Identifying treatment heterogeneity in atrial fibrillation using a novel causal machine learning method.

BACKGROUND: Lifelong oral anticoagulation is recommended in patients with atrial fibrillation (AF) to prevent stroke. Over the last decade, multiple new oral anticoagulants (OACs) have expanded the number of treatment options for these patients. While population-level effectiveness of OACs has been compared, it is unclear if there is variability in benefit and risk across patient subgroups. METHODS: We analyzed claims and medical data for 34,569 patients who initiated a nonvitamin K antagonist oral anticoagulant (non-vitamin K antagonist oral anticoagulant (NOAC); apixaban, dabigatran, and rivaroxaban) or warfarin for nonvalvular AF between 08/01/2010 and 11/29/2017 from the OptumLabs Data Warehouse. A machine learning (ML) method was applied to match different OAC groups on several baseline variables including, age, sex, race, renal function, and CHA2DS2 -VASC score. A causal ML method was then used to discover patient subgroups characterizing the head-to-head treatment effects of the OACs on a primary composite outcome of ischemic stroke, intracranial hemorrhage, and all-cause mortality. RESULTS: The mean age, number of females and white race in the entire cohort of 34,569 patients were 71.2 (SD, 10.7) years, 14,916 (43.1%), and 25,051 (72.5%) respectively. During a mean follow-up of 8.3 (SD, 9.0) months, 2,110 (6.1%) of patients experienced the composite outcome, of whom 1,675 (4.8%) died. The causal ML method identified 5 subgroups with variables favoring apixaban over dabigatran; 2 subgroups favoring apixaban over rivaroxaban; 1 subgroup favoring dabigatran over rivaroxaban; and 1 subgroup favoring rivaroxaban over dabigatran in terms of risk reduction of the primary endpoint. No subgroup favored warfarin and most dabigatran vs warfarin users favored neither drug. The variables that most influenced favoring one subgroup over another included Age, history of ischemic stroke, thromboembolism, estimated glomerular filtration rate, Race, and myocardial infarction. CONCLUSIONS: Among patients with AF treated with a NOAC or warfarin, a causal ML method identified patient subgroups with differences in outcomes associated with OAC use. The findings suggest that the effects of OACs are heterogeneous across subgroups of AF patients, which could help personalize the choice of OAC. Future prospective studies are needed to better understand the clinical impact of the subgroups with respect to OAC selection.

A prediction model for asthma exacerbations after stopping asthma biologics.

BACKGROUND: Little is known regarding the prediction of the risks of asthma exacerbation after stopping asthma biologics. OBJECTIVE: To develop and validate a predictive model for the risk of asthma exacerbations after stopping asthma biologics using machine learning models. METHODS: We identified 3057 people with asthma who stopped asthma biologics in the OptumLabs Database Warehouse and considered a wide range of demographic and clinical risk factors to predict subsequent outcomes. The primary outcome used to assess success after stopping was having no exacerbations in the 6 months after stopping the biologic. Elastic-net logistic regression (GLMnet), random forest, and gradient boosting machine models were used with 10-fold cross-validation within a development (80%) cohort and validation cohort (20%). RESULTS: The mean age of the total cohort was 47.1 (SD, 17.1) years, 1859 (60.8%) were women, 2261 (74.0%) were White, and 1475 (48.3%) were in the Southern region of the United States. The elastic-net logistic regression model yielded an area under the curve (AUC) of 0.75 (95% confidence interval [CI], 0.71-0.78) in the development and an AUC of 0.72 in the validation cohort. The random forest model yielded an AUC of 0.75 (95% CI, 0.68-0.79) in the development cohort and an AUC of 0.72 in the validation cohort. The gradient boosting machine model yielded an AUC of 0.76 (95% CI, 0.72-0.80) in the development cohort and an AUC of 0.74 in the validation cohort. CONCLUSION: Outcomes after stopping asthma biologics can be predicted with moderate accuracy using machine learning methods.

Patient Satisfaction With a Multisite, Multiregional Remote Patient Monitoring Program for Acute and Chronic Condition Management: Survey-Based Analysis.

