Targeted Learning with an Undersmoothed Lasso Propensity Score Model for Large-Scale Covariate Adjustment in Healthcare Database Studies.

Lasso regression is widely used for large-scale propensity score (PS) estimation in healthcare database studies. In these settings, previous work has shown that undersmoothing (overfitting) Lasso PS models can improve confounding control, but it can also cause problems of non-overlap in covariate distributions. It remains unclear how to select the degree of undersmoothing when fitting large-scale Lasso PS models to improve confounding control while avoiding issues that can result from reduced covariate overlap. Here, we used simulations to evaluate the performance of using collaborative-controlled targeted learning to data-adaptively select the degree of undersmoothing when fitting large-scale PS models within both singly and doubly robust frameworks to reduce bias in causal estimators. Simulations showed that collaborative learning can data-adaptively select the degree of undersmoothing to reduce bias in estimated treatment effects. Results further showed that when fitting undersmoothed Lasso PS-models, the use of cross-fitting was important for avoiding non-overlap in covariate distributions and reducing bias in causal estimates.

smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies.

Objectives: Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions. Materials and methods: We developed the smdi (structural missing data investigations) R package based on results of a previous simulation study which considered structural assumptions of common missing data mechanisms in EHR. Results: smdi enables users to run principled missing data investigations on partially observed confounders and implement functions to visualize, describe, and infer potential missingness patterns and mechanisms based on observed data. Conclusions: The smdi R package is freely available on CRAN and can provide valuable insights into underlying missingness patterns and mechanisms and thereby help improve the robustness of real-world evidence studies.

Association between changes in prices and out-of-pocket costs for brand-name clinician-administered drugs.

OBJECTIVE: To determine whether annual changes in prices for clinician-administered drugs are associated with changes in patient out-of-pocket costs. DATA SOURCES AND STUDY SETTING: National commercial claims database, 2009 to 2018. STUDY DESIGN: In a serial, cross-sectional study, we calculated the annual percent change in manufacturer list prices and net prices after rebates. We used two-part generalized linear models to assess the relationship between annual changes in price with (1) the percentage of individuals incurring any out-of-pocket costs and (2) the percent change in median non-zero out-of-pocket costs. DATA COLLECTION/EXTRACTION METHODS: We created annual cohorts of privately insured individuals who used one of 52 brand-name clinician-administered drugs. PRINCIPAL FINDINGS: List prices increased 4.4%/yr (interquartile range [IQR], 1.1% to 6.0%) and net prices 3.3%/yr (IQR, 0.3% to 5.5%). The median percentage of patients with any out-of-pocket costs increased from 38% in 2009 to 48% in 2018, and median non-zero annual out-of-pocket costs increased by 9.6%/yr (IQR, 4.1% to 15.4%). There was no association between changes in prices and out-of-pocket costs for individual drugs. CONCLUSIONS: From 2009 to 2018, prices and out-of-pocket costs for brand-name clinician-administered drugs increased, but these were not directly related for individual drugs. This may be due to changes to insurance benefit design and private insurer drug reimbursement rates.

Comparative Safety and Effectiveness of Warfarin or Rivaroxaban Versus Apixaban in Patients With Advanced CKD and Atrial Fibrillation: Nationwide US Cohort Study.

