De-identified Data Research Articles

Prediction of Nursing Need Proxies Using Vital Signs and Biomarkers Data: Application of Deep Learning Models.

To develop deep learning models to predict nursing need proxies among hospitalised patients and compare their predictive efficacy to that of a traditional regression model. This methodological study employed a cross-sectional secondary data analysis. This study used de-identified electronic health records data from 20,855 adult patients aged 20 years or older, admitted to the general wards at a tertiary hospital. The models utilised patient information covering the preceding 2 days, comprising vital signs, biomarkers and demographic data. To create nursing need proxies, we identified the six highest-workload nursing tasks. We structured the collected data sequentially to facilitate processing via recurrent neural network (RNN) and long short-term memory (LSTM) algorithms. The STROBE checklist for cross-sectional studies was used for reporting. Both the RNN and LSTM predicted nursing need proxies more effectively than the traditional regression model. However, upon testing the models using a sample case dataset, we observed a notable reduction in prediction accuracy during periods marked by rapid change. The RNN and LSTM, which enhanced predictive performance for nursing needs, were developed using iterative learning processes. The RNN and LSTM demonstrated predictive capabilities superior to the traditional multiple regression model for nursing need proxies. Applying these predictive models in clinical settings where medical care complexity and diversity are increasing could substantially mitigate the uncertainties inherent in decision-making processes. We used de-identified electronic health record data of 20,855 adult patients about vital signs, biomarkers and nursing activities. The authors state that they have adhered to relevant EQUATOR guidelines: STROBE statement for cross-sectional studies. Despite widespread adoption of deep learning algorithms in various industries, their application in nursing administration for workload distribution and staffing adequacy remains limited. This study amalgamated deep learning technology to develop a predictive model to proactively forecast nursing need proxies. Our study demonstrates that both the RNN and LSTM models outperform a traditional regression model in predicting nursing need proxies. The proactive application of deep learning methods for nursing need prediction could help facilitate timely detection of changes in patient nursing demands, enabling the effective and safe nursing services.

Six Month Follow up of the one of the First Patients to Complete MDMA-Assisted Therapy for PTSD Outside of Clinical Trials in Australia: Implications for Translating Clinical Research to Clinical Practice

Purpose: The goal of this case report is to illustrate the implementation of 3,4-methylenedioxymethamphetamine (MDMA)-assisted therapy for posttraumatic stress disorder (PTSD) and learning points for health professionals in the translation of psychedelic assisted therapy research into clinical practice. We present our key learnings and 6-month follow up data from one of the first patients to complete MDMA-assisted therapy for PTSD outside of clinical trials, in an Australian outpatient clinic setting. Methodology: The patient described in this case report had chronic PTSD, depression and anxiety due to experiencing domestic violence, including sexual, physical, and psychological abuse in her adolescence. Treatment was informed by the Multidisciplinary Association of Psychedelic Studies (MAPS) MDMA-assisted therapy manual. After completing treatment, she enrolled in our weekly integration peer support group, including meditation workshops and family support sessions. Data was gathered using the Australian National University (ANU) Psychedelic-assisted therapy Australian national outcome database (https://medicine-psychology.anu.edu.au/research/research-projects/australian-interventional-pharmacotherapy-psychedelic-assisted-psychotherapy), which collates de-identified questionnaire data and outcomes from MDMA, psilocybin and ketamine assisted psychotherapy. Outcome data was gathered at baseline, the beginning of preparation, in the week after each dosing session, at the end of the program. Follow up data is collected at 3-, 6-, 9- and 12-months post-treatment. At the time of writing, she had completed 3 and 6 month follow up. Tables and figures are used to present the outcome data. Findings: Our patient no longer met DSM-V criteria for PTSD or major depressive disorder at the end of treatment, and at 6-month follow up. Her Impact of Events Scale – Revised (IES-R) score was 61 at intake, reduced to 0 after the second dosing session and remained 0 at 3 and 6-months follow up. There were no adverse events, with the only side effect reported being loss of appetite in the first 24 hours after each dosing session. All scores on depression and anxiety measures reduced to the normal ranges after the second dosing session and remained in the normal ranges at her six month follow up. After the second dosing session reported having greater self-compassion and being less troubled by the trauma memories. After the third dosing session, she began to feel comfortable leaving her children with their father or trusted family member to run errands for the first time, and an increased sense of connectedness with herself and with others. All these gains were maintained at three- and six-months post-treatment, illustrating a case of safe and effective implementation of MDMA-assisted therapy for PTSD in an outpatient mental health service. Unique Contribution to Theory, Practice and Policy: Our case study offers one model of clinical practice, contributing to Australian practitioner’s implementation of safe and effective MDMA-assisted therapy in mental health services. We reflect on learning points for practitioners such as whether MDMA or psilocybin would be the most effective treatment, for a patient with both PTSD and treatment resistant depression. Recommendations for practitioners include the importance of working in a dyad, accessing ongoing supervision in the early stages of implementation, and the benefits of the having had legal experience with psychedelic medicines as part of therapist training.

