Gout risk in adults with pre-diabetes initiating metformin.

OBJECTIVE: Despite the strong association between gout and pre-diabetes, the role of metformin in gout among individuals with pre-diabetes remains uncertain. We compared the incidence rates of gout in adults with pre-diabetes starting metformin with those not using antidiabetic treatments. METHODS: We conducted a new-user, propensity score-matched cohort study using electronic health records from an academic health system (2007-2022). Pre-diabetes was defined based on haemoglobin A1c levels. Metformin users were identified and followed from the first metformin prescription date. Non-users of antidiabetic medications were matched to metformin users based on propensity score and the start of follow-up. The primary outcome was incident gout. Cox proportional hazards models estimated the HR for metformin. Linear regression analyses assessed the association between metformin use and changes in serum urate (SU) or C-reactive protein (CRP). RESULTS: We identified 25 064 individuals with pre-diabetes and propensity score-matched 1154 metformin initiators to 13 877 non-users. Baseline characteristics were well balanced (all standardised mean differences <0.1). The median follow-up was 3.9 years. The incidence rate of gout per 1000 person-years was lower in metformin users 7.1 (95% CI 5.1 to 10) compared with non-users 9.5 (95% CI 8.8 to 10.2). Metformin initiation was associated with a reduced relative risk of gout (HR 0.68, 95% CI 0.48 to 0.96). No relationship was found between metformin and changes in SU or CRP. CONCLUSIONS: Metformin use was associated with a reduced risk of gout among adults with pre-diabetes, suggesting that metformin may be important in lowering gout risk in individuals with pre-diabetes.

Examining lack of referrals to heart valve specialists as mechanisms of potential underutilization of aortic valve replacement.

BACKGROUND: Recent studies suggest that aortic valve replacement (AVR) remains underutilized. AIMS: Investigate the potential role of non-referral to heart valve specialists (HVS) on AVR utilization. METHODS: Patients with severe aortic stenosis (AS) between 2015 and 2018, who met class I indication for intervention, were identified. Baseline data and process-related parameters were collected to analyze referral predictors and evaluate outcomes. RESULTS: Among 981 patients meeting criteria AVR, 790 patients (80.5%) were assessed by HVS within six months of index TTE. Factors linked to reduced referral included increasing age (OR: 0.95; 95% CI: 0.94-0.97; P < .001), unmarried status (OR: 0.59; 95% CI: 0.43-0.83; P = .002) and inpatient TTE (OR: 0.27; 95% CI: 0.19-0.38; P < .001). Conversely, higher hematocrit (OR: 1.13; 95% CI: 1.09-1.16; P < .001) and eGFR (OR: 1.01; 95% CI: 1.00-1.02; P = .003), mean aortic valve gradient (OR: 1.03; 95% CI: 1.01-1.04; P < .001) and preserved LVEF (OR: 1.59; 95% CI: 1.02-2.48; P = .04), were associated with increased referral likelihood. Moreover, patients assessed by HVS referral as a time-dependent covariate had a significantly lower two-year mortality risk than those who were not (aHR: 0.30; 95% CI: 0.23-0.39; P < .001). CONCLUSION: A substantial proportion of severe AS patients meeting indications for AVR are not evaluated by HVS and experience markedly increased mortality. Further research is warranted to assess the efficacy of care delivery mechanisms, such as e-consults, and telemedicine, to improve access to HVS expertise.

Therapeutic inertia in treatment of older adults with type II diabetes at high risk for hypoglycemia.

Patients 80 years or older with HbA1c <7.0% (53 mmol/mol) treated with multiple daily insulin injections had low rates of rapid-acting insulin deprescription and initiation of diabetes medications with lower risk of hypoglycemia. Further investigation is needed to elucidate factors contributing to potentially inappropriately aggressive treatment of these patients.

EXIST: EXamining rIsk of excesS adiposiTy-Machine learning to predict obesity-related complications.

