Publications

OBJECTIVE: We aimed to estimate Coccidioides serologic screening rates before initiation of biologic disease-modifying antirheumatic drugs including tofacitinib (b/tsDMARDs), conventional synthetic disease-modifying antirheumatic drugs (csDMARDs), and/or noninhaled corticosteroids.

METHODS: This retrospective cohort study used 2011 to 2016 US Medicare claims data and included beneficiaries with rheumatic or autoimmune disease residing in regions within Arizona, California, and Texas endemic for Coccidioides spp. with ≥1 prescription for a b/tsDMARD, csDMARD, and/or noninhaled corticosteroid. We estimated prior-year serologic screening incidence before initiating b/tsDMARDs, csDMARD, and/or noninhaled corticosteroid.

RESULTS: During 2012 to 2016, 4,331 beneficiaries filled 64,049 prescriptions for b/tsDMARDs, csDMARDs, and noninhaled corticosteroids. Arizona's estimated screening rate was 20.1% (95% confidence interval [95% CI] 14.5-25.7) in the year before prescription initiation for b/tsDMARDs, 8.1% (95% CI 6.5-9.7) before csDMARDs, and 6.9% (95% CI: 5.6-8.2) before corticosteroids. Screening rates for b/tsDMARDs (2.8%, 95% CI 0.0-6.7), csDMARDs (1.0%, 95% CI 0.0-2.0), and corticosteroids (0.8%, 95% CI: 0.4-1.1) were negligible in California and undetected in Texas. Adjusted screening rate before prescription for b/tsDMARDs in Arizona increased from 14.5% (95% CI 7.5-21.5) in 2012 to 26.7% (95% CI 17.6-35.8) in 2016. Rheumatologists prescribing b/tsDMARDs in Arizona screened more than other providers (20.9% [95% CI 13.9-27.9] vs 12.9% [95% CI 5.9-20.0]).

CONCLUSION: Coccidioides serologic screening rates among Medicare beneficiaries with rheumatic/autoimmune diseases on b/tsDMARDs, csDMARDs, and noninhaled corticosteroids was low in Coccidioides spp.-US endemic regions between 2012 and 2016. Alignment of screening recommendations and clinical practice is needed.

OBJECTIVE: To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of health (SDOH).

METHODS: This prognostic study with a retrospective cohort design used OneFlorida data (linked electronic health records (EHRs) from 1240 practices/clinics in Florida). The study cohort included adults (aged ≥ 18) with type 2 diabetes, HbA1C ≥ 7% (53 mmol/mol), ≥one ambulatory visit, and ≥one antihyperglycemic medication prescribed (excluded patients prescribed insulin before HbA1C). The outcome was therapeutic inertia, defined as absence of treatment intensification within six months after HbA1C ≥ 7% (53 mmol/mol). The predictors were patient, provider, and healthcare system factors. Machine learning methods included gradient boosting machines (GBM), random forests (RF), elastic net (EN), and least absolute shrinkage and selection operator (LASSO). The DeLong test compared the discriminative ability (represented by C-statistics) between models.

RESULTS: The cohort included 31,087 patients with type 2 diabetes (mean age = 58.89 (SD = 13.27) years, 50.50% male, 58.89% White). The therapeutic inertia prevalence was 39.80% among the 68,445 records. GBM outperformed (C-statistic from testing sample = 0.84, 95% CI = 0.83-0.84) RF (C-statistic = 0.80, 95% CI = 0.79-0.80), EN (C-statistic = 0.80, 95% CI = 0.80-0.81), and LASSO (C-statistic = 0.80, 95% CI = 0.80-0.81), p < 0.05. Area-level SDOH significantly increased the discriminative ability versus models without SDOH (C-statistic for GBM = 0.84, 95% CI = 0.84-0.85 vs. 0.84, 95% CI = 0.83-0.84), p < 0.05.

CONCLUSIONS: Using EHRs of patients with type 2 diabetes from a large state, machine learning predicted therapeutic inertia (prevalence = 40%). The model's ability to predict patients at high risk of therapeutic inertia is clinically applicable to diabetes care.