BACKGROUND: Remote patient monitoring (RPM) is an option for continuously managing the care of patients in the comfort of their homes or locations outside hospitals and clinics. Patient engagement with RPM programs is essential for achieving successful outcomes and high quality of care. When relying on technology to facilitate monitoring and shifting disease management to the home environment, it is important to understand the patients' experiences to enable quality improvement. OBJECTIVE: This study aimed to describe patients' experiences and overall satisfaction with an RPM program for acute and chronic conditions in a multisite, multiregional health care system. METHODS: Between January 1, 2021, and August 31, 2022, a patient experience survey was delivered via email to all patients enrolled in the RPM program. The survey encompassed 19 questions across 4 categories regarding comfort, equipment, communication, and overall experience, as well as 2 open-ended questions. Descriptive analysis of the survey response data was performed using frequency distribution and percentages. RESULTS: Surveys were sent to 8535 patients. The survey response rate was 37.16% (3172/8535) and the completion rate was 95.23% (3172/3331). Survey results indicated that 88.97% (2783/3128) of participants agreed or strongly agreed that the program helped them feel comfortable managing their health from home. Furthermore, 93.58% (2873/3070) were satisfied with the RPM program and ready to graduate when meeting the program goals. In addition, patient confidence in this model of care was confirmed by 92.76% (2846/3068) of the participants who would recommend RPM to people with similar conditions. There were no differences in ease of technology use according to age. Those with high school or less education were more likely to agree that the equipment and educational materials helped them feel more informed about their care plans than those with higher education levels. CONCLUSIONS: This multisite, multiregional RPM program has become a reliable health care delivery model for the management of acute and chronic conditions outside hospitals and clinics. Program participants reported an excellent overall experience and a high level of satisfaction in managing their health from the comfort of their home environment.

Asthma biologic trial eligibility and real-world outcomes in the United States.

OBJECTIVE: To compare the outcomes of real-world patients who would have been eligible for asthma biologics to those who would not have been eligible. METHODS: We used data from the OptumLabs Data Warehouse (OLDW) to categorize patients into eligible and ineligible groups based on clinical trials (n = 19 trials) used for Food and Drug Administration (FDA) approval. We then compared the change in the number of asthma exacerbations before and after biological initiation between the two groups. RESULTS: The percentage of people who would have been eligible for asthma biologic clinical trials ranged from 0-10.2%. The eligible group had a greater reduction in number of asthma exacerbations compared to the ineligible group based on eligibility criteria from 1 omalizumab trial (1.52, 95% CI 1.25, 1.8 in eligible vs. 0.47, 95% CI 0.43, 0.52 in ineligible) and from 1 dupilumab trial (1.6, 95% CI 0.92, 2.28 in eligible vs. 0.52, 95% CI 0.38, 0.65 ineligible). Notably, 15 of the 19 trials had fewer than 11 eligible people, limiting additional comparisons. CONCLUSIONS: Fewer than 1 in 10 people in the United States treated with asthma biologics would have been eligible to participate in the trial for the biologic they used. Where comparisons could be made, trial eligible people have a greater reduction in exacerbations.Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2010749 .

Persistence of asthma biologic use in a US claims database.

BACKGROUND: Little is known on the persistence of asthma biologic use in clinical practice. OBJECTIVE: To evaluate the persistence of asthma biologic use and time to clinical response in clinical practice. METHODS: A cohort of people with asthma who used at least 1 asthma biologic was constructed using data from 2003 to 2019 in the OptumLabs Data Warehouse. Treatment persistence was defined by the length of time that a person continuously used an asthma biologic, allowing for a lapse in use up to 4 months before confirming that a person stopped. Clinical response to treatment (defined as a decline in asthma exacerbations of at least 50% compared with the 6 months before starting an asthma biologic) was described over time and in relation to biologic persistence. RESULTS: There were 9575 people who had at least 1 episode of asthma biologic use. There were 5319 people (64%, 95% confidence interval, 63%-65%) who completed 6 months or more on an asthma biologic and 3284 (45%, 95% confidence interval, 44%-46%) who completed 12 months or more. Of people with 1 or more asthma exacerbation 6 months before index biologic use, 63%, 76%, 80%, and 81% realized a 50% or more reduction in postindex asthma exacerbations in the first 6 months, 6 to 12 months, 12 to 18 months, and 18 to 24 months, respectively. CONCLUSION: Between 48% and 64% of people remained on an asthma biologic for 6 months or more after first use. Most people who achieved a reduction in asthma exacerbations did so in the first 6 months of treatment.