RATIONALE & OBJECTIVE: Head-to-head data comparing the effectiveness and safety of oral anticoagulants in patients with atrial fibrillation (AF) and advanced chronic kidney disease (CKD) are lacking. We compared the safety and effectiveness of warfarin or rivaroxaban versus apixaban in patients with AF and non-dialysis-dependent CKD stage 4/5. STUDY DESIGN: Propensity score-matched cohort study. SETTING & PARTICIPANTS: 2 nationwide US claims databases, Medicare and Optum's deidentified Clinformatics Data Mart Database, were searched for the interval from January 1, 2013, through March 31, 2022, for patients with nonvalvular AF and CKD stage 4/5 who initiated warfarin versus apixaban (matched cohort, n=12,488) and rivaroxaban versus apixaban (matched cohort, n = 5,720). EXPOSURES: Warfarin, rivaroxaban, or apixaban. OUTCOMES: Primary outcomes included major bleeding and ischemic stroke. Secondary outcomes included all-cause mortality, major gastrointestinal bleeding, and intracranial bleeding. ANALYTICAL APPROACH: Cox regression was used to estimate HRs, and 1:1 propensity-score matching was used to adjust for 80 potential confounders. RESULTS: Compared with apixaban, warfarin initiation was associated with a higher rate of major bleeding (HR, 1.85; 95% CI, 1.59-2.15), including major gastrointestinal bleeding (1.86; 1.53-2.25) and intracranial bleeding (2.15; 1.42-3.25). Compared with apixaban, rivaroxaban was also associated with a higher rate of major bleeding (1.69; 1.33-2.15). All-cause mortality was similar for warfarin (1.08; 0.98-1.18) and rivaroxaban (0.94; 0.81-1.10) versus apixaban. Furthermore, no statistically significant differences for ischemic stroke were observed for warfarin (1.14; 0.83-1.57) or rivaroxaban (0.71; 0.40-1.24) versus apixaban, but the CIs were wide. Similar results were observed for warfarin versus apixaban in the positive control cohort of patients with CKD stage 3, consistent with randomized trial findings. LIMITATIONS: Few ischemic stroke events, potential residual confounding. CONCLUSIONS: In patients with AF and advanced CKD, rivaroxaban and warfarin were associated with higher rates of major bleeding compared with apixaban, suggesting a superior safety profile for apixaban in this high-risk population. PLAIN-LANGUAGE SUMMARY: Different anticoagulants have been shown to reduce the risk of stroke in patients with atrial fibrillation, such as warfarin and direct oral anticoagulants like apixaban and rivaroxaban. Unfortunately, the large-scale randomized trials that compared direct anticoagulants versus warfarin excluded patients with advanced chronic kidney disease. Therefore, the comparative safety and effectiveness of warfarin, apixaban, and rivaroxaban are uncertain in this population. In this study, we used administrative claims data from the United States to answer this question. We found that warfarin and rivaroxaban were associated with increased risks of major bleeding compared with apixaban. There were few stroke events, with no major differences among the 3 drugs in the risk of stroke. In conclusion, this study suggests that apixaban has a better safety profile than warfarin and rivaroxaban.

Improving the accuracy of automated gout flare ascertainment using natural language processing of electronic health records and linked Medicare claims data.

BACKGROUND: We aimed to determine whether integrating concepts from the notes from the electronic health record (EHR) data using natural language processing (NLP) could improve the identification of gout flares. METHODS: Using Medicare claims linked with EHR, we selected gout patients who initiated the urate-lowering therapy (ULT). Patients' 12-month baseline period and on-treatment follow-up were segmented into 1-month units. We retrieved EHR notes for months with gout diagnosis codes and processed notes for NLP concepts. We selected a random sample of 500 patients and reviewed each of their notes for the presence of a physician-documented gout flare. Months containing at least 1 note mentioning gout flares were considered months with events. We used 60% of patients to train predictive models with LASSO. We evaluated the models by the area under the curve (AUC) in the validation data and examined positive/negative predictive values (P/NPV). RESULTS: We extracted and labeled 839 months of follow-up (280 with gout flares). The claims-only model selected 20 variables (AUC = 0.69). The NLP concept-only model selected 15 (AUC = 0.69). The combined model selected 32 claims variables and 13 NLP concepts (AUC = 0.73). The claims-only model had a PPV of 0.64 [0.50, 0.77] and an NPV of 0.71 [0.65, 0.76], whereas the combined model had a PPV of 0.76 [0.61, 0.88] and an NPV of 0.71 [0.65, 0.76]. CONCLUSION: Adding NLP concept variables to claims variables resulted in a small improvement in the identification of gout flares. Our data-driven claims-only model and our combined claims/NLP-concept model outperformed existing rule-based claims algorithms reliant on medication use, diagnosis, and procedure codes.

Scalable Incident Detection via Natural Language Processing and Probabilistic Language Models.