IMPORTANCE: Accurately predicting major bleeding events in non-valvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized treatment and improving patient outcomes, especially with emerging alternatives like left atrial appendage closure devices. The left atrial appendage closure devices reduce stroke risk comparably but with significantly fewer non-procedural bleeding events.

OBJECTIVE: To evaluate the performance of machine learning (ML) risk models in predicting clinically significant bleeding events requiring hospitalization and hemorrhagic stroke in non-valvular AF patients on DOACs compared to conventional bleeding risk scores (HAS-BLED, ORBIT, and ATRIA) at the index visit to a cardiologist for AF management.

DESIGN: Prognostic modeling with retrospective cohort study design using electronic health record (EHR) data, with clinical follow-up at one-, two-, and five-years.

SETTING: University of Pittsburgh Medical Center (UPMC) system.

PARTICIPANTS: 24,468 non-valvular AF patients aged ≥18 years treated with DOACs, excluding those with prior history of significant bleeding, other indications for DOACs, on warfarin or contraindicated to DOACs.

EXPOSURES: DOAC therapy for non-valvular AF.

MAIN OUTCOMES AND MEASURES: The primary endpoint was clinically significant bleeding requiring hospitalization within one year of index visit. The models incorporated demographic, clinical, and laboratory variables available in the EHR at the index visit.

RESULTS: Among 24,468 patients, 553 (2.3%) had bleeding events within one year, 829 (3.5%) within two years, and 1,292 (5.8%) within five years of index visit. We evaluated multivariate logistic regression and ML models including random forest, classification trees, k-nearest neighbor, naive Bayes, and extreme gradient boosting (XGBoost) which modestly outperformed HAS-BLED, ATRIA, and ORBIT scores in predicting clinically significant bleeding at 1-year follow-up. The best performing model (random forest) showed area under the curve (AUC-ROC) 0.76 (0.70-0.81), G-Mean score of 0.67, net reclassification index 0.14 compared to 0.57 (0.50-0.63), G-Mean score of 0.57 for HASBLED score, p-value for difference <0.001. The ML models had improved performance compared to conventional risk across time-points of 2-year and 5-years and within the subgroup of hemorrhagic stroke. SHAP analysis identified novel risk factors including measures from body mass index, cholesterol profile, and insurance type beyond those used in conventional risk scores.

CONCLUSIONS AND RELEVANCE: Our findings demonstrate the superior performance of ML models compared to conventional bleeding risk scores and identify novel risk factors highlighting the potential for personalized bleeding risk assessment in AF patients on DOACs.

Background: Chronic cough (CC), characterized as a cough lasting >8 weeks, is a common multi-factorial syndrome in the community, especially in older adults. Methods: Using a pre-existing algorithm to identify patients with CC within the 2011-2018 Medicare beneficiaries, we examined trends in gabapentinoid use through repeated cross-sectional analyses and identified distinct utilization trajectories using group-based trajectory modeling (GBTM) in a retrospective cohort study. Individuals without CC but with any respiratory conditions related to cough served as a comparator group. Results: Among patients with CC, gabapentinoid use increased from 18.6% in 2011 to 24.1% in 2018 (p = 0.002), with a similar upward trend observed in the non-CC cohort but with overall lower usage (14.7% to 18.4%; p < 0.001). Patients with CC had significantly higher burdens of respiratory and non-respiratory comorbidities, as well as greater healthcare service and medication use compared to the non-CC cohort. The GBTM analyses identified three distinct gabapentinoid utilization trajectories for CC and non-CC patients: no use (77.3% vs. 84.5%), low use (13.9% vs. 10.3%), and high use (8.8% vs. 5.2%). Conclusions: Future studies are needed to evaluate the safety and effectiveness of gabapentinoid use in patients with refractory or unexplained CC in real-world settings.

IMPORTANCE: One-third of older adults in the US have depression, often treated with psychotherapy and antidepressants. Previous studies suggesting an increased risk of falls and related injuries (FRI) associated with antidepressant use may be affected by confounding by indication or immortal time bias.