Drug labeling changes and pediatric hematology/oncology prescribing: Measuring the impact of U.S. legislation.

BACKGROUND/AIMS: The Pediatric Research Equity Act and Best Pharmaceuticals for Children Act are intended to promote the conduct of clinical trials that generate pediatric-specific evidence about drug safety and efficacy. This study assesses the quality of evidence generated through Pediatric Research Equity Act-mandated and Best Pharmaceuticals for Children Act-incentivized clinical trials of hematology/oncology drugs and characterizes subsequent changes in pediatric drug utilization rates. METHODS: Trial characteristics (blinding, randomization, and comparator group) were determined for clinical trials that supported pediatric label changes. Using data from OptumLabs® Data Warehouse, a de-identified administrative claims database, we calculated pediatric utilization rates for each drug. We calculated monthly utilization rates from January 2003 (or from the first month in which data were available) to December 2018. RESULTS: We identified 11 hematology/oncology drugs that underwent pediatric label changes under the Pediatric Research Equity Act Pediatric Research Equity Act and/or Best Pharmaceuticals for Children Act, and we identified 15 trials supporting these changes. Of these trials, 36% (5/14) were randomized, 31% (4/13) were blinded, and 36% (5/14) used a comparator group. A median of 49 children (interquartile range 29.5) received the drug under investigation across these trials. Pediatric label changes were not associated with subsequent changes in pediatric drug utilization. Although some drugs saw increased pediatric use after gaining new pediatric indications, this pattern was not consistently observed. In addition, there was no evidence to suggest that drugs were utilized less frequently after they failed to receive pediatric indications. CONCLUSIONS: Clinical trials of hematology/oncology drugs conducted under the Pediatric Research Equity Act Pediatric Research Equity Act and Best Pharmaceuticals for Children Act generally have low methodological rigor, and the resulting label changes are not consistently associated with changes in pediatric utilization. Alternative regulatory strategies and study designs may be necessary to maximize the impact of newly generated knowledge on drug utilization.

Association between facility volume and mortality of patients with classic Hodgkin lymphoma.

BACKGROUND: Prior studies in oncology have shown that a higher annual facility patient volume is associated with reduced mortality. Because classic Hodgkin lymphoma is uncommon but highly curable, this study used the National Cancer Database (2003-2014) to analyze whether such a relationship exists for this disease. METHODS: The facilities were classified by quartiles, and random intercepts were used to account for clustering of patients within facilities. A Cox regression model was used to determine the volume-outcome relationship. RESULTS: There were 47,633 patients with classic Hodgkin lymphoma treated at 1310 facilities. The first quartile (Q1), which included 58.4% of the facilities, treated 3 or fewer patients per year, whereas the fourth quartile (Q4), which included 5.9% of the facilities, treated more than 9 patients per year. Compared with the patients treated at Q4 facilities, those treated at lower quartile facilities had a higher risk of death (hazard ratio for the third quartile [HR], 1.19; 95% confidence interval [CI], 1.1-1.29; HR for the second quartile, 1.28; 95% CI, 1.19-1.38; HR for Q1, 1.29; 95% CI, 1.2-1.39) after adjustments for all other factors (P < .0001). Compared with facilities treating 10 patients per year, facilities treating 40 patients per year had approximately 27% lower overall mortality rates. CONCLUSIONS: Patients with classic Hodgkin lymphoma treated at high-volume centers had lower overall mortality than those treated at lower volume centers. Because this is a highly curable malignancy, such differences may suggest a benefit from referral to higher volume facilities or the emulation of their care models.

ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial.