Background: Obesity is associated with an increased risk of multiple conditions, ranging from heart disease to cancer. However, there are few predictive models for these outcomes that have been developed specifically for people with overweight/obesity. Objective: To develop predictive models for obesity-related complications in patients with overweight and obesity. Methods: Electronic health record data of adults with body mass index 25-80 kg/m2 treated in primary care practices between 2000 and 2019 were utilized to develop and evaluate predictive models for nine long-term clinical outcomes using a) Lasso-Cox models and b) a machine-learning method random survival forests (RSF). Models were trained on a training dataset and evaluated on a test dataset over 100 replicates. Parsimonious models of <10 variables were also developed using Lasso-Cox. Results: Over a median follow-up of 5.6 years, study outcome incidence in the cohort of 433,272 patients ranged from 1.8% for knee replacement to 11.7% for atherosclerotic cardiovascular disease. Harrell C-index averaged over replicates ranged from 0.702 for liver outcomes to 0.896 for death for RSF, and from 0.694 for liver outcomes to 0.891 for death for Lasso-Cox. The Harrell C-index for parsimonious models ranged from 0.675 for liver outcomes to 0.850 for knee replacement. Conclusions: Predictive modeling can identify patients at high risk of obesity-related complications. Interpretable Cox models achieve results close to those of machine learning methods and could be helpful for population health management and clinical treatment decisions.

Impact of possible errors in natural language processing-derived data on downstream epidemiologic analysis.

Objective: To assess the impact of potential errors in natural language processing (NLP) on the results of epidemiologic studies. Materials and Methods: We utilized data from three outcomes research studies where the primary predictor variable was generated using NLP. For each of these studies, Monte Carlo simulations were applied to generate datasets simulating potential errors in NLP-derived variables. We subsequently fit the original regression models to these partially simulated datasets and compared the distribution of coefficient estimates to the original study results. Results: Among the four models evaluated, the mean change in the point estimate of the relationship between the predictor variable and the outcome ranged from -21.9% to 4.12%. In three of the four models, significance of this relationship was not eliminated in a single of the 500 simulations, and in one model it was eliminated in 12% of simulations. Mean changes in the estimates for confounder variables ranged from 0.27% to 2.27% and significance of the relationship was eliminated between 0% and 9.25% of the time. No variables underwent a shift in the direction of its interpretation. Discussion: Impact of simulated NLP errors on the results of epidemiologic studies was modest, with only small changes in effect estimates and no changes in the interpretation of the findings (direction and significance of association with the outcome) for either the NLP-generated variables or other variables in the models. Conclusion: NLP errors are unlikely to affect the results of studies that use NLP as the source of data.

Natural Language Processing for the Ascertainment and Phenotyping of Left Ventricular Hypertrophy and Hypertrophic Cardiomyopathy on Echocardiogram Reports.

Extracting and accurately phenotyping electronic health documentation is critical for medical research and clinical care. We sought to develop a highly accurate and open-source natural language processing (NLP) module to ascertain and phenotype left ventricular hypertrophy (LVH) and hypertrophic cardiomyopathy (HCM) diagnoses from echocardiogram reports within a diverse hospital network. After the initial development on 17,250 echocardiogram reports, 700 unique reports from 6 hospitals were randomly selected from data repositories within the Mass General Brigham healthcare system and manually adjudicated by physicians for 10 subtypes of LVH and diagnoses of HCM. Using an open-source NLP system, the module was formally tested on 300 training set reports and validated on 400 reports. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated to assess the discriminative accuracy of the NLP module. The NLP demonstrated robust performance across the 10 LVH subtypes, with the overall sensitivity and specificity exceeding 96%. In addition, the NLP module demonstrated excellent performance in detecting HCM diagnoses, with sensitivity and specificity exceeding 93%. In conclusion, we designed a highly accurate NLP module to determine the presence of LVH and HCM on echocardiogram reports. Our work demonstrates the feasibility and accuracy of NLP to detect diagnoses on imaging reports, even when described in free text. This module has been placed in the public domain to advance research, trial recruitment, and population health management for patients with LVH-associated conditions.

Using Renin Activity to Guide Mineralocorticoid Receptor Antagonist Therapy in Patients with Low Renin and Hypertension.