Comprehensive Analysis of TEK Variants in Patients With Vascular Malformations.

Pathogenic variants in the receptor tyrosine kinase TIE2, encoded by TEK, are known to cause vascular malformations (VMs). In this study, we retrospectively reviewed the deidentified data generated through clinical NGS testing in our laboratory and found 88 VM cases with a total of 107 clinically significant TEK variants. Among those, 23 unique variants at the amino acid level were identified, including five novel (p.Cys1040Arg, p.Arg1099PhefsTer12, p.Glu1109Ter, p.Phe1111LeufsTer7, p.Phe1111ValfsTer7) and 18 previously published variants. Missense variants were identified more often in the tyrosine kinase domain, while all nonsense/frameshift variants were clustered in the C-terminal tail (CTT). In addition, most variants occurred as solitary alterations, whereas certain variants always co-occurred with a second TEK variant. Five patterns of TEK variants (P1-P5) were identified: (P1) Arg849 + another variant; (P2) Tyr897 + another variant; (P3) Leu914 single variants; (P4) Arg915/918 single variants; and (P5) CTT single /co-occurring variants. This study provides the most comprehensive view of pathogenic TEK variants in VMs to date.

Examining the Digital Technology Usage of Older Adults as Determinants of Life Satisfaction in the Digital Divide.

The digital divide has become a growing source of inequality, as older adults face challenges in adopting and using digital technologies in daily life. Few studies have explored how different types of digital technology usage in various life domains among older adults impact their overall well-being. This study addresses this gap by suggesting effective directions for reducing the cross-generational digital divide. To this end, it analyzed de-identified secondary data from 864 South Korean adults above 65years, surveyed by the National Information Society Agency (NIA). The findings show that digital efficacy positively affects older adults' life satisfaction, mediated by their perceptions of digital technology. Additionally, social networking through digital technology positively influences their life satisfaction through the same mediation. Our findings suggest that policymakers should prioritize the development of educational programs to improve digital efficacy and social networking, which will ultimately enhance older adults' well-being.

Predictive modelling of linear growth faltering among pediatric patients with Diarrhea in Rural Western Kenya: an explainable machine learning approach

IntroductionStunting affects one-fifth of children globally with diarrhea accounting for an estimated 13.5% of stunting. Identifying risk factors for its precursor, linear growth faltering (LGF), is critical to designing interventions. Moreover, developing new predictive models for LGF using more recent data offers opportunity to enhance model accuracy, interpretability and capture new insights. We employed machine learning (ML) to derive and validate a predictive model for LGF among children enrolled with diarrhea in the Vaccine Impact on Diarrhea in Africa (VIDA) study and the Enterics for Global Heath (EFGH) ― Shigella study in rural western Kenya.MethodsWe used 7 diverse ML algorithms to retrospectively build prognostic models for the prediction of LGF (≥ 0.5 decrease in height/length for age z-score [HAZ]) among children 6–35 months. We used de-identified data from the VIDA study (n = 1,106) combined with synthetic data (n = 8,894) in model development, which entailed split-sampling and K-fold cross-validation with over-sampling technique, and data from EFGH-Shigella study (n = 655) for temporal validation. Potential predictors (n = 65) included demographic, household-level characteristics, illness history, anthropometric and clinical data were identified using boruta feature selection with an explanatory model analysis used to enhance interpretability.ResultsThe prevalence of LGF in the development and temporal validation cohorts was 187 (16.9%) and 147 (22.4%), respectively. Feature selection identified the following 6 variables used in model development, ranked by importance: age (16.6%), temperature (6.0%), respiratory rate (4.1%), SAM (3.4%), rotavirus vaccination (3.3%), and skin turgor (2.1%). While all models showed good prediction capability, the gradient boosting model achieved the best performance (area under the curve % [95% Confidence Interval]: 83.5 [81.6–85.4] and 65.6 [60.8–70.4]) on the development and temporal validation datasets, respectively.ConclusionOur findings accentuate the enduring relevance of established predictors of LGF whilst demonstrating the practical utility of ML algorithms for rapid identification of at-risk children.

How to Teach an "Old Dog" New Tricks: Improving Clinical Efficiency in a Well-Established Cochlear Implant Program.