BACKGROUND: A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automatically screen for low EF, encouraging clinicians to obtain a confirmatory transthoracic echocardiogram (TTE) for previously undiagnosed patients, thereby facilitating early diagnosis and treatment. OBJECTIVES: To prospectively evaluate a novel artificial intelligence (AI) screening tool for detecting low EF in primary care practices. DESIGN: The EAGLE trial is a pragmatic two-arm cluster randomized trial (NCT04000087) that will randomize >100 clinical teams (i.e., clusters) to either intervention (access to the new AI screening tool) or control (usual care) at 48 primary care practices across Minnesota and Wisconsin. The trial is expected to involve approximately 400 clinicians and 20,000 patients. The primary endpoint is newly discovered EF ≤50%. Eligible patients will include adults who undergo ECG for any reason and have not been previously diagnosed with low EF. Data will be pulled from the EHR, and no contact will be made with patients. A positive deviance qualitative study and a post-implementation survey will be conducted among select clinicians to identify facilitators and barriers to using the new screening report. SUMMARY: This trial will examine the effectiveness of the AI-enabled ECG for detection of asymptomatic low EF in routine primary care practices and will be among the first to prospectively evaluate the value of AI in real-world practice. Its findings will inform future implementation strategies for the translation of other AI-enabled algorithms.

Randomized comparison of a reduced-visit prenatal care model enhanced with remote monitoring.

BACKGROUND: Standard prenatal care, consisting of 12-14 visits per pregnancy, is expensive and resource intensive, with limited evidence supporting the structure, rhythm, or components of care. Some studies suggest a reduced-frequency prenatal care model is as safe as the standard model of care for low-risk pregnant women, but evidence is limited. We developed and evaluated an innovative, technology-enhanced, reduced prenatal visit model (OB Nest). OBJECTIVE: To evaluate the acceptability and effectiveness of OB Nest, a reduced-frequency prenatal care model enhanced with remote home monitoring devices and nursing support. STUDY DESIGN: A single-center randomized controlled trial, composed of pregnant women, aged 18-36 years, recruited from an outpatient obstetric tertiary academic center in the Midwest United States. OB Nest care consisted of 8 onsite appointments with an obstetric provider; 6 virtual visits consisting of phone or online communication with an assigned nurse, supplemented with fetal Doppler and sphygmomanometer home monitoring devices; and access to an online community of pregnant women. Usual care consisted of 12 prescheduled prenatal clinic appointments with obstetric providers. Acceptability of OB Nest was measured by validated surveys of patient satisfaction with care at 36 weeks; perception of stress at 14, 24, and 36 weeks; and perceived quality of care at 36 weeks of gestation. Effectiveness was analyzed by comparing adherence to the American College of Obstetricians and Gynecologists recommended routine prenatal and ancillary services, maternal and fetal safety outcomes, and healthcare utilization. RESULTS: Three hundred pregnant women at <13 weeks of gestation were recruited and randomized to OB Nest or usual care (150 in each arm) using a minimization algorithm. Demographic characteristics were similar between groups. Compared to usual care, patients in OB Nest had higher satisfaction on a 100-point validated modified Littlefield and Adams Satisfaction scale (OB Nest = 93.9% vs usual care = 78.9%, P < .01). Pregnancy-related stress, measured, on a 0-2 point PreNatal Maternal Stress validated scale, with higher scores indicating higher levels of stress, was lower among OB Nest participants at 14 weeks (OB Nest = 0.32 vs usual care = 0.41, P < .01) and at 36 weeks of gestation (OB Nest = 0.34 vs usual care = 0.40, P < .03). There was no statistical difference in perceived quality of care. Adherence to the provision of American College of Obstetricians and Gynecologists prenatal services was similar in both arms. Maternal and fetal clinical outcomes were similar between groups. Total reported nursing time was higher in OB Nest (OB Nest = 171.2 minutes vs usual care = 108.2 minutes, 95% confidence interval, 48.7-77.4). CONCLUSION: OB Nest is an innovative, acceptable, and effective reduced-frequency prenatal care model. Compared to routine prenatal care, OB Nest resulted in higher patient satisfaction and lower prenatal stress, while reducing the number of appointments with clinicians and maintaining care standards for pregnant women. This program is a step toward evidence-driven prenatal care that improves patient satisfaction.

Genders of patients and clinicians and their effect on shared decision making: a participant-level meta-analysis.