BACKGROUND: Mineralocorticoid receptor antagonists (MRAs) are often empirically used for patients with low-renin hypertension (LRH) or probable primary aldosteronism (PA) who decline surgery. However, the optimal approach to MRA therapy is unknown. Studies have shown that a rise in renin is an effective biomarker of prevention of cardiovascular complications of PA. This study aimed to determine whether empiric MRA therapy in patients with LRH or probable PA targeting unsuppressed renin is associated with a decrease in blood pressure and/or proteinuria. METHODS: Retrospective single-center cohort study from 2005 to 2021 included adults with LRH or probable PA (renin activity <1.0 ng/ml/h and detectable aldosterone levels). All patients were empirically treated with an MRA, targeting renin ≥1.0 ng/ml/h. RESULTS: Out of 39 patients studied, 32 (82.1%) achieved unsuppressed renin. Systolic and diastolic blood pressure decreased from 148.0 and 81.2 to 125.8 and 71.6 mm Hg, respectively (P < 0.001 for both). Similar blood pressure reductions were seen whether patients had high (>10 ng/dl) or low (<10 ng/dl) aldosterone levels. The majority (24/39; 61.5%) of patients had at least one baseline anti-hypertensive medication stopped. Among the six patients who had detectable proteinuria and albumin-to-creatinine (ACR) measurements post-treatment, the mean ACR decreased from 179.0 to 36.1 mg/g (P = 0.03). None of the patients studied had to completely stop treatment due to adverse reactions. CONCLUSIONS: Empiric MRA therapy in patients with LRH or probable PA targeting unsuppressed renin can safely and effectively improve blood pressure control and reduce proteinuria.

Assessment of Sex Disparities in Nonacceptance of Statin Therapy and Low-Density Lipoprotein Cholesterol Levels Among Patients at High Cardiovascular Risk.

Importance: Many patients at high cardiovascular risk-women more commonly than men-are not receiving statins. Anecdotally, it is common for patients to not accept statin therapy recommendations by their clinicians. However, population-based data on nonacceptance of statin therapy by patients are lacking. Objectives: To evaluate sex disparities in nonacceptance of statin therapy and assess their association with low-density lipoprotein (LDL) cholesterol control. Design, Setting, and Participants: A retrospective cohort study was conducted from January 1, 2019, to December 31, 2022, of statin-naive patients with atherosclerotic cardiovascular disease, diabetes, or LDL cholesterol levels of 190 mg/dL (to convert to millimoles per liter, multiply by 0.0259) or more who were treated at Mass General Brigham between January 1, 2000, and December 31, 2018. Exposure: Recommendation of statin therapy by the patient's clinician, ascertained from the combination of electronic health record prescription data and natural language processing of electronic clinician notes. Main Outcomes and Measures: Time to achieve an LDL cholesterol level of less than 100 mg/dL. Results: Of 24 212 study patients (mean [SD] age, 58.8 [13.0] years; 12 294 women [50.8%]), 5308 (21.9%) did not accept the initial recommendation of statin therapy. Nonacceptance of statin therapy was more common among women than men (24.1% [2957 of 12 294] vs 19.7% [2351 of 11 918]; P < .001) and was similarly higher in every subgroup in the analysis stratified by comorbidities. In multivariable analysis, female sex was associated with lower odds of statin therapy acceptance (0.82 [95% CI, 0.78-0.88]). Patients who did vs did not accept a statin therapy recommendation achieved an LDL cholesterol level of less than 100 mg/dL over a median of 1.5 years (IQR, 0.4-5.5 years) vs 4.4 years (IQR, 1.3-11.1 years) (P < .001). In a multivariable analysis adjusted for demographic characteristics and comorbidities, nonacceptance of statin therapy was associated with a longer time to achieve an LDL cholesterol level of less than 100 mg/dL (hazard ratio, 0.57 [95% CI, 0.55-0.60]). Conclusions and Relevance: This cohort study suggests that nonacceptance of a statin therapy recommendation was common among patients at high cardiovascular risk and was particularly common among women. It was associated with significantly higher LDL cholesterol levels, potentially increasing the risk for cardiovascular events. Further research is needed to understand the reasons for nonacceptance of statin therapy by patients and to develop methods to ensure that all patients receive optimal therapy in accordance with their preferences and priorities.

Outcomes in patients with cardiometabolic disease who develop hyperkalemia while treated with a renin-angiotensin-aldosterone system inhibitor.

