Citations

Background: Patients with atrial fibrillation and severely decreased kidney function were excluded from the pivotal non–vitamin K antagonist oral anticoagulants (NOAC) trials, thereby raising questions about comparative safety and effectiveness in patients with reduced kidney function. The study aimed to compare oral anticoagulants across the range of kidney function in patients with atrial fibrillation. Methods and Results: Using a US administrative claims database with linked laboratory data, 34 569 new users of oral anticoagulants with atrial fibrillation and estimated glomerular filtration rate ≥15 mL/(min·1.73 m 2 ) were identified between October 1, 2010 to November 29, 2017. The proportion of patients using NOACs declined with decreasing kidney function—73.5%, 69.6%, 65.4%, 59.5%, and 45.0% of the patients were prescribed a NOAC in estimated glomerular filtration rate ≥90, 60 to 90, 45 to 60, 30 to 45, 15 to 30 mL/min per 1.73 m 2 groups, respectively. Stabilized inverse probability of treatment weighting was used to balance 4 treatment groups (apixaban, dabigatran, rivaroxaban, and warfarin) on 66 baseline characteristics. In comparison to warfarin, apixaban was associated with a lower risk of stroke (hazard ratio [HR], 0.57 [0.43–0.75]; P <0.001), major bleeding (HR, 0.51 [0.44–0.61]; P <0.001), and mortality (HR, 0.68 [0.56–0.83]; P <0.001); dabigatran was associated with a similar risk of stroke but a lower risk of major bleeding (HR, 0.57 [0.43–0.75]; P <0.001) and mortality (HR, 0.68 [0.48-0.98]; P =0.04); rivaroxaban was associated with a lower risk of stroke (HR, 0.69 [0.51–0.94]; P =0.02), major bleeding (HR, 0.84 [0.72–0.99]; P =0.04), and mortality (HR, 0.73 [0.58–0.91]; P =0.006). There was no significant interaction between treatment and estimated glomerular filtration rate categories for any outcome. When comparing one NOAC to another NOAC, there was no significant difference in mortality, but some differences existed for stroke or major bleeding. No relationship between treatments and falsification end points was found, suggesting no evidence for substantial residual confounding. Conclusions: Relative to warfarin, NOACs are used less frequently as kidney function declines. However, NOACs appears to have similar or better comparative effectiveness and safety across the range of kidney function.

SummaryBackgroundGastrointestinal bleeding (GIB) frequently occurs following percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS) with the prescription of P2Y12 inhibiting antiplatelet agents. Compared with clopidogrel, the newer P2Y12 inhibitors lower major adverse cardiac events with similar or possibly higher major bleeding events. The comparative GIB rates of these medications remain poorly understood.AimTo compare GIB rates associated with clopidogrel, prasugrel and ticagrelor using national medical and pharmacy claims data from privately insured and Medicare Advantage enrollees .MethodsPropensity score and inverse probability treatment weighting were used to balance baseline characteristics among treatment groups. The 1‐year GIB risk was calculated using weighted Cox proportional hazard models and expressed as hazard ratios (HR) with 95% confidence intervals (CI) and number needed to harm (NNH).ResultsWe identified 37 019 patients with ACS (non‐ST elevation ACS [NSTE‐ACS] and ST‐elevation myocardial infarction [STEMI]) within 14 days of a PCI (mean age 63 years and 70% male). Clopidogrel prescription was most common (69%) with prasugrel (16%) and ticagrelor (14%) prescribed less frequently. When compared with clopidogrel, ticagrelor was associated with a 34% risk reduction (HR 0.66; 95% CI: 0.54–0.81) in GIB overall and with NSTE‐ACS, and a 37% GIB risk reduction (HR 0.63; 95% CI: 0.42–0.93) in STEMI patients. When compared with clopidogrel, prasugrel was associated with a 21% risk reduction (HR 0.79; 95% CI: 0.64–0.97) overall, a 36% GIB risk reduction (HR 0.64; 95% CI: 0.49–0.85) in STEMI patients but no reduction of GIB risk in NSTE‐ACS patients.ConclusionsIn the first year following PCI, ticagrelor or prasugrel are associated with fewer GIB events than clopidogrel.

Abstract Objective: Increasing physical activity (PA) is regularly cited as a modifiable target to improve health outcomes and quality of life in the aging population, especially postmenopausal women who exhibit low bone mineral density (BMD) and high fracture risk. In this cross-sectional study, we aimed to quantify real-world PA and its association with BMD in postmenopausal women. Methods: Seventy postmenopausal women, aged 46 to 79 years, received a dual-energy X-ray absorptiometry scan measuring total hip BMD and wore bilateral triaxial accelerometers on the ankles for 7 days to measure PA in their free-living environment. Custom step detection and peak vertical ground reaction force estimation algorithms, sensitive to both quantity and intensity of PA, were used to calculate a daily bone density index (BDI) for each participant. Multiple regression was used to quantify the relationship between total hip BMD, age, step counts, and mean BDI over the span of 7 days of data collection. Results: All participants completed the full 7 days of PA monitoring, totaling more than 7 million detected steps. Participants averaged 14,485 ± 4,334 steps daily with mean peak vertical ground reaction force stepping loads of 675 ± 121 N during daily living. Across the population, total hip BMD was found to be significantly correlated with objective estimates of mean BDI (r = 0.44), as well as participant age (r = 0.285). Conclusion: Despite having higher-than-expected PA, the low stepping loads observed in this cohort, along with half of the participants having low BMD measures, underscores the need for PA intensity to be considered in the management of postmenopausal bone health.

OBJECTIVENonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion.METHODSThe authors queried the 2012–2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion. Outcomes assessed included discharge to nonhome facility and unplanned readmissions within 30 days after surgery. A total of 7 machine learning algorithms were evaluated. Predictive hierarchical clustering of procedure codes was used to increase model performance. Model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC), as well as sensitivity, specificity, and positive and negative predictive values. These performance metrics were computed for both the imputed and unimputed (missing values dropped) datasets.RESULTSA total of 59,145 spinal fusion cases were analyzed. The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC > 0.80, range 0.85–0.87) for predicting nonhome discharge. The generalized linear model showed comparable performance to other machine learning algorithms. By comparison, all models showed poorer predictive performance for unplanned readmission, with AUC ranging between 0.63 and 0.66. Better predictive performance was noted with models using imputed data.CONCLUSIONSIn an analysis of patients undergoing spinal fusion, multiple machine learning algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions. These results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision making.

PURPOSE Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although machine learning methods can readily learn complex nonlinear relationships, many methods are criticized as inadequate because of limited interpretability. We propose using unsupervised clustering of the continuous output of machine learning models to provide discrete risk stratification for predicting time to first treatment in a cohort of patients with CLL. PATIENTS AND METHODS A total of 737 treatment-naïve patients with CLL diagnosed at Mayo Clinic were included in this study. We compared predictive abilities for two survival models (Cox proportional hazards and random survival forest) and four classification methods (logistic regression, support vector machines, random forest, and gradient boosting machine). Probability of treatment was then stratified. RESULTS Machine learning methods did not yield significantly more accurate predictions of time to first treatment. However, automated risk stratification provided by clustering was able to better differentiate patients who were at risk for treatment within 1 year than models developed using standard survival analysis techniques. CONCLUSION Clustering the posterior probabilities of machine learning models provides a way to better interpret machine learning models.

Background: Stroke prevention using warfarin is challenging in AF patients with CKD, due to high bleeding risk and difficulties in the INR control. NOACs provide alternative options, but all have greater degrees of renal clearance. This study aimed to compare the outcomes of apixaban, dabigatran, rivaroxaban, and warfarin across the range of kidney function in patients with AF. Methods: Using a US administrative database including private insurance or Medicare Advantage patients with linked claims and laboratory data, we identified 34,569 new users of oral anticoagulants with AF and eGFR ≥15 between 10/1/2010-11/29/2017. Stabilized IPTW balanced four treatment groups on 66 baseline characteristics. The primary outcomes included stroke, major bleeding, and mortality. Weighted Cox proportional hazards models compared treatments in the overall population and in each eGFR subgroup, with mortality as a competing risk for stroke and major bleeding. Results: The proportion of patients using warfarin increased as the kidney function declined - 26.5%, 30.4%, 34.6%, 40.5%, and 55.0% of patients were prescribed warfarin in eGFR ≥90, 60-90, 45-60, 30-45, 15-30 groups, respectively. In comparison to warfarin, apixaban was associated with a lower risk of stroke, major bleeding, and mortality; dabigatran was associated with a similar risk of stroke, and a lower risk of major bleeding and mortality; rivaroxaban was associated with a lower risk of stroke, major bleeding, and mortality (Figure). When comparing one NOAC to another NOAC, apixaban and dabigatran were associated with a lower risk of major bleeding than rivaroxaban (HR 0.61 [0.51-0.73], p<0.001 for apixaban versus rivaroxaban; HR 0.67 [0.50-0.90], p=0.007 for dabigatran versus rivaroxaban); dabigatran was associated with a higher risk of stroke than apixaban (HR 1.65 [1.11-2.46], p=0.01); there was no difference in mortality. There was no significant interaction between treatment and eGFR categories for any outcome, but the number of patients with low eGFR was small. Conclusions: In practice, relative to warfarin, NOACs are progressively less commonly used with increasing degree of renal dysfunction. However, each NOAC was consistently associated with at least equivalent effectiveness and safety compared with warfarin across the range of kidney function.

Background: Individualized diabetes management would benefit from prospectively identifying well-controlled patients at risk of losing glycemic control. Objectives: To identify patterns of hemoglobin A1c (HbA1c) change among patients with stable controlled diabetes. Research Design: Cohort study using OptumLabs Data Warehouse, 2001–2013. We develop and apply a machine learning framework that uses a Bayesian estimation of the mixture of generalized linear mixed effect models to discover glycemic trajectories, and a random forest feature contribution method to identify patient characteristics predictive of their future glycemic trajectories. Subjects: The study cohort consisted of 27,005 US adults with type 2 diabetes, age 18 years and older, and stable index HbA1c <7.0%. Measures: HbA1c values during 24 months of observation. Results: We compared models with k=1, 2, 3, 4, 5 trajectories and baseline variables including patient age, sex, race/ethnicity, comorbidities, medications, and HbA1c. The k=3 model had the best fit, reflecting 3 distinct trajectories of glycemic change: (T1) rapidly deteriorating HbA1c among 302 (1.1%) youngest (mean, 55.2 y) patients with lowest mean baseline HbA1c, 6.05%; (T2) gradually deteriorating HbA1c among 902 (3.3%) patients (mean, 56.5 y) with highest mean baseline HbA1c, 6.53%; and (T3) stable glycemic control among 25,800 (95.5%) oldest (mean, 58.5 y) patients with mean baseline HbA1c 6.21%. After 24 months, HbA1c rose to 8.75% in T1 and 8.40% in T2, but remained stable at 6.56% in T3. Conclusions: Patients with controlled type 2 diabetes follow 3 distinct trajectories of glycemic control. This novel application of advanced analytic methods can facilitate individualized and population diabetes care by proactively identifying high risk patients.