BACKGROUND: Gender differences in communication styles between clinicians and patients have been postulated to impact patient care, but the extent to which the gender dyad structure impacts outcomes in shared decision making remains unclear. METHODS: Participant-level meta-analysis of 775 clinical encounters within 7 randomized trials where decision aids, shared decision making tools, were used at the point of care. Outcomes analysed include decisional conflict scale scores, satisfaction with the clinical encounter, concordance between stated decision and action taken, and degree of patient engagement by the clinician using the OPTION scale. An estimated minimal important difference was used to determine if nonsignificant results could be explained by low power. RESULTS: We did not find a statistically significant interaction between clinician/patient gender mix and arm for decisional conflict, satisfaction with the clinical encounter or patient engagement. A borderline significant interaction (p = 0.05) was observed for one outcome: concordance between stated decision and action taken, where encounters with female clinician/male patient showed increased concordance in the decision aid arm compared to control (8% more concordant encounters). All other gender dyads showed decreased concordance with decision aid use (6% fewer concordant encounters for same-gender, 16% fewer concordant encounters for male clinician/female patient). CONCLUSIONS: In this participant-level meta-analysis of 7 randomized trials, decision aids used at the point of care demonstrated comparable efficacy across gender dyads. Purported barriers to shared decision making based on gender were not detected when tested for a minimum detected difference. TRIAL REGISTRATIONS: ClinicalTrials.gov NCT00888537, NCT01077037, NCT01029288, NCT00388050, NCT00578981, NCT00949611, NCT00217061.

Guidelines for Data and Safety Monitoring in Pragmatic Randomized Clinical Trials Using Case Studies.

Pragmatic randomized clinical trials (pRCTs) have a unique set of considerations for data and safety monitoring. Because of their unconventional trial designs coupled with collection of multilevel data and implementation outcomes in real-world settings, thoughtful consideration is needed on the presentation of the trial design and accruing data to facilitate review and decision-making by the trial's data and safety monitoring board (DSMB). To our knowledge, there is limited information available in practical guidelines for generalists and medical general practitioners on what to monitor and to report to the DSMB during the conduct of pRCTs and what the DSMB should focus on in its review of reports. This article discusses these matters in the context of 3 case studies focusing on a set of critical data and safety monitoring questions that would be of interest to the generalist conducting pRCTs. In considering these questions, we provide tabular and graphical illustrations of how data can be presented to the DSMB while drawing attention to those areas that the DSMB should focus on in its review of the trial. The strategies and viewpoints discussed herein provide practical guidelines and can serve as a resource for the generalist conducting pRCTs.

Association of Area-Level Socioeconomic Deprivation With Hypoglycemic and Hyperglycemic Crises in US Adults With Diabetes.