BACKGROUND: Many patients with indications for renin-angiotensin-aldosterone system inhibitor (RAASi) therapy are not receiving these medications. Concern about hyperkalemia is thought to contribute to this lack of evidence-based therapy. METHODS: A retrospective cohort study included adult patients in primary care practices affiliated with an integrated health care delivery system treated with RAASi between 2000 and 2019 for any of the following indications: (a) coronary artery disease (CAD); (b) heart failure (HF) with a left ventricle ejection fraction ≤ 40%; (c) diabetes mellitus (DM) with proteinuria; or (d) chronic kidney disease (CKD) with proteinuria. Relationship between hyperkalemia (K > 5.0 mEg/L) over the first 12 months of follow-up and a composite end point of cardiovascular events, renal dysfunction, and all-cause mortality was evaluated. RESULTS: Among 82,732 study patients, 7,727 (9.34%) developed hyperkalemia. Patients with hyperkalemia were older (69.0 vs 64.6) and more likely to have CAD (57.8 vs 53.7%), CKD (57.3 vs 51.1%), HF (19.3 vs 9.7%), and DM (45.3 vs 33.3%) (P < .001 for all). Five-year cumulative risk of the primary outcome was higher in patients who did (63.9%; 95% CI: 62.8%-65.1%) versus did not (37.2%; 95% CI: 36.8%-37.6%) develop hyperkalemia. Five-year cumulative risk of ED visit or hospitalization for hyperkalemia was 15.6% (14.7%-16.6%) for patients with versus 2.7% (95% CI: 2.6-2.9) for patients without hyperkalemia, rising to 25.9% (95% CI: 22.4-29.9) for patients with severe (K > 6.0 mEq/dL) hyperkalemia. Patients who experienced hyperkalemia were more likely (34.4%) than patients who did not (29.2%) to deintensify RAASi therapy (P < .001). Five-year cumulative risk of the primary outcome was higher in patients who lowered RAASi dose (50.4%; 95% CI: 48.5%-52.4%) or stopped RAASi therapy completely (49.3%; 95% CI: 48.5%-50.1%), compared to patients who continued RAASi therapy (36.1%; 95% CI: 25.7-36.5). Similar findings were observed in multivariable analyses and for individual components of the primary outcome. CONCLUSIONS: Hyperkalemia is a common complication of RAASi therapy and is associated with an increased risk of multiple adverse outcomes. Patients who have their RAASi medications deintensified after a hyperkalemic event have higher incidence of cardiovascular events, renal dysfunction and death.

Evaluating the clinical and mechanistic effects of eplerenone and amiloride monotherapy, and combination therapy with cinacalcet, in primary hyperparathyroidism: A placebo-controlled randomized trial.

OBJECTIVES: Human physiology and epidemiology studies have demonstrated complex interactions between the renin-angiotensin-aldosterone system, parathyroid hormone and calcium homeostasis. Several of these studies have suggested that aldosterone inhibition may lower parathyroid hormone (PTH) levels. The objective of this study was to assess the effect of 4 weeks of maximally tolerated mineralocorticoid receptor antagonist therapy with eplerenone on PTH levels in patients with primary hyperparathyroidism (P-HPT) when compared to amiloride and placebo. We also investigated the synergistic effect of these interventions when combined with cinacalcet for an additional 2 weeks. DESIGN: Randomized, double-blinded, three parallel-group, placebo-controlled trial. PATIENTS: Patients with P-HPT. RESULTS: Most patients were women (83%) and White (76%). Maximally tolerated doses of eplerenone and amiloride induced significant reductions in blood pressure and increases in renin and aldosterone production; however, despite these physiologic changes, neither intervention induced significant changes in PTH or calcium levels when compared to the placebo. Both eplerenone and amiloride therapy induced significant reductions in procollagen type 1 N-terminal propeptide levels when compared to placebo. When cinacalcet therapy was added, PTH and calcium levels were markedly reduced in all groups; however, there was no significant difference in PTH or serum calcium reductions between groups. CONCLUSIONS: Although maximally tolerated therapy with eplerenone and amiloride induced expected changes in renin, aldosterone and blood pressure, there were no meaningful changes in PTH or serum calcium levels in P-HPT patients. These results suggest that inhibition of aldosterone action does not have a clinically meaningful role in medical therapy for P-HPT.