To evaluate the implementation of a new streamlined service delivery model for cochlear implant (CI) patients at a mature academic CI program. Tertiary referral center. CI candidates and CI users. Implementation of a new CI service delivery model. CI surgical numbers, conversion rate from CI evaluation to surgery, documentation time, number of visits for new versus established CI users, ratio of CI clinical full-time equivalency to CI surgical numbers, time from CI referral to CI evaluation, patient travel burden. De-identified data from the electronic health record (EHR) were used to develop an efficiency improvement plan. With the old clinical model, audiologists' schedules were at capacity, wait for CI evaluation appointments was prolonged, and CI surgical numbers were declining. The new model implemented an interactive electronic medical record, a de-escalated postoperative programming schedule, inclusion of telehealth pre-CI surgery, and an evidence-based approach to CI programming. After a 4-year time period (2019-2022) of implementing clinical improvement strategies, the postoperative CI programming schedule in the first year after activation was reduced from 10 visits (unilateral CI user) and 16 visits (bilateral CI user) to 4 visits total. This saved the patient up to 16 hours of time at the clinic, reduced travel burden, and opened 19 weeks of appointment slots for new patients. Increased utilization of the EHR and telehealth increased the conversion rate from CI evaluation to CI surgery by 33% and decreased the no-show rate by 5%. Annual CI surgical numbers subsequently increased by 45% with the new model, which increased our program's CI utilization rate and reduced our role as a barrier to CI care. If CI programs wish to be instrumental in improving CI utilization rates, clinical care models need to be adapted now in preparation for the projected rise in the number of potential CI candidates. This streamlined clinical efficiency model serves as an example of patient-centered CI care that can be recreated at other institutions. Outcomes from our 4-year strategic initiative will add to the scarcity of literature in this area.

The global, regional, and national burden of urolithiasis in 204 countries and territories, 2000–2021: a systematic analysis for the Global Burden of Disease Study 2021

Urolithiasis is a common urological problem that is associated with high morbidity. A comprehensive assessment of the non-fatal and fatal health trends of urolithiasis by age, sex, and geography over time is necessary to inform policy to control this surgically managed non-communicable disease. This study was conducted using the standard GBD methodology and analytic tools. Cause-specific mortality rate (CSMR) was estimated using vital registration and verbal autopsy data and the Cause of Death Ensemble model (CODEm) modelling tool. CSMR estimates and incidence data from medical insurance claims and hospital discharges were analysed using a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to estimate age-, sex-, and location-specific incidence of urolithiasis between 2000 and 2021. Disability-adjusted life-years (DALYs) were the sum of years of life lost (YLL) and years lived with disability (YLDs). YLLs due to urolithiasis were calculated by multiplying the estimated number of deaths by the standard life expectancy at the age of death. YLDs were estimated by multiplying the disability weight by the symptomatic proportion of urolithiasis cases. The Global Burden of Diseases study used de-identified data, approved by the University of Washington IRB (Study Number 9060). There were 106 million (95% UI 88.3-129.0) incident cases of urolithiasis in 2021, of which 67% were in men (71.1 million [59.4-86.2)]). The global number of incident cases, deaths, and DALYs increased by 26.7% (23.8-29.8), 60.3% (41.5-84.7), and 34.5% (24.6-47.3), respectively, between 2000 and 2021. The global age-standardised incidence rate of urolithiasis experienced a significant decrease of 17.5% (14.7-20.0), while the age-standardised DALYs rate saw a reduction of 15.1% (6.8-21.3). Twelve GBD regions showed declining trends in the age-standardised incidence rate of urolithiasis between 2000 and 2021, and the remaining nine GBD regions had an increasing trend of age-standardised rates of urolithiasis. A significant increase in the age-standardised incidence rate of urolithiasis was observed in Central America, Tropical Latin America, and the Caribbean regions, whereas notable decline was observed in east Asia, eastern Europe, central Europe, and high-income North America. It was observed that the global age-standardised death rate was less than 0.5 per 100,000 across all GBD regions and less than 1 per 100,000 across all SDI quintiles, with fairly stable global age-standardised death rates of urolithiasis between 2000 and 2021. The age-standardised incidence rate of urolithiasis was 837 (688-1034) in low SDI regions and 1443 (12,108-1734) in high-middle SDI regions. Furthermore, the age-standardised DALY rate showed a decreasing trend across all SDI quintiles over the same period: high-middle SDI (-28.9% [-34.4 to-23.0]), middle SDI (-22.6% [-30.5 to-10.9]), and low-middle SDI (-2.9% [-15.8 to 12.9]). Global urolithiasis incidence and DALY rates have decreased, while the death rate has stabilised worldwide, showing significant variability among regions, SDI levels, and countries. This could be due to effective preventive measures c on urolithiasis risk factors, effective public health education, lifestyle changes, and early interventions and improved health care access at the global level. This analysis offers relevant insights into global, regional, and country-specific urolithiasis trends. Bill & Melinda Gates Foundation.