Importance: Social determinants of health play a role in diabetes management and outcomes, including potentially life-threatening complications of severe hypoglycemia and diabetic ketoacidosis (DKA) or hyperglycemic hyperosmolar state (HHS). Although several person-level socioeconomic factors have been associated with these complications, the implications of area-level socioeconomic deprivation are unknown. Objective: To examine the association between area-level deprivation and the risks of experiencing emergency department visits or hospitalizations for hypoglycemic and hyperglycemic crises (ie, DKA or HHS). Design, Setting, and Participants: This cohort study used deidentified administrative claims data for privately insured individuals and Medicare Advantage beneficiaries across the US. The analysis included adults with diabetes who met the claims criteria for diabetes between January 1, 2016, and December 31, 2017. Data analyses were performed from November 17, 2020, to November 11, 2021. Exposures: Area deprivation index (ADI) was derived for each county for 2016 and 2017 using 17 county-level indicators from the American Community Survey. ADI values were applied to patients who were living in each county based on their index dates and were categorized according to county-level ADI quintile (with quintile 1 having the least deprivation and quintile 5 having the most deprivation). Main Outcomes and Measures: The numbers of emergency department visits or hospitalizations related to the primary diagnoses of hypoglycemia and DKA or HHS (ascertained using validated diagnosis codes in the first or primary position of emergency department or hospital claims) between 2016 and 2019 were calculated for each ADI quintile using negative binomial regression models and adjusted for patient age, sex, health plan type, comorbidities, glucose-lowering medication type, and percentage of White residents in the county. Results: The study population included 1 116 361 individuals (563 943 women [50.5%]), with a mean (SD) age of 64.9 (13.2) years. Of these patients, 343 726 (30.8%) resided in counties with the least deprivation (quintile 1) and 121 810 (10.9%) lived in counties with the most deprivation (quintile 5). Adjusted rates of severe hypoglycemia increased from 13.54 (95% CI, 12.91-14.17) per 1000 person-years in quintile 1 counties to 19.13 (95% CI, 17.62-20.63) per 1000 person-years in quintile 5 counties, corresponding to an incidence rate ratio of 1.41 (95% CI, 1.29-1.54; P < .001). Adjusted rates of DKA or HHS increased from 7.49 (95% CI, 6.96-8.02) per 1000 person-years in quintile 1 counties to 8.37 (95% CI, 7.50-9.23) per 1000 person-years in quintile 5 counties, corresponding to an incidence rate ratio of 1.12 (95% CI, 1.00-1.25; P = .049). Conclusions and Relevance: This study found that living in counties with a high area-level deprivation was associated with an increased risk of severe hypoglycemia and DKA or HHS. The concentration of these preventable events in areas of high deprivation signals the need for interventions that target the structural barriers to optimal diabetes management and health.

Pilot implementation to assess the feasibility and care team impact of an app-based interactive care plan to remotely monitor breast cancer survivors.

PURPOSE: To assess the feasibility of an app-based, electronic health record (EHR)-integrated, interactive care plan (ICP) for breast cancer (BC) survivors. METHODS: A single-arm pilot study was conducted with female BC survivors. ICP tasks included quarterly quality of life (QOL) questionnaire; monthly assessments of fatigue, insomnia, sexual dysfunction, hot flashes, and recurrence symptoms; and daily activity reminders. Embedded decision trees escalated recurrence symptoms to providers. On-demand education was available for self-management of treatment-related toxicities. The primary objective was to assess patients' engagement with ICP tasks against feasibility thresholds of 75% completion rate. Secondary objectives were evaluation of the system's functionality to track and escalate symptoms appropriately, and care team impact measured by volume of escalation messages generated. We report preliminary results 6 months after the last patient enrolled. RESULTS: Twenty-three patients enrolled August to November 2020. Mean age was 50.1 years. All patients engaged with at least one ICP task. The monthly average task completion rates were 62% for the QOL questionnaire, 59% for symptom assessments, and 37% for activity reminders. Task completion rate decreased over time. Eleven of 253 symptoms and QOL questionnaires (4.3%) generated messages for care escalation. CONCLUSION: Implementation of an app-based, EHR-integrated ICP in BC survivors was feasible and created minimal provider burden; however, patient engagement was below the feasibility threshold suggesting that changes may enhance broad implementation and adoption. IMPLICATIONS FOR CANCER SURVIVORS: An ICP may facilitate remote monitoring, symptom control, and recurrence surveillance for cancer survivors as strategies to enhance patient engagement are applied.

Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care.

OBJECTIVE: To compare the clinicians' characteristics of "high adopters" and "low adopters" of an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm that alerted for possible low left ventricular ejection fraction (EF) and the subsequent effectiveness of detecting patients with low EF. METHODS: Clinicians in 48 practice sites of a US Midwest health system were cluster-randomized by the care team to usual care or to receive a notification that suggested ordering an echocardiogram in patients flagged as potentially having low EF based on an AI-ECG algorithm. Enrollment was between June 26, 2019, and July 30, 2019; participation concluded on March 31, 2020. This report is focused on those clinicians randomized to receive the notification of the AI-ECG algorithm. At the patient level, data were analyzed for the proportion of patients with positive AI-ECG results. Adoption was defined as the clinician order of an echocardiogram after prompted by the alert. RESULTS: A total of 165 clinicians and 11,573 patients were included in this analysis. Among patients with positive AI-ECG, high adopters (n=41) were twice as likely to diagnose patients with low EF (33.9%) vs low adopters, n=124, (16.9%); odds ratio, 1.62; 95% CI, 1.21 to 2.17). High adopters were more often advanced practice providers (eg, nurse practitioners and physician assistants) vs physicians, Family Medicine vs Internal Medicine specialty, and tended to have less complex patients. CONCLUSION: Clinicians who most frequently followed the recommendations of an AI tool were twice as likely to diagnose low EF. Those clinicians with less complex patients were more likely to be high adopters. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT04000087.