OBJECTIVE: To evaluate the association between FRI risk and first-line treatments in older adults with depression.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study used a target trial emulation framework with a cloning-censoring-weighting approach with Medicare claims data from 2016 to 2019. Participants included fee-for-service beneficiaries aged 65 years or older with newly diagnosed depression. Data were analyzed from October 1, 2023, to March 31, 2024.

EXPOSURES: First-line depression treatments including psychotherapy, sertraline, escitalopram, citalopram, mirtazapine, duloxetine, trazodone, fluoxetine, bupropion, paroxetine, and venlafaxine.

MAIN OUTCOME AND MEASURE: One-year FRI rate, restricted mean survival time (RMST), and adjusted hazard ratio (aHR) with 95% CI.

RESULTS: Among 101 953 eligible beneficiaries (mean [SD] age, 76 [8] years), 63 344 (62.1%) were female, 7404 (7.3%) were Black individuals, and 81 856 (80.3%) were White individuals. Compared with the untreated group, psychotherapy use was not associated with FRI risk (aHR, 0.94 [95% CI, 0.82-1.17]), while other first-line antidepressants were associated with a decreased FRI risk (aHR ranged from 0.74 [95% CI, 0.59-0.89] for bupropion to 0.83 [95% CI, 0.67-0.98] for escitalopram). The FRI incidence ranged from 63 (95% CI, 53-75) per 1000 person-year for those treated with bupropion to 87 (95% CI, 83-90) per 1000 person-year for those who were untreated. The RMST ranged from 349 (95% CI, 346-350) days for those who were untreated to 353 (95% CI, 350-356) days for those treated with bupropion.

CONCLUSIONS AND RELEVANCE: In this cohort study of older Medicare beneficiaries with depression, first-line antidepressants were associated with a decreased FRI risk compared with untreated individuals. These findings provide valuable insights into their safety profiles, aiding clinicians in their consideration for treating depression in older adults.

OBJECTIVES: Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation.

METHODS: This retrospective study used United States (US) 2018-2021 MarketScan commercial claims data of insured individuals aged 18-64 who initiated buprenorphine between July 2018 and December 2020 with no buprenorphine prescriptions in the previous six months. We measured buprenorphine prescription discontinuation gaps of ≥30 days within 12 months of initiating treatment. We developed predictive models employing logistic regression, decision tree classifier, random forest, extreme gradient boosting, Adaboost, and random forest-extreme gradient boosting ensemble. We applied recursive feature elimination with cross-validation to reduce dimensionality and identify the most predictive features while maintaining model robustness. For model validation, we used several statistics to evaluate performance, such as C-statistics and precision-recall curves. We focused on two distinct treatment stages: at the time of treatment initiation and one and three months after treatment initiation. We employed SHapley Additive exPlanations (SHAP) analysis that helped us explain the contributions of different features in predicting buprenorphine discontinuation. We stratified patients into risk subgroups based on their predicted likelihood of treatment discontinuation, dividing them into decile subgroups. Additionally, we used a calibration plot to analyze the reliability of the models.

RESULTS: A total of 30,373 patients initiated buprenorphine and 14.98% (4551) discontinued treatment. C-statistic varied between 0.56 and 0.76 for the first-stage models including patient-level demographic and clinical variables. Inclusion of proportion of days covered (PDC) measured after one month and three months following treatment initiation significantly increased the models' discriminative power (C-statistics: 0.60 to 0.82). Random forest (C-statistics: 0.76, 0.79 and 0.82 with baseline predictors, one-month PDC and three-months PDC, respectively) outperformed other ML models in discriminative performance in all stages (C-statistics: 0.56 to 0.77). Most influential risk factors of discontinuation included early stage medication adherence, age, and initial days of supply.

CONCLUSION: ML algorithms demonstrated a good discriminative power in identifying patients at higher risk of buprenorphine care discontinuity. The proposed framework may help healthcare providers optimize treatment strategies and deliver targeted interventions to improve buprenorphine care continuity.