An Indian multicentre real-world study on long-term quality of life outcomes following bariatric surgery.

The purpose of this study was to assess the impact of metabolic and bariatric surgery (MBS) on Quality of Life (QoL) in Indian patients with obesity over 10 years. A retrospective chart review was conducted at 11 centres for individuals with MBS between February 2013 and May 2022. Patient medical records provided the source of de-identified data. Data from 2132 individuals with a mean age of 43.28 ± 11.96 years was analysed. There were 37.43% men and 62.57% females in the study population. The study population had a mean preoperative body mass index (BMI) of 45.71 ± 10.38 kg/m2. The Bariatric Analysis and Reporting Outcome System (BAROS) scoring method showed a higher overall QoL score throughout all follow-up periods, with 'very good' outcomes at one, three and 7 years and 'good' outcomes at 5 and 10 years. Improvements in QoL were associated with a substantial improvement (p < .01) in BMI at every follow-up time point. Following MBS, individuals with obesity exhibited a substantial and long-term improvement in their overall QoL for up to 10 years. This study presents Indian data on QoL, which is considered one of the most important decision-making factors for or against an intervention.

Association of Opioid Tapering with Pain-Related Emergency Department Visits, Hospitalizations, and Primary Care Visits: A Retrospective Cohort Study.

Tapering of chronic opioids has increased, with subsequent reports of exacerbated pain among patients who tapered. We aimed to evaluate the association between opioid dose tapering and subsequent pain-related healthcare utilization (ED visits, hospitalizations and primary care visits). We conducted a retrospective cohort study from 2015-2019 using data from the Optum Labs Data Warehouse that contains de-identified retrospective administrative claims data for commercial and Medicare Advantage enrollees in the US. Adults aged ≥18 years who were prescribed stable doses of opioids, ≥50 morphine milligram equivalents (MME)/day, during a 12-month baseline period. Tapering was defined as ≥ 15% relative reduction in mean daily opioid dose during one of 6 overlapping 60-day periods. Tapered patient-periods were subclassified as tapered-and-continued (MME > 0) vs. tapered-and-discontinued (MME = 0). We modeled monthly counts of visits for pain diagnoses up to 12 months after cohort entry using negative binomial regression as a function of tapering, baseline utilization, and patient level-covariates. Among 47,033 patients, 13,793 patients tapered. Compared to no taper, any taper was associated with more ED visits for pain (adjusted incidence rate ratio [aIRR] 1.21, 95% CI: 1.11-1.30), tapered then continued status was associated with more ED visits (aIRR 1.23, CI: 1.14-1.32) and hospitalizations (aIRR 1.14, CI: 1.03-1.27) for pain, and tapered-and-discontinued was associated with fewer primary care visits for pain (aIRR 0.68, CI: 0.61-0.76). These associations suggest that opioid tapering may lead to increased emergency and hospital utilization for acute pain and possibly a decreased perceived need for primary care for those whose opioids were discontinued.

Mental health of Australian frontline nurses during the COVID-19 pandemic: Results of a large national survey

This paper investigates the effects of work demands on burnout indices of emotional exhaustion, depersonalization and personal accomplishment, and mental health indices of anxiety, depression and stress, among Australian nurses and midwives. We used de-identified self-reported survey data from approximately 11,000 Australian nurses and midwives during the pandemic. Linear and quantile regression analyses explored how working conditions affect different aspects of nurses and midwives' burnout and mental health. Results show how working conditions affect burnout and mental health heterogeneously depending on the severity of the mental health symptoms. Increased quantitative and emotional work demands significantly impact occupational burnout indices of emotional exhaustion and depersonalization, and mental health indices of anxiety, depression, and stress among Australian nurses and midwives. Quantitative and emotional demands have more significant effects on people with higher levels of anxiety, depression, stress, and depersonalization than on those with milder or lesser symptoms. Given recent national and international policy focus on psychosocial hazards at work, this paper suggests that governments and health care providers need to monitor such hazards among nurses and midwives and introduce policies that reduce excessive quantitative or emotional burden to minimise risk of burnout and poor mental health and support good mental health among nurses and midwives.

