex care management needs of ADRD patients and their caregivers. This study’s objective was to develop a risk‐assessment tool for hospitalized patients with ADRD. By supporting timely and better identification of who is at risk and why, already‐scarce resources can be allocated more efficiently to reduce readmissions.MethodsWe used 2016‐2019 EMR data from the University of Michigan health system (Michigan Medicine) and applied machine learning techniques (Random Forest, XGBoost, and Logistic LASSO) to develop a readmission risk‐assessment tool. We identified 2,899 individuals with ADRD who had at least one index hospital admission. All data features available in EMR – demographics, lab results, prior counts of healthcare use, and characteristics of index hospitalization – were included in our predictive models. Additionally, we geocoded the street address of place of residence using the National Neighborhood Data Archive (NaNDA) using the U.S. Census tract‐level information to include two composite measures of socioeconomic status: disadvantage and affluence.ResultsThe readmission rate for ADRD patients was 22% versus 17% for the general population. The best predictive model was the Random Forest (area under the receiver operating characteristic curve = 0.66; sensitivity = 0.64; specificity = 0.61). The accuracy of our model (0.61) was 42% higher than the LACE score (0.43), which is currently used by the hospital for all patients. The top 5 predictors of 30‐day readmission among people with ADRD included length of hospital stay, frailty index, living in a disadvantaged neighborhood, and total prior‐year healthcare charges.ConclusionADRD patients are highly vulnerable and require many resources, with substantially greater readmission risk and elevated rates of other adverse health events. Leveraging EMR data in a readmission risk‐assessment tool can help inform appropriate and efficient coordination and transitions of care for ADRD patients. Our risk‐assessment tool can identify ADRD patients at high risk for readmission and why they are at higher risk. This can enable better decision‐making upon discharge.

CORR Insights®: Did a New Design of the Oxford Unicompartmental Knee Prosthesis Result in Improved Survival? A Study From the Norwegian Arthroplasty Register 2012-2021.

1Clinical Assistant Professor, Department of Orthopaedic Surgery, University of Michigan Health System, Ann Arbor, MI, USA B. R. Hallstrom, University of Michigan Health System, Department of Orthopaedic Surgery, 2912 Taubman Center, Box 5328, 1500 East Medical Center Drive, Ann Arbor, MI 48109-5328, USA, Email: [email protected] This CORR Insights® is a commentary on the article “Did a New Design of the Oxford Unicompartmental Knee Prosthesis Result in Improved Survival? A Study From the Norwegian Arthroplasty Register 2012-2021” by Skåden and colleagues available at: DOI: 10.1097/CORR.0000000000002671. The author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request. The opinions expressed are those of the writer, and do not reflect the opinion or policy of CORR® or The Association of Bone and Joint Surgeons®.

Learn Labial Salivary Gland Biopsy Online.

From the *Division of Rheumatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI †Division of Rheumatology, Department of Medicine, University of Wisconsin–Madison, Madison, WI Robert W. Ike ORCiD ID https://orcid.org/0000-0003-4715-4384 Sara S. McCoy ORCiD ID: 0000-0002-5293-4185 R.W.I. and S.S.M. declare no conflict of interest. S.S.M. does basic research into aspects of Sjögren disease, supported by the Clinical and Translational Science Award program, through the NIH National Center for Advancing Translational Sciences (grant 1KL2TR002374), the National Institutes of Dental and Craniofacial Research (grant 1R03DE031340), and the National Cancer Institute (grant P50 CA278595). Correspondence: Robert W. Ike, MD, 1611 Harbal Dr, Ann Arbor MI 48105. E-mail: [email protected].

Why It Is Important to Understand the Microbiome within the Context of Surgical Disease

Surgical InfectionsVol. 24, No. 3 ForewordWhy It Is Important to Understand the Microbiome within the Context of Surgical DiseaseLena M. NapolitanoLena M. NapolitanoAddress correspondence to: Dr. Lena M. Napolitano, Department of Surgery, University of Michigan Health System, 1500 East Medical Center Drive, Ann Arbor, MI 48109-5033, USA E-mail Address: lenan@umich.eduDepartment of Surgery, University of Michigan Health System, Ann Arbor, Michigan, USA.Search for more papers by this authorPublished Online:30 Mar 2023https://doi.org/10.1089/sur.2022.417AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"Why It Is Important to Understand the Microbiome within the Context of Surgical Disease." Surgical Infections, 24(3), pp. 205–206FiguresReferencesRelatedDetails Volume 24Issue 3Apr 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Lena M. Napolitano.Why It Is Important to Understand the Microbiome within the Context of Surgical Disease.Surgical Infections.Apr 2023.205-206.http://doi.org/10.1089/sur.2022.417Published in Volume: 24 Issue 3: March 30, 2023PDF download

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Department of Family Medicine, M7300 Medical Science, University of Michigan Health System, Ann Arbor, MI Address for correspondence: Robert Kiningham, MD, FACSM, Department of Family Medicine, University of Michigan Health System, M7300 Medical Science, 1150 W. Medical Center Dr. SPC 5625, Ann Arbor, MI 48109; E-mail: [email protected].