Asthma Patients Who Stop Asthma Biologics Have a Similar Risk of Asthma Exacerbations as Those Who Continue Asthma Biologics.

BACKGROUND: There is limited information about outcomes associated with stopping asthma biologics. OBJECTIVE: To compare outcomes in people who stopped or continued asthma biologics. METHODS: We identified a cohort of people with asthma who stopped or continued asthma biologics in the Optum Labs Database Warehouse, using a propensity matching method for case and control groups with the variables of age, sex, race, region, insurance, income, specialist access, Charlson comorbidity, specific medical conditions, pre-index exacerbation count, pre-index rescue inhaler pharmacy fills, and pre-index inhaled corticosteroid with or without long-acting ß-agonist pharmacy fills. Primary outcome used to assess failure of stopping was an increase of 50% or more in the asthma exacerbation rate in the 6 months after discontinuing the biologic compared with the 6-month period before biologic initiation. RESULTS: Among a cohort of 4960 asthma biologic users, 1249 were observed to stop use after 6 to 12 months of use. We identified a matched cohort of 1247 stoppers and 1247 people who continued biologic use for at least 18 months. In the first 6 months after stopping or sham stopping, 10.2% of stoppers and 9.5% of continuers had an increase of 50% or more in asthma exacerbations. We found a similar adjusted odds of failing among stoppers and continuers (odds ratio = 1.085; 95% confidence interval, 0.833-1.413). CONCLUSIONS: An increase in asthma exacerbations is infrequently observed in people who stopped asthma biologics and was observed at similar rates as in matched controls who continued asthma biologics.

Adherence to Asthma Biologics: Implications for Patient Selection, Step Therapy, and Outcomes.

BACKGROUND: Little is known about adherence to asthma biologics. RESEARCH QUESTION: Is adherence to inhaled corticosteroid (ICS) associated with subsequent asthma biologic adherence? STUDY DESIGN AND METHODS: We analyzed individuals with asthma who started asthma biologics in the OptumLab Data Warehouse and used that data until October 2019. We calculated proportion days covered (PDC) for ICS ± long-acting ß-agonists in the 6 months before and after asthma biologics were started and asthma biologic PDC for the first 6 months of use. We performed a multivariable analysis to identify factors associated with asthma biologic PDC ≥0.75, ICS PDC ≥0.75 during the 6-month period after asthma biologic were started, and achievement of a ≥50% reduction in asthma exacerbations during the first 6 months of asthma biologic use. RESULTS: We identified 5,319 people who started asthma biologics. The mean PDC for asthma biologics was 0.76 (95% CI, 0.75-0.77) in the first 6 months after starting, higher than the mean PDCs for ICS in the 6 months before (0.44 [95% CI, 0.43-0.45]) and after (0.40 [95% CI, 0.39-0.40]) starting the asthma biologic. PDC ≥0.75 for ICS 6 months before index biologic use is associated with PDC for asthma biologics ≥0.75 (OR, 1.25; 95% CI, 1.10-1.43) and for ICS during the first 6 months of biologic use (OR, 9.93; 95% CI, 8.55-11.53). Neither ICS PDC ≥0.75 (OR, 0.92; 95% CI, 0.74-1.14) nor asthma biologic PDC ≥0.75 (OR, 1.15; 95% CI, 0.97-1.36) is associated with a statistically significant reduction in asthma exacerbations during the first 6 months of asthma biologic use among people with any exacerbation in the 6 months before first use. INTERPRETATION: Adherence to asthma biologic is higher than to ICS and is associated with different factors.

Association Between Area-Level Socioeconomic Deprivation and Diabetes Care Quality in US Primary Care Practices.