Abstract 4139194: Predicting Cholesterol Screening Behavior After Age 50 Using Machine Learning: Insights from the Health and Retirement Study

Background: In the U.S., about 8% of adults never received cholesterol screening. Although machine learning (ML) has been used to develop decision tools for Atherosclerotic Cardiovascular Disease (ASCVD) risk prediction, its application in behavioral forecasting has not yet been explored in the context of cholesterol screening behaviors. This study aimed to examine the performance and accuracy of ML algorithms in forecasting cholesterol screening behaviors in adults after age 50. Methods: This analysis used deidentified data from the Health and Retirement Study (HRS) 2004-2018. HRS is a longitudinal survey among 23,000 households in the U.S. Participants were excluded from the current analysis if they passed away by 2019, ever had ASCVD or stroke, were under age 50 at baseline, or had missing data in self-reported cholesterol screening. In total, 7176 participants (mean age [SD]=62 [8]) met the inclusion criteria; participants were randomly split into a training set (80%) and a testing set (20%). The synthetic minority oversampling technique was used to solve the imbalance distribution of the rare event. Five ML algorithms were used: random forest, gradient boosting machine (GBM), XGBoost, Support Vector Machine (SVM), and logistic regression. Accuracy, AUROC, and positive predictive value (PPV) were used to compare model performance. The average gain was evaluated for feature importance in the demographic and health domains. Results: In total, 232 (3.2%) respondents did not receive any cholesterol screening from 2008 to 2018. Experiments with five ML algorithms suggested that XGBoost with deeper trees and learning rate performed better in classifying those who did not screen for cholesterol levels over 10 years. Adding prior cholesterol screening history (2004-2006) into the model significantly improved model performance. Hypertension, self-rated health, and smoking were the major health features, while insurance, poverty, and work status were the major demographic features in the predictive model (accuracy=0.97; AUROC=0.88; PPV=0.42). Conclusion: Findings underscore the potential utility of ML models in predicting cholesterol screening behaviors after age 50. This could be the basis for developing decision tools for clinicians to identify those with a lower chance of cholesterol screening or make reminders accordingly. The low-cost predictive model might improve the uptake of preventive screening behaviors in middle-aged and older adults.

Abstract 4116300: Valvular Abnormalities and Risk of Abdominal Aortic Aneurysm Associated with Fluoroquinolone Use; A Retrospective Cohort Study

Introduction: Fluoroquinolones (FQ) are efficacious antibiotics whose antimicrobial effect is achieved through bacterial DNA gyrase inhibition. However, there is evidence suggesting that FQ may cause impaired collagen synthesis and an increase in tissue collagen degradation. Collagen serves as a predominant structural constituent of the aortic wall and heart valves. These structures undergo tissue maintenance, which necessitates the regulated expression of MMPs and TGF-β. In this study, we hypothesized that prolonged usage of FQs may increase risk of valvular abnormalities and abdominal aortic aneurysm. Methods: We conducted a retrospective cohort study utilizing de-identified patient data from the TriNetX platform, an extensive electronic health records (EHR) system. The cohorts consisted of smoker patients who used FQs for more than two consecutive weeks, Cohort A, N= 352,215 and smoker patients without a history of FQ use, Cohort B, N=350,912. Propensity score matching balanced baseline characteristics between cohorts: age, sex, ethnicity, and comorbidities. Results: The risk of developing AAA among smokers with a history of FQ use was 1.222% while the risk was 0.806% among smokers without history of FQ use (P&lt; 0.0001, 95% CI (0.63,0.691), Risk Ratio (RR)= 0.66). The risk of developing nonrheumatic (NR) aortic insufficiency among smokers with history of FQ use was 1.479% while smokers with no history of fluoroquinolone use had a 0.95% risk (P&lt;&lt;0.0001, 95% CI (0.616,0.67), RR= 0.643). The risk of developing NR mitral insufficiency in smokers with history of FQ use was 3.873% versus their smoker FQ naive counterparts risk of 2.317% (P&lt; 0.0001, 95% CI (0.582,0.615), RR= 0.598). NR tricuspid insufficiency in smokers with FQ usage was also increased versus their smoker FQ naive counterparts at 3.089% and 1.758%, respectively (P&lt; 0.0001, 95% CI (0.552,0.587), RR =0.569). Conclusions: This study uncovered a significant association between FQ use, risk of AAA, and NR-valvular insufficiencies among smokers. In particular, male smokers are at highest risk of developing AAA. This same population is at risk for prostatitis that would likely need treatment with FQs, further increasing their risk. Despite the efficacy of this class of antibiotics, caution is paramount when prescribing FQs in individuals who are actively smoking or have any connective tissue pathologies.