278 Eight Pharmacokinetic-related Genetic Variants Were Not Associated with Bleeding from Direct Oral Anticoagulants in Non-valvular Atrial Fibrillation Patients

OBJECTIVES/GOALS: Assess the association of PK-related single nucleotide variants (SNVs) with the risk of bleeding from DOACs in non-valvular AF patients. METHODS/STUDY POPULATION: A retrospective cohort study was carried out with 2,364 Caucasians with non-valvular AF and treated with rivaroxaban or apixaban. Patients were genotyped as part of the genomic biobank at the University of Michigan Health System. The primary endpoint was a composite of major and clinically relevant non-major (CRNM) bleeding. Cox proportional hazards regression with time-varying analysis assessed the association of 8 SNVs in 5 PK genes (ABCB1, ABCG2, CYP3A4, CYP3A5, CYP2J2) with the risk of bleeding from DOACs in unadjusted and covariate-adjusted models. Six tests were performed as 3 of the SNVs are in the same haplotype. P-values below the Bonferroni-corrected level of 8.33e-3 were considered statistically significant. RESULTS/ANTICIPATED RESULTS: A total of 412 (17.4%) major and CRNM bleeding events occurred over 2.27 ± 2.03 years of follow-up. None of the PK SNVs were significantly associated with bleeding risk on DOACs in both unadjusted and covariate-adjusted Cox regression models. DISCUSSION/SIGNIFICANCE: The effects of these eight genetic variants on exposure to DOACs may not be strong enough to translate into differences in clinical outcomes. Especially if the genetic inheritance underlying the risk of bleeding from DOACs is polygenic, reinforcing the need for further genomic studies on this subject.

Highlights From the Circulation Family of Journals.

Highlights From the Circulation Family of Journals.

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant

Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown. To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent. This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography. Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non-COVID-19 illness. Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non-COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non-COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non-COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non-COVID-19 illness with 90.2% accuracy. The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.

G. Paul DeRosa, MD Obituary 1939-2022.

*Department of Orthopaedics, University of Michigan Health System, Ann Arbor, Michigan †Director of Pediatrics Orthopaedics, Rainbow Babies and Children's Hospital, Cleveland, Ohio The authors declare no conflicts of interest. Reprints: Robert N. Hensinger, MD, University of Michigan, Ann Arbor 48109, MI. E-mail: [email protected].

Saving life and limb.

Academic Emergency MedicineEarly View REFLECTION Saving life and limb Folafoluwa O. Odetola MB ChB, MPH, Corresponding Author Folafoluwa O. Odetola MB ChB, MPH [email protected] orcid.org/0000-0001-8001-9630 Department of Pediatrics, Division of Pediatric Critical Care Medicine, University of Michigan Health System, Ann Arbor, MI, USA Correspondence Folafoluwa O. Odetola, Department of Pediatrics, Division of Pediatric Critical Care Medicine, University of Michigan Health System, G030W, 2800 Plymouth Road, Ann Arbor, MI 48109, USA. Email: [email protected]Search for more papers by this author Folafoluwa O. Odetola MB ChB, MPH, Corresponding Author Folafoluwa O. Odetola MB ChB, MPH [email protected] orcid.org/0000-0001-8001-9630 Department of Pediatrics, Division of Pediatric Critical Care Medicine, University of Michigan Health System, Ann Arbor, MI, USA Correspondence Folafoluwa O. Odetola, Department of Pediatrics, Division of Pediatric Critical Care Medicine, University of Michigan Health System, G030W, 2800 Plymouth Road, Ann Arbor, MI 48109, USA. Email: [email protected]mich.eduSearch for more papers by this author First published: 06 January 2023 https://doi.org/10.1111/acem.14657 Funding information: None. Supervising Editor: Dr. Brian Zink. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation

PREDICTIVE MODEL OF 30-DAY HOSPITAL READMISSION FOR PATIENTS WITH ALZHEIMER’S DISEASE

Abstract Hospitals are insufficiently equipped for patients with Alzheimer’s disease and related dementia (ADRD). Thus, 30-day hospital readmission is higher and costlier among ADRD patients than the general population of older adults. Our objective was to develop a risk-assessment tool for hospitalized patients with ADRD. We used 2016-2019 electronic medical record (EMR) data from the University of Michigan health system and applied machine learning techniques to develop a readmission risk-assessment tool. We identified 2,899 individuals with ADRD who had at least one index hospital admission. All data features available in EMR – demographics, lab results, prior counts of healthcare use, and characteristics of index hospitalization – were included in our predictive models. Additionally, we geocoded the street address of patients using the National Neighborhood Data Archive using the U.S. Census tract-level information to include two composite measures of socioeconomic status: disadvantage and affluence. The readmission rate for ADRD patients was 22% versus 17% for the general population. The best predictive model was the Random Forest (area under the receiver operating characteristic curve=0.66; sensitivity=0.64; specificity=0.61). The accuracy of our model (0.61) was 42% higher than the LACE score (0.43), which is currently used by the hospital for all patients. The top 5 predictors of 30-day readmission among people with ADRD included length of hospital stay, frailty index, living in a disadvantaged neighborhood, and total prior-year healthcare charges. Our risk-assessment tool identifies ADRD patients at high risk of readmission and why they are at higher risk. The tool enables better decision-making before discharge.