Importance: Diabetes management operates under a complex interrelationship between behavioral, social, and economic factors that affect a patient's ability to self-manage and access care. Objective: To examine the association between 2 complementary area-based metrics, area deprivation index (ADI) score and rurality, and optimal diabetes care. Design, Setting, and Participants: This cross-sectional study analyzed the electronic health records of patients who were receiving care at any of the 75 Mayo Clinic or Mayo Clinic Health System primary care practices in Minnesota, Iowa, and Wisconsin in 2019. Participants were adults with diabetes aged 18 to 75 years. All data were abstracted and analyzed between June 1 and November 30, 2020. Main Outcomes and Measures: The primary outcome was the attainment of all 5 components of the D5 metric of optimal diabetes care: glycemic control (hemoglobin A1c <8.0%), blood pressure (BP) control (systolic BP <140 mm Hg and diastolic BP <90 mm Hg), lipid control (use of statin therapy according to recommended guidelines), aspirin use (for patients with ischemic vascular disease), and no tobacco use. The proportion of patients receiving optimal diabetes care was calculated as a function of block group-level ADI score (a composite measure of 17 US Census indicators) and zip code-level rurality (calculated using Rural-Urban Commuting Area codes). Odds of achieving the D5 metric and its components were assessed using logistic regression that was adjusted for demographic characteristics, coronary artery disease history, and primary care team specialty. Results: Among the 31 934 patients included in the study (mean [SD] age, 59 [11.7] years; 17 645 men [55.3%]), 13 138 (41.1%) achieved the D5 metric of optimal diabetes care. Overall, 4090 patients (12.8%) resided in the least deprived quintile (quintile 1) of block groups and 1614 (5.1%) lived in the most deprived quintile (quintile 5), while 9193 patients (28.8%) lived in rural areas and 2299 (7.2%) in highly rural areas. The odds of meeting the D5 metric were lower for individuals residing in quintile 5 vs quintile 1 block groups (odds ratio [OR], 0.72; 95% CI, 0.67-0.78). Patients residing in rural (OR, 0.84; 95% CI, 0.73-0.97) and highly rural (OR, 0.81; 95% CI, 0.72-0.91) zip codes were also less likely to attain the D5 metric compared with those in urban areas. Conclusions and Relevance: This cross-sectional study found that patients living in more deprived and rural areas were significantly less likely to attain high-quality diabetes care compared with those living in less deprived and urban areas. The results call for geographically targeted population health management efforts by health systems, public health agencies, and payers.

Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01-1.61), P = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08-1.91), P = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P < 0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.

Increasing Allergy-Related Emergency Department Visits in the United States, 2007 to 2015.

BACKGROUND: Allergic reactions, angioedema, and anaphylaxis are commonly treated in the emergency department (ED). Contemporary evidence suggests that these conditions may be increasing in the United States. OBJECTIVE: To evaluate the contemporary epidemiology and trends of ED visits for allergic reactions, angioedema, and anaphylaxis in the United States from 2007 to 2015. METHODS: Using de-identified data from the National Hospital Ambulatory Medical Care Survey from 2007 to 2015, we identified cases of acute allergic reactions, angioedema, and anaphylaxis through International Classification of Diseases, 9th Revision, Clinical Modification codes and conducted a retrospective analysis of rates and trends of these allergy-related ED visits. RESULTS: There was a 14% overall increase in allergy-related ED visits between 2007 and 2015. Approximately 10 million ED visits in this time frame were associated with allergy-related conditions accounting for 0.85% (95% CI, 0.79-0.90) of all ED visits in the United States. Almost 3% of allergy-related ED visits were coded as anaphylaxis of which 46.1% (95% CI, 27.5-64.6) received epinephrine. Patients younger than 10 years had a higher relative risk (1.3; 95% CI, 1.0-1.6; P = .027) of allergy-related ED visits per 1000 ED visits than patients 65 years and older, and women also had a higher relative risk (1.4; 95% CI, 1.2-1.5; P < .001) than men. CONCLUSIONS: Allergy-related ED visits increased 14% from 2007 to 2015, with the highest relative risk occurring in patients younger than 10 years. These data provide further evidence of increasing allergic conditions in the United States.