COVID-19 outcomes in patients taking cardioprotective medications.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) caused a worldwide pandemic and has led to over five million deaths. Many cardiovascular risk factors (e.g. obesity or diabetes) are associated with an increased risk of adverse outcomes in COVID-19. On the other hand, it has been suggested that medications used to treat cardiometabolic conditions may have protective effects for patients with COVID-19. OBJECTIVES: To determine whether patients taking four classes of cardioprotective medications-aspirin, metformin, renin angiotensin aldosterone system inhibitors (RAASi) and statins-have a lower risk of adverse outcomes of COVID-19. METHODS: We conducted a retrospective cohort study of primary care patients at a large integrated healthcare delivery system who had a positive COVID-19 test between March 2020 and March 2021. We compared outcomes of patients who were taking one of the study medications at the time of the COVID-19 test to patients who took a medication from the same class in the past (to minimize bias by indication). The following outcomes were compared: a) hospitalization; b) ICU admission; c) intubation; and d) death. Multivariable analysis was used to adjust for patient demographics and comorbidities. RESULTS: Among 13,585 study patients, 1,970 (14.5%) were hospitalized; 763 (5.6%) were admitted to an ICU; 373 (2.8%) were intubated and 720 (5.3%) died. In bivariate analyses, patients taking metformin, RAASi and statins had lower risk of hospitalization, ICU admission and death. However, in multivariable analysis, only the lower risk of death remained statistically significant. Patients taking aspirin had a significantly higher risk of hospitalization in both bivariate and multivariable analyses. CONCLUSIONS: Cardioprotective medications were not associated with a consistent benefit in COVID-19. As vaccination and effective treatments are not yet universally accessible worldwide, research should continue to determine whether affordable and widely available medications could be utilized to decrease the risks of this disease.

Development and Validation of Inpatient Hypoglycemia Models Centered Around the Insulin Ordering Process.

BACKGROUND: The insulin ordering process is an opportunity to provide clinicians with hypoglycemia risk predictions, but few hypoglycemia models centered around the insulin ordering process exist. METHODS: We used data on adult patients, admitted in 2019 to non-ICU floors of a large teaching hospital, who had orders for subcutaneous insulin. Our outcome was hypoglycemia, defined as a blood glucose (BG) <70 mg/dL within 24 hours after ordering insulin. We trained and evaluated models to predict hypoglycemia at the time of placing an insulin order, using logistic regression, random forest, and extreme gradient boosting (XGBoost). We compared performance using area under the receiver operating characteristic curve (AUCs) and precision-recall curves. We determined recall at our goal precision of 0.30. RESULTS: Of 21 052 included insulin orders, 1839 (9%) were followed by a hypoglycemic event within 24 hours. Logistic regression, random forest, and XGBoost models had AUCs of 0.81, 0.80, and 0.79, and recall of 0.44, 0.49, and 0.32, respectively. The most significant predictor was the lowest BG value in the 24 hours preceding the order. Predictors related to the insulin order being placed at the time of the prediction were useful to the model but less important than the patient's history of BG values over time. CONCLUSIONS: Hypoglycemia within the next 24 hours can be predicted at the time an insulin order is placed, providing an opportunity to integrate decision support into the medication ordering process to make insulin therapy safer.

Evaluation of the clinical utility of the PromarkerD in-vitro test in predicting diabetic kidney disease and rapid renal decline through a conjoint analysis.

BACKGROUND: Early identification of patients at risk of developing diabetic kidney disease or rapid renal decline is imperative for appropriate patient management, but traditional methods of predicting renal decline are limited. OBJECTIVE: This study evaluated the impact of PromarkerD, a biomarker-based blood test predicting the risk of diabetic kidney disease (DKD) and rapid renal decline. METHODS: Conjoint analysis clarified the importance of PromarkerD and other patient attributes to physician decisions for type 2 diabetes patients. Forty-two patient profiles were generated, with varying levels of albuminuria, estimated glomerular filtration rate (eGFR), blood pressure, hemoglobin A1c (HbA1c), age, and PromarkerD result. A web-based survey asked each physician to make monitoring/treatment decisions about eight randomly selected profiles. Data were analyzed using multivariable logit models. RESULTS: Two hundred three primary care physicians and 197 endocrinologists completed the survey. PromarkerD result was most important for increasing the frequency of risk factor monitoring. PromarkerD was second to HbA1c in importance for deciding to prescribe sodium/glucose cotransporter-2 inhibitors (SGLT2s) with a DKD indication, second to blood pressure for increasing the dose of lisinopril, and second to eGFR for replacing ibuprofen with a non-nephrotoxic medication. Compared with no PromarkerD results, a high-risk PromarkerD result was associated with significantly higher odds of increasing monitoring frequency (odds ratio [OR]: 2.56, 95% confidence interval: 1.90-3.45), prescribing SGLT2s (OR: 1.98 [1.56-2.52]), increasing lisinopril dose (OR: 1.48 [1.17-1.87]), and replacing ibuprofen (OR: 1.78 [1.32-2.40]). A low-risk PromarkerD result was associated with significantly lower odds of increasing monitoring frequency (OR: 0.48 [0.37-0.64]), prescribing SGLT2s (OR: 0.70 [0.56-0.88]), and replacing ibuprofen (OR: 0.75 [0.57-0.99]). CONCLUSION: PromarkerD could increase adoption of renoprotective interventions in patients at high risk for renal decline and lower the likelihood of aggressive treatment in those at low risk. Further studies are needed to assess patient outcomes with PromarkerD in real-world practice.