Post marketing safety surveillance depends in part on the ability to detect concerning clinical events at scale. Spontaneous reporting might be an effective component of safety surveillance, but it requires awareness and understanding among healthcare professionals to achieve its potential. Reliance on readily available structured data such as diagnostic codes risk under-coding and imprecision. Clinical textual data might bridge these gaps, and natural language processing (NLP) has been shown to aid in scalable phenotyping across healthcare records in multiple clinical domains. In this study, we developed and validated a novel incident phenotyping approach using unstructured clinical textual data agnostic to Electronic Health Record (EHR) and note type. It's based on a published, validated approach (PheRe) used to ascertain social determinants of health and suicidality across entire healthcare records. To demonstrate generalizability, we validated this approach on two separate phenotypes that share common challenges with respect to accurate ascertainment: 1) suicide attempt; 2) sleep-related behaviors. With samples of 89,428 records and 35,863 records for suicide attempt and sleep-related behaviors, respectively, we conducted silver standard (diagnostic coding) and gold standard (manual chart review) validation. We showed Area Under the Precision-Recall Curve of ∼ 0.77 (95% CI 0.75-0.78) for suicide attempt and AUPR ∼ 0.31 (95% CI 0.28-0.34) for sleep-related behaviors. We also evaluated performance by coded race and demonstrated differences in performance by race were dissimilar across phenotypes and require algorithmovigilance and debiasing prior to implementation.

Real-World Treatment Effectiveness of Disease-Modifying Antirheumatic Drugs by Serostatus Among Patients With Rheumatoid Arthritis.

OBJECTIVE: The objective of this study was to compare the clinical effectiveness of biologic disease-modifying antirheumatic drugs (bDMARDs) or Janus kinase inhibitors (JAKi) among seropositive versus seronegative patients with rheumatoid arthritis (RA) in a real-world setting. METHODS: We used Optum's deidentified Clinformatics Data Mart Database (January 1, 2004, to March 31, 2021) linked with outpatient laboratory test results. The study population was adult patients with RA who initiated a bDMARD or JAKi. The index date was the dispensing of the first-ever study drug. At least 1-year continuous enrollment before and after the index date was required. Disenrollment due to death after the index date was allowed. Serostatus was defined using laboratory test results or the International Classification of Diseases, 10th Revision code M05x or M06.0x any time prior to the index date. Treatment effectiveness was measured based on a claims-based composite endpoint at 1-year post index, including nonoccurrence of any of the following: addition of conventional synthetic DMARDs, addition of or switching to new bDMARDs/JAKi, initiation of glucocorticoids, increased glucocorticoid dose, or death. Log-binomial regression models were constructed to estimate the risk ratio (RR) with 95% confidence interval (CI) comparing seropositive patients with seronegative patients, adjusting for more than 60 baseline covariates. RESULTS: We identified a total of 7813 seropositive patients and 4202 seronegative patients. The mean (±SD) age was 56.7 (±14.0) years; 77.9% were female. The risk of 1-year treatment effectiveness was 70.2% among seropositive patients and 69.8% among seronegative patients. The adjusted RR (95% CI) was 1.00 (0.98-1.02). CONCLUSION: In this real-world cohort study, seropositive and seronegative patients with RA had similar 1-year treatment effectiveness after initiating a bDMARD/JAKi.

Sodium-glucose cotransporter 2 inhibitors vs. sitagliptin in heart failure and type 2 diabetes: an observational cohort study.

AIMS: The effectiveness of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in patients with heart failure (HF) in routine clinical practice is not extensively studied. This study aimed to evaluate the comparative effectiveness of SGLT2i vs. sitagliptin in older adults with HF and type 2 diabetes and to investigate whether there were any differences between agents within the SGLT2i class or for reduced and preserved ejection fraction. METHODS AND RESULTS: Using Medicare claims data (April 2013 to December 2019), 16 253 SGLT2i initiators vs. 43 352 initiators of sitagliptin aged ≥65 years with type 2 diabetes and HF were included. The primary outcome was a composite of all-cause mortality, hospitalization for HF or urgent visit requiring intravenous diuretics; secondary outcomes included its individual components. Propensity score fine stratification weighted Cox regression was used to adjust for 100 pre-exposure characteristics. Mean age was 74 years; 49.8% were women. Initiation of SGLT2i vs. sitagliptin was associated with a lower risk of the primary composite outcome [adjusted hazard ratio (HR) 0.72; 95% confidence interval 0.67-0.77]. The adjusted HRs were 0.70 (0.63-0.78) for all-cause mortality, 0.64 (0.58-0.70) for hospitalization for HF, and 0.77 (0.69-0.86) for urgent visit requiring intravenous diuretics. Similar associations with the primary composite outcome were observed for all three agents within the SGLT2i class, for reduced and preserved ejection fraction, and subgroups based on demographics, comorbidities, and other HF treatments. Bias-calibrated HRs for the primary endpoint using negative and positive control outcomes ranged between 0.81 and 0.89, suggesting that the observed benefit could not be fully explained by residual confounding. CONCLUSION: In routine US clinical practice, SGLT2i demonstrated robust clinical effectiveness in older adults with HF and type 2 diabetes compared with sitagliptin, with no evidence of heterogeneity across the SGLT2i class or across ejection fraction.

Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.

INTRODUCTION: Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance. METHODS: To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices. RESULTS: We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations. CONCLUSION: Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance.

Emulation of Randomized Clinical Trials With Nonrandomized Database Analyses: Results of 32 Clinical Trials.

Importance: Nonrandomized studies using insurance claims databases can be analyzed to produce real-world evidence on the effectiveness of medical products. Given the lack of baseline randomization and measurement issues, concerns exist about whether such studies produce unbiased treatment effect estimates. Objective: To emulate the design of 30 completed and 2 ongoing randomized clinical trials (RCTs) of medications with database studies using observational analogues of the RCT design parameters (population, intervention, comparator, outcome, time [PICOT]) and to quantify agreement in RCT-database study pairs. Design, Setting, and Participants: New-user cohort studies with propensity score matching using 3 US claims databases (Optum Clinformatics, MarketScan, and Medicare). Inclusion-exclusion criteria for each database study were prespecified to emulate the corresponding RCT. RCTs were explicitly selected based on feasibility, including power, key confounders, and end points more likely to be emulated with real-world data. All 32 protocols were registered on ClinicalTrials.gov before conducting analyses. Emulations were conducted from 2017 through 2022. Exposures: Therapies for multiple clinical conditions were included. Main Outcomes and Measures: Database study emulations focused on the primary outcome of the corresponding RCT. Findings of database studies were compared with RCTs using predefined metrics, including Pearson correlation coefficients and binary metrics based on statistical significance agreement, estimate agreement, and standardized difference. Results: In these highly selected RCTs, the overall observed agreement between the RCT and the database emulation results was a Pearson correlation of 0.82 (95% CI, 0.64-0.91), with 75% meeting statistical significance, 66% estimate agreement, and 75% standardized difference agreement. In a post hoc analysis limited to 16 RCTs with closer emulation of trial design and measurements, concordance was higher (Pearson r, 0.93; 95% CI, 0.79-0.97; 94% meeting statistical significance, 88% estimate agreement, 88% standardized difference agreement). Weaker concordance occurred among 16 RCTs for which close emulation of certain design elements that define the research question (PICOT) with data from insurance claims was not possible (Pearson r, 0.53; 95% CI, 0.00-0.83; 56% meeting statistical significance, 50% estimate agreement, 69% standardized difference agreement). Conclusions and Relevance: Real-world evidence studies can reach similar conclusions as RCTs when design and measurements can be closely emulated, but this may be difficult to achieve. Concordance in results varied depending on the agreement metric. Emulation differences, chance, and residual confounding can contribute to divergence in results and are difficult to disentangle.

The Impact of Longitudinal Data-Completeness of Electronic Health Record Data on the Prediction Performance of Clinical Risk Scores.

The impact of electronic health record (EHR) discontinuity (i.e., receiving care outside of a given EHR system) on EHR-based risk prediction is unknown. We aimed to assess the impact of EHR-continuity on the performance of clinical risk scores. The study cohort consisted of patients aged ≥ 65 years with ≥ 1 EHR encounter in the 2 networks in Massachusetts (MA; 2007/1/1-2017/12/31, internal training and validation dataset), and one network in North Carolina (NC; 2007/1/1-2016/12/31, external validation dataset) that were linked with Medicare claims data. Risk scores were calculated using EHR data alone vs. linked EHR-claims data (not subject to misclassification due to EHR-discontinuity): (i) combined comorbidity score (CCS), (ii) claim-based frailty score (CFI), (iii) CHAD2 DS2 -VASc, and (iv) Hypertension, Abnormal renal/liver function, Stroke, Bleeding, Labile, Elderly, and Drugs (HAS-BLED). We assessed the performance of CCS and CFI predicting death, CHAD2 DS2 -VASc predicting ischemic stroke, and HAS-BLED predicting bleeding by area under receiver operating characteristic curve (AUROC), stratified by quartiles of predicted EHR-continuity (Q1-4). There were 319,740 patients in the MA systems and 125,380 in the NC system. In the external validation dataset, AUROC for EHR-based CCS predicting 1-year risk of death was 0.583 in Q1 (lowest) EHR-continuity group, which increased to 0.739 in Q4 (highest) EHR-continuity group. The corresponding improvement in AUROC was 0.539 to 0.647 for CFI, 0.556 to 0.637 for CHAD2 DS2 -VASc, and 0.517 to 0.556 for HAS-BLED. The AUROC in Q4 EHR-continuity group based on EHR alone approximates that based on EHR-claims data. The prediction performance of four clinical risk scores was substantially worse in patients with lower vs. high EHR-continuity.