Abstract 14063: Eight Pharmacokinetic-Related Genetic Variants Were Not Associated With Bleeding From Direct Oral Anticoagulants in Non-Valvular Atrial Fibrillation Patients

Background. Atrial fibrillation (AF) is the leading cause of ischemic stroke and treatment has focused on reducing stroke risk through anticoagulation. Direct Oral Anticoagulants (DOACs) are the first-line guideline-recommended therapy in non-valvular AF since they are as effective and safer than warfarin. Although patients bleed less overall from DOACs, bleeding still is the main safety concern with this therapy. Previous studies have shown that genetic variants modify the function of metabolic enzymes or transporters involved in the pharmacokinetics (PK) of DOACs, which then affect DOAC plasma concentrations. However, whether those functional PK genetic variants are significantly associated with the risk of bleeding on DOACs is unknown. Aim. Assess the association of PK-related single nucleotide variants (SNVs) with the risk of bleeding from DOACs in non-valvular AF patients. Methods. A retrospective cohort study was carried out with 2,364 Caucasians with non-valvular AF and treated with rivaroxaban or apixaban. Patients were genotyped as part of the genomic biobank at the University of Michigan Health System. The primary endpoint was a composite of major and clinically relevant non-major (CRNM) bleeding. Cox proportional hazards regression with time-varying analysis assessed the association of 8 SNVs in 5 PK genes ( ABCB1 , ABCG2 , CYP3A4 , CYP3A5 , CYP2J2 ) with the risk of bleeding from DOACs in unadjusted and covariate-adjusted models. Six tests were performed as 3 of the SNVs are in the same haplotype. P-values below the Bonferroni-corrected level of 8.33e-3 were considered statistically significant. Results. A total of 412 (17.4%) major and CRNM bleeding events occurred over 2.27 ± 2.03 years of follow-up. None of the PK SNVs were significantly associated with bleeding risk on DOACs (Table 1). Conclusion. SNVs that affect the function of PK genes were not significantly associated with major and CRNM bleeding from rivaroxaban or apixaban in 2,364 AF patients.

Abstract 14037: Nonspecific Tools Performed Better in Discriminating Major Bleeding From Direct Oral Anticoagulants Than Specific Tools in a Real-World Population With Atrial Fibrillation

Background. Direct oral anticoagulants (DOACs) are the first-line guideline-recommended therapy for preventing stroke in non-valvular atrial fibrillation (AF) patients. DOACs are increasingly being prescribed in the US compared to warfarin because of lower bleeding risk overall, but the proportion of Emergency Department visits involving DOACs-related bleeding increased from 2.3% in 2011 to 37.9% in 2017. Therefore, tools that accurately predict bleeding risk from DOACs are needed. Aim. This study compared the performance of six tools for predicting major bleeding risk in non-valvular AF patients on DOACs. Methods. A retrospective cohort study was carried out with 2,364 Caucasians diagnosed with non-valvular AF and treated with rivaroxaban or apixaban at the University of Michigan Health System. Bleeding risk was calculated using six existing tools: CHA 2 DS 2 -VASc, Anticoagulation-specific Bleeding Score (ABS), HAS-BLED, ATRIA, HEMORR2HAGES, and ORBIT. Major bleeding events were retrieved from medical records and classified according to International Society of Thrombosis and Haemostasis criteria. Discrimination was estimated using the c-statistic, and the areas under the curves (AUC) were compared using the Hanley-McNeil test. RGui was used for all statistical analyses and p-values &lt;0.05 were considered statistically significant. Results. A total of 97 (4.1%) patients experienced major bleeding on DOACs over 2.27 ± 2.03 years of follow-up. HAS-BLED and ABS are tools for specifically predicting major bleeding risk, but both had significantly lower performance than ATRIA, HEMORR2HAGES, and ORBIT ( Figure 1 ). There was no significant difference in the discriminative performance between HAS-BLED and ABS, as well as between ATRIA, HEMORR2HAGES, and ORBIT. Conclusion. ATRIA, HEMORR2HAGES, and ORBIT were the tools that performed better in discriminating major bleeding in AF patients if compared to HAS-BLED and ABS.

Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification.

Electronic health records (EHR) are not designed for population-based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow-up to the methodological work, we demonstrate how to apply these methods for two real-world case studies, and we evaluate their performance. In both examples, we use individual patient-level data collected through the University of Michigan Health System and various external population-based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age-related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR-based research.

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Department of Family Medicine, University of Michigan Health System, Ann Arbor, MI Address for correspondence: Robert B. Kiningham, MD, FACSM, Department of Family Medicine, University of Michigan Health System, M7300 Medical Science, 1150 W. Medical Center Dr. SPC 5625, Ann Arbor, MI 48109; E-mail: [email protected].