Quality of Care Among Patients with Diabetes and Cerebrovascular Disease. Insights from The Diabetes Collaborative Registry.

BACKGROUND: Although secondary cardiovascular prevention is a focus among patients with type 2 diabetes (T2D) and coronary artery disease (CAD) or peripheral artery disease (PAD), the application of guideline-recommended therapy in T2D patients and isolated cerebrovascular disease (CeVD) remains unknown. METHODS: In a US outpatient registry, T2D patients with established cardiovascular disease from 2014-2018 were categorized as: isolated CeVD, CeVD plus CAD or PAD, or isolated CAD/PAD. In each group, we determined the proportion with optimal secondary prevention (hemoglobin [Hb]A1C <8%, blood pressure <130/80 mm Hg, use of antithrombotics, use of statins, non-smoking/cessation counseling, and use of glucose-lowering medications with cardioprotective effects (sodium-glucose cotransporter [SGLT]-2 inhibitors, glucagon-like peptide [GLP]-1 receptor agonists, thiazolidinediones [TZDs]). Hierarchical Poisson regression was used to estimate relative rate of achieving each target across groups, adjusted for age and chronic kidney disease (where relevant). RESULTS: Our study included 727,467 T2D outpatients with cardiovascular disease (isolated CeVD [n = 99,777], CeVD plus CAD/PAD [n = 158,361], isolated CAD/PAD [n = 469,329]). Compared with isolated CAD/PAD patients, isolated CeVD patients more often had an HbA1c <8% (adjusted relative risk [aRR] 1.10; 95% confidence interval [CI], 1.08-1.11) but less often had a blood pressure of ≤130/80 mm Hg (aRR 0.93; 95% CI, 0.92-0.94) or were prescribed antithrombotics (0.84; 95% CI, 0.83-0.85), statins (0.86; 95% CI, 0.85-0.87), GLP-1 agonists (0.75; 95% CI, 0.73-0.78), SGLT2 inhibitors (0.73; 95% CI, 0.71-0.76), and TZDs (aRR 0.76; 95% CI, 0.73-0.78). CONCLUSION: Among T2D patients, those with isolated CeVD had the lowest rates of secondary cardiovascular prevention goals attainment. More focus is needed on secondary prevention in patients with CeVD.

Trends in Utilization of Aortic Valve Replacement for Severe Aortic Stenosis.

BACKGROUND: Despite the rapid growth of aortic valve replacement (AVR) for aortic stenosis (AS), limited data suggest symptomatic severe AS remains undertreated. OBJECTIVES: This study sought to investigate temporal trends in AVR utilization among patients with a clinical indication for AVR. METHODS: Patients with severe AS (aortic valve area <1 cm2) on transthoracic echocardiograms from 2000 to 2017 at 2 large academic medical centers were classified based on clinical guideline indications for AVR and divided into 4 AS subgroups: high gradient with normal left ventricular ejection fraction (LVEF) (HG-NEF), high gradient with low LVEF (HG-LEF), low gradient with normal LVEF (LG-NEF), and low gradient with low LVEF (LG-LEF). Utilization of AVR was examined and predictors identified. RESULTS: Of 10,795 patients, 6,150 (57%) had an indication or potential indication for AVR, of whom 2,977 (48%) received AVR. The frequency of AVR varied by AS subtype with LG groups less likely to receive an AVR (HG-NEF: 70%, HG-LEF: 53%, LG-NEF: 32%, LG-LEF: 38%, P < 0.001). AVR volumes grew over the 18-year study period but were paralleled by comparable growth in the number of patients with an indication for AVR. In patients with a Class I indication, younger age, coronary artery disease, smoking history, higher hematocrit, outpatient index transthoracic echocardiogram, and LVEF ≥0.5 were independently associated with an increased likelihood of receiving an AVR. AVR was associated with improved survival in each AS-subgroup. CONCLUSIONS: Over an 18-year period, the proportion of patients with an indication for AVR who did not receive AVR has remained substantial despite the rapid growth of AVR volumes.