Comparative Safety of Gout Treatment Strategies on Cardiovascular Outcomes Using Observational Data: Clone-censor-weight Target Trial Emulation Approach.

BACKGROUND: We sought to examine the cardiovascular safety of intensive treat-to-target serum urate strategies for gout using Medicare claims data linked to electronic health record laboratory data. METHODS: We selected patients with gout who initiated urate-lowering therapy. We emulated a hypothetical trial comparing the rate of major adverse cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death) among seven different strategies over 24 months. Three aspects were considered in defining increasingly intensive strategies: (1) continuation of urate-lowering therapy, (2) serum urate monitoring, and (3) modification of urate-lowering therapy when serum urate >6 mg/dl. We applied the "clone-censor-weight" method to account for baseline and time-varying confounding. RESULTS: We identified 4402 patients with gout who initiated urate-lowering therapy (mean age 77; male 60%). During a total of 6611 person-years (PY) of follow-up under usual care, the rate of major cardiovascular events (first and recurrent) was 4.5/100 PY (95% CI = 4.0, 5.1). The rate ratios (RRs) suggested reductions (RR point estimates 0.88-0.84) compared with usual care. All 95% CIs were imprecise, but their upper bounds excluded substantial increase in RRs. RRs were closer to 1.0 for the analysis focusing on the first major adverse cardiovascular event during follow-up and on comparison to the strategy requiring continuation of urate-lowering therapy (but not necessarily titration). CONCLUSIONS: Our treatment strategy trial emulation did not find increased risk of major adverse cardiovascular events with intensive urate-lowering strategies. Results may provide reassurance of the cardiovascular safety of intensive treat-to-target serum urate strategies recommended by rheumatology societies.

Finerenone in chronic kidney disease and type 2 diabetes: the known and the unknown.

The novel nonsteroidal mineralocorticoid receptor antagonist finerenone has been shown to reduce the risk of kidney and cardiovascular outcomes in patients with type 2 diabetes and chronic kidney disease. In this issue of Kidney International, Bakris et al. present new data on the kidney efficacy of finerenone across subgroups of estimated glomerular filtration rate and urinary albumin-to-creatinine ratio, as well as safety data. We attempt to place these results in context by discussing the benefits and risks of finerenone, as well as the generalizability of the study findings to routine care settings.

Hydroxychloroquine lowers Alzheimer's disease and related dementias risk and rescues molecular phenotypes related to Alzheimer's disease.

We recently nominated cytokine signaling through the Janus-kinase-signal transducer and activator of transcription (JAK/STAT) pathway as a potential AD drug target. As hydroxychloroquine (HCQ) has recently been shown to inactivate STAT3, we hypothesized that it may impact AD pathogenesis and risk. Among 109,124 rheumatoid arthritis patients from routine clinical care, HCQ initiation was associated with a lower risk of incident AD compared to methotrexate initiation across 4 alternative analyses schemes addressing specific types of biases including informative censoring, reverse causality, and outcome misclassification (hazard ratio [95% confidence interval] of 0.92 [0.83-1.00], 0.87 [0.81-0.93], 0.84 [0.76-0.93], and 0.87 [0.75-1.01]). We additionally show that HCQ exerts dose-dependent effects on late long-term potentiation (LTP) and rescues impaired hippocampal synaptic plasticity prior to significant accumulation of amyloid plaques and neurodegeneration in APP/PS1 mice. Additionally, HCQ treatment enhances microglial clearance of Aß1-42, lowers neuroinflammation, and reduces tau phosphorylation in cell culture-based phenotypic assays. Finally, we show that HCQ inactivates STAT3 in microglia, neurons, and astrocytes suggesting a plausible mechanism associated with its observed effects on AD pathogenesis. HCQ, a relatively safe and inexpensive drug in current use may be a promising disease-modifying AD treatment. This hypothesis merits testing through adequately powered clinical trials in at-risk individuals during preclinical stages of disease progression.

Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.

BACKGROUND: We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19. METHODS: We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including >40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 h. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration. RESULTS: Overall, 3706 patients (2587 in the development and 1119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79-0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = -0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78. CONCLUSIONS: Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors.

Risk of malignant melanoma and non-melanoma skin cancer in rheumatoid arthritis patients initiating methotrexate versus hydroxychloroquine: a cohort study.

OBJECTIVES: To characterise the incidence rate of skin cancer associated with methotrexate and hydroxychloroquine in older adults with rheumatoid arthritis (RA). METHODS: RA patients aged ≥65 years who initiated methotrexate or hydroxychloroquine as their first disease modifying antirheumatic drugs (DMARDs). The primary outcome was new occurrence of any skin cancer (i.e. malignant melanoma or non-melanoma skin cancer; NMSC) based on validated algorithms (positive predictive value >83%). Secondary outcomes were malignant melanoma, NMSC, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). We estimated the incidence rates (IRs) and hazard ratios (HRs) for each outcome in the 1:1 propensity score (PS)-matched methotrexate and hydroxychloroquine groups. RESULTS: We included 24,577 PS-matched pairs of methotrexate and hydroxychloroquine initiators. Compared with hydroxychloroquine (IR 25.20/1,000 person-years), methotrexate initiators (IR 26.21/1,000 person-years) had a similar risk of any skin cancer [HR 1.03 -(95%CI 0.92, 1.14)] over a mean follow-up of 388 days. The HR (95%CI) associated with methotrexate was 1.39 (0.87, 2.21) for malignant melanoma, 1.01(0.90, 1.12) for NMSC, 1.37 (1.13, 1.66) for BCC, and 0.79 (0.63, 0.99) for SCC compared with hydroxychloroquine. CONCLUSIONS: In this large cohort of older RA patients initiating methotrexate or hydroxychloroquine as their first DMARD, we found no difference in the risk of skin cancer including malignant melanoma and NMSC. However, for specific components of NMSC, methotrexate initiators had higher risk of BCC but lower risk of SCC compared with hydroxychloroquine initiators.

Risk of Acute Kidney Injury Among Older Adults With Heart Failure and With Reduced Ejection Fraction Treated With Angiotensin-Neprilysin Inhibitor vs Renin-Angiotensin System Inhibitor in Routine Clinical Care.

BACKGROUND: The acute hemodynamic effects of sacubitril/valsartan, an angiotensin receptor neprilysin inhibitor (ARNI), may result in early changes in kidney function, raising concerns about acute kidney injury (AKI), particularly in those who are naïve to renin-angiotensin system inhibitors (RASis). METHODS: We conducted a cohort study using U.S. Medicare fee-for-service claims data (2014-2017). Patients with HFrEF ≥ 65 years newly initiating ARNI or RASi, with no prior use of either drug class, were included. The primary outcome was hospitalization due to AKI as the primary discharge diagnosis, and the secondary outcome included AKI as a primary or secondary discharge diagnosis. AKI risks were described under an as-treated follow-up approach, with censoring on treatment discontinuation, switch, insurance disenrollment, death, or administrative censoring as well as an intent-to-treat approach. Propensity-score-based fine-stratification weighting was used to account for potential confounding by 81 pre-exposure characteristics. Cumulative incidence functions were used to report absolute risks, and Cox proportional hazards models were used to provide hazard ratios (HR) and 95% confidence intervals (CI). RESULTS: We included 27,166 patients with a mean (SD) age of 73 (7.3) years, and 4155 (15.3%) were initiating ARNI. After propensity score weighting, the 180-day cumulative incidence was 2.7% (2.4%-3.1%) among RASi initiators and 2.7% (2.2%-3.5%) among ARNI initiators for the primary outcome, and it was 6.5% (6.0%-7.1%) and 6.1% (5.2%-7.1%), respectively, for the secondary outcome under as-treated follow-up. HR (95% CI) comparing ARNI with RASi were 0.91 (95% CI: 0.72-1.16) for the primary outcome and 0.92 (95% CI: 0.79-1.08) for the secondary outcome. Similar results were observed in the intent-to-treat analysis. CONCLUSIONS: Among a large cohort of U.S. Medicare beneficiaries with HFrEF, ARNI treatment was not associated with higher rates of AKI than RASi treatment. These results provide reassurance for providers considering ARNI initiation in older patients who are RASi-naïve.