Abstract 4118122: Effect of Beta Blockers on Exercise Capacity, Diastolic Function, and Quality of Life in Patients with Heart Failure with Preserved Ejection Fraction

Introduction: In recent landmark HFpEF trials, 75-80% of patients are taking beta blockers (BB) without clear evidence of clinical or symptomatic benefit. Patients with HFpEF commonly have comorbidities that have been classically managed with BB (AF, CAD, HTN), though the utility of this medication class for these conditions is being reconsidered. It remains unclear what impact BB use, in patients with HFpEF, has on exercise capacity, diastolic function, and quality of life. Hypothesis: We hypothesized that BB use is associated with worse exercise capacity, diastolic function, and quality of life in HFpEF patients. Methods: This retrospective cohort study was completed using deidentified patient data from the INDIE-HFpEF and RELAX clinical trials (two trials of HFpEF patients). Patients were divided into two groups based on BB use at trial enrollment. The primary outcomes were exercise capacity, diastolic function, and quality of life at baseline evaluation. A multivariate logistic regression model was used to assess for statistically significant differences after controlling for potentially confounding variables including age, sex, BMI, AF, prior MI, COPD, DM, HTN, serum creatinine, and NYHA functional class. Results: A total of 320 patients were included in this analysis, with 230 on BB (72%). Baseline demographics are shown in Table 1. Patients studied were older (68 years old), white (90%), obese (average BMI 34 kg/m 2 ) individuals with an average LVEF of 61%. Outcomes and multivariate linear regression results are shown in Table 2. After controlling for potential confounding variables, there were no differences in baseline exercise capacity [peak VO2 (mL/kg/min): 12.1 vs 13.2, P=0.233], diastolic function [average E/e’: 14 vs 12, P=0.125; LAVI (mL/m 2 ): 42 vs 37, P = 0.665; peak TR velocity (m/s): 2.8 vs 2.7, P = 0.165; PASP (mmHg): 41 vs 36, P = 0.357], quality of life [KCCQ: 54 vs 58, P = 0.206; MLHFQ 43 vs 46, P = 0.762], or NT-proBNP (633 vs 342; pg/mL, P = 0.208) in HFpEF patients on BB compared to those not on BB. Conclusion: There was no association with BB use and worse exercise capacity, diastolic function, or quality of life in HFpEF patients. This may be due to heterogenous treatment effects, with differing impact of BB based upon the underlying pathophysiology of unique HFpEF phenotypes. To definitively determine the impact of BB use in patients with HFpEF, both on clinical outcomes and symptoms, randomized controlled trials are necessary.

Abstract 4137535: Gastrointestinal Bleeding Risk: Diltiazem versus Metoprolol in Patients on Rivaroxaban, Apixaban, or Dabigatran.

Introduction: A recent JAMA study by Ray et al., reported severe bleeding risk among older adults with atrial fibrillation (Afib) receiving apixaban or rivaroxaban along with diltiazem compared to metoprolol. Our investigation extended to evaluating the gastrointestinal (GI) bleeding risk associated with various Xa inhibitors and dabigatran when combined with either diltiazem, or metoprolol among all adults ages 18 and older. Method: We conducted a comparative retrospective cohort study using de-identified patient data obtained from the global network of the TriNetX platform, a comprehensive electronic health records system comprising 120 healthcare systems and more than 147 million patients worldwide. The cohorts were divided into patients treated with diltiazem vs. metoprolol while receiving apixaban or rivaroxaban (Cohort A), patients receiving metoprolol with apixaban vs. rivaroxaban treatment (Cohort B), patients treated with diltiazem with apixaban vs rivaroxaban (Cohort C), and patients treated with diltiazem vs metoprolol who received dabigatran (Cohort D). The risk of gastrointestinal hemorrhage among the cohorts was evaluated. Propensity score matching was employed to balance baseline characteristics between the cohorts, including age, sex, ethnicity, and comorbidities. Results: The risk of GI bleed in patients taking diltiazem with either apixaban or rivaroxaban was 6.3%, while the risk in patients taking metoprolol with either of these Xa inhibitors was 5.7 % (p&lt; 0.0001, 95%CI (1.074,1.111), RR 1.09. In patients taking metoprolol with apixaban, GI bleed risk was 5.4%, compared to 6.4% in those taking metoprolol with rivaroxaban (p&lt; 0.0001, 95% CI (0.838, 0.868), RR 0.85. For patients taking diltiazem with rivaroxaban, GI bleed risk was 7.3%, whereas it was 5.8% in those taking diltiazem with apixaban (p&lt; 0.0001, 95% CI (1.194, 1.265), RR 1.23. Risk of GI bleed in patients taking diltiazem with dabigatran was 7.4%% while it was 6.71% for patients treated with metoprolol and dabigatran (p&lt; 0.0011, CI 95% (1.038,1.16), RR 1.097) Discussion: Global patients’ data in this study suggested similar findings reported by Ray et al. Additionally, among all ages adults with Afib, treatment with metoprolol and apixaban was associated with the lower risk of GI bleed when compared with metoprolol and rivaroxaban. While the highest risk of GI bleed was associated with concurrent diltiazem and dabigatran use.

Real-world use of multiplex point-of-care molecular testing or laboratory-based molecular testing for influenza-like illness in a 2021 to 2022 US outpatient sample.

