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.

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.

Strong multiperiod forerunning light jet within a double-prism gap: a counterintuitive manifestation of causality.

We report on the effect of a strong and protracted advanced response in pulse transmission and reflection in a double-prism scheme. In distinction to the well-known activity on superluminal-like tunneling of an electromagnetic pulse through a gap of a double prism, we consider an optical pulse refracting to a gap and sliding therein. The formation of a multiperiod light jet running within the gap well before the incident pulse is shown with account of normal material dispersion and excitation of leaky modes in the gap. Conditions for the paradoxical appearance of the forerunning jet are revealed to have a geometrical nature with a specific relation between phase and group velocities involved. This deduction assigns the stated effect to the counterintuitive manifestation of causality with no reference to superluminal propagation.

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.

Adherence and persistence among people with type 2 diabetes newly initiating oral semaglutide versus DPP-4is in a US real-world setting.

AIMS: To investigate real-world treatment adherence and persistence in people with type 2 diabetes newly initiating oral semaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1 RA), or a dipeptidyl peptidase-4 inhibitor (DPP-4i). METHODS: This retrospective cohort study used the Merative™ MarketScan® Commercial and Medicare databases. Index date was the first fill for the cohort medication. Adherence was defined as proportion of days covered (PDC) over the 12-month post-index period ('adherent' = ≥0.8). Persistence was number of days until discontinuation, based on a 45-day gap. Results were compared between cohorts using inverse probability treatment weighting. RESULTS: Oral semaglutide (n=5485) and DPP-4i (n=4980) cohorts had similar percentages of people who were adherent (PDC ≥0.8; 41.6 % vs. 42.9 %; P = 0.182) and persistent for ≥9 months (45.0 % vs. 46.3 %; P = 0.185). The DPP-4i cohort used significantly more anti-diabetic medication (ADM) classes over the post-index period (mean±SD: 2.6±1.0 vs. 2.9±1.1, P < 0.001), with 23.2 % filling a GLP-1 RA in the post-period. CONCLUSIONS: Adherence and persistence were similar between cohorts. However, there are potential benefits to prescribing oral semaglutide over DPP-4is, including reduced need for additional ADM.

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.