Background/Objectives: Concurrent opioid (OPI) and benzodiazepine (BZD) use may exacerbate injurious fall risk (e.g., falls and fractures) compared to no use or use alone. Yet, patients may need concurrent OPI-BZD use for co-occurring conditions (e.g., pain and anxiety). Therefore, we examined the association between longitudinal OPI-BZD dosing patterns and subsequent injurious fall risk. Methods: We conducted a retrospective cohort study including non-cancer fee-for-service Medicare beneficiaries initiating OPI and/or BZD in 2016-2018. We identified OPI-BZD use patterns during the 3 months following OPI and/or BZD initiation (i.e., trajectory period) using group-based multi-trajectory models. We estimated the time to first injurious falls within the 3-month post-trajectory period using inverse-probability-of-treatment-weighted Cox proportional hazards models. Results: Among 622,588 beneficiaries (age ≥ 65 = 84.6%, female = 58.1%, White = 82.7%; having injurious falls = 0.45%), we identified 13 distinct OPI-BZD trajectories: Group (A): Very-low OPI-only (early discontinuation) (44.9% of the cohort); (B): Low OPI-only (rapid decline) (15.1%); (C): Very-low OPI-only (late discontinuation) (7.7%); (D): Low OPI-only (gradual decline) (4.0%); (E): Moderate OPI-only (rapid decline) (2.3%); (F): Very-low BZD-only (late discontinuation) (11.5%); (G): Low BZD-only (rapid decline) (4.5%); (H): Low BZD-only (stable) (3.1%); (I): Moderate BZD-only (gradual decline) (2.1%); (J): Very-low OPI (rapid decline)/Very-low BZD (late discontinuation) (2.9%); (K): Very-low OPI (rapid decline)/Very-low BZD (increasing) (0.9%); (L): Very-low OPI (stable)/Low BZD (stable) (0.6%); and (M): Low OPI (gradual decline)/Low BZD (gradual decline) (0.6%). Compared with Group (A), six trajectories had an increased 3-month injurious falls risk: (C): HR = 1.78, 95% CI = 1.58-2.01; (D): HR = 2.24, 95% CI = 1.93-2.59; (E): HR = 2.60, 95% CI = 2.18-3.09; (H): HR = 2.02, 95% CI = 1.70-2.40; (L): HR = 2.73, 95% CI = 1.98-3.76; and (M): HR = 1.96, 95% CI = 1.32-2.91. Conclusions: Our findings suggest that 3-month injurious fall risk varied across OPI-BZD trajectories, highlighting the importance of considering both dose and duration when assessing injurious fall risk of OPI-BZD use among older adults.

BACKGROUND: Although biological plausibility suggests that fluoroquinolones could lead to rhegmatogenous retinal detachment (RRD) through collagen degradation, real-world evidence on their relative risk of RRD is inconsistent, with limited information on absolute risk estimates.

OBJECTIVE: The study aimed to estimate the RRD risk associated with fluoroquinolones versus other antibiotics with similar indications (i.e., comparison antibiotics).

METHODS: We conducted a retrospective cohort study analyzing claims data from adult patients who initiated fluoroquinolones or amoxicillin/clavulanate or ampicillin/sulbactam or extended-spectrum cephalosporins using the Taiwan National Health Insurance Research Database (2009-2018) and the United States IBM MarketScan Database (2011-2020). Patients were followed for up to 90 days after cohort entry. For each country's data, after 1:1 propensity score (PS) matching, we used Cox regression models to estimate RRD risks, presented with hazard ratios (HR) with 95% confidence interval (95% CI). We used random-effects meta-analyses to derive pooled HRs across both counties.

RESULTS: Of 24,172,032 eligible patients comprising 7,944,620 insured Taiwanese (mean age [SD], 46 [18] years; 45% male) and 16,227,412 United States commercially insured individuals (mean age [SD], 47 [16] years; 40% male), 10,137,468 patients initiated fluoroquinolones, 10,203,794 initiated amoxicillin/clavulanate or ampicillin/sulbactam, and 3,830,770 initiated extended-spectrum cephalosporins. After PS matching, similar RRD incidence rates were observed between fluoroquinolones and amoxicillin/clavulanate or ampicillin/sulbactam users (0.33 [95% CI, 0.19-0.56] versus 0.35 [95% CI, 0.26-0.46] per 1,000 person-years), yielding an HR of 0.97 (95% CI, 0.76-1.23). The RRD incidence rates were also similar comparing fluoroquinolones to extended-spectrum cephalosporins (0.36 [95% CI, 0.22-0.57] versus 0.34 [95% CI, 0.22-0.50] per 1,000 person-years; HR, 1.08 [95% CI, 0.92-1.27]). The comparative safety profiles remained consistent by country, various patient characteristic (e.g., diabetes or ophthalmic conditions), type of fluoroquinolones, follow-up duration, or treatment setting.