While molecular testing is recommended for symptomatic patients suspected of having coronavirus disease 2019 (COVID-19), limited data are available examining real-world use of tests for severe acute respiratory syndrome coronavirus (SARS-CoV-2) and the impact of SARS-CoV-2 testing on patient outcomes. In this retrospective cohort study using de-identified administrative claims data in the Optum Labs Data Warehouse, we identified 2 groups of patients with ≥1 outpatient claims with a procedure code for SARS-CoV-2 testing between January 2021 and September 2022. Group 1 had ≥1 claims with CPT code 0240U or 0241U ("Xpert Xpress") (N = 51,602); Group 2 had ≥1 claims for laboratory-based molecular testing (N = 317,192). Outcomes assessed on the identification date and through the 90-day follow-up included claims evidence of use of SARS-CoV-2, influenza, and respiratory syncytial virus (RSV) tests, diagnosis of active COVID-19, influenza, or RSV, and use of treatments (antivirals for COVID-19, influenza, and RSV and other treatments for COVID-19 and RSV). Patients in Group 1 had fewer tests for SARS-CoV-2, influenza, or RSV (mean ± standard deviation 1.6±1.4 versus 2.6±2.6, standardized difference -0.45), faster time to diagnosis of COVID-19 (median 0 versus 4 days, standardized difference -0.27) or influenza (median 0 versus 5 days, standardized difference -0.74), and faster time to treatment of COVID-19, influenza, or RSV (median 1 versus 5 days, standardized difference 0.16) than patients in Group 2. In this nationwide real-world study of outpatient testing, use of point-of-care molecular multiplex SARS-CoV-2 testing resulted in fewer claims for SARS-CoV-2, influenza, and RSV tests, faster time to diagnosis, and faster time to treatment than laboratory-based molecular testing.

Boston Marathon athlete performance outcomes and intra-event medical encounter risk associated with low energy availability indicators

ObjectiveTo determine the association between survey-based self-reported problematic low energy availability indicators (LEA-I) and race performance and intra-event medical encounters during the Boston Marathon.Methods1030 runners who were registered for the...

DISP-19. ETHNIC DISPARITIES IN METASTASIS PATTERNS AND TREATMENT UTILIZATION AMONG METASTATIC BREAST CANCER PATIENTS WITH BRAIN METASTASIS: A RETROSPECTIVE ANALYSIS OF US HEALTH SYSTEMS DATA (2019-2023)

Abstract INTRODUCTION The treatment landscape for metastatic breast cancer (MBC) with brain metastasis (BM) has evolved due to novel therapies. However, outcomes remain poor and vary among groups. This study investigated ethnic disparities in metastasis (mets) patterns and treatment use among MBCBM patients (pts). METHODS We analyzed deidentified data from seven US health systems in the Guardian Research Network from December 2019 to December 2023. Pts with MBC were categorized by IHC scores: hormone receptor+/HER2-, HER2-low (IHC 1+ or 2+ with negative FISH), HER2 positive, and Triple Negative (TNBC). We used descriptive and comparative statistical analysis. RESULTS Of 421 MBC pts, 200 (47.5%) had HER2+, 167 (39.7%) had HR+/HER2-, and 54 (12.8%) had TNBC. 159 (37.7%) were reclassified as HER2-low, primarily from HR+/HER2- (127; 76.0%) and TNBC (32; 59.3%). Bone mets developed in 29.6% of pts, more prevalent in HER2+ (39.5%), TNBC (25.9%), and HR+/HER2- (19.1%) groups. Non-Hispanics showed higher BM prevalence (74.4% vs. 25.6%). HR+/HER2- BM pts had higher bone mets (71.88%) than HER2+ BM (65.82%). TNBC BM pts had the highest rate of liver mets (57.1%). HER2-low BM pts showed mets across liver (40%), lung (34.29%), and bone (68.57%). In the HER2+ BM group, non-Hispanics had higher liver (40.13% vs. 32.35%) and bone mets (69.08% vs. 52.94%) than Hispanics. In TNBC BM group, Hispanics had higher liver mets (66.67% vs. 35%), while non-Hispanics had higher lung mets (32.5% vs. 11.11%). Both ethnic groups in HR+/HER2- category had high bone mets (78.95% vs. 73.33%). There were disparities in the use of CDK4/6 inhibitors, ICIs, and mTOR inhibitors, with Hispanics frequently using them. CONCLUSION HR+/HER2- BM pts were most affected by bone mets, followed by HER2+ BM. TNBC BM pts predominantly had liver mets. Non-Hispanics had higher BM prevalence. Hispanics frequently used ICIs, CDK4/6 and mTOR inhibitors, which were more frequently used by Hispanics. Further research should explore these disparities in mets patterns, treatment choices, and responses.