Leveraging structured and unstructured electronic health record data to detect reasons for suboptimal statin therapy use in patients with atherosclerotic cardiovascular disease.

OBJECTIVE: To determine whether natural language processing (NLP) of unstructured medical text can improve identification of ASCVD patients not using high-intensity statin therapy (HIST) due to statin-associated side effects (SASEs) and other reasons. METHODS: Reviewers annotated reasons for not prescribing HIST in notes of 1152 randomly selected patients from across the VA healthcare system treated for ASCVD but not receiving HIST. Developers used reviewer annotations to train the Canary NLP tool to detect and extract notes containing one or more of these reasons. Negative predictive value (NPV), sensitivity, specificity and Area Under the Curve (AUC) were used to assess accuracy at detecting documents containing reasons when using structured data, NLP-extracted unstructured data, or both data sources combined. RESULTS: At least one documented reason for not prescribing HIST occurred in 47% of notes. The most frequent reasons were SASEs (41%) and general intolerance (20%). When identifying notes containing any documented reason for not using HIST, adding NLP-extracted, unstructured data significantly (p<0.05) increased sensitivity (0.69 (95% confidence interval [CI] 0.60-0.76) to 0.89 (95% CI 0.81-0.93)), NPV (0.90 (95% CI 0.87 to 0.93) to 0.96 (95% CI 0.93-0.98)), and AUC (0.84 (95% confidence interval [CI] 0.81-0.88) to 0.91 (95% CI 0.90-0.93)) compared to structured data alone. CONCLUSIONS: NLP extraction of data from unstructured text can improve identification of reasons for patients not being on HIST over structured data alone. The additional information provided through NLP of unstructured free text should help in tailoring and implementing system-level interventions to improve HIST use in patients with ASCVD.

Association of patient, provider and facility related characteristics with statin associated side effects and statin use: Insight from the Veteran's Affairs healthcare system.

BACKGROUND: Statin associated side effects (SASE) are a leading cause of statin discontinuation. OBJECTIVE: We evaluated patient, provider, and facility characteristics associated with SASEs and whether these characteristics impact statin utilization. METHODS: Patients with atherosclerotic cardiovascular disease (ASCVD) receiving care across the Veterans Affairs healthcare system from October 1, 2014 to September 30, 2015 were included. Multivariable logistic regression analyses were performed to determine (a) factors associated with SASE and (b) factors associated with statin use in those with SASE. RESULTS: Our cohort included 1,225,576 patients with ASCVD. Of these, 171,189 (13.7%) had at least 1 reported SASE since year 2000. The most significant odds for SASEs were observed with female sex (odds ratio [OR] 1.40, 95% confidence interval [CI] 1.36, 1.45), White race (OR 1.43, 95% CI 1.41, 1.45), hypertension (OR 1.37, 95% CI 1.33, 1.41) and ischemic heart disease (IHD: OR 1.45, 95% CI 1.43, 1.47). Lower odds were noted with care at a teaching facility (OR 0.89, 95% CI 0.88, 0.90). Factors most associated with being on a statin among patients with SASE included having diabetes (OR 1.18, 95% CI 1.15, 1.20), IHD (OR 1.39, 95% CI 1.35, 1.43) and a higher number of cardiology visits (OR 1.08, 95% CI 1.07, 1.09), while female sex was associated with lower odds (OR 0.65, 95% CI 0.61, 0.69). CONCLUSION: There are significant disparities in statin use by sex, ASCVD type, and comorbidities among secondary prevention patients with SASE, which represent areas for improvement in optimizing statin utilization.