CONCLUSION: This large-scale study, leveraging real-world data from Taiwan and the United States, showed a low and comparable RRD risk among adults who initiated fluoroquinolones or other antibiotics with similar indications. This suggests that the RRD risk should not deter the use of fluoroquinolone when clinically indicated.

BACKGROUND: Opioid use disorder contributes to maternal morbidity and mortality in the United States. Little is known about how the patterns of buprenorphine dose and duration throughout pregnancy may affect neonatal and postpartum outcomes.

OBJECTIVE: To determine the associations between trajectories of buprenorphine utilization and dose during pregnancy on maternal and neonatal health outcomes.

STUDY DESIGN: Retrospective cohort study among 2925 pregnant persons with opioid use disorder, followed from the estimated start date of pregnancy through 90 days after delivery. We used administrative healthcare data from Medicaid-enrolled individuals to assess buprenorphine dose and use and maternal (postpartum buprenorphine continuation and overdose) and neonatal (low birthweight, neonatal abstinence syndrome (NAS)) outcomes. Group-based trajectory modelling was used to identify trajectories of buprenorphine dose and use during pregnancy. Weighted multivariable logistic regression assessed the association between buprenorphine trajectories and outcomes.

RESULTS: We identified 8 trajectories of buprenorphine utilization and dose during pregnancy. Regression analyses found that high doses of buprenorphine and a longer duration of buprenorphine use during pregnancy was associated with higher odds of postpartum buprenorphine continuation and reduced rates of overdose. Higher doses and longer duration of buprenorphine treatment were not associated with an increase in NAS or term low birth weight, relative to moderate or low doses or shorter treatment duration.

CONCLUSION: A longer duration and higher dose of buprenorphine treatment during pregnancy were associated with improved odds of postpartum buprenorphine continuation and were not associated with adverse neonatal outcomes.

BACKGROUND: Pre-exposure prophylaxis (PrEP) effectiveness is highly dependent on medication adherence, which is associated with differential HIV risks and possibly sexually transmitted infection (STI).

METHODS: This retrospective cohort study of PrEP users (01/01/2012-12/31/2021) used the MarketScan database of commercially insured enrollees to examine PrEP adherence trajectory groups' associations with HIV and STI acquisition risks. Distinct PrEP adherence trajectories were identified by group-based trajectory modeling among individuals who used oral PrEP. The primary outcome was HIV acquisition incidence, and secondary was STI rate, compared among trajectory groups. Inverse probability treatment weighting time-varying Cox proportional hazards models assessed HIV acquisition, and Poisson regression models assessed STI.

RESULTS: Among 23 258 oral PrEP users, 4 distinct PrEP adherence patterns were identified: minimal use (10.5% of the cohort), rapidly declining (25.4%), gradually declining (24.3%), and consistently high (39.8%). Compared with the minimal use group, the gradually declining (adjusted hazard ratio [AHR], 0.53; 95% CI, 0.31-0.90) and consistently high (AHR, 0.50; 95% CI, 0.30-0.84) PrEP adherence groups showed decreased HIV incidence risks. Compared with the minimal use group, the rapidly declining (adjusted incidence rate ratio [AIRR], 1.35; 95% CI, 1.07-1.72), gradually declining (AIRR, 1.73; 95% CI, 1.38-2.18), and consistently high (AIRR, 2.06; 95% CI, 1.64-2.58) groups were associated with increased STI risk.

CONCLUSIONS: These findings underscore the benefits of continuing and remaining adherent to PrEP and may also inform public health strategies, clinical guidelines, and interventions aimed at maximizing the effectiveness of PrEP in reducing new HIV infections while developing targeted strategies to prevent STIs with PrEP use.