Investigating the Fairness of Deep Learning Models in Breast Cancer Diagnosis Based on Race and Ethnicity

Breast cancer is the leading cancer affecting women globally. Despite deep learning models making significant strides in diagnosing and treating this disease, ensuring fair outcomes across diverse populations presents a challenge, particularly when certain demographic groups are underrepresented in training datasets. Addressing the fairness of AI models across varied demographic backgrounds is crucial. This study analyzes demographic representation within the publicly accessible Emory Breast Imaging Dataset (EMBED), which includes de-identified mammography and clinical data. We spotlight the data disparities among racial and ethnic groups and assess the biases in mammography image classification models trained on this dataset, specifically ResNet-50 and Swin Transformer V2. Our evaluation of classification accuracies across these groups reveals significant variations in model performance, highlighting concerns regarding the fairness of AI diagnostic tools. This paper emphasizes the imperative need for fairness in AI and suggests directions for future research aimed at increasing the inclusiveness and dependability of these technologies in healthcare settings. Code is available at: https://github.com/kuanhuang0624/EMBEDFairModels.

Medical students as teachers: impact of peer-led teaching sessions on NBME exam performance

Introduction/BackgroundPeer-led teaching sessions (PLTS) supplement conventional medical education, allowing students to share knowledge effectively. PLTS have shown significant academic benefits for student instructors. However, the impact on attendees, especially concerning NBME-style examinations, remains underexplored.MethodsThis study analyzed de-identified data from 67 first-year medical students at Kirk Kerkorian School of Medicine, divided into PLTS attendees (study group) and non-attendees (control group) based on session participation. Data were collected before two NBME examinations, with attendees defined as those attending at least five Peer-led teaching sessions. Academic performance was measured using NBME Customized Assessment Services (CAS) exam scores. Statistical analyses, including p-tests, chi-square, and t-tests, assessed differences in performance and potential biases based on MCAT scores, first-generation status, and socioeconomic background.ResultsThe study group initially had 26 students, and the control group had 31 students. Post-first examination, the study group adjusted to 22 students, and the control group to 35 students. MCAT scores and demographics showed no significant differences between groups. The first examination revealed a significant higher score in PLTS attendees’ scores (4.28% increase, p = 0.036), indicating a positive impact of PLTS on basic science knowledge. The second examination on organ systems physiology showed no significant difference (p = 0.722).DiscussionPLTS effectively enhance foundational science understanding but may be less impactful for advanced topics. The diminishing effect may result from increased use of diverse study resources and adaptation to medical school. Future research should explore long-term benefits, optimal implementation strategies, and the impact of instructor quality and engagement. Therefore, the overall conclusion is that PLTS positively influence NBME-style exam performance in early medical education, but benefits decrease over time.

Disparities in Colorectal Cancer Screening Before and After the Onset of the COVID Pandemic.

Colorectal cancer (CRC) screening is underutilized among those with lower socioeconomic status and in racial and ethnic minoritized populations who have been disproportionately impacted by COVID. To compare disparities in CRC screening before and after the onset of the COVID pandemic among privately insured individuals. Retrospective cohort study using deidentified claims data from the USA between January 1, 2017, and December 31, 2022. Blue Cross Blue Shield beneficiaries aged 50-75years with average risk of CRC. Mean screening use was compared by demographic and area-level socioeconomic factors between the periods preceding (January 1,2017 to February 28,2020) and following (July 1,2020 to December 31,2022) the onset of the COVID pandemic. Difference-in-differences analysis was used to evaluate changes in screening differences. Our study included 21,724,223 beneficiaries. Compared to males, females had higher screening in both periods (p < 0.05), and this sex difference in screening increased 1.63% (95% confidence interval [CI]: 1.32%, 1.94%) following the onset of the pandemic. Compared to residents in areas with high socioeconomic status (SES), low SES area residents had lower screening (p < 0.001) during both periods. Furthermore, this difference grew 4.32% (95% CI, 3.76%, 4.88%) during the post-onset period. Metropolitan area residents had higher screening than non-metropolitan area residents during both periods (p < 0.001); however, this difference decreased 0.77% (95% CI, 0.34%, 1.20%) during the post-onset period. Among beneficiaries with high risk of CRC, the difference in screening based on social deprivation index and metropolitan area status increased 6.99% (95% CI, 5.77%, 8.20%) and 1.82% (95% CI, 0.88%, 2.74%), respectively. Among privately insured individuals, CRC screening after the COVID pandemic recovered unevenly based on sex, area-level socioeconomic measures, and metropolitan area status, with pre-pandemic disparities persisting and even worsening for some of the factors.